Guideline-Grounded Evidence Accumulation for High-Stakes Agent Verification
This work addresses the critical problem of reliable verification for high-stakes decision-making agents, particularly in the medical domain, where accurate and trustworthy diagnoses are crucial for clinicians and patients.
The authors tackled the problem of verifying decisions made by LLM-powered agents in high-stakes scenarios, such as clinical diagnosis, and achieved a 12% improvement in AUROC and 50% reduction in Brier score compared to the best baseline. Their framework, GLEAN, demonstrated effectiveness in both discrimination and calibration.
As LLM-powered agents have been used for high-stakes decision-making, such as clinical diagnosis, it becomes critical to develop reliable verification of their decisions to facilitate trustworthy deployment. Yet, existing verifiers usually underperform owing to a lack of domain knowledge and limited calibration. To address this, we establish GLEAN, an agent verification framework with Guideline-grounded Evidence Accumulation that compiles expert-curated protocols into trajectory-informed, well-calibrated correctness signals. GLEAN evaluates the step-wise alignment with domain guidelines and aggregates multi-guideline ratings into surrogate features, which are accumulated along the trajectory and calibrated into correctness probabilities using Bayesian logistic regression. Moreover, the estimated uncertainty triggers active verification, which selectively collects additional evidence for uncertain cases via expanding guideline coverage and performing differential checks. We empirically validate GLEAN with agentic clinical diagnosis across three diseases from the MIMIC-IV dataset, surpassing the best baseline by 12% in AUROC and 50% in Brier score reduction, which confirms the effectiveness in both discrimination and calibration. In addition, the expert study with clinicians recognizes GLEAN's utility in practice.