MedReseacher-R1: Expert-Level Medical Deep Researcher via A
  Knowledge-Informed Trajectory Synthesis Framework
Authors: name: Ailing Yu; name: Lan Yao; name: Jingnan Liu; name: Zhe Chen; name: Jiajun Yin; name: Yuan Wang; name: Xinhao Liao; name: Zhiling Ye; name: Ji Li; name: Yun Yue; name: Hansong Xiao; name: Hualei Zhou; name: Chunxiao Guo; name: Peng Wei; name: Jinjie Gu
Published: 2025-08-20 17:51:20

Abstract: Recent developments in Large Language Model (LLM)-based agents have shown
impressive capabilities spanning multiple domains, exemplified by deep research
systems that demonstrate superior performance on complex information-seeking
and synthesis tasks. While general-purpose deep research agents have shown
impressive capabilities, they struggle significantly with medical domain
challenges, as evidenced by leading proprietary systems achieving limited
accuracy on complex medical benchmarks. The key limitations are: (1) the model
lacks sufficient dense medical knowledge for clinical reasoning, and (2) the
framework is constrained by the absence of specialized retrieval tools tailored
for medical contexts.We present a medical deep research agent that addresses
these challenges through two core innovations. First, we develop a novel data
synthesis framework using medical knowledge graphs, extracting the longest
chains from subgraphs around rare medical entities to generate complex
multi-hop question-answer pairs. Second, we integrate a custom-built private
medical retrieval engine alongside general-purpose tools, enabling accurate
medical information synthesis. Our approach generates 2100+ diverse
trajectories across 12 medical specialties, each averaging 4.2 tool
interactions.Through a two-stage training paradigm combining supervised
fine-tuning and online reinforcement learning with composite rewards, our
MedResearcher-R1-32B model demonstrates exceptional performance, establishing
new state-of-the-art results on medical benchmarks while maintaining
competitive performance on general deep research tasks. Our work demonstrates
that strategic domain-specific innovations in architecture, tool design, and
training data construction can enable smaller open-source models to outperform
much larger proprietary systems in specialized domains.