12 citations · 14 across the 10 of their papers we have counts for
12 papers
RIVO: Lightweight Adaptive Rerouting for Deep Research Agents
Mingchen Li, Jiatan Huang, Hong Yu
Deep Research agents rely on long-horizon reasoning and iterative tool use to solve complex information-seeking tasks. However, adaptive decision-making over these trajectories rem…
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…
Where LLM Annotators Fail: Label-Free Learning on Graphs with LLMs
Safal Thapaliya, Jiatan Huang, Chuxu Zhang
Node classification on graphs often requires labeled nodes, yet obtaining labels at graph scale is expensive. When node attributes contain semantic content, such as paper abstracts…
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…
Same Signal, Opposite Meaning: Direction-Informed Adaptive Learning for LLM Agents
Ziming Li, Jiatan Huang, Xiaoguang Guo +3
Adaptive test-time compute for LLM agents aims to invoke extra computation only when it improves performance. Existing methods typically use confidence-, uncertainty-, or difficult…
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…
EvolveRouter: Co-Evolving Routing and Prompt for Multi-Agent Question Answering
Jiatan Huang, Zheyuan Zhang, Kaiwen Shi +2
Large language model agents often exhibit complementary strengths, making routing a promising approach for multi-agent question answering. However, existing routing methods remain…
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…
RiTeK: A Dataset for Large Language Models Complex Reasoning over Textual Knowledge Graphs in Medicine
Jiatan Huang, Mingchen Li, Zonghai Yao +8
Answering complex real-world questions in the medical domain often requires accurate retrieval from medical Textual Knowledge Graphs (medical TKGs), as the relational path informat…
CancerLLM: A Large Language Model in Cancer Domain
Mingchen Li, Jiatan Huang, Jeremy Yeung +5
Medical Large Language Models (LLMs) have demonstrated impressive performance on a wide variety of medical NLP tasks; however, there still lacks a LLM specifically designed for phe…
Benchmarking Retrieval-Augmented Large Language Models in Biomedical NLP: Application, Robustness, and Self-Awareness
Mingchen Li, Zaifu Zhan, Han Yang +3
Large language models (LLM) have demonstrated remarkable capabilities in various biomedical natural language processing (NLP) tasks, leveraging the demonstration within the input c…