Guideline-Grounded Evidence Accumulation for High-Stakes Agent Verification
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 traject...
| Authors | Yichi Zhang, Nabeel Seedat, Yinpeng Dong et al. |
|---|---|
| Primary category | cs.AI |
| Published | 3 March 2026 |
| Last updated at source | 3 March 2026 |
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