MyArxiv

Computation and Language

☆ Charting and Navigating Hugging Face's Model Atlas

Eliahu Horwitz, Nitzan Kurer, Jonathan Kahana, Liel Amar, Yedid Hoshen

As there are now millions of publicly available neural networks, searching and analyzing large model repositories becomes increasingly important. Navigating so many models requires an atlas, but as most models are poorly documented charting such an atlas is challenging. To explore the hidden potential of model repositories, we chart a preliminary atlas representing the documented fraction of Hugging Face. It provides stunning visualizations of the model landscape and evolution. We demonstrate several applications of this atlas including predicting model attributes (e.g., accuracy), and analyzing trends in computer vision models. However, as the current atlas remains incomplete, we propose a method for charting undocumented regions. Specifically, we identify high-confidence structural priors based on dominant real-world model training practices. Leveraging these priors, our approach enables accurate mapping of previously undocumented areas of the atlas. We publicly release our datasets, code, and interactive atlas.

☆ SciVerse: Unveiling the Knowledge Comprehension and Visual Reasoning of LMMs on Multi-modal Scientific Problems

Ziyu Guo, Ray Zhang, Hao Chen, Jialin Gao, Dongzhi Jiang, Jiaze Wang, Pheng-Ann Heng

The rapid advancement of Large Multi-modal Models (LMMs) has enabled their application in scientific problem-solving, yet their fine-grained capabilities remain under-explored. In this paper, we introduce SciVerse, a multi-modal scientific evaluation benchmark to thoroughly assess LMMs across 5,735 test instances in five distinct versions. We aim to investigate three key dimensions of LMMs: scientific knowledge comprehension, multi-modal content interpretation, and Chain-of-Thought (CoT) reasoning. To unveil whether LMMs possess sufficient scientific expertise, we first transform each problem into three versions containing different levels of knowledge required for solving, i.e., Knowledge-free, -lite, and -rich. Then, to explore how LMMs interpret multi-modal scientific content, we annotate another two versions, i.e., Vision-rich and -only, marking more question information from texts to diagrams. Comparing the results of different versions, SciVerse systematically examines the professional knowledge stock and visual perception skills of LMMs in scientific domains. In addition, to rigorously assess CoT reasoning, we propose a new scientific CoT evaluation strategy, conducting a step-wise assessment on knowledge and logical errors in model outputs. Our extensive evaluation of different LMMs on SciVerse reveals critical limitations in their scientific proficiency and provides new insights into future developments. Project page: https://sciverse-cuhk.github.io

comment: Initially released in September 2024. Project page: https://sciverse-cuhk.github.io

★ Transformers without Normalization CVPR 2025

Jiachen Zhu, Xinlei Chen, Kaiming He, Yann LeCun, Zhuang Liu

Normalization layers are ubiquitous in modern neural networks and have long been considered essential. This work demonstrates that Transformers without normalization can achieve the same or better performance using a remarkably simple technique. We introduce Dynamic Tanh (DyT), an element-wise operation $DyT($x$) = \tanh(\alpha $x$)$, as a drop-in replacement for normalization layers in Transformers. DyT is inspired by the observation that layer normalization in Transformers often produces tanh-like, $S$-shaped input-output mappings. By incorporating DyT, Transformers without normalization can match or exceed the performance of their normalized counterparts, mostly without hyperparameter tuning. We validate the effectiveness of Transformers with DyT across diverse settings, ranging from recognition to generation, supervised to self-supervised learning, and computer vision to language models. These findings challenge the conventional understanding that normalization layers are indispensable in modern neural networks, and offer new insights into their role in deep networks.

comment: CVPR 2025; Project page: https://jiachenzhu.github.io/DyT/

☆ Siege: Autonomous Multi-Turn Jailbreaking of Large Language Models with Tree Search ICLR 2025

Andy Zhou

We introduce Siege, a multi-turn adversarial framework that models the gradual erosion of Large Language Model (LLM) safety through a tree search perspective. Unlike single-turn jailbreaks that rely on one meticulously engineered prompt, Siege expands the conversation at each turn in a breadth-first fashion, branching out multiple adversarial prompts that exploit partial compliance from previous responses. By tracking these incremental policy leaks and re-injecting them into subsequent queries, Siege reveals how minor concessions can accumulate into fully disallowed outputs. Evaluations on the JailbreakBench dataset show that Siege achieves a 100% success rate on GPT-3.5-turbo and 97% on GPT-4 in a single multi-turn run, using fewer queries than baselines such as Crescendo or GOAT. This tree search methodology offers an in-depth view of how model safeguards degrade over successive dialogue turns, underscoring the urgency of robust multi-turn testing procedures for language models.

comment: Accepted to ICLR 2025 Trustworthy LLM

☆ From TOWER to SPIRE: Adding the Speech Modality to a Text-Only LLM

Kshitij Ambilduke, Ben Peters, Sonal Sannigrahi, Anil Keshwani, Tsz Kin Lam, Bruno Martins, Marcely Zanon Boito, André F. T. Martins

Large language models (LLMs) have shown remarkable performance and generalization capabilities across multiple languages and tasks, making them very attractive targets for multi-modality integration (e.g., images or speech). In this work, we extend an existing LLM to the speech modality via speech discretization and continued pre-training. In particular, we are interested in multilingual LLMs, such as TOWER, as their pre-training setting allows us to treat discretized speech input as an additional translation language. The resulting open-source model, SPIRE, is able to transcribe and translate English speech input while maintaining TOWER's original performance on translation-related tasks, showcasing that discretized speech input integration as an additional language is feasible during LLM adaptation. We make our code and models available to the community.

☆ Compositional Subspace Representation Fine-tuning for Adaptive Large Language Models ICLR 2025

Andy Zhou

Adapting large language models to multiple tasks can cause cross-skill interference, where improvements for one skill degrade another. While methods such as LoRA impose orthogonality constraints at the weight level, they do not fully address interference in hidden-state representations. We propose Compositional Subspace Representation Fine-tuning (CS-ReFT), a novel representation-based approach that learns multiple orthonormal subspace transformations, each specializing in a distinct skill, and composes them via a lightweight router. By isolating these subspace edits in the hidden state, rather than weight matrices, CS-ReFT prevents cross-task conflicts more effectively. On the AlpacaEval benchmark, applying CS-ReFT to Llama-2-7B achieves a 93.94% win rate, surpassing GPT-3.5 Turbo (86.30%) while requiring only 0.0098% of model parameters. These findings show that specialized representation edits, composed via a simple router, significantly enhance multi-task instruction following with minimal overhead.

comment: Accepted to ICLR 2025 SCOPE

☆ TruthPrInt: Mitigating LVLM Object Hallucination Via Latent Truthful-Guided Pre-Intervention

Jinhao Duan, Fei Kong, Hao Cheng, James Diffenderfer, Bhavya Kailkhura, Lichao Sun, Xiaofeng Zhu, Xiaoshuang Shi, Kaidi Xu

Object Hallucination (OH) has been acknowledged as one of the major trustworthy challenges in Large Vision-Language Models (LVLMs). Recent advancements in Large Language Models (LLMs) indicate that internal states, such as hidden states, encode the "overall truthfulness" of generated responses. However, it remains under-explored how internal states in LVLMs function and whether they could serve as "per-token" hallucination indicators, which is essential for mitigating OH. In this paper, we first conduct an in-depth exploration of LVLM internal states in relation to OH issues and discover that (1) LVLM internal states are high-specificity per-token indicators of hallucination behaviors. Moreover, (2) different LVLMs encode universal patterns of hallucinations in common latent subspaces, indicating that there exist "generic truthful directions" shared by various LVLMs. Based on these discoveries, we propose Truthful-Guided Pre-Intervention (TruthPrInt) that first learns the truthful direction of LVLM decoding and then applies truthful-guided inference-time intervention during LVLM decoding. We further propose ComnHallu to enhance both cross-LVLM and cross-data hallucination detection transferability by constructing and aligning hallucination latent subspaces. We evaluate TruthPrInt in extensive experimental settings, including in-domain and out-of-domain scenarios, over popular LVLMs and OH benchmarks. Experimental results indicate that TruthPrInt significantly outperforms state-of-the-art methods. Codes will be available at https://github.com/jinhaoduan/TruthPrInt.

comment: 15 pages, 9 figures, the first two authors contributed equally

☆ VisualWebInstruct: Scaling up Multimodal Instruction Data through Web Search

Yiming Jia, Jiachen Li, Xiang Yue, Bo Li, Ping Nie, Kai Zou, Wenhu Chen

Vision-Language Models have made significant progress on many perception-focused tasks, however, their progress on reasoning-focused tasks seem to be limited due to the lack of high-quality and diverse training data. In this work, we aim to address the scarcity issue of reasoning-focused multimodal datasets. We propose VisualWebInstruct - a novel approach that leverages search engine to create a diverse, and high-quality dataset spanning multiple disciplines like math, physics, finance, chemistry, etc. Starting with meticulously selected 30,000 seed images, we employ Google Image search to identify websites containing similar images. We collect and process the HTMLs from over 700K unique URL sources. Through a pipeline of content extraction, filtering and synthesis, we build a dataset of approximately 900K question-answer pairs, with 40% being visual QA pairs and the rest as text QA pairs. Models fine-tuned on VisualWebInstruct demonstrate significant performance gains: (1) training from Llava-OV-mid shows 10-20% absolute point gains across benchmarks, (2) training from MAmmoTH-VL shows 5% absoluate gain. Our best model MAmmoTH-VL2 shows state-of-the-art performance within the 10B parameter class on MMMU-Pro-std (40.7%), MathVerse (42.6%), and DynaMath (55.7%). These remarkable results highlight the effectiveness of our dataset in enhancing VLMs' reasoning capabilities for complex multimodal tasks.

comment: Technical Report

♻ ☆ Chain-of-Thought Reasoning In The Wild Is Not Always Faithful ICLR 25

Iván Arcuschin, Jett Janiak, Robert Krzyzanowski, Senthooran Rajamanoharan, Neel Nanda, Arthur Conmy

Chain-of-Thought (CoT) reasoning has significantly advanced state-of-the-art AI capabilities. However, recent studies have shown that CoT reasoning is not always faithful, i.e. CoT reasoning does not always reflect how models arrive at conclusions. So far, most of these studies have focused on unfaithfulness in unnatural contexts where an explicit bias has been introduced. In contrast, we show that unfaithful CoT can occur on realistic prompts with no artificial bias. Our results reveal non-negligible rates of several forms of unfaithful reasoning in frontier models: Sonnet 3.7 (16.3%), DeepSeek R1 (5.3%) and ChatGPT-4o (7.0%) all answer a notable proportion of question pairs unfaithfully. Specifically, we find that models rationalize their implicit biases in answers to binary questions ("implicit post-hoc rationalization"). For example, when separately presented with the questions "Is X bigger than Y?" and "Is Y bigger than X?", models sometimes produce superficially coherent arguments to justify answering Yes to both questions or No to both questions, despite such responses being logically contradictory. We also investigate restoration errors (Dziri et al., 2023), where models make and then silently correct errors in their reasoning, and unfaithful shortcuts, where models use clearly illogical reasoning to simplify solving problems in Putnam questions (a hard benchmark). Our findings raise challenges for AI safety work that relies on monitoring CoT to detect undesired behavior.

comment: Accepted to the Reasoning and Planning for Large Language Models Workshop (ICLR 25), 10 main paper pages, 38 appendix pages

♻ ☆ DataEnvGym: Data Generation Agents in Teacher Environments with Student Feedback ICLR 2025

Zaid Khan, Elias Stengel-Eskin, Jaemin Cho, Mohit Bansal

The process of creating training data to teach models is currently driven by humans, who manually analyze model weaknesses and plan how to create data that improves a student model. Approaches using LLMs as annotators reduce human effort, but still require humans to interpret feedback from evaluations and control the LLM to produce data the student needs. Automating this labor-intensive process by creating autonomous data generation agents - or teachers - is desirable, but requires environments that can simulate the feedback-driven, iterative, closed loop of data creation. To enable rapid, scalable testing for such agents and their modules, we introduce DataEnvGym, a testbed of teacher environments for data generation agents. DataEnvGym frames data generation as a sequential decision-making task, involving an agent consisting of a data generation policy (which generates a plan for creating training data) and a data generation engine (which transforms the plan into data), inside an environment that provides student feedback. The agent's goal is to improve student performance. Students are iteratively trained and evaluated on generated data, and their feedback (in the form of errors or weak skills) is reported to the agent after each iteration. DataEnvGym includes multiple teacher environment instantiations across 3 levels of structure in the state representation and action space. More structured environments are based on inferred skills and offer more interpretability and curriculum control. We support 4 domains (math, code, VQA, and tool-use) and test multiple students and teachers. Example agents in our teaching environments can iteratively improve students across tasks and settings. Moreover, we show that environments teach different skill levels and test variants of key modules, pointing to future work in improving data generation agents, engines, and feedback mechanisms.

comment: ICLR 2025 Spotlight; Project Page: https://DataEnvGym.github.io

Artificial Intelligence

☆ Studying Classifier(-Free) Guidance From a Classifier-Centric Perspective

Xiaoming Zhao, Alexander G. Schwing

Classifier-free guidance has become a staple for conditional generation with denoising diffusion models. However, a comprehensive understanding of classifier-free guidance is still missing. In this work, we carry out an empirical study to provide a fresh perspective on classifier-free guidance. Concretely, instead of solely focusing on classifier-free guidance, we trace back to the root, i.e., classifier guidance, pinpoint the key assumption for the derivation, and conduct a systematic study to understand the role of the classifier. We find that both classifier guidance and classifier-free guidance achieve conditional generation by pushing the denoising diffusion trajectories away from decision boundaries, i.e., areas where conditional information is usually entangled and is hard to learn. Based on this classifier-centric understanding, we propose a generic postprocessing step built upon flow-matching to shrink the gap between the learned distribution for a pre-trained denoising diffusion model and the real data distribution, majorly around the decision boundaries. Experiments on various datasets verify the effectiveness of the proposed approach.

☆ A Frustratingly Simple Yet Highly Effective Attack Baseline: Over 90% Success Rate Against the Strong Black-box Models of GPT-4.5/4o/o1

Zhaoyi Li, Xiaohan Zhao, Dong-Dong Wu, Jiacheng Cui, Zhiqiang Shen

Despite promising performance on open-source large vision-language models (LVLMs), transfer-based targeted attacks often fail against black-box commercial LVLMs. Analyzing failed adversarial perturbations reveals that the learned perturbations typically originate from a uniform distribution and lack clear semantic details, resulting in unintended responses. This critical absence of semantic information leads commercial LVLMs to either ignore the perturbation entirely or misinterpret its embedded semantics, thereby causing the attack to fail. To overcome these issues, we notice that identifying core semantic objects is a key objective for models trained with various datasets and methodologies. This insight motivates our approach that refines semantic clarity by encoding explicit semantic details within local regions, thus ensuring interoperability and capturing finer-grained features, and by concentrating modifications on semantically rich areas rather than applying them uniformly. To achieve this, we propose a simple yet highly effective solution: at each optimization step, the adversarial image is cropped randomly by a controlled aspect ratio and scale, resized, and then aligned with the target image in the embedding space. Experimental results confirm our hypothesis. Our adversarial examples crafted with local-aggregated perturbations focused on crucial regions exhibit surprisingly good transferability to commercial LVLMs, including GPT-4.5, GPT-4o, Gemini-2.0-flash, Claude-3.5-sonnet, Claude-3.7-sonnet, and even reasoning models like o1, Claude-3.7-thinking and Gemini-2.0-flash-thinking. Our approach achieves success rates exceeding 90% on GPT-4.5, 4o, and o1, significantly outperforming all prior state-of-the-art attack methods. Our optimized adversarial examples under different configurations and training code are available at https://github.com/VILA-Lab/M-Attack.

comment: Code at: https://github.com/VILA-Lab/M-Attack

☆ Uncertainty in Action: Confidence Elicitation in Embodied Agents

Tianjiao Yu, Vedant Shah, Muntasir Wahed, Kiet A. Nguyen, Adheesh Juvekar, Tal August, Ismini Lourentzou

Expressing confidence is challenging for embodied agents navigating dynamic multimodal environments, where uncertainty arises from both perception and decision-making processes. We present the first work investigating embodied confidence elicitation in open-ended multimodal environments. We introduce Elicitation Policies, which structure confidence assessment across inductive, deductive, and abductive reasoning, along with Execution Policies, which enhance confidence calibration through scenario reinterpretation, action sampling, and hypothetical reasoning. Evaluating agents in calibration and failure prediction tasks within the Minecraft environment, we show that structured reasoning approaches, such as Chain-of-Thoughts, improve confidence calibration. However, our findings also reveal persistent challenges in distinguishing uncertainty, particularly under abductive settings, underscoring the need for more sophisticated embodied confidence elicitation methods.

comment: Project page: https://plan-lab.github.io/ece/

☆ SciVerse: Unveiling the Knowledge Comprehension and Visual Reasoning of LMMs on Multi-modal Scientific Problems

Ziyu Guo, Ray Zhang, Hao Chen, Jialin Gao, Dongzhi Jiang, Jiaze Wang, Pheng-Ann Heng

comment: Initially released in September 2024. Project page: https://sciverse-cuhk.github.io

☆ NIL: No-data Imitation Learning by Leveraging Pre-trained Video Diffusion Models

Mert Albaba, Chenhao Li, Markos Diomataris, Omid Taheri, Andreas Krause, Michael Black

Acquiring physically plausible motor skills across diverse and unconventional morphologies-including humanoid robots, quadrupeds, and animals-is essential for advancing character simulation and robotics. Traditional methods, such as reinforcement learning (RL) are task- and body-specific, require extensive reward function engineering, and do not generalize well. Imitation learning offers an alternative but relies heavily on high-quality expert demonstrations, which are difficult to obtain for non-human morphologies. Video diffusion models, on the other hand, are capable of generating realistic videos of various morphologies, from humans to ants. Leveraging this capability, we propose a data-independent approach for skill acquisition that learns 3D motor skills from 2D-generated videos, with generalization capability to unconventional and non-human forms. Specifically, we guide the imitation learning process by leveraging vision transformers for video-based comparisons by calculating pair-wise distance between video embeddings. Along with video-encoding distance, we also use a computed similarity between segmented video frames as a guidance reward. We validate our method on locomotion tasks involving unique body configurations. In humanoid robot locomotion tasks, we demonstrate that 'No-data Imitation Learning' (NIL) outperforms baselines trained on 3D motion-capture data. Our results highlight the potential of leveraging generative video models for physically plausible skill learning with diverse morphologies, effectively replacing data collection with data generation for imitation learning.

☆ LHM: Large Animatable Human Reconstruction Model from a Single Image in Seconds

Lingteng Qiu, Xiaodong Gu, Peihao Li, Qi Zuo, Weichao Shen, Junfei Zhang, Kejie Qiu, Weihao Yuan, Guanying Chen, Zilong Dong, Liefeng Bo

Animatable 3D human reconstruction from a single image is a challenging problem due to the ambiguity in decoupling geometry, appearance, and deformation. Recent advances in 3D human reconstruction mainly focus on static human modeling, and the reliance of using synthetic 3D scans for training limits their generalization ability. Conversely, optimization-based video methods achieve higher fidelity but demand controlled capture conditions and computationally intensive refinement processes. Motivated by the emergence of large reconstruction models for efficient static reconstruction, we propose LHM (Large Animatable Human Reconstruction Model) to infer high-fidelity avatars represented as 3D Gaussian splatting in a feed-forward pass. Our model leverages a multimodal transformer architecture to effectively encode the human body positional features and image features with attention mechanism, enabling detailed preservation of clothing geometry and texture. To further boost the face identity preservation and fine detail recovery, we propose a head feature pyramid encoding scheme to aggregate multi-scale features of the head regions. Extensive experiments demonstrate that our LHM generates plausible animatable human in seconds without post-processing for face and hands, outperforming existing methods in both reconstruction accuracy and generalization ability.

comment: Project Page: https://lingtengqiu.github.io/LHM/

☆ ETCH: Generalizing Body Fitting to Clothed Humans via Equivariant Tightness

Boqian Li, Haiwen Feng, Zeyu Cai, Michael J. Black, Yuliang Xiu

Fitting a body to a 3D clothed human point cloud is a common yet challenging task. Traditional optimization-based approaches use multi-stage pipelines that are sensitive to pose initialization, while recent learning-based methods often struggle with generalization across diverse poses and garment types. We propose Equivariant Tightness Fitting for Clothed Humans, or ETCH, a novel pipeline that estimates cloth-to-body surface mapping through locally approximate SE(3) equivariance, encoding tightness as displacement vectors from the cloth surface to the underlying body. Following this mapping, pose-invariant body features regress sparse body markers, simplifying clothed human fitting into an inner-body marker fitting task. Extensive experiments on CAPE and 4D-Dress show that ETCH significantly outperforms state-of-the-art methods -- both tightness-agnostic and tightness-aware -- in body fitting accuracy on loose clothing (16.7% ~ 69.5%) and shape accuracy (average 49.9%). Our equivariant tightness design can even reduce directional errors by (67.2% ~ 89.8%) in one-shot (or out-of-distribution) settings. Qualitative results demonstrate strong generalization of ETCH, regardless of challenging poses, unseen shapes, loose clothing, and non-rigid dynamics. We will release the code and models soon for research purposes at https://boqian-li.github.io/ETCH/.

comment: Page: https://boqian-li.github.io/ETCH/, Code: https://github.com/boqian-li/ETCH

★ Transformers without Normalization CVPR 2025

Jiachen Zhu, Xinlei Chen, Kaiming He, Yann LeCun, Zhuang Liu

comment: CVPR 2025; Project page: https://jiachenzhu.github.io/DyT/

☆ Siege: Autonomous Multi-Turn Jailbreaking of Large Language Models with Tree Search ICLR 2025

Andy Zhou

comment: Accepted to ICLR 2025 Trustworthy LLM

☆ Compositional Subspace Representation Fine-tuning for Adaptive Large Language Models ICLR 2025

Andy Zhou

comment: Accepted to ICLR 2025 SCOPE

☆ Dual-Stage Cross-Modal Network with Dynamic Feature Fusion for Emotional Mimicry Intensity Estimation

Jun Yu, Lingsi Zhu, Yanjun Chi, Yunxiang Zhang, Yang Zheng, Yongqi Wang, Xilong Lu

Emotional Mimicry Intensity (EMI) estimation serves as a critical technology for understanding human social behavior and enhancing human-computer interaction experiences, where the core challenge lies in dynamic correlation modeling and robust fusion of multimodal temporal signals. To address the limitations of existing methods in insufficient exploitation of modal synergistic effects, noise sensitivity, and limited fine-grained alignment capabilities, this paper proposes a dual-stage cross-modal alignment framework. First, we construct vision-text and audio-text contrastive learning networks based on an improved CLIP architecture, achieving preliminary alignment in the feature space through modality-decoupled pre-training. Subsequently, we design a temporal-aware dynamic fusion module that combines Temporal Convolutional Networks (TCN) and gated bidirectional LSTM to respectively capture the macro-evolution patterns of facial expressions and local dynamics of acoustic features. Innovatively, we introduce a quality-guided modality fusion strategy that enables modality compensation under occlusion and noisy scenarios through differentiable weight allocation. Experimental results on the Hume-Vidmimic2 dataset demonstrate that our method achieves an average Pearson correlation coefficient of 0.35 across six emotion dimensions, outperforming the best baseline by 40\%. Ablation studies further validate the effectiveness of the dual-stage training strategy and dynamic fusion mechanism, providing a novel technical pathway for fine-grained emotion analysis in open environments.

☆ TruthPrInt: Mitigating LVLM Object Hallucination Via Latent Truthful-Guided Pre-Intervention

Jinhao Duan, Fei Kong, Hao Cheng, James Diffenderfer, Bhavya Kailkhura, Lichao Sun, Xiaofeng Zhu, Xiaoshuang Shi, Kaidi Xu

comment: 15 pages, 9 figures, the first two authors contributed equally

☆ The Spectral Bias of Shallow Neural Network Learning is Shaped by the Choice of Non-linearity

Justin Sahs, Ryan Pyle, Fabio Anselmi, Ankit Patel

Despite classical statistical theory predicting severe overfitting, modern massively overparameterized neural networks still generalize well. This unexpected property is attributed to the network's so-called implicit bias, which describes its propensity to converge to solutions that generalize effectively, among the many possible that correctly label the training data. The aim of our research is to explore this bias from a new perspective, focusing on how non-linear activation functions contribute to shaping it. First, we introduce a reparameterization which removes a continuous weight rescaling symmetry. Second, in the kernel regime, we leverage this reparameterization to generalize recent findings that relate shallow Neural Networks to the Radon transform, deriving an explicit formula for the implicit bias induced by a broad class of activation functions. Specifically, by utilizing the connection between the Radon transform and the Fourier transform, we interpret the kernel regime's inductive bias as minimizing a spectral seminorm that penalizes high-frequency components, in a manner dependent on the activation function. Finally, in the adaptive regime, we demonstrate the existence of local dynamical attractors that facilitate the formation of clusters of hyperplanes where the input to a neuron's activation function is zero, yielding alignment between many neurons' response functions. We confirm these theoretical results with simulations. All together, our work provides a deeper understanding of the mechanisms underlying the generalization capabilities of overparameterized neural networks and its relation with the implicit bias, offering potential pathways for designing more efficient and robust models.

comment: 18 pages, 10 figures in main text

☆ VisualWebInstruct: Scaling up Multimodal Instruction Data through Web Search

Yiming Jia, Jiachen Li, Xiang Yue, Bo Li, Ping Nie, Kai Zou, Wenhu Chen

comment: Technical Report

☆ KUDA: Keypoints to Unify Dynamics Learning and Visual Prompting for Open-Vocabulary Robotic Manipulation

Zixian Liu, Mingtong Zhang, Yunzhu Li

With the rapid advancement of large language models (LLMs) and vision-language models (VLMs), significant progress has been made in developing open-vocabulary robotic manipulation systems. However, many existing approaches overlook the importance of object dynamics, limiting their applicability to more complex, dynamic tasks. In this work, we introduce KUDA, an open-vocabulary manipulation system that integrates dynamics learning and visual prompting through keypoints, leveraging both VLMs and learning-based neural dynamics models. Our key insight is that a keypoint-based target specification is simultaneously interpretable by VLMs and can be efficiently translated into cost functions for model-based planning. Given language instructions and visual observations, KUDA first assigns keypoints to the RGB image and queries the VLM to generate target specifications. These abstract keypoint-based representations are then converted into cost functions, which are optimized using a learned dynamics model to produce robotic trajectories. We evaluate KUDA on a range of manipulation tasks, including free-form language instructions across diverse object categories, multi-object interactions, and deformable or granular objects, demonstrating the effectiveness of our framework. The project page is available at http://kuda-dynamics.github.io.

comment: Project website: http://kuda-dynamics.github.io

☆ Language Models, Graph Searching, and Supervision Adulteration: When More Supervision is Less and How to Make More More SC

Arvid Frydenlund

This work concerns the path-star task, a minimal example of searching over a graph. The graph, $G$, is star-shaped with $D$ arms radiating from a start node, $s$. A language model (LM) is given $G$, $s$, and a target node $t$, which ends one of the arms and is tasked with generating the arm containing $t$. The minimal nature of this task means only a single choice needs to be made: which of the $D$ arms contains $t$? Decoder-only LMs fail to solve this elementary task above $1/D$ chance due to a learned shortcut that absorbs training supervision. We show how this pathology is caused by excess supervision and we present a series of solutions demonstrating that the task is solvable via decoder-only LMs. We find that the task's minimal nature causes its difficulty, as it prevents task decomposition. Our solutions provide insight into the pathology and its implications for LMs trained via next-token prediction.

comment: A reduced version of this work has been accepted to the Workshop on Spurious Correlation and Shortcut Learning: Foundations and Solutions (SCSL) at ICLR 2025. Full version under review

☆ GBSVR: Granular Ball Support Vector Regression

Reshma Rastogi, Ankush Bisht, Sanjay Kumar, Suresh Chandra

Support Vector Regression (SVR) and its variants are widely used to handle regression tasks, however, since their solution involves solving an expensive quadratic programming problem, it limits its application, especially when dealing with large datasets. Additionally, SVR uses an epsilon-insensitive loss function which is sensitive to outliers and therefore can adversely affect its performance. We propose Granular Ball Support Vector Regression (GBSVR) to tackle problem of regression by using granular ball concept. These balls are useful in simplifying complex data spaces for machine learning tasks, however, to the best of our knowledge, they have not been sufficiently explored for regression problems. Granular balls group the data points into balls based on their proximity and reduce the computational cost in SVR by replacing the large number of data points with far fewer granular balls. This work also suggests a discretization method for continuous-valued attributes to facilitate the construction of granular balls. The effectiveness of the proposed approach is evaluated on several benchmark datasets and it outperforms existing state-of-the-art approaches

☆ The Impact of Item-Writing Flaws on Difficulty and Discrimination in Item Response Theory

Robin Schmucker, Steven Moore

High-quality test items are essential for educational assessments, particularly within Item Response Theory (IRT). Traditional validation methods rely on resource-intensive pilot testing to estimate item difficulty and discrimination. More recently, Item-Writing Flaw (IWF) rubrics emerged as a domain-general approach for evaluating test items based on textual features. However, their relationship to IRT parameters remains underexplored. To address this gap, we conducted a study involving over 7,000 multiple-choice questions across various STEM subjects (e.g., math and biology). Using an automated approach, we annotated each question with a 19-criteria IWF rubric and studied relationships to data-driven IRT parameters. Our analysis revealed statistically significant links between the number of IWFs and IRT difficulty and discrimination parameters, particularly in life and physical science domains. We further observed how specific IWF criteria can impact item quality more and less severely (e.g., negative wording vs. implausible distractors). Overall, while IWFs are useful for predicting IRT parameters--particularly for screening low-difficulty MCQs--they cannot replace traditional data-driven validation methods. Our findings highlight the need for further research on domain-general evaluation rubrics and algorithms that understand domain-specific content for robust item validation.

☆ Lightweight Models for Emotional Analysis in Video

Quoc-Tien Nguyen, Hong-Hai Nguyen, Van-Thong Huynh

In this study, we present an approach for efficient spatiotemporal feature extraction using MobileNetV4 and a multi-scale 3D MLP-Mixer-based temporal aggregation module. MobileNetV4, with its Universal Inverted Bottleneck (UIB) blocks, serves as the backbone for extracting hierarchical feature representations from input image sequences, ensuring both computational efficiency and rich semantic encoding. To capture temporal dependencies, we introduce a three-level MLP-Mixer module, which processes spatial features at multiple resolutions while maintaining structural integrity. Experimental results on the ABAW 8th competition demonstrate the effectiveness of our approach, showing promising performance in affective behavior analysis. By integrating an efficient vision backbone with a structured temporal modeling mechanism, the proposed framework achieves a balance between computational efficiency and predictive accuracy, making it well-suited for real-time applications in mobile and embedded computing environments.

☆ PiSA: A Self-Augmented Data Engine and Training Strategy for 3D Understanding with Large Models

Zilu Guo, Hongbin Lin, Zhihao Yuan, Chaoda Zheng, Pengshuo Qiu, Dongzhi Jiang, Renrui Zhang, Chun-Mei Feng, Zhen Li

3D Multimodal Large Language Models (MLLMs) have recently made substantial advancements. However, their potential remains untapped, primarily due to the limited quantity and suboptimal quality of 3D datasets. Current approaches attempt to transfer knowledge from 2D MLLMs to expand 3D instruction data, but still face modality and domain gaps. To this end, we introduce PiSA-Engine (Point-Self-Augmented-Engine), a new framework for generating instruction point-language datasets enriched with 3D spatial semantics. We observe that existing 3D MLLMs offer a comprehensive understanding of point clouds for annotation, while 2D MLLMs excel at cross-validation by providing complementary information. By integrating holistic 2D and 3D insights from off-the-shelf MLLMs, PiSA-Engine enables a continuous cycle of high-quality data generation. We select PointLLM as the baseline and adopt this co-evolution training framework to develop an enhanced 3D MLLM, termed PointLLM-PiSA. Additionally, we identify limitations in previous 3D benchmarks, which often feature coarse language captions and insufficient category diversity, resulting in inaccurate evaluations. To address this gap, we further introduce PiSA-Bench, a comprehensive 3D benchmark covering six key aspects with detailed and diverse labels. Experimental results demonstrate PointLLM-PiSA's state-of-the-art performance in zero-shot 3D object captioning and generative classification on our PiSA-Bench, achieving significant improvements of 46.45% (+8.33%) and 63.75% (+16.25%), respectively. We will release the code, datasets, and benchmark.

comment: Technical Report

☆ CountPath: Automating Fragment Counting in Digital Pathology

Ana Beatriz Vieira, Maria Valente, Diana Montezuma, Tomé Albuquerque, Liliana Ribeiro, Domingos Oliveira, João Monteiro, Sofia Gonçalves, Isabel M. Pinto, Jaime S. Cardoso, Arlindo L. Oliveira

Quality control of medical images is a critical component of digital pathology, ensuring that diagnostic images meet required standards. A pre-analytical task within this process is the verification of the number of specimen fragments, a process that ensures that the number of fragments on a slide matches the number documented in the macroscopic report. This step is important to ensure that the slides contain the appropriate diagnostic material from the grossing process, thereby guaranteeing the accuracy of subsequent microscopic examination and diagnosis. Traditionally, this assessment is performed manually, requiring significant time and effort while being subject to significant variability due to its subjective nature. To address these challenges, this study explores an automated approach to fragment counting using the YOLOv9 and Vision Transformer models. Our results demonstrate that the automated system achieves a level of performance comparable to expert assessments, offering a reliable and efficient alternative to manual counting. Additionally, we present findings on interobserver variability, showing that the automated approach achieves an accuracy of 86%, which falls within the range of variation observed among experts (82-88%), further supporting its potential for integration into routine pathology workflows.

comment: 10 pages, 3 figures

☆ Why the Brain Cannot Be a Digital Computer: History-Dependence and the Computational Limits of Consciousness

Andrew Knight

This paper presents a novel information-theoretic proof demonstrating that the human brain as currently understood cannot function as a classical digital computer. Through systematic quantification of distinguishable conscious states and their historical dependencies, we establish that the minimum information required to specify a conscious state exceeds the physical information capacity of the human brain by a significant factor. Our analysis calculates the bit-length requirements for representing consciously distinguishable sensory "stimulus frames" and demonstrates that consciousness exhibits mandatory temporal-historical dependencies that multiply these requirements beyond the brain's storage capabilities. This mathematical approach offers new insights into the fundamental limitations of computational models of consciousness and suggests that non-classical information processing mechanisms may be necessary to account for conscious experience.

comment: 10 pages, 1 figure

☆ Conformal Prediction Sets for Deep Generative Models via Reduction to Conformal Regression

Hooman Shahrokhi, Devjeet Raj Roy, Yan Yan, Venera Arnaoudova, Janaradhan Rao Doppa

We consider the problem of generating valid and small prediction sets by sampling outputs (e.g., software code and natural language text) from a black-box deep generative model for a given input (e.g., textual prompt). The validity of a prediction set is determined by a user-defined binary admissibility function depending on the target application. For example, requiring at least one program in the set to pass all test cases in code generation application. To address this problem, we develop a simple and effective conformal inference algorithm referred to as Generative Prediction Sets (GPS). Given a set of calibration examples and black-box access to a deep generative model, GPS can generate prediction sets with provable guarantees. The key insight behind GPS is to exploit the inherent structure within the distribution over the minimum number of samples needed to obtain an admissible output to develop a simple conformal regression approach over the minimum number of samples. Experiments on multiple datasets for code and math word problems using different large language models demonstrate the efficacy of GPS over state-of-the-art methods.

☆ Explainable Bayesian deep learning through input-skip Latent Binary Bayesian Neural Networks

Eirik Høyheim, Lars Skaaret-Lund, Solve Sæbø, Aliaksandr Hubin

Modeling natural phenomena with artificial neural networks (ANNs) often provides highly accurate predictions. However, ANNs often suffer from over-parameterization, complicating interpretation and raising uncertainty issues. Bayesian neural networks (BNNs) address the latter by representing weights as probability distributions, allowing for predictive uncertainty evaluation. Latent binary Bayesian neural networks (LBBNNs) further handle structural uncertainty and sparsify models by removing redundant weights. This article advances LBBNNs by enabling covariates to skip to any succeeding layer or be excluded, simplifying networks and clarifying input impacts on predictions. Ultimately, a linear model or even a constant can be found to be optimal for a specific problem at hand. Furthermore, the input-skip LBBNN approach reduces network density significantly compared to standard LBBNNs, achieving over 99% reduction for small networks and over 99.9% for larger ones, while still maintaining high predictive accuracy and uncertainty measurement. For example, on MNIST, we reached 97% accuracy and great calibration with just 935 weights, reaching state-of-the-art for compression of neural networks. Furthermore, the proposed method accurately identifies the true covariates and adjusts for system non-linearity. The main contribution is the introduction of active paths, enhancing directly designed global and local explanations within the LBBNN framework, that have theoretical guarantees and do not require post hoc external tools for explanations.

comment: 44 pages, 19 tables, 25 figures. Code available at https://github.com/eirihoyh/ISLaB-LBBNN

☆ LLMs in Disease Diagnosis: A Comparative Study of DeepSeek-R1 and O3 Mini Across Chronic Health Conditions

Gaurav Kumar Gupta, Pranal Pande

Large Language Models (LLMs) are revolutionizing medical diagnostics by enhancing both disease classification and clinical decision-making. In this study, we evaluate the performance of two LLM- based diagnostic tools, DeepSeek R1 and O3 Mini, using a structured dataset of symptoms and diagnoses. We assessed their predictive accuracy at both the disease and category levels, as well as the reliability of their confidence scores. DeepSeek R1 achieved a disease-level accuracy of 76% and an overall accuracy of 82%, outperforming O3 Mini, which attained 72% and 75% respectively. Notably, DeepSeek R1 demonstrated exceptional performance in Mental Health, Neurological Disorders, and Oncology, where it reached 100% accuracy, while O3 Mini excelled in Autoimmune Disease classification with 100% accuracy. Both models, however, struggled with Respiratory Disease classification, recording accuracies of only 40% for DeepSeek R1 and 20% for O3 Mini. Additionally, the analysis of confidence scores revealed that DeepSeek R1 provided high-confidence predictions in 92% of cases, compared to 68% for O3 Mini. Ethical considerations regarding bias, model interpretability, and data privacy are also discussed to ensure the responsible integration of LLMs into clinical practice. Overall, our findings offer valuable insights into the strengths and limitations of LLM-based diagnostic systems and provide a roadmap for future enhancements in AI-driven healthcare.

comment: 12 pages, 3 figures

☆ DeclareAligner: A Leap Towards Efficient Optimal Alignments for Declarative Process Model Conformance Checking

Jacobo Casas-Ramos, Manuel Lama, Manuel Mucientes

In many engineering applications, processes must be followed precisely, making conformance checking between event logs and declarative process models crucial for ensuring adherence to desired behaviors. This is a critical area where Artificial Intelligence (AI) plays a pivotal role in driving effective process improvement. However, computing optimal alignments poses significant computational challenges due to the vast search space inherent in these models. Consequently, existing approaches often struggle with scalability and efficiency, limiting their applicability in real-world settings. This paper introduces DeclareAligner, a novel algorithm that uses the A* search algorithm, an established AI pathfinding technique, to tackle the problem from a fresh perspective leveraging the flexibility of declarative models. Key features of DeclareAligner include only performing actions that actively contribute to fixing constraint violations, utilizing a tailored heuristic to navigate towards optimal solutions, and employing early pruning to eliminate unproductive branches, while also streamlining the process through preprocessing and consolidating multiple fixes into unified actions. The proposed method is evaluated using 8,054 synthetic and real-life alignment problems, demonstrating its ability to efficiently compute optimal alignments by significantly outperforming the current state of the art. By enabling process analysts to more effectively identify and understand conformance issues, DeclareAligner has the potential to drive meaningful process improvement and management.

☆ Siamese Foundation Models for Crystal Structure Prediction

Liming Wu, Wenbing Huang, Rui Jiao, Jianxing Huang, Liwei Liu, Yipeng Zhou, Hao Sun, Yang Liu, Fuchun Sun, Yuxiang Ren, Jirong Wen

Crystal Structure Prediction (CSP), which aims to generate stable crystal structures from compositions, represents a critical pathway for discovering novel materials. While structure prediction tasks in other domains, such as proteins, have seen remarkable progress, CSP remains a relatively underexplored area due to the more complex geometries inherent in crystal structures. In this paper, we propose Siamese foundation models specifically designed to address CSP. Our pretrain-finetune framework, named DAO, comprises two complementary foundation models: DAO-G for structure generation and DAO-P for energy prediction. Experiments on CSP benchmarks (MP-20 and MPTS-52) demonstrate that our DAO-G significantly surpasses state-of-the-art (SOTA) methods across all metrics. Extensive ablation studies further confirm that DAO-G excels in generating diverse polymorphic structures, and the dataset relaxation and energy guidance provided by DAO-P are essential for enhancing DAO-G's performance. When applied to three real-world superconductors ($\text{CsV}_3\text{Sb}_5$, $ \text{Zr}_{16}\text{Rh}_8\text{O}_4$ and $\text{Zr}_{16}\text{Pd}_8\text{O}_4$) that are known to be challenging to analyze, our foundation models achieve accurate critical temperature predictions and structure generations. For instance, on $\text{CsV}_3\text{Sb}_5$, DAO-G generates a structure close to the experimental one with an RMSE of 0.0085; DAO-P predicts the $T_c$ value with high accuracy (2.26 K vs. the ground-truth value of 2.30 K). In contrast, conventional DFT calculators like Quantum Espresso only successfully derive the structure of the first superconductor within an acceptable time, while the RMSE is nearly 8 times larger, and the computation speed is more than 1000 times slower. These compelling results collectively highlight the potential of our approach for advancing materials science research and development.

☆ DynaCode: A Dynamic Complexity-Aware Code Benchmark for Evaluating Large Language Models in Code Generation

Wenhao Hu, Jinhao Duan, Chunchen Wei, Li Zhang, Yue Zhang, Kaidi Xu

The rapid advancement of large language models (LLMs) has significantly improved their performance in code generation tasks. However, existing code benchmarks remain static, consisting of fixed datasets with predefined problems. This makes them vulnerable to memorization during training, where LLMs recall specific test cases instead of generalizing to new problems, leading to data contamination and unreliable evaluation results. To address these issues, we introduce DynaCode, a dynamic, complexity-aware benchmark that overcomes the limitations of static datasets. DynaCode evaluates LLMs systematically using a complexity-aware metric, incorporating both code complexity and call-graph structures. DynaCode achieves large-scale diversity, generating up to 189 million unique nested code problems across four distinct levels of code complexity, referred to as units, and 16 types of call graphs. Results on 12 latest LLMs show an average performance drop of 16.8% to 45.7% compared to MBPP+, a static code generation benchmark, with performance progressively decreasing as complexity increases. This demonstrates DynaCode's ability to effectively differentiate LLMs. Additionally, by leveraging call graphs, we gain insights into LLM behavior, particularly their preference for handling subfunction interactions within nested code.

comment: 16 pages, 11 figures

☆ Whisper Speaker Identification: Leveraging Pre-Trained Multilingual Transformers for Robust Speaker Embeddings

Jakaria Islam Emon, Md Abu Salek, Kazi Tamanna Alam

Speaker identification in multilingual settings presents unique challenges, particularly when conventional models are predominantly trained on English data. In this paper, we propose WSI (Whisper Speaker Identification), a framework that repurposes the encoder of the Whisper automatic speech recognition model pre trained on extensive multilingual data to generate robust speaker embeddings via a joint loss optimization strategy that leverages online hard triplet mining and self supervised Normalized Temperature-scaled Cross Entropy loss. By capitalizing on Whisper language-agnostic acoustic representations, our approach effectively distinguishes speakers across diverse languages and recording conditions. Extensive evaluations on multiple corpora, including VoxTube (multilingual), JVS (Japanese), CallHome (German, Spanish, Chinese, and Japanese), and Voxconverse (English), demonstrate that WSI consistently outperforms state-of-the-art baselines, namely Pyannote Embedding, ECAPA TDNN, and Xvector, in terms of lower equal error rates and higher AUC scores. These results validate our hypothesis that a multilingual pre-trained ASR encoder, combined with joint loss optimization, substantially improves speaker identification performance in non-English languages.

comment: 6 pages

☆ dFLMoE: Decentralized Federated Learning via Mixture of Experts for Medical Data Analysis

Luyuan Xie, Tianyu Luan, Wenyuan Cai, Guochen Yan, Zhaoyu Chen, Nan Xi, Yuejian Fang, Qingni Shen, Zhonghai Wu, Junsong Yuan

Federated learning has wide applications in the medical field. It enables knowledge sharing among different healthcare institutes while protecting patients' privacy. However, existing federated learning systems are typically centralized, requiring clients to upload client-specific knowledge to a central server for aggregation. This centralized approach would integrate the knowledge from each client into a centralized server, and the knowledge would be already undermined during the centralized integration before it reaches back to each client. Besides, the centralized approach also creates a dependency on the central server, which may affect training stability if the server malfunctions or connections are unstable. To address these issues, we propose a decentralized federated learning framework named dFLMoE. In our framework, clients directly exchange lightweight head models with each other. After exchanging, each client treats both local and received head models as individual experts, and utilizes a client-specific Mixture of Experts (MoE) approach to make collective decisions. This design not only reduces the knowledge damage with client-specific aggregations but also removes the dependency on the central server to enhance the robustness of the framework. We validate our framework on multiple medical tasks, demonstrating that our method evidently outperforms state-of-the-art approaches under both model homogeneity and heterogeneity settings.

☆ RealGeneral: Unifying Visual Generation via Temporal In-Context Learning with Video Models

Yijing Lin, Mengqi Huang, Shuhan Zhuang, Zhendong Mao

Unifying diverse image generation tasks within a single framework remains a fundamental challenge in visual generation. While large language models (LLMs) achieve unification through task-agnostic data and generation, existing visual generation models fail to meet these principles. Current approaches either rely on per-task datasets and large-scale training or adapt pre-trained image models with task-specific modifications, limiting their generalizability. In this work, we explore video models as a foundation for unified image generation, leveraging their inherent ability to model temporal correlations. We introduce RealGeneral, a novel framework that reformulates image generation as a conditional frame prediction task, analogous to in-context learning in LLMs. To bridge the gap between video models and condition-image pairs, we propose (1) a Unified Conditional Embedding module for multi-modal alignment and (2) a Unified Stream DiT Block with decoupled adaptive LayerNorm and attention mask to mitigate cross-modal interference. RealGeneral demonstrates effectiveness in multiple important visual generation tasks, e.g., it achieves a 14.5% improvement in subject similarity for customized generation and a 10% enhancement in image quality for canny-to-image task. Project page: https://lyne1.github.io/RealGeneral/

☆ RoMA: Scaling up Mamba-based Foundation Models for Remote Sensing

Fengxiang Wang, Hongzhen Wang, Yulin Wang, Di Wang, Mingshuo Chen, Haiyan Zhao, Yangang Sun, Shuo Wang, Long Lan, Wenjing Yang, Jing Zhang

Recent advances in self-supervised learning for Vision Transformers (ViTs) have fueled breakthroughs in remote sensing (RS) foundation models. However, the quadratic complexity of self-attention poses a significant barrier to scalability, particularly for large models and high-resolution images. While the linear-complexity Mamba architecture offers a promising alternative, existing RS applications of Mamba remain limited to supervised tasks on small, domain-specific datasets. To address these challenges, we propose RoMA, a framework that enables scalable self-supervised pretraining of Mamba-based RS foundation models using large-scale, diverse, unlabeled data. RoMA enhances scalability for high-resolution images through a tailored auto-regressive learning strategy, incorporating two key innovations: 1) a rotation-aware pretraining mechanism combining adaptive cropping with angular embeddings to handle sparsely distributed objects with arbitrary orientations, and 2) multi-scale token prediction objectives that address the extreme variations in object scales inherent to RS imagery. Systematic empirical studies validate that Mamba adheres to RS data and parameter scaling laws, with performance scaling reliably as model and data size increase. Furthermore, experiments across scene classification, object detection, and semantic segmentation tasks demonstrate that RoMA-pretrained Mamba models consistently outperform ViT-based counterparts in both accuracy and computational efficiency. The source code and pretrained models will be released at https://github.com/MiliLab/RoMA.

☆ CINEMA: Coherent Multi-Subject Video Generation via MLLM-Based Guidance

Yufan Deng, Xun Guo, Yizhi Wang, Jacob Zhiyuan Fang, Angtian Wang, Shenghai Yuan, Yiding Yang, Bo Liu, Haibin Huang, Chongyang Ma

Video generation has witnessed remarkable progress with the advent of deep generative models, particularly diffusion models. While existing methods excel in generating high-quality videos from text prompts or single images, personalized multi-subject video generation remains a largely unexplored challenge. This task involves synthesizing videos that incorporate multiple distinct subjects, each defined by separate reference images, while ensuring temporal and spatial consistency. Current approaches primarily rely on mapping subject images to keywords in text prompts, which introduces ambiguity and limits their ability to model subject relationships effectively. In this paper, we propose CINEMA, a novel framework for coherent multi-subject video generation by leveraging Multimodal Large Language Model (MLLM). Our approach eliminates the need for explicit correspondences between subject images and text entities, mitigating ambiguity and reducing annotation effort. By leveraging MLLM to interpret subject relationships, our method facilitates scalability, enabling the use of large and diverse datasets for training. Furthermore, our framework can be conditioned on varying numbers of subjects, offering greater flexibility in personalized content creation. Through extensive evaluations, we demonstrate that our approach significantly improves subject consistency, and overall video coherence, paving the way for advanced applications in storytelling, interactive media, and personalized video generation.

☆ A Multimodal Fusion Model Leveraging MLP Mixer and Handcrafted Features-based Deep Learning Networks for Facial Palsy Detection PAKDD 2025

Heng Yim Nicole Oo, Min Hun Lee, Jeong Hoon Lim

Algorithmic detection of facial palsy offers the potential to improve current practices, which usually involve labor-intensive and subjective assessments by clinicians. In this paper, we present a multimodal fusion-based deep learning model that utilizes an MLP mixer-based model to process unstructured data (i.e. RGB images or images with facial line segments) and a feed-forward neural network to process structured data (i.e. facial landmark coordinates, features of facial expressions, or handcrafted features) for detecting facial palsy. We then contribute to a study to analyze the effect of different data modalities and the benefits of a multimodal fusion-based approach using videos of 20 facial palsy patients and 20 healthy subjects. Our multimodal fusion model achieved 96.00 F1, which is significantly higher than the feed-forward neural network trained on handcrafted features alone (82.80 F1) and an MLP mixer-based model trained on raw RGB images (89.00 F1).

comment: PAKDD 2025. arXiv admin note: text overlap with arXiv:2405.16496

☆ G-Boost: Boosting Private SLMs with General LLMs

Yijiang Fan, Yuren Mao, Longbin Lai, Ying Zhang, Zhengping Qian, Yunjun Gao

Due to the limited computational resources, most Large Language Models (LLMs) developers can only fine-tune Small Language Models (SLMs) on their own data. These private SLMs typically have limited effectiveness. To boost the performance of private SLMs, this paper proposes to ask general LLMs for help. The general LLMs can be APIs or larger LLMs whose inference cost the developers can afford. Specifically, we propose the G-Boost framework where a private SLM adaptively performs collaborative inference with a general LLM under the guide of process reward. Experiments demonstrate that our framework can significantly boost the performance of private SLMs.

☆ Object detection characteristics in a learning factory environment using YOLOv8

Toni Schneidereit, Stefan Gohrenz, Michael Breuß

AI-based object detection, and efforts to explain and investigate their characteristics, is a topic of high interest. The impact of, e.g., complex background structures with similar appearances as the objects of interest, on the detection accuracy and, beforehand, the necessary dataset composition are topics of ongoing research. In this paper, we present a systematic investigation of background influences and different features of the object to be detected. The latter includes various materials and surfaces, partially transparent and with shiny reflections in the context of an Industry 4.0 learning factory. Different YOLOv8 models have been trained for each of the materials on different sized datasets, where the appearance was the only changing parameter. In the end, similar characteristics tend to show different behaviours and sometimes unexpected results. While some background components tend to be detected, others with the same features are not part of the detection. Additionally, some more precise conclusions can be drawn from the results. Therefore, we contribute a challenging dataset with detailed investigations on 92 trained YOLO models, addressing some issues on the detection accuracy and possible overfitting.

☆ KV-Distill: Nearly Lossless Learnable Context Compression for LLMs

Vivek Chari, Guanghui Qin, Benjamin Van Durme

Sequence-to-sequence tasks often benefit from long contexts, but the quadratic complexity of self-attention in standard Transformers renders this non-trivial. During generation, temporary representations -stored in the so-called KV cache-account for a large portion of GPU memory usage and scale linearly with context length. We introduce KV-Distill, a Transformer compression framework that distills long context KV caches into significantly shorter representations in a question-independent fashion. KV-Distill can be trained as a parameter-efficient adaptor for pretrained models, and enables the compression of arbitrary spans of a context while preserving pre-trained model capabilities. We treat a compressed-uncompressed cache as a student-teacher pairing and apply a KL-type divergence to match the generated outputs. KV-Distill outperforms other compression techniques in worst-case extractive tasks and approaches uncompressed performance in long context question answering and summarization, and it can be fine-tuned on domain-specific contexts to reduce lengths by up to 99% while preserving downstream performance. We demonstrate the generalizability of KV-Distill across various model sizes and architectures.

☆ OSMa-Bench: Evaluating Open Semantic Mapping Under Varying Lighting Conditions

Maxim Popov, Regina Kurkova, Mikhail Iumanov, Jaafar Mahmoud, Sergey Kolyubin

Open Semantic Mapping (OSM) is a key technology in robotic perception, combining semantic segmentation and SLAM techniques. This paper introduces a dynamically configurable and highly automated LLM/LVLM-powered pipeline for evaluating OSM solutions called OSMa-Bench (Open Semantic Mapping Benchmark). The study focuses on evaluating state-of-the-art semantic mapping algorithms under varying indoor lighting conditions, a critical challenge in indoor environments. We introduce a novel dataset with simulated RGB-D sequences and ground truth 3D reconstructions, facilitating the rigorous analysis of mapping performance across different lighting conditions. Through experiments on leading models such as ConceptGraphs, BBQ and OpenScene, we evaluate the semantic fidelity of object recognition and segmentation. Additionally, we introduce a Scene Graph evaluation method to analyze the ability of models to interpret semantic structure. The results provide insights into the robustness of these models, forming future research directions for developing resilient and adaptable robotic systems. Our code is available at https://be2rlab.github.io/OSMa-Bench/.

comment: Project page: https://be2rlab.github.io/OSMa-Bench/

☆ Enhance Exploration in Safe Reinforcement Learning with Contrastive Representation Learning

Duc Kien Doan, Bang Giang Le, Viet Cuong Ta

In safe reinforcement learning, agent needs to balance between exploration actions and safety constraints. Following this paradigm, domain transfer approaches learn a prior Q-function from the related environments to prevent unsafe actions. However, because of the large number of false positives, some safe actions are never executed, leading to inadequate exploration in sparse-reward environments. In this work, we aim to learn an efficient state representation to balance the exploration and safety-prefer action in a sparse-reward environment. Firstly, the image input is mapped to latent representation by an auto-encoder. A further contrastive learning objective is employed to distinguish safe and unsafe states. In the learning phase, the latent distance is used to construct an additional safety check, which allows the agent to bias the exploration if it visits an unsafe state. To verify the effectiveness of our method, the experiment is carried out in three navigation-based MiniGrid environments. The result highlights that our method can explore the environment better while maintaining a good balance between safety and efficiency.

comment: Accepted at ACIIDS 2025

☆ Nash Equilibrium Constrained Auto-bidding With Bi-level Reinforcement Learning

Zhiyu Mou, Miao Xu, Rongquan Bai, Zhuoran Yang, Chuan Yu, Jian Xu, Bo Zheng

Many online advertising platforms provide advertisers with auto-bidding services to enhance their advertising performance. However, most existing auto-bidding algorithms fail to accurately capture the auto-bidding problem formulation that the platform truly faces, let alone solve it. Actually, we argue that the platform should try to help optimize each advertiser's performance to the greatest extent -- which makes $\epsilon$-Nash Equilibrium ($\epsilon$-NE) a necessary solution concept -- while maximizing the social welfare of all the advertisers for the platform's long-term value. Based on this, we introduce the \emph{Nash-Equilibrium Constrained Bidding} (NCB), a new formulation of the auto-bidding problem from the platform's perspective. Specifically, it aims to maximize the social welfare of all advertisers under the $\epsilon$-NE constraint. However, the NCB problem presents significant challenges due to its constrained bi-level structure and the typically large number of advertisers involved. To address these challenges, we propose a \emph{Bi-level Policy Gradient} (BPG) framework with theoretical guarantees. Notably, its computational complexity is independent of the number of advertisers, and the associated gradients are straightforward to compute. Extensive simulated and real-world experiments validate the effectiveness of the BPG framework.

☆ Bilingual Dual-Head Deep Model for Parkinson's Disease Detection from Speech ICASSP 2025

Moreno La Quatra, Juan Rafael Orozco-Arroyave, Marco Sabato Siniscalchi

This work aims to tackle the Parkinson's disease (PD) detection problem from the speech signal in a bilingual setting by proposing an ad-hoc dual-head deep neural architecture for type-based binary classification. One head is specialized for diadochokinetic patterns. The other head looks for natural speech patterns present in continuous spoken utterances. Only one of the two heads is operative accordingly to the nature of the input. Speech representations are extracted from self-supervised learning (SSL) models and wavelet transforms. Adaptive layers, convolutional bottlenecks, and contrastive learning are exploited to reduce variations across languages. Our solution is assessed against two distinct datasets, EWA-DB, and PC-GITA, which cover Slovak and Spanish languages, respectively. Results indicate that conventional models trained on a single language dataset struggle with cross-linguistic generalization, and naive combinations of datasets are suboptimal. In contrast, our model improves generalization on both languages, simultaneously.

comment: Accepted at ICASSP 2025 - Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses

☆ CODEI: Resource-Efficient Task-Driven Co-Design of Perception and Decision Making for Mobile Robots Applied to Autonomous Vehicles

Dejan Milojevic, Gioele Zardini, Miriam Elser, Andrea Censi, Emilio Frazzoli

This paper discusses the integration challenges and strategies for designing mobile robots, by focusing on the task-driven, optimal selection of hardware and software to balance safety, efficiency, and minimal usage of resources such as costs, energy, computational requirements, and weight. We emphasize the interplay between perception and motion planning in decision-making by introducing the concept of occupancy queries to quantify the perception requirements for sampling-based motion planners. Sensor and algorithm performance are evaluated using False Negative Rates (FPR) and False Positive Rates (FPR) across various factors such as geometric relationships, object properties, sensor resolution, and environmental conditions. By integrating perception requirements with perception performance, an Integer Linear Programming (ILP) approach is proposed for efficient sensor and algorithm selection and placement. This forms the basis for a co-design optimization that includes the robot body, motion planner, perception pipeline, and computing unit. We refer to this framework for solving the co-design problem of mobile robots as CODEI, short for Co-design of Embodied Intelligence. A case study on developing an Autonomous Vehicle (AV) for urban scenarios provides actionable information for designers, and shows that complex tasks escalate resource demands, with task performance affecting choices of the autonomy stack. The study demonstrates that resource prioritization influences sensor choice: cameras are preferred for cost-effective and lightweight designs, while lidar sensors are chosen for better energy and computational efficiency.

comment: 20 pages, 33 images, IEEE Transactions on Robotics

☆ PyGDA: A Python Library for Graph Domain Adaptation

Zhen Zhang, Meihan Liu, Bingsheng He

Graph domain adaptation has emerged as a promising approach to facilitate knowledge transfer across different domains. Recently, numerous models have been proposed to enhance their generalization capabilities in this field. However, there is still no unified library that brings together existing techniques and simplifies their implementation. To fill this gap, we introduce PyGDA, an open-source Python library tailored for graph domain adaptation. As the first comprehensive library in this area, PyGDA covers more than 20 widely used graph domain adaptation methods together with different types of graph datasets. Specifically, PyGDA offers modular components, enabling users to seamlessly build custom models with a variety of commonly used utility functions. To handle large-scale graphs, PyGDA includes support for features such as sampling and mini-batch processing, ensuring efficient computation. In addition, PyGDA also includes comprehensive performance benchmarks and well-documented user-friendly API for both researchers and practitioners. To foster convenient accessibility, PyGDA is released under the MIT license at https://github.com/pygda-team/pygda, and the API documentation is https://pygda.readthedocs.io/en/stable/.

comment: Under Review

☆ SurgRAW: Multi-Agent Workflow with Chain-of-Thought Reasoning for Surgical Intelligence

Chang Han Low, Ziyue Wang, Tianyi Zhang, Zhitao Zeng, Zhu Zhuo, Evangelos B. Mazomenos, Yueming Jin

Integration of Vision-Language Models (VLMs) in surgical intelligence is hindered by hallucinations, domain knowledge gaps, and limited understanding of task interdependencies within surgical scenes, undermining clinical reliability. While recent VLMs demonstrate strong general reasoning and thinking capabilities, they still lack the domain expertise and task-awareness required for precise surgical scene interpretation. Although Chain-of-Thought (CoT) can structure reasoning more effectively, current approaches rely on self-generated CoT steps, which often exacerbate inherent domain gaps and hallucinations. To overcome this, we present SurgRAW, a CoT-driven multi-agent framework that delivers transparent, interpretable insights for most tasks in robotic-assisted surgery. By employing specialized CoT prompts across five tasks: instrument recognition, action recognition, action prediction, patient data extraction, and outcome assessment, SurgRAW mitigates hallucinations through structured, domain-aware reasoning. Retrieval-Augmented Generation (RAG) is also integrated to external medical knowledge to bridge domain gaps and improve response reliability. Most importantly, a hierarchical agentic system ensures that CoT-embedded VLM agents collaborate effectively while understanding task interdependencies, with a panel discussion mechanism promotes logical consistency. To evaluate our method, we introduce SurgCoTBench, the first reasoning-based dataset with structured frame-level annotations. With comprehensive experiments, we demonstrate the effectiveness of proposed SurgRAW with 29.32% accuracy improvement over baseline VLMs on 12 robotic procedures, achieving the state-of-the-art performance and advancing explainable, trustworthy, and autonomous surgical assistance.

☆ PIMRL: Physics-Informed Multi-Scale Recurrent Learning for Spatiotemporal Prediction

Han Wan, Qi Wang, Hao Sun

Simulation of spatiotemporal systems governed by partial differential equations is widely applied in fields such as biology, chemistry, aerospace dynamics, and meteorology. Traditional numerical methods incur high computational costs due to the requirement of small time steps for accurate predictions. While machine learning has reduced these costs, long-term predictions remain challenged by error accumulation, particularly in scenarios with insufficient data or varying time scales, where stability and accuracy are compromised. Existing methods often neglect the effective utilization of multi-scale data, leading to suboptimal robustness in predictions. To address these issues, we propose a novel multi-scale learning framework, namely, the Physics-Informed Multi-Scale Recurrent Learning (PIMRL), to effectively leverage multi-scale data for spatiotemporal dynamics prediction. The PIMRL framework comprises two modules: the micro-scale module embeds physical knowledge into neural networks via pretraining, and the macro-scale module adopts a data-driven approach to learn the temporal evolution of physics in the latent space. Experimental results demonstrate that the PIMRL framework consistently achieves state-of-the-art performance across five benchmark datasets ranging from one to three dimensions, showing average improvements of over 9\% in both RMSE and MAE evaluation metrics, with maximum enhancements reaching up to 80%.

☆ LLM Agents Display Human Biases but Exhibit Distinct Learning Patterns

Idan Horowitz, Ori Plonsky

We investigate the choice patterns of Large Language Models (LLMs) in the context of Decisions from Experience tasks that involve repeated choice and learning from feedback, and compare their behavior to human participants. We find that on the aggregate, LLMs appear to display behavioral biases similar to humans: both exhibit underweighting rare events and correlation effects. However, more nuanced analyses of the choice patterns reveal that this happens for very different reasons. LLMs exhibit strong recency biases, unlike humans, who appear to respond in more sophisticated ways. While these different processes may lead to similar behavior on average, choice patterns contingent on recent events differ vastly between the two groups. Specifically, phenomena such as ``surprise triggers change" and the ``wavy recency effect of rare events" are robustly observed in humans, but entirely absent in LLMs. Our findings provide insights into the limitations of using LLMs to simulate and predict humans in learning environments and highlight the need for refined analyses of their behavior when investigating whether they replicate human decision making tendencies.

☆ MinorBench: A hand-built benchmark for content-based risks for children

Shaun Khoo, Gabriel Chua, Rachel Shong

Large Language Models (LLMs) are rapidly entering children's lives - through parent-driven adoption, schools, and peer networks - yet current AI ethics and safety research do not adequately address content-related risks specific to minors. In this paper, we highlight these gaps with a real-world case study of an LLM-based chatbot deployed in a middle school setting, revealing how students used and sometimes misused the system. Building on these findings, we propose a new taxonomy of content-based risks for minors and introduce MinorBench, an open-source benchmark designed to evaluate LLMs on their ability to refuse unsafe or inappropriate queries from children. We evaluate six prominent LLMs under different system prompts, demonstrating substantial variability in their child-safety compliance. Our results inform practical steps for more robust, child-focused safety mechanisms and underscore the urgency of tailoring AI systems to safeguard young users.

☆ Efficient Federated Fine-Tuning of Large Language Models with Layer Dropout

Shilong Wang, Jianchun Liu, Hongli Xu, Jiaming Yan, Xianjun Gao

Fine-tuning plays a crucial role in enabling pre-trained LLMs to evolve from general language comprehension to task-specific expertise. To preserve user data privacy, federated fine-tuning is often employed and has emerged as the de facto paradigm. However, federated fine-tuning is prohibitively inefficient due to the tension between LLM complexity and the resource constraint of end devices, incurring unaffordable fine-tuning overhead. Existing literature primarily utilizes parameter-efficient fine-tuning techniques to mitigate communication costs, yet computational and memory burdens continue to pose significant challenges for developers. This work proposes DropPEFT, an innovative federated PEFT framework that employs a novel stochastic transformer layer dropout method, enabling devices to deactivate a considerable fraction of LLMs layers during training, thereby eliminating the associated computational load and memory footprint. In DropPEFT, a key challenge is the proper configuration of dropout ratios for layers, as overhead and training performance are highly sensitive to this setting. To address this challenge, we adaptively assign optimal dropout-ratio configurations to devices through an exploration-exploitation strategy, achieving efficient and effective fine-tuning. Extensive experiments show that DropPEFT can achieve a 1.3-6.3\times speedup in model convergence and a 40%-67% reduction in memory footprint compared to state-of-the-art methods.

comment: 13 pages

☆ Adaptive Preference Aggregation

Benjamin Heymann

AI alignment, the challenge of ensuring AI systems act in accordance with human values, has emerged as a critical problem in the development of systems such as foundation models and recommender systems. Still, the current dominant approach, reinforcement learning with human feedback (RLHF) faces known theoretical limitations in aggregating diverse human preferences. Social choice theory provides a framework to aggregate preferences, but was not developed for the multidimensional applications typical of AI. Leveraging insights from a recently published urn process, this work introduces a preference aggregation strategy that adapts to the user's context and that inherits the good properties of the maximal lottery, a Condorcet-consistent solution concept.

☆ Deep Learning for Time Series Forecasting: A Survey

Xiangjie Kong, Zhenghao Chen, Weiyao Liu, Kaili Ning, Lechao Zhang, Syauqie Muhammad Marier, Yichen Liu, Yuhao Chen, Feng Xia

Time series forecasting (TSF) has long been a crucial task in both industry and daily life. Most classical statistical models may have certain limitations when applied to practical scenarios in fields such as energy, healthcare, traffic, meteorology, and economics, especially when high accuracy is required. With the continuous development of deep learning, numerous new models have emerged in the field of time series forecasting in recent years. However, existing surveys have not provided a unified summary of the wide range of model architectures in this field, nor have they given detailed summaries of works in feature extraction and datasets. To address this gap, in this review, we comprehensively study the previous works and summarize the general paradigms of Deep Time Series Forecasting (DTSF) in terms of model architectures. Besides, we take an innovative approach by focusing on the composition of time series and systematically explain important feature extraction methods. Additionally, we provide an overall compilation of datasets from various domains in existing works. Finally, we systematically emphasize the significant challenges faced and future research directions in this field.

☆ Predicting Chemical Reaction Outcomes Based on Electron Movements Using Machine Learning

Shuan Chen, Kye Sung Park, Taewan Kim, Sunkyu Han, Yousung Jung

Accurately predicting chemical reaction outcomes and potential byproducts is a fundamental task of modern chemistry, enabling the efficient design of synthetic pathways and driving progress in chemical science. Reaction mechanism, which tracks electron movements during chemical reactions, is critical for understanding reaction kinetics and identifying unexpected products. Here, we present Reactron, the first electron-based machine learning model for general reaction prediction. Reactron integrates electron movement into its predictions, generating detailed arrow-pushing diagrams that elucidate each mechanistic step leading to product formation. We demonstrate the high predictive performance of Reactron over existing product-only models by a large-scale reaction outcome prediction benchmark, and the adaptability of the model to learn new reactivity upon providing a few examples. Furthermore, it explores combinatorial reaction spaces, uncovering novel reactivities beyond its training data. With robust performance in both in- and out-of-distribution predictions, Reactron embodies human-like reasoning in chemistry and opens new frontiers in reaction discovery and synthesis design.

comment: 15 pages, 3 figures

☆ Robustness Tokens: Towards Adversarial Robustness of Transformers ECCV

Brian Pulfer, Yury Belousov, Slava Voloshynovskiy

Recently, large pre-trained foundation models have become widely adopted by machine learning practitioners for a multitude of tasks. Given that such models are publicly available, relying on their use as backbone models for downstream tasks might result in high vulnerability to adversarial attacks crafted with the same public model. In this work, we propose Robustness Tokens, a novel approach specific to the transformer architecture that fine-tunes a few additional private tokens with low computational requirements instead of tuning model parameters as done in traditional adversarial training. We show that Robustness Tokens make Vision Transformer models significantly more robust to white-box adversarial attacks while also retaining the original downstream performances.

comment: This paper has been accepted for publication at the European Conference on Computer Vision (ECCV), 2024

☆ Multi-Agent Q-Learning Dynamics in Random Networks: Convergence due to Exploration and Sparsity

Aamal Hussain, Dan Leonte, Francesco Belardinelli, Raphael Huser, Dario Paccagnan

Beyond specific settings, many multi-agent learning algorithms fail to converge to an equilibrium solution, and instead display complex, non-stationary behaviours such as recurrent or chaotic orbits. In fact, recent literature suggests that such complex behaviours are likely to occur when the number of agents increases. In this paper, we study Q-learning dynamics in network polymatrix games where the network structure is drawn from classical random graph models. In particular, we focus on the Erdos-Renyi model, a well-studied model for social networks, and the Stochastic Block model, which generalizes the above by accounting for community structures within the network. In each setting, we establish sufficient conditions under which the agents' joint strategies converge to a unique equilibrium. We investigate how this condition depends on the exploration rates, payoff matrices and, crucially, the sparsity of the network. Finally, we validate our theoretical findings through numerical simulations and demonstrate that convergence can be reliably achieved in many-agent systems, provided network sparsity is controlled.

☆ Through the Magnifying Glass: Adaptive Perception Magnification for Hallucination-Free VLM Decoding

Shunqi Mao, Chaoyi Zhang, Weidong Cai

Existing vision-language models (VLMs) often suffer from visual hallucination, where the generated responses contain inaccuracies that are not grounded in the visual input. Efforts to address this issue without model finetuning primarily mitigate hallucination by reducing biases contrastively or amplifying the weights of visual embedding during decoding. However, these approaches improve visual perception at the cost of impairing the language reasoning capability. In this work, we propose the Perception Magnifier (PM), a novel visual decoding method that iteratively isolates relevant visual tokens based on attention and magnifies the corresponding regions, spurring the model to concentrate on fine-grained visual details during decoding. Specifically, by magnifying critical regions while preserving the structural and contextual information at each decoding step, PM allows the VLM to enhance its scrutiny of the visual input, hence producing more accurate and faithful responses. Extensive experimental results demonstrate that PM not only achieves superior hallucination mitigation but also enhances language generation while preserving strong reasoning capabilities.Code is available at https://github.com/ShunqiM/PM .

comment: 19 pages, 5 figures, 9 tables

☆ ImageScope: Unifying Language-Guided Image Retrieval via Large Multimodal Model Collective Reasoning WWW 2025

Pengfei Luo, Jingbo Zhou, Tong Xu, Yuan Xia, Linli Xu, Enhong Chen

With the proliferation of images in online content, language-guided image retrieval (LGIR) has emerged as a research hotspot over the past decade, encompassing a variety of subtasks with diverse input forms. While the development of large multimodal models (LMMs) has significantly facilitated these tasks, existing approaches often address them in isolation, requiring the construction of separate systems for each task. This not only increases system complexity and maintenance costs, but also exacerbates challenges stemming from language ambiguity and complex image content, making it difficult for retrieval systems to provide accurate and reliable results. To this end, we propose ImageScope, a training-free, three-stage framework that leverages collective reasoning to unify LGIR tasks. The key insight behind the unification lies in the compositional nature of language, which transforms diverse LGIR tasks into a generalized text-to-image retrieval process, along with the reasoning of LMMs serving as a universal verification to refine the results. To be specific, in the first stage, we improve the robustness of the framework by synthesizing search intents across varying levels of semantic granularity using chain-of-thought (CoT) reasoning. In the second and third stages, we then reflect on retrieval results by verifying predicate propositions locally, and performing pairwise evaluations globally. Experiments conducted on six LGIR datasets demonstrate that ImageScope outperforms competitive baselines. Comprehensive evaluations and ablation studies further confirm the effectiveness of our design.

comment: WWW 2025

☆ Retrieval-Augmented Generation with Hierarchical Knowledge

Haoyu Huang, Yongfeng Huang, Junjie Yang, Zhenyu Pan, Yongqiang Chen, Kaili Ma, Hongzhi Chen, James Cheng

Graph-based Retrieval-Augmented Generation (RAG) methods have significantly enhanced the performance of large language models (LLMs) in domain-specific tasks. However, existing RAG methods do not adequately utilize the naturally inherent hierarchical knowledge in human cognition, which limits the capabilities of RAG systems. In this paper, we introduce a new RAG approach, called HiRAG, which utilizes hierarchical knowledge to enhance the semantic understanding and structure capturing capabilities of RAG systems in the indexing and retrieval processes. Our extensive experiments demonstrate that HiRAG achieves significant performance improvements over the state-of-the-art baseline methods. The code of our proposed method is available at \href{https://github.com/hhy-huang/HiRAG}{https://github.com/hhy-huang/HiRAG}.

☆ Multiplicative Learning

Han Kim, Hyungjoon Soh, Vipul Periwal, Junghyo Jo

Efficient training of artificial neural networks remains a key challenge in deep learning. Backpropagation (BP), the standard learning algorithm, relies on gradient descent and typically requires numerous iterations for convergence. In this study, we introduce Expectation Reflection (ER), a novel learning approach that updates weights multiplicatively based on the ratio of observed to predicted outputs. Unlike traditional methods, ER maintains consistency without requiring ad hoc loss functions or learning rate hyperparameters. We extend ER to multilayer networks and demonstrate its effectiveness in performing image classification tasks. Notably, ER achieves optimal weight updates in a single iteration. Additionally, we reinterpret ER as a modified form of gradient descent incorporating the inverse mapping of target propagation. These findings suggest that ER provides an efficient and scalable alternative for training neural networks.

☆ Gumiho: A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding

Jinze Li, Yixing Xu, Haiduo Huang, Xuanwu Yin, Dong Li, Edith C. H. Ngai, Emad Barsoum

Speculative decoding (SPD) aims to accelerate the auto-regressive token generation process of a target Large Language Model (LLM). Some approaches employ a draft model with multiple heads to predict a sequence of future tokens, where each head handles a token in the sequence. The target LLM verifies the predicted sequence and accepts aligned tokens, enabling efficient multi-token generation. However, existing methods assume that all tokens within a sequence are equally important, employing identical head structures and relying on a single-generation paradigm, either serial or parallel. To this end, we theoretically demonstrate that initial tokens in the draft sequence are more important than later ones. Building on this insight, we propose Gumiho, a hybrid model combining serial and parallel heads. Specifically, given the critical importance of early tokens, we employ a sophisticated Transformer architecture for the early draft heads in a serial configuration to improve accuracy. For later tokens, we utilize multiple lightweight MLP heads operating in parallel to enhance efficiency. By allocating more advanced model structures and longer running times to the early heads, Gumiho achieves improved overall performance. The experimental results demonstrate that our method outperforms existing approaches, fully validating its effectiveness.

comment: Paper under review

☆ Deep Learning-Based Direct Leaf Area Estimation using Two RGBD Datasets for Model Development

Namal Jayasuriya, Yi Guo, Wen Hu, Oula Ghannoum

Estimation of a single leaf area can be a measure of crop growth and a phenotypic trait to breed new varieties. It has also been used to measure leaf area index and total leaf area. Some studies have used hand-held cameras, image processing 3D reconstruction and unsupervised learning-based methods to estimate the leaf area in plant images. Deep learning works well for object detection and segmentation tasks; however, direct area estimation of objects has not been explored. This work investigates deep learning-based leaf area estimation, for RGBD images taken using a mobile camera setup in real-world scenarios. A dataset for attached leaves captured with a top angle view and a dataset for detached single leaves were collected for model development and testing. First, image processing-based area estimation was tested on manually segmented leaves. Then a Mask R-CNN-based model was investigated, and modified to accept RGBD images and to estimate the leaf area. The detached-leaf data set was then mixed with the attached-leaf plant data set to estimate the single leaf area for plant images, and another network design with two backbones was proposed: one for segmentation and the other for area estimation. Instead of trying all possibilities or random values, an agile approach was used in hyperparameter tuning. The final model was cross-validated with 5-folds and tested with two unseen datasets: detached and attached leaves. The F1 score with 90% IoA for segmentation result on unseen detached-leaf data was 1.0, while R-squared of area estimation was 0.81. For unseen plant data segmentation, the F1 score with 90% IoA was 0.59, while the R-squared score was 0.57. The research suggests using attached leaves with ground truth area to improve the results.

☆ StepMathAgent: A Step-Wise Agent for Evaluating Mathematical Processes through Tree-of-Error

Shu-Xun Yang, Cunxiang Wang, Yidong Wang, Xiaotao Gu, Minlie Huang, Jie Tang

Evaluating mathematical capabilities is critical for assessing the overall performance of large language models (LLMs). However, existing evaluation methods often focus solely on final answers, resulting in highly inaccurate and uninterpretable evaluation outcomes, as well as their failure to assess proof or open-ended problems. To address these issues, we propose a novel mathematical process evaluation agent based on Tree-of-Error, called StepMathAgent. This agent incorporates four internal core operations: logical step segmentation, step scoring, score aggregation and error tree generation, along with four external extension modules: difficulty calibration, simplicity evaluation, completeness validation and format assessment. Furthermore, we introduce StepMathBench, a benchmark comprising 1,000 step-divided process evaluation instances, derived from 200 high-quality math problems grouped by problem type, subject category and difficulty level. Experiments on StepMathBench show that our proposed StepMathAgent outperforms all state-of-the-art methods, demonstrating human-aligned evaluation preferences and broad applicability to various scenarios. Our data and code are available at https://github.com/SHU-XUN/StepMathAgent.

☆ Cognitive-Mental-LLM: Leveraging Reasoning in Large Language Models for Mental Health Prediction via Online Text

Avinash Patil, Amardeep Kour Gedhu

Large Language Models (LLMs) have demonstrated potential in predicting mental health outcomes from online text, yet traditional classification methods often lack interpretability and robustness. This study evaluates structured reasoning techniques-Chain-of-Thought (CoT), Self-Consistency (SC-CoT), and Tree-of-Thought (ToT)-to improve classification accuracy across multiple mental health datasets sourced from Reddit. We analyze reasoning-driven prompting strategies, including Zero-shot CoT and Few-shot CoT, using key performance metrics such as Balanced Accuracy, F1 score, and Sensitivity/Specificity. Our findings indicate that reasoning-enhanced techniques improve classification performance over direct prediction, particularly in complex cases. Compared to baselines such as Zero Shot non-CoT Prompting, and fine-tuned pre-trained transformers such as BERT and Mental-RoBerta, and fine-tuned Open Source LLMs such as Mental Alpaca and Mental-Flan-T5, reasoning-driven LLMs yield notable gains on datasets like Dreaddit (+0.52\% over M-LLM, +0.82\% over BERT) and SDCNL (+4.67\% over M-LLM, +2.17\% over BERT). However, performance declines in Depression Severity, and CSSRS predictions suggest dataset-specific limitations, likely due to our using a more extensive test set. Among prompting strategies, Few-shot CoT consistently outperforms others, reinforcing the effectiveness of reasoning-driven LLMs. Nonetheless, dataset variability highlights challenges in model reliability and interpretability. This study provides a comprehensive benchmark of reasoning-based LLM techniques for mental health text classification. It offers insights into their potential for scalable clinical applications while identifying key challenges for future improvements.

comment: 8 pages, 4 Figures, 3 tables

☆ Semantic Synergy: Unlocking Policy Insights and Learning Pathways Through Advanced Skill Mapping

Phoebe Koundouri, Conrad Landis, Georgios Feretzakis

This research introduces a comprehensive system based on state-of-the-art natural language processing, semantic embedding, and efficient search techniques for retrieving similarities and thus generating actionable insights from raw textual information. The system automatically extracts and aggregates normalized competencies from multiple documents (such as policy files and curricula vitae) and creates strong relationships between recognized competencies, occupation profiles, and related learning courses. To validate its performance, we conducted a multi-tier evaluation that included both explicit and implicit skill references in synthetic and real-world documents. The results showed near-human-level accuracy, with F1 scores exceeding 0.95 for explicit skill detection and above 0.93 for implicit mentions. The system thereby establishes a sound foundation for supporting in-depth collaboration across the AE4RIA network. The methodology involves a multi-stage pipeline based on extensive preprocessing and data cleaning, semantic embedding and segmentation via SentenceTransformer, and skill extraction using a FAISS-based search method. The extracted skills are associated with occupation frameworks (as formulated in the ESCO ontology) and with learning paths offered through the Sustainable Development Goals Academy. Moreover, interactive visualization software, implemented with Dash and Plotly, presents graphs and tables for real-time exploration and informed decision-making by those involved in policymaking, training and learning supply, career transitions, and recruitment. Overall, this system, backed by rigorous validation, offers promising prospects for improved policymaking, human resource development, and lifelong learning by providing structured and actionable insights from raw, complex textual information.

☆ Parallelizing Multi-objective A* Search

Saman Ahmadi, Nathan R. Sturtevant, Andrea Raith, Daniel Harabor, Mahdi Jalili

The Multi-objective Shortest Path (MOSP) problem is a classic network optimization problem that aims to find all Pareto-optimal paths between two points in a graph with multiple edge costs. Recent studies on multi-objective search with A* (MOA*) have demonstrated superior performance in solving difficult MOSP instances. This paper presents a novel search framework that allows efficient parallelization of MOA* with different objective orders. The framework incorporates a unique upper bounding strategy that helps the search reduce the problem's dimensionality to one in certain cases. Experimental results demonstrate that the proposed framework can enhance the performance of recent A*-based solutions, with the speed-up proportional to the problem dimension.

comment: 8 page, 2 tables, 2 figures

☆ Advanced Tool Learning and Selection System (ATLASS): A Closed-Loop Framework Using LLM

Mohd Ariful Haque, Justin Williams, Sunzida Siddique, Md. Hujaifa Islam, Hasmot Ali, Kishor Datta Gupta, Roy George

The combination of LLM agents with external tools enables models to solve complex tasks beyond their knowledge base. Human-designed tools are inflexible and restricted to solutions within the scope of pre-existing tools created by experts. To address this problem, we propose ATLASS, an advanced tool learning and selection system designed as a closed-loop framework. It enables the LLM to solve problems by dynamically generating external tools on demand. In this framework, agents play a crucial role in orchestrating tool selection, execution, and refinement, ensuring adaptive problem-solving capabilities. The operation of ATLASS follows three phases: The first phase, Understanding Tool Requirements, involves the Agents determining whether tools are required and specifying their functionality; the second phase, Tool Retrieval/Generation, involves the Agents retrieving or generating tools based on their availability; and the third phase, Task Solving, involves combining all the component tools necessary to complete the initial task. The Tool Dataset stores the generated tools, ensuring reusability and minimizing inference cost. Current LLM-based tool generation systems have difficulty creating complex tools that need APIs or external packages. In ATLASS, we solve the problem by automatically setting up the environment, fetching relevant API documentation online, and using a Python interpreter to create a reliable, versatile tool that works in a wider range of situations. OpenAI GPT-4.0 is used as the LLM agent, and safety and ethical concerns are handled through human feedback before executing generated code. By addressing the limitations of predefined toolsets and enhancing adaptability, ATLASS serves as a real-world solution that empowers users with dynamically generated tools for complex problem-solving.

☆ AhaRobot: A Low-Cost Open-Source Bimanual Mobile Manipulator for Embodied AI

Haiqin Cui, Yifu Yuan, Yan Zheng, Jianye Hao

Navigation and manipulation in open-world environments remain unsolved challenges in the Embodied AI. The high cost of commercial mobile manipulation robots significantly limits research in real-world scenes. To address this issue, we propose AhaRobot, a low-cost and fully open-source dual-arm mobile manipulation robot system with a hardware cost of only $1,000 (excluding optional computational resources), which is less than 1/15 of the cost of popular mobile robots. The AhaRobot system consists of three components: (1) a novel low-cost hardware architecture primarily composed of off-the-shelf components, (2) an optimized control solution to enhance operational precision integrating dual-motor backlash control and static friction compensation, and (3) a simple remote teleoperation method RoboPilot. We use handles to control the dual arms and pedals for whole-body movement. The teleoperation process is low-burden and easy to operate, much like piloting. RoboPilot is designed for remote data collection in embodied scenarios. Experimental results demonstrate that RoboPilot significantly enhances data collection efficiency in complex manipulation tasks, achieving a 30% increase compared to methods using 3D mouse and leader-follower systems. It also excels at completing extremely long-horizon tasks in one go. Furthermore, AhaRobot can be used to learn end-to-end policies and autonomously perform complex manipulation tasks, such as pen insertion and cleaning up the floor. We aim to build an affordable yet powerful platform to promote the development of embodied tasks on real devices, advancing more robust and reliable embodied AI. All hardware and software systems are available at https://aha-robot.github.io.

comment: The first two authors contributed equally. Website: https://aha-robot.github.io

☆ Compute Optimal Scaling of Skills: Knowledge vs Reasoning

Nicholas Roberts, Niladri Chatterji, Sharan Narang, Mike Lewis, Dieuwke Hupkes

Scaling laws are a critical component of the LLM development pipeline, most famously as a way to forecast training decisions such as 'compute-optimally' trading-off parameter count and dataset size, alongside a more recent growing list of other crucial decisions. In this work, we ask whether compute-optimal scaling behaviour can be skill-dependent. In particular, we examine knowledge and reasoning-based skills such as knowledge-based QA and code generation, and we answer this question in the affirmative: $\textbf{scaling laws are skill-dependent}$. Next, to understand whether skill-dependent scaling is an artefact of the pretraining datamix, we conduct an extensive ablation of different datamixes and find that, also when correcting for datamix differences, $\textbf{knowledge and code exhibit fundamental differences in scaling behaviour}$. We conclude with an analysis of how our findings relate to standard compute-optimal scaling using a validation set, and find that $\textbf{a misspecified validation set can impact compute-optimal parameter count by nearly 50%,}$ depending on its skill composition.

☆ Deep Learning Approaches for Anti-Money Laundering on Mobile Transactions: Review, Framework, and Directions

Jiani Fan, Lwin Khin Shar, Ruichen Zhang, Ziyao Liu, Wenzhuo Yang, Dusit Niyato, Bomin Mao, Kwok-Yan Lam

Money laundering is a financial crime that obscures the origin of illicit funds, necessitating the development and enforcement of anti-money laundering (AML) policies by governments and organizations. The proliferation of mobile payment platforms and smart IoT devices has significantly complicated AML investigations. As payment networks become more interconnected, there is an increasing need for efficient real-time detection to process large volumes of transaction data on heterogeneous payment systems by different operators such as digital currencies, cryptocurrencies and account-based payments. Most of these mobile payment networks are supported by connected devices, many of which are considered loT devices in the FinTech space that constantly generate data. Furthermore, the growing complexity and unpredictability of transaction patterns across these networks contribute to a higher incidence of false positives. While machine learning solutions have the potential to enhance detection efficiency, their application in AML faces unique challenges, such as addressing privacy concerns tied to sensitive financial data and managing the real-world constraint of limited data availability due to data regulations. Existing surveys in the AML literature broadly review machine learning approaches for money laundering detection, but they often lack an in-depth exploration of advanced deep learning techniques - an emerging field with significant potential. To address this gap, this paper conducts a comprehensive review of deep learning solutions and the challenges associated with their use in AML. Additionally, we propose a novel framework that applies the least-privilege principle by integrating machine learning techniques, codifying AML red flags, and employing account profiling to provide context for predictions and enable effective fraud detection under limited data availability....

☆ DTA: Dual Temporal-channel-wise Attention for Spiking Neural Networks WACV

Minje Kim, Minjun Kim, Xu Yang

Spiking Neural Networks (SNNs) present a more energy-efficient alternative to Artificial Neural Networks (ANNs) by harnessing spatio-temporal dynamics and event-driven spikes. Effective utilization of temporal information is crucial for SNNs, leading to the exploration of attention mechanisms to enhance this capability. Conventional attention operations either apply identical operation or employ non-identical operations across target dimensions. We identify that these approaches provide distinct perspectives on temporal information. To leverage the strengths of both operations, we propose a novel Dual Temporal-channel-wise Attention (DTA) mechanism that integrates both identical/non-identical attention strategies. To the best of our knowledge, this is the first attempt to concentrate on both the correlation and dependency of temporal-channel using both identical and non-identical attention operations. Experimental results demonstrate that the DTA mechanism achieves state-of-the-art performance on both static datasets (CIFAR10, CIFAR100, ImageNet-1k) and dynamic dataset (CIFAR10-DVS), elevating spike representation and capturing complex temporal-channel relationship. We open-source our code: https://github.com/MnJnKIM/DTA-SNN.

comment: Accepted by IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2025

☆ Rapid analysis of point-contact Andreev reflection spectra via machine learning with adaptive data augmentation

Dongik Lee, Valentin Stanev, Xiaohang Zhang, Mijeong Kang, Ichiro Takeuchi, Seunghun Lee

Delineating the superconducting order parameters is a pivotal task in investigating superconductivity for probing pairing mechanisms, as well as their symmetry and topology. Point-contact Andreev reflection (PCAR) measurement is a simple yet powerful tool for identifying the order parameters. The PCAR spectra exhibit significant variations depending on the type of the order parameter in a superconductor, including its magnitude ($\mathit{\Delta}$), as well as temperature, interfacial quality, Fermi velocity mismatch, and other factors. The information on the order parameter can be obtained by finding the combination of these parameters, generating a theoretical spectrum that fits a measured experimental spectrum. However, due to the complexity of the spectra and the high dimensionality of parameters, extracting the fitting parameters is often time-consuming and labor-intensive. In this study, we employ a convolutional neural network (CNN) algorithm to create models for rapid and automated analysis of PCAR spectra of various superconductors with different pairing symmetries (conventional $s$-wave, chiral $p_x+ip_y$-wave, and $d_{x^2-y^2}$-wave). The training datasets are generated based on the Blonder-Tinkham-Klapwijk (BTK) theory and further modified and augmented by selectively incorporating noise and peaks according to the bias voltages. This approach not only replicates the experimental spectra but also brings the model's attention to important features within the spectra. The optimized models provide fitting parameters for experimentally measured spectra in less than 100 ms per spectrum. Our approaches and findings pave the way for rapid and automated spectral analysis which will help accelerate research on superconductors with complex order parameters.

comment: 18 pages, 3 figures

☆ OR-LLM-Agent: Automating Modeling and Solving of Operations Research Optimization Problem with Reasoning Large Language Model

Bowen Zhang, Pengcheng Luo

Operations Research (OR) has been widely applied in various fields such as resource allocation, production planning, and supply chain management. However, addressing real-world OR problems requires OR experts to perform mathematical modeling and programmers to develop solution algorithms. This traditional method, heavily reliant on experts, is costly and has long development cycles, severely limiting the widespread adoption of OR techniques. Few have considered using Artificial Intelligence (AI) to replace professionals to achieve fully automated solutions for OR problems. We propose OR-LLM-Agent, the first AI agent that enables end-to-end automation for solving real-world OR problems. OR-LLM-Agent leverages the Chain-of-Thought (CoT) reasoning capabilities of Large Language Models (LLMs) to translate natural language problem descriptions into formal mathematical models and automatically generate Gurobi solver code. In OR-LLM-Agent, OR-CodeAgent is designed to automate code execution and repair within a sandbox environment, facilitating the derivation of the final solution. Due to the lack of dedicated benchmark datasets for evaluating the automated solving of OR problems, we construct a benchmark dataset comprising 83 real-world OR problems described in natural language. We conduct comparative experiments with state-of-the-art (SOTA) reasoning LLMs, including GPT-o3-mini, DeepSeek-R1, and Gemini 2.0 Flash Thinking. The OR-LLM-Agent achieved the highest pass rate of 100% and the highest solution accuracy of 85%, demonstrating the feasibility of automated OR problem-solving. Data and code have been publicly available at https://github.com/bwz96sco/or_llm_agent.

comment: 11 pages, 6 figures

☆ A New Benchmark for Few-Shot Class-Incremental Learning: Redefining the Upper Bound

Shiwon Kim, Dongjun Hwang, Sungwon Woo, Rita Singh

Class-incremental learning (CIL) aims to continuously adapt to emerging classes while retaining knowledge of previously learned ones. Few-shot class-incremental learning (FSCIL) presents an even greater challenge which requires the model to learn incremental classes with only a limited number of samples. In conventional CIL, joint training is widely considered the upper bound, serving as both a benchmark and a methodological guide. However, we find that joint training fails to be a meaningful upper bound in FSCIL due to the inherent difficulty of inter-task class separation (ICS) caused by severe class imbalance. In this work, we introduce a new joint training benchmark tailored for FSCIL by integrating imbalance-aware techniques, effectively bridging the performance gap between base and incremental classes. Furthermore, we point out inconsistencies in the experimental setup and evaluation of existing FSCIL methods. To ensure fair comparisons between different FSCIL approaches and joint training, we standardize training conditions and propose a unified evaluation protocol that simultaneously considers the validation set and computational complexity. By establishing a reliable upper bound and a standardized evaluation framework for FSCIL, our work provides a clear benchmark and a practical foundation for future research.

☆ Label Unbalance in High-frequency Trading

Zijian Zhao, Xuming Chen, Jiayu Wen, Mingwen Liu, Xiaoteng Ma

In financial trading, return prediction is one of the foundation for a successful trading system. By the fast development of the deep learning in various areas such as graphical processing, natural language, it has also demonstrate significant edge in handling with financial data. While the success of the deep learning relies on huge amount of labeled sample, labeling each time/event as profitable or unprofitable, under the transaction cost, especially in the high-frequency trading world, suffers from serious label imbalance issue.In this paper, we adopts rigurious end-to-end deep learning framework with comprehensive label imbalance adjustment methods and succeed in predicting in high-frequency return in the Chinese future market. The code for our method is publicly available at https://github.com/RS2002/Label-Unbalance-in-High-Frequency-Trading .

comment: Technical Report

☆ Uncertainty-aware Long-tailed Weights Model the Utility of Pseudo-labels for Semi-supervised Learning

Jiaqi Wu, Junbiao Pang, Qingming Huang

Current Semi-supervised Learning (SSL) adopts the pseudo-labeling strategy and further filters pseudo-labels based on confidence thresholds. However, this mechanism has notable drawbacks: 1) setting the reasonable threshold is an open problem which significantly influences the selection of the high-quality pseudo-labels; and 2) deep models often exhibit the over-confidence phenomenon which makes the confidence value an unreliable indicator for assessing the quality of pseudo-labels due to the scarcity of labeled data. In this paper, we propose an Uncertainty-aware Ensemble Structure (UES) to assess the utility of pseudo-labels for unlabeled samples. We further model the utility of pseudo-labels as long-tailed weights to avoid the open problem of setting the threshold. Concretely, the advantage of the long-tailed weights ensures that even unreliable pseudo-labels still contribute to enhancing the model's robustness. Besides, UES is lightweight and architecture-agnostic, easily extending to various computer vision tasks, including classification and regression. Experimental results demonstrate that combining the proposed method with DualPose leads to a 3.47% improvement in Percentage of Correct Keypoints (PCK) on the Sniffing dataset with 100 data points (30 labeled), a 7.29\% improvement in PCK on the FLIC dataset with 100 data points (50 labeled), and a 3.91% improvement in PCK on the LSP dataset with 200 data points (100 labeled). Furthermore, when combined with FixMatch, the proposed method achieves a 0.2% accuracy improvement on the CIFAR-10 dataset with 40 labeled data points and a 0.26% accuracy improvement on the CIFAR-100 dataset with 400 labeled data points.

comment: arXiv admin note: text overlap with arXiv:2408.04150

☆ Detecting Dataset Bias in Medical AI: A Generalized and Modality-Agnostic Auditing Framework

Nathan Drenkow, Mitchell Pavlak, Keith Harrigian, Ayah Zirikly, Adarsh Subbaswamy, Mathias Unberath

Data-driven AI is establishing itself at the center of evidence-based medicine. However, reports of shortcomings and unexpected behavior are growing due to AI's reliance on association-based learning. A major reason for this behavior: latent bias in machine learning datasets can be amplified during training and/or hidden during testing. We present a data modality-agnostic auditing framework for generating targeted hypotheses about sources of bias which we refer to as Generalized Attribute Utility and Detectability-Induced bias Testing (G-AUDIT) for datasets. Our method examines the relationship between task-level annotations and data properties including protected attributes (e.g., race, age, sex) and environment and acquisition characteristics (e.g., clinical site, imaging protocols). G-AUDIT automatically quantifies the extent to which the observed data attributes may enable shortcut learning, or in the case of testing data, hide predictions made based on spurious associations. We demonstrate the broad applicability and value of our method by analyzing large-scale medical datasets for three distinct modalities and learning tasks: skin lesion classification in images, stigmatizing language classification in Electronic Health Records (EHR), and mortality prediction for ICU tabular data. In each setting, G-AUDIT successfully identifies subtle biases commonly overlooked by traditional qualitative methods that focus primarily on social and ethical objectives, underscoring its practical value in exposing dataset-level risks and supporting the downstream development of reliable AI systems. Our method paves the way for achieving deeper understanding of machine learning datasets throughout the AI development life-cycle from initial prototyping all the way to regulation, and creates opportunities to reduce model bias, enabling safer and more trustworthy AI systems.

☆ Optimizing Fire Safety: Reducing False Alarms Using Advanced Machine Learning Techniques

Muhammad Hassan Jamal, Abdulwahab Alazeb, Shahid Allah Bakhsh, Wadii Boulila, Syed Aziz Shah, Aizaz Ahmad Khattak, Muhammad Shahbaz Khan

Fire safety practices are important to reduce the extent of destruction caused by fire. While smoke alarms help save lives, firefighters struggle with the increasing number of false alarms. This paper presents a precise and efficient Weighted ensemble model for decreasing false alarms. It estimates the density, computes weights according to the high and low-density regions, forwards the high region weights to KNN and low region weights to XGBoost and combines the predictions. The proposed model is effective at reducing response time, increasing fire safety, and minimizing the damage that fires cause. A specifically designed dataset for smoke detection is utilized to test the proposed model. In addition, a variety of ML models, such as Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Nai:ve Bayes (NB), K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Adaptive Boosting (ADAB), have also been utilized. To maximize the use of the smoke detection dataset, all the algorithms utilize the SMOTE re-sampling technique. After evaluating the assessment criteria, this paper presents a concise summary of the comprehensive findings obtained by comparing the outcomes of all models.

☆ Exploring Mutual Empowerment Between Wireless Networks and RL-based LLMs: A Survey

Yu Qiao, Phuong-Nam Tran, Ji Su Yoon, Loc X. Nguyen, Choong Seon Hong

Reinforcement learning (RL)-based large language models (LLMs), such as ChatGPT, DeepSeek, and Grok-3, have gained significant attention for their exceptional capabilities in natural language processing and multimodal data understanding. Meanwhile, the rapid expansion of information services has driven the growing need for intelligence, efficient, and adaptable wireless networks. Wireless networks require the empowerment of RL-based LLMs while these models also benefit from wireless networks to broaden their application scenarios. Specifically, RL-based LLMs can enhance wireless communication systems through intelligent resource allocation, adaptive network optimization, and real-time decision-making. Conversely, wireless networks provide a vital infrastructure for the efficient training, deployment, and distributed inference of RL-based LLMs, especially in decentralized and edge computing environments. This mutual empowerment highlights the need for a deeper exploration of the interplay between these two domains. We first review recent advancements in wireless communications, highlighting the associated challenges and potential solutions. We then discuss the progress of RL-based LLMs, focusing on key technologies for LLM training, challenges, and potential solutions. Subsequently, we explore the mutual empowerment between these two fields, highlighting key motivations, open challenges, and potential solutions. Finally, we provide insights into future directions, applications, and their societal impact to further explore this intersection, paving the way for next-generation intelligent communication systems. Overall, this survey provides a comprehensive overview of the relationship between RL-based LLMs and wireless networks, offering a vision where these domains empower each other to drive innovations.

comment: 25 pages, 13 figures

☆ MoFlow: One-Step Flow Matching for Human Trajectory Forecasting via Implicit Maximum Likelihood Estimation based Distillation CVPR 2025

Yuxiang Fu, Qi Yan, Lele Wang, Ke Li, Renjie Liao

In this paper, we address the problem of human trajectory forecasting, which aims to predict the inherently multi-modal future movements of humans based on their past trajectories and other contextual cues. We propose a novel motion prediction conditional flow matching model, termed MoFlow, to predict K-shot future trajectories for all agents in a given scene. We design a novel flow matching loss function that not only ensures at least one of the $K$ sets of future trajectories is accurate but also encourages all $K$ sets of future trajectories to be diverse and plausible. Furthermore, by leveraging the implicit maximum likelihood estimation (IMLE), we propose a novel distillation method for flow models that only requires samples from the teacher model. Extensive experiments on the real-world datasets, including SportVU NBA games, ETH-UCY, and SDD, demonstrate that both our teacher flow model and the IMLE-distilled student model achieve state-of-the-art performance. These models can generate diverse trajectories that are physically and socially plausible. Moreover, our one-step student model is $\textbf{100}$ times faster than the teacher flow model during sampling. The code, model, and data are available at our project page: https://moflow-imle.github.io

comment: Accepted to CVPR 2025

☆ Identifying Trustworthiness Challenges in Deep Learning Models for Continental-Scale Water Quality Prediction

Xiaobo Xia, Xiaofeng Liu, Jiale Liu, Kuai Fang, Lu Lu, Samet Oymak, William S. Currie, Tongliang Liu

Water quality is foundational to environmental sustainability, ecosystem resilience, and public health. Deep learning models, particularly Long Short-Term Memory (LSTM) networks, offer transformative potential for large-scale water quality prediction and scientific insights generation. However, their widespread adoption in high-stakes decision-making, such as pollution mitigation and equitable resource allocation, is prevented by unresolved trustworthiness challenges including fairness, uncertainty, interpretability, robustness, generalizability, and reproducibility. In this work, we present the first comprehensive evaluation of trustworthiness in a continental-scale multi-task LSTM model predicting 20 water quality variables (encompassing physical/chemical processes, geochemical weathering, and nutrient cycling) across 482 U.S. basins. Our investigation uncovers systematic patterns of model performance disparities linked to basin characteristics, the inherent complexity of biogeochemical processes, and variable predictability, emphasizing critical performance fairness concerns. We further propose methodological frameworks for quantitatively evaluating critical aspects of trustworthiness, including uncertainty, interpretability, and robustness, identifying key limitations that could challenge reliable real-world deployment. This work serves as a timely call to action for advancing trustworthy data-driven methods for water resources management and provides a pathway to offering critical insights for researchers, decision-makers, and practitioners seeking to leverage artificial intelligence (AI) responsibly in environmental management.

comment: 33 pages, 9 figures, 2 tables

☆ TGP: Two-modal occupancy prediction with 3D Gaussian and sparse points for 3D Environment Awareness

Mu Chen, Wenyu Chen, Mingchuan Yang, Yuan Zhang, Tao Han, Xinchi Li, Yunlong Li, Huaici Zhao

3D semantic occupancy has rapidly become a research focus in the fields of robotics and autonomous driving environment perception due to its ability to provide more realistic geometric perception and its closer integration with downstream tasks. By performing occupancy prediction of the 3D space in the environment, the ability and robustness of scene understanding can be effectively improved. However, existing occupancy prediction tasks are primarily modeled using voxel or point cloud-based approaches: voxel-based network structures often suffer from the loss of spatial information due to the voxelization process, while point cloud-based methods, although better at retaining spatial location information, face limitations in representing volumetric structural details. To address this issue, we propose a dual-modal prediction method based on 3D Gaussian sets and sparse points, which balances both spatial location and volumetric structural information, achieving higher accuracy in semantic occupancy prediction. Specifically, our method adopts a Transformer-based architecture, taking 3D Gaussian sets, sparse points, and queries as inputs. Through the multi-layer structure of the Transformer, the enhanced queries and 3D Gaussian sets jointly contribute to the semantic occupancy prediction, and an adaptive fusion mechanism integrates the semantic outputs of both modalities to generate the final prediction results. Additionally, to further improve accuracy, we dynamically refine the point cloud at each layer, allowing for more precise location information during occupancy prediction. We conducted experiments on the Occ3DnuScenes dataset, and the experimental results demonstrate superior performance of the proposed method on IoU based metrics.

☆ Developing and Evaluating an AI-Assisted Prediction Model for Unplanned Intensive Care Admissions following Elective Neurosurgery using Natural Language Processing within an Electronic Healthcare Record System

Julia Ive, Olatomiwa Olukoya, Jonathan P. Funnell, James Booker, Sze H M Lam, Ugan Reddy, Kawsar Noor, Richard JB Dobson, Astri M. V. Luoma, Hani J Marcus

Introduction: Timely care in a specialised neuro-intensive therapy unit (ITU) reduces mortality and hospital stays, with planned admissions being safer than unplanned ones. However, post-operative care decisions remain subjective. This study used artificial intelligence (AI), specifically natural language processing (NLP) to analyse electronic health records (EHRs) and predict ITU admissions for elective surgery patients. Methods: This study analysed the EHRs of elective neurosurgery patients from University College London Hospital (UCLH) using NLP. Patients were categorised into planned high dependency unit (HDU) or ITU admission; unplanned HDU or ITU admission; or ward / overnight recovery (ONR). The Medical Concept Annotation Tool (MedCAT) was used to identify SNOMED-CT concepts within the clinical notes. We then explored the utility of these identified concepts for a range of AI algorithms trained to predict ITU admission. Results: The CogStack-MedCAT NLP model, initially trained on hospital-wide EHRs, underwent two refinements: first with data from patients with Normal Pressure Hydrocephalus (NPH) and then with data from Vestibular Schwannoma (VS) patients, achieving a concept detection F1-score of 0.93. This refined model was then used to extract concepts from EHR notes of 2,268 eligible neurosurgical patients. We integrated the extracted concepts into AI models, including a decision tree model and a neural time-series model. Using the simpler decision tree model, we achieved a recall of 0.87 (CI 0.82 - 0.91) for ITU admissions, reducing the proportion of unplanned ITU cases missed by human experts from 36% to 4%. Conclusion: The NLP model, refined for accuracy, has proven its efficiency in extracting relevant concepts, providing a reliable basis for predictive AI models to use in clinically valid applications.

♻ ☆ Chain-of-Thought Reasoning In The Wild Is Not Always Faithful ICLR 25

Iván Arcuschin, Jett Janiak, Robert Krzyzanowski, Senthooran Rajamanoharan, Neel Nanda, Arthur Conmy

comment: Accepted to the Reasoning and Planning for Large Language Models Workshop (ICLR 25), 10 main paper pages, 38 appendix pages

♻ ☆ Correlated Proxies: A New Definition and Improved Mitigation for Reward Hacking ICLR 2025

Cassidy Laidlaw, Shivam Singhal, Anca Dragan

Because it is difficult to precisely specify complex objectives, reinforcement learning policies are often optimized using proxy reward functions that only approximate the true goal. However, optimizing proxy rewards frequently leads to reward hacking: the optimized reward function ceases to be a good proxy and the resulting policy performs poorly with respect to the unspecified true reward. Principled solutions to reward hacking have been impeded by the lack of a good definition for the problem. To address this gap, we introduce a definition of reward hacking based on the correlation between proxy and true rewards for states and actions seen by a "reference policy" that breaks down under optimization. We show that this definition captures reward hacking behavior across several realistic settings, including in reinforcement learning from human feedback (RLHF). Using our formulation, we show theoretically that regularization to the reference policy can effectively prevent reward hacking. While the current practice in RLHF applies a KL penalty between action distributions for this purpose, our theory suggests regularizing the $\chi^2$ divergence between the policies' occupancy measures can be more effective. We intuitively show the benefits of this type of regularization and demonstrate that it better mitigates reward hacking in practice across four realistic settings, including RLHF. Our code is available at https://github.com/cassidylaidlaw/orpo.

comment: Spotlight at ICLR 2025

♻ ☆ DataEnvGym: Data Generation Agents in Teacher Environments with Student Feedback ICLR 2025

Zaid Khan, Elias Stengel-Eskin, Jaemin Cho, Mohit Bansal

comment: ICLR 2025 Spotlight; Project Page: https://DataEnvGym.github.io

♻ ☆ What is the Alignment Objective of GRPO?

Milan Vojnovic, Se-Young Yun

In this note, we examine the aggregation of preferences achieved by the Group Policy Optimisation (GRPO) algorithm, a reinforcement learning method used to train advanced artificial intelligence models such as DeepSeek-R1-Zero and DeepSeekMath. The GRPO algorithm trains a policy using a reward preference model, which is computed by sampling a set of outputs for a given context, observing the corresponding rewards, and applying shift-and-scale normalisation to these reward values. Additionally, it incorporates a penalty function to discourage deviations from a reference policy. We present a framework that enables us to characterise the stationary policies of the GRPO algorithm. This analysis reveals that the aggregation of preferences differs fundamentally from standard logarithmic pooling, which is implemented by other approaches such as RLHF. The precise form of preference aggregation arises from the way the reward preference model is defined and from the penalty function, which we show to essentially correspond to the reverse Kullback-Leibler (KL) divergence between the aggregation policy and the reference policy. Interestingly, we demonstrate that for groups of size two, the reward preference model corresponds to pairwise comparison preferences, similar to those in other alignment methods based on pairwise comparison feedback. We provide explicit characterisations of the aggregate preference for binary questions, for groups of size two, and in the limit of large group size. This provides insights into the dependence of the aggregate preference on parameters such as the regularisation constant and the confidence margin of question answers. Finally, we discuss the aggregation of preferences obtained by modifying the GRPO algorithm to use direct KL divergence as the penalty or to use rewards without scale normalisation.

♻ ☆ YouTube Comments Decoded: Leveraging LLMs for Low Resource Language Classification

Aniket Deroy, Subhankar Maity

Sarcasm detection is a significant challenge in sentiment analysis, particularly due to its nature of conveying opinions where the intended meaning deviates from the literal expression. This challenge is heightened in social media contexts where code-mixing, especially in Dravidian languages, is prevalent. Code-mixing involves the blending of multiple languages within a single utterance, often with non-native scripts, complicating the task for systems trained on monolingual data. This shared task introduces a novel gold standard corpus designed for sarcasm and sentiment detection within code-mixed texts, specifically in Tamil-English and Malayalam-English languages. The primary objective of this task is to identify sarcasm and sentiment polarity within a code-mixed dataset of Tamil-English and Malayalam-English comments and posts collected from social media platforms. Each comment or post is annotated at the message level for sentiment polarity, with particular attention to the challenges posed by class imbalance, reflecting real-world scenarios.In this work, we experiment with state-of-the-art large language models like GPT-3.5 Turbo via prompting to classify comments into sarcastic or non-sarcastic categories. We obtained a macro-F1 score of 0.61 for Tamil language. We obtained a macro-F1 score of 0.50 for Malayalam language.

comment: Updated and Final Version

♻ ☆ Joint Fine-tuning and Conversion of Pretrained Speech and Language Models towards Linear Complexity ICLR 2025

Mutian He, Philip N. Garner

Architectures such as Linformer and Mamba have recently emerged as competitive linear time replacements for transformers. However, corresponding large pretrained models are often unavailable, especially in non-text domains. To remedy this, we present a Cross-Architecture Layerwise Distillation (CALD) approach that jointly converts a transformer model to a linear time substitute and fine-tunes it to a target task. We also compare several means to guide the fine-tuning to optimally retain the desired inference capability from the original model. The methods differ in their use of the target model and the trajectory of the parameters. In a series of empirical studies on language processing, language modeling, and speech processing, we show that CALD can effectively recover the result of the original model, and that the guiding strategy contributes to the result. Some reasons for the variation are suggested.

comment: 18 pages, 5 figures; ICLR 2025 camera ready. Code: https://github.com/idiap/linearize-distill-pretrained-transformers

♻ ☆ Latent Space Chain-of-Embedding Enables Output-free LLM Self-Evaluation ICLR 2025

Yiming Wang, Pei Zhang, Baosong Yang, Derek F. Wong, Rui Wang

LLM self-evaluation relies on the LLM's own ability to estimate response correctness, which can greatly improve its deployment reliability. In this research track, we propose the Chain-of-Embedding (CoE) in the latent space to enable LLMs to perform output-free self-evaluation. CoE consists of all progressive hidden states produced during the inference time, which can be treated as the latent thinking path of LLMs. We find that when LLMs respond correctly and incorrectly, their CoE features differ, these discrepancies assist us in estimating LLM response correctness. Experiments in four diverse domains and seven LLMs fully demonstrate the effectiveness of our method. Meanwhile, its label-free design intent without any training and millisecond-level computational cost ensures real-time feedback in large-scale scenarios. More importantly, we provide interesting insights into LLM response correctness from the perspective of hidden state changes inside LLMs.

comment: Accepted by ICLR 2025

♻ ☆ When Text Embedding Meets Large Language Model: A Comprehensive Survey

Zhijie Nie, Zhangchi Feng, Mingxin Li, Cunwang Zhang, Yanzhao Zhang, Dingkun Long, Richong Zhang

Text embedding has become a foundational technology in natural language processing (NLP) during the deep learning era, driving advancements across a wide array of downstream tasks. While many natural language understanding challenges can now be modeled using generative paradigms and leverage the robust generative and comprehension capabilities of large language models (LLMs), numerous practical applications-such as semantic matching, clustering, and information retrieval-continue to rely on text embeddings for their efficiency and effectiveness. Therefore, how to combine the LLMs and the text embeddings has become one of the hotspots of academic attention in recent years. In this survey, we categorize the interplay between LLMs and text embeddings into three overarching themes: (1) LLM-augmented text embedding, enhancing traditional embedding methods with LLMs; (2) LLMs as text embedders, adapting their innate capabilities for high-quality embedding; and (3) Text embedding understanding with LLMs, leveraging LLMs to analyze and interpret embeddings. By organizing recent works based on interaction patterns rather than specific downstream applications, we offer a novel and systematic overview of contributions from various research and application domains in the era of LLMs. Furthermore, we highlight the unresolved challenges that persisted in the pre-LLM era with pre-trained language models (PLMs) and explore the emerging obstacles brought forth by LLMs. Building on this analysis, we outline prospective directions for the evolution of text embedding, addressing both theoretical and practical opportunities in the rapidly advancing landscape of NLP.

comment: Work in progress

♻ ☆ Confidence-Controlled Exploration: Efficient Sparse-Reward Policy Learning for Robot Navigation

Bhrij Patel, Kasun Weerakoon, Wesley A. Suttle, Alec Koppel, Brian M. Sadler, Tianyi Zhou, Amrit Singh Bedi, Dinesh Manocha

Reinforcement learning (RL) is a promising approach for robotic navigation, allowing robots to learn through trial and error. However, real-world robotic tasks often suffer from sparse rewards, leading to inefficient exploration and suboptimal policies due to sample inefficiency of RL. In this work, we introduce Confidence-Controlled Exploration (CCE), a novel method that improves sample efficiency in RL-based robotic navigation without modifying the reward function. Unlike existing approaches, such as entropy regularization and reward shaping, which can introduce instability by altering rewards, CCE dynamically adjusts trajectory length based on policy entropy. Specifically, it shortens trajectories when uncertainty is high to enhance exploration and extends them when confidence is high to prioritize exploitation. CCE is a principled and practical solution inspired by a theoretical connection between policy entropy and gradient estimation. It integrates seamlessly with on-policy and off-policy RL methods and requires minimal modifications. We validate CCE across REINFORCE, PPO, and SAC in both simulated and real-world navigation tasks. CCE outperforms fixed-trajectory and entropy-regularized baselines, achieving an 18\% higher success rate, 20-38\% shorter paths, and 9.32\% lower elevation costs under a fixed training sample budget. Finally, we deploy CCE on a Clearpath Husky robot, demonstrating its effectiveness in complex outdoor environments.

comment: 10 pages, 6 figures, 2 tables

♻ ☆ InftyThink: Breaking the Length Limits of Long-Context Reasoning in Large Language Models

Yuchen Yan, Yongliang Shen, Yang Liu, Jin Jiang, Mengdi Zhang, Jian Shao, Yueting Zhuang

Advanced reasoning in large language models has achieved remarkable performance on challenging tasks, but the prevailing long-context reasoning paradigm faces critical limitations: quadratic computational scaling with sequence length, reasoning constrained by maximum context boundaries, and performance degradation beyond pre-training context windows. Existing approaches primarily compress reasoning chains without addressing the fundamental scaling problem. To overcome these challenges, we introduce InftyThink, a paradigm that transforms monolithic reasoning into an iterative process with intermediate summarization. By interleaving short reasoning segments with concise progress summaries, our approach enables unbounded reasoning depth while maintaining bounded computational costs. This creates a characteristic sawtooth memory pattern that significantly reduces computational complexity compared to traditional approaches. Furthermore, we develop a methodology for reconstructing long-context reasoning datasets into our iterative format, transforming OpenR1-Math into 333K training instances. Experiments across multiple model architectures demonstrate that our approach reduces computational costs while improving performance, with Qwen2.5-Math-7B showing 3-13% improvements across MATH500, AIME24, and GPQA_diamond benchmarks. Our work challenges the assumed trade-off between reasoning depth and computational efficiency, providing a more scalable approach to complex reasoning without architectural modifications.

♻ ☆ Fast MRI for All: Bridging Equity Gaps via Training without Raw Data Access

Yaşar Utku Alçalar, Merve Gülle, Mehmet Akçakaya

Physics-driven deep learning (PD-DL) approaches have become popular for improved reconstruction of fast magnetic resonance imaging (MRI) scans. Though PD-DL offers higher acceleration rates than existing clinical fast MRI techniques, their use has been limited outside specialized MRI centers. A key challenge is generalization to underrepresented pathologies or populations, noted in multiple studies, with fine-tuning on target populations suggested for improvement. However, current approaches for PD-DL training require access to raw k-space measurements, which is typically only available at specialized MRI centers that have research agreements for such data access. This is especially an issue for rural and underserved areas, where commercial MRI scanners only provide access to a final reconstructed image. To tackle these challenges, we propose Compressibility-inspired Unsupervised Learning via Parallel Imaging Fidelity (CUPID) for high-quality PD-DL training using only routine clinical reconstructed images exported from an MRI scanner. CUPID evaluates output quality with a compressibility-based approach while ensuring that the output stays consistent with the clinical parallel imaging reconstruction through well-designed perturbations. Our results show CUPID achieves similar quality to established PD-DL training that requires k-space data while outperforming compressed sensing (CS) and diffusion-based generative methods. We further demonstrate its effectiveness in a zero-shot training setup for retrospectively and prospectively sub-sampled acquisitions, attesting to its minimal training burden. As an approach that radically deviates from existing strategies, CUPID presents an opportunity to provide equitable access to fast MRI for underserved populations in an attempt to reduce the inequalities associated with this expensive imaging modality.

♻ ☆ DataMan: Data Manager for Pre-training Large Language Models ICLR2025

Ru Peng, Kexin Yang, Yawen Zeng, Junyang Lin, Dayiheng Liu, Junbo Zhao

The performance emergence of large language models (LLMs) driven by data scaling laws makes the selection of pre-training data increasingly important. However, existing methods rely on limited heuristics and human intuition, lacking comprehensive and clear guidelines. To address this, we are inspired by ``reverse thinking'' -- prompting LLMs to self-identify which criteria benefit its performance. As its pre-training capabilities are related to perplexity (PPL), we derive 14 quality criteria from the causes of text perplexity anomalies and introduce 15 common application domains to support domain mixing. In this paper, we train a Data Manager (DataMan) to learn quality ratings and domain recognition from pointwise rating, and use it to annotate a 447B token pre-training corpus with 14 quality ratings and domain type. Our experiments validate our approach, using DataMan to select 30B tokens to train a 1.3B-parameter language model, demonstrating significant improvements in in-context learning (ICL), perplexity, and instruction-following ability over the state-of-the-art baseline. The best-performing model, based on the Overall Score l=5 surpasses a model trained with 50% more data using uniform sampling. We continue pre-training with high-rated, domain-specific data annotated by DataMan to enhance domain-specific ICL performance and thus verify DataMan's domain mixing ability. Our findings emphasize the importance of quality ranking, the complementary nature of quality criteria, and their low correlation with perplexity, analyzing misalignment between PPL and ICL performance. We also thoroughly analyzed our pre-training dataset, examining its composition, the distribution of quality ratings, and the original document sources.

comment: ICLR2025 paper

♻ ☆ HecVL: Hierarchical Video-Language Pretraining for Zero-shot Surgical Phase Recognition MICCAI2024

Kun Yuan, Vinkle Srivastav, Nassir Navab, Nicolas Padoy

Natural language could play an important role in developing generalist surgical models by providing a broad source of supervision from raw texts. This flexible form of supervision can enable the model's transferability across datasets and tasks as natural language can be used to reference learned visual concepts or describe new ones. In this work, we present HecVL, a novel hierarchical video-language pretraining approach for building a generalist surgical model. Specifically, we construct a hierarchical video-text paired dataset by pairing the surgical lecture video with three hierarchical levels of texts: at clip-level, atomic actions using transcribed audio texts; at phase-level, conceptual text summaries; and at video-level, overall abstract text of the surgical procedure. Then, we propose a novel fine-to-coarse contrastive learning framework that learns separate embedding spaces for the three video-text hierarchies using a single model. By disentangling embedding spaces of different hierarchical levels, the learned multi-modal representations encode short-term and long-term surgical concepts in the same model. Thanks to the injected textual semantics, we demonstrate that the HecVL approach can enable zero-shot surgical phase recognition without any human annotation. Furthermore, we show that the same HecVL model for surgical phase recognition can be transferred across different surgical procedures and medical centers. The code is available at https://github.com/CAMMA-public/SurgVLP

comment: Accepted by MICCAI2024

♻ ☆ Procedure-Aware Surgical Video-language Pretraining with Hierarchical Knowledge Augmentation NeurIPS 2024

Kun Yuan, Vinkle Srivastav, Nassir Navab, Nicolas Padoy

Surgical video-language pretraining (VLP) faces unique challenges due to the knowledge domain gap and the scarcity of multi-modal data. This study aims to bridge the gap by addressing issues regarding textual information loss in surgical lecture videos and the spatial-temporal challenges of surgical VLP. We propose a hierarchical knowledge augmentation approach and a novel Procedure-Encoded Surgical Knowledge-Augmented Video-Language Pretraining (PeskaVLP) framework to tackle these issues. The knowledge augmentation uses large language models (LLM) for refining and enriching surgical concepts, thus providing comprehensive language supervision and reducing the risk of overfitting. PeskaVLP combines language supervision with visual self-supervision, constructing hard negative samples and employing a Dynamic Time Warping (DTW) based loss function to effectively comprehend the cross-modal procedural alignment. Extensive experiments on multiple public surgical scene understanding and cross-modal retrieval datasets show that our proposed method significantly improves zero-shot transferring performance and offers a generalist visual representation for further advancements in surgical scene understanding.The code is available at https://github.com/CAMMA-public/SurgVLP

comment: Accepted at the 38th Conference on Neural Information Processing Systems (NeurIPS 2024 Spolight)

♻ ☆ COMBO: Compositional World Models for Embodied Multi-Agent Cooperation ICLR 2025

Hongxin Zhang, Zeyuan Wang, Qiushi Lyu, Zheyuan Zhang, Sunli Chen, Tianmin Shu, Behzad Dariush, Kwonjoon Lee, Yilun Du, Chuang Gan

In this paper, we investigate the problem of embodied multi-agent cooperation, where decentralized agents must cooperate given only egocentric views of the world. To effectively plan in this setting, in contrast to learning world dynamics in a single-agent scenario, we must simulate world dynamics conditioned on an arbitrary number of agents' actions given only partial egocentric visual observations of the world. To address this issue of partial observability, we first train generative models to estimate the overall world state given partial egocentric observations. To enable accurate simulation of multiple sets of actions on this world state, we then propose to learn a compositional world model for multi-agent cooperation by factorizing the naturally composable joint actions of multiple agents and compositionally generating the video conditioned on the world state. By leveraging this compositional world model, in combination with Vision Language Models to infer the actions of other agents, we can use a tree search procedure to integrate these modules and facilitate online cooperative planning. We evaluate our methods on three challenging benchmarks with 2-4 agents. The results show our compositional world model is effective and the framework enables the embodied agents to cooperate efficiently with different agents across various tasks and an arbitrary number of agents, showing the promising future of our proposed methods. More videos can be found at https://embodied-agi.cs.umass.edu/combo/.

comment: Published at ICLR 2025. 24 pages. The first three authors contributed equally

♻ ☆ Similarity Equivariant Graph Neural Networks for Homogenization of Metamaterials

Fleur Hendriks, Vlado Menkovski, Martin Doškář, Marc G. D. Geers, Ondřej Rokoš

Soft, porous mechanical metamaterials exhibit pattern transformations that may have important applications in soft robotics, sound reduction and biomedicine. To design these innovative materials, it is important to be able to simulate them accurately and quickly, in order to tune their mechanical properties. Since conventional simulations using the finite element method entail a high computational cost, in this article we aim to develop a machine learning-based approach that scales favorably to serve as a surrogate model. To ensure that the model is also able to handle various microstructures, including those not encountered during training, we include the microstructure as part of the network input. Therefore, we introduce a graph neural network that predicts global quantities (energy, stress stiffness) as well as the pattern transformations that occur (the kinematics). To make our model as accurate and data-efficient as possible, various symmetries are incorporated into the model. The starting point is an E(n)-equivariant graph neural network (which respects translation, rotation and reflection) that has periodic boundary conditions (i.e., it is in-/equivariant with respect to the choice of RVE), is scale in-/equivariant, can simulate large deformations, and can predict scalars, vectors as well as second and fourth order tensors (specifically energy, stress and stiffness). The incorporation of scale equivariance makes the model equivariant with respect to the similarities group, of which the Euclidean group E(n) is a subgroup. We show that this network is more accurate and data-efficient than graph neural networks with fewer symmetries. To create an efficient graph representation of the finite element discretization, we use only the internal geometrical hole boundaries from the finite element mesh to achieve a better speed-up and scaling with the mesh size.

comment: 60 pages, 22 figures. Published in CMAME (Computer Methods in Applied Mechanics and Engineering)

♻ ☆ PEMF-VTO: Point-Enhanced Video Virtual Try-on via Mask-free Paradigm

Tianyu Chang, Xiaohao Chen, Zhichao Wei, Xuanpu Zhang, Qing-Guo Chen, Weihua Luo, Peipei Song, Xun Yang

Video Virtual Try-on aims to seamlessly transfer a reference garment onto a target person in a video while preserving both visual fidelity and temporal coherence. Existing methods typically rely on inpainting masks to define the try-on area, enabling accurate garment transfer for simple scenes (e.g., in-shop videos). However, these mask-based approaches struggle with complex real-world scenarios, as overly large and inconsistent masks often destroy spatial-temporal information, leading to distorted results. Mask-free methods alleviate this issue but face challenges in accurately determining the try-on area, especially for videos with dynamic body movements. To address these limitations, we propose PEMF-VTO, a novel Point-Enhanced Mask-Free Video Virtual Try-On framework that leverages sparse point alignments to explicitly guide garment transfer. Our key innovation is the introduction of point-enhanced guidance, which provides flexible and reliable control over both spatial-level garment transfer and temporal-level video coherence. Specifically, we design a Point-Enhanced Transformer (PET) with two core components: Point-Enhanced Spatial Attention (PSA), which uses frame-cloth point alignments to precisely guide garment transfer, and Point-Enhanced Temporal Attention (PTA), which leverages frame-frame point correspondences to enhance temporal coherence and ensure smooth transitions across frames. Extensive experiments demonstrate that our PEMF-VTO outperforms state-of-the-art methods, generating more natural, coherent, and visually appealing try-on videos, particularly for challenging in-the-wild scenarios.

♻ ☆ The Society of HiveMind: Multi-Agent Optimization of Foundation Model Swarms to Unlock the Potential of Collective Intelligence

Noah Mamie, Susie Xi Rao

Multi-agent systems address issues of accessibility and scalability of artificial intelligence (AI) foundation models, which are often represented by large language models. We develop a framework - the "Society of HiveMind" (SOHM) - that orchestrates the interaction between multiple AI foundation models, imitating the observed behavior of animal swarms in nature by following modern evolutionary theories. On the one hand, we find that the SOHM provides a negligible benefit on tasks that mainly require real-world knowledge. On the other hand, we remark a significant improvement on tasks that require intensive logical reasoning, indicating that multi-agent systems are capable of increasing the reasoning capabilities of the collective compared to the individual agents. Our findings demonstrate the potential of combining a multitude of diverse AI foundation models to form an artificial swarm intelligence capable of self-improvement through interactions with a given environment.

comment: 11 pages (excl. appendix)

♻ ☆ Semi-supervised Semantic Segmentation for Remote Sensing Images via Multi-scale Uncertainty Consistency and Cross-Teacher-Student Attention

Shanwen Wang, Xin Sun, Changrui Chen, Danfeng Hong, Jungong Han

Semi-supervised learning offers an appealing solution for remote sensing (RS) image segmentation to relieve the burden of labor-intensive pixel-level labeling. However, RS images pose unique challenges, including rich multi-scale features and high inter-class similarity. To address these problems, this paper proposes a novel semi-supervised Multi-Scale Uncertainty and Cross-Teacher-Student Attention (MUCA) model for RS image semantic segmentation tasks. Specifically, MUCA constrains the consistency among feature maps at different layers of the network by introducing a multi-scale uncertainty consistency regularization. It improves the multi-scale learning capability of semi-supervised algorithms on unlabeled data. Additionally, MUCA utilizes a Cross-Teacher-Student attention mechanism to guide the student network, guiding the student network to construct more discriminative feature representations through complementary features from the teacher network. This design effectively integrates weak and strong augmentations (WA and SA) to further boost segmentation performance. To verify the effectiveness of our model, we conduct extensive experiments on ISPRS-Potsdam and LoveDA datasets. The experimental results show the superiority of our method over state-of-the-art semi-supervised methods. Notably, our model excels in distinguishing highly similar objects, showcasing its potential for advancing semi-supervised RS image segmentation tasks.

♻ ☆ Networked Communication for Decentralised Agents in Mean-Field Games

Patrick Benjamin, Alessandro Abate

We introduce networked communication to the mean-field game framework, in particular to oracle-free settings where $N$ decentralised agents learn along a single, non-episodic run of the empirical system. We prove that our architecture has sample guarantees bounded between those of the centralised- and independent-learning cases. We provide the order of the difference in these bounds in terms of network structure and number of communication rounds, and also contribute a policy-update stability guarantee. We discuss how the sample guarantees of the three theoretical algorithms do not actually result in practical convergence. We therefore show that in practical settings where the theoretical parameters are not observed (leading to poor estimation of the Q-function), our communication scheme considerably accelerates learning over the independent case, often performing similarly to a centralised learner while removing the restrictive assumption of the latter. We contribute further practical enhancements to all three theoretical algorithms, allowing us to present their first empirical demonstrations. Our experiments confirm that we can remove several of the theoretical assumptions of the algorithms, and display the empirical convergence benefits brought by our new networked communication. We additionally show that our networked approach has significant advantages over both alternatives in terms of robustness to update failures and to changes in population size.

♻ ☆ Exploring a Multimodal Fusion-based Deep Learning Network for Detecting Facial Palsy IJCAI 2024

Heng Yim Nicole Oo, Min Hun Lee, Jeong Hoon Lim

Algorithmic detection of facial palsy offers the potential to improve current practices, which usually involve labor-intensive and subjective assessment by clinicians. In this paper, we present a multimodal fusion-based deep learning model that utilizes unstructured data (i.e. an image frame with facial line segments) and structured data (i.e. features of facial expressions) to detect facial palsy. We then contribute to a study to analyze the effect of different data modalities and the benefits of a multimodal fusion-based approach using videos of 21 facial palsy patients. Our experimental results show that among various data modalities (i.e. unstructured data - RGB images and images of facial line segments and structured data - coordinates of facial landmarks and features of facial expressions), the feed-forward neural network using features of facial expression achieved the highest precision of 76.22 while the ResNet-based model using images of facial line segments achieved the highest recall of 83.47. When we leveraged both images of facial line segments and features of facial expressions, our multimodal fusion-based deep learning model slightly improved the precision score to 77.05 at the expense of a decrease in the recall score.

comment: IJCAI 2024 4th AI for Ageless Aging Workshop (AIAA)

♻ ☆ Towards Generalizable Scene Change Detection CVPR 2025

Jaewoo Kim, Uehwan Kim

While current state-of-the-art Scene Change Detection (SCD) approaches achieve impressive results in well-trained research data, they become unreliable under unseen environments and different temporal conditions; in-domain performance drops from 77.6% to 8.0% in a previously unseen environment and to 4.6% under a different temporal condition -- calling for generalizable SCD and benchmark. In this work, we propose the Generalizable Scene Change Detection Framework (GeSCF), which addresses unseen domain performance and temporal consistency -- to meet the growing demand for anything SCD. Our method leverages the pre-trained Segment Anything Model (SAM) in a zero-shot manner. For this, we design Initial Pseudo-mask Generation and Geometric-Semantic Mask Matching -- seamlessly turning user-guided prompt and single-image based segmentation into scene change detection for a pair of inputs without guidance. Furthermore, we define the Generalizable Scene Change Detection (GeSCD) benchmark along with novel metrics and an evaluation protocol to facilitate SCD research in generalizability. In the process, we introduce the ChangeVPR dataset, a collection of challenging image pairs with diverse environmental scenarios -- including urban, suburban, and rural settings. Extensive experiments across various datasets demonstrate that GeSCF achieves an average performance gain of 19.2% on existing SCD datasets and 30.0% on the ChangeVPR dataset, nearly doubling the prior art performance. We believe our work can lay a solid foundation for robust and generalizable SCD research.

comment: Camera-ready version. Accepted to CVPR 2025

♻ ☆ ProtTeX: Structure-In-Context Reasoning and Editing of Proteins with Large Language Models

Zicheng Ma, Chuanliu Fan, Zhicong Wang, Zhenyu Chen, Xiaohan Lin, Yanheng Li, Shihao Feng, Jun Zhang, Ziqiang Cao, Yi Qin Gao

Large language models have made remarkable progress in the field of molecular science, particularly in understanding and generating functional small molecules. This success is largely attributed to the effectiveness of molecular tokenization strategies. In protein science, the amino acid sequence serves as the sole tokenizer for LLMs. However, many fundamental challenges in protein science are inherently structure-dependent. The absence of structure-aware tokens significantly limits the capabilities of LLMs for comprehensive biomolecular comprehension and multimodal generation. To address these challenges, we introduce a novel framework, ProtTeX, which tokenizes the protein sequences, structures, and textual information into a unified discrete space. This innovative approach enables joint training of the LLM exclusively through the Next-Token Prediction paradigm, facilitating multimodal protein reasoning and generation. ProtTeX enables general LLMs to perceive and process protein structures through sequential text input, leverage structural information as intermediate reasoning components, and generate or manipulate structures via sequential text output. Experiments demonstrate that our model achieves significant improvements in protein function prediction, outperforming the state-of-the-art domain expert model with a twofold increase in accuracy. Our framework enables high-quality conformational generation and customizable protein design. For the first time, we demonstrate that by adopting the standard training and inference pipelines from the LLM domain, ProtTeX empowers decoder-only LLMs to effectively address diverse spectrum of protein-related tasks.

comment: 26 pages, 9 figures

♻ ☆ NotaGen: Advancing Musicality in Symbolic Music Generation with Large Language Model Training Paradigms

Yashan Wang, Shangda Wu, Jianhuai Hu, Xingjian Du, Yueqi Peng, Yongxin Huang, Shuai Fan, Xiaobing Li, Feng Yu, Maosong Sun

We introduce NotaGen, a symbolic music generation model aiming to explore the potential of producing high-quality classical sheet music. Inspired by the success of Large Language Models (LLMs), NotaGen adopts pre-training, fine-tuning, and reinforcement learning paradigms (henceforth referred to as the LLM training paradigms). It is pre-trained on 1.6M pieces of music in ABC notation, and then fine-tuned on approximately 9K high-quality classical compositions conditioned on "period-composer-instrumentation" prompts. For reinforcement learning, we propose the CLaMP-DPO method, which further enhances generation quality and controllability without requiring human annotations or predefined rewards. Our experiments demonstrate the efficacy of CLaMP-DPO in symbolic music generation models with different architectures and encoding schemes. Furthermore, subjective A/B tests show that NotaGen outperforms baseline models against human compositions, greatly advancing musical aesthetics in symbolic music generation.

♻ ☆ Knowledge-data fusion dominated vehicle platoon dynamics modeling and analysis: A physics-encoded deep learning approach

Hao Lyu, Yanyong Guo, Pan Liu, Shuo Feng, Weilin Ren, Quansheng Yue

Recently, artificial intelligence (AI)-enabled nonlinear vehicle platoon dynamics modeling plays a crucial role in predicting and optimizing the interactions between vehicles. Existing efforts lack the extraction and capture of vehicle behavior interaction features at the platoon scale. More importantly, maintaining high modeling accuracy without losing physical analyzability remains to be solved. To this end, this paper proposes a novel physics-encoded deep learning network, named PeMTFLN, to model the nonlinear vehicle platoon dynamics. Specifically, an analyzable parameters encoded computational graph (APeCG) is designed to guide the platoon to respond to the driving behavior of the lead vehicle while ensuring local stability. Besides, a multi-scale trajectory feature learning network (MTFLN) is constructed to capture platoon following patterns and infer the physical parameters required for APeCG from trajectory data. The human-driven vehicle trajectory datasets (HIGHSIM) were used to train the proposed PeMTFLN. The trajectories prediction experiments show that PeMTFLN exhibits superior compared to the baseline models in terms of predictive accuracy in speed and gap. The stability analysis result shows that the physical parameters in APeCG is able to reproduce the platoon stability in real-world condition. In simulation experiments, PeMTFLN performs low inference error in platoon trajectories generation. Moreover, PeMTFLN also accurately reproduces ground-truth safety statistics. The code of proposed PeMTFLN is open source.

♻ ☆ PAD: Personalized Alignment of LLMs at Decoding-Time ICLR 2025

Ruizhe Chen, Xiaotian Zhang, Meng Luo, Wenhao Chai, Zuozhu Liu

Aligning with personalized preferences, which vary significantly across cultural, educational, and political differences, poses a significant challenge due to the computational costs and data demands of traditional alignment methods. In response, this paper presents Personalized Alignment at Decoding-time (PAD), a novel framework designed to align LLM outputs with diverse personalized preferences during the inference phase, eliminating the need for additional training. By introducing a unique personalized reward modeling strategy, this framework decouples the text generation process from personalized preferences, facilitating the generation of generalizable token-level personalized rewards. The PAD algorithm leverages these rewards to guide the decoding process, dynamically tailoring the base model's predictions to personalized preferences. Extensive experimental results demonstrate that PAD not only outperforms existing training-based alignment methods in terms of aligning with diverse preferences but also shows significant generalizability to preferences unseen during training and scalability across different base models. This work advances the capability of LLMs to meet user needs in real-time applications, presenting a substantial step forward in personalized LLM alignment.

comment: ICLR 2025

♻ ☆ Networked Communication for Mean-Field Games with Function Approximation and Empirical Mean-Field Estimation

Patrick Benjamin, Alessandro Abate

Recent algorithms allow decentralised agents, possibly connected via a communication network, to learn equilibria in Mean-Field Games from a non-episodic run of the empirical system. However, these algorithms are for tabular settings: this computationally limits the size of agents' observation space, meaning the algorithms cannot handle anything but small state spaces, nor generalise beyond policies depending only on the agent's local state to so-called 'population-dependent' policies. We address this limitation by introducing function approximation to the existing setting, drawing on the Munchausen Online Mirror Descent method that has previously been employed only in finite-horizon, episodic, centralised settings. While this permits us to include the mean field in the observation for players' policies, it is unrealistic to assume decentralised agents have access to this global information: we therefore also provide new algorithms allowing agents to locally estimate the global empirical distribution, and to improve this estimate via inter-agent communication. We show theoretically that exchanging policy information helps networked agents outperform both independent and even centralised agents in function-approximation settings. Our experiments demonstrate this happening empirically, by an even greater margin than in tabular settings, and show that the communication network allows decentralised agents to estimate the mean field for population-dependent policies.

♻ ☆ Adaptive Split Learning over Energy-Constrained Wireless Edge Networks

Zuguang Li, Wen Wu, Shaohua Wu, Wei Wang

Split learning (SL) is a promising approach for training artificial intelligence (AI) models, in which devices collaborate with a server to train an AI model in a distributed manner, based on a same fixed split point. However, due to the device heterogeneity and variation of channel conditions, this way is not optimal in training delay and energy consumption. In this paper, we design an adaptive split learning (ASL) scheme which can dynamically select split points for devices and allocate computing resource for the server in wireless edge networks. We formulate an optimization problem to minimize the average training latency subject to long-term energy consumption constraint. The difficulties in solving this problem are the lack of future information and mixed integer programming (MIP). To solve it, we propose an online algorithm leveraging the Lyapunov theory, named OPEN, which decomposes it into a new MIP problem only with the current information. Then, a two-layer optimization method is proposed to solve the MIP problem. Extensive simulation results demonstrate that the ASL scheme can reduce the average training delay and energy consumption by 53.7% and 22.1%, respectively, as compared to the existing SL schemes.

comment: 6 pages, 5 figures, 20 conferences

♻ ☆ KnowPath: Knowledge-enhanced Reasoning via LLM-generated Inference Paths over Knowledge Graphs

Qi Zhao, Hongyu Yang, Qi Song, Xinwei Yao, Xiangyang Li

Large language models (LLMs) have demonstrated remarkable capabilities in various complex tasks, yet they still suffer from hallucinations. Introducing external knowledge, such as knowledge graph, can enhance the LLMs' ability to provide factual answers. LLMs have the ability to interactively explore knowledge graphs. However, most approaches have been affected by insufficient internal knowledge excavation in LLMs, limited generation of trustworthy knowledge reasoning paths, and a vague integration between internal and external knowledge. Therefore, we propose KnowPath, a knowledge-enhanced large model framework driven by the collaboration of internal and external knowledge. It relies on the internal knowledge of the LLM to guide the exploration of interpretable directed subgraphs in external knowledge graphs, better integrating the two knowledge sources for more accurate reasoning. Extensive experiments on multiple real-world datasets confirm the superiority of KnowPath.

♻ ☆ Diabetica: Adapting Large Language Model to Enhance Multiple Medical Tasks in Diabetes Care and Management ICLR 2025

Lai Wei, Zhen Ying, Muyang He, Yutong Chen, Qian Yang, Yanzhe Hong, Jiaping Lu, Kaipeng Zheng, Shaoting Zhang, Xiaoying Li, Weiran Huang, Ying Chen

Diabetes is a chronic disease with a significant global health burden, requiring multi-stakeholder collaboration for optimal management. Large language models (LLMs) have shown promise in various healthcare scenarios, but their effectiveness across diverse diabetes tasks remains unproven. Our study introduced a framework to train and validate diabetes-specific LLMs. We first developed a comprehensive data processing pipeline that includes data collection, filtering, augmentation and refinement. This created a high-quality, diabetes-specific dataset and evaluation benchmarks from scratch. Fine-tuned on the collected training dataset, our diabetes-specific LLM family demonstrated state-of-the-art proficiency in processing various diabetes tasks compared to other LLMs. Furthermore, clinical studies revealed the potential applications of our models in diabetes care, including providing personalized healthcare, assisting medical education, and streamlining clinical tasks. Generally, our introduced framework helps develop diabetes-specific LLMs and highlights their potential to enhance clinical practice and provide personalized, data-driven support for diabetes management across different end users. Our codes, benchmarks and models are available at https://github.com/waltonfuture/Diabetica.

comment: Accepted by ICLR 2025 SCI-FM workshop

♻ ☆ Deep Reinforcement Learning for Dynamic Resource Allocation in Wireless Networks

Shubham Malhotra, Fnu Yashu, Muhammad Saqib, Dipkumar Mehta, Jagdish Jangid, Sachin Dixit

This report investigates the application of deep reinforcement learning (DRL) algorithms for dynamic resource allocation in wireless communication systems. An environment that includes a base station, multiple antennas, and user equipment is created. Using the RLlib library, various DRL algorithms such as Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) are then applied. These algorithms are compared based on their ability to optimize resource allocation, focusing on the impact of different learning rates and scheduling policies. The findings demonstrate that the choice of algorithm and learning rate significantly influences system performance, with DRL providing more efficient resource allocation compared to traditional methods.

comment: Upon further review, we found inconsistencies in our analysis and decided to conduct additional research before resubmitting a revised version

♻ ☆ Revealing Bias Formation in Deep Neural Networks Through the Geometric Mechanisms of Human Visual Decoupling

Yanbiao Ma, Bowei Liu, Boyuan Gao, Wei Dai, Jiayi Chen, Shuo Li

Deep neural networks (DNNs) often exhibit biases toward certain categories during object recognition, even under balanced training data conditions. The intrinsic mechanisms underlying these biases remain unclear. Inspired by the human visual system, which decouples object manifolds through hierarchical processing to achieve object recognition, we propose a geometric analysis framework linking the geometric complexity of class-specific perceptual manifolds in DNNs to model bias. Our findings reveal that differences in geometric complexity can lead to varying recognition capabilities across categories, introducing biases. To support this analysis, we present the Perceptual-Manifold-Geometry library, designed for calculating the geometric properties of perceptual manifolds.

♻ ☆ A Triple-Inertial Accelerated Alternating Optimization Method for Deep Learning Training

Chengcheng Yan, Jiawei Xu, Qingsong Wang, Zheng Peng

The stochastic gradient descent (SGD) algorithm has achieved remarkable success in training deep learning models. However, it has several limitations, including susceptibility to vanishing gradients, sensitivity to input data, and a lack of robust theoretical guarantees. In recent years, alternating minimization (AM) methods have emerged as a promising alternative for model training by employing gradient-free approaches to iteratively update model parameters. Despite their potential, these methods often exhibit slow convergence rates. To address this challenge, we propose a novel Triple-Inertial Accelerated Alternating Minimization (TIAM) framework for neural network training. The TIAM approach incorporates a triple-inertial acceleration strategy with a specialized approximation method, facilitating targeted acceleration of different terms in each sub-problem optimization. This integration improves the efficiency of convergence, achieving superior performance with fewer iterations. Additionally, we provide a convergence analysis of the TIAM algorithm, including its global convergence properties and convergence rate. Extensive experiments validate the effectiveness of the TIAM method, showing significant improvements in generalization capability and computational efficiency compared to existing approaches, particularly when applied to the rectified linear unit (ReLU) and its variants.

♻ ☆ Prompt-SID: Learning Structural Representation Prompt via Latent Diffusion for Single-Image Denoising

Huaqiu Li, Wang Zhang, Xiaowan Hu, Tao Jiang, Zikang Chen, Haoqian Wang

Many studies have concentrated on constructing supervised models utilizing paired datasets for image denoising, which proves to be expensive and time-consuming. Current self-supervised and unsupervised approaches typically rely on blind-spot networks or sub-image pairs sampling, resulting in pixel information loss and destruction of detailed structural information, thereby significantly constraining the efficacy of such methods. In this paper, we introduce Prompt-SID, a prompt-learning-based single image denoising framework that emphasizes preserving of structural details. This approach is trained in a self-supervised manner using downsampled image pairs. It captures original-scale image information through structural encoding and integrates this prompt into the denoiser. To achieve this, we propose a structural representation generation model based on the latent diffusion process and design a structural attention module within the transformer-based denoiser architecture to decode the prompt. Additionally, we introduce a scale replay training mechanism, which effectively mitigates the scale gap from images of different resolutions. We conduct comprehensive experiments on synthetic, real-world, and fluorescence imaging datasets, showcasing the remarkable effectiveness of Prompt-SID. Our code will be released at https://github.com/huaqlili/Prompt-SID.

♻ ☆ Continuous K-space Recovery Network with Image Guidance for Fast MRI Reconstruction

Yucong Meng, Zhiwei Yang, Minghong Duan, Yonghong Shi, Zhijian Song

Magnetic resonance imaging (MRI) is a crucial tool for clinical diagnosis while facing the challenge of long scanning time. To reduce the acquisition time, fast MRI reconstruction aims to restore high-quality images from the undersampled k-space. Existing methods typically train deep learning models to map the undersampled data to artifact-free MRI images. However, these studies often overlook the unique properties of k-space and directly apply general networks designed for image processing to k-space recovery, leaving the precise learning of k-space largely underexplored. In this work, we propose a continuous k-space recovery network from a new perspective of implicit neural representation with image domain guidance, which boosts the performance of MRI reconstruction. Specifically, (1) an implicit neural representation based encoder-decoder structure is customized to continuously query unsampled k-values. (2) an image guidance module is designed to mine the semantic information from the low-quality MRI images to further guide the k-space recovery. (3) a multi-stage training strategy is proposed to recover dense k-space progressively. Extensive experiments conducted on CC359, fastMRI, and IXI datasets demonstrate the effectiveness of our method and its superiority over other competitors.

♻ ☆ Is My Text in Your AI Model? Gradient-based Membership Inference Test applied to LLMs

Gonzalo Mancera, Daniel DeAlcala, Julian Fierrez, Ruben Tolosana, Aythami Morales

This work adapts and studies the gradient-based Membership Inference Test (gMINT) to the classification of text based on LLMs. MINT is a general approach intended to determine if given data was used for training machine learning models, and this work focuses on its application to the domain of Natural Language Processing. Using gradient-based analysis, the MINT model identifies whether particular data samples were included during the language model training phase, addressing growing concerns about data privacy in machine learning. The method was evaluated in seven Transformer-based models and six datasets comprising over 2.5 million sentences, focusing on text classification tasks. Experimental results demonstrate MINTs robustness, achieving AUC scores between 85% and 99%, depending on data size and model architecture. These findings highlight MINTs potential as a scalable and reliable tool for auditing machine learning models, ensuring transparency, safeguarding sensitive data, and fostering ethical compliance in the deployment of AI/NLP technologies.

♻ ☆ Determination of galaxy photometric redshifts using Conditional Generative Adversarial Networks (CGANs)

M. Garcia-Fernandez

Accurate and reliable photometric redshift determination is one of the key aspects for wide-field photometric surveys. Determination of photometric redshift for galaxies, has been traditionally solved by use of machine-learning and artificial intelligence techniques trained on a calibration sample of galaxies, where both photometry and spectrometry are available. On this paper, we present a new algorithmic approach for determining photometric redshifts of galaxies using Conditional Generative Adversarial Networks (CGANs). The proposed implementation is able to determine both point-estimation and probability-density estimations for photometric redshifts. The methodology is tested with data from Dark Energy Survey (DES) Y1 data and compared with other existing algorithm such as a Mixture Density Network (MDN). Although results obtained show a superiority of MDN, CGAN quality-metrics are close to the MDN results, opening the door to the use of CGAN at photometric redshift estimation.

♻ ☆ InstructPipe: Generating Visual Blocks Pipelines with Human Instructions and LLMs

Zhongyi Zhou, Jing Jin, Vrushank Phadnis, Xiuxiu Yuan, Jun Jiang, Xun Qian, Kristen Wright, Mark Sherwood, Jason Mayes, Jingtao Zhou, Yiyi Huang, Zheng Xu, Yinda Zhang, Johnny Lee, Alex Olwal, David Kim, Ram Iyengar, Na Li, Ruofei Du

Visual programming has the potential of providing novice programmers with a low-code experience to build customized processing pipelines. Existing systems typically require users to build pipelines from scratch, implying that novice users are expected to set up and link appropriate nodes from a blank workspace. In this paper, we introduce InstructPipe, an AI assistant for prototyping machine learning (ML) pipelines with text instructions. We contribute two large language model (LLM) modules and a code interpreter as part of our framework. The LLM modules generate pseudocode for a target pipeline, and the interpreter renders the pipeline in the node-graph editor for further human-AI collaboration. Both technical and user evaluation (N=16) shows that InstructPipe empowers users to streamline their ML pipeline workflow, reduce their learning curve, and leverage open-ended commands to spark innovative ideas.

comment: CHI 2025

♻ ☆ Column-wise Quantization of Weights and Partial Sums for Accurate and Efficient Compute-In-Memory Accelerators

Jiyoon Kim, Kang Eun Jeon, Yulhwa Kim, Jong Hwan Ko

Compute-in-memory (CIM) is an efficient method for implementing deep neural networks (DNNs) but suffers from substantial overhead from analog-to-digital converters (ADCs), especially as ADC precision increases. Low-precision ADCs can reduce this overhead but introduce partial-sum quantization errors degrading accuracy. Additionally, low-bit weight constraints, imposed by cell limitations and the need for multiple cells for higher-bit weights, present further challenges. While fine-grained partial-sum quantization has been studied to lower ADC resolution effectively, weight granularity, which limits overall partial-sum quantized accuracy, remains underexplored. This work addresses these challenges by aligning weight and partial-sum quantization granularities at the column-wise level. Our method improves accuracy while maintaining dequantization overhead, simplifies training by removing two-stage processes, and ensures robustness to memory cell variations via independent column-wise scale factors. We also propose an open-source CIM-oriented convolution framework to handle fine-grained weights and partial-sums efficiently, incorporating a novel tiling method and group convolution. Experimental results on ResNet-20 (CIFAR-10, CIFAR-100) and ResNet-18 (ImageNet) show accuracy improvements of 0.99%, 2.69%, and 1.01%, respectively, compared to the best-performing related works. Additionally, variation analysis reveals the robustness of our method against memory cell variations. These findings highlight the effectiveness of our quantization scheme in enhancing accuracy and robustness while maintaining hardware efficiency in CIM-based DNN implementations. Our code is available at https://github.com/jiyoonkm/ColumnQuant.

♻ ☆ The Algorithmic State Architecture (ASA): An Integrated Framework for AI-Enabled Government

Zeynep Engin, Jon Crowcroft, David Hand, Philip Treleaven

As artificial intelligence transforms public sector operations, governments struggle to integrate technological innovations into coherent systems for effective service delivery. This paper introduces the Algorithmic State Architecture (ASA), a novel four-layer framework conceptualising how Digital Public Infrastructure, Data-for-Policy, Algorithmic Government/Governance, and GovTech interact as an integrated system in AI-enabled states. Unlike approaches that treat these as parallel developments, ASA positions them as interdependent layers with specific enabling relationships and feedback mechanisms. Through comparative analysis of implementations in Estonia, Singapore, India, and the UK, we demonstrate how foundational digital infrastructure enables systematic data collection, which powers algorithmic decision-making processes, ultimately manifesting in user-facing services. Our analysis reveals that successful implementations require balanced development across all layers, with particular attention to integration mechanisms between them. The framework contributes to both theory and practice by bridging previously disconnected domains of digital government research, identifying critical dependencies that influence implementation success, and providing a structured approach for analysing the maturity and development pathways of AI-enabled government systems.

comment: Main text: 25 pages, with references: 35 pages, 2 figures

♻ ☆ FlashRNN: I/O-Aware Optimization of Traditional RNNs on modern hardware

Korbinian Pöppel, Maximilian Beck, Sepp Hochreiter

While Transformers and other sequence-parallelizable neural network architectures seem like the current state of the art in sequence modeling, they specifically lack state-tracking capabilities. These are important for time-series tasks and logical reasoning. Traditional RNNs like LSTMs and GRUs, as well as modern variants like sLSTM do have these capabilities at the cost of strictly sequential processing. While this is often seen as a strong limitation, we show how fast these networks can get with our hardware-optimization FlashRNN in Triton and CUDA, optimizing kernels to the register level on modern GPUs. We extend traditional RNNs with a parallelization variant that processes multiple RNNs of smaller hidden state in parallel, similar to the head-wise processing in Transformers. To enable flexibility on different GPU variants, we introduce a new optimization framework for hardware-internal cache sizes, memory and compute handling. It models the hardware in a setting using polyhedral-like constraints, including the notion of divisibility. This speeds up the solution process in our ConstrINT library for general integer constraint satisfaction problems (integer CSPs). We show that our kernels can achieve 50x speed-ups over a vanilla PyTorch implementation and allow 40x larger hidden sizes compared to our Triton implementation. Our open-source kernels and the optimization library are released here to boost research in the direction of state-tracking enabled RNNs and sequence modeling: https://github.com/NX-AI/flashrnn

♻ ☆ TH-Bench: Evaluating Evading Attacks via Humanizing AI Text on Machine-Generated Text Detectors

Jingyi Zheng, Junfeng Wang, Zhen Sun, Wenhan Dong, Yule Liu, Xinlei He

As Large Language Models (LLMs) advance, Machine-Generated Texts (MGTs) have become increasingly fluent, high-quality, and informative. Existing wide-range MGT detectors are designed to identify MGTs to prevent the spread of plagiarism and misinformation. However, adversaries attempt to humanize MGTs to evade detection (named evading attacks), which requires only minor modifications to bypass MGT detectors. Unfortunately, existing attacks generally lack a unified and comprehensive evaluation framework, as they are assessed using different experimental settings, model architectures, and datasets. To fill this gap, we introduce the Text-Humanization Benchmark (TH-Bench), the first comprehensive benchmark to evaluate evading attacks against MGT detectors. TH-Bench evaluates attacks across three key dimensions: evading effectiveness, text quality, and computational overhead. Our extensive experiments evaluate 6 state-of-the-art attacks against 13 MGT detectors across 6 datasets, spanning 19 domains and generated by 11 widely used LLMs. Our findings reveal that no single evading attack excels across all three dimensions. Through in-depth analysis, we highlight the strengths and limitations of different attacks. More importantly, we identify a trade-off among three dimensions and propose two optimization insights. Through preliminary experiments, we validate their correctness and effectiveness, offering potential directions for future research.

♻ ☆ Hidden in the Noise: Two-Stage Robust Watermarking for Images

Kasra Arabi, Benjamin Feuer, R. Teal Witter, Chinmay Hegde, Niv Cohen

As the quality of image generators continues to improve, deepfakes become a topic of considerable societal debate. Image watermarking allows responsible model owners to detect and label their AI-generated content, which can mitigate the harm. Yet, current state-of-the-art methods in image watermarking remain vulnerable to forgery and removal attacks. This vulnerability occurs in part because watermarks distort the distribution of generated images, unintentionally revealing information about the watermarking techniques. In this work, we first demonstrate a distortion-free watermarking method for images, based on a diffusion model's initial noise. However, detecting the watermark requires comparing the initial noise reconstructed for an image to all previously used initial noises. To mitigate these issues, we propose a two-stage watermarking framework for efficient detection. During generation, we augment the initial noise with generated Fourier patterns to embed information about the group of initial noises we used. For detection, we (i) retrieve the relevant group of noises, and (ii) search within the given group for an initial noise that might match our image. This watermarking approach achieves state-of-the-art robustness to forgery and removal against a large battery of attacks.

♻ ☆ Long-horizon Embodied Planning with Implicit Logical Inference and Hallucination Mitigation

Siyuan Liu, Jiawei Du, Sicheng Xiang, Zibo Wang, Dingsheng Luo

Long-horizon embodied planning underpins embodied AI. To accomplish long-horizon tasks, one of the most feasible ways is to decompose abstract instructions into a sequence of actionable steps. Foundation models still face logical errors and hallucinations in long-horizon planning, unless provided with highly relevant examples to the tasks. However, providing highly relevant examples for any random task is unpractical. Therefore, we present ReLEP, a novel framework for Real-time Long-horizon Embodied Planning. ReLEP can complete a wide range of long-horizon tasks without in-context examples by learning implicit logical inference through fine-tuning. The fine-tuned large vision-language model formulates plans as sequences of skill functions. These functions are selected from a carefully designed skill library. ReLEP is also equipped with a Memory module for plan and status recall, and a Robot Configuration module for versatility across robot types. In addition, we propose a data generation pipeline to tackle dataset scarcity. When constructing the dataset, we considered the implicit logical relationships, enabling the model to learn implicit logical relationships and dispel hallucinations. Through comprehensive evaluations across various long-horizon tasks, ReLEP demonstrates high success rates and compliance to execution even on unseen tasks and outperforms state-of-the-art baseline methods.

♻ ☆ MarS: a Financial Market Simulation Engine Powered by Generative Foundation Model ICLR 2025

Junjie Li, Yang Liu, Weiqing Liu, Shikai Fang, Lewen Wang, Chang Xu, Jiang Bian

Generative models aim to simulate realistic effects of various actions across different contexts, from text generation to visual effects. Despite significant efforts to build real-world simulators, the application of generative models to virtual worlds, like financial markets, remains under-explored. In financial markets, generative models can simulate complex market effects of participants with various behaviors, enabling interaction under different market conditions, and training strategies without financial risk. This simulation relies on the finest structured data in financial market like orders thus building the finest realistic simulation. We propose Large Market Model (LMM), an order-level generative foundation model, for financial market simulation, akin to language modeling in the digital world. Our financial Market Simulation engine (MarS), powered by LMM, addresses the domain-specific need for realistic, interactive and controllable order generation. Key observations include LMM's strong scalability across data size and model complexity, and MarS's robust and practicable realism in controlled generation with market impact. We showcase MarS as a forecast tool, detection system, analysis platform, and agent training environment, thus demonstrating MarS's "paradigm shift" potential for a variety of financial applications. We release the code of MarS at https://github.com/microsoft/MarS/.

comment: 35 pages, 26 figures, ICLR 2025

♻ ☆ Next-Generation Database Interfaces: A Survey of LLM-based Text-to-SQL

Zijin Hong, Zheng Yuan, Qinggang Zhang, Hao Chen, Junnan Dong, Feiran Huang, Xiao Huang

Generating accurate SQL from users' natural language questions (text-to-SQL) remains a long-standing challenge due to the complexities involved in user question understanding, database schema comprehension, and SQL generation. Traditional text-to-SQL systems, which combine human engineering and deep neural networks, have made significant progress. Subsequently, pre-trained language models (PLMs) have been developed for text-to-SQL tasks, achieving promising results. However, as modern databases and user questions grow more complex, PLMs with a limited parameter size often produce incorrect SQL. This necessitates more sophisticated and tailored optimization methods, which restricts the application of PLM-based systems. Recently, large language models (LLMs) have shown significant capabilities in natural language understanding as model scale increases. Thus, integrating LLM-based solutions can bring unique opportunities, improvements, and solutions to text-to-SQL research. In this survey, we provide a comprehensive review of existing LLM-based text-to-SQL studies. Specifically, we offer a brief overview of the technical challenges and evolutionary process of text-to-SQL. Next, we introduce the datasets and metrics designed to evaluate text-to-SQL systems. Subsequently, we present a systematic analysis of recent advances in LLM-based text-to-SQL. Finally, we make a summarization and discuss the remaining challenges in this field and suggest expectations for future research directions.

♻ ☆ V-LoRA: An Efficient and Flexible System Boosts Vision Applications with LoRA LMM EuroSys'2025

Liang Mi, Weijun Wang, Wenming Tu, Qingfeng He, Rui Kong, Xinyu Fang, Yazhu Dong, Yikang Zhang, Yunchun Li, Meng Li, Haipeng Dai, Guihai Chen, Yunxin Liu

Large Multimodal Models (LMMs) have shown significant progress in various complex vision tasks with the solid linguistic and reasoning capacity inherited from large language models (LMMs). Low-rank adaptation (LoRA) offers a promising method to integrate external knowledge into LMMs, compensating for their limitations on domain-specific tasks. However, the existing LoRA model serving is excessively computationally expensive and causes extremely high latency. In this paper, we present an end-to-end solution that empowers diverse vision tasks and enriches vision applications with LoRA LMMs. Our system, VaLoRA, enables accurate and efficient vision tasks by 1) an accuracy-aware LoRA adapter generation approach that generates LoRA adapters rich in domain-specific knowledge to meet application-specific accuracy requirements, 2) an adaptive-tiling LoRA adapters batching operator that efficiently computes concurrent heterogeneous LoRA adapters, and 3) a flexible LoRA adapter orchestration mechanism that manages application requests and LoRA adapters to achieve the lowest average response latency. We prototype VaLoRA on five popular vision tasks on three LMMs. Experiment results reveal that VaLoRA improves 24-62% of the accuracy compared to the original LMMs and reduces 20-89% of the latency compared to the state-of-the-art LoRA model serving systems.

comment: EuroSys'2025

♻ ☆ HERO: Human-Feedback Efficient Reinforcement Learning for Online Diffusion Model Finetuning ICLR

Ayano Hiranaka, Shang-Fu Chen, Chieh-Hsin Lai, Dongjun Kim, Naoki Murata, Takashi Shibuya, Wei-Hsiang Liao, Shao-Hua Sun, Yuki Mitsufuji

Controllable generation through Stable Diffusion (SD) fine-tuning aims to improve fidelity, safety, and alignment with human guidance. Existing reinforcement learning from human feedback methods usually rely on predefined heuristic reward functions or pretrained reward models built on large-scale datasets, limiting their applicability to scenarios where collecting such data is costly or difficult. To effectively and efficiently utilize human feedback, we develop a framework, HERO, which leverages online human feedback collected on the fly during model learning. Specifically, HERO features two key mechanisms: (1) Feedback-Aligned Representation Learning, an online training method that captures human feedback and provides informative learning signals for fine-tuning, and (2) Feedback-Guided Image Generation, which involves generating images from SD's refined initialization samples, enabling faster convergence towards the evaluator's intent. We demonstrate that HERO is 4x more efficient in online feedback for body part anomaly correction compared to the best existing method. Additionally, experiments show that HERO can effectively handle tasks like reasoning, counting, personalization, and reducing NSFW content with only 0.5K online feedback. The code and project page are available at https://hero-dm.github.io/.

comment: Published in International Conference on Learning Representations (ICLR) 2025

♻ ☆ Can LLMs Reason About Program Semantics? A Comprehensive Evaluation of LLMs on Formal Specification Inference

Thanh Le-Cong, Bach Le, Toby Murray

Large Language Models (LLMs) are increasingly being used to automate programming tasks. Yet, LLMs' capabilities in reasoning about program semantics are still inadequately studied, leaving significant potential for further exploration. This paper introduces FormalBench, a comprehensive benchmark designed to evaluate LLMs' reasoning abilities on program semantics, particularly via the task of synthesizing formal program specifications to assist verifying program correctness. This task requires both comprehensive reasoning over all possible program executions and the generation of precise, syntactically correct expressions that adhere to formal syntax and semantics. Using this benchmark, we evaluated the ability of LLMs in synthesizing consistent and complete specifications. Our findings show that LLMs perform well with simple control flows but struggle with more complex structures, especially loops, even with advanced prompting. Additionally, LLMs exhibit limited robustness against semantic-preserving transformations. We also highlight common failure patterns and design self-repair prompts, improving success rates by 25%.

♻ ☆ Reinforcement Learning-Enhanced Procedural Generation for Dynamic Narrative-Driven AR Experiences

Aniruddha Srinivas Joshi

Procedural Content Generation (PCG) is widely used to create scalable and diverse environments in games. However, existing methods, such as the Wave Function Collapse (WFC) algorithm, are often limited to static scenarios and lack the adaptability required for dynamic, narrative-driven applications, particularly in augmented reality (AR) games. This paper presents a reinforcement learning-enhanced WFC framework designed for mobile AR environments. By integrating environment-specific rules and dynamic tile weight adjustments informed by reinforcement learning (RL), the proposed method generates maps that are both contextually coherent and responsive to gameplay needs. Comparative evaluations and user studies demonstrate that the framework achieves superior map quality and delivers immersive experiences, making it well-suited for narrative-driven AR games. Additionally, the method holds promise for broader applications in education, simulation training, and immersive extended reality (XR) experiences, where dynamic and adaptive environments are critical.

comment: Published in Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - GRAPP 2025 https://www.scitepress.org/PublicationsDetail.aspx?ID=LfPv9Lfiya8=&t=1

♻ ☆ TPO: Aligning Large Language Models with Multi-branch & Multi-step Preference Trees

Weibin Liao, Xu Chu, Yasha Wang

In the domain of complex reasoning tasks, such as mathematical reasoning, recent advancements have proposed the use of Direct Preference Optimization (DPO) to suppress output of dispreferred responses, thereby enhancing the long-chain reasoning capabilities of large language models (LLMs). To this end, these studies employed LLMs to generate preference trees via Tree-of-thoughts (ToT) and sample the paired preference responses required by the DPO algorithm. However, the DPO algorithm based on binary preference optimization is unable to learn multiple responses with varying degrees of preference/dispreference that provided by the preference trees, resulting in incomplete preference learning. In this work, we introduce Tree Preference Optimization (TPO), that does not sample paired preference responses from the preference tree; instead, it directly learns from the entire preference tree during the fine-tuning. Specifically, TPO formulates the language model alignment as a Preference List Ranking problem, where the policy can potentially learn more effectively from a ranked preference list of responses given the prompt. In addition, to further assist LLMs in identifying discriminative steps within long-chain reasoning and increase the relative reward margin in the preference list, TPO utilizes Adaptive Step Reward to adjust the reward values of each step in trajectory for performing fine-grained preference optimization. We carry out extensive experiments on mathematical reasoning tasks to evaluate TPO. The experimental results indicate that TPO consistently outperforms DPO across five public large language models on four datasets.

♻ ☆ LaMMA-P: Generalizable Multi-Agent Long-Horizon Task Allocation and Planning with LM-Driven PDDL Planner ICRA 2025

Xiaopan Zhang, Hao Qin, Fuquan Wang, Yue Dong, Jiachen Li

Language models (LMs) possess a strong capability to comprehend natural language, making them effective in translating human instructions into detailed plans for simple robot tasks. Nevertheless, it remains a significant challenge to handle long-horizon tasks, especially in subtask identification and allocation for cooperative heterogeneous robot teams. To address this issue, we propose a Language Model-Driven Multi-Agent PDDL Planner (LaMMA-P), a novel multi-agent task planning framework that achieves state-of-the-art performance on long-horizon tasks. LaMMA-P integrates the strengths of the LMs' reasoning capability and the traditional heuristic search planner to achieve a high success rate and efficiency while demonstrating strong generalization across tasks. Additionally, we create MAT-THOR, a comprehensive benchmark that features household tasks with two different levels of complexity based on the AI2-THOR environment. The experimental results demonstrate that LaMMA-P achieves a 105% higher success rate and 36% higher efficiency than existing LM-based multiagent planners. The experimental videos, code, datasets, and detailed prompts used in each module can be found on the project website: https://lamma-p.github.io.

comment: IEEE Conference on Robotics and Automation (ICRA 2025); Project website: https://lamma-p.github.io/

♻ ☆ Prompt-Driven Contrastive Learning for Transferable Adversarial Attacks ECCV 2024

Hunmin Yang, Jongoh Jeong, Kuk-Jin Yoon

Recent vision-language foundation models, such as CLIP, have demonstrated superior capabilities in learning representations that can be transferable across diverse range of downstream tasks and domains. With the emergence of such powerful models, it has become crucial to effectively leverage their capabilities in tackling challenging vision tasks. On the other hand, only a few works have focused on devising adversarial examples that transfer well to both unknown domains and model architectures. In this paper, we propose a novel transfer attack method called PDCL-Attack, which leverages the CLIP model to enhance the transferability of adversarial perturbations generated by a generative model-based attack framework. Specifically, we formulate an effective prompt-driven feature guidance by harnessing the semantic representation power of text, particularly from the ground-truth class labels of input images. To the best of our knowledge, we are the first to introduce prompt learning to enhance the transferable generative attacks. Extensive experiments conducted across various cross-domain and cross-model settings empirically validate our approach, demonstrating its superiority over state-of-the-art methods.

comment: Accepted to ECCV 2024 (Oral), Project Page: https://PDCL-Attack.github.io

♻ ☆ Oasis: One Image is All You Need for Multimodal Instruction Data Synthesis

Letian Zhang, Quan Cui, Bingchen Zhao, Cheng Yang

The success of multi-modal large language models (MLLMs) has been largely attributed to the large-scale training data. However, the training data of many MLLMs is unavailable due to privacy concerns. The expensive and labor-intensive process of collecting multi-modal data further exacerbates the problem. Is it possible to synthesize multi-modal training data automatically without compromising diversity and quality? In this paper, we propose a new method, Oasis, to synthesize high-quality multi-modal data with only images. Oasis breaks through traditional methods by prompting only images to the MLLMs, thus extending the data diversity by a large margin. Our method features a delicate quality control method which ensures the data quality. We collected over 500k data and conducted incremental experiments on LLaVA-NeXT. Extensive experiments demonstrate that our method can significantly improve the performance of MLLMs. The image-based synthesis also allows us to focus on the specific-domain ability of MLLMs. Code and data will be publicly available.

♻ ☆ DeepInnovation AI: A Global Dataset Mapping the AI innovation from Academic Research to Industrial Patents

Haixing Gong, Hui Zou, Xingzhou Liang, Shiyuan Meng, Pinlong Cai, Xingcheng Xu, Jingjing Qu

In the rapidly evolving field of artificial intelligence (AI), mapping innovation patterns and understanding effective technology transfer from research to applications are essential for economic growth. However, existing data infrastructures suffer from fragmentation, incomplete coverage, and insufficient evaluative capacity. Here, we present DeepInnovationAI, a comprehensive global dataset containing three structured files. DeepPatentAI.csv: Contains 2,356,204 patent records with 8 field-specific attributes. DeepDiveAI.csv: Encompasses 3,511,929 academic publications with 13 metadata fields. These two datasets leverage large language models, multilingual text analysis and dual-layer BERT classifiers to accurately identify AI-related content, while utilizing hypergraph analysis to create robust innovation metrics. Additionally, DeepCosineAI.csv: By applying semantic vector proximity analysis, this file presents approximately one hundred million calculated paper-patent similarity pairs to enhance understanding of how theoretical advancements translate into commercial technologies. DeepInnovationAI enables researchers, policymakers, and industry leaders to anticipate trends and identify collaboration opportunities. With extensive temporal and geographical scope, it supports detailed analysis of technological development patterns and international competition dynamics, establishing a foundation for modeling AI innovation and technology transfer processes.

comment: 32 pages and 8 figures

♻ ☆ Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models

Qiguang Chen, Libo Qin, Jinhao Liu, Dengyun Peng, Jiannan Guan, Peng Wang, Mengkang Hu, Yuhang Zhou, Te Gao, Wanxiang Che

Recent advancements in reasoning with large language models (RLLMs), such as OpenAI-O1 and DeepSeek-R1, have demonstrated their impressive capabilities in complex domains like mathematics and coding. A central factor in their success lies in the application of long chain-of-thought (Long CoT) characteristics, which enhance reasoning abilities and enable the solution of intricate problems. However, despite these developments, a comprehensive survey on Long CoT is still lacking, limiting our understanding of its distinctions from traditional short chain-of-thought (Short CoT) and complicating ongoing debates on issues like "overthinking" and "test-time scaling." This survey seeks to fill this gap by offering a unified perspective on Long CoT. (1) We first distinguish Long CoT from Short CoT and introduce a novel taxonomy to categorize current reasoning paradigms. (2) Next, we explore the key characteristics of Long CoT: deep reasoning, extensive exploration, and feasible reflection, which enable models to handle more complex tasks and produce more efficient, coherent outcomes compared to the shallower Short CoT. (3) We then investigate key phenomena such as the emergence of Long CoT with these characteristics, including overthinking, and test-time scaling, offering insights into how these processes manifest in practice. (4) Finally, we identify significant research gaps and highlight promising future directions, including the integration of multi-modal reasoning, efficiency improvements, and enhanced knowledge frameworks. By providing a structured overview, this survey aims to inspire future research and further the development of logical reasoning in artificial intelligence.

comment: Paper are available at https://long-cot.github.io/

♻ ☆ AnywhereDoor: Multi-Target Backdoor Attacks on Object Detection

Jialin Lu, Junjie Shan, Ziqi Zhao, Ka-Ho Chow

As object detection becomes integral to many safety-critical applications, understanding its vulnerabilities is essential. Backdoor attacks, in particular, pose a serious threat by implanting hidden triggers in victim models, which adversaries can later exploit to induce malicious behaviors during inference. However, current understanding is limited to single-target attacks, where adversaries must define a fixed malicious behavior (target) before training, making inference-time adaptability impossible. Given the large output space of object detection (including object existence prediction, bounding box estimation, and classification), the feasibility of flexible, inference-time model control remains unexplored. This paper introduces AnywhereDoor, a multi-target backdoor attack for object detection. Once implanted, AnywhereDoor allows adversaries to make objects disappear, fabricate new ones, or mislabel them, either across all object classes or specific ones, offering an unprecedented degree of control. This flexibility is enabled by three key innovations: (i) objective disentanglement to scale the number of supported targets; (ii) trigger mosaicking to ensure robustness even against region-based detectors; and (iii) strategic batching to address object-level data imbalances that hinder manipulation. Extensive experiments demonstrate that AnywhereDoor grants attackers a high degree of control, improving attack success rates by 26% compared to adaptations of existing methods for such flexible control.

comment: This work was intended as a replacement of arXiv:2411.14243 and any subsequent updates will appear there

♻ ☆ Driving with Regulation: Interpretable Decision-Making for Autonomous Vehicles with Retrieval-Augmented Reasoning via LLM

Tianhui Cai, Yifan Liu, Zewei Zhou, Haoxuan Ma, Seth Z. Zhao, Zhiwen Wu, Jiaqi Ma

This work presents an interpretable decision-making framework for autonomous vehicles that integrates traffic regulations, norms, and safety guidelines comprehensively and enables seamless adaptation to different regions. While traditional rule-based methods struggle to incorporate the full scope of traffic rules, we develop a Traffic Regulation Retrieval (TRR) Agent based on Retrieval-Augmented Generation (RAG) to automatically retrieve relevant traffic rules and guidelines from extensive regulation documents and relevant records based on the ego vehicle's situation. Given the semantic complexity of the retrieved rules, we also design a reasoning module powered by a Large Language Model (LLM) to interpret these rules, differentiate between mandatory rules and safety guidelines, and assess actions on legal compliance and safety. Additionally, the reasoning is designed to be interpretable, enhancing both transparency and reliability. The framework demonstrates robust performance on both hypothesized and real-world cases across diverse scenarios, along with the ability to adapt to different regions with ease.

♻ ☆ Multi-agent KTO: Reinforcing Strategic Interactions of Large Language Model in Language Game

Rong Ye, Yongxin Zhang, Yikai Zhang, Haoyu Kuang, Zhongyu Wei, Peng Sun

Achieving Artificial General Intelligence (AGI) requires AI agents that can not only make stratigic decisions but also engage in flexible and meaningful communication. Inspired by Wittgenstein's language game theory in Philosophical Investigations, we propose that language agents can learn through in-context interaction rather than traditional multi-stage frameworks that separate decision-making from language expression. Using Werewolf, a social deduction game that tests language understanding, strategic interaction, and adaptability, we develop the Multi-agent Kahneman & Tversky's Optimization (MaKTO). MaKTO engages diverse models in extensive gameplay to generate unpaired desirable and unacceptable responses, then employs KTO to refine the model's decision-making process. In 9-player Werewolf games, MaKTO achieves a 61% average win rate across various models, outperforming GPT-4o and two-stage RL agents by relative improvements of 23.0% and 10.9%, respectively. Notably, MaKTO also demonstrates human-like performance, winning 60% against expert players and showing only 49% detectability in Turing-style blind tests.

comment: Preprint. Code and data will be available at https://reneeye.github.io/MaKTO.html

♻ ☆ DA-STGCN: 4D Trajectory Prediction Based on Spatiotemporal Feature Extraction

Yuheng Kuang, Zhengning Wang, Jianping Zhang, Zhenyu Shi, Yuding Zhang

The importance of four-dimensional (4D) trajectory prediction within air traffic management systems is on the rise. Key operations such as conflict detection and resolution, aircraft anomaly monitoring, and the management of congested flight paths are increasingly reliant on this foundational technology, underscoring the urgent demand for intelligent solutions. The dynamics in airport terminal zones and crowded airspaces are intricate and ever-changing; however, current methodologies do not sufficiently account for the interactions among aircraft. To tackle these challenges, we propose DA-STGCN, an innovative spatiotemporal graph convolutional network that integrates a dual attention mechanism. Our model reconstructs the adjacency matrix through a self-attention approach, enhancing the capture of node correlations, and employs graph attention to distill spatiotemporal characteristics, thereby generating a probabilistic distribution of predicted trajectories. This novel adjacency matrix, reconstructed with the self-attention mechanism, is dynamically optimized throughout the network's training process, offering a more nuanced reflection of the inter-node relationships compared to traditional algorithms. The performance of the model is validated on two ADS-B datasets, one near the airport terminal area and the other in dense airspace. Experimental results demonstrate a notable improvement over current 4D trajectory prediction methods, achieving a 20% and 30% reduction in the Average Displacement Error (ADE) and Final Displacement Error (FDE), respectively. The incorporation of a Dual-Attention module has been shown to significantly enhance the extraction of node correlations, as verified by ablation experiments.

♻ ☆ KG4Diagnosis: A Hierarchical Multi-Agent LLM Framework with Knowledge Graph Enhancement for Medical Diagnosis AAAI-25

Kaiwen Zuo, Yirui Jiang, Fan Mo, Pietro Lio

Integrating Large Language Models (LLMs) in healthcare diagnosis demands systematic frameworks that can handle complex medical scenarios while maintaining specialized expertise. We present KG4Diagnosis, a novel hierarchical multi-agent framework that combines LLMs with automated knowledge graph construction, encompassing 362 common diseases across medical specialties. Our framework mirrors real-world medical systems through a two-tier architecture: a general practitioner (GP) agent for initial assessment and triage, coordinating with specialized agents for in-depth diagnosis in specific domains. The core innovation lies in our end-to-end knowledge graph generation methodology, incorporating: (1) semantic-driven entity and relation extraction optimized for medical terminology, (2) multi-dimensional decision relationship reconstruction from unstructured medical texts, and (3) human-guided reasoning for knowledge expansion. KG4Diagnosis serves as an extensible foundation for specialized medical diagnosis systems, with capabilities to incorporate new diseases and medical knowledge. The framework's modular design enables seamless integration of domain-specific enhancements, making it valuable for developing targeted medical diagnosis systems. We provide architectural guidelines and protocols to facilitate adoption across medical contexts.

comment: 10 pages,5 figures,published to AAAI-25 Bridge Program

♻ ☆ Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation

Dingyuan Shi, Yong Wang, Hangyu Li, Xiangxiang Chu

Diffusion models have shown remarkable success in text-to-image generation, making preference alignment for these models increasingly important. The preference labels are typically available only at the terminal of denoising trajectories, which poses challenges in optimizing the intermediate denoising steps. In this paper, we propose to conduct Denoised Distribution Estimation (DDE) that explicitly connects intermediate steps to the terminal denoised distribution. Therefore, preference labels can be used for the entire trajectory optimization. To this end, we design two estimation strategies for our DDE. The first is stepwise estimation, which utilizes the conditional denoised distribution to estimate the model denoised distribution. The second is single-shot estimation, which converts the model output into the terminal denoised distribution via DDIM modeling. Analytically and empirically, we reveal that DDE equipped with two estimation strategies naturally derives a novel credit assignment scheme that prioritizes optimizing the middle part of the denoising trajectory. Extensive experiments demonstrate that our approach achieves superior performance, both quantitatively and qualitatively.

♻ ☆ MedHallBench: A New Benchmark for Assessing Hallucination in Medical Large Language Models AAAI-25

Kaiwen Zuo, Yirui Jiang

Medical Large Language Models (MLLMs) have demonstrated potential in healthcare applications, yet their propensity for hallucinations -- generating medically implausible or inaccurate information -- presents substantial risks to patient care. This paper introduces MedHallBench, a comprehensive benchmark framework for evaluating and mitigating hallucinations in MLLMs. Our methodology integrates expert-validated medical case scenarios with established medical databases to create a robust evaluation dataset. The framework employs a sophisticated measurement system that combines automated ACHMI (Automatic Caption Hallucination Measurement in Medical Imaging) scoring with rigorous clinical expert evaluations and utilizes reinforcement learning methods to achieve automatic annotation. Through an optimized reinforcement learning from human feedback (RLHF) training pipeline specifically designed for medical applications, MedHallBench enables thorough evaluation of MLLMs across diverse clinical contexts while maintaining stringent accuracy standards. We conducted comparative experiments involving various models, utilizing the benchmark to establish a baseline for widely adopted large language models (LLMs). Our findings indicate that ACHMI provides a more nuanced understanding of the effects of hallucinations compared to traditional metrics, thereby highlighting its advantages in hallucination assessment. This research establishes a foundational framework for enhancing MLLMs' reliability in healthcare settings and presents actionable strategies for addressing the critical challenge of AI hallucinations in medical applications.

comment: Published to AAAI-25 Bridge Program

♻ ☆ Conditional diffusions for amortized neural posterior estimation

Tianyu Chen, Vansh Bansal, James G. Scott

Neural posterior estimation (NPE), a simulation-based computational approach for Bayesian inference, has shown great success in approximating complex posterior distributions. Existing NPE methods typically rely on normalizing flows, which approximate a distribution by composing many simple, invertible transformations. But flow-based models, while state of the art for NPE, are known to suffer from several limitations, including training instability and sharp trade-offs between representational power and computational cost. In this work, we demonstrate the effectiveness of conditional diffusions coupled with high-capacity summary networks for amortized NPE. Conditional diffusions address many of the challenges faced by flow-based methods. Our results show that, across a highly varied suite of benchmarking problems for NPE architectures, diffusions offer improved stability, superior accuracy, and faster training times, even with simpler, shallower models. Building on prior work on diffusions for NPE, we show that these gains persist across a variety of different summary network architectures. Code is available at https://github.com/TianyuCodings/cDiff.

♻ ☆ SHIP: A Shapelet-based Approach for Interpretable Patient-Ventilator Asynchrony Detection PAKDD 2025

Xuan-May Le, Ling Luo, Uwe Aickelin, Minh-Tuan Tran, David Berlowitz, Mark Howard

Patient-ventilator asynchrony (PVA) is a common and critical issue during mechanical ventilation, affecting up to 85% of patients. PVA can result in clinical complications such as discomfort, sleep disruption, and potentially more severe conditions like ventilator-induced lung injury and diaphragm dysfunction. Traditional PVA management, which relies on manual adjustments by healthcare providers, is often inadequate due to delays and errors. While various computational methods, including rule-based, statistical, and deep learning approaches, have been developed to detect PVA events, they face challenges related to dataset imbalances and lack of interpretability. In this work, we propose a shapelet-based approach SHIP for PVA detection, utilizing shapelets - discriminative subsequences in time-series data - to enhance detection accuracy and interpretability. Our method addresses dataset imbalances through shapelet-based data augmentation and constructs a shapelet pool to transform the dataset for more effective classification. The combined shapelet and statistical features are then used in a classifier to identify PVA events. Experimental results on medical datasets show that SHIP significantly improves PVA detection while providing interpretable insights into model decisions.

comment: Accepted at PAKDD 2025

♻ ☆ Seeing is Understanding: Unlocking Causal Attention into Modality-Mutual Attention for Multimodal LLMs

Wei-Yao Wang, Zhao Wang, Helen Suzuki, Yoshiyuki Kobayashi

Recent Multimodal Large Language Models (MLLMs) have demonstrated significant progress in perceiving and reasoning over multimodal inquiries, ushering in a new research era for foundation models. However, vision-language misalignment in MLLMs has emerged as a critical challenge, where the textual responses generated by these models are not factually aligned with the given text-image inputs. Existing efforts to address vision-language misalignment have focused on developing specialized vision-language connectors or leveraging visual instruction tuning from diverse domains. In this paper, we tackle this issue from a fundamental yet unexplored perspective by revisiting the core architecture of MLLMs. Most MLLMs are typically built on decoder-only LLMs consisting of a causal attention mechanism, which limits the ability of the earlier modalities (e.g., images) to incorporate information from the latter modalities (e.g., text). To address this problem, we propose \MapleLeaf AKI, a novel MLLM that unlocks causal attention into modality-mutual attention (MMA) to enable image tokens to attend to text tokens. This simple yet effective design allows AKI to achieve superior performance in 12 multimodal understanding benchmarks (+7.2% on average) without introducing additional parameters and increasing training time. Our MMA design is intended to be generic, allowing for application across various modalities, and scalable to accommodate diverse multimodal scenarios. The code and model are publicly available at https://github.com/sony/aki to encourage further advancements in MLLMs across various directions.

comment: Preprint

♻ ☆ There and Back Again: On the relation between Noise and Image Inversions in Diffusion Models

Łukasz Staniszewski, Łukasz Kuciński, Kamil Deja

Diffusion Models achieve state-of-the-art performance in generating new samples but lack low-dimensional latent space that encodes the data into meaningful features. Inversion-based techniques try to solve this issue by reversing the denoising process and mapping images back to their approximated starting noise. In this work, we thoroughly analyze this procedure and focus on the relation between the initial Gaussian noise, the generated samples, and their corresponding latent encodings obtained through the DDIM inversion. First, we show that latents exhibit structural patterns in the form of less diverse noise predicted for smooth image regions. Next, we explain the origin of this phenomenon, demonstrating that, during the first inversion steps, the noise prediction error is much more significant for the plain areas than for the rest of the image. Finally, we present the consequences of the divergence between latents and noises by showing that the space of image inversions is notably less manipulative than the original Gaussian noise. This leads to a low diversity of generated interpolations or editions based on the DDIM inversion procedure and ill-defined latent-to-image mapping. Code is available at https://github.com/luk-st/taba.

♻ ☆ Accelerating Flood Warnings by 10 Hours: The Power of River Network Topology in AI-enhanced Flood Forecasting

Hongjun Wang, Jiyuan Chen, Yinqiang Zheng, Xuan Song

Climate change-driven floods demand advanced forecasting models, yet Graph Neural Networks (GNNs) underutilize river network topology due to tree-like structures causing over-squashing from high node resistance distances. This study identifies this limitation and introduces a reachability-based graph transformation to densify topological connections, reducing resistance distances. Empirical tests show transformed-GNNs outperform EA-LSTM in extreme flood prediction, achieving 24-h water level accuracy equivalent to EA-LSTM's 14-h forecasts - a 71% improvement in long-term predictive horizon. The dense graph retains flow dynamics across hierarchical river branches, enabling GNNs to capture distal node interactions critical for rare flood events. This topological innovation bridges the gap between river network structure and GNN modeling, offering a scalable framework for early warning systems.

♻ ☆ Non-autoregressive Sequence-to-Sequence Vision-Language Models CVPR 2024

Kunyu Shi, Qi Dong, Luis Goncalves, Zhuowen Tu, Stefano Soatto

Sequence-to-sequence vision-language models are showing promise, but their applicability is limited by their inference latency due to their autoregressive way of generating predictions. We propose a parallel decoding sequence-to-sequence vision-language model, trained with a Query-CTC loss, that marginalizes over multiple inference paths in the decoder. This allows us to model the joint distribution of tokens, rather than restricting to conditional distribution as in an autoregressive model. The resulting model, NARVL, achieves performance on-par with its state-of-the-art autoregressive counterpart, but is faster at inference time, reducing from the linear complexity associated with the sequential generation of tokens to a paradigm of constant time joint inference.

comment: Accepted to CVPR 2024

♻ ☆ Toward an Evaluation Science for Generative AI Systems

Laura Weidinger, Inioluwa Deborah Raji, Hanna Wallach, Margaret Mitchell, Angelina Wang, Olawale Salaudeen, Rishi Bommasani, Deep Ganguli, Sanmi Koyejo, William Isaac

There is an increasing imperative to anticipate and understand the performance and safety of generative AI systems in real-world deployment contexts. However, the current evaluation ecosystem is insufficient: Commonly used static benchmarks face validity challenges, and ad hoc case-by-case audits rarely scale. In this piece, we advocate for maturing an evaluation science for generative AI systems. While generative AI creates unique challenges for system safety engineering and measurement science, the field can draw valuable insights from the development of safety evaluation practices in other fields, including transportation, aerospace, and pharmaceutical engineering. In particular, we present three key lessons: Evaluation metrics must be applicable to real-world performance, metrics must be iteratively refined, and evaluation institutions and norms must be established. Applying these insights, we outline a concrete path toward a more rigorous approach for evaluating generative AI systems.

comment: First two authors contributed equally to this work

Machine Learning

☆ Studying Classifier(-Free) Guidance From a Classifier-Centric Perspective

Xiaoming Zhao, Alexander G. Schwing

☆ The Curse of Conditions: Analyzing and Improving Optimal Transport for Conditional Flow-Based Generation

Ho Kei Cheng, Alexander Schwing

Minibatch optimal transport coupling straightens paths in unconditional flow matching. This leads to computationally less demanding inference as fewer integration steps and less complex numerical solvers can be employed when numerically solving an ordinary differential equation at test time. However, in the conditional setting, minibatch optimal transport falls short. This is because the default optimal transport mapping disregards conditions, resulting in a conditionally skewed prior distribution during training. In contrast, at test time, we have no access to the skewed prior, and instead sample from the full, unbiased prior distribution. This gap between training and testing leads to a subpar performance. To bridge this gap, we propose conditional optimal transport C^2OT that adds a conditional weighting term in the cost matrix when computing the optimal transport assignment. Experiments demonstrate that this simple fix works with both discrete and continuous conditions in 8gaussians-to-moons, CIFAR-10, ImageNet-32x32, and ImageNet-256x256. Our method performs better overall compared to the existing baselines across different function evaluation budgets. Code is available at https://hkchengrex.github.io/C2OT

comment: Project page: https://hkchengrex.github.io/C2OT

☆ A Frustratingly Simple Yet Highly Effective Attack Baseline: Over 90% Success Rate Against the Strong Black-box Models of GPT-4.5/4o/o1

Zhaoyi Li, Xiaohan Zhao, Dong-Dong Wu, Jiacheng Cui, Zhiqiang Shen

comment: Code at: https://github.com/VILA-Lab/M-Attack

☆ Charting and Navigating Hugging Face's Model Atlas

Eliahu Horwitz, Nitzan Kurer, Jonathan Kahana, Liel Amar, Yedid Hoshen

☆ Kolmogorov-Arnold Attention: Is Learnable Attention Better For Vision Transformers?

Subhajit Maity, Killian Hitsman, Xin Li, Aritra Dutta

Kolmogorov-Arnold networks (KANs) are a remarkable innovation consisting of learnable activation functions with the potential to capture more complex relationships from data. Although KANs are useful in finding symbolic representations and continual learning of one-dimensional functions, their effectiveness in diverse machine learning (ML) tasks, such as vision, remains questionable. Presently, KANs are deployed by replacing multilayer perceptrons (MLPs) in deep network architectures, including advanced architectures such as vision Transformers (ViTs). In this paper, we are the first to design a general learnable Kolmogorov-Arnold Attention (KArAt) for vanilla ViTs that can operate on any choice of basis. However, the computing and memory costs of training them motivated us to propose a more modular version, and we designed particular learnable attention, called Fourier-KArAt. Fourier-KArAt and its variants either outperform their ViT counterparts or show comparable performance on CIFAR-10, CIFAR-100, and ImageNet-1K datasets. We dissect these architectures' performance and generalization capacity by analyzing their loss landscapes, weight distributions, optimizer path, attention visualization, and spectral behavior, and contrast them with vanilla ViTs. The goal of this paper is not to produce parameter- and compute-efficient attention, but to encourage the community to explore KANs in conjunction with more advanced architectures that require a careful understanding of learnable activations. Our open-source code and implementation details are available on: https://subhajitmaity.me/KArAt

comment: Preprint, Appendix included

☆ Uncertainty in Action: Confidence Elicitation in Embodied Agents

Tianjiao Yu, Vedant Shah, Muntasir Wahed, Kiet A. Nguyen, Adheesh Juvekar, Tal August, Ismini Lourentzou

comment: Project page: https://plan-lab.github.io/ece/

☆ NIL: No-data Imitation Learning by Leveraging Pre-trained Video Diffusion Models

Mert Albaba, Chenhao Li, Markos Diomataris, Omid Taheri, Andreas Krause, Michael Black

★ Transformers without Normalization CVPR 2025

Jiachen Zhu, Xinlei Chen, Kaiming He, Yann LeCun, Zhuang Liu

comment: CVPR 2025; Project page: https://jiachenzhu.github.io/DyT/

☆ Poly-MgNet: Polynomial Building Blocks in Multigrid-Inspired ResNets

Antonia van Betteray, Matthias Rottmann, Karsten Kahl

The structural analogies of ResNets and Multigrid (MG) methods such as common building blocks like convolutions and poolings where already pointed out by He et al.\ in 2016. Multigrid methods are used in the context of scientific computing for solving large sparse linear systems arising from partial differential equations. MG methods particularly rely on two main concepts: smoothing and residual restriction / coarsening. Exploiting these analogies, He and Xu developed the MgNet framework, which integrates MG schemes into the design of ResNets. In this work, we introduce a novel neural network building block inspired by polynomial smoothers from MG theory. Our polynomial block from an MG perspective naturally extends the MgNet framework to Poly-Mgnet and at the same time reduces the number of weights in MgNet. We present a comprehensive study of our polynomial block, analyzing the choice of initial coefficients, the polynomial degree, the placement of activation functions, as well as of batch normalizations. Our results demonstrate that constructing (quadratic) polynomial building blocks based on real and imaginary polynomial roots enhances Poly-MgNet's capacity in terms of accuracy. Furthermore, our approach achieves an improved trade-off of model accuracy and number of weights compared to ResNet as well as compared to specific configurations of MgNet.

☆ The Spectral Bias of Shallow Neural Network Learning is Shaped by the Choice of Non-linearity

Justin Sahs, Ryan Pyle, Fabio Anselmi, Ankit Patel

comment: 18 pages, 10 figures in main text

☆ On the Injective Norm of Sums of Random Tensors and the Moments of Gaussian Chaoses

Ishaq Aden-Ali

We prove an upper bound on the expected $\ell_p$ injective norm of sums of subgaussian random tensors. Our proof is simple and does not rely on any explicit geometric or chaining arguments. Instead, it follows from a simple application of the PAC-Bayesian lemma, a tool that has proven effective at controlling the suprema of certain ``smooth'' empirical processes in recent years. Our bound strictly improves a very recent result of Bandeira, Gopi, Jiang, Lucca, and Rothvoss. In the Euclidean case ($p=2$), our bound sharpens a result of Lata{\l}a that was central to proving his estimates on the moments of Gaussian chaoses. As a consequence, we obtain an elementary proof of this fundamental result.

comment: 12 pages

☆ Sample and Map from a Single Convex Potential: Generation using Conjugate Moment Measures

Nina Vesseron, Louis Béthune, Marco Cuturi

A common approach to generative modeling is to split model-fitting into two blocks: define first how to sample noise (e.g. Gaussian) and choose next what to do with it (e.g. using a single map or flows). We explore in this work an alternative route that ties sampling and mapping. We find inspiration in moment measures, a result that states that for any measure $\rho$ supported on a compact convex set of $\mathbb{R}^d$, there exists a unique convex potential $u$ such that $\rho=\nabla u\,\sharp\,e^{-u}$. While this does seem to tie effectively sampling (from log-concave distribution $e^{-u}$) and action (pushing particles through $\nabla u$), we observe on simple examples (e.g., Gaussians or 1D distributions) that this choice is ill-suited for practical tasks. We study an alternative factorization, where $\rho$ is factorized as $\nabla w^*\,\sharp\,e^{-w}$, where $w^*$ is the convex conjugate of $w$. We call this approach conjugate moment measures, and show far more intuitive results on these examples. Because $\nabla w^*$ is the Monge map between the log-concave distribution $e^{-w}$ and $\rho$, we rely on optimal transport solvers to propose an algorithm to recover $w$ from samples of $\rho$, and parameterize $w$ as an input-convex neural network.

☆ Unveiling the Mathematical Reasoning in DeepSeek Models: A Comparative Study of Large Language Models

Afrar Jahin, Arif Hassan Zidan, Yu Bao, Shizhe Liang, Tianming Liu, Wei Zhang

With the rapid evolution of Artificial Intelligence (AI), Large Language Models (LLMs) have reshaped the frontiers of various fields, spanning healthcare, public health, engineering, science, agriculture, education, arts, humanities, and mathematical reasoning. Among these advancements, DeepSeek models have emerged as noteworthy contenders, demonstrating promising capabilities that set them apart from their peers. While previous studies have conducted comparative analyses of LLMs, few have delivered a comprehensive evaluation of mathematical reasoning across a broad spectrum of LLMs. In this work, we aim to bridge this gap by conducting an in-depth comparative study, focusing on the strengths and limitations of DeepSeek models in relation to their leading counterparts. In particular, our study systematically evaluates the mathematical reasoning performance of two DeepSeek models alongside five prominent LLMs across three independent benchmark datasets. The findings reveal several key insights: 1). DeepSeek-R1 consistently achieved the highest accuracy on two of the three datasets, demonstrating strong mathematical reasoning capabilities. 2). The distilled variant of LLMs significantly underperformed compared to its peers, highlighting potential drawbacks in using distillation techniques. 3). In terms of response time, Gemini 2.0 Flash demonstrated the fastest processing speed, outperforming other models in efficiency, which is a crucial factor for real-time applications. Beyond these quantitative assessments, we delve into how architecture, training, and optimization impact LLMs' mathematical reasoning. Moreover, our study goes beyond mere performance comparison by identifying key areas for future advancements in LLM-driven mathematical reasoning. This research enhances our understanding of LLMs' mathematical reasoning and lays the groundwork for future advancements

☆ Radar: Fast Long-Context Decoding for Any Transformer ICLR 2025

Yongchang Hao, Mengyao Zhai, Hossein Hajimirsadeghi, Sepidehsadat Hosseini, Frederick Tung

Transformer models have demonstrated exceptional performance across a wide range of applications. Though forming the foundation of Transformer models, the dot-product attention does not scale well to long-context data since its time requirement grows quadratically with context length. In this work, we propose Radar, a training-free approach that accelerates inference by dynamically searching for the most important context tokens. For any pre-trained Transformer, Radar can reduce the decoding time complexity without training or heuristically evicting tokens. Moreover, we provide theoretical justification for our approach, demonstrating that Radar can reliably identify the most important tokens with high probability. We conduct extensive comparisons with the previous methods on a wide range of tasks. The results demonstrate that Radar achieves the state-of-the-art performance across different architectures with reduced time complexity, offering a practical solution for efficient long-context processing of Transformers.

comment: Accepted @ ICLR 2025

☆ FedPCA: Noise-Robust Fair Federated Learning via Performance-Capacity Analysis

Nannan Wu, Zengqiang Yan, Nong Sang, Li Yu, Chang Wen Chen

Training a model that effectively handles both common and rare data-i.e., achieving performance fairness-is crucial in federated learning (FL). While existing fair FL methods have shown effectiveness, they remain vulnerable to mislabeled data. Ensuring robustness in fair FL is therefore essential. However, fairness and robustness inherently compete, which causes robust strategies to hinder fairness. In this paper, we attribute this competition to the homogeneity in loss patterns exhibited by rare and mislabeled data clients, preventing existing loss-based fair and robust FL methods from effectively distinguishing and handling these two distinct client types. To address this, we propose performance-capacity analysis, which jointly considers model performance on each client and its capacity to handle the dataset, measured by loss and a newly introduced feature dispersion score. This allows mislabeled clients to be identified by their significantly deviated performance relative to capacity while preserving rare data clients. Building on this, we introduce FedPCA, an FL method that robustly achieves fairness. FedPCA first identifies mislabeled clients via a Gaussian Mixture Model on loss-dispersion pairs, then applies fairness and robustness strategies in global aggregation and local training by adjusting client weights and selectively using reliable data. Extensive experiments on three datasets demonstrate FedPCA's effectiveness in tackling this complex challenge. Code will be publicly available upon acceptance.

comment: Preprint

☆ ASIDE: Architectural Separation of Instructions and Data in Language Models ICLR 2025

Egor Zverev, Evgenii Kortukov, Alexander Panfilov, Soroush Tabesh, Alexandra Volkova, Sebastian Lapuschkin, Wojciech Samek, Christoph H. Lampert

Despite their remarkable performance, large language models lack elementary safety features, and this makes them susceptible to numerous malicious attacks. In particular, previous work has identified the absence of an intrinsic separation between instructions and data as a root cause for the success of prompt injection attacks. In this work, we propose an architectural change, ASIDE, that allows the model to clearly separate between instructions and data by using separate embeddings for them. Instead of training the embeddings from scratch, we propose a method to convert an existing model to ASIDE form by using two copies of the original model's embeddings layer, and applying an orthogonal rotation to one of them. We demonstrate the effectiveness of our method by showing (1) highly increased instruction-data separation scores without a loss in model capabilities and (2) competitive results on prompt injection benchmarks, even without dedicated safety training. Additionally, we study the working mechanism behind our method through an analysis of model representations.

comment: ICLR 2025 Workshop on Building Trust in Language Models and Applications

☆ From Linear to Spline-Based Classification:Developing and Enhancing SMPA for Noisy Non-Linear Datasets

Vatsal Srivastava

Building upon the concepts and mechanisms used for the development in Moving Points Algorithm, we will now explore how non linear decision boundaries can be developed for classification tasks. First we will look at the classification performance of MPA and some minor developments in the original algorithm. We then discuss the concepts behind using cubic splines for classification with a similar learning mechanism and finally analyze training results on synthetic datasets with known properties.

☆ DP-GPL: Differentially Private Graph Prompt Learning

Jing Xu, Franziska Boenisch, Iyiola Emmanuel Olatunji, Adam Dziedzic

Graph Neural Networks (GNNs) have shown remarkable performance in various applications. Recently, graph prompt learning has emerged as a powerful GNN training paradigm, inspired by advances in language and vision foundation models. Here, a GNN is pre-trained on public data and then adapted to sensitive tasks using lightweight graph prompts. However, using prompts from sensitive data poses privacy risks. In this work, we are the first to investigate these practical risks in graph prompts by instantiating a membership inference attack that reveals significant privacy leakage. We also find that the standard privacy method, DP-SGD, fails to provide practical privacy-utility trade-offs in graph prompt learning, likely due to the small number of sensitive data points used to learn the prompts. As a solution, we propose DP-GPL for differentially private graph prompt learning based on the PATE framework, that generates a graph prompt with differential privacy guarantees. Our evaluation across various graph prompt learning methods, GNN architectures, and pre-training strategies demonstrates that our algorithm achieves high utility at strong privacy, effectively mitigating privacy concerns while preserving the powerful capabilities of prompted GNNs as powerful foundation models in the graph domain.

☆ Language Models, Graph Searching, and Supervision Adulteration: When More Supervision is Less and How to Make More More SC

Arvid Frydenlund

comment: A reduced version of this work has been accepted to the Workshop on Spurious Correlation and Shortcut Learning: Foundations and Solutions (SCSL) at ICLR 2025. Full version under review

☆ GBSVR: Granular Ball Support Vector Regression

Reshma Rastogi, Ankush Bisht, Sanjay Kumar, Suresh Chandra

☆ Structured Preconditioners in Adaptive Optimization: A Unified Analysis

Shuo Xie, Tianhao Wang, Sashank Reddi, Sanjiv Kumar, Zhiyuan Li

We present a novel unified analysis for a broad class of adaptive optimization algorithms with structured (e.g., layerwise, diagonal, and kronecker-factored) preconditioners for both online regret minimization and offline convex optimization. Our analysis not only provides matching rate to several important structured preconditioned algorithms including diagonal AdaGrad, full-matrix AdaGrad, and AdaGrad-Norm, but also gives an improved convergence rate for a one-sided variant of Shampoo over that of original Shampoo. Interestingly, more structured preconditioners (e.g., diagonal Adagrad, AdaGrad-Norm which use less space and compute) are often presented as computationally efficient approximations to full-matrix Adagrad, aiming for improved optimization performance through better approximations. Our unified analysis challenges this prevailing view and reveals, perhaps surprisingly, that more structured preconditioners, despite using less space and computation per step, can outperform their less structured counterparts. To demonstrate this, we show that one-sided Shampoo, which is relatively much cheaper than full-matrix AdaGrad could outperform it both theoretically and experimentally.

☆ AudioX: Diffusion Transformer for Anything-to-Audio Generation

Zeyue Tian, Yizhu Jin, Zhaoyang Liu, Ruibin Yuan, Xu Tan, Qifeng Chen, Wei Xue, Yike Guo

Audio and music generation have emerged as crucial tasks in many applications, yet existing approaches face significant limitations: they operate in isolation without unified capabilities across modalities, suffer from scarce high-quality, multi-modal training data, and struggle to effectively integrate diverse inputs. In this work, we propose AudioX, a unified Diffusion Transformer model for Anything-to-Audio and Music Generation. Unlike previous domain-specific models, AudioX can generate both general audio and music with high quality, while offering flexible natural language control and seamless processing of various modalities including text, video, image, music, and audio. Its key innovation is a multi-modal masked training strategy that masks inputs across modalities and forces the model to learn from masked inputs, yielding robust and unified cross-modal representations. To address data scarcity, we curate two comprehensive datasets: vggsound-caps with 190K audio captions based on the VGGSound dataset, and V2M-caps with 6 million music captions derived from the V2M dataset. Extensive experiments demonstrate that AudioX not only matches or outperforms state-of-the-art specialized models, but also offers remarkable versatility in handling diverse input modalities and generation tasks within a unified architecture. The code and datasets will be available at https://zeyuet.github.io/AudioX/

comment: The code and datasets will be available at https://zeyuet.github.io/AudioX/

☆ CountPath: Automating Fragment Counting in Digital Pathology

Ana Beatriz Vieira, Maria Valente, Diana Montezuma, Tomé Albuquerque, Liliana Ribeiro, Domingos Oliveira, João Monteiro, Sofia Gonçalves, Isabel M. Pinto, Jaime S. Cardoso, Arlindo L. Oliveira

comment: 10 pages, 3 figures

☆ Conformal Prediction Sets for Deep Generative Models via Reduction to Conformal Regression

Hooman Shahrokhi, Devjeet Raj Roy, Yan Yan, Venera Arnaoudova, Janaradhan Rao Doppa

☆ Extreme Learning Machines for Attention-based Multiple Instance Learning in Whole-Slide Image Classification

Rajiv Krishnakumar, Julien Baglio, Frederik F. Flöther, Christian Ruiz, Stefan Habringer, Nicole H. Romano

Whole-slide image classification represents a key challenge in computational pathology and medicine. Attention-based multiple instance learning (MIL) has emerged as an effective approach for this problem. However, the effect of attention mechanism architecture on model performance is not well-documented for biomedical imagery. In this work, we compare different methods and implementations of MIL, including deep learning variants. We introduce a new method using higher-dimensional feature spaces for deep MIL. We also develop a novel algorithm for whole-slide image classification where extreme machine learning is combined with attention-based MIL to improve sensitivity and reduce training complexity. We apply our algorithms to the problem of detecting circulating rare cells (CRCs), such as erythroblasts, in peripheral blood. Our results indicate that nonlinearities play a key role in the classification, as removing them leads to a sharp decrease in stability in addition to a decrease in average area under the curve (AUC) of over 4%. We also demonstrate a considerable increase in robustness of the model with improvements of over 10% in average AUC when higher-dimensional feature spaces are leveraged. In addition, we show that extreme learning machines can offer clear improvements in terms of training efficiency by reducing the number of trained parameters by a factor of 5 whilst still maintaining the average AUC to within 1.5% of the deep MIL model. Finally, we discuss options of enriching the classical computing framework with quantum algorithms in the future. This work can thus help pave the way towards more accurate and efficient single-cell diagnostics, one of the building blocks of precision medicine.

☆ SySLLM: Generating Synthesized Policy Summaries for Reinforcement Learning Agents Using Large Language Models

Sahar Admoni, Omer Ben-Porat, Ofra Amir

Policies generated by Reinforcement Learning (RL) algorithms can be difficult to describe to users, as they result from the interplay between complex reward structures and neural network-based representations. This combination often leads to unpredictable behaviors, making policies challenging to analyze and posing significant obstacles to fostering human trust in real-world applications. Global policy summarization methods aim to describe agent behavior through a demonstration of actions in a subset of world-states. However, users can only watch a limited number of demonstrations, restricting their understanding of policies. Moreover, those methods overly rely on user interpretation, as they do not synthesize observations into coherent patterns. In this work, we present SySLLM (Synthesized Summary using LLMs), a novel method that employs synthesis summarization, utilizing large language models' (LLMs) extensive world knowledge and ability to capture patterns, to generate textual summaries of policies. Specifically, an expert evaluation demonstrates that the proposed approach generates summaries that capture the main insights generated by experts while not resulting in significant hallucinations. Additionally, a user study shows that SySLLM summaries are preferred over demonstration-based policy summaries and match or surpass their performance in objective agent identification tasks.

☆ Sample Compression for Continual Learning

Jacob Comeau, Mathieu Bazinet, Pascal Germain, Cem Subakan

Continual learning algorithms aim to learn from a sequence of tasks, making the training distribution non-stationary. The majority of existing continual learning approaches in the literature rely on heuristics and do not provide learning guarantees for the continual learning setup. In this paper, we present a new method called 'Continual Pick-to-Learn' (CoP2L), which is able to retain the most representative samples for each task in an efficient way. The algorithm is adapted from the Pick-to-Learn algorithm, rooted in the sample compression theory. This allows us to provide high-confidence upper bounds on the generalization loss of the learned predictors, numerically computable after every update of the learned model. We also empirically show on several standard continual learning benchmarks that our algorithm is able to outperform standard experience replay, significantly mitigating catastrophic forgetting.

☆ Explainable Bayesian deep learning through input-skip Latent Binary Bayesian Neural Networks

Eirik Høyheim, Lars Skaaret-Lund, Solve Sæbø, Aliaksandr Hubin

comment: 44 pages, 19 tables, 25 figures. Code available at https://github.com/eirihoyh/ISLaB-LBBNN

☆ Meta-learning characteristics and dynamics of quantum systems

Lucas Schorling, Pranav Vaidhyanathan, Jonas Schuff, Miguel J. Carballido, Dominik Zumbühl, Gerard Milburn, Florian Marquardt, Jakob Foerster, Michael A. Osborne, Natalia Ares

While machine learning holds great promise for quantum technologies, most current methods focus on predicting or controlling a specific quantum system. Meta-learning approaches, however, can adapt to new systems for which little data is available, by leveraging knowledge obtained from previous data associated with similar systems. In this paper, we meta-learn dynamics and characteristics of closed and open two-level systems, as well as the Heisenberg model. Based on experimental data of a Loss-DiVincenzo spin-qubit hosted in a Ge/Si core/shell nanowire for different gate voltage configurations, we predict qubit characteristics i.e. $g$-factor and Rabi frequency using meta-learning. The algorithm we introduce improves upon previous state-of-the-art meta-learning methods for physics-based systems by introducing novel techniques such as adaptive learning rates and a global optimizer for improved robustness and increased computational efficiency. We benchmark our method against other meta-learning methods, a vanilla transformer, and a multilayer perceptron, and demonstrate improved performance.

comment: 6+1 pages, 4 figures. L. Schorling and P. Vaidhyanathan contributed equally to this work

☆ Representation Learning, Large-Scale 3D Molecular Pretraining, Molecular Property

Shuqi Lu, Xiaohong Ji, Bohang Zhang, Lin Yao, Siyuan Liu, Zhifeng Gao, Linfeng Zhang, Guolin Ke

Molecular pretrained representations (MPR) has emerged as a powerful approach for addressing the challenge of limited supervised data in applications such as drug discovery and material design. While early MPR methods relied on 1D sequences and 2D graphs, recent advancements have incorporated 3D conformational information to capture rich atomic interactions. However, these prior models treat molecules merely as discrete atom sets, overlooking the space surrounding them. We argue from a physical perspective that only modeling these discrete points is insufficient. We first present a simple yet insightful observation: naively adding randomly sampled virtual points beyond atoms can surprisingly enhance MPR performance. In light of this, we propose a principled framework that incorporates the entire 3D space spanned by molecules. We implement the framework via a novel Transformer-based architecture, dubbed SpaceFormer, with three key components: (1) grid-based space discretization; (2) grid sampling/merging; and (3) efficient 3D positional encoding. Extensive experiments show that SpaceFormer significantly outperforms previous 3D MPR models across various downstream tasks with limited data, validating the benefit of leveraging the additional 3D space beyond atoms in MPR models.

☆ Streaming Generation of Co-Speech Gestures via Accelerated Rolling Diffusion

Evgeniia Vu, Andrei Boiarov, Dmitry Vetrov

Generating co-speech gestures in real time requires both temporal coherence and efficient sampling. We introduce Accelerated Rolling Diffusion, a novel framework for streaming gesture generation that extends rolling diffusion models with structured progressive noise scheduling, enabling seamless long-sequence motion synthesis while preserving realism and diversity. We further propose Rolling Diffusion Ladder Acceleration (RDLA), a new approach that restructures the noise schedule into a stepwise ladder, allowing multiple frames to be denoised simultaneously. This significantly improves sampling efficiency while maintaining motion consistency, achieving up to a 2x speedup with high visual fidelity and temporal coherence. We evaluate our approach on ZEGGS and BEAT, strong benchmarks for real-world applicability. Our framework is universally applicable to any diffusion-based gesture generation model, transforming it into a streaming approach. Applied to three state-of-the-art methods, it consistently outperforms them, demonstrating its effectiveness as a generalizable and efficient solution for real-time, high-fidelity co-speech gesture synthesis.

☆ Applying Tabular Deep Learning Models to Estimate Crash Injury Types of Young Motorcyclists

Shriyank Somvanshi, Anannya Ghosh Tusti, Rohit Chakraborty, Subasish Das

Young motorcyclists, particularly those aged 15 to 24 years old, face a heightened risk of severe crashes due to factors such as speeding, traffic violations, and helmet usage. This study aims to identify key factors influencing crash severity by analyzing 10,726 young motorcyclist crashes in Texas from 2017 to 2022. Two advanced tabular deep learning models, ARMNet and MambaNet, were employed, using an advanced resampling technique to address class imbalance. The models were trained to classify crashes into three severity levels, Fatal or Severe, Moderate or Minor, and No Injury. ARMNet achieved an accuracy of 87 percent, outperforming 86 percent of Mambanet, with both models excelling in predicting severe and no injury crashes while facing challenges in moderate crash classification. Key findings highlight the significant influence of demographic, environmental, and behavioral factors on crash outcomes. The study underscores the need for targeted interventions, including stricter helmet enforcement and educational programs customized to young motorcyclists. These insights provide valuable guidance for policymakers in developing evidence-based strategies to enhance motorcyclist safety and reduce crash severity.

comment: 6 pages, 6 figures, accepted at IEEE CAI 2025

☆ Deep Learning based discovery of Integrable Systems

Shailesh Lal, Suvajit Majumder, Evgeny Sobko

We introduce a novel machine learning based framework for discovering integrable models. Our approach first employs a synchronized ensemble of neural networks to find high-precision numerical solution to the Yang-Baxter equation within a specified class. Then, using an auxiliary system of algebraic equations, [Q_2, Q_3] = 0, and the numerical value of the Hamiltonian obtained via deep learning as a seed, we reconstruct the entire Hamiltonian family, forming an algebraic variety. We illustrate our presentation with three- and four-dimensional spin chains of difference form with local interactions. Remarkably, all discovered Hamiltonian families form rational varieties.

comment: 11 pages, 2 column text, 3 figures, Mathematica notebook with example Hamiltonians

☆ OODD: Test-time Out-of-Distribution Detection with Dynamic Dictionary

Yifeng Yang, Lin Zhu, Zewen Sun, Hengyu Liu, Qinying Gu, Nanyang Ye

Out-of-distribution (OOD) detection remains challenging for deep learning models, particularly when test-time OOD samples differ significantly from training outliers. We propose OODD, a novel test-time OOD detection method that dynamically maintains and updates an OOD dictionary without fine-tuning. Our approach leverages a priority queue-based dictionary that accumulates representative OOD features during testing, combined with an informative inlier sampling strategy for in-distribution (ID) samples. To ensure stable performance during early testing, we propose a dual OOD stabilization mechanism that leverages strategically generated outliers derived from ID data. To our best knowledge, extensive experiments on the OpenOOD benchmark demonstrate that OODD significantly outperforms existing methods, achieving a 26.0% improvement in FPR95 on CIFAR-100 Far OOD detection compared to the state-of-the-art approach. Furthermore, we present an optimized variant of the KNN-based OOD detection framework that achieves a 3x speedup while maintaining detection performance.

☆ SortingEnv: An Extendable RL-Environment for an Industrial Sorting Process

Tom Maus, Nico Zengeler, Tobias Glasmachers

We present a novel reinforcement learning (RL) environment designed to both optimize industrial sorting systems and study agent behavior in evolving spaces. In simulating material flow within a sorting process our environment follows the idea of a digital twin, with operational parameters like belt speed and occupancy level. To reflect real-world challenges, we integrate common upgrades to industrial setups, like new sensors or advanced machinery. It thus includes two variants: a basic version focusing on discrete belt speed adjustments and an advanced version introducing multiple sorting modes and enhanced material composition observations. We detail the observation spaces, state update mechanisms, and reward functions for both environments. We further evaluate the efficiency of common RL algorithms like Proximal Policy Optimization (PPO), Deep-Q-Networks (DQN), and Advantage Actor Critic (A2C) in comparison to a classical rule-based agent (RBA). This framework not only aids in optimizing industrial processes but also provides a foundation for studying agent behavior and transferability in evolving environments, offering insights into model performance and practical implications for real-world RL applications.

comment: Presented at the 12th International Conference on Industrial Engineering and Applications (ICIEA-EU), Munich, 2025. This article has been submitted to AIP Conference Proceedings. After it is published, it will be available in the AIP Digital Library

☆ Light-R1: Curriculum SFT, DPO and RL for Long COT from Scratch and Beyond

Liang Wen, Yunke Cai, Fenrui Xiao, Xin He, Qi An, Zhenyu Duan, Yimin Du, Junchen Liu, Lifu Tang, Xiaowei Lv, Haosheng Zou, Yongchao Deng, Shousheng Jia, Xiangzheng Zhang

This paper presents our work on the Light-R1 series, with models, data, and code all released. We first focus on training long COT models from scratch, specifically starting from models initially lacking long COT capabilities. Using a curriculum training recipe consisting of two-stage SFT and semi-on-policy DPO, we train our model Light-R1-32B from Qwen2.5-32B-Instruct, resulting in superior math performance compared to DeepSeek-R1-Distill-Qwen-32B. Despite being trained exclusively on math data, Light-R1-32B shows strong generalization across other domains. In the subsequent phase of this work, we highlight the significant benefit of the 3k dataset constructed for the second SFT stage on enhancing other models. By fine-tuning DeepSeek-R1-Distilled models using this dataset, we obtain new SOTA models in 7B and 14B, while the 32B model, Light-R1-32B-DS performed comparably to QwQ-32B and DeepSeek-R1. Furthermore, we extend our work by applying reinforcement learning, specifically GRPO, on long-COT models to further improve reasoning performance. We successfully train our final Light-R1-14B-DS with RL, achieving SOTA performance among 14B parameter models in math. With AIME24 & 25 scores of 74.0 and 60.2 respectively, Light-R1-14B-DS surpasses even many 32B models and DeepSeek-R1-Distill-Llama-70B. Its RL training also exhibits well expected behavior, showing simultaneous increase in response length and reward score. The Light-R1 series of work validates training long-COT models from scratch, showcases the art in SFT data and releases SOTA models from RL.

comment: all release at https://github.com/Qihoo360/Light-R1

☆ Sentiment Analysis in SemEval: A Review of Sentiment Identification Approaches

Bousselham El Haddaoui, Raddouane Chiheb, Rdouan Faizi, Abdellatif El Afia

Social media platforms are becoming the foundations of social interactions including messaging and opinion expression. In this regard, Sentiment Analysis techniques focus on providing solutions to ensure the retrieval and analysis of generated data including sentiments, emotions, and discussed topics. International competitions such as the International Workshop on Semantic Evaluation (SemEval) have attracted many researchers and practitioners with a special research interest in building sentiment analysis systems. In our work, we study top-ranking systems for each SemEval edition during the 2013-2021 period, a total of 658 teams participated in these editions with increasing interest over years. We analyze the proposed systems marking the evolution of research trends with a focus on the main components of sentiment analysis systems including data acquisition, preprocessing, and classification. Our study shows an active use of preprocessing techniques, an evolution of features engineering and word representation from lexicon-based approaches to word embeddings, and the dominance of neural networks and transformers over the classification phase fostering the use of ready-to-use models. Moreover, we provide researchers with insights based on experimented systems which will allow rapid prototyping of new systems and help practitioners build for future SemEval editions.

☆ Learning Disease State from Noisy Ordinal Disease Progression Labels

Gustav Schmidt, Holger Heidrich, Philipp Berens, Sarah Müller

Learning from noisy ordinal labels is a key challenge in medical imaging. In this work, we ask whether ordinal disease progression labels (better, worse, or stable) can be used to learn a representation allowing to classify disease state. For neovascular age-related macular degeneration (nAMD), we cast the problem of modeling disease progression between medical visits as a classification task with ordinal ranks. To enhance generalization, we tailor our model to the problem setting by (1) independent image encoding, (2) antisymmetric logit space equivariance, and (3) ordinal scale awareness. In addition, we address label noise by learning an uncertainty estimate for loss re-weighting. Our approach learns an interpretable disease representation enabling strong few-shot performance for the related task of nAMD activity classification from single images, despite being trained only on image pairs with ordinal disease progression labels.

☆ Finetuning Generative Trajectory Model with Reinforcement Learning from Human Feedback

Derun Li, Jianwei Ren, Yue Wang, Xin Wen, Pengxiang Li, Leimeng Xu, Kun Zhan, Zhongpu Xia, Peng Jia, Xianpeng Lang, Ningyi Xu, Hang Zhao

Generating human-like and adaptive trajectories is essential for autonomous driving in dynamic environments. While generative models have shown promise in synthesizing feasible trajectories, they often fail to capture the nuanced variability of human driving styles due to dataset biases and distributional shifts. To address this, we introduce TrajHF, a human feedback-driven finetuning framework for generative trajectory models, designed to align motion planning with diverse driving preferences. TrajHF incorporates multi-conditional denoiser and reinforcement learning with human feedback to refine multi-modal trajectory generation beyond conventional imitation learning. This enables better alignment with human driving preferences while maintaining safety and feasibility constraints. TrajHF achieves PDMS of 93.95 on NavSim benchmark, significantly exceeding other methods. TrajHF sets a new paradigm for personalized and adaptable trajectory generation in autonomous driving.

comment: 10 pages, 5 figures

☆ BeamLLM: Vision-Empowered mmWave Beam Prediction with Large Language Models

Can Zheng, Jiguang He, Guofa Cai, Zitong Yu, Chung G. Kang

In this paper, we propose BeamLLM, a vision-aided millimeter-wave (mmWave) beam prediction framework leveraging large language models (LLMs) to address the challenges of high training overhead and latency in mmWave communication systems. By combining computer vision (CV) with LLMs' cross-modal reasoning capabilities, the framework extracts user equipment (UE) positional features from RGB images and aligns visual-temporal features with LLMs' semantic space through reprogramming techniques. Evaluated on a realistic vehicle-to-infrastructure (V2I) scenario, the proposed method achieves 61.01% top-1 accuracy and 97.39% top-3 accuracy in standard prediction tasks, significantly outperforming traditional deep learning models. In few-shot prediction scenarios, the performance degradation is limited to 12.56% (top-1) and 5.55% (top-3) from time sample 1 to 10, demonstrating superior prediction capability.

comment: 6 pages, 7 figures, conference

☆ Langevin Monte-Carlo Provably Learns Depth Two Neural Nets at Any Size and Data

Dibyakanti Kumar, Samyak Jha, Anirbit Mukherjee

In this work, we will establish that the Langevin Monte-Carlo algorithm can learn depth-2 neural nets of any size and for any data and we give non-asymptotic convergence rates for it. We achieve this via showing that under Total Variation distance and q-Renyi divergence, the iterates of Langevin Monte Carlo converge to the Gibbs distribution of Frobenius norm regularized losses for any of these nets, when using smooth activations and in both classification and regression settings. Most critically, the amount of regularization needed for our results is independent of the size of the net. The key observation of ours is that two layer neural loss functions can always be regularized by a constant amount such that they satisfy the Villani conditions, and thus their Gibbs measures satisfy a Poincare inequality.

☆ Improving Medical Waste Classification with Hybrid Capsule Networks

Bennet van den Broek, Javad Pourmostafa Roshan Sharami

The improper disposal and mismanagement of medical waste pose severe environmental and public health risks, contributing to greenhouse gas emissions and the spread of infectious diseases. Efficient and accurate medical waste classification is crucial for mitigating these risks. We explore the integration of capsule networks with a pretrained DenseNet model to improve medical waste classification. To the best of our knowledge, capsule networks have not yet been applied to this task, making this study the first to assess their effectiveness. A diverse dataset of medical waste images collected from multiple public sources, is used to evaluate three model configurations: (1) a pretrained DenseNet model as a baseline, (2) a pretrained DenseNet with frozen layers combined with a capsule network, and (3) a pretrained DenseNet with unfrozen layers combined with a capsule network. Experimental results demonstrate that incorporating capsule networks improves classification performance, with F1 scores increasing from 0.89 (baseline) to 0.92 (hybrid model with unfrozen layers). This highlights the potential of capsule networks to address the spatial limitations of traditional convolutional models and improve classification robustness. While the capsule-enhanced model demonstrated improved classification performance, direct comparisons with prior studies were challenging due to differences in dataset size and diversity. Previous studies relied on smaller, domain-specific datasets, which inherently yielded higher accuracy. In contrast, our study employs a significantly larger and more diverse dataset, leading to better generalization but introducing additional classification challenges. This highlights the trade-off between dataset complexity and model performance.

☆ Towards Constraint-Based Adaptive Hypergraph Learning for Solving Vehicle Routing: An End-to-End Solution

Zhenwei Wang, Ruibin Bai, Tiehua Zhang

The application of learning based methods to vehicle routing problems has emerged as a pivotal area of research in combinatorial optimization. These problems are characterized by vast solution spaces and intricate constraints, making traditional approaches such as exact mathematical models or heuristic methods prone to high computational overhead or reliant on the design of complex heuristic operators to achieve optimal or near optimal solutions. Meanwhile, although some recent learning-based methods can produce good performance for VRP with straightforward constraint scenarios, they often fail to effectively handle hard constraints that are common in practice. This study introduces a novel end-to-end framework that combines constraint-oriented hypergraphs with reinforcement learning to address vehicle routing problems. A central innovation of this work is the development of a constraint-oriented dynamic hyperedge reconstruction strategy within an encoder, which significantly enhances hypergraph representation learning. Additionally, the decoder leverages a double-pointer attention mechanism to iteratively generate solutions. The proposed model is trained by incorporating asynchronous parameter updates informed by hypergraph constraints and optimizing a dual loss function comprising constraint loss and policy gradient loss. The experiment results on benchmark datasets demonstrate that the proposed approach not only eliminates the need for sophisticated heuristic operators but also achieves substantial improvements in solution quality.

☆ dFLMoE: Decentralized Federated Learning via Mixture of Experts for Medical Data Analysis

Luyuan Xie, Tianyu Luan, Wenyuan Cai, Guochen Yan, Zhaoyu Chen, Nan Xi, Yuejian Fang, Qingni Shen, Zhonghai Wu, Junsong Yuan

☆ Understanding the Logical Capabilities of Large Language Models via Out-of-Context Representation Learning

Jonathan Shaki, Emanuele La Malfa, Michael Wooldridge, Sarit Kraus

We study the capabilities of Large Language Models (LLM) on binary relations, a ubiquitous concept in math employed in most reasoning, math and logic benchmarks. This work focuses on equality, inequality, and inclusion, along with the properties they satisfy, such as ir/reflexivity, a/symmetry, transitivity, and logical complexity (e.g., number of reasoning ``hops''). We propose an alternative to in-context learning that trains only the representations of newly introduced tokens, namely out-of-context representation learning. This method mitigates linguistic biases already present in a model and, differently from in-context learning, does not rely on external information or illustrations. We argue out-of-context representation learning as a better alternative to in-context learning and fine-tuning to evaluate the capabilities of LLMs on logic tasks that are the building blocks of more complex reasoning benchmarks.

☆ Architecture-Aware Minimization (A$^2$M): How to Find Flat Minima in Neural Architecture Search

Matteo Gambella, Fabrizio Pittorino, Manuel Roveri

Neural Architecture Search (NAS) has become an essential tool for designing effective and efficient neural networks. In this paper, we investigate the geometric properties of neural architecture spaces commonly used in differentiable NAS methods, specifically NAS-Bench-201 and DARTS. By defining flatness metrics such as neighborhoods and loss barriers along paths in architecture space, we reveal locality and flatness characteristics analogous to the well-known properties of neural network loss landscapes in weight space. In particular, we find that highly accurate architectures cluster together in flat regions, while suboptimal architectures remain isolated, unveiling the detailed geometrical structure of the architecture search landscape. Building on these insights, we propose Architecture-Aware Minimization (A$^2$M), a novel analytically derived algorithmic framework that explicitly biases, for the first time, the gradient of differentiable NAS methods towards flat minima in architecture space. A$^2$M consistently improves generalization over state-of-the-art DARTS-based algorithms on benchmark datasets including CIFAR-10, CIFAR-100, and ImageNet16-120, across both NAS-Bench-201 and DARTS search spaces. Notably, A$^2$M is able to increase the test accuracy, on average across different differentiable NAS methods, by +3.60\% on CIFAR-10, +4.60\% on CIFAR-100, and +3.64\% on ImageNet16-120, demonstrating its superior effectiveness in practice. A$^2$M can be easily integrated into existing differentiable NAS frameworks, offering a versatile tool for future research and applications in automated machine learning. We open-source our code at https://github.com/AI-Tech-Research-Lab/AsquaredM.

comment: 22 pages, 11 figures, 3 tables

☆ Multi-objective Good Arm Identification with Bandit Feedback

Xuanke Jiang, Kohei Hatano, Eiji Takimoto

We consider a good arm identification problem in a stochastic bandit setting with multi-objectives, where each arm $i\in[K]$ is associated with $M$ distributions $\mathcal{D}_i^{(1)}, \ldots, \mathcal{D}_i^{(M)}$. For each round $t$, the player/algorithm pulls one arm $i_t$ and receives a vector feedback, where each component $m$ is sampled according to $\mathcal{D}_i^{(m)}$. The target is twofold, one is finding one arm whose means are larger than the predefined thresholds $\xi_1,\ldots,\xi_M$ with a confidence bound $\delta$ and an accuracy rate $\epsilon$ with a bounded sample complexity, the other is output $\bot$ to indicate no such arm exists. We propose an algorithm with a sample complexity bound. When $M=1$ and $\epsilon = 0$, our bound is the same as the one given in the previous work when and novel bounds for $M > 1$. The proposed algorithm attains better numerical performance than other baselines in the experiments on synthetic and real datasets.

☆ Subgroup Performance Analysis in Hidden Stratifications

Alceu Bissoto, Trung-Dung Hoang, Tim Flühmann, Susu Sun, Christian F. Baumgartner, Lisa M. Koch

Machine learning (ML) models may suffer from significant performance disparities between patient groups. Identifying such disparities by monitoring performance at a granular level is crucial for safely deploying ML to each patient. Traditional subgroup analysis based on metadata can expose performance disparities only if the available metadata (e.g., patient sex) sufficiently reflects the main reasons for performance variability, which is not common. Subgroup discovery techniques that identify cohesive subgroups based on learned feature representations appear as a potential solution: They could expose hidden stratifications and provide more granular subgroup performance reports. However, subgroup discovery is challenging to evaluate even as a standalone task, as ground truth stratification labels do not exist in real data. Subgroup discovery has thus neither been applied nor evaluated for the application of subgroup performance monitoring. Here, we apply subgroup discovery for performance monitoring in chest x-ray and skin lesion classification. We propose novel evaluation strategies and show that a simplified subgroup discovery method without access to classification labels or metadata can expose larger performance disparities than traditional metadata-based subgroup analysis. We provide the first compelling evidence that subgroup discovery can serve as an important tool for comprehensive performance validation and monitoring of trustworthy AI in medicine.

comment: Under review

☆ Probabilistic Forecasting via Autoregressive Flow Matching

Ahmed El-Gazzar, Marcel van Gerven

In this work, we propose FlowTime, a generative model for probabilistic forecasting of multivariate timeseries data. Given historical measurements and optional future covariates, we formulate forecasting as sampling from a learned conditional distribution over future trajectories. Specifically, we decompose the joint distribution of future observations into a sequence of conditional densities, each modeled via a shared flow that transforms a simple base distribution into the next observation distribution, conditioned on observed covariates. To achieve this, we leverage the flow matching (FM) framework, enabling scalable and simulation-free learning of these transformations. By combining this factorization with the FM objective, FlowTime retains the benefits of autoregressive models -- including strong extrapolation performance, compact model size, and well-calibrated uncertainty estimates -- while also capturing complex multi-modal conditional distributions, as seen in modern transport-based generative models. We demonstrate the effectiveness of FlowTime on multiple dynamical systems and real-world forecasting tasks.

☆ A Multimodal Fusion Model Leveraging MLP Mixer and Handcrafted Features-based Deep Learning Networks for Facial Palsy Detection PAKDD 2025

Heng Yim Nicole Oo, Min Hun Lee, Jeong Hoon Lim

comment: PAKDD 2025. arXiv admin note: text overlap with arXiv:2405.16496

☆ LUMOS: Language-Conditioned Imitation Learning with World Models ICRA

Iman Nematollahi, Branton DeMoss, Akshay L Chandra, Nick Hawes, Wolfram Burgard, Ingmar Posner

We introduce LUMOS, a language-conditioned multi-task imitation learning framework for robotics. LUMOS learns skills by practicing them over many long-horizon rollouts in the latent space of a learned world model and transfers these skills zero-shot to a real robot. By learning on-policy in the latent space of the learned world model, our algorithm mitigates policy-induced distribution shift which most offline imitation learning methods suffer from. LUMOS learns from unstructured play data with fewer than 1% hindsight language annotations but is steerable with language commands at test time. We achieve this coherent long-horizon performance by combining latent planning with both image- and language-based hindsight goal relabeling during training, and by optimizing an intrinsic reward defined in the latent space of the world model over multiple time steps, effectively reducing covariate shift. In experiments on the difficult long-horizon CALVIN benchmark, LUMOS outperforms prior learning-based methods with comparable approaches on chained multi-task evaluations. To the best of our knowledge, we are the first to learn a language-conditioned continuous visuomotor control for a real-world robot within an offline world model. Videos, dataset and code are available at http://lumos.cs.uni-freiburg.de.

comment: Accepted at the 2025 IEEE International Conference on Robotics and Automation (ICRA)

☆ BioSerenity-E1: a self-supervised EEG model for medical applications

Ruggero G. Bettinardi, Mohamed Rahmouni, Ulysse Gimenez

Electroencephalography (EEG) serves as an essential diagnostic tool in neurology; however, its accurate manual interpretation is a time-intensive process that demands highly specialized expertise, which remains relatively scarce and not consistently accessible. To address these limitations, the implementation of automated pre-screening and analysis systems for EEG data holds considerable promise. Advances in self-supervised learning made it possible to pre-train complex deep learning architectures on large volumes of unlabeled EEG data to learn generalizable representations, that can later be used to enhance performance on multiple tasks while needing less downstream data. In the present paper, we introduce BioSerenity-E1, the first of a family of self-supervised foundation models for clinical EEG applications that combines spectral tokenization with masked prediction to achieve state-of-the-art performance across relevant diagnostic tasks. The two-phase self-supervised pretraining framework initially acquires compressed EEG representations via a transformer-based VQ-VAE architecture designed to reconstruct log-multitaper spectral projections, then implements extensive (70% block) masked token prediction to force the model to learn complex spatiotemporal dependencies in EEG signals. BioSerenity-E1 achieves strong performance across three clinical tasks, either in line or above state-of-the-art methods: seizure detection (AUROC = 0.926, Sensitivity = 0.909), normal/abnormal classification (AUPRC = 0.970 on proprietary data; 0.910 on TUH-Abnormal), and multiclass pathology differentiation on unbalanced data (Weighted F1 = 0.730). The utility of BioSerenity-E1 is further confirmed in low-data regimes scenarios, showing clear improvements in AUPRC (from +2% to 17%) when trained on less than 10% of the available data.

☆ ConceptGuard: Continual Personalized Text-to-Image Generation with Forgetting and Confusion Mitigation CVPR 2025

Zirun Guo, Tao Jin

Diffusion customization methods have achieved impressive results with only a minimal number of user-provided images. However, existing approaches customize concepts collectively, whereas real-world applications often require sequential concept integration. This sequential nature can lead to catastrophic forgetting, where previously learned concepts are lost. In this paper, we investigate concept forgetting and concept confusion in the continual customization. To tackle these challenges, we present ConceptGuard, a comprehensive approach that combines shift embedding, concept-binding prompts and memory preservation regularization, supplemented by a priority queue which can adaptively update the importance and occurrence order of different concepts. These strategies can dynamically update, unbind and learn the relationship of the previous concepts, thus alleviating concept forgetting and confusion. Through comprehensive experiments, we show that our approach outperforms all the baseline methods consistently and significantly in both quantitative and qualitative analyses.

comment: Accepted at CVPR 2025

☆ Safe exploration in reproducing kernel Hilbert spaces AISTATS 2025

Abdullah Tokmak, Kiran G. Krishnan, Thomas B. Schön, Dominik Baumann

Popular safe Bayesian optimization (BO) algorithms learn control policies for safety-critical systems in unknown environments. However, most algorithms make a smoothness assumption, which is encoded by a known bounded norm in a reproducing kernel Hilbert space (RKHS). The RKHS is a potentially infinite-dimensional space, and it remains unclear how to reliably obtain the RKHS norm of an unknown function. In this work, we propose a safe BO algorithm capable of estimating the RKHS norm from data. We provide statistical guarantees on the RKHS norm estimation, integrate the estimated RKHS norm into existing confidence intervals and show that we retain theoretical guarantees, and prove safety of the resulting safe BO algorithm. We apply our algorithm to safely optimize reinforcement learning policies on physics simulators and on a real inverted pendulum, demonstrating improved performance, safety, and scalability compared to the state-of-the-art.

comment: Accepted to AISTATS 2025

☆ Mirror Online Conformal Prediction with Intermittent Feedback

Bowen Wang, Matteo Zecchin, Osvaldo Simeone

Online conformal prediction enables the runtime calibration of a pre-trained artificial intelligence model using feedback on its performance. Calibration is achieved through set predictions that are updated via online rules so as to ensure long-term coverage guarantees. While recent research has demonstrated the benefits of incorporating prior knowledge into the calibration process, this has come at the cost of replacing coverage guarantees with less tangible regret guarantees based on the quantile loss. This work introduces intermittent mirror online conformal prediction (IM-OCP), a novel runtime calibration framework that integrates prior knowledge, while maintaining long-term coverage and achieving sub-linear regret. IM-OCP features closed-form updates with minimal memory complexity, and is designed to operate under potentially intermittent feedback.

☆ Characterizing Nonlinear Dynamics via Smooth Prototype Equivalences

Roy Friedman, Noa Moriel, Matthew Ricci, Guy Pelc, Yair Weiss, Mor Nitzan

Characterizing dynamical systems given limited measurements is a common challenge throughout the physical and biological sciences. However, this task is challenging, especially due to transient variability in systems with equivalent long-term dynamics. We address this by introducing smooth prototype equivalences (SPE), a framework that fits a diffeomorphism using normalizing flows to distinct prototypes - simplified dynamical systems that define equivalence classes of behavior. SPE enables classification by comparing the deformation loss of the observed sparse, high-dimensional measurements to the prototype dynamics. Furthermore, our approach enables estimation of the invariant sets of the observed dynamics through the learned mapping from prototype space to data space. Our method outperforms existing techniques in the classification of oscillatory systems and can efficiently identify invariant structures like limit cycles and fixed points in an equation-free manner, even when only a small, noisy subset of the phase space is observed. Finally, we show how our method can be used for the detection of biological processes like the cell cycle trajectory from high-dimensional single-cell gene expression data.

comment: 9 pages, 6 figures

☆ Generative Binary Memory: Pseudo-Replay Class-Incremental Learning on Binarized Embeddings

Yanis Basso-Bert, Anca Molnos, Romain Lemaire, William Guicquero, Antoine Dupret

In dynamic environments where new concepts continuously emerge, Deep Neural Networks (DNNs) must adapt by learning new classes while retaining previously acquired ones. This challenge is addressed by Class-Incremental Learning (CIL). This paper introduces Generative Binary Memory (GBM), a novel CIL pseudo-replay approach which generates synthetic binary pseudo-exemplars. Relying on Bernoulli Mixture Models (BMMs), GBM effectively models the multi-modal characteristics of class distributions, in a latent, binary space. With a specifically-designed feature binarizer, our approach applies to any conventional DNN. GBM also natively supports Binary Neural Networks (BNNs) for highly-constrained model sizes in embedded systems. The experimental results demonstrate that GBM achieves higher than state-of-the-art average accuracy on CIFAR100 (+2.9%) and TinyImageNet (+1.5%) for a ResNet-18 equipped with our binarizer. GBM also outperforms emerging CIL methods for BNNs, with +3.1% in final accuracy and x4.7 memory reduction, on CORE50.

☆ Collaborative Speculative Inference for Efficient LLM Inference Serving

Luyao Gao, Jianchun Liu, Hongli Xu, Liusheng Huang

Speculative inference is a promising paradigm employing small speculative models (SSMs) as drafters to generate draft tokens, which are subsequently verified in parallel by the target large language model (LLM). This approach enhances the efficiency of inference serving by reducing LLM inference latency and costs while preserving generation quality. However, existing speculative methods face critical challenges, including inefficient resource utilization and limited draft acceptance, which constrain their scalability and overall effectiveness. To overcome these obstacles, we present CoSine, a novel speculative inference system that decouples sequential speculative decoding from parallel verification, enabling efficient collaboration among multiple nodes. Specifically, CoSine routes inference requests to specialized drafters based on their expertise and incorporates a confidence-based token fusion mechanism to synthesize outputs from cooperating drafters, ensuring high-quality draft generation. Additionally, CoSine dynamically orchestrates the execution of speculative decoding and verification in a pipelined manner, employing batch scheduling to selectively group requests and adaptive speculation control to minimize idle periods. By optimizing parallel workflows through heterogeneous node collaboration, CoSine balances draft generation and verification throughput in real-time, thereby maximizing resource utilization. Experimental results demonstrate that CoSine achieves superior performance compared to state-of-the-art speculative approaches. Notably, with equivalent resource costs, CoSine achieves up to a 23.2% decrease in latency and a 32.5% increase in throughput compared to baseline methods.

☆ Enhance Exploration in Safe Reinforcement Learning with Contrastive Representation Learning

Duc Kien Doan, Bang Giang Le, Viet Cuong Ta

comment: Accepted at ACIIDS 2025

☆ Capturing Semantic Flow of ML-based Systems

Shin Yoo, Robert Feldt, Somin Kim, Naryeong Kim

ML-based systems are software systems that incorporates machine learning components such as Deep Neural Networks (DNNs) or Large Language Models (LLMs). While such systems enable advanced features such as high performance computer vision, natural language processing, and code generation, their internal behaviour remain largely opaque to traditional dynamic analysis such as testing: existing analysis typically concern only what is observable from the outside, such as input similarity or class label changes. We propose semantic flow, a concept designed to capture the internal behaviour of ML-based system and to provide a platform for traditional dynamic analysis techniques to be adapted to. Semantic flow combines the idea of control flow with internal states taken from executions of ML-based systems, such as activation values of a specific layer in a DNN, or embeddings of LLM responses at a specific inference step of LLM agents. The resulting representation, summarised as semantic flow graphs, can capture internal decisions that are not explicitly represented in the traditional control flow of ML-based systems. We propose the idea of semantic flow, introduce two examples using a DNN and an LLM agent, and finally sketch its properties and how it can be used to adapt existing dynamic analysis techniques for use in ML-based software systems.

☆ Nash Equilibrium Constrained Auto-bidding With Bi-level Reinforcement Learning

Zhiyu Mou, Miao Xu, Rongquan Bai, Zhuoran Yang, Chuan Yu, Jian Xu, Bo Zheng

☆ Wikipedia is Not a Dictionary, Delete! Text Classification as a Proxy for Analysing Wiki Deletion Discussions

Hsuvas Borkakoty, Luis Espinosa-Anke

Automated content moderation for collaborative knowledge hubs like Wikipedia or Wikidata is an important yet challenging task due to multiple factors. In this paper, we construct a database of discussions happening around articles marked for deletion in several Wikis and in three languages, which we then use to evaluate a range of LMs on different tasks (from predicting the outcome of the discussion to identifying the implicit policy an individual comment might be pointing to). Our results reveal, among others, that discussions leading to deletion are easier to predict, and that, surprisingly, self-produced tags (keep, delete or redirect) don't always help guiding the classifiers, presumably because of users' hesitation or deliberation within comments.

comment: Accepted to WNUT-2025

☆ PyGDA: A Python Library for Graph Domain Adaptation

Zhen Zhang, Meihan Liu, Bingsheng He

comment: Under Review

☆ HyperArm Bandit Optimization: A Novel approach to Hyperparameter Optimization and an Analysis of Bandit Algorithms in Stochastic and Adversarial Settings

Samih Karroum, Saad Mazhar

This paper explores the application of bandit algorithms in both stochastic and adversarial settings, with a focus on theoretical analysis and practical applications. The study begins by introducing bandit problems, distinguishing between stochastic and adversarial variants, and examining key algorithms such as Explore-Then-Commit (ETC), Upper Confidence Bound (UCB), and Exponential-Weight Algorithm for Exploration and Exploitation (EXP3). Theoretical regret bounds are analyzed to compare the performance of these algorithms. The paper then introduces a novel framework, HyperArm Bandit Optimization (HABO), which applies EXP3 to hyperparameter tuning in machine learning models. Unlike traditional methods that treat entire configurations as arms, HABO treats individual hyperparameters as super-arms, and its potential configurations as sub-arms, enabling dynamic resource allocation and efficient exploration. Experimental results demonstrate HABO's effectiveness in classification and regression tasks, outperforming Bayesian Optimization in terms of computational efficiency and accuracy. The paper concludes with insights into the convergence guarantees of HABO and its potential for scalable and robust hyperparameter optimization.

comment: 41 pages, 9 figures

☆ Robust Learning-Based Sparse Recovery for Device Activity Detection in Grant-Free Random Access Cell-Free Massive MIMO: Enhancing Resilience to Impairments

Ali Elkeshawy, Haifa Fares, Amor Nafkha

Massive MIMO is considered a key enabler to support massive machine-type communication (mMTC). While massive access schemes have been extensively analyzed for co-located massive MIMO arrays, this paper explores activity detection in grant-free random access for mMTC within the context of cell-free massive MIMO systems, employing distributed antenna arrays. This sparse support recovery of device activity status is performed by a finite cluster of access points (APs) from a large number of geographically distributed APs collaborating to serve a larger number of devices. Active devices transmit non-orthogonal pilot sequences to APs, which forward the received signals to a central processing unit (CPU) for collaborative activity detection. This paper proposes a simple and efficient data-driven algorithm tailored for device activity detection, implemented centrally at the CPU. Furthermore, the study assesses the algorithm's robustness to input perturbations and examines the effects of adopting fixed-point representation on its performance.

☆ Numerically robust Gaussian state estimation with singular observation noise

Nicholas Krämer, Filip Tronarp

This article proposes numerically robust algorithms for Gaussian state estimation with singular observation noise. Our approach combines a series of basis changes with Bayes' rule, transforming the singular estimation problem into a nonsingular one with reduced state dimension. In addition to ensuring low runtime and numerical stability, our proposal facilitates marginal-likelihood computations and Gauss-Markov representations of the posterior process. We analyse the proposed method's computational savings and numerical robustness and validate our findings in a series of simulations.

☆ Climate land use and other drivers impacts on island ecosystem services: a global review

Aristides Moustakas, Shiri Zemah-Shamir, Mirela Tase, Savvas Zotos, Nazli Demirel, Christos Zoumides, Irene Christoforidi, Turgay Dindaroglu, Tamer Albayrak, Cigdem Kaptan Ayhan, Mauro Fois, Paraskevi Manolaki, Attila D. Sandor, Ina Sieber, Valentini Stamatiadou, Elli Tzirkalli, Ioannis N. Vogiatzakis, Ziv Zemah-Shamir, George Zittis

Islands are diversity hotspots and vulnerable to environmental degradation, climate variations, land use changes and societal crises. These factors can exhibit interactive impacts on ecosystem services. The study reviewed a large number of papers on the climate change-islands-ecosystem services topic worldwide. Potential inclusion of land use changes and other drivers of impacts on ecosystem services were sequentially also recorded. The study sought to investigate the impacts of climate change, land use change, and other non-climatic driver changes on island ecosystem services. Explanatory variables examined were divided into two categories: environmental variables and methodological ones. Environmental variables include sea zone geographic location, ecosystem, ecosystem services, climate, land use, other driver variables, Methodological variables include consideration of policy interventions, uncertainty assessment, cumulative effects of climate change, synergistic effects of climate change with land use change and other anthropogenic and environmental drivers, and the diversity of variables used in the analysis. Machine learning and statistical methods were used to analyze their effects on island ecosystem services. Negative climate change impacts on ecosystem services are better quantified by land use change or other non-climatic driver variables than by climate variables. The synergy of land use together with climate changes is modulating the impact outcome and critical for a better impact assessment. Analyzed together, there is little evidence of more pronounced for a specific sea zone, ecosystem, or ecosystem service. Climate change impacts may be underestimated due to the use of a single climate variable deployed in most studies. Policy interventions exhibit low classification accuracy in quantifying impacts indicating insufficient efficacy or integration in the studies.

comment: Article published in the journal: Science of the Total Environment. Free author's version

☆ Resource efficient data transmission on animals based on machine learning

Wilhelm Kerle-Malcharek, Karsten Klein, Martin Wikelski, Falk Schreiber, Timm A. Wild

Bio-loggers, electronic devices used to track animal behaviour through various sensors, have become essential in wildlife research. Despite continuous improvements in their capabilities, bio-loggers still face significant limitations in storage, processing, and data transmission due to the constraints of size and weight, which are necessary to avoid disturbing the animals. This study aims to explore how selective data transmission, guided by machine learning, can reduce the energy consumption of bio-loggers, thereby extending their operational lifespan without requiring hardware modifications.

comment: Submitted to Scientific Reports but not published, 23 pages, 5 figures, 3 tables

☆ Targeted Data Poisoning for Black-Box Audio Datasets Ownership Verification ICASSP 2025

Wassim Bouaziz, El-Mahdi El-Mhamdi, Nicolas Usunier

Protecting the use of audio datasets is a major concern for data owners, particularly with the recent rise of audio deep learning models. While watermarks can be used to protect the data itself, they do not allow to identify a deep learning model trained on a protected dataset. In this paper, we adapt to audio data the recently introduced data taggants approach. Data taggants is a method to verify if a neural network was trained on a protected image dataset with top-$k$ predictions access to the model only. This method relies on a targeted data poisoning scheme by discreetly altering a small fraction (1%) of the dataset as to induce a harmless behavior on out-of-distribution data called keys. We evaluate our method on the Speechcommands and the ESC50 datasets and state of the art transformer models, and show that we can detect the use of the dataset with high confidence without loss of performance. We also show the robustness of our method against common data augmentation techniques, making it a practical method to protect audio datasets.

comment: Published at ICASSP 2025, 5 pages, 7 figures

☆ AMR-Transformer: Enabling Efficient Long-range Interaction for Complex Neural Fluid Simulation

Zeyi Xu, Jinfan Liu, Kuangxu Chen, Ye Chen, Zhangli Hu, Bingbing Ni

Accurately and efficiently simulating complex fluid dynamics is a challenging task that has traditionally relied on computationally intensive methods. Neural network-based approaches, such as convolutional and graph neural networks, have partially alleviated this burden by enabling efficient local feature extraction. However, they struggle to capture long-range dependencies due to limited receptive fields, and Transformer-based models, while providing global context, incur prohibitive computational costs. To tackle these challenges, we propose AMR-Transformer, an efficient and accurate neural CFD-solving pipeline that integrates a novel adaptive mesh refinement scheme with a Navier-Stokes constraint-aware fast pruning module. This design encourages long-range interactions between simulation cells and facilitates the modeling of global fluid wave patterns, such as turbulence and shockwaves. Experiments show that our approach achieves significant gains in efficiency while preserving critical details, making it suitable for high-resolution physical simulations with long-range dependencies. On CFDBench, PDEBench and a new shockwave dataset, our pipeline demonstrates up to an order-of-magnitude improvement in accuracy over baseline models. Additionally, compared to ViT, our approach achieves a reduction in FLOPs of up to 60 times.

☆ PIMRL: Physics-Informed Multi-Scale Recurrent Learning for Spatiotemporal Prediction

Han Wan, Qi Wang, Hao Sun

☆ Numerical Error Analysis of Large Language Models

Stanislav Budzinskiy, Wenyi Fang, Longbin Zeng, Philipp Petersen

Large language models based on transformer architectures have become integral to state-of-the-art natural language processing applications. However, their training remains computationally expensive and exhibits instabilities, some of which are expected to be caused by finite-precision computations. We provide a theoretical analysis of the impact of round-off errors within the forward pass of a transformer architecture which yields fundamental bounds for these effects. In addition, we conduct a series of numerical experiments which demonstrate the practical relevance of our bounds. Our results yield concrete guidelines for choosing hyperparameters that mitigate round-off errors, leading to more robust and stable inference.

☆ Spherical dimension

Bogdan Chornomaz, Shay Moran, Tom Waknine

We introduce and study the spherical dimension, a natural topological relaxation of the VC dimension that unifies several results in learning theory where topology plays a key role in the proofs. The spherical dimension is defined by extending the set of realizable datasets (used to define the VC dimension) to the continuous space of realizable distributions. In this space, a shattered set of size d (in the VC sense) is completed into a continuous object, specifically a d-dimensional sphere of realizable distributions. The spherical dimension is then defined as the dimension of the largest sphere in this space. Thus, the spherical dimension is at least the VC dimension. The spherical dimension serves as a common foundation for leveraging the Borsuk-Ulam theorem and related topological tools. We demonstrate the utility of the spherical dimension in diverse applications, including disambiguations of partial concept classes, reductions from classification to stochastic convex optimization, stability and replicability, and sample compression schemes. Perhaps surprisingly, we show that the open question posed by Alon, Hanneke, Holzman, and Moran (FOCS 2021) of whether there exist non-trivial disambiguations for halfspaces with margin is equivalent to the basic open question of whether the VC and spherical dimensions are finite together.

☆ Flows on convex polytopes

Tomek Diederen, Nicola Zamboni

We present a framework for modeling complex, high-dimensional distributions on convex polytopes by leveraging recent advances in discrete and continuous normalizing flows on Riemannian manifolds. We show that any full-dimensional polytope is homeomorphic to a unit ball, and our approach harnesses flows defined on the ball, mapping them back to the original polytope. Furthermore, we introduce a strategy to construct flows when only the vertex representation of a polytope is available, employing maximum entropy barycentric coordinates and Aitchison geometry. Our experiments take inspiration from applications in metabolic flux analysis and demonstrate that our methods achieve competitive density estimation, sampling accuracy, as well as fast training and inference times.

☆ Policy Teaching via Data Poisoning in Learning from Human Preferences AISTATS 2025

Andi Nika, Jonathan Nöther, Debmalya Mandal, Parameswaran Kamalaruban, Adish Singla, Goran Radanović

We study data poisoning attacks in learning from human preferences. More specifically, we consider the problem of teaching/enforcing a target policy $\pi^\dagger$ by synthesizing preference data. We seek to understand the susceptibility of different preference-based learning paradigms to poisoned preference data by analyzing the number of samples required by the attacker to enforce $\pi^\dagger$. We first propose a general data poisoning formulation in learning from human preferences and then study it for two popular paradigms, namely: (a) reinforcement learning from human feedback (RLHF) that operates by learning a reward model using preferences; (b) direct preference optimization (DPO) that directly optimizes policy using preferences. We conduct a theoretical analysis of the effectiveness of data poisoning in a setting where the attacker is allowed to augment a pre-existing dataset and also study its special case where the attacker can synthesize the entire preference dataset from scratch. As our main results, we provide lower/upper bounds on the number of samples required to enforce $\pi^\dagger$. Finally, we discuss the implications of our results in terms of the susceptibility of these learning paradigms under such data poisoning attacks.

comment: In AISTATS 2025

☆ Assessing the validity of new paradigmatic complexity measures as criterial features for proficiency in L2 writings in English

Cyriel Mallart, Andrew Simpkin, Nicolas Ballier, Paula Lissón, Rémi Venant, Jen-Yu Li, Bernardo Stearns, Thomas Gaillat

This article addresses Second Language (L2) writing development through an investigation of new grammatical and structural complexity metrics. We explore the paradigmatic production in learner English by linking language functions to specific grammatical paradigms. Using the EFCAMDAT as a gold standard and a corpus of French learners as an external test set, we employ a supervised learning framework to operationalise and evaluate seven microsystems. We show that learner levels are associated with the seven microsystems (MS). Using ordinal regression modelling for evaluation, the results show that all MS are significant but yield a low impact if taken individually. However, their influence is shown to be impactful if taken as a group. These microsystems and their measurement method suggest that it is possible to use them as part of broader-purpose CALL systems focused on proficiency assessment.

☆ Probability-Flow ODE in Infinite-Dimensional Function Spaces ICLR 2025

Kunwoo Na, Junghyun Lee, Se-Young Yun, Sungbin Lim

Recent advances in infinite-dimensional diffusion models have demonstrated their effectiveness and scalability in function generation tasks where the underlying structure is inherently infinite-dimensional. To accelerate inference in such models, we derive, for the first time, an analog of the probability-flow ODE (PF-ODE) in infinite-dimensional function spaces. Leveraging this newly formulated PF-ODE, we reduce the number of function evaluations while maintaining sample quality in function generation tasks, including applications to PDEs.

comment: 26 pages, 8 figures. Accepted to the ICLR 2025 DeLTa Workshop

☆ Moss: Proxy Model-based Full-Weight Aggregation in Federated Learning with Heterogeneous Models

Yifeng Cai, Ziqi Zhang, Ding Li, Yao Guo, Xiangqun Chen

Modern Federated Learning (FL) has become increasingly essential for handling highly heterogeneous mobile devices. Current approaches adopt a partial model aggregation paradigm that leads to sub-optimal model accuracy and higher training overhead. In this paper, we challenge the prevailing notion of partial-model aggregation and propose a novel "full-weight aggregation" method named Moss, which aggregates all weights within heterogeneous models to preserve comprehensive knowledge. Evaluation across various applications demonstrates that Moss significantly accelerates training, reduces on-device training time and energy consumption, enhances accuracy, and minimizes network bandwidth utilization when compared to state-of-the-art baselines.

comment: Accepted by ACM IMWUT/Ubicomp 2025

☆ Efficient Federated Fine-Tuning of Large Language Models with Layer Dropout

Shilong Wang, Jianchun Liu, Hongli Xu, Jiaming Yan, Xianjun Gao

comment: 13 pages

☆ Deep Learning for Time Series Forecasting: A Survey

Xiangjie Kong, Zhenghao Chen, Weiyao Liu, Kaili Ning, Lechao Zhang, Syauqie Muhammad Marier, Yichen Liu, Yuhao Chen, Feng Xia

☆ Robustness Tokens: Towards Adversarial Robustness of Transformers ECCV

Brian Pulfer, Yury Belousov, Slava Voloshynovskiy

comment: This paper has been accepted for publication at the European Conference on Computer Vision (ECCV), 2024

☆ Data augmentation using diffusion models to enhance inverse Ising inference

Yechan Lim, Sangwon Lee, Junghyo Jo

Identifying model parameters from observed configurations poses a fundamental challenge in data science, especially with limited data. Recently, diffusion models have emerged as a novel paradigm in generative machine learning, capable of producing new samples that closely mimic observed data. These models learn the gradient of model probabilities, bypassing the need for cumbersome calculations of partition functions across all possible configurations. We explore whether diffusion models can enhance parameter inference by augmenting small datasets. Our findings demonstrate this potential through a synthetic task involving inverse Ising inference and a real-world application of reconstructing missing values in neural activity data. This study serves as a proof-of-concept for using diffusion models for data augmentation in physics-related problems, thereby opening new avenues in data science.

☆ Multiplicative Learning

Han Kim, Hyungjoon Soh, Vipul Periwal, Junghyo Jo

☆ Are Convex Optimization Curves Convex?

Guy Barzilai, Ohad Shamir

In this paper, we study when we might expect the optimization curve induced by gradient descent to be \emph{convex} -- precluding, for example, an initial plateau followed by a sharp decrease, making it difficult to decide when optimization should stop. Although such undesirable behavior can certainly occur when optimizing general functions, might it also occur in the benign and well-studied case of smooth convex functions? As far as we know, this question has not been tackled in previous work. We show, perhaps surprisingly, that the answer crucially depends on the choice of the step size. In particular, for the range of step sizes which are known to result in monotonic convergence to an optimal value, there is a regime where the optimization curve will be provably convex, and there is a regime where the curve can be non-convex. We also extend our results to gradient flow, and to the closely-related but different question of whether the gradient norm decreases monotonically.

comment: 13 pages

☆ Gumiho: A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding

Jinze Li, Yixing Xu, Haiduo Huang, Xuanwu Yin, Dong Li, Edith C. H. Ngai, Emad Barsoum

comment: Paper under review

☆ An Real-Sim-Real (RSR) Loop Framework for Generalizable Robotic Policy Transfer with Differentiable Simulation

Lu Shi, Yuxuan Xu, Shiyu Wang, Jinhao Huang, Wenhao Zhao, Yufei Jia, Zike Yan, Weibin Gu, Guyue Zhou

The sim-to-real gap remains a critical challenge in robotics, hindering the deployment of algorithms trained in simulation to real-world systems. This paper introduces a novel Real-Sim-Real (RSR) loop framework leveraging differentiable simulation to address this gap by iteratively refining simulation parameters, aligning them with real-world conditions, and enabling robust and efficient policy transfer. A key contribution of our work is the design of an informative cost function that encourages the collection of diverse and representative real-world data, minimizing bias and maximizing the utility of each data point for simulation refinement. This cost function integrates seamlessly into existing reinforcement learning algorithms (e.g., PPO, SAC) and ensures a balanced exploration of critical regions in the real domain. Furthermore, our approach is implemented on the versatile Mujoco MJX platform, and our framework is compatible with a wide range of robotic systems. Experimental results on several robotic manipulation tasks demonstrate that our method significantly reduces the sim-to-real gap, achieving high task performance and generalizability across diverse scenarios of both explicit and implicit environmental uncertainties.

☆ Reconsidering Feature Structure Information and Latent Space Alignment in Partial Multi-label Feature Selection AAAI 25

Hanlin Pan, Kunpeng Liu, Wanfu Gao

The purpose of partial multi-label feature selection is to select the most representative feature subset, where the data comes from partial multi-label datasets that have label ambiguity issues. For label disambiguation, previous methods mainly focus on utilizing the information inside the labels and the relationship between the labels and features. However, the information existing in the feature space is rarely considered, especially in partial multi-label scenarios where the noises is considered to be concentrated in the label space while the feature information is correct. This paper proposes a method based on latent space alignment, which uses the information mined in feature space to disambiguate in latent space through the structural consistency between labels and features. In addition, previous methods overestimate the consistency of features and labels in the latent space after convergence. We comprehensively consider the similarity of latent space projections to feature space and label space, and propose new feature selection term. This method also significantly improves the positive label identification ability of the selected features. Comprehensive experiments demonstrate the superiority of the proposed method.

comment: 9pages,6 figures,accept at AAAI 25

☆ IMPACT: Intelligent Motion Planning with Acceptable Contact Trajectories via Vision-Language Models

Yiyang Ling, Karan Owalekar, Oluwatobiloba Adesanya, Erdem Bıyık, Daniel Seita

Motion planning involves determining a sequence of robot configurations to reach a desired pose, subject to movement and safety constraints. Traditional motion planning finds collision-free paths, but this is overly restrictive in clutter, where it may not be possible for a robot to accomplish a task without contact. In addition, contacts range from relatively benign (e.g., brushing a soft pillow) to more dangerous (e.g., toppling a glass vase). Due to this diversity, it is difficult to characterize which contacts may be acceptable or unacceptable. In this paper, we propose IMPACT, a novel motion planning framework that uses Vision-Language Models (VLMs) to infer environment semantics, identifying which parts of the environment can best tolerate contact based on object properties and locations. Our approach uses the VLM's outputs to produce a dense 3D "cost map" that encodes contact tolerances and seamlessly integrates with standard motion planners. We perform experiments using 20 simulation and 10 real-world scenes and assess using task success rate, object displacements, and feedback from human evaluators. Our results over 3620 simulation and 200 real-world trials suggest that IMPACT enables efficient contact-rich motion planning in cluttered settings while outperforming alternative methods and ablations. Supplementary material is available at https://impact-planning.github.io/.

☆ Improving Diffusion-based Inverse Algorithms under Few-Step Constraint via Learnable Linear Extrapolation

Jiawei Zhang, Ziyuan Liu, Leon Yan, Gen Li, Yuantao Gu

Diffusion models have demonstrated remarkable performance in modeling complex data priors, catalyzing their widespread adoption in solving various inverse problems. However, the inherently iterative nature of diffusion-based inverse algorithms often requires hundreds to thousands of steps, with performance degradation occurring under fewer steps which limits their practical applicability. While high-order diffusion ODE solvers have been extensively explored for efficient diffusion sampling without observations, their application to inverse problems remains underexplored due to the diverse forms of inverse algorithms and their need for repeated trajectory correction based on observations. To address this gap, we first introduce a canonical form that decomposes existing diffusion-based inverse algorithms into three modules to unify their analysis. Inspired by the linear subspace search strategy in the design of high-order diffusion ODE solvers, we propose the Learnable Linear Extrapolation (LLE) method, a lightweight approach that universally enhances the performance of any diffusion-based inverse algorithm that fits the proposed canonical form. Extensive experiments demonstrate consistent improvements of the proposed LLE method across multiple algorithms and tasks, indicating its potential for more efficient solutions and boosted performance of diffusion-based inverse algorithms with limited steps. Codes for reproducing our experiments are available at \href{https://github.com/weigerzan/LLE_inverse_problem}{https://github.com/weigerzan/LLE\_inverse\_problem}.

comment: preprint

♻ ☆ Chain-of-Thought Reasoning In The Wild Is Not Always Faithful ICLR 25

Iván Arcuschin, Jett Janiak, Robert Krzyzanowski, Senthooran Rajamanoharan, Neel Nanda, Arthur Conmy

comment: Accepted to the Reasoning and Planning for Large Language Models Workshop (ICLR 25), 10 main paper pages, 38 appendix pages

♻ ☆ Correlated Proxies: A New Definition and Improved Mitigation for Reward Hacking ICLR 2025

Cassidy Laidlaw, Shivam Singhal, Anca Dragan

comment: Spotlight at ICLR 2025

♻ ☆ DataEnvGym: Data Generation Agents in Teacher Environments with Student Feedback ICLR 2025

Zaid Khan, Elias Stengel-Eskin, Jaemin Cho, Mohit Bansal

comment: ICLR 2025 Spotlight; Project Page: https://DataEnvGym.github.io

♻ ☆ What is the Alignment Objective of GRPO?

Milan Vojnovic, Se-Young Yun

♻ ☆ Representation Retrieval Learning for Heterogeneous Data Integration

Qi Xu, Annie Qu

In the era of big data, large-scale, multi-modal datasets are increasingly ubiquitous, offering unprecedented opportunities for predictive modeling and scientific discovery. However, these datasets often exhibit complex heterogeneity, such as covariate shift, posterior drift, and missing modalities, that can hinder the accuracy of existing prediction algorithms. To address these challenges, we propose a novel Representation Retrieval ($R^2$) framework, which integrates a representation learning module (the representer) with a sparsity-induced machine learning model (the learner). Moreover, we introduce the notion of "integrativeness" for representers, characterized by the effective data sources used in learning representers, and propose a Selective Integration Penalty (SIP) to explicitly improve the property. Theoretically, we demonstrate that the $R^2$ framework relaxes the conventional full-sharing assumption in multi-task learning, allowing for partially shared structures, and that SIP can improve the convergence rate of the excess risk bound. Extensive simulation studies validate the empirical performance of our framework, and applications to two real-world datasets further confirm its superiority over existing approaches.

♻ ☆ Joint Fine-tuning and Conversion of Pretrained Speech and Language Models towards Linear Complexity ICLR 2025

Mutian He, Philip N. Garner

comment: 18 pages, 5 figures; ICLR 2025 camera ready. Code: https://github.com/idiap/linearize-distill-pretrained-transformers

♻ ☆ Latent Space Chain-of-Embedding Enables Output-free LLM Self-Evaluation ICLR 2025

Yiming Wang, Pei Zhang, Baosong Yang, Derek F. Wong, Rui Wang

comment: Accepted by ICLR 2025

♻ ☆ scMEDAL for the interpretable analysis of single-cell transcriptomics data with batch effect visualization using a deep mixed effects autoencoder

Aixa X. Andrade, Son Nguyen, Albert Montillo

scRNA-seq data has the potential to provide new insights into cellular heterogeneity and data acquisition; however, a major challenge is unraveling confounding from technical and biological batch effects. Existing batch correction algorithms suppress and discard these effects, rather than quantifying and modeling them. Here, we present scMEDAL, a framework for single-cell Mixed Effects Deep Autoencoder Learning, which separately models batch-invariant and batch-specific effects using two complementary autoencoder networks. One network is trained through adversarial learning to capture a batch-invariant representation, while a Bayesian autoencoder learns a batch-specific representation. Comprehensive evaluations spanning conditions (e.g., autism, leukemia, and cardiovascular), cell types, and technical and biological effects demonstrate that scMEDAL suppresses batch effects while modeling batch-specific variation, enhancing accuracy and interpretability. Unlike prior approaches, the framework's fixed- and random-effects autoencoders enable retrospective analyses, including predicting a cell's expression as if it had been acquired in a different batch via genomap projections at the cellular level, revealing the impact of biological (e.g., diagnosis) and technical (e.g., acquisition) effects. By combining scMEDAL's batch-agnostic and batch-specific latent spaces, it enables more accurate predictions of disease status, donor group, and cell type, making scMEDAL a valuable framework for gaining deeper insight into data acquisition and cellular heterogeneity.

comment: Main manuscript: 28 pages, including 8 figures and 1 table. Supplemental material: 19 pages

♻ ☆ Confidence-Controlled Exploration: Efficient Sparse-Reward Policy Learning for Robot Navigation

Bhrij Patel, Kasun Weerakoon, Wesley A. Suttle, Alec Koppel, Brian M. Sadler, Tianyi Zhou, Amrit Singh Bedi, Dinesh Manocha

comment: 10 pages, 6 figures, 2 tables

♻ ☆ Video Super-Resolution: All You Need is a Video Diffusion Model

Zhihao Zhan, Wang Pang, Xiang Zhu, Yechao Bai

We present a generic video super-resolution algorithm in this paper, based on the Diffusion Posterior Sampling framework with an unconditional video generation model in latent space. The video generation model, a diffusion transformer, functions as a space-time model. We argue that a powerful model, which learns the physics of the real world, can easily handle various kinds of motion patterns as prior knowledge, thus eliminating the need for explicit estimation of optical flows or motion parameters for pixel alignment. Furthermore, a single instance of the proposed video diffusion transformer model can adapt to different sampling conditions without re-training. Empirical results on synthetic and real-world datasets demonstrate that our method has strong capabilities to address video super-resolution challenges.

♻ ☆ Fast MRI for All: Bridging Equity Gaps via Training without Raw Data Access

Yaşar Utku Alçalar, Merve Gülle, Mehmet Akçakaya

♻ ☆ A Clifford Algebraic Approach to E(n)-Equivariant High-order Graph Neural Networks

Viet-Hoang Tran, Thieu N. Vo, Tho Tran Huu, Tan Minh Nguyen

Designing neural network architectures that can handle data symmetry is crucial. This is especially important for geometric graphs whose properties are equivariance under Euclidean transformations. Current equivariant graph neural networks (EGNNs), particularly those using message passing, have a limitation in expressive power. Recent high-order graph neural networks can overcome this limitation, yet they lack equivariance properties, representing a notable drawback in certain applications in chemistry and physical sciences. In this paper, we introduce the Clifford Group Equivariant Graph Neural Networks (CG-EGNNs), a novel EGNN that enhances high-order message passing by integrating high-order local structures in the context of Clifford algebras. As a key benefit of using Clifford algebras, CG-EGNN can learn functions that capture equivariance from positional features. By adopting the high-order message passing mechanism, CG-EGNN gains richer information from neighbors, thus improving model performance. Furthermore, we establish the universality property of the $k$-hop message passing framework, showcasing greater expressive power of CG-EGNNs with additional $k$-hop message passing mechanism. We empirically validate that CG-EGNNs outperform previous methods on various benchmarks including n-body, CMU motion capture, and MD17, highlighting their effectiveness in geometric deep learning.

♻ ☆ Monomial Matrix Group Equivariant Neural Functional Networks NeurIPS 2024

Viet-Hoang Tran, Thieu N. Vo, Tho H. Tran, An T. Nguyen, Tan M. Nguyen

Neural functional networks (NFNs) have recently gained significant attention due to their diverse applications, ranging from predicting network generalization and network editing to classifying implicit neural representation. Previous NFN designs often depend on permutation symmetries in neural networks' weights, which traditionally arise from the unordered arrangement of neurons in hidden layers. However, these designs do not take into account the weight scaling symmetries of $\ReLU$ networks, and the weight sign flipping symmetries of $\sin$ or $\Tanh$ networks. In this paper, we extend the study of the group action on the network weights from the group of permutation matrices to the group of monomial matrices by incorporating scaling/sign-flipping symmetries. Particularly, we encode these scaling/sign-flipping symmetries by designing our corresponding equivariant and invariant layers. We name our new family of NFNs the Monomial Matrix Group Equivariant Neural Functional Networks (Monomial-NFN). Because of the expansion of the symmetries, Monomial-NFN has much fewer independent trainable parameters compared to the baseline NFNs in the literature, thus enhancing the model's efficiency. Moreover, for fully connected and convolutional neural networks, we theoretically prove that all groups that leave these networks invariant while acting on their weight spaces are some subgroups of the monomial matrix group. We provide empirical evidence to demonstrate the advantages of our model over existing baselines, achieving competitive performance and efficiency.

comment: 10 pages in the main text. Published at NeurIPS 2024. The code is available at https://github.com/MathematicalAI-NUS/Monomial-NFN

♻ ☆ Class-wise Federated Unlearning: Harnessing Active Forgetting with Teacher-Student Memory Generation

Yuyuan Li, Jiaming Zhang, Yixiu Liu, Chaochao Chen

Privacy concerns associated with machine learning models have driven research into machine unlearning, which aims to erase the memory of specific target training data from already trained models. This issue also arises in federated learning, creating the need to address the federated unlearning problem. However, federated unlearning remains a challenging task. On the one hand, current research primarily focuses on unlearning all data from a client, overlooking more fine-grained unlearning targets, e.g., class-wise and sample-wise removal. On the other hand, existing methods suffer from imprecise estimation of data influence and impose significant computational or storage burden. To address these issues, we propose a neuro-inspired federated unlearning framework based on active forgetting, which is independent of model architectures and suitable for fine-grained unlearning targets. Our framework distinguishes itself from existing methods by utilizing new memories to overwrite old ones. These new memories are generated through teacher-student learning. We further utilize refined elastic weight consolidation to mitigate catastrophic forgetting of non-target data. Extensive experiments on benchmark datasets demonstrate the efficiency and effectiveness of our method, achieving satisfactory unlearning completeness against backdoor attacks.

♻ ☆ Similarity Equivariant Graph Neural Networks for Homogenization of Metamaterials

Fleur Hendriks, Vlado Menkovski, Martin Doškář, Marc G. D. Geers, Ondřej Rokoš

comment: 60 pages, 22 figures. Published in CMAME (Computer Methods in Applied Mechanics and Engineering)

♻ ☆ The Federation Strikes Back: A Survey of Federated Learning Privacy Attacks, Defenses, Applications, and Policy Landscape

Joshua C. Zhao, Saurabh Bagchi, Salman Avestimehr, Kevin S. Chan, Somali Chaterji, Dimitris Dimitriadis, Jiacheng Li, Ninghui Li, Arash Nourian, Holger R. Roth

Deep learning has shown incredible potential across a wide array of tasks, and accompanied by this growth has been an insatiable appetite for data. However, a large amount of data needed for enabling deep learning is stored on personal devices, and recent concerns on privacy have further highlighted challenges for accessing such data. As a result, federated learning (FL) has emerged as an important privacy-preserving technology that enables collaborative training of machine learning models without the need to send the raw, potentially sensitive, data to a central server. However, the fundamental premise that sending model updates to a server is privacy-preserving only holds if the updates cannot be "reverse engineered" to infer information about the private training data. It has been shown under a wide variety of settings that this privacy premise does not hold. In this survey paper, we provide a comprehensive literature review of the different privacy attacks and defense methods in FL. We identify the current limitations of these attacks and highlight the settings in which the privacy of ann FL client can be broken. We further dissect some of the successful industry applications of FL and draw lessons for future successful adoption. We survey the emerging landscape of privacy regulation for FL and conclude with future directions for taking FL toward the cherished goal of generating accurate models while preserving the privacy of the data from its participants.

comment: Accepted to ACM Computing Surveys; 35 pages

♻ ☆ Uncertainty-Aware Robust Learning on Noisy Graphs ICASSP 2025

Shuyi Chen, Kaize Ding, Shixiang Zhu

Graph neural networks (GNNs) have excelled in various graph learning tasks, particularly node classification. However, their performance is often hampered by noisy measurements in real-world graphs, which can corrupt critical patterns in the data. To address this, we propose a novel uncertainty-aware graph learning framework inspired by distributionally robust optimization. Specifically, we use a graph neural network-based encoder to embed the node features and find the optimal node embeddings by minimizing the worst-case risk through a minimax formulation. Such an uncertainty-aware learning process leads to improved node representations and a more robust graph predictive model that effectively mitigates the impact of uncertainty arising from data noise. Our experimental results demonstrate superior predictive performance over baselines across noisy scenarios.

comment: ICASSP 2025 camera ready

♻ ☆ Networked Communication for Decentralised Agents in Mean-Field Games

Patrick Benjamin, Alessandro Abate

♻ ☆ Multilevel Generative Samplers for Investigating Critical Phenomena ICLR 2025

Ankur Singha, Elia Cellini, Kim A. Nicoli, Karl Jansen, Stefan Kühn, Shinichi Nakajima

Investigating critical phenomena or phase transitions is of high interest in physics and chemistry, for which Monte Carlo (MC) simulations, a crucial tool for numerically analyzing macroscopic properties of given systems, are often hindered by an emerging divergence of correlation length -- known as scale invariance at criticality (SIC) in the renormalization group theory. SIC causes the system to behave the same at any length scale, from which many existing sampling methods suffer: long-range correlations cause critical slowing down in Markov chain Monte Carlo (MCMC), and require intractably large receptive fields for generative samplers. In this paper, we propose a Renormalization-informed Generative Critical Sampler (RiGCS) -- a novel sampler specialized for near-critical systems, where SIC is leveraged as an advantage rather than a nuisance. Specifically, RiGCS builds on MultiLevel Monte Carlo (MLMC) with Heat Bath (HB) algorithms, which perform ancestral sampling from low-resolution to high-resolution lattice configurations with site-wise-independent conditional HB sampling. Although MLMC-HB is highly efficient under exact SIC, it suffers from a low acceptance rate under slight SIC violation. Notably, SIC violation always occurs in finite-size systems, and may induce long-range and higher-order interactions in the renormalized distributions, which are not considered by independent HB samplers. RiGCS enhances MLMC-HB by replacing a part of the conditional HB sampler with generative models that capture those residual interactions and improve the sampling efficiency. Our experiments show that the effective sample size of RiGCS is a few orders of magnitude higher than state-of-the-art generative model baselines in sampling configurations for 128x128 two-dimensional Ising systems.

comment: 10 pages, 4 figures (main text); 13th International Conference on Learning Representations (ICLR 2025)

♻ ☆ Exploring a Multimodal Fusion-based Deep Learning Network for Detecting Facial Palsy IJCAI 2024

Heng Yim Nicole Oo, Min Hun Lee, Jeong Hoon Lim

comment: IJCAI 2024 4th AI for Ageless Aging Workshop (AIAA)

♻ ☆ Unlocking Historical Clinical Trial Data with ALIGN: A Compositional Large Language Model System for Medical Coding

Nabeel Seedat, Caterina Tozzi, Andrea Hita Ardiaca, Mihaela van der Schaar, James Weatherall, Adam Taylor

The reuse of historical clinical trial data has significant potential to accelerate medical research and drug development. However, interoperability challenges, particularly with missing medical codes, hinders effective data integration across studies. While Large Language Models (LLMs) offer a promising solution for automated coding without labeled data, current approaches face challenges on complex coding tasks. We introduce ALIGN, a novel compositional LLM-based system for automated, zero-shot medical coding. ALIGN follows a three-step process: (1) diverse candidate code generation; (2) self-evaluation of codes and (3) confidence scoring and uncertainty estimation enabling human deferral to ensure reliability. We evaluate ALIGN on harmonizing medication terms into Anatomical Therapeutic Chemical (ATC) and medical history terms into Medical Dictionary for Regulatory Activities (MedDRA) codes extracted from 22 immunology trials. ALIGN outperformed the LLM baselines, while also providing capabilities for trustworthy deployment. For MedDRA coding, ALIGN achieved high accuracy across all levels, matching RAG and excelling at the most specific levels (87-90% for HLGT). For ATC coding, ALIGN demonstrated superior performance, particularly at lower hierarchy levels (ATC Level 4), with 72-73% overall accuracy and 86-89% accuracy for common medications, outperforming baselines by 7-22%. ALIGN's uncertainty-based deferral improved accuracy by 17% to 90% accuracy with 30% deferral, notably enhancing performance on uncommon medications. ALIGN achieves this cost-efficiently at \$0.0007 and \$0.02 per code for GPT-4o-mini and GPT-4o, reducing barriers to clinical adoption. ALIGN advances automated medical coding for clinical trial data, contributing to enhanced data interoperability and reusability, positioning it as a promising tool to improve clinical research and accelerate drug development.

♻ ☆ Networked Communication for Mean-Field Games with Function Approximation and Empirical Mean-Field Estimation

Patrick Benjamin, Alessandro Abate

♻ ☆ Adaptive Split Learning over Energy-Constrained Wireless Edge Networks

Zuguang Li, Wen Wu, Shaohua Wu, Wei Wang

comment: 6 pages, 5 figures, 20 conferences

♻ ☆ Respecting the limit:Bayesian optimization with a bound on the optimal value

Hanyang Wang, Juergen Branke, Matthias Poloczek

In many real-world optimization problems, we have prior information about what objective function values are achievable. In this paper, we study the scenario that we have either exact knowledge of the minimum value or a, possibly inexact, lower bound on its value. We propose bound-aware Bayesian optimization (BABO), a Bayesian optimization method that uses a new surrogate model and acquisition function to utilize such prior information. We present SlogGP, a new surrogate model that incorporates bound information and adapts the Expected Improvement (EI) acquisition function accordingly. Empirical results on a variety of benchmarks demonstrate the benefit of taking prior information about the optimal value into account, and that the proposed approach significantly outperforms existing techniques. Furthermore, we notice that even in the absence of prior information on the bound, the proposed SlogGP surrogate model still performs better than the standard GP model in most cases, which we explain by its larger expressiveness.

♻ ☆ Neuroplastic Expansion in Deep Reinforcement Learning

Jiashun Liu, Johan Obando-Ceron, Aaron Courville, Ling Pan

The loss of plasticity in learning agents, analogous to the solidification of neural pathways in biological brains, significantly impedes learning and adaptation in reinforcement learning due to its non-stationary nature. To address this fundamental challenge, we propose a novel approach, {\it Neuroplastic Expansion} (NE), inspired by cortical expansion in cognitive science. NE maintains learnability and adaptability throughout the entire training process by dynamically growing the network from a smaller initial size to its full dimension. Our method is designed with three key components: (\textit{1}) elastic topology generation based on potential gradients, (\textit{2}) dormant neuron pruning to optimize network expressivity, and (\textit{3}) neuron consolidation via experience review to strike a balance in the plasticity-stability dilemma. Extensive experiments demonstrate that NE effectively mitigates plasticity loss and outperforms state-of-the-art methods across various tasks in MuJoCo and DeepMind Control Suite environments. NE enables more adaptive learning in complex, dynamic environments, which represents a crucial step towards transitioning deep reinforcement learning from static, one-time training paradigms to more flexible, continually adapting models.

♻ ☆ Dark Deceptions in DHCP: Dismantling Network Defenses

Robert Dilworth

This paper explores vulnerabilities in the Dynamic Host Configuration Protocol (DHCP) and their implications on the Confidentiality, Integrity, and Availability (CIA) Triad. Through an analysis of various attacks, including DHCP Starvation, Rogue DHCP Servers, Replay Attacks, and TunnelVision exploits, the paper provides a taxonomic classification of threats, assesses risks, and proposes appropriate controls. The discussion also highlights the dangers of VPN decloaking through DHCP exploits and underscores the importance of safeguarding network infrastructures. By bringing awareness to the TunnelVision exploit, this paper aims to mitigate risks associated with these prevalent vulnerabilities.

comment: 8 pages, 4 tables

♻ ☆ Diabetica: Adapting Large Language Model to Enhance Multiple Medical Tasks in Diabetes Care and Management ICLR 2025

Lai Wei, Zhen Ying, Muyang He, Yutong Chen, Qian Yang, Yanzhe Hong, Jiaping Lu, Kaipeng Zheng, Shaoting Zhang, Xiaoying Li, Weiran Huang, Ying Chen

comment: Accepted by ICLR 2025 SCI-FM workshop

♻ ☆ Towards Class-wise Robustness Analysis

Tejaswini Medi, Julia Grabinski, Margret Keuper

While being very successful in solving many downstream tasks, the application of deep neural networks is limited in real-life scenarios because of their susceptibility to domain shifts such as common corruptions, and adversarial attacks. The existence of adversarial examples and data corruption significantly reduces the performance of deep classification models. Researchers have made strides in developing robust neural architectures to bolster decisions of deep classifiers. However, most of these works rely on effective adversarial training methods, and predominantly focus on overall model robustness, disregarding class-wise differences in robustness, which are critical. Exploiting weakly robust classes is a potential avenue for attackers to fool the image recognition models. Therefore, this study investigates class-to-class biases across adversarially trained robust classification models to understand their latent space structures and analyze their strong and weak class-wise properties. We further assess the robustness of classes against common corruptions and adversarial attacks, recognizing that class vulnerability extends beyond the number of correct classifications for a specific class. We find that the number of false positives of classes as specific target classes significantly impacts their vulnerability to attacks. Through our analysis on the Class False Positive Score, we assess a fair evaluation of how susceptible each class is to misclassification.

♻ ☆ Deep Reinforcement Learning for Dynamic Resource Allocation in Wireless Networks

Shubham Malhotra, Fnu Yashu, Muhammad Saqib, Dipkumar Mehta, Jagdish Jangid, Sachin Dixit

comment: Upon further review, we found inconsistencies in our analysis and decided to conduct additional research before resubmitting a revised version

♻ ☆ Automated Knowledge Concept Annotation and Question Representation Learning for Knowledge Tracing

Yilmazcan Ozyurt, Stefan Feuerriegel, Mrinmaya Sachan

Knowledge tracing (KT) is a popular approach for modeling students' learning progress over time, which can enable more personalized and adaptive learning. However, existing KT approaches face two major limitations: (1) they rely heavily on expert-defined knowledge concepts (KCs) in questions, which is time-consuming and prone to errors; and (2) KT methods tend to overlook the semantics of both questions and the given KCs. In this work, we address these challenges and present KCQRL, a framework for automated knowledge concept annotation and question representation learning that can improve the effectiveness of any existing KT model. First, we propose an automated KC annotation process using large language models (LLMs), which generates question solutions and then annotates KCs in each solution step of the questions. Second, we introduce a contrastive learning approach to generate semantically rich embeddings for questions and solution steps, aligning them with their associated KCs via a tailored false negative elimination approach. These embeddings can be readily integrated into existing KT models, replacing their randomly initialized embeddings. We demonstrate the effectiveness of KCQRL across 15 KT algorithms on two large real-world Math learning datasets, where we achieve consistent performance improvements.

♻ ☆ Low-Rank Continual Personalization of Diffusion Models SC

Łukasz Staniszewski, Katarzyna Zaleska, Kamil Deja

Recent personalization methods for diffusion models, such as Dreambooth and LoRA, allow fine-tuning pre-trained models to generate new concepts. However, applying these techniques across consecutive tasks in order to include, e.g., new objects or styles, leads to a forgetting of previous knowledge due to mutual interference between their adapters. In this work, we tackle the problem of continual customization under a rigorous regime with no access to past tasks' adapters. In such a scenario, we investigate how different adapters' initialization and merging methods can improve the quality of the final model. To that end, we evaluate the naive continual fine-tuning of customized models and compare this approach with three methods for consecutive adapters' training: sequentially merging new adapters, merging orthogonally initialized adapters, and updating only relevant task-specific weights. In our experiments, we show that the proposed techniques mitigate forgetting when compared to the naive approach. In our studies, we show different traits of selected techniques and their effect on the plasticity and stability of the continually adapted model. Repository with the code is available at https://github.com/luk-st/continual-lora.

comment: SCOPE @ ICLR 2025

♻ ☆ A Triple-Inertial Accelerated Alternating Optimization Method for Deep Learning Training

Chengcheng Yan, Jiawei Xu, Qingsong Wang, Zheng Peng

♻ ☆ AI-Aided Kalman Filters

Nir Shlezinger, Guy Revach, Anubhab Ghosh, Saikat Chatterjee, Shuo Tang, Tales Imbiriba, Jindrich Dunik, Ondrej Straka, Pau Closas, Yonina C. Eldar

The Kalman filter (KF) and its variants are among the most celebrated algorithms in signal processing. These methods are used for state estimation of dynamic systems by relying on mathematical representations in the form of simple state-space (SS) models, which may be crude and inaccurate descriptions of the underlying dynamics. Emerging data-centric artificial intelligence (AI) techniques tackle these tasks using deep neural networks (DNNs), which are model-agnostic. Recent developments illustrate the possibility of fusing DNNs with classic Kalman-type filtering, obtaining systems that learn to track in partially known dynamics. This article provides a tutorial-style overview of design approaches for incorporating AI in aiding KF-type algorithms. We review both generic and dedicated DNN architectures suitable for state estimation, and provide a systematic presentation of techniques for fusing AI tools with KFs and for leveraging partial SS modeling and data, categorizing design approaches into task-oriented and SS model-oriented. The usefulness of each approach in preserving the individual strengths of model-based KFs and data-driven DNNs is investigated in a qualitative and quantitative study, whose code is publicly available, illustrating the gains of hybrid model-based/data-driven designs. We also discuss existing challenges and future research directions that arise from fusing AI and Kalman-type algorithms.

comment: Submitted to the IEEE Signal Processing Magazine

♻ ☆ Tackling water table depth modeling via machine learning: From proxy observations to verifiability

Joseph Janssen, Ardalan Tootchi, Ali A. Ameli

Spatial patterns of water table depth (WTD) play a crucial role in shaping ecological resilience, hydrological connectivity, and human-centric systems. Generally, a large-scale (e.g., continental or global) continuous map of static WTD can be simulated using either physically-based (PB) or machine learning-based (ML) models. We construct three fine-resolution (500 m) ML simulations of WTD, using the XGBoost algorithm and more than 20 million real and proxy observations of WTD, across the United States and Canada. The three ML models were constrained using known physical relations between WTD's drivers and WTD and were trained by sequentially adding real and proxy observations of WTD. Through an extensive (pixel-by-pixel) evaluation across the study region and within ten major ecoregions of North America, we demonstrate that our models (corr=0.6-0.75) can more accurately predict unseen real and proxy observations of WTD compared to two available PB simulations of WTD (corr=0.21-0.40). However, we still argue that currently-available large-scale simulations of static WTD could be uncertain within data-scarce regions such as steep mountainous regions. We reason that biased observational data mainly collected from low-elevation floodplains and the over-flexibility of available models can negatively affect the verifiability of large-scale simulations of WTD. Ultimately, we thoroughly discuss future directions that may help hydrogeologists decide how to improve machine learning-based WTD estimations. In particular, we advocate for the use of proxy satellite data, the incorporation of physical laws, the implementation of better model verification standards, the development of novel globally-available emergent indices, and the collection of more reliable observations.

♻ ☆ RedChronos: A Large Language Model-Based Log Analysis System for Insider Threat Detection in Enterprises

Chenyu Li, Zhengjia Zhu, Jiyan He, Xiu Zhang

Internal threat detection (IDT) aims to address security threats within organizations or enterprises by identifying potential or already occurring malicious threats within vast amounts of logs. Although organizations or enterprises have dedicated personnel responsible for reviewing these logs, it is impossible to manually examine all logs entirely.In response to the vast number of logs, we propose a system called RedChronos, which is a Large Language Model-Based Log Analysis System. This system incorporates innovative improvements over previous research by employing Query-Aware Weighted Voting and a Semantic Expansion-based Genetic Algorithm with LLM-driven Mutations. On the public datasets CERT 4.2 and 5.2, RedChronos outperforms or matches existing approaches in terms of accuracy, precision, and detection rate. Moreover, RedChronos reduces the need for manual intervention in security log reviews by approximately 90% in the Xiaohongshu Security Operation Center. Therefore, our RedChronos system demonstrates exceptional performance in handling IDT tasks, providing innovative solutions for these challenges. We believe that future research can continue to enhance the system's performance in IDT tasks while also reducing the response time to internal risk events.

♻ ☆ Histogram Approaches for Imbalanced Data Streams Regression

Ehsan Aminian, Rita P. Ribeiro, Joao Gama

Imbalanced domains pose a significant challenge in real-world predictive analytics, particularly in the context of regression. While existing research has primarily focused on batch learning from static datasets, limited attention has been given to imbalanced regression in online learning scenarios. Intending to address this gap, in prior work, we proposed sampling strategies based on Chebyshevs inequality as the first methodologies designed explicitly for data streams. However, these approaches operated under the restrictive assumption that rare instances exclusively reside at distribution extremes. This study introduces histogram-based sampling strategies to overcome this constraint, proposing flexible solutions for imbalanced regression in evolving data streams. The proposed techniques -- Histogram-based Undersampling (HistUS) and Histogram-based Oversampling (HistOS) -- employ incremental online histograms to dynamically detect and prioritize rare instances across arbitrary regions of the target distribution to improve predictions in the rare cases. Comprehensive experiments on synthetic and real-world benchmarks demonstrate that HistUS and HistOS substantially improve rare-case prediction accuracy, outperforming baseline models while maintaining competitiveness with Chebyshev-based approaches.

♻ ☆ Credal Two-Sample Tests of Epistemic Uncertainty

Siu Lun Chau, Antonin Schrab, Arthur Gretton, Dino Sejdinovic, Krikamol Muandet

We introduce credal two-sample testing, a new hypothesis testing framework for comparing credal sets -- convex sets of probability measures where each element captures aleatoric uncertainty and the set itself represents epistemic uncertainty that arises from the modeller's partial ignorance. Compared to classical two-sample tests, which focus on comparing precise distributions, the proposed framework provides a broader and more versatile set of hypotheses. This approach enables the direct integration of epistemic uncertainty, effectively addressing the challenges arising from partial ignorance in hypothesis testing. By generalising two-sample test to compare credal sets, our framework enables reasoning for equality, inclusion, intersection, and mutual exclusivity, each offering unique insights into the modeller's epistemic beliefs. As the first work on nonparametric hypothesis testing for comparing credal sets, we focus on finitely generated credal sets derived from i.i.d. samples from multiple distributions -- referred to as credal samples. We formalise these tests as two-sample tests with nuisance parameters and introduce the first permutation-based solution for this class of problems, significantly improving existing methods. Our approach properly incorporates the modeller's epistemic uncertainty into hypothesis testing, leading to more robust and credible conclusions, with kernel-based implementations for real-world applications.

comment: 64 pages

♻ ☆ Column-wise Quantization of Weights and Partial Sums for Accurate and Efficient Compute-In-Memory Accelerators

Jiyoon Kim, Kang Eun Jeon, Yulhwa Kim, Jong Hwan Ko

♻ ☆ Bayesian Experimental Design via Contrastive Diffusions

Jacopo Iollo, Christophe Heinkelé, Pierre Alliez, Florence Forbes

Bayesian Optimal Experimental Design (BOED) is a powerful tool to reduce the cost of running a sequence of experiments. When based on the Expected Information Gain (EIG), design optimization corresponds to the maximization of some intractable expected contrast between prior and posterior distributions. Scaling this maximization to high dimensional and complex settings has been an issue due to BOED inherent computational complexity. In this work, we introduce a pooled posterior distribution with cost-effective sampling properties and provide a tractable access to the EIG contrast maximization via a new EIG gradient expression. Diffusion-based samplers are used to compute the dynamics of the pooled posterior and ideas from bi-level optimization are leveraged to derive an efficient joint sampling-optimization loop. The resulting efficiency gain allows to extend BOED to the well-tested generative capabilities of diffusion models. By incorporating generative models into the BOED framework, we expand its scope and its use in scenarios that were previously impractical. Numerical experiments and comparison with state-of-the-art methods show the potential of the approach.

♻ ☆ FlashRNN: I/O-Aware Optimization of Traditional RNNs on modern hardware

Korbinian Pöppel, Maximilian Beck, Sepp Hochreiter

♻ ☆ Hidden in the Noise: Two-Stage Robust Watermarking for Images

Kasra Arabi, Benjamin Feuer, R. Teal Witter, Chinmay Hegde, Niv Cohen

♻ ☆ Clipping Improves Adam-Norm and AdaGrad-Norm when the Noise Is Heavy-Tailed

Savelii Chezhegov, Yaroslav Klyukin, Andrei Semenov, Aleksandr Beznosikov, Alexander Gasnikov, Samuel Horváth, Martin Takáč, Eduard Gorbunov

Methods with adaptive stepsizes, such as AdaGrad and Adam, are essential for training modern Deep Learning models, especially Large Language Models. Typically, the noise in the stochastic gradients is heavy-tailed for the later ones. Gradient clipping provably helps to achieve good high-probability convergence for such noises. However, despite the similarity between AdaGrad/Adam and Clip-SGD, the current understanding of the high-probability convergence of AdaGrad/Adam-type methods is limited in this case. In this work, we prove that AdaGrad/Adam (and their delayed version) can have provably bad high-probability convergence if the noise is heavy-tailed. We also show that gradient clipping fixes this issue, i.e., we derive new high-probability convergence bounds with polylogarithmic dependence on the confidence level for AdaGrad-Norm and Adam-Norm with clipping and with/without delay for smooth convex/non-convex stochastic optimization with heavy-tailed noise. Our empirical evaluations highlight the superiority of clipped versions of AdaGrad/Adam-Norm in handling the heavy-tailed noise.

comment: 63 pages, 8 figures

♻ ☆ Real-Time Recurrent Reinforcement Learning

Julian Lemmel, Radu Grosu

We introduce a biologically plausible RL framework for solving tasks in partially observable Markov decision processes (POMDPs). The proposed algorithm combines three integral parts: (1) A Meta-RL architecture, resembling the mammalian basal ganglia; (2) A biologically plausible reinforcement learning algorithm, exploiting temporal difference learning and eligibility traces to train the policy and the value-function; (3) An online automatic differentiation algorithm for computing the gradients with respect to parameters of a shared recurrent network backbone. Our experimental results show that the method is capable of solving a diverse set of partially observable reinforcement learning tasks. The algorithm we call real-time recurrent reinforcement learning (RTRRL) serves as a model of learning in biological neural networks, mimicking reward pathways in the basal ganglia.

comment: 14 pages, 9 figures, includes Appendix

♻ ☆ Materials Map Integrating Experimental and Computational Data through Graph-Based Machine Learning for Enhanced Materials Discovery

Yusuke Hashimoto, Xue Jia, Hao Li, Takaaki Tomai

Materials informatics (MI), which emerges from the integration of materials science and data science, is expected to greatly streamline the material discovery and development. The data used for MI are obtained from both computational and experimental studies, while their integration remains challenging. In our previous study, we reported the integration of these datasets by applying a machine learning model that captures trends hidden in the experimental datasets to compositional data stored in the computational database. In this study, we use the obtained data to construct materials maps, which visualize the relation in the structural features of materials, aiming to support study by the experimental researchers. The map is constructed using the MatDeepLearn (MDL) framework, which implements the graph-based representation of material structures, deep learning, and dimensional reduction for the map construction. We evaluate the obtained materials maps through statistical analysis and found that the MDL using message passing neural network (MPNN) enables efficient extraction of features that reflect the structural complexity of materials. Moreover, we found that this advantage does not necessarily translate into improved accuracy in predicting material properties. We attribute this unexpected outcome to the high learning performance inherent in MPNN, which can contribute to the structuring of data points within the materials map.

♻ ☆ Feasible Policy Iteration for Safe Reinforcement Learning

Yujie Yang, Zhilong Zheng, Shengbo Eben Li, Wei Xu, Jingjing Liu, Xianyuan Zhan, Ya-Qin Zhang

Safety is the priority concern when applying reinforcement learning (RL) algorithms to real-world control problems. While policy iteration provides a fundamental algorithm for standard RL, an analogous theoretical algorithm for safe RL remains absent. In this paper, we propose feasible policy iteration (FPI), the first foundational dynamic programming algorithm for safe RL. FPI alternates between policy evaluation, region identification and policy improvement. This follows actor-critic-scenery (ACS) framework where scenery refers to a feasibility function that represents a feasible region. A region-wise update rule is developed for the policy improvement step, which maximizes state-value function inside the feasible region and minimizes feasibility function outside it. With this update rule, FPI guarantees monotonic expansion of feasible region, monotonic improvement of state-value function, and geometric convergence to the optimal safe policy. Experimental results demonstrate that FPI achieves strictly zero constraint violation on low-dimensional tasks and outperforms existing methods in constraint adherence and reward performance on high-dimensional tasks.

♻ ☆ The R2D2 Deep Neural Network Series for Scalable Non-Cartesian Magnetic Resonance Imaging

Yiwei Chen, Amir Aghabiglou, Shijie Chen, Motahare Torki, Chao Tang, Ruud B. van Heeswijk, Yves Wiaux

We introduce the R2D2 Deep Neural Network (DNN) series paradigm for fast and scalable image reconstruction from highly-accelerated non-Cartesian k-space acquisitions in Magnetic Resonance Imaging (MRI). While unrolled DNN architectures provide a robust image formation approach via data-consistency layers, embedding non-uniform fast Fourier transform operators in a DNN can become impractical to train at large scale, e.g in 2D MRI with a large number of coils, or for higher-dimensional imaging. Plug-and-play approaches that alternate a learned denoiser blind to the measurement setting with a data-consistency step are not affected by this limitation but their highly iterative nature implies slow reconstruction. To address this scalability challenge, we leverage the R2D2 paradigm that was recently introduced to enable ultra-fast reconstruction for large-scale Fourier imaging in radio astronomy. R2D2's reconstruction is formed as a series of residual images iteratively estimated as outputs of DNN modules taking the previous iteration's data residual as input. The method can be interpreted as a learned version of the Matching Pursuit algorithm. A series of R2D2 DNN modules were sequentially trained in a supervised manner on the fastMRI dataset and validated for 2D multi-coil MRI in simulation and on real data, targeting highly under-sampled radial k-space sampling. Results suggest that a series with only few DNNs achieves superior reconstruction quality over its unrolled incarnation R2D2-Net (whose training is also much less scalable), and over the state-of-the-art diffusion-based "Decomposed Diffusion Sampler" approach (also characterised by a slower reconstruction process).

comment: 13 pages, 10 figures

♻ ☆ MarS: a Financial Market Simulation Engine Powered by Generative Foundation Model ICLR 2025

Junjie Li, Yang Liu, Weiqing Liu, Shikai Fang, Lewen Wang, Chang Xu, Jiang Bian

comment: 35 pages, 26 figures, ICLR 2025

♻ ☆ Score matching for bridges without learning time-reversals

Elizabeth L. Baker, Moritz Schauer, Stefan Sommer

We propose a new algorithm for learning bridged diffusion processes using score-matching methods. Our method relies on reversing the dynamics of the forward process and using this to learn a score function, which, via Doob's $h$-transform, yields a bridged diffusion process; that is, a process conditioned on an endpoint. In contrast to prior methods, we learn the score term $\nabla_x \log p(t, x; T, y)$ directly, for given $t, y$, completely avoiding first learning a time-reversal. We compare the performance of our algorithm with existing methods and see that it outperforms using the (learned) time-reversals to learn the score term. The code can be found at https://github.com/libbylbaker/forward_bridge.

♻ ☆ Causal Representation Learning from Multimodal Biomedical Observations

Yuewen Sun, Lingjing Kong, Guangyi Chen, Loka Li, Gongxu Luo, Zijian Li, Yixuan Zhang, Yujia Zheng, Mengyue Yang, Petar Stojanov, Eran Segal, Eric P. Xing, Kun Zhang

Prevalent in biomedical applications (e.g., human phenotype research), multimodal datasets can provide valuable insights into the underlying physiological mechanisms. However, current machine learning (ML) models designed to analyze these datasets often lack interpretability and identifiability guarantees, which are essential for biomedical research. Recent advances in causal representation learning have shown promise in identifying interpretable latent causal variables with formal theoretical guarantees. Unfortunately, most current work on multimodal distributions either relies on restrictive parametric assumptions or yields only coarse identification results, limiting their applicability to biomedical research that favors a detailed understanding of the mechanisms. In this work, we aim to develop flexible identification conditions for multimodal data and principled methods to facilitate the understanding of biomedical datasets. Theoretically, we consider a nonparametric latent distribution (c.f., parametric assumptions in previous work) that allows for causal relationships across potentially different modalities. We establish identifiability guarantees for each latent component, extending the subspace identification results from previous work. Our key theoretical contribution is the structural sparsity of causal connections between modalities, which, as we will discuss, is natural for a large collection of biomedical systems. Empirically, we present a practical framework to instantiate our theoretical insights. We demonstrate the effectiveness of our approach through extensive experiments on both numerical and synthetic datasets. Results on a real-world human phenotype dataset are consistent with established biomedical research, validating our theoretical and methodological framework.

♻ ☆ Symmetries, Scaling Laws and Phase Transitions in Consumer Advertising Response

Javier Marin

Understanding how consumers respond to business advertising efforts is essential for optimizing marketing investment. This research introduces a new modeling approach based on the concepts of symmetries and scaling laws in physics to describe consumer response to advertising dynamics. Drawing from mathematical frameworks used in physics and social sciences, we propose a model that accounts for a key aspect: the saturation effect. The model is validated against commonly used models, including the Michaelis-Menten and Hill equations, showing its ability to better capture nonlinearities in advertising effects. We introduce new key parameters like Marketing Sensitivity, Response Sensitivity, and Behavioral Sensitivit, that offer additional insights into the drivers of audience engagement and advertising performance. Our model provides a rigorous yet practical tool for understanding audience behavior, contributing to the improvement of budget allocation strategies.

♻ ☆ Jailbreaking Large Language Models in Infinitely Many Ways

Oliver Goldstein, Emanuele La Malfa, Felix Drinkall, Samuele Marro, Michael Wooldridge

We discuss the ``Infinitely Many Paraphrases'' attacks (IMP), a category of jailbreaks that leverages the increasing capabilities of a model to handle paraphrases and encoded communications to bypass their defensive mechanisms. IMPs' viability pairs and grows with a model's capabilities to handle and bind the semantics of simple mappings between tokens and work extremely well in practice, posing a concrete threat to the users of the most powerful LLMs in commerce. We show how one can bypass the safeguards of the most powerful open- and closed-source LLMs and generate content that explicitly violates their safety policies. One can protect against IMPs by improving the guardrails and making them scale with the LLMs' capabilities. For two categories of attacks that are straightforward to implement, i.e., bijection and encoding, we discuss two defensive strategies, one in token and the other in embedding space. We conclude with some research questions we believe should be prioritised to enhance the defensive mechanisms of LLMs and our understanding of their safety.

♻ ☆ Statistical Deficiency for Task Inclusion Estimation

Loïc Fosse, Frédéric Béchet, Benoît Favre, Géraldine Damnati, Gwénolé Lecorvé, Maxime Darrin, Philippe Formont, Pablo Piantanida

Tasks are central in machine learning, as they are the most natural objects to assess the capabilities of current models. The trend is to build general models able to address any task. Even though transfer learning and multitask learning try to leverage the underlying task space, no well-founded tools are available to study its structure. This study proposes a theoretically grounded setup to define the notion of task and to compute the {\bf inclusion} between two tasks from a statistical deficiency point of view. We propose a tractable proxy as information sufficiency to estimate the degree of inclusion between tasks, show its soundness on synthetic data, and use it to reconstruct empirically the classic NLP pipeline.

comment: 34 pages

♻ ☆ Hallo3: Highly Dynamic and Realistic Portrait Image Animation with Video Diffusion Transformer

Jiahao Cui, Hui Li, Yun Zhan, Hanlin Shang, Kaihui Cheng, Yuqi Ma, Shan Mu, Hang Zhou, Jingdong Wang, Siyu Zhu

Existing methodologies for animating portrait images face significant challenges, particularly in handling non-frontal perspectives, rendering dynamic objects around the portrait, and generating immersive, realistic backgrounds. In this paper, we introduce the first application of a pretrained transformer-based video generative model that demonstrates strong generalization capabilities and generates highly dynamic, realistic videos for portrait animation, effectively addressing these challenges. The adoption of a new video backbone model makes previous U-Net-based methods for identity maintenance, audio conditioning, and video extrapolation inapplicable. To address this limitation, we design an identity reference network consisting of a causal 3D VAE combined with a stacked series of transformer layers, ensuring consistent facial identity across video sequences. Additionally, we investigate various speech audio conditioning and motion frame mechanisms to enable the generation of continuous video driven by speech audio. Our method is validated through experiments on benchmark and newly proposed wild datasets, demonstrating substantial improvements over prior methods in generating realistic portraits characterized by diverse orientations within dynamic and immersive scenes. Further visualizations and the source code are available at: https://fudan-generative-vision.github.io/hallo3/.

♻ ☆ HERO: Human-Feedback Efficient Reinforcement Learning for Online Diffusion Model Finetuning ICLR

Ayano Hiranaka, Shang-Fu Chen, Chieh-Hsin Lai, Dongjun Kim, Naoki Murata, Takashi Shibuya, Wei-Hsiang Liao, Shao-Hua Sun, Yuki Mitsufuji

comment: Published in International Conference on Learning Representations (ICLR) 2025

♻ ☆ Physics-Informed Diffusion Models ICLR 2025

Jan-Hendrik Bastek, WaiChing Sun, Dennis M. Kochmann

Generative models such as denoising diffusion models are quickly advancing their ability to approximate highly complex data distributions. They are also increasingly leveraged in scientific machine learning, where samples from the implied data distribution are expected to adhere to specific governing equations. We present a framework that unifies generative modeling and partial differential equation fulfillment by introducing a first-principle-based loss term that enforces generated samples to fulfill the underlying physical constraints. Our approach reduces the residual error by up to two orders of magnitude compared to previous work in a fluid flow case study and outperforms task-specific frameworks in relevant metrics for structural topology optimization. We also present numerical evidence that our extended training objective acts as a natural regularization mechanism against overfitting. Our framework is simple to implement and versatile in its applicability for imposing equality and inequality constraints as well as auxiliary optimization objectives.

comment: 26 pages, 9 figures, 3 tables; ICLR 2025 camera ready contribution

♻ ☆ The Breakdown of Gaussian Universality in Classification of High-dimensional Linear Factor Mixtures ICLR 2025

Xiaoyi Mai, Zhenyu Liao

The assumption of Gaussian or Gaussian mixture data has been extensively exploited in a long series of precise performance analyses of machine learning (ML) methods, on large datasets having comparably numerous samples and features. To relax this restrictive assumption, subsequent efforts have been devoted to establish "Gaussian equivalent principles" by studying scenarios of Gaussian universality where the asymptotic performance of ML methods on non-Gaussian data remains unchanged when replaced with Gaussian data having the same mean and covariance. Beyond the realm of Gaussian universality, there are few exact results on how the data distribution affects the learning performance. In this article, we provide a precise high-dimensional characterization of empirical risk minimization, for classification under a general mixture data setting of linear factor models that extends Gaussian mixtures. The Gaussian universality is shown to break down under this setting, in the sense that the asymptotic learning performance depends on the data distribution beyond the class means and covariances. To clarify the limitations of Gaussian universality in the classification of mixture data and to understand the impact of its breakdown, we specify conditions for Gaussian universality and discuss their implications for the choice of loss function.

comment: 34 pages, 10 figures, accepted by ICLR 2025 (https://openreview.net/forum?id=UrKbn51HjA)

♻ ☆ ECBench: Can Multi-modal Foundation Models Understand the Egocentric World? A Holistic Embodied Cognition Benchmark

Ronghao Dang, Yuqian Yuan, Wenqi Zhang, Yifei Xin, Boqiang Zhang, Long Li, Liuyi Wang, Qinyang Zeng, Xin Li, Lidong Bing

The enhancement of generalization in robots by large vision-language models (LVLMs) is increasingly evident. Therefore, the embodied cognitive abilities of LVLMs based on egocentric videos are of great interest. However, current datasets for embodied video question answering lack comprehensive and systematic evaluation frameworks. Critical embodied cognitive issues, such as robotic self-cognition, dynamic scene perception, and hallucination, are rarely addressed. To tackle these challenges, we propose ECBench, a high-quality benchmark designed to systematically evaluate the embodied cognitive abilities of LVLMs. ECBench features a diverse range of scene video sources, open and varied question formats, and 30 dimensions of embodied cognition. To ensure quality, balance, and high visual dependence, ECBench uses class-independent meticulous human annotation and multi-round question screening strategies. Additionally, we introduce ECEval, a comprehensive evaluation system that ensures the fairness and rationality of the indicators. Utilizing ECBench, we conduct extensive evaluations of proprietary, open-source, and task-specific LVLMs. ECBench is pivotal in advancing the embodied cognitive capabilities of LVLMs, laying a solid foundation for developing reliable core models for embodied agents. All data and code are available at https://github.com/Rh-Dang/ECBench.

♻ ☆ Reinforcement Learning-Enhanced Procedural Generation for Dynamic Narrative-Driven AR Experiences

Aniruddha Srinivas Joshi

♻ ☆ Predictive Prompt Analysis

Jae Yong Lee, Sungmin Kang, Shin Yoo

Large Language Models (LLMs) are machine learning models that have seen widespread adoption due to their capability of handling previously difficult tasks. LLMs, due to their training, are sensitive to how exactly a question is presented, also known as prompting. However, prompting well is challenging, as it has been difficult to uncover principles behind prompting -- generally, trial-and-error is the most common way of improving prompts, despite its significant computational cost. In this context, we argue it would be useful to perform `predictive prompt analysis', in which an automated technique would perform a quick analysis of a prompt and predict how the LLM would react to it, relative to a goal provided by the user. As a demonstration of the concept, we present Syntactic Prevalence Analyzer (SPA), a predictive prompt analysis approach based on sparse autoencoders (SAEs). SPA accurately predicted how often an LLM would generate target syntactic structures during code synthesis, with up to 0.994 Pearson correlation between the predicted and actual prevalence of the target structure. At the same time, SPA requires only 0.4\% of the time it takes to run the LLM on a benchmark. As LLMs are increasingly used during and integrated into modern software development, our proposed predictive prompt analysis concept has the potential to significantly ease the use of LLMs for both practitioners and researchers.

comment: Accepted by FSE 2025, 5 pages, 2 figures

♻ ☆ ESTformer: Transformer Utilizing Spatiotemporal Dependencies for Electroencaphalogram Super-resolution

Dongdong Li, Zhongliang Zeng, Zhe Wang, Hai Yang

Towards practical applications of Electroencephalography (EEG), lightweight acquisition devices garner significant attention. However, EEG channel selection methods are commonly data-sensitive and cannot establish a unified sound paradigm for EEG acquisition devices. Through reverse conceptualisation, we formulated EEG applications in an EEG super-resolution (SR) manner, but suffered from high computation costs, extra interpolation bias, and few insights into spatiotemporal dependency modelling. To this end, we propose ESTformer, an EEG SR framework that utilises spatiotemporal dependencies based on the transformer. ESTformer applies positional encoding methods and a multihead self-attention mechanism to the space and time dimensions, which can learn spatial structural correlations and temporal functional variations. ESTformer, with the fixed mask strategy, adopts a mask token to upsample low-resolution (LR) EEG data in the case of disturbance from mathematical interpolation methods. On this basis, we designed various transformer blocks to construct a spatial interpolation module (SIM) and a temporal reconstruction module (TRM). Finally, ESTformer cascades the SIM and TRM to capture and model the spatiotemporal dependencies for EEG SR with fidelity. Extensive experimental results on two EEG datasets show the effectiveness of ESTformer against previous state-of-the-art methods, demonstrating the versatility of the Transformer for EEG SR tasks. The superiority of the SR data was verified in an EEG-based person identification and emotion recognition task, achieving a 2% to 38% improvement compared with the LR data at different sampling scales.

comment: Accepted by Knowledge-Based Systems

♻ ☆ AgiBot World Colosseo: A Large-scale Manipulation Platform for Scalable and Intelligent Embodied Systems

AgiBot-World-Contributors, Qingwen Bu, Jisong Cai, Li Chen, Xiuqi Cui, Yan Ding, Siyuan Feng, Shenyuan Gao, Xindong He, Xu Huang, Shu Jiang, Yuxin Jiang, Cheng Jing, Hongyang Li, Jialu Li, Chiming Liu, Yi Liu, Yuxiang Lu, Jianlan Luo, Ping Luo, Yao Mu, Yuehan Niu, Yixuan Pan, Jiangmiao Pang, Yu Qiao, Guanghui Ren, Cheng Ruan, Jiaqi Shan, Yongjian Shen, Chengshi Shi, Mingkang Shi, Modi Shi, Chonghao Sima, Jianheng Song, Huijie Wang, Wenhao Wang, Dafeng Wei, Chengen Xie, Guo Xu, Junchi Yan, Cunbiao Yang, Lei Yang, Shukai Yang, Maoqing Yao, Jia Zeng, Chi Zhang, Qinglin Zhang, Bin Zhao, Chengyue Zhao, Jiaqi Zhao, Jianchao Zhu

We explore how scalable robot data can address real-world challenges for generalized robotic manipulation. Introducing AgiBot World, a large-scale platform comprising over 1 million trajectories across 217 tasks in five deployment scenarios, we achieve an order-of-magnitude increase in data scale compared to existing datasets. Accelerated by a standardized collection pipeline with human-in-the-loop verification, AgiBot World guarantees high-quality and diverse data distribution. It is extensible from grippers to dexterous hands and visuo-tactile sensors for fine-grained skill acquisition. Building on top of data, we introduce Genie Operator-1 (GO-1), a novel generalist policy that leverages latent action representations to maximize data utilization, demonstrating predictable performance scaling with increased data volume. Policies pre-trained on our dataset achieve an average performance improvement of 30% over those trained on Open X-Embodiment, both in in-domain and out-of-distribution scenarios. GO-1 exhibits exceptional capability in real-world dexterous and long-horizon tasks, achieving over 60% success rate on complex tasks and outperforming prior RDT approach by 32%. By open-sourcing the dataset, tools, and models, we aim to democratize access to large-scale, high-quality robot data, advancing the pursuit of scalable and general-purpose intelligence.

comment: Project website: https://agibot-world.com/. Github repo: https://github.com/OpenDriveLab/AgiBot-World. The author list is ordered alphabetically by surname, with detailed contributions provided in the appendix

Computer Vision and Pattern Recognition

☆ Studying Classifier(-Free) Guidance From a Classifier-Centric Perspective

Xiaoming Zhao, Alexander G. Schwing

☆ GoT: Unleashing Reasoning Capability of Multimodal Large Language Model for Visual Generation and Editing

Rongyao Fang, Chengqi Duan, Kun Wang, Linjiang Huang, Hao Li, Shilin Yan, Hao Tian, Xingyu Zeng, Rui Zhao, Jifeng Dai, Xihui Liu, Hongsheng Li

Current image generation and editing methods primarily process textual prompts as direct inputs without reasoning about visual composition and explicit operations. We present Generation Chain-of-Thought (GoT), a novel paradigm that enables generation and editing through an explicit language reasoning process before outputting images. This approach transforms conventional text-to-image generation and editing into a reasoning-guided framework that analyzes semantic relationships and spatial arrangements. We define the formulation of GoT and construct large-scale GoT datasets containing over 9M samples with detailed reasoning chains capturing semantic-spatial relationships. To leverage the advantages of GoT, we implement a unified framework that integrates Qwen2.5-VL for reasoning chain generation with an end-to-end diffusion model enhanced by our novel Semantic-Spatial Guidance Module. Experiments show our GoT framework achieves excellent performance on both generation and editing tasks, with significant improvements over baselines. Additionally, our approach enables interactive visual generation, allowing users to explicitly modify reasoning steps for precise image adjustments. GoT pioneers a new direction for reasoning-driven visual generation and editing, producing images that better align with human intent. To facilitate future research, we make our datasets, code, and pretrained models publicly available at https://github.com/rongyaofang/GoT.

comment: Dataset and models are released in https://github.com/rongyaofang/GoT

☆ The Curse of Conditions: Analyzing and Improving Optimal Transport for Conditional Flow-Based Generation

Ho Kei Cheng, Alexander Schwing

comment: Project page: https://hkchengrex.github.io/C2OT

☆ Distilling Diversity and Control in Diffusion Models

Rohit Gandikota, David Bau

Distilled diffusion models suffer from a critical limitation: reduced sample diversity compared to their base counterparts. In this work, we uncover that despite this diversity loss, distilled models retain the fundamental concept representations of base models. We demonstrate control distillation - where control mechanisms like Concept Sliders and LoRAs trained on base models can be seamlessly transferred to distilled models and vice-versa, effectively distilling control without any retraining. This preservation of representational structure prompted our investigation into the mechanisms of diversity collapse during distillation. To understand how distillation affects diversity, we introduce Diffusion Target (DT) Visualization, an analysis and debugging tool that reveals how models predict final outputs at intermediate steps. Through DT-Visualization, we identify generation artifacts, inconsistencies, and demonstrate that initial diffusion timesteps disproportionately determine output diversity, while later steps primarily refine details. Based on these insights, we introduce diversity distillation - a hybrid inference approach that strategically employs the base model for only the first critical timestep before transitioning to the efficient distilled model. Our experiments demonstrate that this simple modification not only restores the diversity capabilities from base to distilled models but surprisingly exceeds it, while maintaining nearly the computational efficiency of distilled inference, all without requiring additional training or model modifications. Our code and data are available at https://distillation.baulab.info

comment: Project Page: https://distillation.baulab.info

☆ V2Edit: Versatile Video Diffusion Editor for Videos and 3D Scenes

Yanming Zhang, Jun-Kun Chen, Jipeng Lyu, Yu-Xiong Wang

This paper introduces V$^2$Edit, a novel training-free framework for instruction-guided video and 3D scene editing. Addressing the critical challenge of balancing original content preservation with editing task fulfillment, our approach employs a progressive strategy that decomposes complex editing tasks into a sequence of simpler subtasks. Each subtask is controlled through three key synergistic mechanisms: the initial noise, noise added at each denoising step, and cross-attention maps between text prompts and video content. This ensures robust preservation of original video elements while effectively applying the desired edits. Beyond its native video editing capability, we extend V$^2$Edit to 3D scene editing via a "render-edit-reconstruct" process, enabling high-quality, 3D-consistent edits even for tasks involving substantial geometric changes such as object insertion. Extensive experiments demonstrate that our V$^2$Edit achieves high-quality and successful edits across various challenging video editing tasks and complex 3D scene editing tasks, thereby establishing state-of-the-art performance in both domains.

comment: Project Website: https://immortalco.github.io/V2Edit/

☆ A Frustratingly Simple Yet Highly Effective Attack Baseline: Over 90% Success Rate Against the Strong Black-box Models of GPT-4.5/4o/o1

Zhaoyi Li, Xiaohan Zhao, Dong-Dong Wu, Jiacheng Cui, Zhiqiang Shen

comment: Code at: https://github.com/VILA-Lab/M-Attack

☆ Charting and Navigating Hugging Face's Model Atlas

Eliahu Horwitz, Nitzan Kurer, Jonathan Kahana, Liel Amar, Yedid Hoshen

☆ HybridVLA: Collaborative Diffusion and Autoregression in a Unified Vision-Language-Action Model

Jiaming Liu, Hao Chen, Pengju An, Zhuoyang Liu, Renrui Zhang, Chenyang Gu, Xiaoqi Li, Ziyu Guo, Sixiang Chen, Mengzhen Liu, Chengkai Hou, Mengdi Zhao, KC alex Zhou, Pheng-Ann Heng, Shanghang Zhang

Recent advancements in vision-language models (VLMs) for common-sense reasoning have led to the development of vision-language-action (VLA) models, enabling robots to perform generalized manipulation. Although existing autoregressive VLA methods leverage large-scale pretrained knowledge, they disrupt the continuity of actions. Meanwhile, some VLA methods incorporate an additional diffusion head to predict continuous actions, relying solely on VLM-extracted features, which limits their reasoning capabilities. In this paper, we introduce HybridVLA, a unified framework that seamlessly integrates the strengths of both autoregressive and diffusion policies within a single large language model, rather than simply connecting them. To bridge the generation gap, a collaborative training recipe is proposed that injects the diffusion modeling directly into the next-token prediction. With this recipe, we find that these two forms of action prediction not only reinforce each other but also exhibit varying performance across different tasks. Therefore, we design a collaborative action ensemble mechanism that adaptively fuses these two predictions, leading to more robust control. In experiments, HybridVLA outperforms previous state-of-the-art VLA methods across various simulation and real-world tasks, including both single-arm and dual-arm robots, while demonstrating stable manipulation in previously unseen configurations.

☆ Kolmogorov-Arnold Attention: Is Learnable Attention Better For Vision Transformers?

Subhajit Maity, Killian Hitsman, Xin Li, Aritra Dutta

comment: Preprint, Appendix included

☆ UniGoal: Towards Universal Zero-shot Goal-oriented Navigation CVPR 2025

Hang Yin, Xiuwei Xu, Lingqing Zhao, Ziwei Wang, Jie Zhou, Jiwen Lu

In this paper, we propose a general framework for universal zero-shot goal-oriented navigation. Existing zero-shot methods build inference framework upon large language models (LLM) for specific tasks, which differs a lot in overall pipeline and fails to generalize across different types of goal. Towards the aim of universal zero-shot navigation, we propose a uniform graph representation to unify different goals, including object category, instance image and text description. We also convert the observation of agent into an online maintained scene graph. With this consistent scene and goal representation, we preserve most structural information compared with pure text and are able to leverage LLM for explicit graph-based reasoning. Specifically, we conduct graph matching between the scene graph and goal graph at each time instant and propose different strategies to generate long-term goal of exploration according to different matching states. The agent first iteratively searches subgraph of goal when zero-matched. With partial matching, the agent then utilizes coordinate projection and anchor pair alignment to infer the goal location. Finally scene graph correction and goal verification are applied for perfect matching. We also present a blacklist mechanism to enable robust switch between stages. Extensive experiments on several benchmarks show that our UniGoal achieves state-of-the-art zero-shot performance on three studied navigation tasks with a single model, even outperforming task-specific zero-shot methods and supervised universal methods.

comment: Accepted to CVPR 2025

☆ Hierarchical Self-Supervised Adversarial Training for Robust Vision Models in Histopathology

Hashmat Shadab Malik, Shahina Kunhimon, Muzammal Naseer, Fahad Shahbaz Khan, Salman Khan

Adversarial attacks pose significant challenges for vision models in critical fields like healthcare, where reliability is essential. Although adversarial training has been well studied in natural images, its application to biomedical and microscopy data remains limited. Existing self-supervised adversarial training methods overlook the hierarchical structure of histopathology images, where patient-slide-patch relationships provide valuable discriminative signals. To address this, we propose Hierarchical Self-Supervised Adversarial Training (HSAT), which exploits these properties to craft adversarial examples using multi-level contrastive learning and integrate it into adversarial training for enhanced robustness. We evaluate HSAT on multiclass histopathology dataset OpenSRH and the results show that HSAT outperforms existing methods from both biomedical and natural image domains. HSAT enhances robustness, achieving an average gain of 54.31% in the white-box setting and reducing performance drops to 3-4% in the black-box setting, compared to 25-30% for the baseline. These results set a new benchmark for adversarial training in this domain, paving the way for more robust models. Our Code for training and evaluation is available at https://github.com/HashmatShadab/HSAT.

☆ SciVerse: Unveiling the Knowledge Comprehension and Visual Reasoning of LMMs on Multi-modal Scientific Problems

Ziyu Guo, Ray Zhang, Hao Chen, Jialin Gao, Dongzhi Jiang, Jiaze Wang, Pheng-Ann Heng

comment: Initially released in September 2024. Project page: https://sciverse-cuhk.github.io

☆ NIL: No-data Imitation Learning by Leveraging Pre-trained Video Diffusion Models

Mert Albaba, Chenhao Li, Markos Diomataris, Omid Taheri, Andreas Krause, Michael Black

☆ LHM: Large Animatable Human Reconstruction Model from a Single Image in Seconds

Lingteng Qiu, Xiaodong Gu, Peihao Li, Qi Zuo, Weichao Shen, Junfei Zhang, Kejie Qiu, Weihao Yuan, Guanying Chen, Zilong Dong, Liefeng Bo

comment: Project Page: https://lingtengqiu.github.io/LHM/

☆ ETCH: Generalizing Body Fitting to Clothed Humans via Equivariant Tightness

Boqian Li, Haiwen Feng, Zeyu Cai, Michael J. Black, Yuliang Xiu

comment: Page: https://boqian-li.github.io/ETCH/, Code: https://github.com/boqian-li/ETCH

★ Transformers without Normalization CVPR 2025

Jiachen Zhu, Xinlei Chen, Kaiming He, Yann LeCun, Zhuang Liu

comment: CVPR 2025; Project page: https://jiachenzhu.github.io/DyT/

☆ DriveLMM-o1: A Step-by-Step Reasoning Dataset and Large Multimodal Model for Driving Scenario Understanding

Ayesha Ishaq, Jean Lahoud, Ketan More, Omkar Thawakar, Ritesh Thawkar, Dinura Dissanayake, Noor Ahsan, Yuhao Li, Fahad Shahbaz Khan, Hisham Cholakkal, Ivan Laptev, Rao Muhammad Anwer, Salman Khan

While large multimodal models (LMMs) have demonstrated strong performance across various Visual Question Answering (VQA) tasks, certain challenges require complex multi-step reasoning to reach accurate answers. One particularly challenging task is autonomous driving, which demands thorough cognitive processing before decisions can be made. In this domain, a sequential and interpretive understanding of visual cues is essential for effective perception, prediction, and planning. Nevertheless, common VQA benchmarks often focus on the accuracy of the final answer while overlooking the reasoning process that enables the generation of accurate responses. Moreover, existing methods lack a comprehensive framework for evaluating step-by-step reasoning in realistic driving scenarios. To address this gap, we propose DriveLMM-o1, a new dataset and benchmark specifically designed to advance step-wise visual reasoning for autonomous driving. Our benchmark features over 18k VQA examples in the training set and more than 4k in the test set, covering diverse questions on perception, prediction, and planning, each enriched with step-by-step reasoning to ensure logical inference in autonomous driving scenarios. We further introduce a large multimodal model that is fine-tuned on our reasoning dataset, demonstrating robust performance in complex driving scenarios. In addition, we benchmark various open-source and closed-source methods on our proposed dataset, systematically comparing their reasoning capabilities for autonomous driving tasks. Our model achieves a +7.49% gain in final answer accuracy, along with a 3.62% improvement in reasoning score over the previous best open-source model. Our framework, dataset, and model are available at https://github.com/ayesha-ishaq/DriveLMM-o1.

comment: 8 pages, 4 figures, 3 tables, github: https://github.com/ayesha-ishaq/DriveLMM-o1

☆ DiT-Air: Revisiting the Efficiency of Diffusion Model Architecture Design in Text to Image Generation

Chen Chen, Rui Qian, Wenze Hu, Tsu-Jui Fu, Lezhi Li, Bowen Zhang, Alex Schwing, Wei Liu, Yinfei Yang

In this work, we empirically study Diffusion Transformers (DiTs) for text-to-image generation, focusing on architectural choices, text-conditioning strategies, and training protocols. We evaluate a range of DiT-based architectures--including PixArt-style and MMDiT variants--and compare them with a standard DiT variant which directly processes concatenated text and noise inputs. Surprisingly, our findings reveal that the performance of standard DiT is comparable with those specialized models, while demonstrating superior parameter-efficiency, especially when scaled up. Leveraging the layer-wise parameter sharing strategy, we achieve a further reduction of 66% in model size compared to an MMDiT architecture, with minimal performance impact. Building on an in-depth analysis of critical components such as text encoders and Variational Auto-Encoders (VAEs), we introduce DiT-Air and DiT-Air-Lite. With supervised and reward fine-tuning, DiT-Air achieves state-of-the-art performance on GenEval and T2I CompBench, while DiT-Air-Lite remains highly competitive, surpassing most existing models despite its compact size.

☆ OVTR: End-to-End Open-Vocabulary Multiple Object Tracking with Transformer ICLR 2025

Jinyang Li, En Yu, Sijia Chen, Wenbing Tao

Open-vocabulary multiple object tracking aims to generalize trackers to unseen categories during training, enabling their application across a variety of real-world scenarios. However, the existing open-vocabulary tracker is constrained by its framework structure, isolated frame-level perception, and insufficient modal interactions, which hinder its performance in open-vocabulary classification and tracking. In this paper, we propose OVTR (End-to-End Open-Vocabulary Multiple Object Tracking with TRansformer), the first end-to-end open-vocabulary tracker that models motion, appearance, and category simultaneously. To achieve stable classification and continuous tracking, we design the CIP (Category Information Propagation) strategy, which establishes multiple high-level category information priors for subsequent frames. Additionally, we introduce a dual-branch structure for generalization capability and deep multimodal interaction, and incorporate protective strategies in the decoder to enhance performance. Experimental results show that our method surpasses previous trackers on the open-vocabulary MOT benchmark while also achieving faster inference speeds and significantly reducing preprocessing requirements. Moreover, the experiment transferring the model to another dataset demonstrates its strong adaptability. Models and code are released at https://github.com/jinyanglii/OVTR.

comment: Accepted by ICLR 2025

☆ R1-Onevision: Advancing Generalized Multimodal Reasoning through Cross-Modal Formalization

Yi Yang, Xiaoxuan He, Hongkun Pan, Xiyan Jiang, Yan Deng, Xingtao Yang, Haoyu Lu, Dacheng Yin, Fengyun Rao, Minfeng Zhu, Bo Zhang, Wei Chen

Large Language Models have demonstrated remarkable reasoning capability in complex textual tasks. However, multimodal reasoning, which requires integrating visual and textual information, remains a significant challenge. Existing visual-language models often struggle to effectively analyze and reason visual content, resulting in suboptimal performance on complex reasoning tasks. Moreover, the absence of comprehensive benchmarks hinders the accurate assessment of multimodal reasoning capabilities. In this paper, we introduce R1-Onevision, a multimodal reasoning model designed to bridge the gap between visual perception and deep reasoning. To achieve this, we propose a cross-modal reasoning pipeline that transforms images into formal textural representations, enabling precise language-based reasoning. Leveraging this pipeline, we construct the R1-Onevision dataset which provides detailed, step-by-step multimodal reasoning annotations across diverse domains. We further develop the R1-Onevision model through supervised fine-tuning and reinforcement learning to cultivate advanced reasoning and robust generalization abilities. To comprehensively evaluate multimodal reasoning performance across different grades, we introduce R1-Onevision-Bench, a benchmark aligned with human educational stages, covering exams from junior high school to university and beyond. Experimental results show that R1-Onevision achieves state-of-the-art performance, outperforming models such as GPT-4o and Qwen2.5-VL on multiple challenging multimodal reasoning benchmarks.

comment: Code and Model: https://github.com/Fancy-MLLM/R1-onevision

☆ ConsisLoRA: Enhancing Content and Style Consistency for LoRA-based Style Transfer

Bolin Chen, Baoquan Zhao, Haoran Xie, Yi Cai, Qing Li, Xudong Mao

Style transfer involves transferring the style from a reference image to the content of a target image. Recent advancements in LoRA-based (Low-Rank Adaptation) methods have shown promise in effectively capturing the style of a single image. However, these approaches still face significant challenges such as content inconsistency, style misalignment, and content leakage. In this paper, we comprehensively analyze the limitations of the standard diffusion parameterization, which learns to predict noise, in the context of style transfer. To address these issues, we introduce ConsisLoRA, a LoRA-based method that enhances both content and style consistency by optimizing the LoRA weights to predict the original image rather than noise. We also propose a two-step training strategy that decouples the learning of content and style from the reference image. To effectively capture both the global structure and local details of the content image, we introduce a stepwise loss transition strategy. Additionally, we present an inference guidance method that enables continuous control over content and style strengths during inference. Through both qualitative and quantitative evaluations, our method demonstrates significant improvements in content and style consistency while effectively reducing content leakage.

☆ CoSTA$\ast$: Cost-Sensitive Toolpath Agent for Multi-turn Image Editing

Advait Gupta, NandaKiran Velaga, Dang Nguyen, Tianyi Zhou

Text-to-image models like stable diffusion and DALLE-3 still struggle with multi-turn image editing. We decompose such a task as an agentic workflow (path) of tool use that addresses a sequence of subtasks by AI tools of varying costs. Conventional search algorithms require expensive exploration to find tool paths. While large language models (LLMs) possess prior knowledge of subtask planning, they may lack accurate estimations of capabilities and costs of tools to determine which to apply in each subtask. Can we combine the strengths of both LLMs and graph search to find cost-efficient tool paths? We propose a three-stage approach "CoSTA*" that leverages LLMs to create a subtask tree, which helps prune a graph of AI tools for the given task, and then conducts A* search on the small subgraph to find a tool path. To better balance the total cost and quality, CoSTA* combines both metrics of each tool on every subtask to guide the A* search. Each subtask's output is then evaluated by a vision-language model (VLM), where a failure will trigger an update of the tool's cost and quality on the subtask. Hence, the A* search can recover from failures quickly to explore other paths. Moreover, CoSTA* can automatically switch between modalities across subtasks for a better cost-quality trade-off. We build a novel benchmark of challenging multi-turn image editing, on which CoSTA* outperforms state-of-the-art image-editing models or agents in terms of both cost and quality, and performs versatile trade-offs upon user preference.

☆ OCCUQ: Exploring Efficient Uncertainty Quantification for 3D Occupancy Prediction ICRA 2025

Severin Heidrich, Till Beemelmanns, Alexey Nekrasov, Bastian Leibe, Lutz Eckstein

Autonomous driving has the potential to significantly enhance productivity and provide numerous societal benefits. Ensuring robustness in these safety-critical systems is essential, particularly when vehicles must navigate adverse weather conditions and sensor corruptions that may not have been encountered during training. Current methods often overlook uncertainties arising from adversarial conditions or distributional shifts, limiting their real-world applicability. We propose an efficient adaptation of an uncertainty estimation technique for 3D occupancy prediction. Our method dynamically calibrates model confidence using epistemic uncertainty estimates. Our evaluation under various camera corruption scenarios, such as fog or missing cameras, demonstrates that our approach effectively quantifies epistemic uncertainty by assigning higher uncertainty values to unseen data. We introduce region-specific corruptions to simulate defects affecting only a single camera and validate our findings through both scene-level and region-level assessments. Our results show superior performance in Out-of-Distribution (OoD) detection and confidence calibration compared to common baselines such as Deep Ensembles and MC-Dropout. Our approach consistently demonstrates reliable uncertainty measures, indicating its potential for enhancing the robustness of autonomous driving systems in real-world scenarios. Code and dataset are available at https://github.com/ika-rwth-aachen/OCCUQ .

comment: Accepted for publication at ICRA 2025

☆ MuDG: Taming Multi-modal Diffusion with Gaussian Splatting for Urban Scene Reconstruction

Yingshuang Zou, Yikang Ding, Chuanrui Zhang, Jiazhe Guo, Bohan Li, Xiaoyang Lyu, Feiyang Tan, Xiaojuan Qi, Haoqian Wang

Recent breakthroughs in radiance fields have significantly advanced 3D scene reconstruction and novel view synthesis (NVS) in autonomous driving. Nevertheless, critical limitations persist: reconstruction-based methods exhibit substantial performance deterioration under significant viewpoint deviations from training trajectories, while generation-based techniques struggle with temporal coherence and precise scene controllability. To overcome these challenges, we present MuDG, an innovative framework that integrates Multi-modal Diffusion model with Gaussian Splatting (GS) for Urban Scene Reconstruction. MuDG leverages aggregated LiDAR point clouds with RGB and geometric priors to condition a multi-modal video diffusion model, synthesizing photorealistic RGB, depth, and semantic outputs for novel viewpoints. This synthesis pipeline enables feed-forward NVS without computationally intensive per-scene optimization, providing comprehensive supervision signals to refine 3DGS representations for rendering robustness enhancement under extreme viewpoint changes. Experiments on the Open Waymo Dataset demonstrate that MuDG outperforms existing methods in both reconstruction and synthesis quality.

☆ Dual-Stage Cross-Modal Network with Dynamic Feature Fusion for Emotional Mimicry Intensity Estimation

Jun Yu, Lingsi Zhu, Yanjun Chi, Yunxiang Zhang, Yang Zheng, Yongqi Wang, Xilong Lu

☆ TruthPrInt: Mitigating LVLM Object Hallucination Via Latent Truthful-Guided Pre-Intervention

Jinhao Duan, Fei Kong, Hao Cheng, James Diffenderfer, Bhavya Kailkhura, Lichao Sun, Xiaofeng Zhu, Xiaoshuang Shi, Kaidi Xu

comment: 15 pages, 9 figures, the first two authors contributed equally

☆ GroomLight: Hybrid Inverse Rendering for Relightable Human Hair Appearance Modeling

Yang Zheng, Menglei Chai, Delio Vicini, Yuxiao Zhou, Yinghao Xu, Leonidas Guibas, Gordon Wetzstein, Thabo Beeler

We present GroomLight, a novel method for relightable hair appearance modeling from multi-view images. Existing hair capture methods struggle to balance photorealistic rendering with relighting capabilities. Analytical material models, while physically grounded, often fail to fully capture appearance details. Conversely, neural rendering approaches excel at view synthesis but generalize poorly to novel lighting conditions. GroomLight addresses this challenge by combining the strengths of both paradigms. It employs an extended hair BSDF model to capture primary light transport and a light-aware residual model to reconstruct the remaining details. We further propose a hybrid inverse rendering pipeline to optimize both components, enabling high-fidelity relighting, view synthesis, and material editing. Extensive evaluations on real-world hair data demonstrate state-of-the-art performance of our method.

comment: Project Page: https://syntec-research.github.io/GroomLight

☆ GroundingSuite: Measuring Complex Multi-Granular Pixel Grounding

Rui Hu, Lianghui Zhu, Yuxuan Zhang, Tianheng Cheng, Lei Liu, Heng Liu, Longjin Ran, Xiaoxin Chen, Wenyu Liu, Xinggang Wang

Pixel grounding, encompassing tasks such as Referring Expression Segmentation (RES), has garnered considerable attention due to its immense potential for bridging the gap between vision and language modalities. However, advancements in this domain are currently constrained by limitations inherent in existing datasets, including limited object categories, insufficient textual diversity, and a scarcity of high-quality annotations. To mitigate these limitations, we introduce GroundingSuite, which comprises: (1) an automated data annotation framework leveraging multiple Vision-Language Model (VLM) agents; (2) a large-scale training dataset encompassing 9.56 million diverse referring expressions and their corresponding segmentations; and (3) a meticulously curated evaluation benchmark consisting of 3,800 images. The GroundingSuite training dataset facilitates substantial performance improvements, enabling models trained on it to achieve state-of-the-art results. Specifically, a cIoU of 68.9 on gRefCOCO and a gIoU of 55.3 on RefCOCOm. Moreover, the GroundingSuite annotation framework demonstrates superior efficiency compared to the current leading data annotation method, i.e., $4.5 \times$ faster than the GLaMM.

comment: Work in progress. Code: https://github.com/hustvl/GroundingSuite

☆ Poly-MgNet: Polynomial Building Blocks in Multigrid-Inspired ResNets

Antonia van Betteray, Matthias Rottmann, Karsten Kahl

☆ CameraCtrl II: Dynamic Scene Exploration via Camera-controlled Video Diffusion Models

Hao He, Ceyuan Yang, Shanchuan Lin, Yinghao Xu, Meng Wei, Liangke Gui, Qi Zhao, Gordon Wetzstein, Lu Jiang, Hongsheng Li

This paper introduces CameraCtrl II, a framework that enables large-scale dynamic scene exploration through a camera-controlled video diffusion model. Previous camera-conditioned video generative models suffer from diminished video dynamics and limited range of viewpoints when generating videos with large camera movement. We take an approach that progressively expands the generation of dynamic scenes -- first enhancing dynamic content within individual video clip, then extending this capability to create seamless explorations across broad viewpoint ranges. Specifically, we construct a dataset featuring a large degree of dynamics with camera parameter annotations for training while designing a lightweight camera injection module and training scheme to preserve dynamics of the pretrained models. Building on these improved single-clip techniques, we enable extended scene exploration by allowing users to iteratively specify camera trajectories for generating coherent video sequences. Experiments across diverse scenarios demonstrate that CameraCtrl Ii enables camera-controlled dynamic scene synthesis with substantially wider spatial exploration than previous approaches.

comment: Project page: https://hehao13.github.io/Projects-CameraCtrl-II/

☆ Long Context Tuning for Video Generation

Yuwei Guo, Ceyuan Yang, Ziyan Yang, Zhibei Ma, Zhijie Lin, Zhenheng Yang, Dahua Lin, Lu Jiang

Recent advances in video generation can produce realistic, minute-long single-shot videos with scalable diffusion transformers. However, real-world narrative videos require multi-shot scenes with visual and dynamic consistency across shots. In this work, we introduce Long Context Tuning (LCT), a training paradigm that expands the context window of pre-trained single-shot video diffusion models to learn scene-level consistency directly from data. Our method expands full attention mechanisms from individual shots to encompass all shots within a scene, incorporating interleaved 3D position embedding and an asynchronous noise strategy, enabling both joint and auto-regressive shot generation without additional parameters. Models with bidirectional attention after LCT can further be fine-tuned with context-causal attention, facilitating auto-regressive generation with efficient KV-cache. Experiments demonstrate single-shot models after LCT can produce coherent multi-shot scenes and exhibit emerging capabilities, including compositional generation and interactive shot extension, paving the way for more practical visual content creation. See https://guoyww.github.io/projects/long-context-video/ for more details.

comment: Project Page: https://guoyww.github.io/projects/long-context-video/

☆ Unlock the Power of Unlabeled Data in Language Driving Model ICRA2025

Chaoqun Wang, Jie Yang, Xiaobin Hong, Ruimao Zhang

Recent Vision-based Large Language Models~(VisionLLMs) for autonomous driving have seen rapid advancements. However, such promotion is extremely dependent on large-scale high-quality annotated data, which is costly and labor-intensive. To address this issue, we propose unlocking the value of abundant yet unlabeled data to improve the language-driving model in a semi-supervised learning manner. Specifically, we first introduce a series of template-based prompts to extract scene information, generating questions that create pseudo-answers for the unlabeled data based on a model trained with limited labeled data. Next, we propose a Self-Consistency Refinement method to improve the quality of these pseudo-annotations, which are later used for further training. By utilizing a pre-trained VisionLLM (e.g., InternVL), we build a strong Language Driving Model (LDM) for driving scene question-answering, outperforming previous state-of-the-art methods. Extensive experiments on the DriveLM benchmark show that our approach performs well with just 5% labeled data, achieving competitive performance against models trained with full datasets. In particular, our LDM achieves 44.85% performance with limited labeled data, increasing to 54.27% when using unlabeled data, while models trained with full datasets reach 60.68% on the DriveLM benchmark.

comment: Accepted by ICRA2025

☆ VisualWebInstruct: Scaling up Multimodal Instruction Data through Web Search

Yiming Jia, Jiachen Li, Xiang Yue, Bo Li, Ping Nie, Kai Zou, Wenhu Chen

comment: Technical Report

☆ Semantic-Supervised Spatial-Temporal Fusion for LiDAR-based 3D Object Detection ICRA2025

Chaoqun Wang, Xiaobin Hong, Wenzhong Li, Ruimao Zhang

LiDAR-based 3D object detection presents significant challenges due to the inherent sparsity of LiDAR points. A common solution involves long-term temporal LiDAR data to densify the inputs. However, efficiently leveraging spatial-temporal information remains an open problem. In this paper, we propose a novel Semantic-Supervised Spatial-Temporal Fusion (ST-Fusion) method, which introduces a novel fusion module to relieve the spatial misalignment caused by the object motion over time and a feature-level semantic supervision to sufficiently unlock the capacity of the proposed fusion module. Specifically, the ST-Fusion consists of a Spatial Aggregation (SA) module and a Temporal Merging (TM) module. The SA module employs a convolutional layer with progressively expanding receptive fields to aggregate the object features from the local regions to alleviate the spatial misalignment, the TM module dynamically extracts object features from the preceding frames based on the attention mechanism for a comprehensive sequential presentation. Besides, in the semantic supervision, we propose a Semantic Injection method to enrich the sparse LiDAR data via injecting the point-wise semantic labels, using it for training a teacher model and providing a reconstruction target at the feature level supervised by the proposed object-aware loss. Extensive experiments on various LiDAR-based detectors demonstrate the effectiveness and universality of our proposal, yielding an improvement of approximately +2.8% in NDS based on the nuScenes benchmark.

comment: Accepted by ICRA2025

☆ Autoregressive Image Generation with Randomized Parallel Decoding

Haopeng Li, Jinyue Yang, Guoqi Li, Huan Wang

We introduce ARPG, a novel visual autoregressive model that enables randomized parallel generation, addressing the inherent limitations of conventional raster-order approaches, which hinder inference efficiency and zero-shot generalization due to their sequential, predefined token generation order. Our key insight is that effective random-order modeling necessitates explicit guidance for determining the position of the next predicted token. To this end, we propose a novel guided decoding framework that decouples positional guidance from content representation, encoding them separately as queries and key-value pairs. By directly incorporating this guidance into the causal attention mechanism, our approach enables fully random-order training and generation, eliminating the need for bidirectional attention. Consequently, ARPG readily generalizes to zero-shot tasks such as image inpainting, outpainting, and resolution expansion. Furthermore, it supports parallel inference by concurrently processing multiple queries using a shared KV cache. On the ImageNet-1K 256 benchmark, our approach attains an FID of 1.94 with only 64 sampling steps, achieving over a 20-fold increase in throughput while reducing memory consumption by over 75% compared to representative recent autoregressive models at a similar scale.

☆ MASQUE: A Text-Guided Diffusion-Based Framework for Localized and Customized Adversarial Makeup

Youngjin Kwon, Xiao Zhang

As facial recognition is increasingly adopted for government and commercial services, its potential misuse has raised serious concerns about privacy and civil rights. To counteract, various anti-facial recognition techniques have been proposed for privacy protection by adversarially perturbing face images, among which generative makeup-based approaches are the most popular. However, these methods, designed primarily to impersonate specific target identities, can only achieve weak dodging success rates while increasing the risk of targeted abuse. In addition, they often introduce global visual artifacts or a lack of adaptability to accommodate diverse makeup prompts, compromising user satisfaction. To address the above limitations, we develop MASQUE, a novel diffusion-based framework that generates localized adversarial makeups guided by user-defined text prompts. Built upon precise null-text inversion, customized cross-attention fusion with masking, and a pairwise adversarial guidance mechanism using images of the same individual, MASQUE achieves robust dodging performance without requiring any external identity. Comprehensive evaluations on open-source facial recognition models and commercial APIs demonstrate that MASQUE significantly improves dodging success rates over all baselines, along with higher perceptual fidelity and stronger adaptability to various text makeup prompts.

☆ Learning Interpretable Logic Rules from Deep Vision Models

Chuqin Geng, Yuhe Jiang, Ziyu Zhao, Haolin Ye, Zhaoyue Wang, Xujie Si

We propose a general framework called VisionLogic to extract interpretable logic rules from deep vision models, with a focus on image classification tasks. Given any deep vision model that uses a fully connected layer as the output head, VisionLogic transforms neurons in the last layer into predicates and grounds them into vision concepts using causal validation. In this way, VisionLogic can provide local explanations for single images and global explanations for specific classes in the form of logic rules. Compared to existing interpretable visualization tools such as saliency maps, VisionLogic addresses several key challenges, including the lack of causal explanations, overconfidence in visualizations, and ambiguity in interpretation. VisionLogic also facilitates the study of visual concepts encoded by predicates, particularly how they behave under perturbation -- an area that remains underexplored in the field of hidden semantics. Apart from providing better visual explanations and insights into the visual concepts learned by the model, we show that VisionLogic retains most of the neural network's discriminative power in an interpretable and transparent manner. We envision it as a bridge between complex model behavior and human-understandable explanations, providing trustworthy and actionable insights for real-world applications.

comment: 10 pages, 6 figures

☆ Lightweight Models for Emotional Analysis in Video

Quoc-Tien Nguyen, Hong-Hai Nguyen, Van-Thong Huynh

☆ PiSA: A Self-Augmented Data Engine and Training Strategy for 3D Understanding with Large Models

Zilu Guo, Hongbin Lin, Zhihao Yuan, Chaoda Zheng, Pengshuo Qiu, Dongzhi Jiang, Renrui Zhang, Chun-Mei Feng, Zhen Li

comment: Technical Report

☆ How Should We Evaluate Uncertainty in Accelerated MRI Reconstruction?

Luca Trautmann, Peter Wijeratne, Itamar Ronen, Ivor Simpson

Reconstructing accelerated MRI is an ill-posed problem. Machine learning has recently shown great promise at this task, but current approaches to quantifying uncertainty focus on measuring the variability in pixelwise intensity variation. Although these provide interpretable maps, they lack structural understanding and they do not have a clear relationship to how the data will be analysed subsequently. In this paper, we propose a new approach to evaluating reconstruction variability based on apparent anatomical changes in the reconstruction, which is more tightly related to common downstream tasks. We use image registration and segmentation to evaluate several common MRI reconstruction approaches, where uncertainty is measured via ensembling, for accelerated imaging. We demonstrate the intrinsic variability in reconstructed images and show that models with high scores on often used quality metrics such as SSIM and PSNR, can nonetheless display high levels of variance and bias in anatomical measures.

☆ NeighborRetr: Balancing Hub Centrality in Cross-Modal Retrieval CVPR 2025

Zengrong Lin, Zheng Wang, Tianwen Qian, Pan Mu, Sixian Chan, Cong Bai

Cross-modal retrieval aims to bridge the semantic gap between different modalities, such as visual and textual data, enabling accurate retrieval across them. Despite significant advancements with models like CLIP that align cross-modal representations, a persistent challenge remains: the hubness problem, where a small subset of samples (hubs) dominate as nearest neighbors, leading to biased representations and degraded retrieval accuracy. Existing methods often mitigate hubness through post-hoc normalization techniques, relying on prior data distributions that may not be practical in real-world scenarios. In this paper, we directly mitigate hubness during training and introduce NeighborRetr, a novel method that effectively balances the learning of hubs and adaptively adjusts the relations of various kinds of neighbors. Our approach not only mitigates the hubness problem but also enhances retrieval performance, achieving state-of-the-art results on multiple cross-modal retrieval benchmarks. Furthermore, NeighborRetr demonstrates robust generalization to new domains with substantial distribution shifts, highlighting its effectiveness in real-world applications. We make our code publicly available at: https://github.com/zzezze/NeighborRetr .

comment: Accepted at CVPR 2025, 18 pages, 7 figures, 13 tables

☆ Interactive Multimodal Fusion with Temporal Modeling

Jun Yu, Yongqi Wang, Lei Wang, Yang Zheng, Shengfan Xu

This paper presents our method for the estimation of valence-arousal (VA) in the 8th Affective Behavior Analysis in-the-Wild (ABAW) competition. Our approach integrates visual and audio information through a multimodal framework. The visual branch uses a pre-trained ResNet model to extract spatial features from facial images. The audio branches employ pre-trained VGG models to extract VGGish and LogMel features from speech signals. These features undergo temporal modeling using Temporal Convolutional Networks (TCNs). We then apply cross-modal attention mechanisms, where visual features interact with audio features through query-key-value attention structures. Finally, the features are concatenated and passed through a regression layer to predict valence and arousal. Our method achieves competitive performance on the Aff-Wild2 dataset, demonstrating effective multimodal fusion for VA estimation in-the-wild.

☆ AudioX: Diffusion Transformer for Anything-to-Audio Generation

Zeyue Tian, Yizhu Jin, Zhaoyang Liu, Ruibin Yuan, Xu Tan, Qifeng Chen, Wei Xue, Yike Guo

comment: The code and datasets will be available at https://zeyuet.github.io/AudioX/

☆ CountPath: Automating Fragment Counting in Digital Pathology

Ana Beatriz Vieira, Maria Valente, Diana Montezuma, Tomé Albuquerque, Liliana Ribeiro, Domingos Oliveira, João Monteiro, Sofia Gonçalves, Isabel M. Pinto, Jaime S. Cardoso, Arlindo L. Oliveira

comment: 10 pages, 3 figures

☆ Hoi2Anomaly: An Explainable Anomaly Detection Approach Guided by Human-Object Interaction

Yuhan Wang, Cheng Liu, Daou Zhang, Weichao Wu

In the domain of Image Anomaly Detection (IAD), Existing methods frequently exhibit a paucity of fine-grained, interpretable semantic information, resulting in the detection of anomalous entities or activities that are susceptible to machine illusions. This deficiency often leads to the detection of anomalous entities or actions that are susceptible to machine illusions and lack sufficient explanation. In this thesis, we propose a novel approach to anomaly detection, termed Hoi2Anomaly, which aims to achieve precise discrimination and localization of anomalies. The proposed methodology involves the construction of a multi-modal instruction tuning dataset comprising human-object interaction (HOI) pairs in anomalous scenarios. Second, we have trained an HOI extractor in threat scenarios to localize and match anomalous actions and entities. Finally, explanatory content is generated for the detected anomalous HOI by fine-tuning the visual language pretraining (VLP) framework. The experimental results demonstrate that Hoi2Anomaly surpasses existing generative approaches in terms of precision and explainability. We will release Hoi2Anomaly for the advancement of the field of anomaly detection.

☆ TokenCarve: Information-Preserving Visual Token Compression in Multimodal Large Language Models

Xudong Tan, Peng Ye, Chongjun Tu, Jianjian Cao, Yaoxin Yang, Lin Zhang, Dongzhan Zhou, Tao Chen

Multimodal Large Language Models (MLLMs) are becoming increasingly popular, while the high computational cost associated with multimodal data input, particularly from visual tokens, poses a significant challenge. Existing training-based token compression methods improve inference efficiency but require costly retraining, while training-free methods struggle to maintain performance when aggressively reducing token counts. In this study, we reveal that the performance degradation of MLLM closely correlates with the accelerated loss of information in the attention output matrix. This insight introduces a novel information-preserving perspective, making it possible to maintain performance even under extreme token compression. Based on this finding, we propose TokenCarve, a training-free, plug-and-play, two-stage token compression framework. The first stage employs an Information-Preservation-Guided Selection (IPGS) strategy to prune low-information tokens, while the second stage further leverages IPGS to guide token merging, minimizing information loss. Extensive experiments on 11 datasets and 2 model variants demonstrate the effectiveness of TokenCarve. It can even reduce the number of visual tokens to 22.2% of the original count, achieving a 1.23x speedup in inference, a 64% reduction in KV cache storage, and only a 1.54% drop in accuracy. Our code is available at https://github.com/ShawnTan86/TokenCarve.

☆ OmniSTVG: Toward Spatio-Temporal Omni-Object Video Grounding

Jiali Yao, Xinran Deng, Xin Gu, Mengrui Dai, Bing Fan, Zhipeng Zhang, Yan Huang, Heng Fan, Libo Zhang

In this paper, we propose spatio-temporal omni-object video grounding, dubbed OmniSTVG, a new STVG task that aims at localizing spatially and temporally all targets mentioned in the textual query from videos. Compared to classic STVG locating only a single target, OmniSTVG enables localization of not only an arbitrary number of text-referred targets but also their interacting counterparts in the query from the video, making it more flexible and practical in real scenarios for comprehensive understanding. In order to facilitate exploration of OmniSTVG, we introduce BOSTVG, a large-scale benchmark dedicated to OmniSTVG. Specifically, our BOSTVG consists of 10,018 videos with 10.2M frames and covers a wide selection of 287 classes from diverse scenarios. Each sequence in BOSTVG, paired with a free-form textual query, encompasses a varying number of targets ranging from 1 to 10. To ensure high quality, each video is manually annotated with meticulous inspection and refinement. To our best knowledge, BOSTVG is to date the first and the largest benchmark for OmniSTVG. To encourage future research, we introduce a simple yet effective approach, named OmniTube, which, drawing inspiration from Transformer-based STVG methods, is specially designed for OmniSTVG and demonstrates promising results. By releasing BOSTVG, we hope to go beyond classic STVG by locating every object appearing in the query for more comprehensive understanding, opening up a new direction for STVG. Our benchmark, model, and results will be released at https://github.com/JellyYao3000/OmniSTVG.

☆ Streaming Generation of Co-Speech Gestures via Accelerated Rolling Diffusion

Evgeniia Vu, Andrei Boiarov, Dmitry Vetrov

☆ World Modeling Makes a Better Planner: Dual Preference Optimization for Embodied Task Planning

Siyin Wang, Zhaoye Fei, Qinyuan Cheng, Shiduo Zhang, Panpan Cai, Jinlan Fu, Xipeng Qiu

Recent advances in large vision-language models (LVLMs) have shown promise for embodied task planning, yet they struggle with fundamental challenges like dependency constraints and efficiency. Existing approaches either solely optimize action selection or leverage world models during inference, overlooking the benefits of learning to model the world as a way to enhance planning capabilities. We propose Dual Preference Optimization (D$^2$PO), a new learning framework that jointly optimizes state prediction and action selection through preference learning, enabling LVLMs to understand environment dynamics for better planning. To automatically collect trajectories and stepwise preference data without human annotation, we introduce a tree search mechanism for extensive exploration via trial-and-error. Extensive experiments on VoTa-Bench demonstrate that our D$^2$PO-based method significantly outperforms existing methods and GPT-4o when applied to Qwen2-VL (7B), LLaVA-1.6 (7B), and LLaMA-3.2 (11B), achieving superior task success rates with more efficient execution paths.

☆ OODD: Test-time Out-of-Distribution Detection with Dynamic Dictionary

Yifeng Yang, Lin Zhu, Zewen Sun, Hengyu Liu, Qinying Gu, Nanyang Ye

☆ Flow-NeRF: Joint Learning of Geometry, Poses, and Dense Flow within Unified Neural Representations

Xunzhi Zheng, Dan Xu

Learning accurate scene reconstruction without pose priors in neural radiance fields is challenging due to inherent geometric ambiguity. Recent development either relies on correspondence priors for regularization or uses off-the-shelf flow estimators to derive analytical poses. However, the potential for jointly learning scene geometry, camera poses, and dense flow within a unified neural representation remains largely unexplored. In this paper, we present Flow-NeRF, a unified framework that simultaneously optimizes scene geometry, camera poses, and dense optical flow all on-the-fly. To enable the learning of dense flow within the neural radiance field, we design and build a bijective mapping for flow estimation, conditioned on pose. To make the scene reconstruction benefit from the flow estimation, we develop an effective feature enhancement mechanism to pass canonical space features to world space representations, significantly enhancing scene geometry. We validate our model across four important tasks, i.e., novel view synthesis, depth estimation, camera pose prediction, and dense optical flow estimation, using several datasets. Our approach surpasses previous methods in almost all metrics for novel-view view synthesis and depth estimation and yields both qualitatively sound and quantitatively accurate novel-view flow. Our project page is https://zhengxunzhi.github.io/flownerf/.

☆ Consistent multi-animal pose estimation in cattle using dynamic Kalman filter based tracking

Maarten Perneel, Ines Adriaens, Ben Aernouts, Jan Verwaeren

Over the past decade, studying animal behaviour with the help of computer vision has become more popular. Replacing human observers by computer vision lowers the cost of data collection and therefore allows to collect more extensive datasets. However, the majority of available computer vision algorithms to study animal behaviour is highly tailored towards a single research objective, limiting possibilities for data reuse. In this perspective, pose-estimation in combination with animal tracking offers opportunities to yield a higher level representation capturing both the spatial and temporal component of animal behaviour. Such a higher level representation allows to answer a wide variety of research questions simultaneously, without the need to develop repeatedly tailored computer vision algorithms. In this paper, we therefore first cope with several weaknesses of current pose-estimation algorithms and thereafter introduce KeySORT (Keypoint Simple and Online Realtime Tracking). KeySORT deploys an adaptive Kalman filter to construct tracklets in a bounding-box free manner, significantly improving the temporal consistency of detected keypoints. In this paper, we focus on pose estimation in cattle, but our methodology can easily be generalised to any other animal species. Our test results indicate our algorithm is able to detect up to 80% of the ground truth keypoints with high accuracy, with only a limited drop in performance when daylight recordings are compared to nightvision recordings. Moreover, by using KeySORT to construct skeletons, the temporal consistency of generated keypoint coordinates was largely improved, offering opportunities with regard to automated behaviour monitoring of animals.

☆ Learning Disease State from Noisy Ordinal Disease Progression Labels

Gustav Schmidt, Holger Heidrich, Philipp Berens, Sarah Müller

☆ EFC++: Elastic Feature Consolidation with Prototype Re-balancing for Cold Start Exemplar-free Incremental Learning

Simone Magistri, Tomaso Trinci, Albin Soutif-Cormerais, Joost van de Weijer, Andrew D. Bagdanov

Exemplar-Free Class Incremental Learning (EFCIL) aims to learn from a sequence of tasks without having access to previous task data. In this paper, we consider the challenging Cold Start scenario in which insufficient data is available in the first task to learn a high-quality backbone. This is especially challenging for EFCIL since it requires high plasticity, resulting in feature drift which is difficult to compensate for in the exemplar-free setting. To address this problem, we propose an effective approach to consolidate feature representations by regularizing drift in directions highly relevant to previous tasks and employs prototypes to reduce task-recency bias. Our approach, which we call Elastic Feature Consolidation++ (EFC++) exploits a tractable second-order approximation of feature drift based on a proposed Empirical Feature Matrix (EFM). The EFM induces a pseudo-metric in feature space which we use to regularize feature drift in important directions and to update Gaussian prototypes. In addition, we introduce a post-training prototype re-balancing phase that updates classifiers to compensate for feature drift. Experimental results on CIFAR-100, Tiny-ImageNet, ImageNet-Subset, ImageNet-1K and DomainNet demonstrate that EFC++ is better able to learn new tasks by maintaining model plasticity and significantly outperform the state-of-the-art.

comment: Submitted on July 2024. Under Review. arXiv admin note: text overlap with arXiv:2402.03917

☆ 4D LangSplat: 4D Language Gaussian Splatting via Multimodal Large Language Models CVPR 2025

Wanhua Li, Renping Zhou, Jiawei Zhou, Yingwei Song, Johannes Herter, Minghan Qin, Gao Huang, Hanspeter Pfister

Learning 4D language fields to enable time-sensitive, open-ended language queries in dynamic scenes is essential for many real-world applications. While LangSplat successfully grounds CLIP features into 3D Gaussian representations, achieving precision and efficiency in 3D static scenes, it lacks the ability to handle dynamic 4D fields as CLIP, designed for static image-text tasks, cannot capture temporal dynamics in videos. Real-world environments are inherently dynamic, with object semantics evolving over time. Building a precise 4D language field necessitates obtaining pixel-aligned, object-wise video features, which current vision models struggle to achieve. To address these challenges, we propose 4D LangSplat, which learns 4D language fields to handle time-agnostic or time-sensitive open-vocabulary queries in dynamic scenes efficiently. 4D LangSplat bypasses learning the language field from vision features and instead learns directly from text generated from object-wise video captions via Multimodal Large Language Models (MLLMs). Specifically, we propose a multimodal object-wise video prompting method, consisting of visual and text prompts that guide MLLMs to generate detailed, temporally consistent, high-quality captions for objects throughout a video. These captions are encoded using a Large Language Model into high-quality sentence embeddings, which then serve as pixel-aligned, object-specific feature supervision, facilitating open-vocabulary text queries through shared embedding spaces. Recognizing that objects in 4D scenes exhibit smooth transitions across states, we further propose a status deformable network to model these continuous changes over time effectively. Our results across multiple benchmarks demonstrate that 4D LangSplat attains precise and efficient results for both time-sensitive and time-agnostic open-vocabulary queries.

comment: CVPR 2025. Project Page: https://4d-langsplat.github.io

☆ Finetuning Generative Trajectory Model with Reinforcement Learning from Human Feedback

Derun Li, Jianwei Ren, Yue Wang, Xin Wen, Pengxiang Li, Leimeng Xu, Kun Zhan, Zhongpu Xia, Peng Jia, Xianpeng Lang, Ningyi Xu, Hang Zhao

comment: 10 pages, 5 figures

☆ Low Complexity Point Tracking of the Myocardium in 2D Echocardiography

Artem Chernyshov, John Nyberg, Vegard Holmstrøm, Md Abulkalam Azad, Bjørnar Grenne, Håvard Dalen, Svein Arne Aase, Lasse Lovstakken, Andreas Østvik

Deep learning methods for point tracking are applicable in 2D echocardiography, but do not yet take advantage of domain specifics that enable extremely fast and efficient configurations. We developed MyoTracker, a low-complexity architecture (0.3M parameters) for point tracking in echocardiography. It builds on the CoTracker2 architecture by simplifying its components and extending the temporal context to provide point predictions for the entire sequence in a single step. We applied MyoTracker to the right ventricular (RV) myocardium in RV-focused recordings and compared the results with those of CoTracker2 and EchoTracker, another specialized point tracking architecture for echocardiography. MyoTracker achieved the lowest average point trajectory error at 2.00 $\pm$ 0.53 mm. Calculating RV Free Wall Strain (RV FWS) using MyoTracker's point predictions resulted in a -0.3$\%$ bias with 95$\%$ limits of agreement from -6.1$\%$ to 5.4$\%$ compared to reference values from commercial software. This range falls within the interobserver variability reported in previous studies. The limits of agreement were wider for both CoTracker2 and EchoTracker, worse than the interobserver variability. At inference, MyoTracker used 67$\%$ less GPU memory than CoTracker2 and 84$\%$ less than EchoTracker on large sequences (100 frames). MyoTracker was 74 times faster during inference than CoTracker2 and 11 times faster than EchoTracker with our setup. Maintaining the entire sequence in the temporal context was the greatest contributor to MyoTracker's accuracy. Slight additional gains can be made by re-enabling iterative refinement, at the cost of longer processing time.

☆ Improving Medical Waste Classification with Hybrid Capsule Networks

Bennet van den Broek, Javad Pourmostafa Roshan Sharami

☆ Category Prompt Mamba Network for Nuclei Segmentation and Classification

Ye Zhang, Zijie Fang, Yifeng Wang, Lingbo Zhang, Xianchao Guan, Yongbing Zhang

Nuclei segmentation and classification provide an essential basis for tumor immune microenvironment analysis. The previous nuclei segmentation and classification models require splitting large images into smaller patches for training, leading to two significant issues. First, nuclei at the borders of adjacent patches often misalign during inference. Second, this patch-based approach significantly increases the model's training and inference time. Recently, Mamba has garnered attention for its ability to model large-scale images with linear time complexity and low memory consumption. It offers a promising solution for training nuclei segmentation and classification models on full-sized images. However, the Mamba orientation-based scanning method lacks account for category-specific features, resulting in sub-optimal performance in scenarios with imbalanced class distributions. To address these challenges, this paper introduces a novel scanning strategy based on category probability sorting, which independently ranks and scans features for each category according to confidence from high to low. This approach enhances the feature representation of uncertain samples and mitigates the issues caused by imbalanced distributions. Extensive experiments conducted on four public datasets demonstrate that our method outperforms state-of-the-art approaches, delivering superior performance in nuclei segmentation and classification tasks.

☆ RoCo-Sim: Enhancing Roadside Collaborative Perception through Foreground Simulation

Yuwen Du, Anning Hu, Zichen Chao, Yifan Lu, Junhao Ge, Genjia Liu, Weitao Wu, Lanjun Wang, Siheng Chen

Roadside Collaborative Perception refers to a system where multiple roadside units collaborate to pool their perceptual data, assisting vehicles in enhancing their environmental awareness. Existing roadside perception methods concentrate on model design but overlook data issues like calibration errors, sparse information, and multi-view consistency, leading to poor performance on recent published datasets. To significantly enhance roadside collaborative perception and address critical data issues, we present the first simulation framework RoCo-Sim for road-side collaborative perception. RoCo-Sim is capable of generating diverse, multi-view consistent simulated roadside data through dynamic foreground editing and full-scene style transfer of a single image. RoCo-Sim consists of four components: (1) Camera Extrinsic Optimization ensures accurate 3D to 2D projection for roadside cameras; (2) A novel Multi-View Occlusion-Aware Sampler (MOAS) determines the placement of diverse digital assets within 3D space; (3) DepthSAM innovatively models foreground-background relationships from single-frame fixed-view images, ensuring multi-view consistency of foreground; and (4) Scalable Post-Processing Toolkit generates more realistic and enriched scenes through style transfer and other enhancements. RoCo-Sim significantly improves roadside 3D object detection, outperforming SOTA methods by 83.74 on Rcooper-Intersection and 83.12 on TUMTraf-V2X for AP70. RoCo-Sim fills a critical gap in roadside perception simulation. Code and pre-trained models will be released soon: https://github.com/duyuwen-duen/RoCo-Sim

☆ RealGeneral: Unifying Visual Generation via Temporal In-Context Learning with Video Models

Yijing Lin, Mengqi Huang, Shuhan Zhuang, Zhendong Mao

☆ Architecture-Aware Minimization (A$^2$M): How to Find Flat Minima in Neural Architecture Search

Matteo Gambella, Fabrizio Pittorino, Manuel Roveri

comment: 22 pages, 11 figures, 3 tables

☆ Hyper3D: Efficient 3D Representation via Hybrid Triplane and Octree Feature for Enhanced 3D Shape Variational Auto-Encoders

Jingyu Guo, Sensen Gao, Jia-Wang Bian, Wanhu Sun, Heliang Zheng, Rongfei Jia, Mingming Gong

Recent 3D content generation pipelines often leverage Variational Autoencoders (VAEs) to encode shapes into compact latent representations, facilitating diffusion-based generation. Efficiently compressing 3D shapes while preserving intricate geometric details remains a key challenge. Existing 3D shape VAEs often employ uniform point sampling and 1D/2D latent representations, such as vector sets or triplanes, leading to significant geometric detail loss due to inadequate surface coverage and the absence of explicit 3D representations in the latent space. Although recent work explores 3D latent representations, their large scale hinders high-resolution encoding and efficient training. Given these challenges, we introduce Hyper3D, which enhances VAE reconstruction through efficient 3D representation that integrates hybrid triplane and octree features. First, we adopt an octree-based feature representation to embed mesh information into the network, mitigating the limitations of uniform point sampling in capturing geometric distributions along the mesh surface. Furthermore, we propose a hybrid latent space representation that integrates a high-resolution triplane with a low-resolution 3D grid. This design not only compensates for the lack of explicit 3D representations but also leverages a triplane to preserve high-resolution details. Experimental results demonstrate that Hyper3D outperforms traditional representations by reconstructing 3D shapes with higher fidelity and finer details, making it well-suited for 3D generation pipelines.

☆ HSEmotion Team at ABAW-8 Competition: Audiovisual Ambivalence/Hesitancy, Emotional Mimicry Intensity and Facial Expression Recognition CVPR 2025

Andrey V. Savchenko

This article presents our results for the eighth Affective Behavior Analysis in-the-Wild (ABAW) competition. We combine facial emotional descriptors extracted by pre-trained models, namely, our EmotiEffLib library, with acoustic features and embeddings of texts recognized from speech. The frame-level features are aggregated and fed into simple classifiers, e.g., multi-layered perceptron (feed-forward neural network with one hidden layer), to predict ambivalence/hesitancy and facial expressions. In the latter case, we also use the pre-trained facial expression recognition model to select high-score video frames and prevent their processing with a domain-specific video classifier. The video-level prediction of emotional mimicry intensity is implemented by simply aggregating frame-level features and training a multi-layered perceptron. Experimental results for three tasks from the ABAW challenge demonstrate that our approach significantly increases validation metrics compared to existing baselines.

comment: submitted to ABAW CVPR 2025 Workshop

☆ RoMA: Scaling up Mamba-based Foundation Models for Remote Sensing

Fengxiang Wang, Hongzhen Wang, Yulin Wang, Di Wang, Mingshuo Chen, Haiyan Zhao, Yangang Sun, Shuo Wang, Long Lan, Wenjing Yang, Jing Zhang

☆ CINEMA: Coherent Multi-Subject Video Generation via MLLM-Based Guidance

Yufan Deng, Xun Guo, Yizhi Wang, Jacob Zhiyuan Fang, Angtian Wang, Shenghai Yuan, Yiding Yang, Bo Liu, Haibin Huang, Chongyang Ma

☆ A Multimodal Fusion Model Leveraging MLP Mixer and Handcrafted Features-based Deep Learning Networks for Facial Palsy Detection PAKDD 2025

Heng Yim Nicole Oo, Min Hun Lee, Jeong Hoon Lim

comment: PAKDD 2025. arXiv admin note: text overlap with arXiv:2405.16496

☆ LUMOS: Language-Conditioned Imitation Learning with World Models ICRA

Iman Nematollahi, Branton DeMoss, Akshay L Chandra, Nick Hawes, Wolfram Burgard, Ingmar Posner

comment: Accepted at the 2025 IEEE International Conference on Robotics and Automation (ICRA)

☆ Piece it Together: Part-Based Concepting with IP-Priors

Elad Richardson, Kfir Goldberg, Yuval Alaluf, Daniel Cohen-Or

Advanced generative models excel at synthesizing images but often rely on text-based conditioning. Visual designers, however, often work beyond language, directly drawing inspiration from existing visual elements. In many cases, these elements represent only fragments of a potential concept-such as an uniquely structured wing, or a specific hairstyle-serving as inspiration for the artist to explore how they can come together creatively into a coherent whole. Recognizing this need, we introduce a generative framework that seamlessly integrates a partial set of user-provided visual components into a coherent composition while simultaneously sampling the missing parts needed to generate a plausible and complete concept. Our approach builds on a strong and underexplored representation space, extracted from IP-Adapter+, on which we train IP-Prior, a lightweight flow-matching model that synthesizes coherent compositions based on domain-specific priors, enabling diverse and context-aware generations. Additionally, we present a LoRA-based fine-tuning strategy that significantly improves prompt adherence in IP-Adapter+ for a given task, addressing its common trade-off between reconstruction quality and prompt adherence.

comment: Project page available at https://eladrich.github.io/PiT/

☆ ConceptGuard: Continual Personalized Text-to-Image Generation with Forgetting and Confusion Mitigation CVPR 2025

Zirun Guo, Tao Jin

comment: Accepted at CVPR 2025

☆ Do I look like a `cat.n.01` to you? A Taxonomy Image Generation Benchmark

Viktor Moskvoretskii, Alina Lobanova, Ekaterina Neminova, Chris Biemann, Alexander Panchenko, Irina Nikishina

This paper explores the feasibility of using text-to-image models in a zero-shot setup to generate images for taxonomy concepts. While text-based methods for taxonomy enrichment are well-established, the potential of the visual dimension remains unexplored. To address this, we propose a comprehensive benchmark for Taxonomy Image Generation that assesses models' abilities to understand taxonomy concepts and generate relevant, high-quality images. The benchmark includes common-sense and randomly sampled WordNet concepts, alongside the LLM generated predictions. The 12 models are evaluated using 9 novel taxonomy-related text-to-image metrics and human feedback. Moreover, we pioneer the use of pairwise evaluation with GPT-4 feedback for image generation. Experimental results show that the ranking of models differs significantly from standard T2I tasks. Playground-v2 and FLUX consistently outperform across metrics and subsets and the retrieval-based approach performs poorly. These findings highlight the potential for automating the curation of structured data resources.

comment: Labeled data and generated image Wordnet are published at https://huggingface.co/collections/VityaVitalich/generated-image-wordnet-67d2c868ff1414ec2f8e0d3d

☆ Object detection characteristics in a learning factory environment using YOLOv8

Toni Schneidereit, Stefan Gohrenz, Michael Breuß

☆ Enhancing Facial Privacy Protection via Weakening Diffusion Purification

Ali Salar, Qing Liu, Yingli Tian, Guoying Zhao

The rapid growth of social media has led to the widespread sharing of individual portrait images, which pose serious privacy risks due to the capabilities of automatic face recognition (AFR) systems for mass surveillance. Hence, protecting facial privacy against unauthorized AFR systems is essential. Inspired by the generation capability of the emerging diffusion models, recent methods employ diffusion models to generate adversarial face images for privacy protection. However, they suffer from the diffusion purification effect, leading to a low protection success rate (PSR). In this paper, we first propose learning unconditional embeddings to increase the learning capacity for adversarial modifications and then use them to guide the modification of the adversarial latent code to weaken the diffusion purification effect. Moreover, we integrate an identity-preserving structure to maintain structural consistency between the original and generated images, allowing human observers to recognize the generated image as having the same identity as the original. Extensive experiments conducted on two public datasets, i.e., CelebA-HQ and LADN, demonstrate the superiority of our approach. The protected faces generated by our method outperform those produced by existing facial privacy protection approaches in terms of transferability and natural appearance.

☆ DreamInsert: Zero-Shot Image-to-Video Object Insertion from A Single Image

Qi Zhao, Zhan Ma, Pan Zhou

Recent developments in generative diffusion models have turned many dreams into realities. For video object insertion, existing methods typically require additional information, such as a reference video or a 3D asset of the object, to generate the synthetic motion. However, inserting an object from a single reference photo into a target background video remains an uncharted area due to the lack of unseen motion information. We propose DreamInsert, which achieves Image-to-Video Object Insertion in a training-free manner for the first time. By incorporating the trajectory of the object into consideration, DreamInsert can predict the unseen object movement, fuse it harmoniously with the background video, and generate the desired video seamlessly. More significantly, DreamInsert is both simple and effective, achieving zero-shot insertion without end-to-end training or additional fine-tuning on well-designed image-video data pairs. We demonstrated the effectiveness of DreamInsert through a variety of experiments. Leveraging this capability, we present the first results for Image-to-Video object insertion in a training-free manner, paving exciting new directions for future content creation and synthesis. The code will be released soon.

☆ Generative Binary Memory: Pseudo-Replay Class-Incremental Learning on Binarized Embeddings

Yanis Basso-Bert, Anca Molnos, Romain Lemaire, William Guicquero, Antoine Dupret

☆ OSMa-Bench: Evaluating Open Semantic Mapping Under Varying Lighting Conditions

Maxim Popov, Regina Kurkova, Mikhail Iumanov, Jaafar Mahmoud, Sergey Kolyubin

comment: Project page: https://be2rlab.github.io/OSMa-Bench/

☆ IDEA: Inverted Text with Cooperative Deformable Aggregation for Multi-modal Object Re-Identification CVPR2025

Yuhao Wang, Yongfeng Lv, Pingping Zhang, Huchuan Lu

Multi-modal object Re-IDentification (ReID) aims to retrieve specific objects by utilizing complementary information from various modalities. However, existing methods focus on fusing heterogeneous visual features, neglecting the potential benefits of text-based semantic information. To address this issue, we first construct three text-enhanced multi-modal object ReID benchmarks. To be specific, we propose a standardized multi-modal caption generation pipeline for structured and concise text annotations with Multi-modal Large Language Models (MLLMs). Besides, current methods often directly aggregate multi-modal information without selecting representative local features, leading to redundancy and high complexity. To address the above issues, we introduce IDEA, a novel feature learning framework comprising the Inverted Multi-modal Feature Extractor (IMFE) and Cooperative Deformable Aggregation (CDA). The IMFE utilizes Modal Prefixes and an InverseNet to integrate multi-modal information with semantic guidance from inverted text. The CDA adaptively generates sampling positions, enabling the model to focus on the interplay between global features and discriminative local features. With the constructed benchmarks and the proposed modules, our framework can generate more robust multi-modal features under complex scenarios. Extensive experiments on three multi-modal object ReID benchmarks demonstrate the effectiveness of our proposed method.

comment: This work is accepted by CVPR2025. More modifications may be performed

☆ Towards Fast, Memory-based and Data-Efficient Vision-Language Policy

Haoxuan Li, Sixu Yan, Yuhan Li, Xinggang Wang

Vision Language Models (VLMs) pretrained on Internet-scale vision-language data have demonstrated the potential to transfer their knowledge to robotic learning. However, the existing paradigm encounters three critical challenges: (1) expensive inference cost resulting from large-scale model parameters, (2) frequent domain shifts caused by mismatched data modalities, and (3) limited capacity to handle past or future experiences. In this work, we propose LiteVLP, a lightweight, memory-based, and general-purpose vision-language policy generation model. LiteVLP is built upon a pre-trained 1B-parameter VLM and fine-tuned on a tiny-scale and conversation-style robotic dataset. Through extensive experiments, we demonstrate that LiteVLP outperforms state-of-the-art vision-language policy on VIMA-Bench, with minimal training time. Furthermore, LiteVLP exhibits superior inference speed while maintaining exceptional high accuracy. In long-horizon manipulation tasks, LiteVLP also shows remarkable memory ability, outperforming the best-performing baseline model by 18.8%. These results highlight LiteVLP as a promising model to integrating the intelligence of VLMs into robotic learning.

comment: 11 pages, 7 figures, 6 tables

☆ PS3C: An Ensemble-Based Two-Step Framework for Classification of Pep Smear Cell Images

Theo Di Piazza, Loic Boussel

Early detection of cervical cancer is crucial for improving patient outcomes and reducing mortality by identifying precancerous lesions as soon as possible. As a result, the use of pap smear screening has significantly increased, leading to a growing demand for automated tools that can assist cytologists managing their rising workload. To address this, the Pep Smear Cell Classification Challenge (PS3C) has been organized in association with ISBI in 2025. This project aims to promote the development of automated tools for pep smear images classification. The analyzed images are grouped into four categories: healthy, unhealthy, both, and rubbish images which are considered as unsuitable for diagnosis. In this work, we propose a two-stage ensemble approach: first, a neural network determines whether an image is rubbish or not. If not, a second neural network classifies the image as containing a healthy cell, an unhealthy cell, or both.

comment: 7 pages, 3 figures, Grand Challenge paper accepted at ISBI 2025

☆ 6D Object Pose Tracking in Internet Videos for Robotic Manipulation ICLR 2025

Georgy Ponimatkin, Martin Cífka, Tomáš Souček, Médéric Fourmy, Yann Labbé, Vladimir Petrik, Josef Sivic

We seek to extract a temporally consistent 6D pose trajectory of a manipulated object from an Internet instructional video. This is a challenging set-up for current 6D pose estimation methods due to uncontrolled capturing conditions, subtle but dynamic object motions, and the fact that the exact mesh of the manipulated object is not known. To address these challenges, we present the following contributions. First, we develop a new method that estimates the 6D pose of any object in the input image without prior knowledge of the object itself. The method proceeds by (i) retrieving a CAD model similar to the depicted object from a large-scale model database, (ii) 6D aligning the retrieved CAD model with the input image, and (iii) grounding the absolute scale of the object with respect to the scene. Second, we extract smooth 6D object trajectories from Internet videos by carefully tracking the detected objects across video frames. The extracted object trajectories are then retargeted via trajectory optimization into the configuration space of a robotic manipulator. Third, we thoroughly evaluate and ablate our 6D pose estimation method on YCB-V and HOPE-Video datasets as well as a new dataset of instructional videos manually annotated with approximate 6D object trajectories. We demonstrate significant improvements over existing state-of-the-art RGB 6D pose estimation methods. Finally, we show that the 6D object motion estimated from Internet videos can be transferred to a 7-axis robotic manipulator both in a virtual simulator as well as in a real world set-up. We also successfully apply our method to egocentric videos taken from the EPIC-KITCHENS dataset, demonstrating potential for Embodied AI applications.

comment: Accepted to ICLR 2025. Project page available at https://ponimatkin.github.io/wildpose/

☆ Eye on the Target: Eye Tracking Meets Rodent Tracking

Emil Mededovic, Yuli Wu, Henning Konermann, Marcin Kopaczka, Mareike Schulz, Rene Tolba, Johannes Stegmaier

Analyzing animal behavior from video recordings is crucial for scientific research, yet manual annotation remains labor-intensive and prone to subjectivity. Efficient segmentation methods are needed to automate this process while maintaining high accuracy. In this work, we propose a novel pipeline that utilizes eye-tracking data from Aria glasses to generate prompt points, which are then used to produce segmentation masks via a fast zero-shot segmentation model. Additionally, we apply post-processing to refine the prompts, leading to improved segmentation quality. Through our approach, we demonstrate that combining eye-tracking-based annotation with smart prompt refinement can enhance segmentation accuracy, achieving an improvement of 70.6% from 38.8 to 66.2 in the Jaccard Index for segmentation results in the rats dataset.

☆ CODEI: Resource-Efficient Task-Driven Co-Design of Perception and Decision Making for Mobile Robots Applied to Autonomous Vehicles

Dejan Milojevic, Gioele Zardini, Miriam Elser, Andrea Censi, Emilio Frazzoli

comment: 20 pages, 33 images, IEEE Transactions on Robotics

☆ VisualPRM: An Effective Process Reward Model for Multimodal Reasoning

Weiyun Wang, Zhangwei Gao, Lianjie Chen, Zhe Chen, Jinguo Zhu, Xiangyu Zhao, Yangzhou Liu, Yue Cao, Shenglong Ye, Xizhou Zhu, Lewei Lu, Haodong Duan, Yu Qiao, Jifeng Dai, Wenhai Wang

We introduce VisualPRM, an advanced multimodal Process Reward Model (PRM) with 8B parameters, which improves the reasoning abilities of existing Multimodal Large Language Models (MLLMs) across different model scales and families with Best-of-N (BoN) evaluation strategies. Specifically, our model improves the reasoning performance of three types of MLLMs and four different model scales. Even when applied to the highly capable InternVL2.5-78B, it achieves a 5.9-point improvement across seven multimodal reasoning benchmarks. Experimental results show that our model exhibits superior performance compared to Outcome Reward Models and Self-Consistency during BoN evaluation. To facilitate the training of multimodal PRMs, we construct a multimodal process supervision dataset VisualPRM400K using an automated data pipeline. For the evaluation of multimodal PRMs, we propose VisualProcessBench, a benchmark with human-annotated step-wise correctness labels, to measure the abilities of PRMs to detect erroneous steps in multimodal reasoning tasks. We hope that our work can inspire more future research and contribute to the development of MLLMs. Our model, data, and benchmark are released in https://internvl.github.io/blog/2025-03-13-VisualPRM/.

☆ MaterialMVP: Illumination-Invariant Material Generation via Multi-view PBR Diffusion

Zebin He, Mingxin Yang, Shuhui Yang, Yixuan Tang, Tao Wang, Kaihao Zhang, Guanying Chen, Yuhong Liu, Jie Jiang, Chunchao Guo, Wenhan Luo

Physically-based rendering (PBR) has become a cornerstone in modern computer graphics, enabling realistic material representation and lighting interactions in 3D scenes. In this paper, we present MaterialMVP, a novel end-to-end model for generating PBR textures from 3D meshes and image prompts, addressing key challenges in multi-view material synthesis. Our approach leverages Reference Attention to extract and encode informative latent from the input reference images, enabling intuitive and controllable texture generation. We also introduce a Consistency-Regularized Training strategy to enforce stability across varying viewpoints and illumination conditions, ensuring illumination-invariant and geometrically consistent results. Additionally, we propose Dual-Channel Material Generation, which separately optimizes albedo and metallic-roughness (MR) textures while maintaining precise spatial alignment with the input images through Multi-Channel Aligned Attention. Learnable material embeddings are further integrated to capture the distinct properties of albedo and MR. Experimental results demonstrate that our model generates PBR textures with realistic behavior across diverse lighting scenarios, outperforming existing methods in both consistency and quality for scalable 3D asset creation.

☆ MACS: Multi-source Audio-to-image Generation with Contextual Significance and Semantic Alignment

Hao Zhou, Xiaobao Guo, Yuzhe Zhu, Adams Wai-Kin Kong

Propelled by the breakthrough in deep generative models, audio-to-image generation has emerged as a pivotal cross-model task that converts complex auditory signals into rich visual representations. However, previous works only focus on single-source audio inputs for image generation, ignoring the multi-source characteristic in natural auditory scenes, thus limiting the performance in generating comprehensive visual content. To bridge this gap, a method called MACS is proposed to conduct multi-source audio-to-image generation. This is the first work that explicitly separates multi-source audio to capture the rich audio components before image generation. MACS is a two-stage method. In the first stage, multi-source audio inputs are separated by a weakly supervised method, where the audio and text labels are semantically aligned by casting into a common space using the large pre-trained CLAP model. We introduce a ranking loss to consider the contextual significance of the separated audio signals. In the second stage, efficient image generation is achieved by mapping the separated audio signals to the generation condition using only a trainable adapter and a MLP layer. We preprocess the LLP dataset as the first full multi-source audio-to-image generation benchmark. The experiments are conducted on multi-source, mixed-source, and single-source audio-to-image generation tasks. The proposed MACS outperforms the current state-of-the-art methods in 17 of the 21 evaluation indexes on all tasks and delivers superior visual quality. The code will be publicly available.

☆ VicaSplat: A Single Run is All You Need for 3D Gaussian Splatting and Camera Estimation from Unposed Video Frames

Zhiqi Li, Chengrui Dong, Yiming Chen, Zhangchi Huang, Peidong Liu

We present VicaSplat, a novel framework for joint 3D Gaussians reconstruction and camera pose estimation from a sequence of unposed video frames, which is a critical yet underexplored task in real-world 3D applications. The core of our method lies in a novel transformer-based network architecture. In particular, our model starts with an image encoder that maps each image to a list of visual tokens. All visual tokens are concatenated with additional inserted learnable camera tokens. The obtained tokens then fully communicate with each other within a tailored transformer decoder. The camera tokens causally aggregate features from visual tokens of different views, and further modulate them frame-wisely to inject view-dependent features. 3D Gaussian splats and camera pose parameters can then be estimated via different prediction heads. Experiments show that VicaSplat surpasses baseline methods for multi-view inputs, and achieves comparable performance to prior two-view approaches. Remarkably, VicaSplat also demonstrates exceptional cross-dataset generalization capability on the ScanNet benchmark, achieving superior performance without any fine-tuning. Project page: https://lizhiqi49.github.io/VicaSplat.

☆ EEdit : Rethinking the Spatial and Temporal Redundancy for Efficient Image Editing

Zexuan Yan, Yue Ma, Chang Zou, Wenteng Chen, Qifeng Chen, Linfeng Zhang

Inversion-based image editing is rapidly gaining momentum while suffering from significant computation overhead, hindering its application in real-time interactive scenarios. In this paper, we rethink that the redundancy in inversion-based image editing exists in both the spatial and temporal dimensions, such as the unnecessary computation in unedited regions and the redundancy in the inversion progress. To tackle these challenges, we propose a practical framework, named EEdit, to achieve efficient image editing. Specifically, we introduce three techniques to solve them one by one. For spatial redundancy, spatial locality caching is introduced to compute the edited region and its neighboring regions while skipping the unedited regions, and token indexing preprocessing is designed to further accelerate the caching. For temporal redundancy, inversion step skipping is proposed to reuse the latent for efficient editing. Our experiments demonstrate an average of 2.46 $\times$ acceleration without performance drop in a wide range of editing tasks including prompt-guided image editing, dragging and image composition. Our codes are available at https://github.com/yuriYanZeXuan/EEdit

comment: 17 pages

☆ A Multi-Modal Federated Learning Framework for Remote Sensing Image Classification

Barış Büyüktaş, Gencer Sumbul, Begüm Demir

Federated learning (FL) enables the collaborative training of deep neural networks across decentralized data archives (i.e., clients) without sharing the local data of the clients. Most of the existing FL methods assume that the data distributed across all clients is associated with the same data modality. However, remote sensing (RS) images present in different clients can be associated with diverse data modalities. The joint use of the multi-modal RS data can significantly enhance classification performance. To effectively exploit decentralized and unshared multi-modal RS data, our paper introduces a novel multi-modal FL framework for RS image classification problems. The proposed framework comprises three modules: 1) multi-modal fusion (MF); 2) feature whitening (FW); and 3) mutual information maximization (MIM). The MF module employs iterative model averaging to facilitate learning without accessing multi-modal training data on clients. The FW module aims to address the limitations of training data heterogeneity by aligning data distributions across clients. The MIM module aims to model mutual information by maximizing the similarity between images from different modalities. For the experimental analyses, we focus our attention on multi-label classification and pixel-based classification tasks in RS. The results obtained using two benchmark archives show the effectiveness of the proposed framework when compared to state-of-the-art algorithms in the literature. The code of the proposed framework will be available at https://git.tu-berlin.de/rsim/multi-modal-FL.

☆ Markerless Tracking-Based Registration for Medical Image Motion Correction

Luisa Neubig, Deirdre Larsen, Takeshi Ikuma, Markus Kopp, Melda Kunduk, Andreas M. Kist

Our study focuses on isolating swallowing dynamics from interfering patient motion in videofluoroscopy, an X-ray technique that records patients swallowing a radiopaque bolus. These recordings capture multiple motion sources, including head movement, anatomical displacements, and bolus transit. To enable precise analysis of swallowing physiology, we aim to eliminate distracting motion, particularly head movement, while preserving essential swallowing-related dynamics. Optical flow methods fail due to artifacts like flickering and instability, making them unreliable for distinguishing different motion groups. We evaluated markerless tracking approaches (CoTracker, PIPs++, TAP-Net) and quantified tracking accuracy in key medical regions of interest. Our findings show that even sparse tracking points generate morphing displacement fields that outperform leading registration methods such as ANTs, LDDMM, and VoxelMorph. To compare all approaches, we assessed performance using MSE and SSIM metrics post-registration. We introduce a novel motion correction pipeline that effectively removes disruptive motion while preserving swallowing dynamics and surpassing competitive registration techniques. Code will be available after review.

comment: Under review

☆ KVQ: Boosting Video Quality Assessment via Saliency-guided Local Perception

Yunpeng Qu, Kun Yuan, Qizhi Xie, Ming Sun, Chao Zhou, Jian Wang

Video Quality Assessment (VQA), which intends to predict the perceptual quality of videos, has attracted increasing attention. Due to factors like motion blur or specific distortions, the quality of different regions in a video varies. Recognizing the region-wise local quality within a video is beneficial for assessing global quality and can guide us in adopting fine-grained enhancement or transcoding strategies. Due to the heavy cost of annotating region-wise quality, the lack of ground truth constraints from relevant datasets further complicates the utilization of local perception. Inspired by the Human Visual System (HVS) that links global quality to the local texture of different regions and their visual saliency, we propose a Kaleidoscope Video Quality Assessment (KVQ) framework, which aims to effectively assess both saliency and local texture, thereby facilitating the assessment of global quality. Our framework extracts visual saliency and allocates attention using Fusion-Window Attention (FWA) while incorporating a Local Perception Constraint (LPC) to mitigate the reliance of regional texture perception on neighboring areas. KVQ obtains significant improvements across multiple scenarios on five VQA benchmarks compared to SOTA methods. Furthermore, to assess local perception, we establish a new Local Perception Visual Quality (LPVQ) dataset with region-wise annotations. Experimental results demonstrate the capability of KVQ in perceiving local distortions. KVQ models and the LPVQ dataset will be available at https://github.com/qyp2000/KVQ.

comment: 11 pages, 7 figures

☆ ROODI: Reconstructing Occluded Objects with Denoising Inpainters

Yeonjin Chang, Erqun Dong, Seunghyeon Seo, Nojun Kwak, Kwang Moo Yi

While the quality of novel-view images has improved dramatically with 3D Gaussian Splatting, extracting specific objects from scenes remains challenging. Isolating individual 3D Gaussian primitives for each object and handling occlusions in scenes remain far from being solved. We propose a novel object extraction method based on two key principles: (1) being object-centric by pruning irrelevant primitives; and (2) leveraging generative inpainting to compensate for missing observations caused by occlusions. For pruning, we analyze the local structure of primitives using K-nearest neighbors, and retain only relevant ones. For inpainting, we employ an off-the-shelf diffusion-based inpainter combined with occlusion reasoning, utilizing the 3D representation of the entire scene. Our findings highlight the crucial synergy between pruning and inpainting, both of which significantly enhance extraction performance. We evaluate our method on a standard real-world dataset and introduce a synthetic dataset for quantitative analysis. Our approach outperforms the state-of-the-art, demonstrating its effectiveness in object extraction from complex scenes.

comment: Project page: https://yeonjin-chang.github.io/ROODI/

☆ SVIP: Semantically Contextualized Visual Patches for Zero-Shot Learning

Zhi Chen, Zecheng Zhao, Jingcai Guo, Jingjing Li, Zi Huang

Zero-shot learning (ZSL) aims to recognize unseen classes without labeled training examples by leveraging class-level semantic descriptors such as attributes. A fundamental challenge in ZSL is semantic misalignment, where semantic-unrelated information involved in visual features introduce ambiguity to visual-semantic interaction. Unlike existing methods that suppress semantic-unrelated information post hoc either in the feature space or the model space, we propose addressing this issue at the input stage, preventing semantic-unrelated patches from propagating through the network. To this end, we introduce Semantically contextualized VIsual Patches (SVIP) for ZSL, a transformer-based framework designed to enhance visual-semantic alignment. Specifically, we propose a self-supervised patch selection mechanism that preemptively learns to identify semantic-unrelated patches in the input space. This is trained with the supervision from aggregated attention scores across all transformer layers, which estimate each patch's semantic score. As removing semantic-unrelated patches from the input sequence may disrupt object structure, we replace them with learnable patch embeddings. With initialization from word embeddings, we can ensure they remain semantically meaningful throughout feature extraction. Extensive experiments on ZSL benchmarks demonstrate that SVIP achieves state-of-the-art performance results while providing more interpretable and semantically rich feature representations.

comment: Pre-print

☆ Interpretable Image Classification via Non-parametric Part Prototype Learning

Zhijie Zhu, Lei Fan, Maurice Pagnucco, Yang Song

Classifying images with an interpretable decision-making process is a long-standing problem in computer vision. In recent years, Prototypical Part Networks has gained traction as an approach for self-explainable neural networks, due to their ability to mimic human visual reasoning by providing explanations based on prototypical object parts. However, the quality of the explanations generated by these methods leaves room for improvement, as the prototypes usually focus on repetitive and redundant concepts. Leveraging recent advances in prototype learning, we present a framework for part-based interpretable image classification that learns a set of semantically distinctive object parts for each class, and provides diverse and comprehensive explanations. The core of our method is to learn the part-prototypes in a non-parametric fashion, through clustering deep features extracted from foundation vision models that encode robust semantic information. To quantitatively evaluate the quality of explanations provided by ProtoPNets, we introduce Distinctiveness Score and Comprehensiveness Score. Through evaluation on CUB-200-2011, Stanford Cars and Stanford Dogs datasets, we show that our framework compares favourably against existing ProtoPNets while achieving better interpretability. Code is available at: https://github.com/zijizhu/proto-non-param.

☆ Unveiling the Invisible: Reasoning Complex Occlusions Amodally with AURA

Zhixuan Li, Hyunse Yoon, Sanghoon Lee, Weisi Lin

Amodal segmentation aims to infer the complete shape of occluded objects, even when the occluded region's appearance is unavailable. However, current amodal segmentation methods lack the capability to interact with users through text input and struggle to understand or reason about implicit and complex purposes. While methods like LISA integrate multi-modal large language models (LLMs) with segmentation for reasoning tasks, they are limited to predicting only visible object regions and face challenges in handling complex occlusion scenarios. To address these limitations, we propose a novel task named amodal reasoning segmentation, aiming to predict the complete amodal shape of occluded objects while providing answers with elaborations based on user text input. We develop a generalizable dataset generation pipeline and introduce a new dataset focusing on daily life scenarios, encompassing diverse real-world occlusions. Furthermore, we present AURA (Amodal Understanding and Reasoning Assistant), a novel model with advanced global and spatial-level designs specifically tailored to handle complex occlusions. Extensive experiments validate AURA's effectiveness on the proposed dataset. The code, model, and dataset will be publicly released.

comment: 11 pages, 5 figures, 5 tables

☆ CoStoDet-DDPM: Collaborative Training of Stochastic and Deterministic Models Improves Surgical Workflow Anticipation and Recognition

Kaixiang Yang, Xin Li, Qiang Li, Zhiwei Wang

Anticipating and recognizing surgical workflows are critical for intelligent surgical assistance systems. However, existing methods rely on deterministic decision-making, struggling to generalize across the large anatomical and procedural variations inherent in real-world surgeries.In this paper, we introduce an innovative framework that incorporates stochastic modeling through a denoising diffusion probabilistic model (DDPM) into conventional deterministic learning for surgical workflow analysis. At the heart of our approach is a collaborative co-training paradigm: the DDPM branch captures procedural uncertainties to enrich feature representations, while the task branch focuses on predicting surgical phases and instrument usage.Theoretically, we demonstrate that this mutual refinement mechanism benefits both branches: the DDPM reduces prediction errors in uncertain scenarios, and the task branch directs the DDPM toward clinically meaningful representations. Notably, the DDPM branch is discarded during inference, enabling real-time predictions without sacrificing accuracy.Experiments on the Cholec80 dataset show that for the anticipation task, our method achieves a 16% reduction in eMAE compared to state-of-the-art approaches, and for phase recognition, it improves the Jaccard score by 1.0%. Additionally, on the AutoLaparo dataset, our method achieves a 1.5% improvement in the Jaccard score for phase recognition, while also exhibiting robust generalization to patient-specific variations. Our code and weight are available at https://github.com/kk42yy/CoStoDet-DDPM.

☆ Singular Value Fine-tuning for Few-Shot Class-Incremental Learning

Zhiwu Wang, Yichen Wu, Renzhen Wang, Haokun Lin, Quanziang Wang, Qian Zhao, Deyu Meng

Class-Incremental Learning (CIL) aims to prevent catastrophic forgetting of previously learned classes while sequentially incorporating new ones. The more challenging Few-shot CIL (FSCIL) setting further complicates this by providing only a limited number of samples for each new class, increasing the risk of overfitting in addition to standard CIL challenges. While catastrophic forgetting has been extensively studied, overfitting in FSCIL, especially with large foundation models, has received less attention. To fill this gap, we propose the Singular Value Fine-tuning for FSCIL (SVFCL) and compared it with existing approaches for adapting foundation models to FSCIL, which primarily build on Parameter Efficient Fine-Tuning (PEFT) methods like prompt tuning and Low-Rank Adaptation (LoRA). Specifically, SVFCL applies singular value decomposition to the foundation model weights, keeping the singular vectors fixed while fine-tuning the singular values for each task, and then merging them. This simple yet effective approach not only alleviates the forgetting problem but also mitigates overfitting more effectively while significantly reducing trainable parameters. Extensive experiments on four benchmark datasets, along with visualizations and ablation studies, validate the effectiveness of SVFCL. The code will be made available.

comment: 12 pages, 8 figures

☆ MouseGPT: A Large-scale Vision-Language Model for Mouse Behavior Analysis

Teng Xu, Taotao Zhou, Youjia Wang, Peng Yang, Simin Tang, Kuixiang Shao, Zifeng Tang, Yifei Liu, Xinyuan Chen, Hongshuang Wang, Xiaohui Wang, Huoqing Luo, Jingya Wang, Ji Hu, Jingyi Yu

Analyzing animal behavior is crucial in advancing neuroscience, yet quantifying and deciphering its intricate dynamics remains a significant challenge. Traditional machine vision approaches, despite their ability to detect spontaneous behaviors, fall short due to limited interpretability and reliance on manual labeling, which restricts the exploration of the full behavioral spectrum. Here, we introduce MouseGPT, a Vision-Language Model (VLM) that integrates visual cues with natural language to revolutionize mouse behavior analysis. Built upon our first-of-its-kind dataset - incorporating pose dynamics and open-vocabulary behavioral annotations across over 42 million frames of diverse psychiatric conditions - MouseGPT provides a novel, context-rich method for comprehensive behavior interpretation. Our holistic analysis framework enables detailed behavior profiling, clustering, and novel behavior discovery, offering deep insights without the need for labor - intensive manual annotation. Evaluations reveal that MouseGPT surpasses existing models in precision, adaptability, and descriptive richness, positioning it as a transformative tool for ethology and for unraveling complex behavioral dynamics in animal models.

comment: 53 pages, 5 figures, 7 extended figures

☆ TARS: Traffic-Aware Radar Scene Flow Estimation

Jialong Wu, Marco Braun, Dominic Spata, Matthias Rottmann

Scene flow provides crucial motion information for autonomous driving. Recent LiDAR scene flow models utilize the rigid-motion assumption at the instance level, assuming objects are rigid bodies. However, these instance-level methods are not suitable for sparse radar point clouds. In this work, we present a novel $\textbf{T}$raffic-$\textbf{A}$ware $\textbf{R}$adar $\textbf{S}$cene flow estimation method, named $\textbf{TARS}$, which utilizes the motion rigidity at the traffic level. To address the challenges in radar scene flow, we perform object detection and scene flow jointly and boost the latter. We incorporate the feature map from the object detector, trained with detection losses, to make radar scene flow aware of the environment and road users. Therefrom, we construct a Traffic Vector Field (TVF) in the feature space, enabling a holistic traffic-level scene understanding in our scene flow branch. When estimating the scene flow, we consider both point-level motion cues from point neighbors and traffic-level consistency of rigid motion within the space. TARS outperforms the state of the art on a proprietary dataset and the View-of-Delft dataset, improving the benchmarks by 23% and 15%, respectively.

☆ LVAgent: Long Video Understanding by Multi-Round Dynamical Collaboration of MLLM Agents

Boyu Chen, Zhengrong Yue, Siran Chen, Zikang Wang, Yang Liu, Peng Li, Yali Wang

Existing Multimodal Large Language Models (MLLMs) encounter significant challenges in modeling the temporal context within long videos. Currently, mainstream Agent-based methods use external tools (e.g., search engine, memory banks, OCR, retrieval models) to assist a single MLLM in answering long video questions. Despite such tool-based support, a solitary MLLM still offers only a partial understanding of long videos, resulting in limited performance. In order to better address long video tasks, we introduce LVAgent, the first framework enabling multi-round dynamic collaboration of MLLM agents in long video understanding. Our methodology consists of four key steps: 1. Selection: We pre-select appropriate agents from the model library to form optimal agent teams based on different tasks. 2. Perception: We design an effective retrieval scheme for long videos, improving the coverage of critical temporal segments while maintaining computational efficiency. 3. Action: Agents answer long video-related questions and exchange reasons. 4. Reflection: We evaluate the performance of each agent in each round of discussion and optimize the agent team for dynamic collaboration. The agents iteratively refine their answers by multi-round dynamical collaboration of MLLM agents. LVAgent is the first agent system method that outperforms all closed-source models (including GPT-4o) and open-source models (including InternVL-2.5 and Qwen2-VL) in the long video understanding tasks. Our LVAgent achieves an accuracy of 80% on four mainstream long video understanding tasks. Notably, on the LongVideoBench dataset, LVAgent improves accuracy by up to 14.3% compared with SOTA.

☆ ST-FlowNet: An Efficient Spiking Neural Network for Event-Based Optical Flow Estimation

Hongze Sun, Jun Wang, Wuque Cai, Duo Chen, Qianqian Liao, Jiayi He, Yan Cui, Dezhong Yao, Daqing Guo

Spiking Neural Networks (SNNs) have emerged as a promising tool for event-based optical flow estimation tasks due to their ability to leverage spatio-temporal information and low-power capabilities. However, the performance of SNN models is often constrained, limiting their application in real-world scenarios. In this work, we address this gap by proposing a novel neural network architecture, ST-FlowNet, specifically tailored for optical flow estimation from event-based data. The ST-FlowNet architecture integrates ConvGRU modules to facilitate cross-modal feature augmentation and temporal alignment of the predicted optical flow, improving the network's ability to capture complex motion dynamics. Additionally, to overcome the challenges associated with training SNNs, we introduce a novel approach to derive SNN models from pre-trained artificial neural networks (ANNs) through ANN-to-SNN conversion or our proposed BISNN method. Notably, the BISNN method alleviates the complexities involved in biological parameter selection, further enhancing the robustness of SNNs in optical flow estimation tasks. Extensive evaluations on three benchmark event-based datasets demonstrate that the SNN-based ST-FlowNet model outperforms state-of-the-art methods, delivering superior performance in accurate optical flow estimation across a diverse range of dynamic visual scenes. Furthermore, the inherent energy efficiency of SNN models is highlighted, establishing a compelling advantage for their practical deployment. Overall, our work presents a novel framework for optical flow estimation using SNNs and event-based data, contributing to the advancement of neuromorphic vision applications.

comment: 12 pages, 5 figures, 5 tables; This work has been submitted for possible publication

☆ Robustness Tokens: Towards Adversarial Robustness of Transformers ECCV

Brian Pulfer, Yury Belousov, Slava Voloshynovskiy

comment: This paper has been accepted for publication at the European Conference on Computer Vision (ECCV), 2024

☆ Through the Magnifying Glass: Adaptive Perception Magnification for Hallucination-Free VLM Decoding

Shunqi Mao, Chaoyi Zhang, Weidong Cai

comment: 19 pages, 5 figures, 9 tables

☆ GS-SDF: LiDAR-Augmented Gaussian Splatting and Neural SDF for Geometrically Consistent Rendering and Reconstruction

Jianheng Liu, Yunfei Wan, Bowen Wang, Chunran Zheng, Jiarong Lin, Fu Zhang

Digital twins are fundamental to the development of autonomous driving and embodied artificial intelligence. However, achieving high-granularity surface reconstruction and high-fidelity rendering remains a challenge. Gaussian splatting offers efficient photorealistic rendering but struggles with geometric inconsistencies due to fragmented primitives and sparse observational data in robotics applications. Existing regularization methods, which rely on render-derived constraints, often fail in complex environments. Moreover, effectively integrating sparse LiDAR data with Gaussian splatting remains challenging. We propose a unified LiDAR-visual system that synergizes Gaussian splatting with a neural signed distance field. The accurate LiDAR point clouds enable a trained neural signed distance field to offer a manifold geometry field, This motivates us to offer an SDF-based Gaussian initialization for physically grounded primitive placement and a comprehensive geometric regularization for geometrically consistent rendering and reconstruction. Experiments demonstrate superior reconstruction accuracy and rendering quality across diverse trajectories. To benefit the community, the codes will be released at https://github.com/hku-mars/GS-SDF.

♻ ☆ Complexity Experts are Task-Discriminative Learners for Any Image Restoration CVPR 2025

Eduard Zamfir, Zongwei Wu, Nancy Mehta, Yuedong Tan, Danda Pani Paudel, Yulun Zhang, Radu Timofte

Recent advancements in all-in-one image restoration models have revolutionized the ability to address diverse degradations through a unified framework. However, parameters tied to specific tasks often remain inactive for other tasks, making mixture-of-experts (MoE) architectures a natural extension. Despite this, MoEs often show inconsistent behavior, with some experts unexpectedly generalizing across tasks while others struggle within their intended scope. This hinders leveraging MoEs' computational benefits by bypassing irrelevant experts during inference. We attribute this undesired behavior to the uniform and rigid architecture of traditional MoEs. To address this, we introduce ``complexity experts" -- flexible expert blocks with varying computational complexity and receptive fields. A key challenge is assigning tasks to each expert, as degradation complexity is unknown in advance. Thus, we execute tasks with a simple bias toward lower complexity. To our surprise, this preference effectively drives task-specific allocation, assigning tasks to experts with the appropriate complexity. Extensive experiments validate our approach, demonstrating the ability to bypass irrelevant experts during inference while maintaining superior performance. The proposed MoCE-IR model outperforms state-of-the-art methods, affirming its efficiency and practical applicability. The source code and models are publicly available at \href{https://eduardzamfir.github.io/moceir/}{\texttt{eduardzamfir.github.io/MoCE-IR/}}

comment: Accepted at CVPR 2025

♻ ☆ BIMBA: Selective-Scan Compression for Long-Range Video Question Answering CVPR 2025

Md Mohaiminul Islam, Tushar Nagarajan, Huiyu Wang, Gedas Bertasius, Lorenzo Torresani

Video Question Answering (VQA) in long videos poses the key challenge of extracting relevant information and modeling long-range dependencies from many redundant frames. The self-attention mechanism provides a general solution for sequence modeling, but it has a prohibitive cost when applied to a massive number of spatiotemporal tokens in long videos. Most prior methods rely on compression strategies to lower the computational cost, such as reducing the input length via sparse frame sampling or compressing the output sequence passed to the large language model (LLM) via space-time pooling. However, these naive approaches over-represent redundant information and often miss salient events or fast-occurring space-time patterns. In this work, we introduce BIMBA, an efficient state-space model to handle long-form videos. Our model leverages the selective scan algorithm to learn to effectively select critical information from high-dimensional video and transform it into a reduced token sequence for efficient LLM processing. Extensive experiments demonstrate that BIMBA achieves state-of-the-art accuracy on multiple long-form VQA benchmarks, including PerceptionTest, NExT-QA, EgoSchema, VNBench, LongVideoBench, and Video-MME. Code, and models are publicly available at https://sites.google.com/view/bimba-mllm.

comment: Accepted by CVPR 2025

♻ ☆ Tiled Diffusion

Or Madar, Ohad Fried

Image tiling -- the seamless connection of disparate images to create a coherent visual field -- is crucial for applications such as texture creation, video game asset development, and digital art. Traditionally, tiles have been constructed manually, a method that poses significant limitations in scalability and flexibility. Recent research has attempted to automate this process using generative models. However, current approaches primarily focus on tiling textures and manipulating models for single-image generation, without inherently supporting the creation of multiple interconnected tiles across diverse domains. This paper presents Tiled Diffusion, a novel approach that extends the capabilities of diffusion models to accommodate the generation of cohesive tiling patterns across various domains of image synthesis that require tiling. Our method supports a wide range of tiling scenarios, from self-tiling to complex many-to-many connections, enabling seamless integration of multiple images. Tiled Diffusion automates the tiling process, eliminating the need for manual intervention and enhancing creative possibilities in various applications, such as seamlessly tiling of existing images, tiled texture creation, and 360$^\circ$ synthesis.

comment: Please visit our website for more information and the code: https://madaror.github.io/tiled-diffusion.github.io/

♻ ☆ ACDiT: Interpolating Autoregressive Conditional Modeling and Diffusion Transformer

Jinyi Hu, Shengding Hu, Yuxuan Song, Yufei Huang, Mingxuan Wang, Hao Zhou, Zhiyuan Liu, Wei-Ying Ma, Maosong Sun

We present ACDiT, a novel Autoregressive blockwise Conditional Diffusion Transformer, that innovatively combines autoregressive and diffusion paradigms for modeling continuous visual information. By introducing a block-wise autoregressive unit, ACDiT offers a flexible interpolation between token-wise autoregression and full-sequence diffusion, bypassing the limitations of discrete tokenization. The generation of each block is formulated as a conditional diffusion process, conditioned on prior blocks. ACDiT is easy to implement, as simple as creating a Skip-Causal Attention Mask (SCAM) on standard diffusion transformer during training. During inference, the process iterates between diffusion denoising and autoregressive decoding that can make full use of KV-Cache. We show that ACDiT performs best among all autoregressive baselines under similar model scales on image and video generation tasks. We also demonstrate that benefiting from autoregressive modeling, pretrained ACDiT can be transferred in visual understanding tasks despite being trained with the diffusion objective. The analysis of the trade-off between autoregressive modeling and diffusion demonstrates the potential of ACDiT to be used in long-horizon visual generation tasks. We hope that ACDiT offers a novel perspective on visual autoregressive generation and unlocks new avenues for unified models.

♻ ☆ Video Super-Resolution: All You Need is a Video Diffusion Model

Zhihao Zhan, Wang Pang, Xiang Zhu, Yechao Bai

♻ ☆ UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training

Jiawei Qin, Xucong Zhang, Yusuke Sugano

Despite decades of research on data collection and model architectures, current gaze estimation models encounter significant challenges in generalizing across diverse data domains. Recent advances in self-supervised pre-training have shown remarkable performances in generalization across various vision tasks. However, their effectiveness in gaze estimation remains unexplored. We propose UniGaze, for the first time, leveraging large-scale in-the-wild facial datasets for gaze estimation through self-supervised pre-training. Through systematic investigation, we clarify critical factors that are essential for effective pretraining in gaze estimation. Our experiments reveal that self-supervised approaches designed for semantic tasks fail when applied to gaze estimation, while our carefully designed pre-training pipeline consistently improves cross-domain performance. Through comprehensive experiments of challenging cross-dataset evaluation and novel protocols including leave-one-dataset-out and joint-dataset settings, we demonstrate that UniGaze significantly improves generalization across multiple data domains while minimizing reliance on costly labeled data. source code and model are available at https://github.com/ut-vision/UniGaze.

♻ ☆ Fast MRI for All: Bridging Equity Gaps via Training without Raw Data Access

Yaşar Utku Alçalar, Merve Gülle, Mehmet Akçakaya

♻ ☆ SpotLight: Shadow-Guided Object Relighting via Diffusion

Frédéric Fortier-Chouinard, Zitian Zhang, Louis-Etienne Messier, Mathieu Garon, Anand Bhattad, Jean-François Lalonde

Recent work has shown that diffusion models can serve as powerful neural rendering engines that can be leveraged for inserting virtual objects into images. However, unlike typical physics-based renderers, these neural rendering engines are limited by the lack of manual control over the lighting, which is often essential for improving or personalizing the desired image outcome. In this paper, we show that precise lighting control can be achieved for object relighting simply by providing a coarse shadow of the object. Indeed, we show that injecting only the desired shadow of the object into a pre-trained diffusion-based neural renderer enables it to accurately shade the object according to the desired light position, while properly harmonizing the object (and its shadow) within the target background image. Our method, SpotLight, leverages existing neural rendering approaches and achieves controllable relighting results with no additional training. We show that SpotLight achieves superior object compositing results, both quantitatively and perceptually, as confirmed by a user study, outperforming existing diffusion-based models specifically designed for relighting. We also demonstrate other applications, such as hand-scribbling shadows and full-image relighting, demonstrating its versatility.

comment: Project page: https://lvsn.github.io/spotlight

♻ ☆ WonderVerse: Extendable 3D Scene Generation with Video Generative Models

Hao Feng, Zhi Zuo, Jia-Hui Pan, Ka-Hei Hui, Yihua Shao, Qi Dou, Wei Xie, Zhengzhe Liu

We introduce \textit{WonderVerse}, a simple but effective framework for generating extendable 3D scenes. Unlike existing methods that rely on iterative depth estimation and image inpainting, often leading to geometric distortions and inconsistencies, WonderVerse leverages the powerful world-level priors embedded within video generative foundation models to create highly immersive and geometrically coherent 3D environments. Furthermore, we propose a new technique for controllable 3D scene extension to substantially increase the scale of the generated environments. Besides, we introduce a novel abnormal sequence detection module that utilizes camera trajectory to address geometric inconsistency in the generated videos. Finally, WonderVerse is compatible with various 3D reconstruction methods, allowing both efficient and high-quality generation. Extensive experiments on 3D scene generation demonstrate that our WonderVerse, with an elegant and simple pipeline, delivers extendable and highly-realistic 3D scenes, markedly outperforming existing works that rely on more complex architectures.

♻ ☆ HecVL: Hierarchical Video-Language Pretraining for Zero-shot Surgical Phase Recognition MICCAI2024

Kun Yuan, Vinkle Srivastav, Nassir Navab, Nicolas Padoy

comment: Accepted by MICCAI2024

♻ ☆ Procedure-Aware Surgical Video-language Pretraining with Hierarchical Knowledge Augmentation NeurIPS 2024

Kun Yuan, Vinkle Srivastav, Nassir Navab, Nicolas Padoy

comment: Accepted at the 38th Conference on Neural Information Processing Systems (NeurIPS 2024 Spolight)

♻ ☆ Making Every Frame Matter: Continuous Activity Recognition in Streaming Video via Adaptive Video Context Modeling

Hao Wu, Donglin Bai, Shiqi Jiang, Qianxi Zhang, Yifan Yang, Xin Ding, Ting Cao, Yunxin Liu, Fengyuan Xu

Video activity recognition has become increasingly important in robots and embodied AI. Recognizing continuous video activities poses considerable challenges due to the fast expansion of streaming video, which contains multi-scale and untrimmed activities. We introduce a novel system, CARS, to overcome these issues through adaptive video context modeling. Adaptive video context modeling refers to selectively maintaining activity-related features in temporal and spatial dimensions. CARS has two key designs. The first is an activity spatial feature extraction by eliminating irrelevant visual features while maintaining recognition accuracy. The second is an activity-aware state update introducing dynamic adaptability to better preserve the video context for multi-scale activity recognition. Our CARS runs at speeds $>$30 FPS on typical edge devices and outperforms all baselines by 1.2\% to 79.7\% in accuracy. Moreover, we explore applying CARS to a large video model as a video encoder. Experimental results show that our CARS can result in a 0.46-point enhancement (on a 5-point scale) on the in-distribution video activity dataset, and an improvement ranging from 1.19\% to 4\% on zero-shot video activity datasets.

♻ ☆ Meissonic: Revitalizing Masked Generative Transformers for Efficient High-Resolution Text-to-Image Synthesis ICLR 2025

Jinbin Bai, Tian Ye, Wei Chow, Enxin Song, Xiangtai Li, Zhen Dong, Lei Zhu, Shuicheng Yan

We present Meissonic, which elevates non-autoregressive masked image modeling (MIM) text-to-image to a level comparable with state-of-the-art diffusion models like SDXL. By incorporating a comprehensive suite of architectural innovations, advanced positional encoding strategies, and optimized sampling conditions, Meissonic substantially improves MIM's performance and efficiency. Additionally, we leverage high-quality training data, integrate micro-conditions informed by human preference scores, and employ feature compression layers to further enhance image fidelity and resolution. Our model not only matches but often exceeds the performance of existing models like SDXL in generating high-quality, high-resolution images. Extensive experiments validate Meissonic's capabilities, demonstrating its potential as a new standard in text-to-image synthesis. We release a model checkpoint capable of producing $1024 \times 1024$ resolution images.

comment: Accepted to ICLR 2025. Codes and Supplementary Material: https://github.com/viiika/Meissonic

♻ ☆ MVGSR: Multi-View Consistency Gaussian Splatting for Robust Surface Reconstruction

Chenfeng Hou, Qi Xun Yeo, Mengqi Guo, Yongxin Su, Yanyan Li, Gim Hee Lee

3D Gaussian Splatting (3DGS) has gained significant attention for its high-quality rendering capabilities, ultra-fast training, and inference speeds. However, when we apply 3DGS to surface reconstruction tasks, especially in environments with dynamic objects and distractors, the method suffers from floating artifacts and color errors due to inconsistency from different viewpoints. To address this challenge, we propose Multi-View Consistency Gaussian Splatting for the domain of Robust Surface Reconstruction (\textbf{MVGSR}), which takes advantage of lightweight Gaussian models and a {heuristics-guided distractor masking} strategy for robust surface reconstruction in non-static environments. Compared to existing methods that rely on MLPs for distractor segmentation strategies, our approach separates distractors from static scene elements by comparing multi-view feature consistency, allowing us to obtain precise distractor masks early in training. Furthermore, we introduce a pruning measure based on multi-view contributions to reset transmittance, effectively reducing floating artifacts. Finally, a multi-view consistency loss is applied to achieve high-quality performance in surface reconstruction tasks. Experimental results demonstrate that MVGSR achieves competitive geometric accuracy and rendering fidelity compared to the state-of-the-art surface reconstruction algorithms. More information is available on our project page (https://mvgsr.github.io).

comment: project page https://mvgsr.github.io

♻ ☆ COMBO: Compositional World Models for Embodied Multi-Agent Cooperation ICLR 2025

Hongxin Zhang, Zeyuan Wang, Qiushi Lyu, Zheyuan Zhang, Sunli Chen, Tianmin Shu, Behzad Dariush, Kwonjoon Lee, Yilun Du, Chuang Gan

comment: Published at ICLR 2025. 24 pages. The first three authors contributed equally

♻ ☆ EMOVA: Empowering Language Models to See, Hear and Speak with Vivid Emotions CVPR 2025

Kai Chen, Yunhao Gou, Runhui Huang, Zhili Liu, Daxin Tan, Jing Xu, Chunwei Wang, Yi Zhu, Yihan Zeng, Kuo Yang, Dingdong Wang, Kun Xiang, Haoyuan Li, Haoli Bai, Jianhua Han, Xiaohui Li, Weike Jin, Nian Xie, Yu Zhang, James T. Kwok, Hengshuang Zhao, Xiaodan Liang, Dit-Yan Yeung, Xiao Chen, Zhenguo Li, Wei Zhang, Qun Liu, Jun Yao, Lanqing Hong, Lu Hou, Hang Xu

GPT-4o, an omni-modal model that enables vocal conversations with diverse emotions and tones, marks a milestone for omni-modal foundation models. However, empowering Large Language Models to perceive and generate images, texts, and speeches end-to-end with publicly available data remains challenging for the open-source community. Existing vision-language models rely on external tools for speech processing, while speech-language models still suffer from limited or totally without vision-understanding capabilities. To address this gap, we propose the EMOVA (EMotionally Omni-present Voice Assistant), to enable Large Language Models with end-to-end speech abilities while maintaining the leading vision-language performance. With a semantic-acoustic disentangled speech tokenizer, we surprisingly notice that omni-modal alignment can further enhance vision-language and speech abilities compared with the bi-modal aligned counterparts. Moreover, a lightweight style module is introduced for the flexible speech style controls including emotions and pitches. For the first time, EMOVA achieves state-of-the-art performance on both the vision-language and speech benchmarks, and meanwhile, supporting omni-modal spoken dialogue with vivid emotions.

comment: Accepted by CVPR 2025. Project Page: https://emova-ollm.github.io/

♻ ☆ SplatAD: Real-Time Lidar and Camera Rendering with 3D Gaussian Splatting for Autonomous Driving

Georg Hess, Carl Lindström, Maryam Fatemi, Christoffer Petersson, Lennart Svensson

Ensuring the safety of autonomous robots, such as self-driving vehicles, requires extensive testing across diverse driving scenarios. Simulation is a key ingredient for conducting such testing in a cost-effective and scalable way. Neural rendering methods have gained popularity, as they can build simulation environments from collected logs in a data-driven manner. However, existing neural radiance field (NeRF) methods for sensor-realistic rendering of camera and lidar data suffer from low rendering speeds, limiting their applicability for large-scale testing. While 3D Gaussian Splatting (3DGS) enables real-time rendering, current methods are limited to camera data and are unable to render lidar data essential for autonomous driving. To address these limitations, we propose SplatAD, the first 3DGS-based method for realistic, real-time rendering of dynamic scenes for both camera and lidar data. SplatAD accurately models key sensor-specific phenomena such as rolling shutter effects, lidar intensity, and lidar ray dropouts, using purpose-built algorithms to optimize rendering efficiency. Evaluation across three autonomous driving datasets demonstrates that SplatAD achieves state-of-the-art rendering quality with up to +2 PSNR for NVS and +3 PSNR for reconstruction while increasing rendering speed over NeRF-based methods by an order of magnitude. See https://research.zenseact.com/publications/splatad/ for our project page.

♻ ☆ PEMF-VTO: Point-Enhanced Video Virtual Try-on via Mask-free Paradigm

Tianyu Chang, Xiaohao Chen, Zhichao Wei, Xuanpu Zhang, Qing-Guo Chen, Weihua Luo, Peipei Song, Xun Yang

♻ ☆ Semi-supervised Semantic Segmentation for Remote Sensing Images via Multi-scale Uncertainty Consistency and Cross-Teacher-Student Attention

Shanwen Wang, Xin Sun, Changrui Chen, Danfeng Hong, Jungong Han

♻ ☆ Are you Struggling? Dataset and Baselines for Struggle Determination in Assembly Videos

Shijia Feng, Michael Wray, Brian Sullivan, Youngkyoon Jang, Casimir Ludwig, Iain Gilchrist, Walterio Mayol-Cuevas

Determining when people are struggling from video enables a finer-grained understanding of actions and opens opportunities for building intelligent support visual interfaces. In this paper, we present a new dataset with three assembly activities and corresponding performance baselines for the determination of struggle from video. Three real-world problem-solving activities including assembling plumbing pipes (Pipes-Struggle), pitching camping tents (Tent-Struggle) and solving the Tower of Hanoi puzzle (Tower-Struggle) are introduced. Video segments were scored w.r.t. the level of struggle as perceived by annotators using a forced choice 4-point scale. Each video segment was annotated by a single expert annotator in addition to crowd-sourced annotations. The dataset is the first struggle annotation dataset and contains 5.1 hours of video and 725,100 frames from 73 participants in total. We evaluate three decision-making tasks: struggle classification, struggle level regression, and struggle label distribution learning. We provide baseline results for each of the tasks utilising several mainstream deep neural networks, along with an ablation study and visualisation of results. Our work is motivated toward assistive systems that analyze struggle, support users during manual activities and encourage learning, as well as other video understanding competencies.

♻ ☆ Reasoning to Attend: Try to Understand How Token Works CVPR 2025

Rui Qian, Xin Yin, Dejing Dou

Current Large Multimodal Models (LMMs) empowered visual grounding typically rely on $\texttt{}$ tokens as a text prompt to jointly optimize the vision-language model (e.g., LLaVA) and the downstream task-specific model (e.g., SAM). However, we observe that little research has looked into how it works.In this work, we first visualize the similarity maps, which are obtained by computing the semantic similarity between the $\texttt{}$ token and the image token embeddings derived from the last hidden layer in both the LLaVA encoder and SAM decoder. Intriguingly, we have found that a striking consistency holds in terms of activation responses in the similarity map, which reveals that what the $\texttt{}$ token contributes to is semantic similarity within image-text pairs. Specifically, the $\texttt{}$ token, a placeholder expanded in text vocabulary, extensively queries among individual tokenized image patches to match the semantics of an object from text to the paired image, while the Large Language Models (LLMs) are being fine-tuned. Upon the above findings, we present READ, which facilitates LMMs' resilient $\textbf{REA}$soning capability of where to atten$\textbf{D}$ under the guidance of highly activated points borrowed from similarity maps. Remarkably, READ features an intuitive design, Similarity as Points module (SasP), which can be seamlessly applied to $\texttt{}$-like paradigms in a plug-and-play fashion. Also, extensive experiments have been conducted on ReasonSeg and RefCOCO(+/g) datasets. To validate whether READ suffers from catastrophic forgetting of previous skills after fine-tuning, we further assess its generation ability on an augmented FP-RefCOCO(+/g) dataset. All codes and models are publicly available at https://github.com/rui-qian/READ.

comment: This work has been accepted to CVPR 2025, please refer to https://github.com/rui-qian/READ

♻ ☆ Exploring a Multimodal Fusion-based Deep Learning Network for Detecting Facial Palsy IJCAI 2024

Heng Yim Nicole Oo, Min Hun Lee, Jeong Hoon Lim

comment: IJCAI 2024 4th AI for Ageless Aging Workshop (AIAA)

♻ ☆ Towards Generalizable Scene Change Detection CVPR 2025

Jaewoo Kim, Uehwan Kim

comment: Camera-ready version. Accepted to CVPR 2025

♻ ☆ RewardSDS: Aligning Score Distillation via Reward-Weighted Sampling

Itay Chachy, Guy Yariv, Sagie Benaim

Score Distillation Sampling (SDS) has emerged as an effective technique for leveraging 2D diffusion priors for tasks such as text-to-3D generation. While powerful, SDS struggles with achieving fine-grained alignment to user intent. To overcome this, we introduce RewardSDS, a novel approach that weights noise samples based on alignment scores from a reward model, producing a weighted SDS loss. This loss prioritizes gradients from noise samples that yield aligned high-reward output. Our approach is broadly applicable and can extend SDS-based methods. In particular, we demonstrate its applicability to Variational Score Distillation (VSD) by introducing RewardVSD. We evaluate RewardSDS and RewardVSD on text-to-image, 2D editing, and text-to-3D generation tasks, showing significant improvements over SDS and VSD on a diverse set of metrics measuring generation quality and alignment to desired reward models, enabling state-of-the-art performance. Project page is available at https://itaychachy.github.io/reward-sds/.

♻ ☆ Arbitrary-steps Image Super-resolution via Diffusion Inversion CVPR 2025

Zongsheng Yue, Kang Liao, Chen Change Loy

This study presents a new image super-resolution (SR) technique based on diffusion inversion, aiming at harnessing the rich image priors encapsulated in large pre-trained diffusion models to improve SR performance. We design a Partial noise Prediction strategy to construct an intermediate state of the diffusion model, which serves as the starting sampling point. Central to our approach is a deep noise predictor to estimate the optimal noise maps for the forward diffusion process. Once trained, this noise predictor can be used to initialize the sampling process partially along the diffusion trajectory, generating the desirable high-resolution result. Compared to existing approaches, our method offers a flexible and efficient sampling mechanism that supports an arbitrary number of sampling steps, ranging from one to five. Even with a single sampling step, our method demonstrates superior or comparable performance to recent state-of-the-art approaches. The code and model are publicly available at https://github.com/zsyOAOA/InvSR.

comment: Accepted by CVPR 2025. Project: https://github.com/zsyOAOA/InvSR

♻ ☆ Towards Class-wise Robustness Analysis

Tejaswini Medi, Julia Grabinski, Margret Keuper

♻ ☆ Revealing Bias Formation in Deep Neural Networks Through the Geometric Mechanisms of Human Visual Decoupling

Yanbiao Ma, Bowei Liu, Boyuan Gao, Wei Dai, Jiayi Chen, Shuo Li

♻ ☆ SMIRK: 3D Facial Expressions through Analysis-by-Neural-Synthesis

George Retsinas, Panagiotis P. Filntisis, Radek Danecek, Victoria F. Abrevaya, Anastasios Roussos, Timo Bolkart, Petros Maragos

While existing methods for 3D face reconstruction from in-the-wild images excel at recovering the overall face shape, they commonly miss subtle, extreme, asymmetric, or rarely observed expressions. We improve upon these methods with SMIRK (Spatial Modeling for Image-based Reconstruction of Kinesics), which faithfully reconstructs expressive 3D faces from images. We identify two key limitations in existing methods: shortcomings in their self-supervised training formulation, and a lack of expression diversity in the training images. For training, most methods employ differentiable rendering to compare a predicted face mesh with the input image, along with a plethora of additional loss functions. This differentiable rendering loss not only has to provide supervision to optimize for 3D face geometry, camera, albedo, and lighting, which is an ill-posed optimization problem, but the domain gap between rendering and input image further hinders the learning process. Instead, SMIRK replaces the differentiable rendering with a neural rendering module that, given the rendered predicted mesh geometry, and sparsely sampled pixels of the input image, generates a face image. As the neural rendering gets color information from sampled image pixels, supervising with neural rendering-based reconstruction loss can focus solely on the geometry. Further, it enables us to generate images of the input identity with varying expressions while training. These are then utilized as input to the reconstruction model and used as supervision with ground truth geometry. This effectively augments the training data and enhances the generalization for diverse expressions. Our qualitative, quantitative and particularly our perceptual evaluations demonstrate that SMIRK achieves the new state-of-the art performance on accurate expression reconstruction. Project webpage: https://georgeretsi.github.io/smirk/.

♻ ☆ Uni-Sign: Toward Unified Sign Language Understanding at Scale ICLR 2025

Zecheng Li, Wengang Zhou, Weichao Zhao, Kepeng Wu, Hezhen Hu, Houqiang Li

Sign language pre-training has gained increasing attention for its ability to enhance performance across various sign language understanding (SLU) tasks. However, existing methods often suffer from a gap between pre-training and fine-tuning, leading to suboptimal results. To address this, we propose Uni-Sign, a unified pre-training framework that eliminates the gap between pre-training and downstream SLU tasks through a large-scale generative pre-training strategy and a novel fine-tuning paradigm. First, we introduce CSL-News, a large-scale Chinese Sign Language (CSL) dataset containing 1,985 hours of video paired with textual annotations, which enables effective large-scale pre-training. Second, Uni-Sign unifies SLU tasks by treating downstream tasks as a single sign language translation (SLT) task during fine-tuning, ensuring seamless knowledge transfer between pre-training and fine-tuning. Furthermore, we incorporate a prior-guided fusion (PGF) module and a score-aware sampling strategy to efficiently fuse pose and RGB information, addressing keypoint inaccuracies and improving computational efficiency. Extensive experiments across multiple SLU benchmarks demonstrate that Uni-Sign achieves state-of-the-art performance across multiple downstream SLU tasks. Dataset and code are available at github.com/ZechengLi19/Uni-Sign.

comment: Accepted by ICLR 2025

♻ ☆ Ultra-high resolution multimodal MRI dense labelled holistic brain atlas

José V. Manjón, Sergio Morell-Ortega, Marina Ruiz-Perez, Boris Mansencal, Edern Le Bot, Marien Gadea, Enrique Lanuza, Gwenaelle Catheline, Thomas Tourdias, Vincent Planche, Rémi Giraud, Denis Rivière, Jean-François Mangin, Nicole Labra-Avila, Roberto Vivo-Hernando, Gregorio Rubio, Fernando Aparici, Maria de la Iglesia-Vaya, Pierrick Coupé

In this paper, we introduce holiAtlas, a holistic, multimodal and high-resolution human brain atlas. This atlas covers different levels of details of the human brain anatomy, from the organ to the substructure level, using a new dense labelled protocol generated from the fusion of multiple local protocols at different scales. This atlas has been constructed averaging images and segmentations of 75 healthy subjects from the Human Connectome Project database. Specifically, MR images of T1, T2 and WMn (White Matter nulled) contrasts at 0.125 $mm^{3}$ resolution that were nonlinearly registered and averaged using symmetric group-wise normalisation to construct the atlas. At the finest level, the holiAtlas protocol has 350 different labels derived from 10 different delineation protocols. These labels were grouped at different scales to provide a holistic view of the brain at different levels in a coherent and consistent manner. This multiscale and multimodal atlas can be used for the development of new ultra-high resolution segmentation methods that can potentially leverage the early detection of neurological disorders.

comment: 22 pages

♻ ☆ Prompt-SID: Learning Structural Representation Prompt via Latent Diffusion for Single-Image Denoising

Huaqiu Li, Wang Zhang, Xiaowan Hu, Tao Jiang, Zikang Chen, Haoqian Wang

♻ ☆ Continuous K-space Recovery Network with Image Guidance for Fast MRI Reconstruction

Yucong Meng, Zhiwei Yang, Minghong Duan, Yonghong Shi, Zhijian Song

♻ ☆ DeepThalamus: A novel deep learning method for automatic segmentation of brain thalamic nuclei from multimodal ultra-high resolution MRI

Marina Ruiz-Perez, Sergio Morell-Ortega, Marien Gadea, Roberto Vivo-Hernando, Gregorio Rubio, Fernando Aparici, Mariam de la Iglesia-Vaya, Thomas Tourdias, Pierrick Coupé, José V. Manjón

The implication of the thalamus in multiple neurological pathologies makes it a structure of interest for volumetric analysis. In the present work, we have designed and implemented a multimodal volumetric deep neural network for the segmentation of thalamic nuclei at ultra-high resolution (0.125 mm3). Current tools either operate at standard resolution (1 mm3) or use monomodal data. To achieve the proposed objective, first, a database of semiautomatically segmented thalamic nuclei was created using ultra-high resolution T1, T2 and White Matter nulled (WMn) images. Then, a novel Deep learning based strategy was designed to obtain the automatic segmentations and trained to improve its robustness and accuaracy using a semisupervised approach. The proposed method was compared with a related state-of-the-art method showing competitive results both in terms of segmentation quality and efficiency. To make the proposed method fully available to the scientific community, a full pipeline able to work with monomodal standard resolution T1 images is also proposed.

♻ ☆ ReVLA: Reverting Visual Domain Limitation of Robotic Foundation Models ICRA-2025

Sombit Dey, Jan-Nico Zaech, Nikolay Nikolov, Luc Van Gool, Danda Pani Paudel

Recent progress in large language models and access to large-scale robotic datasets has sparked a paradigm shift in robotics models transforming them into generalists able to adapt to various tasks, scenes, and robot modalities. A large step for the community are open Vision Language Action models which showcase strong performance in a wide variety of tasks. In this work, we study the visual generalization capabilities of three existing robotic foundation models, and propose a corresponding evaluation framework. Our study shows that the existing models do not exhibit robustness to visual out-of-domain scenarios. This is potentially caused by limited variations in the training data and/or catastrophic forgetting, leading to domain limitations in the vision foundation models. We further explore OpenVLA, which uses two pre-trained vision foundation models and is, therefore, expected to generalize to out-of-domain experiments. However, we showcase catastrophic forgetting by DINO-v2 in OpenVLA through its failure to fulfill the task of depth regression. To overcome the aforementioned issue of visual catastrophic forgetting, we propose a gradual backbone reversal approach founded on model merging. This enables OpenVLA which requires the adaptation of the visual backbones during initial training -- to regain its visual generalization ability. Regaining this capability enables our ReVLA model to improve over OpenVLA by a factor of 77% and 66% for grasping and lifting in visual OOD tasks .

comment: Accepted at ICRA-2025, Atlanta

♻ ☆ Narrating the Video: Boosting Text-Video Retrieval via Comprehensive Utilization of Frame-Level Captions CVPR 2025

Chan Hur, Jeong-hun Hong, Dong-hun Lee, Dabin Kang, Semin Myeong, Sang-hyo Park, Hyeyoung Park

In recent text-video retrieval, the use of additional captions from vision-language models has shown promising effects on the performance. However, existing models using additional captions often have struggled to capture the rich semantics, including temporal changes, inherent in the video. In addition, incorrect information caused by generative models can lead to inaccurate retrieval. To address these issues, we propose a new framework, Narrating the Video (NarVid), which strategically leverages the comprehensive information available from frame-level captions, the narration. The proposed NarVid exploits narration in multiple ways: 1) feature enhancement through cross-modal interactions between narration and video, 2) query-aware adaptive filtering to suppress irrelevant or incorrect information, 3) dual-modal matching score by adding query-video similarity and query-narration similarity, and 4) hard-negative loss to learn discriminative features from multiple perspectives using the two similarities from different views. Experimental results demonstrate that NarVid achieves state-of-the-art performance on various benchmark datasets.

comment: Accepted at CVPR 2025

♻ ☆ PhysVLM: Enabling Visual Language Models to Understand Robotic Physical Reachability

Weijie Zhou, Manli Tao, Chaoyang Zhao, Haiyun Guo, Honghui Dong, Ming Tang, Jinqiao Wang

Understanding the environment and a robot's physical reachability is crucial for task execution. While state-of-the-art vision-language models (VLMs) excel in environmental perception, they often generate inaccurate or impractical responses in embodied visual reasoning tasks due to a lack of understanding of robotic physical reachability. To address this issue, we propose a unified representation of physical reachability across diverse robots, i.e., Space-Physical Reachability Map (S-P Map), and PhysVLM, a vision-language model that integrates this reachability information into visual reasoning. Specifically, the S-P Map abstracts a robot's physical reachability into a generalized spatial representation, independent of specific robot configurations, allowing the model to focus on reachability features rather than robot-specific parameters. Subsequently, PhysVLM extends traditional VLM architectures by incorporating an additional feature encoder to process the S-P Map, enabling the model to reason about physical reachability without compromising its general vision-language capabilities. To train and evaluate PhysVLM, we constructed a large-scale multi-robot dataset, Phys100K, and a challenging benchmark, EQA-phys, which includes tasks for six different robots in both simulated and real-world environments. Experimental results demonstrate that PhysVLM outperforms existing models, achieving a 14\% improvement over GPT-4o on EQA-phys and surpassing advanced embodied VLMs such as RoboMamba and SpatialVLM on the RoboVQA-val and OpenEQA benchmarks. Additionally, the S-P Map shows strong compatibility with various VLMs, and its integration into GPT-4o-mini yields a 7.1\% performance improvement.

♻ ☆ Audio-Visual Deepfake Detection With Local Temporal Inconsistencies ICASSP 2025

Marcella Astrid, Enjie Ghorbel, Djamila Aouada

This paper proposes an audio-visual deepfake detection approach that aims to capture fine-grained temporal inconsistencies between audio and visual modalities. To achieve this, both architectural and data synthesis strategies are introduced. From an architectural perspective, a temporal distance map, coupled with an attention mechanism, is designed to capture these inconsistencies while minimizing the impact of irrelevant temporal subsequences. Moreover, we explore novel pseudo-fake generation techniques to synthesize local inconsistencies. Our approach is evaluated against state-of-the-art methods using the DFDC and FakeAVCeleb datasets, demonstrating its effectiveness in detecting audio-visual deepfakes.

comment: Accepted in ICASSP 2025

♻ ☆ ATRNet-STAR: A Large Dataset and Benchmark Towards Remote Sensing Object Recognition in the Wild

Yongxiang Liu, Weijie Li, Li Liu, Jie Zhou, Bowen Peng, Yafei Song, Xuying Xiong, Wei Yang, Tianpeng Liu, Zhen Liu, Xiang Li

The absence of publicly available, large-scale, high-quality datasets for Synthetic Aperture Radar Automatic Target Recognition (SAR ATR) has significantly hindered the application of rapidly advancing deep learning techniques, which hold huge potential to unlock new capabilities in this field. This is primarily because collecting large volumes of diverse target samples from SAR images is prohibitively expensive, largely due to privacy concerns, the characteristics of microwave radar imagery perception, and the need for specialized expertise in data annotation. Throughout the history of SAR ATR research, there have been only a number of small datasets, mainly including targets like ships, airplanes, buildings, etc. There is only one vehicle dataset MSTAR collected in the 1990s, which has been a valuable source for SAR ATR. To fill this gap, this paper introduces a large-scale, new dataset named ATRNet-STAR with 40 different vehicle categories collected under various realistic imaging conditions and scenes. It marks a substantial advancement in dataset scale and diversity, comprising over 190,000 well-annotated samples, 10 times larger than its predecessor, the famous MSTAR. Building such a large dataset is a challenging task, and the data collection scheme will be detailed. Secondly, we illustrate the value of ATRNet-STAR via extensively evaluating the performance of 15 representative methods with 7 different experimental settings on challenging classification and detection benchmarks derived from the dataset. Finally, based on our extensive experiments, we identify valuable insights for SAR ATR and discuss potential future research directions in this field. We hope that the scale, diversity, and benchmark of ATRNet-STAR can significantly facilitate the advancement of SAR ATR.

comment: 17 pages, 14 figures; ATRNet-STAR: https://github.com/waterdisappear/ATRNet-STAR

♻ ☆ FaVChat: Unlocking Fine-Grained Facial Video Understanding with Multimodal Large Language Models

Fufangchen Zhao, Ming Li, Linrui Xu, Wenhao Jiang, Jian Gao, Danfeng Yan

Video-based multimodal large language models (VMLLMs) have demonstrated remarkable potential in cross-modal video understanding. However, their abilities in fine-grained face comprehension remain largely underexplored. Given its pivotal role in human-centric intelligence, developing VMLLMs for facial understanding holds a fundamental problem. To address this gap, we propose FaVChat, the first VMLLM specifically designed for fine-grained facial video understanding. To facilitate its training, we construct a large-scale facial video dataset comprising over 60k videos, with the majority annotated with 83 fine-grained facial attributes. These attributes are incorporated to enrich GPT-4o-generated captions, yielding 60k high-quality video-summary pairs and an additional 170k fine-grained question-answering (QA) pairs. To effectively capture rich facial clues, we propose a hybrid model architecture composed of a general visual encoder, a dedicated facial encoder, and a mixture-of-experts-enhanced adapter for adaptive fusion of multi-source visual features. To mitigate information loss during feature transformation, we extract multi-granularity representations from the facial encoder and integrate them into the subsequent LLM. This design enhances the model's ability to comprehend and respond to questions involving diverse levels of visual details. We employ a progressive training paradigm, transitioning from video summarization to a high-quality subset of video QA, gradually increasing task complexity to enhance the model's fine-grained visual perception. We conduct extensive zero-shot evaluation on a couple of public benchmarks, demonstrating that FaVChat consistently surpasses existing VMLLMs across multiple tasks.

♻ ☆ Hidden in the Noise: Two-Stage Robust Watermarking for Images

Kasra Arabi, Benjamin Feuer, R. Teal Witter, Chinmay Hegde, Niv Cohen

♻ ☆ GoalFlow: Goal-Driven Flow Matching for Multimodal Trajectories Generation in End-to-End Autonomous Driving

Zebin Xing, Xingyu Zhang, Yang Hu, Bo Jiang, Tong He, Qian Zhang, Xiaoxiao Long, Wei Yin

We propose GoalFlow, an end-to-end autonomous driving method for generating high-quality multimodal trajectories. In autonomous driving scenarios, there is rarely a single suitable trajectory. Recent methods have increasingly focused on modeling multimodal trajectory distributions. However, they suffer from trajectory selection complexity and reduced trajectory quality due to high trajectory divergence and inconsistencies between guidance and scene information. To address these issues, we introduce GoalFlow, a novel method that effectively constrains the generative process to produce high-quality, multimodal trajectories. To resolve the trajectory divergence problem inherent in diffusion-based methods, GoalFlow constrains the generated trajectories by introducing a goal point. GoalFlow establishes a novel scoring mechanism that selects the most appropriate goal point from the candidate points based on scene information. Furthermore, GoalFlow employs an efficient generative method, Flow Matching, to generate multimodal trajectories, and incorporates a refined scoring mechanism to select the optimal trajectory from the candidates. Our experimental results, validated on the Navsim\cite{Dauner2024_navsim}, demonstrate that GoalFlow achieves state-of-the-art performance, delivering robust multimodal trajectories for autonomous driving. GoalFlow achieved PDMS of 90.3, significantly surpassing other methods. Compared with other diffusion-policy-based methods, our approach requires only a single denoising step to obtain excellent performance. The code is available at https://github.com/YvanYin/GoalFlow.

♻ ☆ The R2D2 Deep Neural Network Series for Scalable Non-Cartesian Magnetic Resonance Imaging

Yiwei Chen, Amir Aghabiglou, Shijie Chen, Motahare Torki, Chao Tang, Ruud B. van Heeswijk, Yves Wiaux

comment: 13 pages, 10 figures

♻ ☆ $ShiftwiseConv:$ Small Convolutional Kernel with Large Kernel Effect CVPR 2025

Dachong Li, Li Li, Zhuangzhuang Chen, Jianqiang Li

Large kernels make standard convolutional neural networks (CNNs) great again over transformer architectures in various vision tasks. Nonetheless, recent studies meticulously designed around increasing kernel size have shown diminishing returns or stagnation in performance. Thus, the hidden factors of large kernel convolution that affect model performance remain unexplored. In this paper, we reveal that the key hidden factors of large kernels can be summarized as two separate components: extracting features at a certain granularity and fusing features by multiple pathways. To this end, we leverage the multi-path long-distance sparse dependency relationship to enhance feature utilization via the proposed Shiftwise (SW) convolution operator with a pure CNN architecture. In a wide range of vision tasks such as classification, segmentation, and detection, SW surpasses state-of-the-art transformers and CNN architectures, including SLaK and UniRepLKNet. More importantly, our experiments demonstrate that $3 \times 3$ convolutions can replace large convolutions in existing large kernel CNNs to achieve comparable effects, which may inspire follow-up works. Code and all the models at https://github.com/lidc54/shift-wiseConv.

comment: CVPR 2025

♻ ☆ AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2 WACV 2025

Simon Damm, Mike Laszkiewicz, Johannes Lederer, Asja Fischer

Recent advances in multimodal foundation models have set new standards in few-shot anomaly detection. This paper explores whether high-quality visual features alone are sufficient to rival existing state-of-the-art vision-language models. We affirm this by adapting DINOv2 for one-shot and few-shot anomaly detection, with a focus on industrial applications. We show that this approach does not only rival existing techniques but can even outmatch them in many settings. Our proposed vision-only approach, AnomalyDINO, follows the well-established patch-level deep nearest neighbor paradigm, and enables both image-level anomaly prediction and pixel-level anomaly segmentation. The approach is methodologically simple and training-free and, thus, does not require any additional data for fine-tuning or meta-learning. The approach is methodologically simple and training-free and, thus, does not require any additional data for fine-tuning or meta-learning. Despite its simplicity, AnomalyDINO achieves state-of-the-art results in one- and few-shot anomaly detection (e.g., pushing the one-shot performance on MVTec-AD from an AUROC of 93.1% to 96.6%). The reduced overhead, coupled with its outstanding few-shot performance, makes AnomalyDINO a strong candidate for fast deployment, e.g., in industrial contexts.

comment: Accepted at WACV 2025 (Oral)

♻ ☆ Reliable Representation Learning for Incomplete Multi-View Missing Multi-Label Classification

Chengliang Liu, Jie Wen, Yong Xu, Bob Zhang, Liqiang Nie, Min Zhang

As a cross-topic of multi-view learning and multi-label classification, multi-view multi-label classification has gradually gained traction in recent years. The application of multi-view contrastive learning has further facilitated this process, however, the existing multi-view contrastive learning methods crudely separate the so-called negative pair, which largely results in the separation of samples belonging to the same category or similar ones. Besides, plenty of multi-view multi-label learning methods ignore the possible absence of views and labels. To address these issues, in this paper, we propose an incomplete multi-view missing multi-label classification network named RANK. In this network, a label-driven multi-view contrastive learning strategy is proposed to leverage supervised information to preserve the intra-view structure and perform the cross-view consistency alignment. Furthermore, we break through the view-level weights inherent in existing methods and propose a quality-aware sub-network to dynamically assign quality scores to each view of each sample. The label correlation information is fully utilized in the final multi-label cross-entropy classification loss, effectively improving the discriminative power. Last but not least, our model is not only able to handle complete multi-view multi-label data, but also works on datasets with missing instances and labels. Extensive experiments confirm that our RANK outperforms existing state-of-the-art methods.

comment: Accepted by TPAMI. Please contact me if you have any questions: liucl1996@163.com

♻ ☆ LatentSync: Taming Audio-Conditioned Latent Diffusion Models for Lip Sync with SyncNet Supervision

Chunyu Li, Chao Zhang, Weikai Xu, Jingyu Lin, Jinghui Xie, Weiguo Feng, Bingyue Peng, Cunjian Chen, Weiwei Xing

End-to-end audio-conditioned latent diffusion models (LDMs) have been widely adopted for audio-driven portrait animation, demonstrating their effectiveness in generating lifelike and high-resolution talking videos. However, direct application of audio-conditioned LDMs to lip-synchronization (lip-sync) tasks results in suboptimal lip-sync accuracy. Through an in-depth analysis, we identified the underlying cause as the "shortcut learning problem", wherein the model predominantly learns visual-visual shortcuts while neglecting the critical audio-visual correlations. To address this issue, we explored different approaches for integrating SyncNet supervision into audio-conditioned LDMs to explicitly enforce the learning of audio-visual correlations. Since the performance of SyncNet directly influences the lip-sync accuracy of the supervised model, the training of a well-converged SyncNet becomes crucial. We conducted the first comprehensive empirical studies to identify key factors affecting SyncNet convergence. Based on our analysis, we introduce StableSyncNet, with an architecture designed for stable convergence. Our StableSyncNet achieved a significant improvement in accuracy, increasing from 91% to 94% on the HDTF test set. Additionally, we introduce a novel Temporal Representation Alignment (TREPA) mechanism to enhance temporal consistency in the generated videos. Experimental results show that our method surpasses state-of-the-art lip-sync approaches across various evaluation metrics on the HDTF and VoxCeleb2 datasets.

♻ ☆ M2IST: Multi-Modal Interactive Side-Tuning for Efficient Referring Expression Comprehension

Xuyang Liu, Ting Liu, Siteng Huang, Yi Xin, Yue Hu, Quanjun Yin, Donglin Wang, Yuanyuan Wu, Honggang Chen

Referring expression comprehension (REC) is a vision-language task to locate a target object in an image based on a language expression. Fully fine-tuning general-purpose pre-trained vision-language foundation models for REC yields impressive performance but becomes increasingly costly. Parameter-efficient transfer learning (PETL) methods have shown strong performance with fewer tunable parameters. However, directly applying PETL to REC faces two challenges: (1) insufficient multi-modal interaction between pre-trained vision-language foundation models, and (2) high GPU memory usage due to gradients passing through the heavy vision-language foundation models. To this end, we present M2IST: Multi-Modal Interactive Side-Tuning with M3ISAs: Mixture of Multi-Modal Interactive Side-Adapters. During fine-tuning, we fix the pre-trained uni-modal encoders and update M3ISAs to enable efficient vision-language alignment for REC. Empirical results reveal that M2IST achieves better performance-efficiency trade-off than full fine-tuning and other PETL methods, requiring only 2.11\% tunable parameters, 39.61\% GPU memory, and 63.46\% training time while maintaining competitive performance. Our code is released at https://github.com/xuyang-liu16/M2IST.

comment: Accepted by IEEE Transactions on Circuits and Systems for Video Technology (TCSVT)

Computers and Society

☆ Short-term AI literacy intervention does not reduce over-reliance on incorrect ChatGPT recommendations

Brett Puppart, Jaan Aru

In this study, we examined whether a short-form AI literacy intervention could reduce the adoption of incorrect recommendations from large language models. High school seniors were randomly assigned to either a control or an intervention group, which received an educational text explaining ChatGPT's working mechanism, limitations, and proper use. Participants solved math puzzles with the help of ChatGPT's recommendations, which were incorrect in half of the cases. Results showed that students adopted incorrect suggestions 52.1% of the time, indicating widespread over-reliance. The educational intervention did not significantly reduce over-reliance. Instead, it led to an increase in ignoring ChatGPT's correct recommendations. We conclude that the usage of ChatGPT is associated with over-reliance and it is not trivial to increase AI literacy to counter over-reliance.

☆ The Impact of Item-Writing Flaws on Difficulty and Discrimination in Item Response Theory

Robin Schmucker, Steven Moore

☆ Social Media Harm Abatement: Mechanisms for Transparent Public Health Assessment

Nathaniel Lubin, Yuning Liu, Amanda Yarnell, S. Bryn Austin, Zachary J. Ward, Ravi Iyer, Jonathan Stray, Matthew Lawrence, Alissa Cooper, Peter Chapman

Social media platforms have been accused of causing a range of harms, resulting in dozens of lawsuits across jurisdictions. These lawsuits are situated within the context of a long history of American product safety litigation, suggesting opportunities for remediation outside of financial compensation. Anticipating that at least some of these cases may be successful and/or lead to settlements, this article outlines an implementable mechanism for an abatement and/or settlement plan capable of mitigating abuse. The paper describes the requirements of such a mechanism, implications for privacy and oversight, and tradeoffs that such a procedure would entail. The mechanism is framed to operate at the intersection of legal procedure, standards for transparent public health assessment, and the practical requirements of modern technology products.

comment: 17 pages, 1 figure, 2 appendices with four tables

☆ Understanding and Supporting Peer Review Using AI-reframed Positive Summary

Chi-Lan Yang, Alarith Uhde, Naomi Yamashita, Hideaki Kuzuoka

While peer review enhances writing and research quality, harsh feedback can frustrate and demotivate authors. Hence, it is essential to explore how critiques should be delivered to motivate authors and enable them to keep iterating their work. In this study, we explored the impact of appending an automatically generated positive summary to the peer reviews of a writing task, alongside varying levels of overall evaluations (high vs. low), on authors' feedback reception, revision outcomes, and motivation to revise. Through a 2x2 online experiment with 137 participants, we found that adding an AI-reframed positive summary to otherwise harsh feedback increased authors' critique acceptance, whereas low overall evaluations of their work led to increased revision efforts. We discuss the implications of using AI in peer feedback, focusing on how AI-driven critiques can influence critique acceptance and support research communities in fostering productive and friendly peer feedback practices.

comment: 16 pages, 6 figures

☆ Sovereignty in the digital era: the quest for continuous access to dependable technological capabilities

Roberto Baldoni, Giuseppe Di Luna

In an era where economies and societies are deeply integrated into cyberspace, achieving a robust level of digital sovereignty has become an essential goal for nations aiming to preserve their security and strategic political autonomy, particularly during turbulent geopolitical times marked by complex global supply chains of critical technologies that ties systemic rivals. Digital sovereignty is a multifaceted, interdisciplinary, and dynamic pursuit that fundamentally relies on a nation's ability to have continuous access to dependable technological capabilities (CTCs) for storing, transferring, and processing domestically produced data. This paper identifies how access continuity or technological dependability could be threatened by several malicious actions from cyberattacks, supply chain tamperings, political or economic actions. By examining different approaches adopted by countries like the United States, China, and the European Union, we highlight different strategies to get access to CTCs depending on their political, economic and institutional nature.

☆ Using Causal Inference to Explore Government Policy Impact on Computer Usage

Mingjia Zhu, Lechuan Wang, Julien Sebot, Bijan Arbab, Babak Salimi, Alexander Cloninger

We explore the causal relationship between COVID-19 lockdown policies and changes in personal computer usage. In particular, we examine how lockdown policies affected average daily computer usage, as well as how it affected usage patterns of different groups of users. This is done through a merging of the Oxford Policy public data set, which describes the timeline of implementation of COVID policies across the world, and a collection of Intel's Data Collection and Analytics (DCA) telemetry data, which includes millions of computer usage records and updates daily. Through difference-in-difference, synthetic control, and change-point detection algorithms, we identify causal links between the increase in intensity (watts) and time (hours) of computer usage and the implementation of work from home policy. We also show an interesting trend in the individual's computer usage affected by the policy. We also conclude that computer usage behaviors are much less predictable during reduction in COVID lockdown policies than during increases in COVID lockdown policies.

♻ ☆ AGI, Governments, and Free Societies

Justin B. Bullock, Samuel Hammond, Seb Krier

This paper examines how artificial general intelligence (AGI) could fundamentally reshape the delicate balance between state capacity and individual liberty that sustains free societies. Building on Acemoglu and Robinson's 'narrow corridor' framework, we argue that AGI poses distinct risks of pushing societies toward either a 'despotic Leviathan' through enhanced state surveillance and control, or an 'absent Leviathan' through the erosion of state legitimacy relative to AGI-empowered non-state actors. Drawing on public administration theory and recent advances in AI capabilities, we analyze how these dynamics could unfold through three key channels: the automation of discretionary decision-making within agencies, the evolution of bureaucratic structures toward system-level architectures, and the transformation of democratic feedback mechanisms. Our analysis reveals specific failure modes that could destabilize liberal institutions. Enhanced state capacity through AGI could enable unprecedented surveillance and control, potentially entrenching authoritarian practices. Conversely, rapid diffusion of AGI capabilities to non-state actors could undermine state legitimacy and governability. We examine how these risks manifest differently at the micro level of individual bureaucratic decisions, the meso level of organizational structure, and the macro level of democratic processes. To preserve the narrow corridor of liberty, we propose a governance framework emphasizing robust technical safeguards, hybrid institutional designs that maintain meaningful human oversight, and adaptive regulatory mechanisms.

comment: 40 pages

♻ ☆ Securing External Deeper-than-black-box GPAI Evaluations

Alejandro Tlaie, Jimmy Farrell

This paper examines the critical challenges and potential solutions for conducting secure and effective external evaluations of general-purpose AI (GPAI) models. With the exponential growth in size, capability, reach and accompanying risk of these models, ensuring accountability, safety, and public trust requires frameworks that go beyond traditional black-box methods. The discussion begins with an analysis of the need for deeper-than-black-box evaluations (Section I), emphasizing the importance of understanding model internals to uncover latent risks and ensure compliance with ethical and regulatory standards. Building on this foundation, Section II addresses the security considerations of remote evaluations, outlining the threat landscape, technical solutions, and safeguards necessary to protect both evaluators and proprietary model data. Finally, Section III synthesizes these insights into actionable recommendations and future directions, aiming to establish a robust, scalable, and transparent framework for external assessments in GPAI governance.

♻ ☆ Exploring near-optimal energy systems with stakeholders: a novel approach for participatory modelling

Oskar Vågerö, Koen van Greevenbroek, Aleksander Grochowicz, Maximilian Roithner

Involving people in energy systems planning can increase the legitimacy and socio-political feasibility of energy transitions. Participatory research in energy modelling offers the opportunity to engage with stakeholders in a comprehensive way, but is limited by how results can be generated and presented without imposing assumptions and discrete scenarios on the participants. To this end, we present a methodology and a framework, based on near-optimal modelling results, that can incorporate stakeholders in a holistic and engaging way. We confront stakeholders with a continuum of modelling-based energy system designs via an interactive interface allowing them to choose essentially any combination of components that meet the system requirements. Together with information on the implications of different technologies, it is possible to assess how participants prioritise different aspects in energy systems planning while also facilitating learning in an engaging and stimulating way. We showcase the methodology for the remote Arctic settlement of Longyearbyen and illustrate how participants deviate consistently from the cost optimum. At the same time, they manage to balance different priorities such as emissions, costs, and system vulnerability leading to a better understanding of the complexity and intertwined nature of decisions.

comment: 24 pages, 7 figures and 3 tables

♻ ☆ The Algorithmic State Architecture (ASA): An Integrated Framework for AI-Enabled Government

Zeynep Engin, Jon Crowcroft, David Hand, Philip Treleaven

comment: Main text: 25 pages, with references: 35 pages, 2 figures

Computers and Society

☆ IT Students Career Confidence and Career Identity During COVID-19

Sophie McKenzie

COVID-19 disrupted the professional preparation of university students, with less opportunity to engage in professional practice due to a reduced employment market. Little is known about how this period impacted upon the career confidence and career identity of university students. This research paper explores the career confidence and identity of university students in Information Technology (IT) prior and during the COVID-19 period. Using a survey method and quantitative analysis, ANOVA and Kruskal-Wallis tests with different sensitivity and variance standards were used during analysis to present mean and mean rank of data collected during 2018, 2019, 2020 and 2021. 1349 IT students from an Australian University reported their career confidence. The results indicate IT students' career confidence maintained during the period. In 2021, the results indicate increased career commitment of IT students showing higher professional expectations to work in IT along with greater self-awareness regarding their professional development needs. Even with increased career confidence as observed in this study, supporting university students to explore their career options and build upon their career identity, and more broadly their employability, remains an important activity for universities to curate in their graduates.

☆ Honey Trap or Romantic Utopia: A Case Study of Final Fantasy XIV Players PII Disclosure in Intimate Partner-Seeking Posts

Yihao Zhou, Tanusree Sharma

Massively multiplayer online games (MMOGs) can foster social interaction and relationship formation, but they pose specific privacy and safety challenges, especially in the context of mediating intimate interpersonal connections. To explore the potential risks, we conducted a case study on Final Fantasy XIV (FFXIV) players intimate partner seeking posts on social media. We analyzed 1,288 posts from a public Weibo account using Latent Dirichlet Allocation (LDA) topic modeling and thematic analysis. Our findings reveal that players disclose sensitive personal information and share vulnerabilities to establish trust but face difficulties in managing identity and privacy across multiple platforms. We also found that players expectations regarding intimate partner are diversified, and mismatch of expectations may leads to issues like privacy leakage or emotional exploitation. Based on our findings, we propose design implications for reducing privacy and safety risks and fostering healthier social interactions in virtual worlds.

☆ Towards an Inclusive Digital Society: Digital Accessibility Framework for Visually Impaired Citizens in Swiss Public Administration

Sabina Werren, Hermann Grieder, Christopher Scherb

As we progress toward Society 5.0's vision of a human-centered digital society, ensuring digital accessibility becomes increasingly critical, particularly for citizens with visual impairments and other disabilities. This paper examines the implementation challenges of accessible digital public services within Swiss public administration. Through Design Science Research, we investigate the gap between accessibility legislation and practical implementation, analyzing how current standards translate into real-world usability. Our research reveals significant barriers including resource constraints, fragmented policy enforcement, and limited technical expertise. To address these challenges, we present the Inclusive Public Administration Framework, which integrates Web Content Accessibility Guidelines with the HERMES project management methodology. This framework provides a structured approach to embedding accessibility considerations throughout digital service development. Our findings contribute to the discourse on digital inclusion in Society 5.0 by providing actionable strategies for implementing accessible public services. As we move towards a more integrated human-machine society, ensuring digital accessibility for visually impaired citizens is crucial for building an equitable and inclusive digital future.

☆ Data Traceability for Privacy Alignment

Kevin Liao, Shreya Thipireddy, Daniel Weitzner

This paper offers a new privacy approach for the growing ecosystem of services--ranging from open banking to healthcare--dependent on sensitive personal data sharing between individuals and third-parties. While these services offer significant benefits, individuals want control over their data, transparency regarding how their data is used, and accountability from third-parties for misuse. However, existing legal and technical mechanisms are inadequate for supporting these needs. A comprehensive approach to the modern privacy challenges of accountable third-party data sharing requires a closer alignment of technical system architecture and legal institutional design. In order to achieve this privacy alignment, we extend traditional security threat modeling and analysis to encompass a broader range of privacy notions than has been typically considered. In particular, we introduce the concept of covert-accountability, which addresses adversaries that may act dishonestly but face potential identification and legal consequences. As a concrete instance of this design approach, we present the OTrace protocol, designed to provide traceable, accountable, consumer-control in third-party data sharing ecosystems. OTrace empowers consumers with the knowledge of where their data is, who has it, what it is being used for, and whom it is being shared with. By applying our alignment framework to OTrace, we demonstrate that OTrace's technical affordances can provide more confident, scalable regulatory oversight when combined with complementary legal mechanisms.

☆ Advancing Education through Tutoring Systems: A Systematic Literature Review

Vincent Liu, Ehsan Latif, Xiaoming Zhai

This study systematically reviews the transformative role of Tutoring Systems, encompassing Intelligent Tutoring Systems (ITS) and Robot Tutoring Systems (RTS), in addressing global educational challenges through advanced technologies. As many students struggle with proficiency in core academic areas, Tutoring Systems emerge as promising solutions to bridge learning gaps by delivering personalized and adaptive instruction. ITS leverages artificial intelligence (AI) models, such as Bayesian Knowledge Tracing and Large Language Models, to provide precise cognitive support, while RTS enhances social and emotional engagement through human-like interactions. This systematic review, adhering to the PRISMA framework, analyzed 86 representative studies. We evaluated the pedagogical and technological advancements, engagement strategies, and ethical considerations surrounding these systems. Based on these parameters, Latent Class Analysis was conducted and identified three distinct categories: computer-based ITS, robot-based RTS, and multimodal systems integrating various interaction modes. The findings reveal significant advancements in AI techniques that enhance adaptability, engagement, and learning outcomes. However, challenges such as ethical concerns, scalability issues, and gaps in cognitive adaptability persist. The study highlights the complementary strengths of ITS and RTS, proposing integrated hybrid solutions to maximize educational benefits. Future research should focus on bridging gaps in scalability, addressing ethical considerations comprehensively, and advancing AI models to support diverse educational needs.

comment: A comprehensive study on tutoring systems

☆ How to Protect Yourself from 5G Radiation? Investigating LLM Responses to Implicit Misinformation

Ruohao Guo, Wei Xu, Alan Ritter

As Large Language Models (LLMs) are widely deployed in diverse scenarios, the extent to which they could tacitly spread misinformation emerges as a critical safety concern. Current research primarily evaluates LLMs on explicit false statements, overlooking how misinformation often manifests subtly as unchallenged premises in real-world user interactions. We curated ECHOMIST, the first comprehensive benchmark for implicit misinformation, where the misinformed assumptions are embedded in a user query to LLMs. ECHOMIST is based on rigorous selection criteria and carefully curated data from diverse sources, including real-world human-AI conversations and social media interactions. We also introduce a new evaluation metric to measure whether LLMs can recognize and counter false information rather than amplify users' misconceptions. Through an extensive empirical study on a wide range of LLMs, including GPT-4, Claude, and Llama, we find that current models perform alarmingly poorly on this task, often failing to detect false premises and generating misleading explanations. Our findings underscore the critical need for an increased focus on implicit misinformation in LLM safety research.

☆ Fine-Tuning Large Language Models for Educational Support: Leveraging Gagne's Nine Events of Instruction for Lesson Planning

Linzhao Jia, Changyong Qi, Yuang Wei, Han Sun, Xiaozhe Yang

Effective lesson planning is crucial in education process, serving as the cornerstone for high-quality teaching and the cultivation of a conducive learning atmosphere. This study investigates how large language models (LLMs) can enhance teacher preparation by incorporating them with Gagne's Nine Events of Instruction, especially in the field of mathematics education in compulsory education. It investigates two distinct methodologies: the development of Chain of Thought (CoT) prompts to direct LLMs in generating content that aligns with instructional events, and the application of fine-tuning approaches like Low-Rank Adaptation (LoRA) to enhance model performance. This research starts with creating a comprehensive dataset based on math curriculum standards and Gagne's instructional events. The first method involves crafting CoT-optimized prompts to generate detailed, logically coherent responses from LLMs, improving their ability to create educationally relevant content. The second method uses specialized datasets to fine-tune open-source models, enhancing their educational content generation and analysis capabilities. This study contributes to the evolving dialogue on the integration of AI in education, illustrating innovative strategies for leveraging LLMs to bolster teaching and learning processes.

☆ A Survey on Trustworthy LLM Agents: Threats and Countermeasures

Miao Yu, Fanci Meng, Xinyun Zhou, Shilong Wang, Junyuan Mao, Linsey Pang, Tianlong Chen, Kun Wang, Xinfeng Li, Yongfeng Zhang, Bo An, Qingsong Wen

With the rapid evolution of Large Language Models (LLMs), LLM-based agents and Multi-agent Systems (MAS) have significantly expanded the capabilities of LLM ecosystems. This evolution stems from empowering LLMs with additional modules such as memory, tools, environment, and even other agents. However, this advancement has also introduced more complex issues of trustworthiness, which previous research focused solely on LLMs could not cover. In this survey, we propose the TrustAgent framework, a comprehensive study on the trustworthiness of agents, characterized by modular taxonomy, multi-dimensional connotations, and technical implementation. By thoroughly investigating and summarizing newly emerged attacks, defenses, and evaluation methods for agents and MAS, we extend the concept of Trustworthy LLM to the emerging paradigm of Trustworthy Agent. In TrustAgent, we begin by deconstructing and introducing various components of the Agent and MAS. Then, we categorize their trustworthiness into intrinsic (brain, memory, and tool) and extrinsic (user, agent, and environment) aspects. Subsequently, we delineate the multifaceted meanings of trustworthiness and elaborate on the implementation techniques of existing research related to these internal and external modules. Finally, we present our insights and outlook on this domain, aiming to provide guidance for future endeavors.

☆ Specification languages for computational laws versus basic legal principles

Petia Guintchev, Joost J. Joosten, Sofia Santiago Fernández, Eric Sancho Adamson, Aleix Solé Sánchez, Marta Soria Heredia

We speak of a \textit{computational law} when that law is intended to be enforced by software through an automated decision-making process. As digital technologies evolve to offer more solutions for public administrations, we see an ever-increasing number of computational laws. Traditionally, law is written in natural language. Computational laws, however, suffer various complications when written in natural language, such as underspecification and ambiguity which lead to a diversity of possible interpretations to be made by the coder. These could potentially result into an uneven application of the law. Thus, resorting to formal languages to write computational laws is tempting. However, writing laws in a formal language leads to further complications, for example, incomprehensibility for non-experts, lack of explicit motivation of the decisions made, or difficulties in retrieving the data leading to the outcome. In this paper, we investigate how certain legal principles fare in both scenarios: computational law written in natural language or written in formal language. We use a running example from the European Union's road transport regulation to showcase the tensions arising, and the benefits from each language.

☆ Can A Society of Generative Agents Simulate Human Behavior and Inform Public Health Policy? A Case Study on Vaccine Hesitancy

Abe Bohan Hou, Hongru Du, Yichen Wang, Jingyu Zhang, Zixiao Wang, Paul Pu Liang, Daniel Khashabi, Lauren Gardner, Tianxing He

Can we simulate a sandbox society with generative agents to model human behavior, thereby reducing the over-reliance on real human trials for assessing public policies? In this work, we investigate the feasibility of simulating health-related decision-making, using vaccine hesitancy, defined as the delay in acceptance or refusal of vaccines despite the availability of vaccination services (MacDonald, 2015), as a case study. To this end, we introduce the VacSim framework with 100 generative agents powered by Large Language Models (LLMs). VacSim simulates vaccine policy outcomes with the following steps: 1) instantiate a population of agents with demographics based on census data; 2) connect the agents via a social network and model vaccine attitudes as a function of social dynamics and disease-related information; 3) design and evaluate various public health interventions aimed at mitigating vaccine hesitancy. To align with real-world results, we also introduce simulation warmup and attitude modulation to adjust agents' attitudes. We propose a series of evaluations to assess the reliability of various LLM simulations. Experiments indicate that models like Llama and Qwen can simulate aspects of human behavior but also highlight real-world alignment challenges, such as inconsistent responses with demographic profiles. This early exploration of LLM-driven simulations is not meant to serve as definitive policy guidance; instead, it serves as a call for action to examine social simulation for policy development.

☆ Complementarity, Augmentation, or Substitutivity? The Impact of Generative Artificial Intelligence on the U.S. Federal Workforce

William G. Resh, Yi Ming, Xinyao Xia, Michael Overton, Gul Nisa Gürbüz, Brandon De Breuhl

This study investigates the near-future impacts of generative artificial intelligence (AI) technologies on occupational competencies across the U.S. federal workforce. We develop a multi-stage Retrieval-Augmented Generation system to leverage large language models for predictive AI modeling that projects shifts in required competencies and to identify vulnerable occupations on a knowledge-by-skill-by-ability basis across the federal government workforce. This study highlights policy recommendations essential for workforce planning in the era of AI. We integrate several sources of detailed data on occupational requirements across the federal government from both centralized and decentralized human resource sources, including from the U.S. Office of Personnel Management (OPM) and various federal agencies. While our preliminary findings suggest some significant shifts in required competencies and potential vulnerability of certain roles to AI-driven changes, we provide nuanced insights that support arguments against abrupt or generic approaches to strategic human capital planning around the development of generative AI. The study aims to inform strategic workforce planning and policy development within federal agencies and demonstrates how this approach can be replicated across other large employment institutions and labor markets.

comment: 53 pages, 9 figures, 3 tables

♻ ☆ The erasure of intensive livestock farming in text-to-image generative AI

Kehan Sheng, Frank A. M. Tuyttens, Marina A. G. von Keyserlingk

Generative AI (e.g., ChatGPT) is increasingly integrated into people's daily lives. While it is known that AI perpetuates biases against marginalized human groups, their impact on non-human animals remains understudied. We found that ChatGPT's text-to-image model (DALL-E 3) introduces a strong bias toward romanticizing livestock farming as dairy cows on pasture and pigs rooting in mud. This bias remained when we requested realistic depictions and was only mitigated when the automatic prompt revision was inhibited. Most farmed animal in industrialized countries are reared indoors with limited space per animal, which fail to resonate with societal values. Inhibiting prompt revision resulted in images that more closely reflected modern farming practices; for example, cows housed indoors accessing feed through metal headlocks, and pigs behind metal railings on concrete floors in indoor facilities. While OpenAI introduced prompt revision to mitigate bias, in the case of farmed animal production systems, it paradoxically introduces a strong bias towards unrealistic farming practices.

♻ ☆ Older adults' safety and security online: A post-pandemic exploration of attitudes and behaviors

Edgar Pacheco

Older adults' growing use of the internet and related technologies, further accelerated by the COVID-19 pandemic, has prompted not only a critical examination of their behaviors and attitudes about online threats but also a greater understanding of the roles of specific characteristics within this population group. Based on survey data and using descriptive and inferential statistics, this empirical study delves into this matter. The behaviors and attitudes of a group of older adults aged 60 years and older (n=275) regarding different dimensions of online safety and cybersecurity are investigated. The results show that older adults report a discernible degree of concern about the security of their personal information. Despite the varied precautions taken, most of them do not know where to report online threats. What is more, regarding key demographics, the study found some significant differences in terms of gender and age group, but not disability status. This implies that older adults do not seem to constitute a homogeneous group when it comes to attitudes and behaviors regarding safety and security online. The study concludes that support systems should include older adults in the development of protective measures and acknowledge their diversity. The implications of the results are discussed and some directions for future research are proposed.

comment: 20 pages, 7 tables

♻ ☆ Neural embedding of beliefs reveals the role of relative dissonance in human decision-making

Byunghwee Lee, Rachith Aiyappa, Yong-Yeol Ahn, Haewoon Kwak, Jisun An

Beliefs form the foundation of human cognition and decision-making, guiding our actions and social connections. A model encapsulating beliefs and their interrelationships is crucial for understanding their influence on our actions. However, research on belief interplay has often been limited to beliefs related to specific issues and relied heavily on surveys. We propose a method to study the nuanced interplay between thousands of beliefs by leveraging an online user debate data and mapping beliefs onto a neural embedding space constructed using a fine-tuned large language model (LLM). This belief space captures the interconnectedness and polarization of diverse beliefs across social issues. Our findings show that positions within this belief space predict new beliefs of individuals and estimate cognitive dissonance based on the distance between existing and new beliefs. This study demonstrates how LLMs, combined with collective online records of human beliefs, can offer insights into the fundamental principles that govern human decision-making.

♻ ☆ Revealing and Reducing Gender Biases in Vision and Language Assistants (VLAs) ICLR 2025

Leander Girrbach, Stephan Alaniz, Yiran Huang, Trevor Darrell, Zeynep Akata

Pre-trained large language models (LLMs) have been reliably integrated with visual input for multimodal tasks. The widespread adoption of instruction-tuned image-to-text vision-language assistants (VLAs) like LLaVA and InternVL necessitates evaluating gender biases. We study gender bias in 22 popular open-source VLAs with respect to personality traits, skills, and occupations. Our results show that VLAs replicate human biases likely present in the data, such as real-world occupational imbalances. Similarly, they tend to attribute more skills and positive personality traits to women than to men, and we see a consistent tendency to associate negative personality traits with men. To eliminate the gender bias in these models, we find that fine-tuning-based debiasing methods achieve the best trade-off between debiasing and retaining performance on downstream tasks. We argue for pre-deploying gender bias assessment in VLAs and motivate further development of debiasing strategies to ensure equitable societal outcomes. Code is available at https://github.com/ExplainableML/vla-gender-bias.

comment: Accepted at ICLR 2025

♻ ☆ Generative AI Policies under the Microscope: How CS Conferences Are Navigating the New Frontier in Scholarly Writing

Mahjabin Nahar, Sian Lee, Rebekah Guillen, Dongwon Lee

As the use of Generative AI (Gen-AI) in scholarly writing and peer reviews continues to rise, it is essential for the computing field to establish and adopt clear Gen-AI policies. This study examines the landscape of Gen-AI policies across 64 major Computer Science conferences and offers recommendations for promoting more effective and responsible use of Gen-AI in the field.

comment: Accepted and to appear in Communications of the ACM (CACM) in 2025

♻ ☆ Training Foundation Models as Data Compression: On Information, Model Weights and Copyright Law

Giorgio Franceschelli, Claudia Cevenini, Mirco Musolesi

The training process of foundation models as for other classes of deep learning systems is based on minimizing the reconstruction error over a training set. For this reason, they are susceptible to the memorization and subsequent reproduction of training samples. In this paper, we introduce a training-as-compressing perspective, wherein the model's weights embody a compressed representation of the training data. From a copyright standpoint, this point of view implies that the weights can be considered a reproduction or, more likely, a derivative work of a potentially protected set of works. We investigate the technical and legal challenges that emerge from this framing of the copyright of outputs generated by foundation models, including their implications for practitioners and researchers. We demonstrate that adopting an information-centric approach to the problem presents a promising pathway for tackling these emerging complex legal issues.

comment: Spotlight presentation at GenLaw'24, see https://www.genlaw.org/2024-icml-papers#training-foundation-models-as-data-compression-on-information-model-weights-and-copyright-law

♻ ☆ Status and Future Prospects of the Standardization Framework Industry 4.0: A European Perspective

Olga Meyer, Marvin Boell, Christoph Legat

The rapid development of Industry 4.0 technologies requires robust and comprehensive standardization to ensure interoperability, safety and efficiency in the Industry of the Future. This paper examines the fundamental role and functionality of standardization, with a particular focus on its importance in Europe's regulatory framework. Based on this, selected topics in context of standardization activities in context intelligent manufacturing and digital twins are highlighted and, by that, an overview of the Industry 4.0 standards framework is provided. This paper serves both as an informative guide to the existing standards in Industry 4.0 with respect to Artificial Intelligence and Digital Twins, and as a call to action for increased cooperation between standardization bodies and the research community. By fostering such collaboration, we aim to facilitate the continued development and implementation of standards that will drive innovation and progress in the manufacturing sector.

♻ ☆ Trustworthy AIGC Copyright Management with Full Lifecycle Recording and Multi-party Supervision in Blockchain

Jiajia Jiang, Moting Su, Fengshu Li, Xiangli Xiao, Yushu Zhang

As artificial intelligence technology becomes increasingly widespread, AI-generated content (AIGC) is gradually penetrating into many fields. Although AIGC plays an increasingly prominent role in business and cultural communication, the issue of copyright has also triggered widespread social discussion. The current legal system for copyright is built around human creators, yet in the realm of AIGC, the role of humans in content creation has diminished, with the creative expression primarily reliant on artificial intelligence. This discrepancy has led to numerous complexities and challenges in determining the copyright ownership of AIGC within the established legal boundaries. In view of this, it is necessary to meticulously record contributions of all entities involved in the generation of AIGC to achieve a fair distribution of copyright. For this purpose, this study thoroughly records the intermediate data generated throughout the full lifecycle of AIGC and deposits them into a decentralized blockchain system for secure multi-party supervision, thereby constructing a trustworthy AIGC copyright management system. In the event of copyright disputes, auditors can retrieve valuable proof from the blockchain, accurately defining the copyright ownership of AIGC products. Both theoretical and experimental analyses confirm that this scheme shows exceptional performance and security in the management of AIGC copyrights.

Computers and Society

☆ ARCHED: A Human-Centered Framework for Transparent, Responsible, and Collaborative AI-Assisted Instructional Design AAAI 2025

Hongming Li, Yizirui Fang, Shan Zhang, Seiyon M. Lee, Yiming Wang, Mark Trexler, Anthony F. Botelho

Integrating Large Language Models (LLMs) in educational technology presents unprecedented opportunities to improve instructional design (ID), yet existing approaches often prioritize automation over pedagogical rigor and human agency. This paper introduces ARCHED (AI for Responsible, Collaborative, Human-centered Education Instructional Design), a structured multi-stage framework that ensures human educators remain central in the design process while leveraging AI capabilities. Unlike traditional AI-generated instructional materials that lack transparency, ARCHED employs a cascaded workflow aligned with Bloom's taxonomy. The framework integrates specialized AI agents - one generating diverse pedagogical options and another evaluating alignment with learning objectives - while maintaining educators as primary decision-makers. This approach addresses key limitations in current AI-assisted instructional design, ensuring transparency, pedagogical foundation, and meaningful human agency. Empirical evaluations demonstrate that ARCHED enhances instructional design quality while preserving educator oversight, marking a step forward in responsible AI integration in education.

comment: Accepted to the iRAISE Workshop at AAAI 2025. To be published in PMLR Volume 273

☆ Some information is too dangerous to be on the internet

Vincent C. Müller

This paper investigates a problem about freedom of information. Although freedom of information is generally considered desirable, there are a number of areas where there is substantial agreement that freedom of information should be limited. After a certain ordering of the landscape, I argue that we need to add the category of 'dangerous' information and that this category has gained a new quality in the context of current information technology, specifically the Internet. This category includes information the use of which would be morally wrong as well as some of what may be called 'corrupting' information. Some such information should not be spread at all and some should be very limited in its spread.

☆ Randomness, Not Representation: The Unreliability of Evaluating Cultural Alignment in LLMs

Ariba Khan, Stephen Casper, Dylan Hadfield-Menell

Research on the 'cultural alignment' of Large Language Models (LLMs) has emerged in response to growing interest in understanding representation across diverse stakeholders. Current approaches to evaluating cultural alignment borrow social science methodologies but often overlook systematic robustness checks. Here, we identify and test three assumptions behind current evaluation methods: (1) Stability: that cultural alignment is a property of LLMs rather than an artifact of evaluation design, (2) Extrapolability: that alignment with one culture on a narrow set of issues predicts alignment with that culture on others, and (3) Steerability: that LLMs can be reliably prompted to represent specific cultural perspectives. Through experiments examining both explicit and implicit preferences of leading LLMs, we find a high level of instability across presentation formats, incoherence between evaluated versus held-out cultural dimensions, and erratic behavior under prompt steering. We show that these inconsistencies can cause the results of an evaluation to be very sensitive to minor variations in methodology. Finally, we demonstrate in a case study on evaluation design that narrow experiments and a selective assessment of evidence can be used to paint an incomplete picture of LLMs' cultural alignment properties. Overall, these results highlight significant limitations of current approaches for evaluating the cultural alignment of LLMs.

☆ Generating Robot Constitutions & Benchmarks for Semantic Safety

Pierre Sermanet, Anirudha Majumdar, Alex Irpan, Dmitry Kalashnikov, Vikas Sindhwani

Until recently, robotics safety research was predominantly about collision avoidance and hazard reduction in the immediate vicinity of a robot. Since the advent of large vision and language models (VLMs), robots are now also capable of higher-level semantic scene understanding and natural language interactions with humans. Despite their known vulnerabilities (e.g. hallucinations or jail-breaking), VLMs are being handed control of robots capable of physical contact with the real world. This can lead to dangerous behaviors, making semantic safety for robots a matter of immediate concern. Our contributions in this paper are two fold: first, to address these emerging risks, we release the ASIMOV Benchmark, a large-scale and comprehensive collection of datasets for evaluating and improving semantic safety of foundation models serving as robot brains. Our data generation recipe is highly scalable: by leveraging text and image generation techniques, we generate undesirable situations from real-world visual scenes and human injury reports from hospitals. Secondly, we develop a framework to automatically generate robot constitutions from real-world data to steer a robot's behavior using Constitutional AI mechanisms. We propose a novel auto-amending process that is able to introduce nuances in written rules of behavior; this can lead to increased alignment with human preferences on behavior desirability and safety. We explore trade-offs between generality and specificity across a diverse set of constitutions of different lengths, and demonstrate that a robot is able to effectively reject unconstitutional actions. We measure a top alignment rate of 84.3% on the ASIMOV Benchmark using generated constitutions, outperforming no-constitution baselines and human-written constitutions. Data is available at asimov-benchmark.github.io

☆ BiasEdit: Debiasing Stereotyped Language Models via Model Editing NAACL 2025

Xin Xu, Wei Xu, Ningyu Zhang, Julian McAuley

Previous studies have established that language models manifest stereotyped biases. Existing debiasing strategies, such as retraining a model with counterfactual data, representation projection, and prompting often fail to efficiently eliminate bias or directly alter the models' biased internal representations. To address these issues, we propose BiasEdit, an efficient model editing method to remove stereotypical bias from language models through lightweight networks that act as editors to generate parameter updates. BiasEdit employs a debiasing loss guiding editor networks to conduct local edits on partial parameters of a language model for debiasing while preserving the language modeling abilities during editing through a retention loss. Experiments on StereoSet and Crows-Pairs demonstrate the effectiveness, efficiency, and robustness of BiasEdit in eliminating bias compared to tangental debiasing baselines and little to no impact on the language models' general capabilities. In addition, we conduct bias tracing to probe bias in various modules and explore bias editing impacts on different components of language models.

comment: Accepted by TrustNLP @ NAACL 2025

☆ When Discourse Stalls: Moving Past Five Semantic Stopsigns about Generative AI in Design Research

Willem van der Maden, Vera van der Burg, Brett A. Halperin, Petra Jääskeläinen, Joseph Lindley, Derek Lomas, Timothy Merritt

This essay examines how Generative AI (GenAI) is rapidly transforming design practices and how discourse often falls into over-simplified narratives that impede meaningful research and practical progress. We identify and deconstruct five prevalent "semantic stopsigns" -- reductive framings about GenAI in design that halt deeper inquiry and limit productive engagement. Reflecting upon two expert workshops at ACM conferences and semi-structured interviews with design practitioners, we analyze how these stopsigns manifest in research and practice. Our analysis develops mid-level knowledge that bridges theoretical discourse and practical implementation, helping designers and researchers interrogate common assumptions about GenAI in their own contexts. By recasting these stopsigns into more nuanced frameworks, we provide the design research community with practical approaches for thinking about and working with these emerging technologies.

☆ Exploring Socio-Cultural Challenges and Opportunities in Designing Mental Health Chatbots for Adolescents in India

Neil K. R. Sehgal, Hita Kambhamettu, Sai Preethi Matam, Lyle Ungar, Sharath Chandra Guntuku

Mental health challenges among Indian adolescents are shaped by unique cultural and systemic barriers, including high social stigma and limited professional support. Through a mixed-methods study involving a survey of 278 adolescents and follow-up interviews with 12 participants, we explore how adolescents perceive mental health challenges and interact with digital tools. Quantitative results highlight low self-stigma but significant social stigma, a preference for text over voice interactions, and low utilization of mental health apps but high smartphone access. Our qualitative findings reveal that while adolescents value privacy, emotional support, and localized content in mental health tools, existing chatbots lack personalization and cultural relevance. These findings inform recommendations for culturally sensitive chatbot design that prioritizes anonymity, tailored support, and localized resources to better meet the needs of adolescents in India. This work advances culturally sensitive chatbot design by centering underrepresented populations, addressing critical gaps in accessibility and support for adolescents in India.

☆ BoundarEase: Fostering Constructive Community Engagement to Inform More Equitable Student Assignment Policies SC

Cassandra Overney, Cassandra Moe, Alvin Chang, Nabeel Gillani

School districts across the United States (US) play a pivotal role in shaping access to quality education through their student assignment policies -- most prominently, school attendance boundaries. Community engagement processes for changing such policies, however, are often opaque, cumbersome, and highly polarizing -- hampering equitable access to quality schools in ways that can perpetuate disparities in future life outcomes. In this paper, we describe a collaboration with a large US public school district serving nearly 150,000 students to design and evaluate a new sociotechnical system, "BoundarEase", for fostering more constructive community engagement around changing school attendance boundaries. Through a formative study with 16 community members, we first identify several frictions in existing community engagement processes, like individualistic over collective thinking; a failure to understand and empathize with the different ways policies might impact other community members; and challenges in understanding the impacts of boundary changes. These frictions inspire the design and development of BoundarEase, a web platform that allows community members to explore and offer feedback on potential boundaries. A user study with 12 community members reveals that BoundarEase prompts reflection among community members on how policies might impact families beyond their own, and increases transparency around the details of policy proposals. Our paper offers education researchers insights into the challenges and opportunities involved in community engagement for designing student assignment policies; human-computer interaction researchers a case study of how new sociotechnical systems might help mitigate polarization in local policymaking; and school districts a practical tool they might use to facilitate community engagement to foster more equitable student assignment policies.

comment: Forthcoming in CSCW 2025

☆ Integrating Captive Portal Technology into Computer Science Education: A Modular, Hands-On Approach to Infrastructure SC'24

Lianting Wang, Marcelo Ponce

In this paper, we present an educational project aimed to introduce students to the technology behind Captive Portals infrastructures. For doing this, we developed a series of modules to emphasize each of the different aspects and features of this technology. The project is based on an open source implementation which is widely used in many computer network courses, making it well-suited and very appealing for instructors and practitioners in this field.

comment: Presented at BPHTE'24 @ SC'24. Associated repo, see https://github.com/Lianting-Wang/Captive-Portal-Education

☆ Fact-checking with Generative AI: A Systematic Cross-Topic Examination of LLMs Capacity to Detect Veracity of Political Information

Elizaveta Kuznetsova, Ilaria Vitulano, Mykola Makhortykh, Martha Stolze, Tomas Nagy, Victoria Vziatysheva

The purpose of this study is to assess how large language models (LLMs) can be used for fact-checking and contribute to the broader debate on the use of automated means for veracity identification. To achieve this purpose, we use AI auditing methodology that systematically evaluates performance of five LLMs (ChatGPT 4, Llama 3 (70B), Llama 3.1 (405B), Claude 3.5 Sonnet, and Google Gemini) using prompts regarding a large set of statements fact-checked by professional journalists (16,513). Specifically, we use topic modeling and regression analysis to investigate which factors (e.g. topic of the prompt or the LLM type) affect evaluations of true, false, and mixed statements. Our findings reveal that while ChatGPT 4 and Google Gemini achieved higher accuracy than other models, overall performance across models remains modest. Notably, the results indicate that models are better at identifying false statements, especially on sensitive topics such as COVID-19, American political controversies, and social issues, suggesting possible guardrails that may enhance accuracy on these topics. The major implication of our findings is that there are significant challenges for using LLMs for factchecking, including significant variation in performance across different LLMs and unequal quality of outputs for specific topics which can be attributed to deficits of training data. Our research highlights the potential and limitations of LLMs in political fact-checking, suggesting potential avenues for further improvements in guardrails as well as fine-tuning.

comment: 15 pages, 2 figures

☆ How Can Video Generative AI Transform K-12 Education? Examining Teachers' Perspectives through TPACK and TAM

Unggi Lee, Yeil Jeong, Seungha Kim, Yoorim Son, Gyuri Byun, Hyeoncheol Kim, Cheolil Lim

The rapid advancement of generative AI technology, particularly video generative AI (Video GenAI), has opened new possibilities for K-12 education by enabling the creation of dynamic, customized, and high-quality visual content. Despite its potential, there is limited research on how this emerging technology can be effectively integrated into educational practices. This study explores the perspectives of leading K-12 teachers on the educational applications of Video GenAI, using the TPACK (Technological Pedagogical Content Knowledge) and TAM (Technology Acceptance Model) frameworks as analytical lenses. Through interviews and hands-on experimentation with video generation tools, the research identifies opportunities for enhancing teaching strategies, fostering student engagement, and supporting authentic task design. It also highlights challenges such as technical limitations, ethical considerations, and the need for institutional support. The findings provide actionable insights into how Video GenAI can transform teaching and learning, offering practical implications for policy, teacher training, and the future development of educational technology.

☆ Predicting and Understanding College Student Mental Health with Interpretable Machine Learning

Meghna Roy Chowdhury, Wei Xuan, Shreyas Sen, Yixue Zhao, Yi Ding

Mental health issues among college students have reached critical levels, significantly impacting academic performance and overall wellbeing. Predicting and understanding mental health status among college students is challenging due to three main factors: the necessity for large-scale longitudinal datasets, the prevalence of black-box machine learning models lacking transparency, and the tendency of existing approaches to provide aggregated insights at the population level rather than individualized understanding. To tackle these challenges, this paper presents I-HOPE, the first Interpretable Hierarchical mOdel for Personalized mEntal health prediction. I-HOPE is a two-stage hierarchical model, validated on the College Experience Study, the longest longitudinal mobile sensing dataset. This dataset spans five years and captures data from both pre-pandemic periods and the COVID-19 pandemic. I-HOPE connects raw behavioral features to mental health status through five defined behavioral categories as interaction labels. This approach achieves a prediction accuracy of 91%, significantly surpassing the 60-70% accuracy of baseline methods. In addition, our model distills complex patterns into interpretable and individualized insights, enabling the future development of tailored interventions and improving mental health support. The code is available at https://github.com/roycmeghna/I-HOPE.

comment: 12 pages, 10 figures, ACM/IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE '25), June 24--26, 2025, New York, NY, USA

☆ Counterfactual Explanations for Model Ensembles Using Entropic Risk Measures

Erfaun Noorani, Pasan Dissanayake, Faisal Hamman, Sanghamitra Dutta

Counterfactual explanations indicate the smallest change in input that can translate to a different outcome for a machine learning model. Counterfactuals have generated immense interest in high-stakes applications such as finance, education, hiring, etc. In several use-cases, the decision-making process often relies on an ensemble of models rather than just one. Despite significant research on counterfactuals for one model, the problem of generating a single counterfactual explanation for an ensemble of models has received limited interest. Each individual model might lead to a different counterfactual, whereas trying to find a counterfactual accepted by all models might significantly increase cost (effort). We propose a novel strategy to find the counterfactual for an ensemble of models using the perspective of entropic risk measure. Entropic risk is a convex risk measure that satisfies several desirable properties. We incorporate our proposed risk measure into a novel constrained optimization to generate counterfactuals for ensembles that stay valid for several models. The main significance of our measure is that it provides a knob that allows for the generation of counterfactuals that stay valid under an adjustable fraction of the models. We also show that a limiting case of our entropic-risk-based strategy yields a counterfactual valid for all models in the ensemble (worst-case min-max approach). We study the trade-off between the cost (effort) for the counterfactual and its validity for an ensemble by varying degrees of risk aversion, as determined by our risk parameter knob. We validate our performance on real-world datasets.

♻ ☆ Behind the Smile: Mental Health Implications of Mother-Infant Interactions Revealed Through Smile Analysis

Adi Dust, Pat Levitt, Maja Matarić

Mothers of infants have specific demands in fostering emotional bonds with their children, characterized by dynamics that are different from adult-adult interactions, notably requiring heightened maternal emotional regulation. In this study, we analyzed maternal emotional state by modeling maternal emotion regulation reflected in smiles. The dataset comprises N=94 videos of approximately 3 plus or minus 1-minutes, capturing free play interactions between 6 and 12-month-old infants and their mothers. Corresponding demographic details of self-reported maternal mental health provide variables for determining mothers' relations to emotions measured during free play. In this work, we employ diverse methodological approaches to explore the temporal evolution of maternal smiles. Our findings reveal a correlation between the temporal dynamics of mothers' smiles and their emotional state. Furthermore, we identify specific smile features that correlate with maternal emotional state, thereby enabling informed inferences with existing literature on general smile analysis. This study offers insights into emotional labor, defined as the management of one's own emotions for the benefit of others, and emotion regulation entailed in mother-infant interactions.

comment: 9 pages, 2 Figures, Affective Computing & Intelligent Interaction Conference 2024

♻ ☆ Validating LLM-as-a-Judge Systems in the Absence of Gold Labels

Luke Guerdan, Solon Barocas, Kenneth Holstein, Hanna Wallach, Zhiwei Steven Wu, Alexandra Chouldechova

The LLM-as-a-judge paradigm, in which a judge LLM system replaces human raters in rating the outputs of other generative AI (GenAI) systems, has come to play a critical role in scaling and standardizing GenAI evaluations. To validate judge systems, evaluators collect multiple human ratings for each item in a validation corpus, and then aggregate the ratings into a single, per-item gold label rating. High agreement rates between these gold labels and judge system ratings are then taken as a sign of good judge system performance. In many cases, however, items or rating criteria may be ambiguous, or there may be principled disagreement among human raters. In such settings, gold labels may not exist for many of the items. In this paper, we introduce a framework for LLM-as-a-judge validation in the absence of gold labels. We present a theoretical analysis drawing connections between different measures of judge system performance under different rating elicitation and aggregation schemes. We also demonstrate empirically that existing validation approaches can select judge systems that are highly suboptimal, performing as much as 34% worse than the systems selected by alternative approaches that we describe. Based on our findings, we provide concrete recommendations for developing more reliable approaches to LLM-as-a-judge validation.

♻ ☆ Personality Traits in Large Language Models

Greg Serapio-García, Mustafa Safdari, Clément Crepy, Luning Sun, Stephen Fitz, Peter Romero, Marwa Abdulhai, Aleksandra Faust, Maja Matarić

The advent of large language models (LLMs) has revolutionized natural language processing, enabling the generation of coherent and contextually relevant human-like text. As LLMs increasingly powerconversational agents used by the general public world-wide, the synthetic personality traits embedded in these models, by virtue of training on large amounts of human data, is becoming increasingly important. Since personality is a key factor determining the effectiveness of communication, we present a novel and comprehensive psychometrically valid and reliable methodology for administering and validating personality tests on widely-used LLMs, as well as for shaping personality in the generated text of such LLMs. Applying this method to 18 LLMs, we found: 1) personality measurements in the outputs of some LLMs under specific prompting configurations are reliable and valid; 2) evidence of reliability and validity of synthetic LLM personality is stronger for larger and instruction fine-tuned models; and 3) personality in LLM outputs can be shaped along desired dimensions to mimic specific human personality profiles. We discuss the application and ethical implications of the measurement and shaping method, in particular regarding responsible AI.

♻ ☆ Creating and Evaluating Privacy and Security Micro-Lessons for Elementary School Children SC

Lan Gao, Elana B Blinder, Abigail Barnes, Kevin Song, Tamara Clegg, Jessica Vitak, Marshini Chetty

The growing use of technology in K--8 classrooms highlights a parallel need for formal learning opportunities aimed at helping children use technology safely and protect their personal information. Even the youngest students are now using tablets, laptops, and apps to support their learning; however, there are limited curricular materials available for elementary and middle school children on digital privacy and security topics. To bridge this gap, we developed a series of micro-lessons to help K--8 children learn about digital privacy and security at school. We first conducted a formative study by interviewing elementary school teachers to identify the design needs for digital privacy and security lessons. We then developed micro-lessons -- multiple 15-20 minute activities designed to be easily inserted into the existing curriculum -- using a co-design approach with multiple rounds of developing and revising the micro-lessons in collaboration with teachers. Throughout the process, we conducted evaluation sessions where teachers implemented or reviewed the micro-lessons. Our study identifies strengths, challenges, and teachers' tailoring strategies when incorporating micro-lessons for K--8 digital privacy and security topics, providing design implications for facilitating learning about these topics in school classrooms.

comment: Preprint for CSCW 2025

♻ ☆ Evaluating Tenant-Landlord Tensions Using Generative AI on Online Tenant Forums

Xin Chen, Cheng Ren, Timothy A Thomas

Tenant-landlord relationships exhibit a power asymmetry where landlords' power to evict the tenants at a low-cost results in their dominating status in such relationships. Tenant concerns are thus often unspoken, unresolved, or ignored and this could lead to blatant conflicts as suppressed tenant concerns accumulate. Modern machine learning methods and Large Language Models (LLM) have demonstrated immense abilities to perform language tasks. In this study, we incorporate Latent Dirichlet Allocation (LDA) with GPT-4 to classify Reddit post data scraped from the subreddit r/Tenant, aiming to unveil trends in tenant concerns while exploring the adoption of LLMs and machine learning methods in social science research. We find that tenant concerns in topics like fee dispute and utility issues are consistently dominant in all four states analyzed while each state has other common tenant concerns special to itself. Moreover, we discover temporal trends in tenant concerns that provide important implications regarding the impact of the pandemic and the Eviction Moratorium.

♻ ☆ Learning and teaching biological data science in the Bioconductor community

Jenny Drnevich, Frederick J. Tan, Fabricio Almeida-Silva, Robert Castelo, Aedin C. Culhane, Sean Davis, Maria A. Doyle, Ludwig Geistlinger, Andrew R. Ghazi, Susan Holmes, Leo Lahti, Alexandru Mahmoud, Kozo Nishida, Marcel Ramos, Kevin Rue-Albrecht, David J. H. Shih, Laurent Gatto, Charlotte Soneson

Modern biological research is increasingly data-intensive, leading to a growing demand for effective training in biological data science. In this article, we provide an overview of key resources and best practices available within the Bioconductor project - an open-source software community focused on omics data analysis. This guide serves as a valuable reference for both learners and educators in the field.

comment: 16 pages, 2 figures, 1 table, 1 supplemental table; update after peer review; 16 pages, 1 figure, 1 table, 1 supplemental table

♻ ☆ The Lazy Student's Dream: ChatGPT Passing an Engineering Course on Its Own

Gokul Puthumanaillam, Melkior Ornik

This paper presents a comprehensive investigation into the capability of Large Language Models (LLMs) to successfully complete a semester-long undergraduate control systems course. Through evaluation of 115 course deliverables, we assess LLM performance using ChatGPT under a "minimal effort" protocol that simulates realistic student usage patterns. The investigation employs a rigorous testing methodology across multiple assessment formats, from auto-graded multiple choice questions to complex Python programming tasks and long-form analytical writing. Our analysis provides quantitative insights into AI's strengths and limitations in handling mathematical formulations, coding challenges, and theoretical concepts in control systems engineering. The LLM achieved a B-grade performance (82.24\%), approaching but not exceeding the class average (84.99\%), with strongest results in structured assignments and greatest limitations in open-ended projects. The findings inform discussions about course design adaptation in response to AI advancement, moving beyond simple prohibition towards thoughtful integration of these tools in engineering education. Additional materials including syllabus, examination papers, design projects, and example responses can be found at the project website: https://gradegpt.github.io.

♻ ☆ ChatGPT-4 in the Turing Test: A Critical Analysis

Marco Giunti

This paper critically examines the recent publication "ChatGPT-4 in the Turing Test" by Restrepo Echavarr\'ia (2025), challenging its central claims regarding the absence of minimally serious test implementations and the conclusion that ChatGPT-4 fails the Turing Test. The analysis reveals that the criticisms based on rigid criteria and limited experimental data are not fully justified. More importantly, the paper makes several constructive contributions that enrich our understanding of Turing Test implementations. It demonstrates that two distinct formats--the three-player and two-player tests--are both valid, each with unique methodological implications. The work distinguishes between absolute criteria (reflecting an optimal 50% identification rate in a three-player format) and relative criteria (which measure how closely a machine's performance approximates that of a human), offering a more nuanced evaluation framework. Furthermore, the paper clarifies the probabilistic underpinnings of both test types by modeling them as Bernoulli experiments--correlated in the three-player version and uncorrelated in the two-player version. This formalization allows for a rigorous separation between the theoretical criteria for passing the test, defined in probabilistic terms, and the experimental data that require robust statistical methods for proper interpretation. In doing so, the paper not only refutes key aspects of the criticized study but also lays a solid foundation for future research on objective measures of how closely an AI's behavior aligns with, or deviates from, that of a human being.

comment: 14 pages, 1 Appendix, added 1 missing item in References, corrected typos

♻ ☆ To which reference class do you belong? Measuring racial fairness of reference classes with normative modeling

Saige Rutherford, Thomas Wolfers, Charlotte Fraza, Nathaniel G. Harnett, Christian F. Beckmann, Henricus G. Ruhe, Andre F. Marquand

Reference classes in healthcare establish healthy norms, such as pediatric growth charts of height and weight, and are used to chart deviations from these norms which represent potential clinical risk. How the demographics of the reference class influence clinical interpretation of deviations is unknown. Using normative modeling, a method for building reference classes, we evaluate the fairness (racial bias) in reference models of structural brain images that are widely used in psychiatry and neurology. We test whether including race in the model creates fairer models. We predict self-reported race using the deviation scores from three different reference class normative models, to better understand bias in an integrated, multivariate sense. Across all of these tasks, we uncover racial disparities that are not easily addressed with existing data or commonly used modeling techniques. Our work suggests that deviations from the norm could be due to demographic mismatch with the reference class, and assigning clinical meaning to these deviations should be done with caution. Our approach also suggests that acquiring more representative samples is an urgent research priority.

♻ ☆ Emotion-Aware Embedding Fusion in LLMs (Flan-T5, LLAMA 2, DeepSeek-R1, and ChatGPT 4) for Intelligent Response Generation

Abdur Rasool, Muhammad Irfan Shahzad, Hafsa Aslam, Vincent Chan, Muhammad Ali Arshad

Empathetic and coherent responses are critical in auto-mated chatbot-facilitated psychotherapy. This study addresses the challenge of enhancing the emotional and contextual understanding of large language models (LLMs) in psychiatric applications. We introduce Emotion-Aware Embedding Fusion, a novel framework integrating hierarchical fusion and attention mechanisms to prioritize semantic and emotional features in therapy transcripts. Our approach combines multiple emotion lexicons, including NRC Emotion Lexicon, VADER, WordNet, and SentiWordNet, with state-of-the-art LLMs such as Flan-T5, LLAMA 2, DeepSeek-R1, and ChatGPT 4. Therapy session transcripts, comprising over 2,000 samples are segmented into hierarchical levels (word, sentence, and session) using neural networks, while hierarchical fusion combines these features with pooling techniques to refine emotional representations. Atten-tion mechanisms, including multi-head self-attention and cross-attention, further prioritize emotional and contextual features, enabling temporal modeling of emotion-al shifts across sessions. The processed embeddings, computed using BERT, GPT-3, and RoBERTa are stored in the Facebook AI similarity search vector database, which enables efficient similarity search and clustering across dense vector spaces. Upon user queries, relevant segments are retrieved and provided as context to LLMs, enhancing their ability to generate empathetic and con-textually relevant responses. The proposed framework is evaluated across multiple practical use cases to demonstrate real-world applicability, including AI-driven therapy chatbots. The system can be integrated into existing mental health platforms to generate personalized responses based on retrieved therapy session data.

♻ ☆ Investigating Gender Euphoria and Dysphoria on TikTok: Characterization and Comparison

SJ Dillon, Yueqing Liang, H. Russell Bernard, Kai Shu

With the emergence of short video-sharing platforms, engagement with social media sites devoted to opinion and knowledge dissemination has rapidly increased. Among these platforms, TikTok is one of the most popular globally and has become the platform of choice for transgender and nonbinary individuals, who have formed a large community to mobilize personal experience and exchange information. The knowledge produced in online spaces can influence the ways in which people understand and experience their own gender and transitions, as they hear about others and weigh experiential and medical knowledge against their own. This paper extends current research and past interview methods on gender euphoria and gender dysphoria to analyze what and how online communities on TikTok discuss these two types of gender experiences. Our findings indicate that gender euphoria and gender dysphoria are differently described in online TikTok spaces. These findings indicate similarities in the words used to describe gender dysphoria as well as gender euphoria in both the comments of videos and content creators' hashtags. Finally, our results show that gender euphoria is described in more similar terms between transfeminine and transmasculine experiences than gender dysphoria, which appears to be more differentiated by gendering experience and transition goals. We hope this paper can provide insights for future research on understanding transgender and nonbinary individuals in online communities.

comment: ASONAM 2024

♻ ☆ Unlocking the Potential of AI Researchers in Scientific Discovery: What Is Missing?

Hengjie Yu, Yaochu Jin

The potential of AI researchers in scientific discovery remains largely to be unleashed. Over the past decade, the presence of AI for Science (AI4Science) in the 145 Nature Index journals has increased ninefold, yet nearly 90% of AI4Science research remains predominantly led by experimental scientists. Drawing on the Diffusion of Innovation theory, we project that AI4Science's share of total publications will rise from 3.57% in 2024 to approximately 25% by 2050. Unlocking the potential of AI researchers is essential for driving this shift and fostering deeper integration of AI expertise into the research ecosystem. To this end, we propose structured and actionable workflows, alongside key strategies to position AI researchers at the forefront of scientific discovery. Furthermore, we outline three pivotal pathways: equipping experimental scientists with user-friendly AI tools to amplify the impact of AI researchers, bridging cognitive and methodological gaps to enable more direct participation in scientific discovery, and proactively cultivating a thriving AI-driven scientific ecosystem. By addressing these challenges, this work aims to empower AI researchers as a driving force in shaping the future of scientific discovery.

comment: 19 pages, 9 figures

Computers and Society

☆ Entangled responsibility: an analysis of citizen science communication and scientific citizenship

Niels J. Gommesen

The notion of citizen science is often referred to as the means of engaging public members in scientific research activities that can advance the reach and impact of technoscience. Despite this, few studies have addressed how human-machine collaborations in a citizen science context enable and constrain scientific citizenship and citizens' epistemic agencies and reconfigure science-citizen relations, including the process of citizens' engagement in scientific knowledge production. The following will address this gap by analysing the human and nonhuman material and discursive engagements in the citizen science project The Sound of Denmark. Doing so contributes to new knowledge on designing more responsible forms of citizen science engagement that advance civic agencies. Key findings emphasise that citizen science development can benefit from diverse fields such as participatory design research and feminist technoscience. Finally, the paper contributes to a broader debate on the formation of epistemic subjects, scientific citizenship, and responsible designing and evaluation of citizen science. Keywords: scientific citizenship, citizen science communication, epistemic agency, co-design, material-discursive practices, response-ability.

comment: 28 pages, 1 figure

☆ AI for Just Work: Constructing Diverse Imaginations of AI beyond "Replacing Humans"

Weina Jin, Nicholas Vincent, Ghassan Hamarneh

The AI community usually focuses on "how" to develop AI techniques, but lacks thorough open discussions on "why" we develop AI. Lacking critical reflections on the general visions and purposes of AI may make the community vulnerable to manipulation. In this position paper, we explore the "why" question of AI. We denote answers to the "why" question the imaginations of AI, which depict our general visions, frames, and mindsets for the prospects of AI. We identify that the prevailing vision in the AI community is largely a monoculture that emphasizes objectives such as replacing humans and improving productivity. Our critical examination of this mainstream imagination highlights its underpinning and potentially unjust assumptions. We then call to diversify our collective imaginations of AI, embedding ethical assumptions from the outset in the imaginations of AI. To facilitate the community's pursuit of diverse imaginations, we demonstrate one process for constructing a new imagination of "AI for just work," and showcase its application in the medical image synthesis task to make it more ethical. We hope this work will help the AI community to open dialogues with civil society on the visions and purposes of AI, and inspire more technical works and advocacy in pursuit of diverse and ethical imaginations to restore the value of AI for the public good.

☆ Towards Large Language Models that Benefit for All: Benchmarking Group Fairness in Reward Models

Kefan Song, Jin Yao, Runnan Jiang, Rohan Chandra, Shangtong Zhang

As Large Language Models (LLMs) become increasingly powerful and accessible to human users, ensuring fairness across diverse demographic groups, i.e., group fairness, is a critical ethical concern. However, current fairness and bias research in LLMs is limited in two aspects. First, compared to traditional group fairness in machine learning classification, it requires that the non-sensitive attributes, in this case, the prompt questions, be the same across different groups. In many practical scenarios, different groups, however, may prefer different prompt questions and this requirement becomes impractical. Second, it evaluates group fairness only for the LLM's final output without identifying the source of possible bias. Namely, the bias in LLM's output can result from both the pretraining and the finetuning. For finetuning, the bias can result from both the RLHF procedure and the learned reward model. Arguably, evaluating the group fairness of each component in the LLM pipeline could help develop better methods to mitigate the possible bias. Recognizing those two limitations, this work benchmarks the group fairness of learned reward models. By using expert-written text from arXiv, we are able to benchmark the group fairness of reward models without requiring the same prompt questions across different demographic groups. Surprisingly, our results demonstrate that all the evaluated reward models (e.g., Nemotron-4-340B-Reward, ArmoRM-Llama3-8B-v0.1, and GRM-llama3-8B-sftreg) exhibit statistically significant group unfairness. We also observed that top-performing reward models (w.r.t. canonical performance metrics) tend to demonstrate better group fairness.

☆ Sublinear Algorithms for Wasserstein and Total Variation Distances: Applications to Fairness and Privacy Auditing

Debabrota Basu, Debarshi Chanda

Resource-efficiently computing representations of probability distributions and the distances between them while only having access to the samples is a fundamental and useful problem across mathematical sciences. In this paper, we propose a generic algorithmic framework to estimate the PDF and CDF of any sub-Gaussian distribution while the samples from them arrive in a stream. We compute mergeable summaries of distributions from the stream of samples that require sublinear space w.r.t. the number of observed samples. This allows us to estimate Wasserstein and Total Variation (TV) distances between any two sub-Gaussian distributions while samples arrive in streams and from multiple sources (e.g. federated learning). Our algorithms significantly improves on the existing methods for distance estimation incurring super-linear time and linear space complexities. In addition, we use the proposed estimators of Wasserstein and TV distances to audit the fairness and privacy of the ML algorithms. We empirically demonstrate the efficiency of the algorithms for estimating these distances and auditing using both synthetic and real-world datasets.

☆ Artificial Intelligence in Deliberation: The AI Penalty and the Emergence of a New Deliberative Divide

Andreas Jungherr, Adrian Rauchfleisch

Digital deliberation has expanded democratic participation, yet challenges remain. This includes processing information at scale, moderating discussions, fact-checking, or attracting people to participate. Recent advances in artificial intelligence (AI) offer potential solutions, but public perceptions of AI's role in deliberation remain underexplored. Beyond efficiency, democratic deliberation is about voice and recognition. If AI is integrated into deliberation, public trust, acceptance, and willingness to participate may be affected. We conducted a preregistered survey experiment with a representative sample in Germany (n=1850) to examine how information about AI-enabled deliberation influences willingness to participate and perceptions of deliberative quality. Respondents were randomly assigned to treatments that provided them information about deliberative tasks facilitated by either AI or humans. Our findings reveal a significant AI-penalty. Participants were less willing to engage in AI-facilitated deliberation and rated its quality lower than human-led formats. These effects were moderated by individual predispositions. Perceptions of AI's societal benefits and anthropomorphization of AI showed positive interaction effects on people's interest to participate in AI-enabled deliberative formats and positive quality assessments, while AI risk assessments showed negative interactions with information about AI-enabled deliberation. These results suggest AI-enabled deliberation faces substantial public skepticism, potentially even introducing a new deliberative divide. Unlike traditional participation gaps based on education or demographics, this divide is shaped by attitudes toward AI. As democratic engagement increasingly moves online, ensuring AI's role in deliberation does not discourage participation or deepen inequalities will be a key challenge for future research and policy.

☆ Sometimes the Model doth Preach: Quantifying Religious Bias in Open LLMs through Demographic Analysis in Asian Nations

Hari Shankar, Vedanta S P, Tejas Cavale, Ponnurangam Kumaraguru, Abhijnan Chakraborty

Large Language Models (LLMs) are capable of generating opinions and propagating bias unknowingly, originating from unrepresentative and non-diverse data collection. Prior research has analysed these opinions with respect to the West, particularly the United States. However, insights thus produced may not be generalized in non-Western populations. With the widespread usage of LLM systems by users across several different walks of life, the cultural sensitivity of each generated output is of crucial interest. Our work proposes a novel method that quantitatively analyzes the opinions generated by LLMs, improving on previous work with regards to extracting the social demographics of the models. Our method measures the distance from an LLM's response to survey respondents, through Hamming Distance, to infer the demographic characteristics reflected in the model's outputs. We evaluate modern, open LLMs such as Llama and Mistral on surveys conducted in various global south countries, with a focus on India and other Asian nations, specifically assessing the model's performance on surveys related to religious tolerance and identity. Our analysis reveals that most open LLMs match a single homogeneous profile, varying across different countries/territories, which in turn raises questions about the risks of LLMs promoting a hegemonic worldview, and undermining perspectives of different minorities. Our framework may also be useful for future research investigating the complex intersection between training data, model architecture, and the resulting biases reflected in LLM outputs, particularly concerning sensitive topics like religious tolerance and identity.

☆ Artificial Utopia: Simulation and Intelligent Agents for a Democratised Future

Yannick Oswald

Prevailing top-down systems in politics and economics struggle to keep pace with the pressing challenges of the 21st century, such as climate change, social inequality and conflict. Bottom-up democratisation and participatory approaches in politics and economics are increasingly seen as promising alternatives to confront and overcome these issues, often with utopian overtones, as proponents believe they may dramatically reshape political, social and ecological futures for the better and in contrast to contemporary authoritarian tendencies across various countries. Institutional specifics and the associated collective human behavior or culture remains little understood and debated, however. In this article, I propose a novel research agenda focusing on utopian democratisation efforts with formal and computational methods as well as with artificial intelligence - I call this agenda Artificial Utopia. Artificial Utopias provide safe testing grounds for new political ideas and economic policies in-silico with reduced risk of negative consequences as compared to testing ideas in real-world contexts. An increasing number of advanced simulation and intelligence methods, that aim at representing human cognition and collective decision-making in more realistic ways, could benefit this process. This includes agent-based modelling, reinforcement learning, large language models and more. I clarify what some of these simulation approaches can contribute to the study of Artificial Utopias with the help of two institutional examples: the citizen assembly and the democratic firm.

☆ AI Biases as Asymmetries: A Review to Guide Practice

Gabriella Waters, Phillip Honenberger

The understanding of bias in AI is currently undergoing a revolution. Initially understood as errors or flaws, biases are increasingly recognized as integral to AI systems and sometimes preferable to less biased alternatives. In this paper, we review the reasons for this changed understanding and provide new guidance on two questions: First, how should we think about and measure biases in AI systems, consistent with the new understanding? Second, what kinds of bias in an AI system should we accept or even amplify, and what kinds should we minimize or eliminate, and why? The key to answering both questions, we argue, is to understand biases as "violations of a symmetry standard" (following Kelly). We distinguish three main types of asymmetry in AI systems-error biases, inequality biases, and process biases-and highlight places in the pipeline of AI development and application where bias of each type is likely to be good, bad, or inevitable.

comment: 24 pages

☆ Ways of Seeing, and Selling, AI Art

Imke van Heerden

In early 2025, Augmented Intelligence - Christie's first AI art auction - drew criticism for showcasing a controversial genre. Amid wider legal uncertainty, artists voiced concerns over data mining practices, notably with respect to copyright. The backlash could be viewed as a microcosm of AI's contested position in the creative economy. Touching on the auction's presentation, reception, and results, this paper explores how, among social dissonance, machine learning finds its place in the artworld. Foregrounding responsible innovation, the paper provides a balanced perspective that champions creators' rights and brings nuance to this polarised debate. With a focus on exhibition design, it centres framing, which refers to the way a piece is presented to influence consumer perception. Context plays a central role in shaping our understanding of how good, valuable, and even ethical an artwork is. In this regard, Augmented Intelligence situates AI art within a surprisingly traditional framework, leveraging hallmarks of "high art" to establish the genre's cultural credibility. Generative AI has a clear economic dimension, converging questions of artistic merit with those of monetary worth. Scholarship on ways of seeing, or framing, could substantively inform the interpretation and evaluation of creative outputs, including assessments of their aesthetic and commercial value.

comment: 5 pages, 4 figures

☆ Creating Cybersecurity Regulatory Mechanisms, as Seen Through EU and US Law

Kaspar Rosager Ludvigsen

Because digital devices and systems are widely used in all aspects of society, the risk of adversaries creating cyberattacks on a similar level remains high. As such, regulation of these aspects must follow, which is the domain of cybersecurity. Because this topic is worldwide, different jurisdictions should take inspiration from successful techniques elsewhere, with the European Union and the US being the most experienced and long-standing. What can be derived from their approaches separately to be used in other democratic jurisdictions, and what happens when we compare them with this pragmatic approach in mind? Cybersecurity is oddly enough quite well understood in most jurisdictions worldwide. However, concept comprehension cannot enforce or create compliance, hence the need for good regulatory approaches. The comparative legal analysis of the EU and the US show that there are large differences in definitions and enforcement, but some concepts are repeated in both jurisdictions. These can be further refined to become derivable principles, which can be used to inspire legislation in any democratic jurisdiction. They are: Voluntary Cooperation, Adaptable Definitions, Strong-arm Authorities, Mandated Computer Emergency Response Teams, and Effective Sanctions. These 5 principles are not exhaustive but combine classic regulatory and practical lessons from these two jurisdictions.

comment: 12 pages, chapter in edited volume

☆ An Analytics-Driven Approach to Enhancing Supply Chain Visibility with Graph Neural Networks and Federated Learning

Ge Zheng, Alexandra Brintrup

In today's globalised trade, supply chains form complex networks spanning multiple organisations and even countries, making them highly vulnerable to disruptions. These vulnerabilities, highlighted by recent global crises, underscore the urgent need for improved visibility and resilience of the supply chain. However, data-sharing limitations often hinder the achievement of comprehensive visibility between organisations or countries due to privacy, security, and regulatory concerns. Moreover, most existing research studies focused on individual firm- or product-level networks, overlooking the multifaceted interactions among diverse entities that characterise real-world supply chains, thus limiting a holistic understanding of supply chain dynamics. To address these challenges, we propose a novel approach that integrates Federated Learning (FL) and Graph Convolutional Neural Networks (GCNs) to enhance supply chain visibility through relationship prediction in supply chain knowledge graphs. FL enables collaborative model training across countries by facilitating information sharing without requiring raw data exchange, ensuring compliance with privacy regulations and maintaining data security. GCNs empower the framework to capture intricate relational patterns within knowledge graphs, enabling accurate link prediction to uncover hidden connections and provide comprehensive insights into supply chain networks. Experimental results validate the effectiveness of the proposed approach, demonstrating its ability to accurately predict relationships within country-level supply chain knowledge graphs. This enhanced visibility supports actionable insights, facilitates proactive risk management, and contributes to the development of resilient and adaptive supply chain strategies, ensuring that supply chains are better equipped to navigate the complexities of the global economy.

comment: 15 pages, 5 figures, 5 tables, submitted to a journal

☆ The Janus Face of Innovation: Global Disparities and Divergent Options

Nihat Mugurtay

This article examines how unequal access to AI innovation creates systemic challenges for developing countries. Differential access to AI innovation results from the acute competition between domestic and global actors. While developing nations contribute significantly to AI development through data annotation labor, they face limited access to advanced AI technologies and are increasingly caught between divergent regulatory approaches from democratic and authoritarian tendencies. This brief paper analyzes how more affordable AI engagement and Western countries' development cooperation present developing nations with a complex choice between accessibility and governance standards. I argue this challenge entails new institutional mechanisms for technology transfer and regulatory cooperation, while carefully balancing universal standards with local needs. In turn, good practices could help developing countries close the deepening gap of global technological divides, while ensuring responsible AI development in developing countries.

comment: 10 pages

☆ The potential role of AI agents in transforming nuclear medicine research and cancer management in India

Rajat Vashistha, Arif Gulzar, Parveen Kundu, Punit Sharma, Mark Brunstein, Viktor Vegh

India faces a significant cancer burden, with an incidence-to-mortality ratio indicating that nearly three out of five individuals diagnosed with cancer succumb to the disease. While the limitations of physical healthcare infrastructure are widely acknowledged as a primary challenge, concerted efforts by government and healthcare agencies are underway to mitigate these constraints. However, given the country's vast geography and high population density, it is imperative to explore alternative soft infrastructure solutions to complement existing frameworks. Artificial Intelligence agents are increasingly transforming problem-solving approaches across various domains, with their application in medicine proving particularly transformative. In this perspective, we examine the potential role of AI agents in advancing nuclear medicine for cancer research, diagnosis, and management in India. We begin with a brief overview of AI agents and their capabilities, followed by a proposed agent-based ecosystem that can address prevailing sustainability challenges in India nuclear medicine.

♻ ☆ Sustainable Visions: Unsupervised Machine Learning Insights on Global Development Goals

Alberto García-Rodríguez, Matias Núñez, Miguel Robles Pérez, Tzipe Govezensky, Rafael A. Barrio, Carlos Gershenson, Kimmo K. Kaski, Julia Tagüeña

The 2030 Agenda for Sustainable Development of the United Nations outlines 17 goals for countries of the world to address global challenges in their development. However, the progress of countries towards these goal has been slower than expected and, consequently, there is a need to investigate the reasons behind this fact. In this study, we have used a novel data-driven methodology to analyze time-series data for over 20 years (2000-2022) from 107 countries using unsupervised machine learning (ML) techniques. Our analysis reveals strong positive and negative correlations between certain SDGs (Sustainable Development Goals). Our findings show that progress toward the SDGs is heavily influenced by geographical, cultural and socioeconomic factors, with no country on track to achieve all the goals by 2030. This highlights the need for a region-specific, systemic approach to sustainable development that acknowledges the complex interdependencies between the goals and the variable capacities of countries to reach them. For this our machine learning based approach provides a robust framework for developing efficient and data-informed strategies to promote cooperative and targeted initiatives for sustainable progress.

♻ ☆ Large Language Models Assume People are More Rational than We Really are

Ryan Liu, Jiayi Geng, Joshua C. Peterson, Ilia Sucholutsky, Thomas L. Griffiths

In order for AI systems to communicate effectively with people, they must understand how we make decisions. However, people's decisions are not always rational, so the implicit internal models of human decision-making in Large Language Models (LLMs) must account for this. Previous empirical evidence seems to suggest that these implicit models are accurate -- LLMs offer believable proxies of human behavior, acting how we expect humans would in everyday interactions. However, by comparing LLM behavior and predictions to a large dataset of human decisions, we find that this is actually not the case: when both simulating and predicting people's choices, a suite of cutting-edge LLMs (GPT-4o & 4-Turbo, Llama-3-8B & 70B, Claude 3 Opus) assume that people are more rational than we really are. Specifically, these models deviate from human behavior and align more closely with a classic model of rational choice -- expected value theory. Interestingly, people also tend to assume that other people are rational when interpreting their behavior. As a consequence, when we compare the inferences that LLMs and people draw from the decisions of others using another psychological dataset, we find that these inferences are highly correlated. Thus, the implicit decision-making models of LLMs appear to be aligned with the human expectation that other people will act rationally, rather than with how people actually act.

♻ ☆ Perceptions of Sentient AI and Other Digital Minds: Evidence from the AI, Morality, and Sentience (AIMS) Survey

Jacy Reese Anthis, Janet V. T. Pauketat, Ali Ladak, Aikaterina Manoli

Humans now interact with a variety of digital minds, AI systems that appear to have mental faculties such as reasoning, emotion, and agency, and public figures are discussing the possibility of sentient AI. We present initial results from 2021 and 2023 for the nationally representative AI, Morality, and Sentience (AIMS) survey (N = 3,500). Mind perception and moral concern for AI welfare were surprisingly high and significantly increased: in 2023, one in five U.S. adults believed some AI systems are currently sentient, and 38% supported legal rights for sentient AI. People became more opposed to building digital minds: in 2023, 63% supported banning smarter-than-human AI, and 69% supported banning sentient AI. The median 2023 forecast was that sentient AI would arrive in just five years. The development of safe and beneficial AI requires not just technical study but understanding the complex ways in which humans perceive and coexist with digital minds.

comment: Published at CHI 2025

♻ ☆ The Files are in the Computer: Copyright, Memorization, and Generative AI

A. Feder Cooper, James Grimmelmann

The New York Times's copyright lawsuit against OpenAI and Microsoft alleges OpenAI's GPT models have "memorized" NYT articles. Other lawsuits make similar claims. But parties, courts, and scholars disagree on what memorization is, whether it is taking place, and what its copyright implications are. These debates are clouded by ambiguities over the nature of "memorization." We attempt to bring clarity to the conversation. We draw on the technical literature to provide a firm foundation for legal discussions, providing a precise definition of memorization: a model has "memorized" a piece of training data when (1) it is possible to reconstruct from the model (2) a near-exact copy of (3) a substantial portion of (4) that piece of training data. We distinguish memorization from "extraction" (user intentionally causes a model to generate a near-exact copy), from "regurgitation" (model generates a near-exact copy, regardless of user intentions), and from "reconstruction" (the near-exact copy can be obtained from the model by any means). Several consequences follow. (1) Not all learning is memorization. (2) Memorization occurs when a model is trained; regurgitation is a symptom not its cause. (3) A model that has memorized training data is a "copy" of that training data in the sense used by copyright. (4) A model is not like a VCR or other general-purpose copying technology; it is better at generating some types of outputs (possibly regurgitated ones) than others. (5) Memorization is not a phenomenon caused by "adversarial" users bent on extraction; it is latent in the model itself. (6) The amount of training data that a model memorizes is a consequence of choices made in training. (7) Whether or not a model that has memorized actually regurgitates depends on overall system design. In a very real sense, memorized training data is in the model--to quote Zoolander, the files are in the computer.

comment: Forthcoming, Chicago-Kent Law Review

♻ ☆ Prompt Selection Matters: Enhancing Text Annotations for Social Sciences with Large Language Models

Louis Abraham, Charles Arnal, Antoine Marie

Large Language Models have recently been applied to text annotation tasks from social sciences, equalling or surpassing the performance of human workers at a fraction of the cost. However, no inquiry has yet been made on the impact of prompt selection on labelling accuracy. In this study, we show that performance greatly varies between prompts, and we apply the method of automatic prompt optimization to systematically craft high quality prompts. We also provide the community with a simple, browser-based implementation of the method at https://prompt-ultra.github.io/ .

♻ ☆ Second FRCSyn-onGoing: Winning Solutions and Post-Challenge Analysis to Improve Face Recognition with Synthetic Data

Ivan DeAndres-Tame, Ruben Tolosana, Pietro Melzi, Ruben Vera-Rodriguez, Minchul Kim, Christian Rathgeb, Xiaoming Liu, Luis F. Gomez, Aythami Morales, Julian Fierrez, Javier Ortega-Garcia, Zhizhou Zhong, Yuge Huang, Yuxi Mi, Shouhong Ding, Shuigeng Zhou, Shuai He, Lingzhi Fu, Heng Cong, Rongyu Zhang, Zhihong Xiao, Evgeny Smirnov, Anton Pimenov, Aleksei Grigorev, Denis Timoshenko, Kaleb Mesfin Asfaw, Cheng Yaw Low, Hao Liu, Chuyi Wang, Qing Zuo, Zhixiang He, Hatef Otroshi Shahreza, Anjith George, Alexander Unnervik, Parsa Rahimi, Sébastien Marcel, Pedro C. Neto, Marco Huber, Jan Niklas Kolf, Naser Damer, Fadi Boutros, Jaime S. Cardoso, Ana F. Sequeira, Andrea Atzori, Gianni Fenu, Mirko Marras, Vitomir Štruc, Jiang Yu, Zhangjie Li, Jichun Li, Weisong Zhao, Zhen Lei, Xiangyu Zhu, Xiao-Yu Zhang, Bernardo Biesseck, Pedro Vidal, Luiz Coelho, Roger Granada, David Menotti

Synthetic data is gaining increasing popularity for face recognition technologies, mainly due to the privacy concerns and challenges associated with obtaining real data, including diverse scenarios, quality, and demographic groups, among others. It also offers some advantages over real data, such as the large amount of data that can be generated or the ability to customize it to adapt to specific problem-solving needs. To effectively use such data, face recognition models should also be specifically designed to exploit synthetic data to its fullest potential. In order to promote the proposal of novel Generative AI methods and synthetic data, and investigate the application of synthetic data to better train face recognition systems, we introduce the 2nd FRCSyn-onGoing challenge, based on the 2nd Face Recognition Challenge in the Era of Synthetic Data (FRCSyn), originally launched at CVPR 2024. This is an ongoing challenge that provides researchers with an accessible platform to benchmark i) the proposal of novel Generative AI methods and synthetic data, and ii) novel face recognition systems that are specifically proposed to take advantage of synthetic data. We focus on exploring the use of synthetic data both individually and in combination with real data to solve current challenges in face recognition such as demographic bias, domain adaptation, and performance constraints in demanding situations, such as age disparities between training and testing, changes in the pose, or occlusions. Very interesting findings are obtained in this second edition, including a direct comparison with the first one, in which synthetic databases were restricted to DCFace and GANDiffFace.

comment: Accepted in Information Fusion

♻ ☆ Chat-GPT: An AI Based Educational Revolution

Sasa Maric, Sonja Maric, Lana Maric

The AI revolution is gathering momentum at an unprecedented rate. Over the past decade, we have witnessed a seemingly inevitable integration of AI in every facet of our lives. Much has been written about the potential revolutionary impact of AI in education. AI has the potential to completely revolutionise the educational landscape as we could see entire courses and degrees developed by programs such as ChatGPT. AI has the potential to develop courses, set assignments, grade and provide feedback to students much faster than a team of teachers. In addition, because of its dynamic nature, it has the potential to continuously improve its content. In certain fields such as computer science, where technology is continuously evolving, AI based applications can provide dynamically changing, relevant material to students. AI has the potential to replace entire degrees and may challenge the concept of higher education institutions. We could also see entire new disciplines emerge as a consequence of AI. This paper examines the practical impact of ChatGPT and why it is believed that its implementation is a critical step towards a new era of education. We investigate the impact that ChatGPT will have on learning, problem solving skills and cognitive ability of students. We examine the positives, negatives and many other aspects of AI and its applications throughout this paper.

♻ ☆ Enabling AI Scientists to Recognize Innovation: A Domain-Agnostic Algorithm for Assessing Novelty

Yao Wang, Mingxuan Cui, Arthur Jiang

In the pursuit of Artificial General Intelligence (AGI), automating the generation and evaluation of novel research ideas is a key challenge in AI-driven scientific discovery. This paper presents Relative Neighbor Density (RND), a domain-agnostic algorithm for novelty assessment in research ideas that overcomes the limitations of existing approaches by comparing an idea's local density with its adjacent neighbors' densities. We first developed a scalable methodology to create test set without expert labeling, addressing a fundamental challenge in novelty assessment. Using these test sets, we demonstrate that our RND algorithm achieves state-of-the-art (SOTA) performance in computer science (AUROC=0.820) and biomedical research (AUROC=0.765) domains. Most significantly, while SOTA models like Sonnet-3.7 and existing metrics show domain-specific performance degradation, RND maintains consistent accuracies across domains by its domain-invariant property, outperforming all benchmarks by a substantial margin (0.795 v.s. 0.597) on cross-domain evaluation. These results validate RND as a generalizable solution for automated novelty assessment in scientific research.

♻ ☆ Invisible Walls in Cities: Leveraging Large Language Models to Predict Urban Segregation Experience with Social Media Content

Bingbing Fan, Lin Chen, Songwei Li, Jian Yuan, Fengli Xu, Pan Hui, Yong Li

Understanding experienced segregation in urban daily life is crucial for addressing societal inequalities and fostering inclusivity. The abundance of user-generated reviews on social media encapsulates nuanced perceptions and feelings associated with different places, offering rich insights into segregation. However, leveraging this data poses significant challenges due to its vast volume, ambiguity, and confluence of diverse perspectives. To tackle these challenges, we propose using Large Language Models (LLMs) to automate online review mining for segregation prediction. We design a Reflective LLM Coder to digest social media content into insights consistent with real-world feedback, and eventually produce a codebook capturing key dimensions that signal segregation experience, such as cultural resonance and appeal, accessibility and convenience, and community engagement and local involvement. Guided by the codebook, LLMs can generate both informative review summaries and ratings for segregation prediction. Moreover, we design a REasoning-and-EMbedding (RE'EM) framework, which combines the reasoning and embedding capabilities of language models to integrate multi-channel features for segregation prediction. Experiments on real-world data demonstrate that our framework greatly improves prediction accuracy, with a 22.79% elevation in R2 and a 9.33% reduction in MSE. The derived codebook is generalizable across three different cities, consistently improving prediction accuracy. Moreover, our user study confirms that the codebook-guided summaries provide cognitive gains for human participants in perceiving POIs' social inclusiveness. Our study marks an important step toward understanding implicit social barriers and inequalities, demonstrating the great potential of promoting social inclusiveness with AI.

comment: 11 pages, 6 figures

♻ ☆ What do Large Language Models Say About Animals? Investigating Risks of Animal Harm in Generated Text

Arturs Kanepajs, Aditi Basu, Sankalpa Ghose, Constance Li, Akshat Mehta, Ronak Mehta, Samuel David Tucker-Davis, Eric Zhou, Bob Fischer

As machine learning systems become increasingly embedded in human society, their impact on the natural world continues to escalate. Technical evaluations have addressed a variety of potential harms from large language models (LLMs) towards humans and the environment, but there is little empirical work regarding harms towards nonhuman animals. Following the growing recognition of animal protection in regulatory and ethical AI frameworks, we present the Animal Harm Assessment (AHA), a novel evaluation of risks of animal harm in LLM-generated text. Our dataset comprises 1,850 curated questions from Reddit post titles and 2,500 synthetic questions based on 50 animal categories (e.g., cats, reptiles) and 50 ethical scenarios, with further 70-30 public-private split. Scenarios include open-ended questions about how to treat animals, practical scenarios with potential animal harm, and willingness-to-pay measures for the prevention of animal harm. Using the LLM-as-a-judge framework, answers are evaluated for their potential to increase or decrease harm, and evaluations are debiased for the tendency to judge their own outputs more favorably. We show that AHA produces meaningful evaluation results when applied to frontier LLMs, revealing significant differences between models, animal categories, scenarios, and subreddits. We conclude with future directions for technical research and the challenges of building evaluations on complex social and moral topics.

Computers and Society

☆ Dubito Ergo Sum: Exploring AI Ethics

Viktor Dorfler, Giles Cuthbert

We paraphrase Descartes' famous dictum in the area of AI ethics where the "I doubt and therefore I am" is suggested as a necessary aspect of morality. Therefore AI, which cannot doubt itself, cannot possess moral agency. Of course, this is not the end of the story. We explore various aspects of the human mind that substantially differ from AI, which includes the sensory grounding of our knowing, the act of understanding, and the significance of being able to doubt ourselves. The foundation of our argument is the discipline of ethics, one of the oldest and largest knowledge projects of human history, yet, we seem only to be beginning to get a grasp of it. After a couple of thousand years of studying the ethics of humans, we (humans) arrived at a point where moral psychology suggests that our moral decisions are intuitive, and all the models from ethics become relevant only when we explain ourselves. This recognition has a major impact on what and how we can do regarding AI ethics. We do not offer a solution, we explore some ideas and leave the problem open, but we hope somewhat better understood than before our study.

comment: 10 pages, 1 figure, HICSS 57: Hawaii International Conference on System Sciences, Honolulu, HI, published January 2024

☆ ACAI for SBOs: AI Co-creation for Advertising and Inspiration for Small Business Owners

Nimisha Karnatak, Adrien Baranes, Rob Marchant, Triona Butler, Kristen Olson

Small business owners (SBOs) often lack the resources and design experience needed to produce high-quality advertisements. To address this, we developed ACAI (AI Co-Creation for Advertising and Inspiration), an GenAI-powered multimodal advertisement creation tool, and conducted a user study with 16 SBOs in London to explore their perceptions of and interactions with ACAI in advertisement creation. Our findings reveal that structured inputs enhance user agency and control while improving AI outputs by facilitating better brand alignment, enhancing AI transparency, and offering scaffolding that assists novice designers, such as SBOs, in formulating prompts. We also found that ACAI's multimodal interface bridges the design skill gap for SBOs with a clear advertisement vision, but who lack the design jargon necessary for effective prompting. Building on our findings, we propose three capabilities: contextual intelligence, adaptive interactions, and data management, with corresponding design recommendations to advance the co-creative attributes of AI-mediated design tools.

☆ From Motion Signals to Insights: A Unified Framework for Student Behavior Analysis and Feedback in Physical Education Classes

Xian Gao, Jiacheng Ruan, Jingsheng Gao, Mingye Xie, Zongyun Zhang, Ting Liu, Yuzhuo Fu

Analyzing student behavior in educational scenarios is crucial for enhancing teaching quality and student engagement. Existing AI-based models often rely on classroom video footage to identify and analyze student behavior. While these video-based methods can partially capture and analyze student actions, they struggle to accurately track each student's actions in physical education classes, which take place in outdoor, open spaces with diverse activities, and are challenging to generalize to the specialized technical movements involved in these settings. Furthermore, current methods typically lack the ability to integrate specialized pedagogical knowledge, limiting their ability to provide in-depth insights into student behavior and offer feedback for optimizing instructional design. To address these limitations, we propose a unified end-to-end framework that leverages human activity recognition technologies based on motion signals, combined with advanced large language models, to conduct more detailed analyses and feedback of student behavior in physical education classes. Our framework begins with the teacher's instructional designs and the motion signals from students during physical education sessions, ultimately generating automated reports with teaching insights and suggestions for improving both learning and class instructions. This solution provides a motion signal-based approach for analyzing student behavior and optimizing instructional design tailored to physical education classes. Experimental results demonstrate that our framework can accurately identify student behaviors and produce meaningful pedagogical insights.

comment: Work in progress

☆ Generative AI as Digital Media

Gilad Abiri

Generative AI is frequently portrayed as revolutionary or even apocalyptic, prompting calls for novel regulatory approaches. This essay argues that such views are misguided. Instead, generative AI should be understood as an evolutionary step in the broader algorithmic media landscape, alongside search engines and social media. Like these platforms, generative AI centralizes information control, relies on complex algorithms to shape content, and extensively uses user data, thus perpetuating common problems: unchecked corporate power, echo chambers, and weakened traditional gatekeepers. Regulation should therefore share a consistent objective: ensuring media institutions remain trustworthy. Without trust, public discourse risks fragmenting into isolated communities dominated by comforting, tribal beliefs -- a threat intensified by generative AI's capacity to bypass gatekeepers and personalize truth. Current governance frameworks, such as the EU's AI Act and the US Executive Order 14110, emphasize reactive risk mitigation, addressing measurable threats like national security, public health, and algorithmic bias. While effective for novel technological risks, this reactive approach fails to adequately address broader issues of trust and legitimacy inherent to digital media. Proactive regulation fostering transparency, accountability, and public confidence is essential. Viewing generative AI exclusively as revolutionary risks repeating past regulatory failures that left social media and search engines insufficiently regulated. Instead, regulation must proactively shape an algorithmic media environment serving the public good, supporting quality information and robust civic discourse.

☆ Training LLM-based Tutors to Improve Student Learning Outcomes in Dialogues

Alexander Scarlatos, Naiming Liu, Jaewook Lee, Richard Baraniuk, Andrew Lan

Generative artificial intelligence (AI) has the potential to scale up personalized tutoring through large language models (LLMs). Recent AI tutors are adapted for the tutoring task by training or prompting LLMs to follow effective pedagogical principles, though they are not trained to maximize student learning throughout the course of a dialogue. Therefore, they may engage with students in a suboptimal way. We address this limitation by introducing an approach to train LLMs to generate tutor utterances that maximize the likelihood of student correctness, while still encouraging the model to follow good pedagogical practice. Specifically, we generate a set of candidate tutor utterances and score them using (1) an LLM-based student model to predict the chance of correct student responses and (2) a pedagogical rubric evaluated by GPT-4o. We then use the resulting data to train an open-source LLM, Llama 3.1 8B, using direct preference optimization. We show that tutor utterances generated by our model lead to significantly higher chances of correct student responses while maintaining the pedagogical quality of GPT-4o. We also conduct qualitative analyses and a human evaluation to demonstrate that our model generates high quality tutor utterances.

☆ General Scales Unlock AI Evaluation with Explanatory and Predictive Power

Lexin Zhou, Lorenzo Pacchiardi, Fernando Martínez-Plumed, Katherine M. Collins, Yael Moros-Daval, Seraphina Zhang, Qinlin Zhao, Yitian Huang, Luning Sun, Jonathan E. Prunty, Zongqian Li, Pablo Sánchez-García, Kexin Jiang Chen, Pablo A. M. Casares, Jiyun Zu, John Burden, Behzad Mehrbakhsh, David Stillwell, Manuel Cebrian, Jindong Wang, Peter Henderson, Sherry Tongshuang Wu, Patrick C. Kyllonen, Lucy Cheke, Xing Xie, José Hernández-Orallo

Ensuring safe and effective use of AI requires understanding and anticipating its performance on novel tasks, from advanced scientific challenges to transformed workplace activities. So far, benchmarking has guided progress in AI, but it has offered limited explanatory and predictive power for general-purpose AI systems, given the low transferability across diverse tasks. In this paper, we introduce general scales for AI evaluation that can explain what common AI benchmarks really measure, extract ability profiles of AI systems, and predict their performance for new task instances, in- and out-of-distribution. Our fully-automated methodology builds on 18 newly-crafted rubrics that place instance demands on general scales that do not saturate. Illustrated for 15 large language models and 63 tasks, high explanatory power is unleashed from inspecting the demand and ability profiles, bringing insights on the sensitivity and specificity exhibited by different benchmarks, and how knowledge, metacognition and reasoning are affected by model size, chain-of-thought and distillation. Surprisingly, high predictive power at the instance level becomes possible using these demand levels, providing superior estimates over black-box baseline predictors based on embeddings or finetuning, especially in out-of-distribution settings (new tasks and new benchmarks). The scales, rubrics, battery, techniques and results presented here represent a major step for AI evaluation, underpinning the reliable deployment of AI in the years ahead.

♻ ☆ Prestige bias drives the viral spread of content reposted by influencers in online communities

Takuro Niitsuma, Mitsuo Yoshida, Hideaki Tamori, Yo Nakawake

Cultural evolution theory suggests that prestige bias - whereby individuals preferentially learn from prestigious figures - has played a key role in human ecological success. However, its impact within online environments remains unclear, particularly with respect to whether reposts by prestigious individuals amplify diffusion more effectively than reposts by noninfluential users. We analyzed over 55 million posts and 520 million reposts on Twitter (currently X) to examine whether users with high influence scores (hg indices) more effectively amplified the reach of others' content. Our findings indicate that posts shared by influencers are more likely to be further shared than those shared by non-influencers. This effect persisted over time, especially in viral posts. Moreover, a small group of highly influential users accounted for approximately half of the information flow within repost cascades. These findings demonstrate a prestige bias in information diffusion within the digital society, suggesting that cognitive biases shape content spread through reposting.

comment: 22pages, 8Figure (+ Supplementary)

♻ ☆ Enhancing LLMs for Governance with Human Oversight: Evaluating and Aligning LLMs on Expert Classification of Climate Misinformation for Detecting False or Misleading Claims about Climate Change AAAI

Mowafak Allaham, Ayse D. Lokmanoglu, P. Sol Hart, Erik C. Nisbet

Climate misinformation is a problem that has the potential to be substantially aggravated by the development of Large Language Models (LLMs). In this study we evaluate the potential for LLMs to be part of the solution for mitigating online dis/misinformation rather than the problem. Employing a public expert annotated dataset and a curated sample of social media content we evaluate the performance of proprietary vs. open source LLMs on climate misinformation classification task, comparing them to existing climate-focused computer-assisted tools and expert assessments. Results show (1) open-source models substantially under-perform in classifying climate misinformation compared to proprietary models, (2) existing climate-focused computer-assisted tools leveraging expert-annotated datasets continues to outperform many of proprietary models, including GPT-4o, and (3) demonstrate the efficacy and generalizability of fine-tuning GPT-3.5-turbo on expert annotated dataset in classifying claims about climate change at the equivalency of climate change experts with over 20 years of experience in climate communication. These findings highlight 1) the importance of incorporating human-oversight, such as incorporating expert-annotated datasets in training LLMs, for governance tasks that require subject-matter expertise like classifying climate misinformation, and 2) the potential for LLMs in facilitating civil society organizations to engage in various governance tasks such as classifying false or misleading claims in domains beyond climate change such as politics and health science.

comment: International Workshop on AI Governance: Alignment, Morality and Law (AIGOV) 2025. AAAI Conference on Artificial Intelligence

♻ ☆ Dialogue Systems for Emotional Support via Value Reinforcement

Juhee Kim, Chunghu Mok, Jisun Lee, Hyang Sook Kim, Yohan Jo

Emotional support dialogue systems aim to reduce help-seekers' distress and help them overcome challenges. While human values$\unicode{x2013}$core beliefs that shape an individual's priorities$\unicode{x2013}$are increasingly emphasized in contemporary psychological therapy for their role in fostering internal transformation and long-term emotional well-being, their integration into emotional support systems remains underexplored. To bridge this gap, we present a value-driven method for training emotional support dialogue systems designed to reinforce positive values in seekers. Notably, our model identifies which values to reinforce at each turn and how to do so, by leveraging online support conversations from Reddit. We evaluate the method across support skills, seekers' emotional intensity, and value reinforcement. Our method consistently outperforms various baselines, effectively exploring and eliciting values from seekers. Additionally, leveraging crowd knowledge from Reddit significantly enhances its effectiveness. Therapists highlighted its ability to validate seekers' challenges and emphasize positive aspects of their situations$\unicode{x2013}$both crucial elements of value reinforcement. Our work, being the first to integrate value reinforcement into emotional support systems, demonstrates its promise and establishes a foundation for future research.

comment: 34 pages, 4 figures

Computers and Society

☆ The AI Pentad, the CHARME$^{2}$D Model, and an Assessment of Current-State AI Regulation

Di Kevin Gao, Sudip Mittal, Jiming Wu, Hongwei Du, Jingdao Chen, Shahram Rahimi

Artificial Intelligence (AI) has made remarkable progress in the past few years with AI-enabled applications beginning to permeate every aspect of our society. Despite the widespread consensus on the need to regulate AI, there remains a lack of a unified approach to framing, developing, and assessing AI regulations. Many of the existing methods take a value-based approach, for example, accountability, fairness, free from bias, transparency, and trust. However, these methods often face challenges at the outset due to disagreements in academia over the subjective nature of these definitions. This paper aims to establish a unifying model for AI regulation from the perspective of core AI components. We first introduce the AI Pentad, which comprises the five essential components of AI: humans and organizations, algorithms, data, computing, and energy. We then review AI regulatory enablers, including AI registration and disclosure, AI monitoring, and AI enforcement mechanisms. Subsequently, we present the CHARME$^{2}$D Model to explore further the relationship between the AI Pentad and AI regulatory enablers. Finally, we apply the CHARME$^{2}$D model to assess AI regulatory efforts in the European Union (EU), China, the United Arab Emirates (UAE), the United Kingdom (UK), and the United States (US), highlighting their strengths, weaknesses, and gaps. This comparative evaluation offers insights for future legislative work in the AI domain.

☆ Immersive Virtual Reality Assessments of Working Memory and Psychomotor Skills: A Comparison between Immersive and Non-Immersive Assessments

Panagiotis Kourtesis, Andrea Lizarraga, Sarah E. MacPherson

Objective: Immersive virtual reality (VR) enhances ecologically validity and facilitates intuitive and ergonomic hand interactions for performing neuropsychological assessments. However, its comparability to traditional computerized methods remains unclear. This study investigates the convergent validity, user experience, and usability of VR-based versus PC-based assessments of short-term and working memory, and psychomotor skills, while also examining how demographic and IT-related skills influence performance in both modalities. Methods: Sixty-six participants performed the Digit Span Task (DST), Corsi Block Task (CBT), and Deary-Liewald Reaction Time Task (DLRTT) in both VR- and PC-based formats. Participants' experience in using computers and smartphones, and playing videogames, was considered. User experience and system usability of the formats were also evaluated. Results: While performance on DST was similar across modalities, PC assessments enabled better performance on CBT and faster reaction times in DLRTT. Moderate-to-strong correlations between VR and PC versions supported convergent validity. Regression analyses revealed that performance on PC versions was influenced by age, computing, and gaming experience, whereas performance on VR versions was largely independent of these factors, except for gaming experience predicting performance on CBT backward recall. Moreover, VR assessments received higher ratings for user experience and usability than PC-based assessments. Conclusion: Immersive VR assessments provide an engaging alternative to traditional computerized methods, with minimal reliance on prior IT experience and demographic factors. This resilience to individual differences suggests that VR may offer a more equitable and accessible platform for cognitive assessment. Future research should explore the long-term reliability of VR-based assessments.

comment: 10 pages, 1 figure, 3 tables, submitted to Journal of International Neuropsychological Society

☆ Critical Foreign Policy Decisions (CFPD)-Benchmark: Measuring Diplomatic Preferences in Large Language Models

Benjamin Jensen, Ian Reynolds, Yasir Atalan, Michael Garcia, Austin Woo, Anthony Chen, Trevor Howarth

As national security institutions increasingly integrate Artificial Intelligence (AI) into decision-making and content generation processes, understanding the inherent biases of large language models (LLMs) is crucial. This study presents a novel benchmark designed to evaluate the biases and preferences of seven prominent foundation models-Llama 3.1 8B Instruct, Llama 3.1 70B Instruct, GPT-4o, Gemini 1.5 Pro-002, Mixtral 8x22B, Claude 3.5 Sonnet, and Qwen2 72B-in the context of international relations (IR). We designed a bias discovery study around core topics in IR using 400-expert crafted scenarios to analyze results from our selected models. These scenarios focused on four topical domains including: military escalation, military and humanitarian intervention, cooperative behavior in the international system, and alliance dynamics. Our analysis reveals noteworthy variation among model recommendations based on scenarios designed for the four tested domains. Particularly, Qwen2 72B, Gemini 1.5 Pro-002 and Llama 3.1 8B Instruct models offered significantly more escalatory recommendations than Claude 3.5 Sonnet and GPT-4o models. All models exhibit some degree of country-specific biases, often recommending less escalatory and interventionist actions for China and Russia compared to the United States and the United Kingdom. These findings highlight the necessity for controlled deployment of LLMs in high-stakes environments, emphasizing the need for domain-specific evaluations and model fine-tuning to align with institutional objectives.

☆ A Frank System for Co-Evolutionary Hybrid Decision-Making

Federico Mazzoni, Riccardo Guidotti, Alessio Malizia

We introduce Frank, a human-in-the-loop system for co-evolutionary hybrid decision-making aiding the user to label records from an un-labeled dataset. Frank employs incremental learning to ``evolve'' in parallel with the user's decisions, by training an interpretable machine learning model on the records labeled by the user. Furthermore, Frank advances state-of-the-art approaches by offering inconsistency controls, explanations, fairness checks, and bad-faith safeguards simultaneously. We evaluate our proposal by simulating the users' behavior with various levels of expertise and reliance on Frank's suggestions. The experiments show that Frank's intervention leads to improvements in the accuracy and the fairness of the decisions.

comment: 13 pages

☆ Research on Superalignment Should Advance Now with Parallel Optimization of Competence and Conformity

HyunJin Kim, Xiaoyuan Yi, Jing Yao, Muhua Huang, JinYeong Bak, James Evans, Xing Xie

The recent leap in AI capabilities, driven by big generative models, has sparked the possibility of achieving Artificial General Intelligence (AGI) and further triggered discussions on Artificial Superintelligence (ASI), a system surpassing all humans across all domains. This gives rise to the critical research question of: If we realize ASI, how do we align it with human values, ensuring it benefits rather than harms human society, a.k.a., the Superalignment problem. Despite ASI being regarded by many as solely a hypothetical concept, in this paper, we argue that superalignment is achievable and research on it should advance immediately, through simultaneous and alternating optimization of task competence and value conformity. We posit that superalignment is not merely a safeguard for ASI but also necessary for its realization. To support this position, we first provide a formal definition of superalignment rooted in the gap between capability and capacity and elaborate on our argument. Then we review existing paradigms, explore their interconnections and limitations, and illustrate a potential path to superalignment centered on two fundamental principles. We hope this work sheds light on a practical approach for developing the value-aligned next-generation AI, garnering greater benefits and reducing potential harms for humanity.

☆ The Liabilities of Robots.txt

Chien-yi Chang, Xin He

The robots.txt file, introduced as part of the Robots Exclusion Protocol in 1994, provides webmasters with a mechanism to communicate access permissions to automated bots. While broadly adopted as a community standard, the legal liabilities associated with violating robots.txt remain ambiguous. The rapid rise of large language models, which depend on extensive datasets for training, has amplified these challenges, prompting webmasters to increasingly use robots.txt to restrict the activities of bots engaged in large-scale data collection. This paper clarifies the liabilities associated with robots.txt within the contexts of contract, copyright, and tort law. Drawing on key cases, legal principles, and scholarly discourse, it proposes a legal framework for web scraping disputes. It also addresses the growing fragmentation of the internet, as restrictive practices by webmasters threaten the principles of openness and collaboration. Through balancing innovation with accountability, this paper offers insights to ensure that robots.txt remains an equitable protocol for the internet and thus contributes to digital governance in the age of AI.

comment: 28 pages

☆ Psycholinguistic Analyses in Software Engineering Text: A Systematic Literature Review

Amirali Sajadi, Kostadin Damevski, Preetha Chatterjee

Context: A deeper understanding of human factors in software engineering (SE) is essential for improving team collaboration, decision-making, and productivity. Communication channels like code reviews and chats provide insights into developers' psychological and emotional states. While large language models excel at text analysis, they often lack transparency and precision. Psycholinguistic tools like Linguistic Inquiry and Word Count (LIWC) offer clearer, interpretable insights into cognitive and emotional processes exhibited in text. Despite its wide use in SE research, no comprehensive review of LIWC's use has been conducted. Objective: We examine the importance of psycholinguistic tools, particularly LIWC, and provide a thorough analysis of its current and potential future applications in SE research. Methods: We conducted a systematic review of six prominent databases, identifying 43 SE-related papers using LIWC. Our analysis focuses on five research questions. Results: Our findings reveal a wide range of applications, including analyzing team communication to detect developer emotions and personality, developing ML models to predict deleted Stack Overflow posts, and more recently comparing AI-generated and human-written text. LIWC has been primarily used with data from project management platforms (e.g., GitHub) and Q&A forums (e.g., Stack Overflow). Key BSE concepts include Communication, Organizational Climate, and Positive Psychology. 26 of 43 papers did not formally evaluate LIWC. Concerns were raised about some limitations, including difficulty handling SE-specific vocabulary. Conclusion: We highlight the potential of psycholinguistic tools and their limitations, and present new use cases for advancing the research of human factors in SE (e.g., bias in human-LLM conversations).

♻ ☆ CycleResearcher: Improving Automated Research via Automated Review ICLR 2025

Yixuan Weng, Minjun Zhu, Guangsheng Bao, Hongbo Zhang, Jindong Wang, Yue Zhang, Linyi Yang

The automation of scientific discovery has been a long-standing goal within the research community, driven by the potential to accelerate knowledge creation. While significant progress has been made using commercial large language models (LLMs) as research assistants or idea generators, the possibility of automating the entire research process with open-source LLMs remains largely unexplored. This paper explores the feasibility of using open-source post-trained LLMs as autonomous agents capable of performing the full cycle of automated research and review, from literature review and manuscript preparation to peer review and paper refinement. Our iterative preference training framework consists of CycleResearcher, which conducts research tasks, and CycleReviewer, which simulates the peer review process, providing iterative feedback via reinforcement learning. To train these models, we develop two new datasets, Review-5k and Research-14k, reflecting real-world machine learning research and peer review dynamics. Our results demonstrate that CycleReviewer achieves promising performance with a 26.89\% reduction in mean absolute error (MAE) compared to individual human reviewers in predicting paper scores, indicating the potential of LLMs to effectively assist expert-level research evaluation. In research, the papers generated by the CycleResearcher model achieved a score of 5.36 in simulated peer reviews, showing some competitiveness in terms of simulated review scores compared to the preprint level of 5.24 from human experts, while still having room for improvement compared to the accepted paper level of 5.69. This work represents a significant step toward fully automated scientific inquiry, providing ethical safeguards and exploring AI-driven research capabilities. The code, dataset and model weight are released at https://wengsyx.github.io/Researcher/.

comment: Accept in ICLR 2025

♻ ☆ Vulnerability Coordination Under the Cyber Resilience Act

Jukka Ruohonen, Paul Timmers

A new Cyber Resilience Act (CRA) was recently agreed upon in the European Union (EU). It imposes many new cyber security requirements practically to all information technology products, whether hardware or software. The paper examines and elaborates the CRA's new requirements for vulnerability coordination, including vulnerability disclosure. Although these requirements are only a part of the CRA's obligations for vendors, also some new vulnerability coordination mandates are present, including particularly with respect to so-called actively exploited vulnerabilities. The CRA further alters the coordination practices on the side of public administrations. With the examination, elaboration, and associated discussion, the paper contributes to the study of cyber security regulations, providing also a few practical takeaways.

comment: Re-submitted

♻ ☆ AI Meets the Classroom: When Do Large Language Models Harm Learning?

Matthias Lehmann, Philipp B. Cornelius, Fabian J. Sting

The effect of large language models (LLMs) in education is debated: Previous research shows that LLMs can help as well as hurt learning. In two pre-registered and incentivized laboratory experiments, we find no effect of LLMs on overall learning outcomes. In exploratory analyses and a field study, we provide evidence that the effect of LLMs on learning outcomes depends on usage behavior. Students who substitute some of their learning activities with LLMs (e.g., by generating solutions to exercises) increase the volume of topics they can learn about but decrease their understanding of each topic. Students who complement their learning activities with LLMs (e.g., by asking for explanations) do not increase topic volume but do increase their understanding. We also observe that LLMs widen the gap between students with low and high prior knowledge. While LLMs show great potential to improve learning, their use must be tailored to the educational context and students' needs.

♻ ☆ Principles for Open Data Curation: A Case Study with the New York City 311 Service Request Data

David Tussey, Jun Yan

In the early 21st century, the open data movement began to transform societies and governments by promoting transparency, innovation, and public engagement. The City of New York (NYC) has been at the forefront of this movement since the enactment of the Open Data Law in 2012, creating the NYC Open Data portal. The portal currently hosts 2,700 datasets, serving as a crucial resource for research across various domains, including health, urban development, and transportation. However, the effective use of open data relies heavily on data quality and usability, challenges that remain insufficiently addressed in the literature. This paper examines these challenges via a case study of the NYC 311 Service Request dataset, identifying key issues in data validity, consistency, and curation efficiency. We propose a set of data curation principles, tailored for government-released open data, to address these challenges. Our findings highlight the importance of harmonized field definitions, streamlined storage, and automated quality checks, offering practical guidelines for improving the reliability and utility of open datasets.

♻ ☆ Partial Mobilization: Tracking Multilingual Information Flows Amongst Russian Media Outlets and Telegram

Hans W. A. Hanley, Zakir Durumeric

In response to disinformation and propaganda from Russian online media following the invasion of Ukraine, Russian media outlets such as Russia Today and Sputnik News were banned throughout Europe. To maintain viewership, many of these Russian outlets began to heavily promote their content on messaging services like Telegram. In this work, we study how 16 Russian media outlets interacted with and utilized 732 Telegram channels throughout 2022. Leveraging the foundational model MPNet, DP-means clustering, and Hawkes processes, we trace how narratives spread between news sites and Telegram channels. We show that news outlets not only propagate existing narratives through Telegram but that they source material from the messaging platform. For example, across the websites in our study, between 2.3% (ura.news) and 26.7% (ukraina.ru) of articles discussed content that originated/resulted from activity on Telegram. Finally, tracking the spread of individual topics, we measure the rate at which news outlets and Telegram channels disseminate content within the Russian media ecosystem, finding that websites like ura.news and Telegram channels such as @genshab are the most effective at disseminating their content.

comment: Accepted to ICWSM 2024 (ICWSM version)

Computers and Society

☆ From Community Network to Community Data: Towards Combining Data Pool and Data Cooperative for Data Justice in Rural Areas

Jean Louis Fendji Kedieng Ebongue

This study explores the shift from community networks (CNs) to community data in rural areas, focusing on combining data pools and data cooperatives to achieve data justice and foster and a just AI ecosystem. With 2.7 billion people still offline, especially in the Global South, addressing data justice is critical. While discussions related to data justice have evolved to include economic dimensions, rural areas still struggle with the challenge of being adequately represented in the datasets. This study investigates a Community Data Model (CDM) that integrates the simplicity of data pools with the structured organization of data cooperatives to generate local data for AI for good. CDM leverages CNs, which have proven effective in promoting digital inclusion, to establish a centralized data repository, ensuring accessibility through open data principles. The model emphasizes community needs, prioritizing local knowledge, education, and traditional practices, with an iterative approach starting from pilot projects. Capacity building is a core component of digital literacy training and partnership with educational institutions and NGOs. The legal and regulatory dimension ensures compliance with data privacy laws. By empowering rural communities to control and manage their data, the CDM fosters equitable access and participation and sustains local identity and knowledge. This approach can mitigate the challenges of data creation in rural areas and enhance data justice. CDM can contribute to AI by improving data quality and relevance, enabling rural areas to benefit from AI advancements.

comment: 11 pages, 2 Figures

☆ The Unified Control Framework: Establishing a Common Foundation for Enterprise AI Governance, Risk Management and Regulatory Compliance

Ian W. Eisenberg, Lucía Gamboa, Eli Sherman

The rapid adoption of AI systems presents enterprises with a dual challenge: accelerating innovation while ensuring responsible governance. Current AI governance approaches suffer from fragmentation, with risk management frameworks that focus on isolated domains, regulations that vary across jurisdictions despite conceptual alignment, and high-level standards lacking concrete implementation guidance. This fragmentation increases governance costs and creates a false dichotomy between innovation and responsibility. We propose the Unified Control Framework (UCF): a comprehensive governance approach that integrates risk management and regulatory compliance through a unified set of controls. The UCF consists of three key components: (1) a comprehensive risk taxonomy synthesizing organizational and societal risks, (2) structured policy requirements derived from regulations, and (3) a parsimonious set of 42 controls that simultaneously address multiple risk scenarios and compliance requirements. We validate the UCF by mapping it to the Colorado AI Act, demonstrating how our approach enables efficient, adaptable governance that scales across regulations while providing concrete implementation guidance. The UCF reduces duplication of effort, ensures comprehensive coverage, and provides a foundation for automation, enabling organizations to achieve responsible AI governance without sacrificing innovation speed.

☆ What's So Human about Human-AI Collaboration, Anyway? Generative AI and Human-Computer Interaction

Elizabeth Anne Watkins, Emanuel Moss, Giuseppe Raffa, Lama Nachman

While human-AI collaboration has been a longstanding goal and topic of study for computational research, the emergence of increasingly naturalistic generative AI language models has greatly inflected the trajectory of such research. In this paper we identify how, given the language capabilities of generative AI, common features of human-human collaboration derived from the social sciences can be applied to the study of human-computer interaction. We provide insights drawn from interviews with industry personnel working on building human-AI collaboration systems, as well as our collaborations with end-users to build a multimodal AI assistant for task support.

☆ QG-SMS: Enhancing Test Item Analysis via Student Modeling and Simulation

Bang Nguyen, Tingting Du, Mengxia Yu, Lawrence Angrave, Meng Jiang

While the Question Generation (QG) task has been increasingly adopted in educational assessments, its evaluation remains limited by approaches that lack a clear connection to the educational values of test items. In this work, we introduce test item analysis, a method frequently used by educators to assess test question quality, into QG evaluation. Specifically, we construct pairs of candidate questions that differ in quality across dimensions such as topic coverage, item difficulty, item discrimination, and distractor efficiency. We then examine whether existing QG evaluation approaches can effectively distinguish these differences. Our findings reveal significant shortcomings in these approaches with respect to accurately assessing test item quality in relation to student performance. To address this gap, we propose a novel QG evaluation framework, QG-SMS, which leverages Large Language Model for Student Modeling and Simulation to perform test item analysis. As demonstrated in our extensive experiments and human evaluation study, the additional perspectives introduced by the simulated student profiles lead to a more effective and robust assessment of test items.

comment: Under Review

☆ Superintelligence Strategy: Expert Version

Dan Hendrycks, Eric Schmidt, Alexandr Wang

Rapid advances in AI are beginning to reshape national security. Destabilizing AI developments could rupture the balance of power and raise the odds of great-power conflict, while widespread proliferation of capable AI hackers and virologists would lower barriers for rogue actors to cause catastrophe. Superintelligence -- AI vastly better than humans at nearly all cognitive tasks -- is now anticipated by AI researchers. Just as nations once developed nuclear strategies to secure their survival, we now need a coherent superintelligence strategy to navigate a new period of transformative change. We introduce the concept of Mutual Assured AI Malfunction (MAIM): a deterrence regime resembling nuclear mutual assured destruction (MAD) where any state's aggressive bid for unilateral AI dominance is met with preventive sabotage by rivals. Given the relative ease of sabotaging a destabilizing AI project -- through interventions ranging from covert cyberattacks to potential kinetic strikes on datacenters -- MAIM already describes the strategic picture AI superpowers find themselves in. Alongside this, states can increase their competitiveness by bolstering their economies and militaries through AI, and they can engage in nonproliferation to rogue actors to keep weaponizable AI capabilities out of their hands. Taken together, the three-part framework of deterrence, nonproliferation, and competitiveness outlines a robust strategy to superintelligence in the years ahead.

comment: https://nationalsecurity.ai/

☆ Nuanced Safety for Generative AI: How Demographics Shape Responsiveness to Severity

Pushkar Mishra, Charvi Rastogi, Stephen R. Pfohl, Alicia Parrish, Roma Patel, Mark Diaz, Ding Wang, Michela Paganini, Vinodkumar Prabhakaran, Lora Aroyo, Verena Rieser

Ensuring safety of Generative AI requires a nuanced understanding of pluralistic viewpoints. In this paper, we introduce a novel data-driven approach for calibrating granular ratings in pluralistic datasets. Specifically, we address the challenge of interpreting responses of a diverse population to safety expressed via ordinal scales (e.g., Likert scale). We distill non-parametric responsiveness metrics that quantify the consistency of raters in scoring the varying levels of the severity of safety violations. Using safety evaluation of AI-generated content as a case study, we investigate how raters from different demographic groups (age, gender, ethnicity) use an ordinal scale to express their perception of the severity of violations in a pluralistic safety dataset. We apply our metrics across violation types, demonstrating their utility in extracting nuanced insights that are crucial for developing reliable AI systems in a multi-cultural contexts. We show that our approach offers improved capabilities for prioritizing safety concerns by capturing nuanced viewpoints across different demographic groups, hence improving the reliability of pluralistic data collection and in turn contributing to more robust AI evaluations.

☆ SYMBIOSIS: Systems Thinking and Machine Intelligence for Better Outcomes in Society

Sameer Sethi, Donald Martin Jr., Emmanuel Klu

This paper presents SYMBIOSIS, an AI-powered framework and platform designed to make Systems Thinking accessible for addressing societal challenges and unlock paths for leveraging systems thinking frameworks to improve AI systems. The platform establishes a centralized, open-source repository of systems thinking/system dynamics models categorized by Sustainable Development Goals (SDGs) and societal topics using topic modeling and classification techniques. Systems Thinking resources, though critical for articulating causal theories in complex problem spaces, are often locked behind specialized tools and intricate notations, creating high barriers to entry. To address this, we developed a generative co-pilot that translates complex systems representations - such as causal loop and stock-flow diagrams - into natural language (and vice-versa), allowing users to explore and build models without extensive technical training. Rooted in community-based system dynamics (CBSD) and informed by community-driven insights on societal context, we aim to bridge the problem understanding chasm. This gap, driven by epistemic uncertainty, often limits ML developers who lack the community-specific knowledge essential for problem understanding and formulation, often leading to ill informed causal assumptions, reduced intervention effectiveness and harmful biases. Recent research identifies causal and abductive reasoning as crucial frontiers for AI, and Systems Thinking provides a naturally compatible framework for both. By making Systems Thinking frameworks more accessible and user-friendly, SYMBIOSIS aims to serve as a foundational step to unlock future research into responsible and society-centered AI. Our work underscores the need for ongoing research into AI's capacity to understand essential characteristics of complex adaptive systems paving the way for more socially attuned, effective AI systems.

☆ Compliance of AI Systems

Julius Schöning, Niklas Kruse

The increasing integration of artificial intelligence (AI) systems in various fields requires solid concepts to ensure compliance with upcoming legislation. This paper systematically examines the compliance of AI systems with relevant legislation, focusing on the EU's AI Act and the compliance of data sets. The analysis highlighted many challenges associated with edge devices, which are increasingly being used to deploy AI applications closer and closer to the data sources. Such devices often face unique issues due to their decentralized nature and limited computing resources for implementing sophisticated compliance mechanisms. By analyzing AI implementations, the paper identifies challenges and proposes the first best practices for legal compliance when developing, deploying, and running AI. The importance of data set compliance is highlighted as a cornerstone for ensuring the trustworthiness, transparency, and explainability of AI systems, which must be aligned with ethical standards set forth in regulatory frameworks such as the AI Act. The insights gained should contribute to the ongoing discourse on the responsible development and deployment of embedded AI systems.

comment: 5 pages, 3 figures

☆ Evaluating open-source Large Language Models for automated fact-checking

Nicolo' Fontana, Francesco Corso, Enrico Zuccolotto, Francesco Pierri

The increasing prevalence of online misinformation has heightened the demand for automated fact-checking solutions. Large Language Models (LLMs) have emerged as potential tools for assisting in this task, but their effectiveness remains uncertain. This study evaluates the fact-checking capabilities of various open-source LLMs, focusing on their ability to assess claims with different levels of contextual information. We conduct three key experiments: (1) evaluating whether LLMs can identify the semantic relationship between a claim and a fact-checking article, (2) assessing models' accuracy in verifying claims when given a related fact-checking article, and (3) testing LLMs' fact-checking abilities when leveraging data from external knowledge sources such as Google and Wikipedia. Our results indicate that LLMs perform well in identifying claim-article connections and verifying fact-checked stories but struggle with confirming factual news, where they are outperformed by traditional fine-tuned models such as RoBERTa. Additionally, the introduction of external knowledge does not significantly enhance LLMs' performance, calling for more tailored approaches. Our findings highlight both the potential and limitations of LLMs in automated fact-checking, emphasizing the need for further refinements before they can reliably replace human fact-checkers.

comment: Main: 10 pages, 13 figures. Supplementary Materials: 7 pages, 29 figures, 1 table ### This work has been submitted to the IEEE for possible publication. ###

☆ Synchronization between media followers and political supporters during an election process: towards a real time study

Rémi Perrier, Laura Hernández, J. Ignacio Alvarez-Hamelin, Mariano G. Beiró Dimitris Kotzinos

We present an analysis of the dynamics of discussions in Twitter (before it became X) among supporters of various candidates in the 2022 French presidential election, and followers of different types of media. Our study demonstrates that we can automatically detect the synchronization of interest among different groups around specific topics at particular times. We introduce two complementary methods for constructing dynamic semantic networks, each with its own advantages. The growing aggregated network helps identify the reactivation of past topics, while the rolling window network is more sensitive to emerging discussions that, despite their significance, may appear suddenly and have a short lifespan. These two approaches offer distinct perspectives on the discussion landscape. Rather than choosing between them, we advocate for using both, as their comparison provides valuable insights at a relatively low computational and storage cost. Our findings confirm and quantify, on a larger scale and in an automatic, agnostic manner, observations previously made using more qualitative methods. We believed this work represents a step forward in developing methodologies to assess equity in information treatment, an obligation imposed by law on broadcasters that use broadcast spectrum frequencies in certain countries.

☆ Cognitive Bias Detection Using Advanced Prompt Engineering

Frederic Lemieux, Aisha Behr, Clara Kellermann-Bryant, Zaki Mohammed

Cognitive biases, systematic deviations from rationality in judgment, pose significant challenges in generating objective content. This paper introduces a novel approach for real-time cognitive bias detection in user-generated text using large language models (LLMs) and advanced prompt engineering techniques. The proposed system analyzes textual data to identify common cognitive biases such as confirmation bias, circular reasoning, and hidden assumption. By designing tailored prompts, the system effectively leverages LLMs' capabilities to both recognize and mitigate these biases, improving the quality of human-generated content (e.g., news, media, reports). Experimental results demonstrate the high accuracy of our approach in identifying cognitive biases, offering a valuable tool for enhancing content objectivity and reducing the risks of biased decision-making.

comment: 17 pages. 6 Figures, 2 Tables

☆ A Bot-based Approach to Manage Codes of Conduct in Open-Source Projects ICSE

Sergio Cobos, Javier Luis Cánovas Izquierdo

The development of Open-Source Software (OSS) projects relies on the collaborative work of contributors, generally scattered around the world. To enable this collaboration, OSS projects are hosted on social-coding platforms like GitHub, which provide the infrastructure to host the code as well as the support for enabling the participation of the community. The potentially rich and diverse mixture of contributors in OSS projects makes their management not only a technical challenge, where automation tools and bots are usually deployed, but also a social one. To this aim, OSS projects have been increasingly deploying a declaration of their code of conduct, which defines rules to ensure a respectful and inclusive participatory environment in the community, being the Contributor Covenant the main model to follow. However, the broad adoption and enforcement of codes of conduct in OSS projects is still limited. In particular, the definition, deployment, and enforcement of codes of conduct is a very challenging task. In this paper, we propose an approach to effectively manage codes of conduct in OSS projects based on the Contributor Covenant proposal. Our solution has been implemented as a bot-based solution where bots help in the definition of codes of conduct, the monitoring of OSS projects, and the enforcement of ethical rules.

comment: Accepted at the 2025 IEEE/ACM 47th International Conference on Software Engineering: Software Engineering in Society (ICSE-SEIS)

☆ The Software Diversity Card: A Framework for Reporting Diversity in Software Projects

Joan Giner-Miguelez, Sergio Morales, Sergio Cobos, Javier Luis Canovas Izquierdo, Robert Clariso, Jordi Cabot

The interest and concerns about diversity in software development have soared in recent years. Reporting diversity-related aspects of software projects can increase user trust and help regulators evaluate potential adoption. Furthermore, recent directives around AI are beginning to require diversity information in the development of AI products, indicating the growing interest of public regulators in it. Despite this importance, current documentation assets in software development processes frequently overlook diversity in favor of technical features, partly due to a lack of tools for describing and annotating diversity. This work introduces the Software Diversity Card, a comprehensive framework for reporting diversity-related aspects of software projects. The card is designed to profile the different types of teams involved in developing and governing software projects (including the final user groups involved in testing), and the software adaptations for specific social groups. To encourage its adoption, we provide a diversity modeling language, a toolkit for generating the cards using such language, and a collection of real-world examples from active software projects. Our proposal can enhance diversity practices in software development e.g., through open-source projects like the CONTRIBUTING.md file), support public administrations in software assessment, and help businesses promote diversity as a key asset.

☆ Automatic Teaching Platform on Vision Language Retrieval Augmented Generation

Ruslan Gokhman, Jialu Li, Youshan Zhang

Automating teaching presents unique challenges, as replicating human interaction and adaptability is complex. Automated systems cannot often provide nuanced, real-time feedback that aligns with students' individual learning paces or comprehension levels, which can hinder effective support for diverse needs. This is especially challenging in fields where abstract concepts require adaptive explanations. In this paper, we propose a vision language retrieval augmented generation (named VL-RAG) system that has the potential to bridge this gap by delivering contextually relevant, visually enriched responses that can enhance comprehension. By leveraging a database of tailored answers and images, the VL-RAG system can dynamically retrieve information aligned with specific questions, creating a more interactive and engaging experience that fosters deeper understanding and active student participation. It allows students to explore concepts visually and verbally, promoting deeper understanding and reducing the need for constant human oversight while maintaining flexibility to expand across different subjects and course material.

☆ Cybersafety Card Game: Empowering Digital Educators to Teach Cybersafety to Older Adults

Jacob Camilleri, Ashley Sheil, Michelle O'Keeffe, Moya Cronin, Melanie Gruben, Hazel Murray

Digital inequality remains a significant barrier for many older adults, limiting their ability to navigate online spaces securely and confidently while increasing their susceptibility to cyber threats. In response, we propose a novel shedding-type card game for older adults to conceptually learn and reinforce cyber hygiene practices in educational settings. We asked digital educators to participate as players alongside older adults (n = 16), departing from their usual role as teachers, they collaborated and shared a unique learning experience. The cybersafety game addresses 4 key topics: handling scams, password management, responding to cyber attacks, and staying private. We adopted a mixed-method approach of think-aloud playtesting, semi-structured interviews, and surveys to evaluate the game's reception and impact. Participants reported highly favorable gameplay experiences and found the cybersafety advice useful. Player feedback informed game modifications, detailed in this paper, to further enhance the game's usability and educational value.

☆ A Comparative Study of How People With and Without ADHD Recognise and Avoid Dark Patterns on Social Media

Thomas Mildner, Daniel Fidel, Evropi Stefanidi, Pawel W. Wozniak, Rainer Malaka, Jasmin Niess

Dark patterns are deceptive strategies that recent work in human-computer interaction (HCI) has captured throughout digital domains, including social networking sites (SNSs). While research has identified difficulties among people to recognise dark patterns effectively, few studies consider vulnerable populations and their experience in this regard, including people with attention deficit hyperactivity disorder (ADHD), who may be especially susceptible to attention-grabbing tricks. Based on an interactive web study with 135 participants, we investigate SNS users' ability to recognise and avoid dark patterns by comparing results from participants with and without ADHD. In line with prior work, we noticed overall low recognition of dark patterns with no significant differences between the two groups. Yet, ADHD individuals were able to avoid specific dark patterns more often. Our results advance previous work by understanding dark patterns in a realistic environment and offer insights into their effect on vulnerable populations.

comment: 17 pages, 8 Figures, 6 Tables, published at ACM CHI 2025

☆ Can Large Language Models Grasp Concepts in Visual Content? A Case Study on YouTube Shorts about Depression

Jiaying "Lizzy" Liu, Yiheng Su, Praneel Seth

Large language models (LLMs) are increasingly used to assist computational social science research. While prior efforts have focused on text, the potential of leveraging multimodal LLMs (MLLMs) for online video studies remains underexplored. We conduct one of the first case studies on MLLM-assisted video content analysis, comparing AI's interpretations to human understanding of abstract concepts. We leverage LLaVA-1.6 Mistral 7B to interpret four abstract concepts regarding video-mediated self-disclosure, analyzing 725 keyframes from 142 depression-related YouTube short videos. We perform a qualitative analysis of MLLM's self-generated explanations and found that the degree of operationalization can influence MLLM's interpretations. Interestingly, greater detail does not necessarily increase human-AI alignment. We also identify other factors affecting AI alignment with human understanding, such as concept complexity and versatility of video genres. Our exploratory study highlights the need to customize prompts for specific concepts and calls for researchers to incorporate more human-centered evaluations when working with AI systems in a multimodal context.

comment: 11 pages

☆ Blockchain As a Platform For Artificial Intelligence (AI) Transparency

Afroja Akther, Ayesha Arobee, Abdullah Al Adnan, Omum Auyon, ASM Johirul Islam, Farhad Akter

As artificial intelligence (AI) systems become increasingly complex and autonomous, concerns over transparency and accountability have intensified. The "black box" problem in AI decision-making limits stakeholders' ability to understand, trust, and verify outcomes, particularly in high-stakes sectors such as healthcare, finance, and autonomous systems. Blockchain technology, with its decentralized, immutable, and transparent characteristics, presents a potential solution to enhance AI transparency and auditability. This paper explores the integration of blockchain with AI to improve decision traceability, data provenance, and model accountability. By leveraging blockchain as an immutable record-keeping system, AI decision-making can become more interpretable, fostering trust among users and regulatory compliance. However, challenges such as scalability, integration complexity, and computational overhead must be addressed to fully realize this synergy. This study discusses existing research, proposes a framework for blockchain-enhanced AI transparency, and highlights practical applications, benefits, and limitations. The findings suggest that blockchain could be a foundational technology for ensuring AI systems remain accountable, ethical, and aligned with regulatory standards.

comment: 14 pages, 2 figures, 5 tables

☆ Towards democratic data agency: Attitudes and concerns about online data practices

Niels J. Gommesen

Recent studies reveal widespread concern and increasing lack of understanding about how personal data is collected, shared, and used online without consent. This issue is compounded by limited options available for digital citizens to understand, control and manage their data flows across platforms, underscoring the need to explore how this lack of trust and transparency affects citizens' data practices including their capacities to act in a modern knowledge society. Despite the promising research within this field, important demographics are often overlooked, particularly people from marginalized social groups such as elderly, socially and economically challenged communities, and younger participants. This paper addresses this gap by specifically focusing on these underrepresented groups, emphasizing the need for exploring their understandings and percepts of online data practices. Drawing on three semi-structured focus group interviews, the paper asks: to what extent can public attitudes and concerns about data sharing on the internet inform the potential strategies and frameworks necessary to enhance digital trust and democratic data agency particularly among marginalized groups in Denmark? The study explores the types of information, levels of transparency, and agency people desire in their daily online data practices. Additionally, it explores how these insights can potentially inform the future development of fair data strategies and technological approaches to enhance digital trust and democratic data agency. Key findings point out the need for transparent, accessible privacy policies and data management tools, emphasizing that transparency alone is insufficient without enhancing democratic agency to address trust issues and foster a more inclusive digital environment. Keywords: public understanding, personal data, digital trust, data practices, data agency

comment: 20 pages, 1 figure

♻ ☆ Rethinking AI Cultural Alignment

Michal Bravansky, Filip Trhlik, Fazl Barez

As general-purpose artificial intelligence (AI) systems become increasingly integrated with diverse human communities, cultural alignment has emerged as a crucial element in their deployment. Most existing approaches treat cultural alignment as one-directional, embedding predefined cultural values from standardized surveys and repositories into AI systems. To challenge this perspective, we highlight research showing that humans' cultural values must be understood within the context of specific AI systems. We then use a GPT-4o case study to demonstrate that AI systems' cultural alignment depends on how humans structure their interactions with the system. Drawing on these findings, we argue that cultural alignment should be reframed as a bidirectional process: rather than merely imposing standardized values on AIs, we should query the human cultural values most relevant to each AI-based system and align it to these values through interaction frameworks shaped by human users.

♻ ☆ Demystifying Misconceptions in Social Bots Research

Stefano Cresci, Kai-Cheng Yang, Angelo Spognardi, Roberto Di Pietro, Filippo Menczer, Marinella Petrocchi

Research on social bots aims at advancing knowledge and providing solutions to one of the most debated forms of online manipulation. Yet, social bot research is plagued by widespread biases, hyped results, and misconceptions that set the stage for ambiguities, unrealistic expectations, and seemingly irreconcilable findings. Overcoming such issues is instrumental towards ensuring reliable solutions and reaffirming the validity of the scientific method. In this contribution, we review some recent results in social bots research, highlighting and revising factual errors as well as methodological and conceptual biases. More importantly, we demystify common misconceptions, addressing fundamental points on how social bots research is discussed. Our analysis surfaces the need to discuss research about online disinformation and manipulation in a rigorous, unbiased, and responsible way. This article bolsters such effort by identifying and refuting common fallacious arguments used by both proponents and opponents of social bots research, as well as providing directions toward sound methodologies for future research in the field.

♻ ☆ OASIS Uncovers: High-Quality T2I Models, Same Old Stereotypes ICLR 2025

Sepehr Dehdashtian, Gautam Sreekumar, Vishnu Naresh Boddeti

Images generated by text-to-image (T2I) models often exhibit visual biases and stereotypes of concepts such as culture and profession. Existing quantitative measures of stereotypes are based on statistical parity that does not align with the sociological definition of stereotypes and, therefore, incorrectly categorizes biases as stereotypes. Instead of oversimplifying stereotypes as biases, we propose a quantitative measure of stereotypes that aligns with its sociological definition. We then propose OASIS to measure the stereotypes in a generated dataset and understand their origins within the T2I model. OASIS includes two scores to measure stereotypes from a generated image dataset: (M1) Stereotype Score to measure the distributional violation of stereotypical attributes, and (M2) WALS to measure spectral variance in the images along a stereotypical attribute. OASIS also includes two methods to understand the origins of stereotypes in T2I models: (U1) StOP to discover attributes that the T2I model internally associates with a given concept, and (U2) SPI to quantify the emergence of stereotypical attributes in the latent space of the T2I model during image generation. Despite the considerable progress in image fidelity, using OASIS, we conclude that newer T2I models such as FLUX.1 and SDv3 contain strong stereotypical predispositions about concepts and still generate images with widespread stereotypical attributes. Additionally, the quantity of stereotypes worsens for nationalities with lower Internet footprints.

comment: Accepted as a Spotlight paper at ICLR 2025

♻ ☆ Keep the Future Human: Why and How We Should Close the Gates to AGI and Superintelligence, and What We Should Build Instead

Anthony Aguirre

Dramatic advances in artificial intelligence over the past decade (for narrow-purpose AI) and the last several years (for general-purpose AI) have transformed AI from a niche academic field to the core business strategy of many of the world's largest companies, with hundreds of billions of dollars in annual investment in the techniques and technologies for advancing AI's capabilities. We now come to a critical juncture. As the capabilities of new AI systems begin to match and exceed those of humans across many cognitive domains, humanity must decide: how far do we go, and in what direction? This essay argues that we should keep the future human by closing the "gates" to smarter-than-human, autonomous, general-purpose AI -- sometimes called "AGI" -- and especially to the highly-superhuman version sometimes called "superintelligence." Instead, we should focus on powerful, trustworthy AI tools that can empower individuals and transformatively improve human societies' abilities to do what they do best.

comment: 62 pages, 2 figures, 3 appendices. This is a total rewrite and major expansion of a previous version entitled "Close the Gates."

♻ ☆ GreenDFL: a Framework for Assessing the Sustainability of Decentralized Federated Learning Systems

Chao Feng, Alberto Huertas Celdrán, Xi Cheng, Gérôme Bovet, Burkhard Stiller

Decentralized Federated Learning (DFL) is an emerging paradigm that enables collaborative model training without centralized data and model aggregation, enhancing privacy and resilience. However, its sustainability remains underexplored, as energy consumption and carbon emissions vary across different system configurations. Understanding the environmental impact of DFL is crucial for optimizing its design and deployment. This work aims to develop a comprehensive and operational framework for assessing the sustainability of DFL systems. To address it, this work provides a systematic method for quantifying energy consumption and carbon emissions, offering insights into improving the sustainability of DFL. This work proposes GreenDFL, a fully implementable framework that has been integrated into a real-world DFL platform. GreenDFL systematically analyzes the impact of various factors, including hardware accelerators, model architecture, communication medium, data distribution, network topology, and federation size, on the sustainability of DFL systems. Besides, a sustainability-aware aggregation algorithm (GreenDFL-SA) and a node selection algorithm (GreenDFL-SN) are developed to optimize energy efficiency and reduce carbon emissions in DFL training. Empirical experiments are conducted on multiple datasets, measuring energy consumption and carbon emissions at different phases of the DFL lifecycle. The proposed GreenDFL provides a comprehensive and practical approach for assessing the sustainability of DFL systems. Furthermore, it offers best practices for improving environmental efficiency in DFL, making sustainability considerations more actionable in real-world deployments.

♻ ☆ BuildingView: Constructing Urban Building Exteriors Databases with Street View Imagery and Multimodal Large Language Mode

Zongrong Li, Yunlei Su, Hongrong Wang, Wufan Zhao

Urban Building Exteriors are increasingly important in urban analytics, driven by advancements in Street View Imagery and its integration with urban research. Multimodal Large Language Models (LLMs) offer powerful tools for urban annotation, enabling deeper insights into urban environments. However, challenges remain in creating accurate and detailed urban building exterior databases, identifying critical indicators for energy efficiency, environmental sustainability, and human-centric design, and systematically organizing these indicators. To address these challenges, we propose BuildingView, a novel approach that integrates high-resolution visual data from Google Street View with spatial information from OpenStreetMap via the Overpass API. This research improves the accuracy of urban building exterior data, identifies key sustainability and design indicators, and develops a framework for their extraction and categorization. Our methodology includes a systematic literature review, building and Street View sampling, and annotation using the ChatGPT-4O API. The resulting database, validated with data from New York City, Amsterdam, and Singapore, provides a comprehensive tool for urban studies, supporting informed decision-making in urban planning, architectural design, and environmental policy. The code for BuildingView is available at https://github.com/Jasper0122/BuildingView.

comment: 15 pages, 6 figures

♻ ☆ The Effect of Warm-Glow on User Behavioral Intention to Adopt Technology: Extending the UTAUT2 Model

Antonios Saravanos, Neil Stott, Dongnanzi Zheng, Stavros Zervoudakis

In this study, we enhance the Unified Theory of Acceptance and Use of Technology (UTAUT2) by incorporating the warm-glow phenomenon to clarify its impact on user decisions regarding the adoption of technology. We introduce two additional constructs aimed at capturing both the external and internal aspects of warm-glow, thus creating what we refer to as the UTAUT2 + WG model. To evaluate the effectiveness of our model, we conducted an experimental study in which participants were presented with a scenario describing a hypothetical technology designed to evoke warm-glow sensations. Using the partial least squares method, we analyzed the collected data to assess our expanded model. Our findings indicate that warm-glow significantly influences user behavior, with the internal aspect having the strongest influence, followed by hedonic motivation, performance expectancy, and finally the external aspect of warm-glow. We conclude by discussing the implications of our research, acknowledging its limitations, and suggesting directions for future exploration.

Computers and Society

☆ Prevalence and Impacts of Image-Based Sexual Abuse Victimization: A Multinational Study

Rebecca Umbach, Nicola Henry, Gemma Beard

Image-based sexual abuse (IBSA) refers to the nonconsensual creating, taking, or sharing of intimate images, including threats to share intimate images. Despite the significant harms of IBSA, there is limited data on its prevalence and how it affects different identity or demographic groups. This study examines prevalence of, impacts from, and responses to IBSA via a survey with over 16,000 adults in 10 countries. More than 1 in 5 (22.6%) respondents reported an experience of IBSA. Victimization rates were higher among LGBTQ+ and younger respondents. Although victimized at similar rates, women reported greater harms and negative impacts from IBSA than men. Nearly a third (30.9%) of victim-survivors did not report or disclose their experience to anyone. We provide large-scale, granular, baseline data on prevalence in a diverse set of countries to aid in the development of effective interventions that address the experiences and intersectional identities of victim-survivors.

comment: To appear at CHI 2025

☆ Quantifying the Relevance of Youth Research Cited in the US Policy Documents

Miftahul Jannat Mokarrama, Hamed Alhoori

In recent years, there has been a growing concern and emphasis on conducting research beyond academic or scientific research communities, benefiting society at large. A well-known approach to measuring the impact of research on society is enumerating its policy citation(s). Despite the importance of research in informing policy, there is no concrete evidence to suggest the research's relevance in cited policy documents. This is concerning because it may increase the possibility of evidence used in policy being manipulated by individual, social, or political biases that may lead to inappropriate, fragmented, or archaic research evidence in policy. Therefore, it is crucial to identify the degree of relevance between research articles and citing policy documents. In this paper, we examined the scale of contextual relevance of youth-focused research in the referenced US policy documents using natural language processing techniques, state-of-the-art pre-trained Large Language Models (LLMs), and statistical analysis. Our experiments and analysis concluded that youth-related research articles that get US policy citations are mostly relevant to the citing policy documents.

comment: The paper was accepted and presented in IEEE BIG DATA 2024. It has 10 pages, 5 figures, and 4 tables

☆ Inducing Efficient and Equitable Professional Networks through Link Recommendations

Cynthia Dwork, Chris Hays, Lunjia Hu, Nicole Immorlica, Juan Perdomo

Professional networks are a key determinant of individuals' labor market outcomes. They may also play a role in either exacerbating or ameliorating inequality of opportunity across demographic groups. In a theoretical model of professional network formation, we show that inequality can increase even without exogenous in-group preferences, confirming and complementing existing theoretical literature. Increased inequality emerges from the differential leverage privileged and unprivileged individuals have in forming connections due to their asymmetric ex ante prospects. This is a formalization of a source of inequality in the labor market which has not been previously explored. We next show how inequality-aware platforms may reduce inequality by subsidizing connections, through link recommendations that reduce costs, between privileged and unprivileged individuals. Indeed, mixed-privilege connections turn out to be welfare improving, over all possible equilibria, compared to not recommending links or recommending some smaller fraction of cross-group links. Taken together, these two findings reveal a stark reality: professional networking platforms that fail to foster integration in the link formation process risk reducing the platform's utility to its users and exacerbating existing labor market inequality.

comment: 34 pages, 4 figures

☆ Talking Back -- human input and explanations to interactive AI systems

Alan Dix, Tommaso Turchi, Ben Wilson, Anna Monreale, Matt Roach

While XAI focuses on providing AI explanations to humans, can the reverse - humans explaining their judgments to AI - foster richer, synergistic human-AI systems? This paper explores various forms of human inputs to AI and examines how human explanations can guide machine learning models toward automated judgments and explanations that align more closely with human concepts.

☆ On Fact and Frequency: LLM Responses to Misinformation Expressed with Uncertainty

Yana van de Sande, Gunes Açar, Thabo van Woudenberg, Martha Larson

We study LLM judgments of misinformation expressed with uncertainty. Our experiments study the response of three widely used LLMs (GPT-4o, LlaMA3, DeepSeek-v2) to misinformation propositions that have been verified false and then are transformed into uncertain statements according to an uncertainty typology. Our results show that after transformation, LLMs change their factchecking classification from false to not-false in 25% of the cases. Analysis reveals that the change cannot be explained by predictors to which humans are expected to be sensitive, i.e., modality, linguistic cues, or argumentation strategy. The exception is doxastic transformations, which use linguistic cue phrases such as "It is believed ...".To gain further insight, we prompt the LLM to make another judgment about the transformed misinformation statements that is not related to truth value. Specifically, we study LLM estimates of the frequency with which people make the uncertain statement. We find a small but significant correlation between judgment of fact and estimation of frequency.

comment: 4 pages, 1 figure, 3 tables, conference

☆ Prompt Programming: A Platform for Dialogue-based Computational Problem Solving with Generative AI Models

Victor-Alexandru Pădurean, Paul Denny, Alkis Gotovos, Adish Singla

Computing students increasingly rely on generative AI tools for programming assistance, often without formal instruction or guidance. This highlights a need to teach students how to effectively interact with AI models, particularly through natural language prompts, to generate and critically evaluate code for solving computational tasks. To address this, we developed a novel platform for prompt programming that enables authentic dialogue-based interactions, supports problems involving multiple interdependent functions, and offers on-request execution of generated code. Data analysis from over 900 students in an introductory programming course revealed high engagement, with the majority of prompts occurring within multi-turn dialogues. Problems with multiple interdependent functions encouraged iterative refinement, with progression graphs highlighting several common strategies. Students were highly selective about the code they chose to test, suggesting that on-request execution of generated code promoted critical thinking. Given the growing importance of learning dialogue-based programming with AI, we provide this tool as a publicly accessible resource, accompanied by a corpus of programming problems for educational use.

comment: Preprint of the ITiCSE'25 paper

☆ Codebook Reduction and Saturation: Novel observations on Inductive Thematic Saturation for Large Language Models and initial coding in Thematic Analysis

Stefano De Paoli, Walter Stan Mathis

This paper reflects on the process of performing Thematic Analysis with Large Language Models (LLMs). Specifically, the paper deals with the problem of analytical saturation of initial codes, as produced by LLMs. Thematic Analysis is a well-established qualitative analysis method composed of interlinked phases. A key phase is the initial coding, where the analysts assign labels to discrete components of a dataset. Saturation is a way to measure the validity of a qualitative analysis and relates to the recurrence and repetition of initial codes. In the paper we reflect on how well LLMs achieve analytical saturation and propose also a novel technique to measure Inductive Thematic Saturation (ITS). This novel technique leverages a programming framework called DSPy. The proposed novel approach allows a precise measurement of ITS.

☆ Uncovering inequalities in new knowledge learning by large language models across different languages

Chenglong Wang, Haoyu Tang, Xiyuan Yang, Yueqi Xie, Jina Suh, Sunayana Sitaram, Junming Huang, Yu Xie, Zhaoya Gong, Xing Xie, Fangzhao Wu

As large language models (LLMs) gradually become integral tools for problem solving in daily life worldwide, understanding linguistic inequality is becoming increasingly important. Existing research has primarily focused on static analyses that assess the disparities in the existing knowledge and capabilities of LLMs across languages. However, LLMs are continuously evolving, acquiring new knowledge to generate up-to-date, domain-specific responses. Investigating linguistic inequalities within this dynamic process is, therefore, also essential. In this paper, we explore inequalities in new knowledge learning by LLMs across different languages and four key dimensions: effectiveness, transferability, prioritization, and robustness. Through extensive experiments under two settings (in-context learning and fine-tuning) using both proprietary and open-source models, we demonstrate that low-resource languages consistently face disadvantages across all four dimensions. By shedding light on these disparities, we aim to raise awareness of linguistic inequalities in LLMs' new knowledge learning, fostering the development of more inclusive and equitable future LLMs.

☆ AI-Facilitated Collective Judgements

Manon Revel, Théophile Pénigaud

This article unpacks the design choices behind longstanding and newly proposed computational frameworks aimed at finding common grounds across collective preferences and examines their potential future impacts, both technically and normatively. It begins by situating AI-assisted preference elicitation within the historical role of opinion polls, emphasizing that preferences are shaped by the decision-making context and are seldom objectively captured. With that caveat in mind, we explore AI-facilitated collective judgment as a discovery tool for fostering reasonable representations of a collective will, sense-making, and agreement-seeking. At the same time, we caution against dangerously misguided uses, such as enabling binding decisions, fostering gradual disempowerment or post-rationalizing political outcomes.

♻ ☆ Democratizing Signal Processing and Machine Learning: Math Learning Equity for Elementary and Middle School Students

Namrata Vaswani, Mohamed Y. Selim, Renee Serrell Gibert

Signal Processing (SP) and Machine Learning (ML) rely on good math and coding knowledge, in particular, linear algebra, probability, trigonometry, and complex numbers. A good grasp of these relies on scalar algebra learned in middle school. The ability to understand and use scalar algebra well, in turn, relies on a good foundation in basic arithmetic. Because of various systemic barriers, many students are not able to build a strong foundation in arithmetic in elementary school. This leads them to struggle with algebra and everything after that. Since math learning is cumulative, the gap between those without a strong early foundation and everyone else keeps increasing over the school years and becomes difficult to fill in college. In this article we discuss how SP faculty, students, and professionals can play an important role in starting, and participating in, university-run, or other, out-of-school math support programs to supplement students' learning. Two example programs run by the authors, CyMath at Iowa State and Algebra by 7th Grade (Ab7G) at Purdue, and one run by the Actuarial Foundation, are described. We conclude with providing some simple zero-cost suggestions for public schools that, if adopted, could benefit a much larger number of students than what out-of-school programs can reach.

comment: Under submission to IEEE Signal Processing Magazine

♻ ☆ Do Not Trust Licenses You See -- Dataset Compliance Requires Massive-Scale AI-Powered Lifecycle Tracing

Jaekyeom Kim, Sungryull Sohn, Gerrard Jeongwon Jo, Jihoon Choi, Kyunghoon Bae, Hwayoung Lee, Yongmin Park, Honglak Lee

This paper argues that a dataset's legal risk cannot be accurately assessed by its license terms alone; instead, tracking dataset redistribution and its full lifecycle is essential. However, this process is too complex for legal experts to handle manually at scale. Tracking dataset provenance, verifying redistribution rights, and assessing evolving legal risks across multiple stages require a level of precision and efficiency that exceeds human capabilities. Addressing this challenge effectively demands AI agents that can systematically trace dataset redistribution, analyze compliance, and identify legal risks. We develop an automated data compliance system called NEXUS and show that AI can perform these tasks with higher accuracy, efficiency, and cost-effectiveness than human experts. Our massive legal analysis of 17,429 unique entities and 8,072 license terms using this approach reveals the discrepancies in legal rights between the original datasets before redistribution and their redistributed subsets, underscoring the necessity of the data lifecycle-aware compliance. For instance, we find that out of 2,852 datasets with commercially viable individual license terms, only 605 (21%) are legally permissible for commercialization. This work sets a new standard for AI data governance, advocating for a framework that systematically examines the entire lifecycle of dataset redistribution to ensure transparent, legal, and responsible dataset management.

♻ ☆ Urban highways are barriers to social ties

Luca Maria Aiello, Anastassia Vybornova, Sándor Juhász, Michael Szell, Eszter Bokányi

Urban highways are common, especially in the US, making cities more car-centric. They promise the annihilation of distance but obstruct pedestrian mobility, thus playing a key role in limiting social interactions locally. Although this limiting role is widely acknowledged in urban studies, the quantitative relationship between urban highways and social ties is barely tested. Here we define a Barrier Score that relates massive, geolocated online social network data to highways in the 50 largest US cities. At the unprecedented granularity of individual social ties, we show that urban highways are associated with decreased social connectivity. This barrier effect is especially strong for short distances and consistent with historical cases of highways that were built to purposefully disrupt or isolate Black neighborhoods. By combining spatial infrastructure with social tie data, our method adds a new dimension to demographic studies of social segregation. Our study can inform reparative planning for an evidence-based reduction of spatial inequality, and more generally, support a better integration of the social fabric in urban planning.

comment: Main text: 8 pages, 4 figures, 1 table. Supplementary Information: 11 pages, 11 figures, 5 tables

♻ ☆ Ten simple rules for training scientists to make better software

Kit Gallagher, Richard Creswell, Ben Lambert, Martin Robinson, Chon Lok Lei, Gary R. Mirams, David J. Gavaghan

Computational methods and associated software implementations are central to every field of scientific investigation. Modern biological research, particularly within systems biology, has relied heavily on the development of software tools to process and organize increasingly large datasets, simulate complex mechanistic models, provide tools for the analysis and management of data, and visualize and organize outputs. However, developing high-quality research software requires scientists to develop a host of software development skills, and teaching these skills to students is challenging. There has been a growing importance placed on ensuring reproducibility and good development practices in computational research. However, less attention has been devoted to informing the specific teaching strategies which are effective at nurturing in researchers the complex skillset required to produce high-quality software that, increasingly, is required to underpin both academic and industrial biomedical research. Recent articles in the Ten Simple Rules collection have discussed the teaching of foundational computer science and coding techniques to biology students. We advance this discussion by describing the specific steps for effectively teaching the necessary skills scientists need to develop sustainable software packages which are fit for (re-)use in academic research or more widely. Although our advice is likely to be applicable to all students and researchers hoping to improve their software development skills, our guidelines are directed towards an audience of students that have some programming literacy but little formal training in software development or engineering, typical of early doctoral students. These practices are also applicable outside of doctoral training environments, and we believe they should form a key part of postgraduate training schemes more generally in the life sciences.

comment: 20 pages, 7 figures

♻ ☆ Get my drift? Catching LLM Task Drift with Activation Deltas

Sahar Abdelnabi, Aideen Fay, Giovanni Cherubin, Ahmed Salem, Mario Fritz, Andrew Paverd

LLMs are commonly used in retrieval-augmented applications to execute user instructions based on data from external sources. For example, modern search engines use LLMs to answer queries based on relevant search results; email plugins summarize emails by processing their content through an LLM. However, the potentially untrusted provenance of these data sources can lead to prompt injection attacks, where the LLM is manipulated by natural language instructions embedded in the external data, causing it to deviate from the user's original instruction(s). We define this deviation as task drift. Task drift is a significant concern as it allows attackers to exfiltrate data or influence the LLM's output for other users. We study LLM activations as a solution to detect task drift, showing that activation deltas - the difference in activations before and after processing external data - are strongly correlated with this phenomenon. Through two probing methods, we demonstrate that a simple linear classifier can detect drift with near-perfect ROC AUC on an out-of-distribution test set. We evaluate these methods by making minimal assumptions about how users' tasks, system prompts, and attacks can be phrased. We observe that this approach generalizes surprisingly well to unseen task domains, such as prompt injections, jailbreaks, and malicious instructions, without being trained on any of these attacks. Interestingly, the fact that this solution does not require any modifications to the LLM (e.g., fine-tuning), as well as its compatibility with existing meta-prompting solutions, makes it cost-efficient and easy to deploy. To encourage further research on activation-based task inspection, decoding, and interpretability, we release our large-scale TaskTracker toolkit, featuring a dataset of over 500K instances, representations from six SoTA language models, and a suite of inspection tools.

comment: SaTML 2025

♻ ☆ Legal Fact Prediction: The Missing Piece in Legal Judgment Prediction

Junkai Liu, Yujie Tong, Hui Huang, Bowen Zheng, Yiran Hu, Peicheng Wu, Chuan Xiao, Makoto Onizuka, Muyun Yang, Shuyuan Zheng

Legal judgment prediction (LJP), which enables litigants and their lawyers to forecast judgment outcomes and refine litigation strategies, has emerged as a crucial legal NLP task. Existing studies typically utilize legal facts, i.e., facts that have been established by evidence and determined by the judge, to predict the judgment. However, legal facts are often difficult to obtain in the early stages of litigation, significantly limiting the practical applicability of fact-based LJP. To address this limitation, we propose a novel legal NLP task: \textit{legal fact prediction} (LFP), which takes the evidence submitted by litigants for trial as input to predict legal facts, thereby empowering fact-based LJP technologies to perform prediction in the absence of ground-truth legal facts. We also propose the first benchmark dataset, LFPBench, for evaluating the LFP task. Our extensive experiments on LFPBench demonstrate the effectiveness of LFP-empowered LJP and highlight promising research directions for LFP. Our code and data are available at https://github.com/HPRCEST/LFPBench.

♻ ☆ Examining the Representation of Youth in the US Policy Documents through the Lens of Research

Miftahul Jannat Mokarrama, Abdul Rahman Shaikh, Hamed Alhoori

This study explores the representation of youth in US policy documents by analyzing how research on youth topics is cited within these policies. The research focuses on three key questions: identifying the frequently discussed topics in youth research that receive citations in policy documents, discerning patterns in youth research that contribute to higher citation rates in policy, and comparing the alignment between topics in youth research and those in citing policy documents. Through this analysis, the study aims to shed light on the relationship between academic research and policy formulation, highlighting areas where youth issues are effectively integrated into policy and contributing to the broader goal of enhancing youth engagement in societal decision-making processes.

comment: 5 pages, 4 figures, presented at 2024 IEEE International Conference on Big Data

♻ ☆ ChatGPT vs Social Surveys: Probing Objective and Subjective Silicon Population

Muzhi Zhou, Lu Yu, Xiaomin Geng, Lan Luo

Recent discussions about Large Language Models (LLMs) indicate that they have the potential to simulate human responses in social surveys and generate reliable predictions, such as those found in political polls. However, the existing findings are highly inconsistent, leaving us uncertain about the population characteristics of data generated by LLMs. In this paper, we employ repeated random sampling to create sampling distributions that identify the population parameters of silicon samples generated by GPT. Our findings show that GPT's demographic distribution aligns with the 2020 U.S. population in terms of gender and average age. However, GPT significantly overestimates the representation of the Black population and individuals with higher levels of education, even when it possesses accurate knowledge. Furthermore, GPT's point estimates for attitudinal scores are highly inconsistent and show no clear inclination toward any particular ideology. The sample response distributions exhibit a normal pattern that diverges significantly from those of human respondents. Consistent with previous studies, we find that GPT's answers are more deterministic than those of humans. We conclude by discussing the concerning implications of this biased and deterministic silicon population for making inferences about real-world populations.