arXiv AI
Jun 2
VESTA: Visual Exploration with Statistical Tool Agents
arXiv:2606. 00384v1 Announce Type: new Abstract: Fitting quantitative models to data is a central step in scientific workflows, yet it remains one of the least automated.
AI agents
Tool use, function calling, orchestration and the protocols that let models act rather than only answer.
arXiv AI
Jun 2
Self-Healing Agentic Orchestrators for Reliable Tool-Augmented Large Language Model Systems
arXiv:2606. 01416v1 Announce Type: new Abstract: Tool-augmented large language model (LLM) agents rely on orchestration layers that coordinate planning, retrieval, tool invocation, validation, memory, and recovery.
arXiv AI
Jun 2
An Open-Source Benchmark and Baseline for Multi-temporal Referring Segmentation
arXiv:2606. 00987v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have shown strong visual understanding and language-guided grounding abilities, yet their capacity for multi-temporal visual reasoning remains underexplored.
arXiv AI
Jun 2
MobEvolve: An Agentic Self-Evolving Heuristic System for Interpretable Human Mobility Generation
arXiv:2606. 01640v1 Announce Type: new Abstract: Human mobility generation aims to synthesize realistic trip chains for target populations based on individual features.
arXiv Machine Learning
Jun 2
B-GRTO: Bootstrapped Group Relative Tool Optimization for Referring Segmentation
arXiv:2605. 23500v2 Announce Type: replace-cross Abstract: Segmentation is a fundamental task in computer vision, underpinning pixel-level scene understanding and serving as a cornerstone for applications ranging from autonomous perception to medical image analysis.
arXiv Machine Learning
Jun 2
Towards Sparse Video Understanding and Reasoning
arXiv:2602. 13602v2 Announce Type: replace-cross Abstract: We present \revise (\underline{Re}asoning with \underline{Vi}deo \underline{S}parsity), a multi-round agent for video question answering (VQA).
arXiv AI
Jun 2
Beyond Offline A/B Testing: Context-Aware Agent Simulation for Recommender System Evaluation
arXiv:2604. 09549v2 Announce Type: replace-cross Abstract: Recommender systems are central to online services, enabling users to navigate through massive amounts of content across various domains.
arXiv AI
Jun 2
JenBridge: Adaptive Long-Form Video Soundtracking across Scene Transitions
arXiv:2606. 01703v1 Announce Type: cross Abstract: We address the challenge of generating high-fidelity, long-form soundtracks that remain coherent across scene transitions.
arXiv AI
Jun 2
Emergence of Exploration in Policy Gradient Reinforcement Learning via Retrying
arXiv:2606. 00151v1 Announce Type: cross Abstract: In reinforcement learning (RL), agents benefit from exploration only because they repeatedly encounter similar states: trying different actions can improve performance or reduce uncertainty; without such retries, a greedy policy is optimal.
arXiv AI
Jun 2
Identifying High-Confidence Social Biases in LLMs for Trustworthy Conversational Tutoring Agents
arXiv:2606. 01584v1 Announce Type: cross Abstract: Conversational tutoring agents have been shown to improve learning engagement and student outcomes, and large language models (LLMs) are increasingly used in these systems to provide scalable, personalized feedback.
arXiv AI
Jun 2
PBT-Bench: Benchmarking AI Agents on Property-Based Testing
arXiv:2605. 15229v3 Announce Type: replace-cross Abstract: Existing code benchmarks measure whether an agent can produce any test that reproduces a known bug, or whether it can produce a patch that fixes a described issue.
arXiv AI
Jun 2
Structure Enables Effective Self-Localization of Errors in LLMs
arXiv:2602. 02416v2 Announce Type: replace Abstract: Self-correction in language models remains elusive.
arXiv AI
Jun 2
Do Multimodal Agents Really Benefit from Tool Use? A Systematic Study of Capability Gains
arXiv:2606. 02357v1 Announce Type: cross Abstract: Tool-augmented multimodal agents show strong benchmark gains, often taken as evidence that agents have learned to use tools.
arXiv AI
Jun 2
Monitoring Agentic Systems Before They're Reliable
arXiv:2606. 02494v1 Announce Type: cross Abstract: Agentic systems entering production typically operate as partially integrated assemblies where structural defects, not task-level errors, dominate the failure landscape.
arXiv AI
Jun 2
Empathic and agentic artificial intelligence in nursing: perspectives on a human-centered framework for cancer care navigation in the United States
arXiv:2606. 00010v1 Announce Type: cross Abstract: For patients experiencing cancer, nurse navigation can ease the burden of complex care by enhancing coordination of health services and patient outcomes.
arXiv AI
Jun 2
Taming System Complexity: Demystifying Software Engineering Agents in Diagnosing Linux Kernel Faults
arXiv:2505. 19489v2 Announce Type: replace Abstract: The Linux kernel is a critical system, serving as the foundation for numerous systems.
arXiv AI
Jun 2
Algorithmic Fragility and Persona Bias in LLM-Generated Autistic Communication
arXiv:2605. 26397v2 Announce Type: replace-cross Abstract: Safety alignment reduces explicitly harmful outputs but inadvertently encodes a sanitized, neuronormative representation of marginalized communication.
arXiv Machine Learning
Jun 2
NestRL: A Nested Training Regime for Mutual Adaptation in Human-AI Teaming
arXiv:2602. 17737v2 Announce Type: replace-cross Abstract: Mutual adaptation is a central challenge in human-AI teaming, as humans naturally adjust their strategies in response to an AI agent's behavior.
arXiv AI
Jun 2
SPADE-Bench: Evaluating Spontaneous Strategic Deception in Agents via Plan-Action Divergence
arXiv:2606. 02380v1 Announce Type: cross Abstract: As LLM-based agents expand their operational scope, reliability becomes a prerequisite for real-world deployment.
arXiv AI
Jun 2
Towards a General Intelligence and Interface for Wearable Health Data
arXiv:2605. 22759v2 Announce Type: replace Abstract: While ubiquitous wearable sensors capture a wealth of behavioral and physiological information, effectively transforming these signals into personalized health insights is challenging.