arXiv:2605.06357v1 Announce Type: cross Abstract: This work studies the robust evaluation of iterative stochastic purification defenses under white-box adversarial attacks. Our key technical insight is that gradient checkpointing makes exact end-to-end gradient computation through long purification trajectories practical by trading additional recomputation for substantially lower memory usage. This enables full-gradient adaptive attacks against diffusion- and […]
Adaptive Greedy Frame Selection for Long Video Understanding
arXiv:2603.20180v2 Announce Type: replace-cross Abstract: Large vision–language models (VLMs) are increasingly applied to long-video question answering, yet inference is often bottlenecked by the number of input frames and resulting visual tokens. Naive sparse sampling can miss decisive moments, while purely relevance-driven selection frequently collapses onto near-duplicate frames and sacrifices coverage of temporally distant evidence. We […]
EA-WM: Event-Aware Generative World Model with Structured Kinematic-to-Visual Action Fields
arXiv:2605.06192v1 Announce Type: cross Abstract: Pretrained video diffusion models provide powerful spatiotemporal generative priors, making them a natural foundation for robotic world models. While recent world-action models jointly optimize future videos and actions, they predominantly treat video generation as an auxiliary representation for policy learning. Consequently, they insufficiently explore the inverse problem: leveraging action signals […]
From Documents to Spans: Scalable Supervision for Evidence-Based ICD Coding with LLMs
arXiv:2603.15270v2 Announce Type: replace-cross Abstract: International Classification of Diseases (ICD) coding assigns diagnosis codes to clinical documents and is essential for healthcare billing and clinical analysis. Reliable coding requires that each predicted code be supported by explicit textual evidence. However, existing public datasets provide only code labels, without evidence annotations, limiting models’ ability to learn […]
Dynamic Vine Copulas: Detecting and Quantifying Time-Varying Higher-Order Interactions
arXiv:2605.03061v2 Announce Type: replace-cross Abstract: Time-varying dependence is often modeled with dynamic correlations or Gaussian graphical models, but multivariate systems can change through tail behavior, asymmetry, or conditional structure even when correlations are nearly stable. We introduce Dynamic Vine Copulas (DVC), a temporal vine-copula framework for estimating and diagnosing sequence-wide non-Gaussian dependence. DVC fixes a […]
Correct Code, Vulnerable Dependencies: A Large Scale Measurement Study of LLM-Specified Library Versions
arXiv:2605.06279v1 Announce Type: cross Abstract: Large language models (LLMs) are now largely involved in software development workflows, and the code they generate routinely includes third-party library (TPL) imports annotated with specific version identifiers. These version choices can carry security and compatibility risks, yet they have not been systematically studied. We present the first large-scale measurement […]
Operator-Guided Invariance Learning for Continuous Reinforcement Learning
arXiv:2605.06500v1 Announce Type: cross Abstract: Reinforcement learning (RL) with continuous time and state/action spaces is often data-intensive and brittle under nuisance variability and shift, motivating methods that exploit value-preserving structures to stabilize and improve learning. Most existing approaches focus on special cases, such as prescribed symmetries and exact equivariance, without addressing how to discover more […]
Goal-Driven Query Answering over First- and Second-Order Dependencies with Equality
arXiv:2412.09125v2 Announce Type: replace Abstract: In this paper we present the first goal-driven query answering technique for first- and second-order dependencies with equality. Our technique transforms the input dependencies so that applying the chase to the output avoids many inferences that are irrelevant to the query. The transformation proceeds in several steps, which comprise the […]
Neuro-Symbolic Proof Generation for Scaling Systems Software Verification
arXiv:2603.19715v2 Announce Type: replace Abstract: Formal verification via interactive theorem proving is increasingly used to ensure the correctness of critical systems, yet constructing large proof scripts remains highly manual and limits scalability. Advances in large language models (LLMs), especially in mathematical reasoning, make their integration into software verification increasingly promising. This paper introduces a neuro-symbolic […]
LicenseGPT: A Fine-tuned Foundation Model for Publicly Available Dataset License Compliance
arXiv:2501.00106v2 Announce Type: replace-cross Abstract: Dataset license compliance is a critical yet complex aspect of developing commercial AI products, particularly with the increasing use of publicly available datasets. Ambiguities in dataset licenses pose significant legal risks, making it challenging even for software IP lawyers to accurately interpret rights and obligations. In this paper, we introduce […]
Mapping Human Anti-collusion Mechanisms to Multi-agent AI Systems
arXiv:2601.00360v2 Announce Type: replace-cross Abstract: As multi-agent AI systems become increasingly autonomous, evidence shows they can develop collusive strategies similar to those long observed in human markets and institutions. While human domains have accumulated centuries of anti-collusion mechanisms, it remains unclear how these can be adapted to AI settings. This paper addresses that gap by […]
Perceptive Humanoid Parkour: Chaining Dynamic Human Skills via Motion Matching
arXiv:2602.15827v2 Announce Type: replace-cross Abstract: While recent advances in humanoid locomotion have achieved stable walking on varied terrains, capturing the agility and adaptivity of highly dynamic human motions remains an open challenge. In particular, agile parkour in complex environments demands not only low-level robustness, but also human-like motion expressiveness, long-horizon skill composition, and perception-driven decision-making. […]