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arXiv · 2508.04915

ConfAgents: A Conformal-Guided Multi-Agent Framework for Cost-Efficient Medical Diagnosis

Huiya Zhao·Yinghao Zhu·Zixiang Wang·Yasha Wang·Junyi Gao·Liantao Ma
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Abstract

The efficacy of AI agents in healthcare research is hindered by their reliance on static, predefined strategies. This creates a critical limitation: agents can become better tool-users but cannot learn to become better strategic planners, a crucial skill for complex domains like healthcare. We introduce HealthFlow, a self-evolving AI agent that overcomes this limitation through a novel meta-level evolution mechanism. HealthFlow autonomously refines its own high-level problem-solving policies by distilling procedural successes and failures into a durable, strategic knowledge base. To anchor our research and facilitate reproducible evaluation, we introduce EHRFlowBench, a new benchmark featuring complex, realistic health data analysis tasks derived from peer-reviewed clinical research. Our comprehensive experiments demonstrate that HealthFlow's self-evolving approach significantly outperforms state-of-the-art agent frameworks. This work marks a necessary shift from building better tool-users to designing smarter, self-evolving task-managers, paving the way for more autonomous and effective AI for scientific discovery.

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Huiya Zhao, Yinghao Zhu, Zixiang Wang, Yasha Wang, Junyi Gao, Liantao Ma. 2025-08-06. ConfAgents: A Conformal-Guided Multi-Agent Framework for Cost-Efficient Medical Diagnosis. https://arxiv.org/abs/2508.04915

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