most citedGraph-R1: Towards Agentic GraphRAG Framework via End-to-end Reinforcement Learning

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cs.AI2026

TIDE: Trajectory-based Diagnostic Evaluation of Test-Time Improvement in LLM Agents

Hang Yan, Xinyu Che, Fangzhi Xu +7

Recent advances in autonomous LLM agents demonstrate their ability to improve performance through iterative interaction with the environment. We define this paradigm as Test-Time I…

cs.AI2026

Towards Efficient and Robust Linguistic Emotion Diagnosis for Mental Health via Multi-Agent Instruction Refinement

Jian Zhang, Zhangqi Wang, Zhiyuan Wang +5

Linguistic expressions of emotions such as depression, anxiety, and trauma-related states are pervasive in clinical notes, counseling dialogues, and online mental health communitie…

cs.AI2026

-Bench: Benchmarking Memory-Driven Scientific Reasoning via Anchor and Attractor Activation

Jian Zhang, Yu He, Zhiyuan Wang +5

Scientific reasoning relies not only on logical inference but also on activating prior knowledge and experiential structures. Memory can efficiently reuse knowledge and enhance rea…

cs.AI2026

MAXS: Meta-Adaptive Exploration with LLM Agents

Jian Zhang, Zhiyuan Wang, Zhangqi Wang +7

Large Language Model (LLM) Agents exhibit inherent reasoning abilities through the collaboration of multiple tools. However, during agent inference, existing methods often suffer f…

cs.AI2025

MAPS: Multi-Agent Personality Shaping for Collaborative Reasoning

Jian Zhang, Zhiyuan Wang, Zhangqi Wang +6

Collaborative reasoning with multiple agents offers the potential for more robust and diverse problem-solving. However, existing approaches often suffer from homogeneous agent beha…

cs.AI2025

Towards Unified Neurosymbolic Reasoning on Knowledge Graphs

Qika Lin, Fangzhi Xu, Hao Lu +5

Knowledge Graph (KG) reasoning has received significant attention in the fields of artificial intelligence and knowledge engineering, owing to its ability to autonomously deduce ne…