Publications (5)
Multi-Agent Deep Research: Training Multi-Agent Systems with M-GRPO
Haoyang Hong, Jiajun Yin, Yuan Wang +14
Multi-agent systems perform well on general reasoning tasks. However, the lack of training in specialized areas hinders their accuracy. Current training methods train a unified lar…
MedResearcher-R1: Expert-Level Medical Deep Researcher via A Knowledge-Informed Trajectory Synthesis Framework
Ailing Yu, Lan Yao, Jingnan Liu +13
Recent developments in Large Language Model (LLM)-based agents have shown impressive capabilities spanning multiple domains, exemplified by deep research systems that demonstrate s…
GAPS: A Clinically Grounded, Automated Benchmark for Evaluating AI Clinicians
Xiuyuan Chen, Tao Sun, Dexin Su +37
Current benchmarks for AI clinician systems, often based on multiple-choice exams or manual rubrics, fail to capture the depth, robustness, and safety required for real-world clini…
Agent2World: Learning to Generate Symbolic World Models via Adaptive Multi-Agent Feedback
Mengkang Hu, Bowei Xia, Yuran Wu +9
Symbolic world models (e.g., PDDL domains or executable simulators) are central to model-based planning, but training LLMs to generate such world models is limited by the lack of l…
ClawMark: A Living-World Benchmark for Multi-Turn, Multi-Day, Multimodal Coworker Agents
Fanqing Meng, Lingxiao Du, Zijian Wu +46
Language-model agents are increasingly used as persistent coworkers that assist users across multiple working days. During such workflows, the surrounding environment may change in…