Toward Interpretable Analysis of Whole-slide Pathology Images via Large Language Model-based Agentic Reasoning

Analyzing whole-slide pathology images (WSIs) requires aniterative, evidence-driven reasoning process that parallels how pathol-ogists dynamically zoom, refocus, and self-correct while collecting ev-idence. However, existing computational pipelines often lack this rea-soning trajectory, resulting in opaque and unjustifiable predictions. Tobridge this gap, we present PathAgent, a training-free, large languagemodel (LLM)-based agent framework that emulates the reflective, step-wise analytical approach of human experts. PathAgent can autonomouslyexplore WSIs, iteratively and precisely locating significant micro-regionsusing the Navigator module, extracting morphological visual cues usingthe Perceptor, and integrating these findings into the continuously evolv-ing natural language trajectories in the Executor. The entire sequenceof observations and decisions forms a structured evidence trajectory,yielding traceable and evidence-grounded analyses. Evaluated across fivechallenging datasets spanning both whole-slide and patch-level settings,PathAgent exhibits strong zero-shot generalization and achieves perfor-mance comparable to human experts, surpassing task-specific baselinesacross diverse pathology tasks, including molecular subtype classifica-tion, histological grading and tumor type diagnosis. Human collaborativeevaluations demonstrate that PathAgent produces diagnostic decisionshighly consistent with expert-selected regions. These results demonstratethat PathAgent enables interpretable and clinically grounded WSI analy-sis. The code is available at https://github.com/G14nTDo4/PathAgent.