arXiv:2603.21298v1 Announce Type: cross Abstract: Combating hate speech on social media is critical for securing cyberspace, yet relies heavily on the efficacy of automated detection systems. As content formats evolve, hate speech is transitioning from solely plain text to complex multimodal expressions, making implicit attacks harder to spot. Current systems, however, often falter on these […]
REMI: Reconstructing Episodic Memory During Internally Driven Path Planning
arXiv:2507.02064v2 Announce Type: cross Abstract: Grid cells in the medial entorhinal cortex (MEC) and place cells in the hippocampus (HC) both form spatial representations. Grid cells fire in triangular grid patterns, while place cells fire at specific locations and respond to contextual cues. How do these interacting systems support not only spatial encoding but also […]
Compensating Visual Insufficiency with Stratified Language Guidance for Long-Tail Class Incremental Learning
arXiv:2603.21708v1 Announce Type: new Abstract: Long-tail class incremental learning (LT CIL) remains highly challenging because the scarcity of samples in tail classes not only hampers their learning but also exacerbates catastrophic forgetting under continuously evolving and imbalanced data distributions. To tackle these issues, we exploit the informativeness and scalability of language knowledge. Specifically, we analyze […]
Guideline-grounded retrieval-augmented generation for ophthalmic clinical decision support
arXiv:2603.21925v1 Announce Type: new Abstract: In this work, we propose Oph-Guid-RAG, a multimodal visual RAG system for ophthalmology clinical question answering and decision support. We treat each guideline page as an independent evidence unit and directly retrieve page images, preserving tables, flowcharts, and layout information. We further design a controllable retrieval framework with routing and […]
Persistent local Laplacian prediction of protein-ligand binding affinities
arXiv:2603.21503v1 Announce Type: new Abstract: Accurate prediction of protein-ligand binding affinity remains a central challenge in structure-based drug discovery. The effectiveness of machine learning models critically depends on the quality of molecular descriptors, for which advanced mathematical frameworks provide powerful tools. In this work, we employ a novel mathematical theory, termed the persistent local Laplacian […]
EnterpriseLab: A Full-Stack Platform for developing and deploying agents in Enterprises
arXiv:2603.21630v1 Announce Type: new Abstract: Deploying AI agents in enterprise environments requires balancing capability with data sovereignty and cost constraints. While small language models offer privacy-preserving alternatives to frontier models, their specialization is hindered by fragmented development pipelines that separate tool integration, data generation, and training. We introduce EnterpriseLab, a full-stack platform that unifies these […]
JCAS-MARL: Joint Communication and Sensing UAV Networks via Resource-Constrained Multi-Agent Reinforcement Learning
arXiv:2603.20265v1 Announce Type: cross Abstract: Multi-UAV networks are increasingly deployed for large-scale inspection and monitoring missions, where operational performance depends on the coordination of sensing reliability, communication quality, and energy constraints. In particular, the rapid increase in overflowing waste bins and illegal dumping sites has created a need for efficient detection of waste hotspots. In […]
The Myhill-Nerode Theorem for Bounded Interaction: Canonical Abstractions via Agent-Bounded Indistinguishability
arXiv:2603.21399v1 Announce Type: new Abstract: Any capacity-limited observer induces a canonical quotient on its environment: two situations that no bounded agent can distinguish are, for that agent, the same. We formalise this for finite POMDPs. A fixed probe family of finite-state controllers induces a closed-loop Wasserstein pseudometric on observation histories and a probe-exact quotient merging […]
Beyond Detection: Governing GenAI in Academic Peer Review as a Sociotechnical Challenge
arXiv:2603.20214v1 Announce Type: cross Abstract: Generative AI tools are increasingly entering academic peer review workflows, raising questions about fairness, accountability, and the legitimacy of evaluative judgment. While these systems promise efficiency gains amid growing reviewer overload, their use introduces new sociotechnical risks. This paper presents a convergent mixed-method study combining discourse analysis of 448 social […]
Emergency Lane-Change Simulation: A Behavioral Guidance Approach for Risky Scenario Generation
arXiv:2603.20234v1 Announce Type: cross Abstract: In contemporary autonomous driving testing, virtual simulation has become an important approach due to its efficiency and cost effectiveness. However, existing methods usually rely on reinforcement learning to generate risky scenarios, making it difficult to efficiently learn realistic emergency behaviors. To address this issue, we propose a behavior guided method […]
AdaRubric: Task-Adaptive Rubrics for LLM Agent Evaluation
arXiv:2603.21362v1 Announce Type: new Abstract: LLM-as-Judge evaluation fails agent tasks because a fixed rubric cannot capture what matters for this task: code debugging demands Correctness and Error Handling; web navigation demands Goal Alignment and Action Efficiency. We present ADARUBRIC, which closes this gap by generating task-specific evaluation rubrics on the fly from task descriptions, scoring […]
Behavioural feasible set: Value alignment constraints on AI decision support
arXiv:2603.21435v1 Announce Type: new Abstract: When organisations adopt commercial AI systems for decision support, they inherit value judgements embedded by vendors that are neither transparent nor renegotiable. The governance puzzle is not whether AI can support decisions but which recommendations the system can actually produce given how its vendor has configured it. I formalise this […]