5 papers
Counterfactual Graph for Multi-Agent LLM Calibration
Jiatan Huang, Mingchen Li, Ziming Li +3
Multi-agent LLM systems often treat agreement as evidence: when many agents in a panel give the same answer, that answer is assumed to be more reliable. We show that this assumptio…
In-Context Optimization for Retrieval-Augmented Generation: A Gradient-Descent Perspective
Mingchen Li, Jiatan Huang, Chuxu Zhang +2
In-context learning has recently been linked to implicit gradient descent in linear self-attention models, suggesting that context can induce a forward-pass update. Retrieval-augme…
RICE-PO: Turning Retrieval Interactions into Credit Signals for Reasoning Agents
Mingchen Li, Hansi Zeng, Zhuo Qian +4
Retrieval is increasingly moving from one-shot matching toward interactive reasoning, where language agents iteratively inspect evidence, reformulate queries, and search again. Tra…
Efficient and Effective Internal Memory Retrieval for LLM-Based Healthcare Prediction
Mingchen Li, Jiatan Huang, Zonghai Yao +1
Large language models (LLMs) hold significant promise for healthcare, yet their reliability in high-stakes clinical settings is often compromised by hallucinations and a lack of gr…
GLEN-Bench: A Graph-Language based Benchmark for Nutritional Health
Jiatan Huang, Zheyuan Zhang, Tianyi Ma +4
Nutritional interventions are important for managing chronic health conditions, but current computational methods provide limited support for personalized dietary guidance. We iden…