Guideline-Grounded Evidence Accumulation for High-Stakes Agent Verification
arXiv:2603.02798v1 Announce Type: new Abstract: 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 an
arXiv:2603.02798v1 Announce Type: new Abstract: 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.
Executive Summary
This article introduces GLEAN, a novel agent verification framework designed to ensure reliable and trustworthy high-stakes decision-making by Large Language Models (LLMs). GLEAN addresses existing limitations in verifiers by integrating domain knowledge and calibration through Guideline-grounded Evidence Accumulation. The framework evaluates step-wise alignment with domain guidelines, aggregates multi-guideline ratings, and calibrates correctness probabilities using Bayesian logistic regression. Empirical validation demonstrates GLEAN's effectiveness in both discrimination and calibration, surpassing baseline performance in agentic clinical diagnosis. The study also highlights GLEAN's utility in practice, as recognized by clinicians in an expert study. This research presents a significant advancement in agent verification, with implications for the deployment of LLMs in high-stakes applications.
Key Points
- ▸ GLEAN introduces a novel framework for agent verification, addressing existing limitations in domain knowledge and calibration.
- ▸ The framework uses Guideline-grounded Evidence Accumulation to evaluate step-wise alignment with domain guidelines and aggregate multi-guideline ratings.
- ▸ Empirical validation demonstrates GLEAN's effectiveness in both discrimination and calibration, surpassing baseline performance in agentic clinical diagnosis.
Merits
Strength in Calibration
GLEAN's use of Bayesian logistic regression for correctness probability calibration allows for accurate and reliable assessment of LLM decisions.
Integration of Domain Knowledge
The framework's incorporation of domain guidelines and expert-curated protocols enables the compilation of well-calibrated correctness signals.
Demerits
Limited Domain Scope
GLEAN's effectiveness may be limited to specific domains, such as clinical diagnosis, and may require adaptation for other high-stakes applications.
Dependence on Expert Knowledge
The framework's reliance on expert-curated protocols and domain guidelines may introduce bias and limitations if not properly managed.
Expert Commentary
The introduction of GLEAN represents a significant advancement in agent verification, addressing critical limitations in existing verifiers. By integrating domain knowledge and calibration, GLEAN offers a robust framework for evaluating the correctness of LLM decisions. The empirical validation demonstrates GLEAN's effectiveness in both discrimination and calibration, underscoring the importance of rigorous verification and validation in high-stakes decision-making applications. The article's findings also highlight the need for continued research in AI explainability and transparency, as well as the importance of expert knowledge and collaboration in developing reliable and trustworthy AI systems.
Recommendations
- ✓ Further investigation into the application of GLEAN in other high-stakes domains, such as finance or healthcare, would provide valuable insights into its scalability and adaptability.
- ✓ The development of more advanced verification frameworks, building on GLEAN's strengths, could further enhance the reliability and trustworthiness of AI-driven decision-making.