5 papers
GuideSkill: Evolving Executable LLM Agent Skills for Guideline-Grounded Clinical Reasoning
Lang Cao, Yuhao Shen, Tianyang Luo +3
Clinical practice guidelines (CPGs) encode diagnostic criteria, but LLM systems typically retrieve guideline text or absorb it through training rather than execute its rules. We in…
ExpWeaver: LLM Agents Learn from Experience via Latent RAG
Tao Feng, Tianyang Luo, Jingjun Xu +5
Experience learning has achieved promising results in enhancing LLM agent planning and reasoning by integrating past interactions as reusable knowledge. However, existing methods r…
ExpGraph: Model-Agnostic Experience Learning with Graph-Structured Memory for LLM Agents
Tao Feng, Chongrui Ye, Tianyang Luo +8
Large language model (LLM) agents have shown strong capabilities in reasoning, tool use, and multi-step interaction, but they often solve tasks from scratch and fail to reuse succe…
ElasticMem: Latent Memory as a Learnable Resource for LLM Agents
Tao Feng, Chongrui Ye, Tianyang Luo +5
Long-term memory is essential for LLM agents to reason coherently across extended interactions, personalize responses, and reuse past experience. However, existing memory-augmented…
MemReward: Graph-Based Experience Memory for LLM Reward Prediction with Limited Labels
Tianyang Luo, Tao Feng, Zhigang Hua +4
Reinforcement learning has emerged as a powerful paradigm for improving large language model (LLM) reasoning, where rollouts are sampled from the policy and reward signals computed…