DEEPMED：通过多跳Med-Search数据和转向控制的代理训练与推理构建医疗深度研究代理
DEEPMED: Building a Medical DeepResearch Agent via Multi-hop Med-Search Data and Turn-Controlled Agentic Training & Inference
AI总结：
DeepMed通过多跳Med-Search数据和转向控制训练推理，提升医疗领域深度推理性能。
摘要：
医疗推理模型仍受参数知识的限制，因此容易出现遗忘和幻觉。DeepResearch（DR）模型通过可验证的证据从工具中生成输出，并在一般领域表现良好，但其直接转移到医疗领域时获得的收益相对有限。我们将其归因于两个缺口：任务特征和工具使用扩展。医疗问题需要在知识密集的临床背景下进行证据解释；而一般的DR模型可以检索信息，但往往缺乏临床背景推理，因此“找到但无法使用”，性能受限于医疗能力。此外，在医疗场景中，盲目扩展工具调用会注入噪声上下文，导致敏感的医疗推理偏离，并促使沿着错误路径重复寻找证据。因此，我们提出了DeepMed。在数据方面，我们部署了一种多跳Med-Search QA合成方法，使模型能够将DR范式应用于医疗领域。在训练方面，我们引入了难度感知的回合惩罚，以抑制过度的工具调用增长。在推理方面，我们引入了监控器，帮助在受控的步骤数内验证假设，并避免上下文旋转。总体而言，在七个医疗基准测试中，DeepMed在基础模型上平均提高了9.79%，并优于更大的医疗推理和DR模型。
英文摘要：
Medical reasoning models remain constrained by parametric knowledge and are thus susceptible to forgetting and hallucinations. DeepResearch (DR) models ground outputs in verifiable evidence from tools and perform strongly in general domains, but their direct transfer to medical field yields relatively limited gains. We attribute this to two gaps: task characteristic and tool-use scaling. Medical questions require evidence interpretation in a knowledge-intensive clinical context; while general DR models can retrieve information, they often lack clinical-context reasoning and thus "find it but fail to use it," leaving performance limited by medical abilities. Moreover, in medical scenarios, blindly scaling tool-call can inject noisy context, derailing sensitive medical reasoning and prompting repetitive evidence-seeking along incorrect paths. Therefore, we propose DeepMed. For data, we deploy a multi-hop med-search QA synthesis method supporting the model to apply the DR paradigm in medical contexts. For training, we introduce a difficulty-aware turn-penalty to suppress excessive tool-call growth. For inference, we bring a monitor to help validate hypotheses within a controlled number of steps and avoid context rot. Overall, on seven medical benchmarks, DeepMed improves its base model by 9.79\% on average and outperforms larger medical reasoning and DR models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
