SSE-Bio: A Structured Self-Evolving Agent with Agentic Retrieval Policy for Multi-Hop Biomedical Reasoning
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
The Flow has not summarised this story yet — read it at arXiv AI.
MedResearcher‑R1 is a medical deep‑research agent that uses a Knowledge‑Informed Trajectory Synthesis (KISA) framework to build medical knowledge graphs and generate complex multi‑hop question‑answer pairs, especially for rare medical entities. It also incorporates a medical retrieval engine and general‑purpose tools to reliably synthesize medical evidence. The resulting 2,100+ trajectories across 12 specialties enable supervised fine‑tuning and reinforcement learning, producing a 32B model that outperforms larger proprietary systems on MedBrowseComp and shows strong performance on GAIA and xBench benchmarks.
arXiv:2610.08327v1 Announce Type: new Abstract: Large language models (LLMs) have shown promise in medical question answering and clinical reasoning, yet their improvement remains constrained by stat...
The paper introduces a challenge‑aware adaptive recruitment system for medical multi‑agent reasoning. An external CHALLENGER monitors the evolving reasoning trace to detect factual or expertise gaps, triggering targeted knowledge retrieval or specialist recruitment mid‑discussion. Experiments on the MEDAGENTSBENCH hard subset show that challenge‑aware in‑between retrieval and Challenger‑triggered specialist recruitment improve accuracy, while naive combinations degrade performance, underscoring the importance of timing and coordination in resource recruitment.
arXiv:2607. 06452v1 Announce Type: cross Abstract: Biomedical question answering requires not only accurate extraction of information from scientific literature but also reliable integration of evidence across multiple documents.
arXiv:2603. 03292v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) exhibit high reasoning capacity in medical question-answering, but their tendency to produce hallucinations and outdated knowledge poses critical risks in healthcare fields.
arXiv:2601. 07055v2 Announce Type: replace Abstract: As high-quality data becomes increasingly difficult to obtain, self-evolution without curated training data has emerged as a promising paradigm.