arXiv:2603.25524v1 Announce Type: cross Abstract: Long-term behavioral monitoring of individual animals is crucial for studying behavioral changes that occur over different time scales, especially for conservation and evolutionary biology. Computer vision methods have proven to benefit biodiversity monitoring, but automated behavior monitoring in wild populations remains challenging. This stems from the lack of datasets that […]
AIP: Agent Identity Protocol for Verifiable Delegation Across MCP and A2A
arXiv:2603.24775v1 Announce Type: cross Abstract: AI agents increasingly call tools via the Model Context Protocol (MCP) and delegate to other agents via Agent-to-Agent (A2A), yet neither protocol verifies agent identity. A scan of approximately 2,000 MCP servers found all lacked authentication. In our survey, we did not identify a prior implemented protocol that jointly combines […]
System-Anchored Knee Estimation for Low-Cost Context Window Selection in PDE Forecasting
arXiv:2603.25025v1 Announce Type: new Abstract: Autoregressive neural PDE simulators predict the evolution of physical fields one step at a time from a finite history, but low-cost context-window selection for such simulators remains an unformalized problem. Existing approaches to context-window selection in time-series forecasting include exhaustive validation, direct low-cost search, and system-theoretic memory estimation, but they […]
Dissecting Model Failures in Abdominal Aortic Aneurysm Segmentation through Explainability-Driven Analysis
arXiv:2603.24801v1 Announce Type: cross Abstract: Computed tomography image segmentation of complex abdominal aortic aneurysms (AAA) often fails because the models assign internal focus to irrelevant structures or do not focus on thin, low-contrast targets. Where the model looks is the primary training signal, and thus we propose an Explainable AI (XAI) guided encoder shaping framework. […]
A Mentalistic Interface for Probing Folk-Psychological Attribution to Non-Humanoid Robots
arXiv:2603.25646v1 Announce Type: cross Abstract: This paper presents an experimental platform for studying intentional-state attribution toward a non-humanoid robot. The system combines a simulated robot, realistic task environments, and large language model-based explanatory layers that can express the same behavior in mentalistic, teleological, or mechanistic terms. By holding behavior constant while varying the explanatory frame, […]
FODMP: Fast One-Step Diffusion of Movement Primitives Generation for Time-Dependent Robot Actions
arXiv:2603.24806v1 Announce Type: cross Abstract: Diffusion models are increasingly used for robot learning, but current designs face a clear trade-off. Action-chunking diffusion policies like ManiCM are fast to run, yet they only predict short segments of motion. This makes them reactive, but unable to capture time-dependent motion primitives, such as following a spring-damper-like behavior with […]
From Stateless to Situated: Building a Psychological World for LLM-Based Emotional Support
arXiv:2603.25031v1 Announce Type: new Abstract: In psychological support and emotional companionship scenarios, the core limitation of large language models (LLMs) lies not merely in response quality, but in their reliance on local next-token prediction, which prevents them from maintaining the temporal continuity, stage awareness, and user consent boundaries required for multi-turn intervention. This stateless characteristic […]
Learning From Developers: Towards Reliable Patch Validation at Scale for Linux
arXiv:2603.24825v1 Announce Type: cross Abstract: Patch reviewing is critical for software development, especially in distributed open-source development, which highly depends on voluntary work, such as Linux. This paper studies the past 10 years of patch reviews of the Linux memory management subsystem to characterize the challenges involved in patch reviewing at scale. Our study reveals […]
PixelSmile: Toward Fine-Grained Facial Expression Editing
arXiv:2603.25728v1 Announce Type: cross Abstract: Fine-grained facial expression editing has long been limited by intrinsic semantic overlap. To address this, we construct the Flex Facial Expression (FFE) dataset with continuous affective annotations and establish FFE-Bench to evaluate structural confusion, editing accuracy, linear controllability, and the trade-off between expression editing and identity preservation. We propose PixelSmile, […]
STAR-GO: Improving Protein Function Prediction by Learning to Hierarchically Integrate Ontology-Informed Semantic Embeddings
arXiv:2512.05245v2 Announce Type: replace Abstract: Accurate prediction of protein function is essential for elucidating molecular mechanisms and advancing biological and therapeutic discovery. Yet experimental annotation lags far behind the rapid growth of protein sequence data. Computational approaches address this gap by associating proteins with Gene Ontology (GO) terms, which encode functional knowledge through hierarchical relations […]
Mechanistically Interpreting Compression in Vision-Language Models
arXiv:2603.25035v1 Announce Type: new Abstract: Compressed vision-language models (VLMs) are widely used to reduce memory and compute costs, making them a suitable choice for real-world deployment. However, compressing these models raises concerns about whether internal computations and safety behaviors are preserved. In this work, we use causal circuit analysis and crosscoder-based feature comparisons to examine […]
Gaze patterns predict preference and confidence in pairwise AI image evaluation
arXiv:2603.24849v1 Announce Type: cross Abstract: Preference learning methods, such as Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO), rely on pairwise human judgments, yet little is known about the cognitive processes underlying these judgments. We investigate whether eye-tracking can reveal preference formation during pairwise AI-generated image evaluation. Thirty participants completed 1,800 trials […]