arXiv AI
Jun 26
Life After Benchmark Saturation: A Case Study of CORE-Bench
arXiv:2606. 26158v1 Announce Type: new Abstract: When a benchmark's accuracy saturates, it is often retired and replaced with a more challenging version.
AI agents
Tool use, function calling, orchestration and the protocols that let models act rather than only answer.
arXiv Machine Learning
Jun 26
BrepCoder: A Unified Multimodal Large Language Model for Multi-task B-rep Reasoning
arXiv:2602. 22284v3 Announce Type: replace Abstract: Recent advancements in deep learning have actively addressed complex challenges within the Computer-Aided Design (CAD) domain.
arXiv AI
Jun 26
Context Recycling for Long-Horizon LLM Inference
arXiv:2606. 26105v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit strong capabilities in short-context reasoning but degrade in performance over long conversational horizons due to context window limitations and inefficient token usage.
arXiv AI
Jun 26
Where Do CoT Training Gains Land in LLM based Agents?
arXiv:2606. 26935v1 Announce Type: new Abstract: Chain-of-thought (CoT) reasoning is widely used in language-model agents, but prior work has shown that verbalized CoT is not always faithful and may instead reflect post-hoc reasoning, which means the model already knows the answer before reasoning.
arXiv AI
Jun 26
Content-Based Smart E-Mail Dispatcher Using Large Language Models
arXiv:2606. 26593v1 Announce Type: new Abstract: Email communication has become an integral part of personal and professional life, but handling its vast volume is still a significant issue for large organisations.
arXiv AI
Jun 26
Reducing Conversational Escalation in Large Language Model Dialogue with Nonviolent Communication Constraints
arXiv:2606. 26106v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used in emotionally charged situations involving interpersonal conflict, frustration, and distress.
arXiv AI
Jun 26
Diagnosing Task Insensitivity in Language Agents
arXiv:2606. 26918v1 Announce Type: new Abstract: Large language models can serve as capable long-horizon agents, but their out-of-distribution (OOD) generalization remains weak.
arXiv AI
Jun 26
Empowering GUI Agents via Autonomous Experience Exploration and Hindsight Experience Utilization for Task Planning
arXiv:2606. 27330v1 Announce Type: cross Abstract: Multimodal web agents can assist humans in operating repetitive GUI tasks, where effective task planning is essential for decomposing complex tasks into executable actions.
arXiv Machine Learning
Jun 26
Finding the Time to Think: Learning Planning Budgets in Real-Time RL
arXiv:2606. 26463v1 Announce Type: new Abstract: Deliberating takes time.
arXiv Machine Learning
Jun 26
Revisiting Action Factorization for Complex Action Spaces
arXiv:2606. 26574v1 Announce Type: new Abstract: Many real-world control problems involve hybrid discrete-continuous action spaces.
arXiv AI
Jun 26
Learning to Select Maximum Clique Algorithms: From Traditional Machine Learning to a Dual-Channel Hybrid Neural Architecture
arXiv:2508. 08005v4 Announce Type: replace-cross Abstract: The Maximum Clique Problem (MCP) is an NP-hard problem with wide-ranging applications in fields such as bioinformatics, network science, and social computing, yet no single algorithm consistently outperforms all others across diverse graph instances.
arXiv AI
Jun 26
Localizing RL-Induced Tool Use to a Single Crosscoder Feature
arXiv:2606. 26474v1 Announce Type: cross Abstract: Fine-tuning through RL reshapes the internal representations of language models to enable agentic behaviors such as tool use, yet the mechanistic basis of these changes remains poorly understood.
arXiv AI
Jun 26
A Guideline-Aware AI Agent for Zero-Shot Target Volume Auto-Delineation
arXiv:2603. 09448v2 Announce Type: replace-cross Abstract: Delineating the clinical target volume (CTV) in radiotherapy involves complex margins constrained by tumor location and anatomical barriers.
arXiv AI
Jun 26
Confidence-Aware Tool Orchestration for Robust Video Understanding
arXiv:2606. 26904v1 Announce Type: cross Abstract: Video reasoning language models implicitly assume that every input frame is equally reliable.
arXiv AI
Jun 26
Closing the Loop to Discover Psychological Theories with an Automated Cognitive Scientist
arXiv:2606. 26448v1 Announce Type: cross Abstract: Across the sciences, autonomous systems are increasingly being used in closed-loop discovery, proposing new theories and designing and running experiments to test them.
arXiv AI
Jun 26
Agentic Analysis for Agentic Infrastructure: An LLM-Powered Pipeline for Comparative Governance of DAO and Corporate AI Protocols
arXiv:2606. 26203v1 Announce Type: new Abstract: As AI agent protocols proliferate, the governance structures shaping their interoperability standards remain empirically underexamined.
arXiv AI
Jun 26
State Representation Matters in Deep Reinforcement Learning: Application to Energy Trading
arXiv:2606. 27032v1 Announce Type: cross Abstract: Energy trading decisions depend not only on current market prices, but also on expected future market conditions, and operational constraints.
arXiv AI
Jun 26
ShareLock: A Stealthy Multi-Tool Threshold Poisoning Attack Against MCP
arXiv:2606. 27027v1 Announce Type: cross Abstract: With the rapid evolution of LLM-driven agents, Model Context Protocol (MCP), an open protocol bridging LLMs with external tools, has quickly become foundational to modern agent ecosystems.
arXiv AI
Jun 26
AgentX: Towards Agent-Driven Self-Iteration of Industrial Recommender Systems
arXiv:2606. 26859v1 Announce Type: new Abstract: Recommendation algorithm iteration is moving from an artisanal, engineer-bound process toward an industrialized research loop, but this transition remains blocked by a structural execution bottleneck: the idea-to-launch cycle still depends on human engineers to generate hypotheses, modify production code, launch A/B experiments, and attribute online results.
arXiv Machine Learning
Jun 26
Sample-efficient Transfer Reinforcement Learning via Adaptive Reward Shaping and Policy-Ratio Reweighting Strategy
arXiv:2606. 26527v1 Announce Type: new Abstract: Transfer learning improves policy learning efficiency by reusing knowledge from source tasks, providing a feasible paradigm for safe and efficient autonomous highway lane changing decision-making.