aiqbench analysis
This research introduces Agent Step Value (ASV), a new metric designed to detect and localize inconsistencies in step rewards when evaluation channels change. By demonstrating significant sign disagreements between direct scoring and generated-view channels, the study highlights critical measurement problems inherent in reusing evaluator-derived signals for black-box agent traces. The findings offer actionable interventions, such as source-only generation contracts, which can restore reward directionality before artifacts corrupt substantive support claims.
Agent Step Value: Auditing Evaluator-Channel Reversals in Black-Box Agent Traces
arXiv:2607.04419v4 Announce Type: replace Abstract: Pooling, substituting, or reusing evaluator-derived step rewards assumes that their direction survives a change of evaluation channel. The same frozen transition can violate that assumption. Process rewards vary agent states, while evaluator audits vary scoring configurations; neither first difference isolates their interaction. We define Agent Step Value (ASV) as a channel-indexed target-margin gain and identify the state-by-channel interaction on complete matched faces. Across frozen PubMed question-answering transitions, direct scoring yields a positive mean ASV, while the generated-view channel yields a negative mean. Two matched replay waves reproduce this reversal, and cross-channel sign disagreement exceeds same-channel retry disagreement by 48.0 percentage points. Matched retrieval faces localize the reversal to the generated-view coordinate and trace its direction across a readout-and-stack bridge. A source-only generation contract restores the positive mean direction on artifact-bearing retrievals and removes parser-detected substantive support claims from artifact-free before-state views. ASV turns channel sensitivity into an identified measurement problem that can be localized and tested by intervention before step rewards are reused.
Abstract:Pooling, substituting, or reusing evaluator-derived step rewards assumes that their direction survives a change of evaluation channel. The same frozen transition can violate that assumption. Process rewards vary agent states, while evaluator audits vary scoring configurations; neither first difference isolates their interaction. We define Agent Step Value (ASV) as a channel-indexed target-margin gain and identify the state-by-channel interaction on complete matched faces. Across frozen PubMed question-answering transitions, direct scoring yields a positive mean ASV, while the generated-view channel yields a negative mean. Two matched replay waves reproduce this reversal, and cross-channel sign disagreement exceeds same-channel retry disagreement by 48.0 percentage points. Matched retrieval faces localize the reversal to the generated-view coordinate and trace its direction across a readout-and-stack bridge. A source-only generation contract restores the positive mean direction on artifact-bearing retrievals and removes parser-detected substantive support claims from artifact-free before-state views. ASV turns channel sensitivity into an identified measurement problem that can be localized and tested by intervention before step rewards are reused.
Artificial Intelligence (cs.AI)
arXiv:2607.04419 [cs.AI]
(or arXiv:2607.04419v4 [cs.AI] for this version)
Full text under the publisher's open licence · 174 words
Related coverage
Agent Step Value: Auditing Evaluation-Channel Sensitivity in Black-Box Agent Traces
arXiv · Oct 12, 2026
Why I Left Google DeepMind
LessWrong · Jul 15, 2026
What We Learned Trying to Catch AI Liars: An Aletheia's Quest Retrospective
EleutherAI · Aug 25, 2026
Brief independent investigation of agents’ behavior, reasoning and collaboration in the OpenAI / Hugging Face hacking incident
METR · Aug 26, 2026