From the 1 of 11 linked papers with an AI index.
11 papers
ToolAtlas: Learning Once, Reusing Everywhere with Tool-Side Memory
Yue Fang, Zhibang Yang, Fangkai Yang +5
ToolAtlas introduces a graph‑based, provider‑side memory that records tool capabilities, failure limits, and how tools can be combined, allowing LLM agents to query this memory and…
The Weakest Link Tells It All: Outcome-Supervised Process Reward Modeling via Learnable Credit Assignment
Tianyu Jia, Yue Fang, Hongxin Ding +6
Process reward models (PRMs) enhance the reasoning capabilities of large language models (LLMs) by providing fine-grained feedback, yet training PRMs typically requires expensive s…
EvoRubrics: Dynamic Rubrics as Rewards via Adversarial Co-Evolution for LLM Reinforcement Learning
Hongxin Ding, Baixiang Huang, Yue Fang +6
Rubric-based rewards offer interpretable and fine-grained optimization signals for reinforcement learning in open-ended tasks where verifiable answers are unavailable. However, pre…
ProMed: Shapley Information Gain Guided Reinforcement Learning for Proactive Medical LLMs
Hongxin Ding, Baixiang Huang, Yue Fang +8
Interactive medical questioning is essential in clinical consultations, where physicians must actively gather necessary patient information. Yet existing medical Large Language Mod…
ScaffoldAgent: Utility-Guided Dynamic Outline Optimization for Open-Ended Deep Research
Zhibang Yang, Xinke Jiang, Yuzhen Xiao +9
Open-ended deep research (OEDR) requires systems to acquire knowledge through multi-round retrieval and generate coherent long-form reports. The outline plays a central role as a s…
GraphWalker: Patient Analogy Meets Information Gain for Clinical Reasoning with Large Language Models
Yue Fang, Weibin Liao, Yuxin Guo +8
Clinical reasoning over electronic health records (EHRs) is a fundamental yet challenging task in modern healthcare. While large language models (LLMs) offer a promising paradigm v…