Evidence-First Agentic Verification

TL;DR

Sauron checks both environment state and execution traces, validating evidence before accepting an agent's success claim. The work connects Agents (LLM Agents) with reward verification in Agentic Reinforcement Learning. (source)

Authors & Org

Reza Sanayei, Razvan-Gabriel Dumitru, Sunjie Hou, MohammadHossein Rezaei, Haris Riaz, Robert Vacareanu, Minglai Yang, Ying Liu, Mihai Surdeanu, Daniel Yue Zhang. Affiliations are not established by the abstract record. First submitted 2026-10-13. (source)

Method

For long-horizon, partly verifiable tasks, Sauron investigates state and trace rather than trusting self-reports. It bounds queries and investigates related criteria together to reduce redundant work; each verdict includes an auditable evidence receipt. (source)

Results

Across 2,586 human-labeled criteria from 100 personal-assistant episodes, the authors report the highest F1 among 5 verifiers on all 8 backbones. With GPT-5.6 Sol, F1 is 0.96, versus Gandalf's 0.92, at 23% lower cost. (source)

On a paired fault-injection suite spanning 13 families, Sauron detects 97% of injected failures and flags 2% of clean controls. On TRAIL it leads Gandalf and TRAIL's judge on all 3 quality metrics, whose numerical values are absent from the abstract. These are author-reported results, not independently reproduced measurements. (source)

Significance

Interpretation: verification should produce inspectable evidence rather than reward a persuasive completion report. The false-positive control is as relevant as failure detection when verifier judgments become training rewards. (source)

Open Questions

The abstract does not establish verifier behavior under adaptive attacks, total training benefit, or transfer beyond the tested environments. Full paper and code were not reviewed. See Eval Harness Configuration for evaluation-setting sensitivity. (source)

Cite

“Evidence-First Agentic Verification.” arXiv:2610.17724. This note uses the abstract and metadata.