The rapid proliferation of scientific knowledge presents a grand challenge: transforming this vast repository of information into an active engine for discovery, especially in high-stakes domains like healthcare. Current AI agents, however, are constrained by static, predefined strategies, limiting their ability to navigate the complex, evolving ecosystem of scientific research. This paper introduces HealthFlow, a self-evolving AI agent that overcomes this limitation through a novel meta-level evolution mechanism. HealthFlow autonomously refines its high-level problem-solving policies by distilling procedural successes and failures into a durable, structured knowledge base, enabling it to learn not just how to use tools, but how to strategize. To anchor our research and provide a community resource, we introduce EHRFlowBench, a new benchmark featuring complex health data analysis tasks systematically derived from peer-reviewed scientific literature. Our experiments demonstrate that HealthFlow's self-evolving approach significantly outperforms state-of-the-art agent frameworks. This work offers a new paradigm for intelligent systems that can learn to operationalize the procedural knowledge embedded in scientific content, marking a critical step toward more autonomous and effective AI for healthcare scientific discovery.

翻译：科学知识的快速膨胀带来了一个重大挑战：如何将这一庞大的信息库转化为推动发现的活跃引擎，尤其是在医疗健康等高风险领域。然而，当前的AI智能体受限于静态、预定义的策略，限制了其在复杂且不断演化的科学研究生态系统中进行探索的能力。本文提出了HealthFlow，一种通过新颖的元级演化机制克服这一局限的自演化AI智能体。HealthFlow能够自主优化其高层问题解决策略，其方法是将程序性的成功与失败提炼并存储到一个持久化、结构化的知识库中，从而使其不仅学习如何使用工具，更学习如何制定策略。为了锚定我们的研究并为社区提供资源，我们引入了EHRFlowBench，这是一个新的基准测试集，其包含从同行评审的科学文献中系统推导出的复杂健康数据分析任务。我们的实验表明，HealthFlow的自演化方法显著优于最先进的智能体框架。这项工作为智能系统提供了一种新范式，使其能够学习如何将科学内容中蕴含的程序性知识付诸实践，标志着在实现更自主、更有效的医疗健康科学发现AI方面迈出了关键一步。