XPathAgent: Concept-to-Evidence Reasoning via Reliability-Guided Routing on Heterogeneous Pathology tools

Computational pathology provides diverse specialist models spanning organs, spatial resolutions, and tissue properties. Integrating these heterogeneous pathology tools is essential for whole-slide image analysis, yet existing pathology agent methods rely on fixed foundation models and validation strategies, limiting generalizability while providing limited transparency into how tool outputs are selected, validated, and integrated. We present XPathAgent, a plug-and-play framework for concept-to-evidence reasoning over heterogeneous pathology tools via reliability-guided routing. The framework operates through three key modules: (1) a Diagnostic Atlas that maps clinical queries to pathology concepts and corresponding feasible tool candidates; (2) a Concept–Evidence Tree that dynamically refines tool-to-region assignments as new evidence accrue; and (3) Invariance-Calibrated Reliability (ICR), which guides tool routing by measuring tool invariance under controlled input degradation while penalizing correlated errors. Validated measurements propagate into concept- and task-level estimates, yielding a fully traceable reasoning path. Compared with existing foundation models and agentic approaches, XPathAgent attains top performance on all 13 benchmarks across subtyping, grading, metastasis detection, stromal TIL scoring, and survival prediction, establishing concept-driven orchestration as a robust paradigm for trustworthy pathology agents.