PathFound: An Agentic Multimodal Model Activating Evidence-seeking Pathological Diagnosis

February 09, 2026

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Title: PathFound: An Agentic Multimodal Model Activating Evidence-seeking Pathological Diagnosis
ArXiv ID: 2512.23545
Date: 2025-12-29
Authors: Shengyi Hua, Jianfeng Wu, Tianle Shen, Kangzhe Hu, Zhongzhen Huang, Shujuan Ni, Zhihong Zhang, Yuan Li, Zhe Wang, Xiaofan Zhang

📝 Abstract

Recent pathological foundation models have substantially advanced visual representation learning and multimodal interaction. However, most models still rely on a static inference paradigm in which whole-slide images are processed once to produce predictions, without reassessment or targeted evidence acquisition under ambiguous diagnoses. This contrasts with clinical diagnostic workflows that refine hypotheses through repeated slide observations and further examination requests. We propose PathFound, an agentic multimodal model designed to support evidence-seeking inference in pathological diagnosis. PathFound integrates the power of pathological visual foundation models, visionlanguage models, and reasoning models trained with reinforcement learning to perform proactive information acquisition and diagnosis refinement by progressing through the initial diagnosis, evidence-seeking, and final decision stages. Across several large multimodal models, adopting this strategy consistently improves diagnostic accuracy, indicating the effectiveness of evidence-seeking workflows in computational pathology. Among these models, PathFound achieves st...

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PathFound: An Agentic Multimodal Model Activating Evidence-seeking Pathological Diagnosis

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