Evaluating Agentic Harness Systems for Autonomous Computational Pathology
Autonomous computational pathology (ACP) converts high-level pathology analysis goals into executable, traceable and clinically bounded workflows. Realizing this capability requires adapting general agentic harness systems to pathology-specific tasks, tools, evidence standards and clinical claim boundaries. We contribute ACP-Bench, a framework that adapts existing harness systems from computational pathology support toward ACP workflow capability. ACP-Bench evaluates 41 pathology workflow tasks, including 24 biomarker, 7 morphology and 10 prognosis tasks spanning 6 body-system groups and 9 endpoint families. The benchmark evaluates 9 models and 3 harness groups (Claude Code, Codex and Ope...
| Authors | Jie Lin, Zongyi Chen, Qiaoling Zheng et al. |
|---|---|
| Primary category | cs.CV |
| Published | 1 July 2026 |
| Last updated at source | 1 July 2026 |
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