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cs.AI2026

Dissecting Failure Dynamics in Large Language Model Reasoning

Wei Zhu, Jian Zhang, Lixing Yu +2

Large Language Models (LLMs) achieve strong performance through extended inference-time deliberation, yet how their reasoning failures arise remains poorly understood. By analyzing…

cs.AI2026

Toward Clinically Explainable AI for Medical Diagnosis: A Foundation Model with Human-Compatible Reasoning via Reinforcement Learning

Qika Lin, Yifan Zhu, Bin Pu +14

The clinical adoption of artificial intelligence (AI) in medical diagnostics is critically hampered by its black-box nature, which prevents clinicians from verifying the rationale…

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

ErrEval: Error-Aware Evaluation for Question Generation through Explicit Diagnostics

Weiping Fu, Bifan Wei, Jingyi Hao +7

Automatic Question Generation (QG) often produces outputs with critical defects, such as factual hallucinations and answer mismatches. However, existing evaluation methods, includi…

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…