EndoGov multi-agent system achieves guideline-compliant endometrial cancer risk stratification with 0.943 accuracy on TCGA-UCEC dataset

EndoGov is a two-tier multi-agent expert system that factorizes decision-making as D(x) = G(P(x), R), where specialist agents P extract structured evidence and a governance agent G applies an executable rule set R. Tier 1 comprises pathology, molecular, and clinical agents; Tier 2 queries an evidence-level-weighted Guideline Knowledge Graph using deterministic hard-path rules for high-priority overrides and constrained soft-path reasoning for ambiguous cases.

On TCGA-UCEC (n=541), EndoGov achieved 0.943 accuracy, 0.973 macro AUC, and a conditional logic-violation rate (C-LVR) of 0.93% among trigger-exposed cases. On CPTAC-UCEC (n=95), EndoGov reached 0.842 accuracy compared with < 0.31 for locked-transfer neural baselines.

End-to-end safety decomposition localized residual failures primarily to upstream molecular detection rather than downstream governance. Backend-swap experiments showed that hard-path compliance is invariant to the LLM backend, indicating that explicit clinical-rule governance can provide guideline-compliant, auditable endometrial cancer risk assignment while preserving competitive discrimination.