@inproceedings{zhang-li-2026-agent,
title = "Agent Step Value: Auditing Evaluation-Channel Sensitivity in Black-Box Agent Traces",
author = "Zhang, Andrew and
Li, Chengzhan",
editor = "Kamalloo, Ehsan and
Gontier, Nicolas and
Rozanov, Nikolai and
Hashemzadeh, Maryam and
Arora, Akhil",
booktitle = "Proceedings of the Second Workshop for {RE}search on Agent Language Models ({REALM} 2026)",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.realm-1.12/",
pages = "161--176",
ISBN = "979-8-89176-485-9",
abstract = "Reusing evaluator-derived step rewards across evaluation channels raises a measurement question: does the assigned direction remain stable? We define Agent Step Value (ASV) as a channel-indexed before/after target-margin gain and identify the state-by-channel interaction from a matched 2 {\texttimes} 2 design. Across frozen PubMed question-answering transitions, cross-channel gain signs disagree on 48.3{\%} of comparisons, compared with 0.3{\%} for same-channel retries. Transition interactions are nearly balanced in sign (median +0.014; 10{\%} trimmed mean -0.033), but asymmetric negative magnitudes produce a positive direct mean and a negative generated-view mean under the empirical target mixture. Equal-target weighting preserves a negative mean interaction but not this strict mean reversal. Matched retrieval designs localize the negative component to the generated-view coordinate and retain its direction across three sign checks that vary readout and evaluator stack. In a matched 512-token source-contract follow-up, source-only generation restores the positive mean direction on artifact-bearing retrievals and removes parser-detected support claims from artifact-free before-state views. These results support an explanation in which generated support inflates the pre-retrieval baseline. The resulting audit can localize channel sensitivity and test an intervention before step rewards are reused."
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<abstract>Reusing evaluator-derived step rewards across evaluation channels raises a measurement question: does the assigned direction remain stable? We define Agent Step Value (ASV) as a channel-indexed before/after target-margin gain and identify the state-by-channel interaction from a matched 2 × 2 design. Across frozen PubMed question-answering transitions, cross-channel gain signs disagree on 48.3% of comparisons, compared with 0.3% for same-channel retries. Transition interactions are nearly balanced in sign (median +0.014; 10% trimmed mean -0.033), but asymmetric negative magnitudes produce a positive direct mean and a negative generated-view mean under the empirical target mixture. Equal-target weighting preserves a negative mean interaction but not this strict mean reversal. Matched retrieval designs localize the negative component to the generated-view coordinate and retain its direction across three sign checks that vary readout and evaluator stack. In a matched 512-token source-contract follow-up, source-only generation restores the positive mean direction on artifact-bearing retrievals and removes parser-detected support claims from artifact-free before-state views. These results support an explanation in which generated support inflates the pre-retrieval baseline. The resulting audit can localize channel sensitivity and test an intervention before step rewards are reused.</abstract>
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%0 Conference Proceedings
%T Agent Step Value: Auditing Evaluation-Channel Sensitivity in Black-Box Agent Traces
%A Zhang, Andrew
%A Li, Chengzhan
%Y Kamalloo, Ehsan
%Y Gontier, Nicolas
%Y Rozanov, Nikolai
%Y Hashemzadeh, Maryam
%Y Arora, Akhil
%S Proceedings of the Second Workshop for REsearch on Agent Language Models (REALM 2026)
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-485-9
%F zhang-li-2026-agent
%X Reusing evaluator-derived step rewards across evaluation channels raises a measurement question: does the assigned direction remain stable? We define Agent Step Value (ASV) as a channel-indexed before/after target-margin gain and identify the state-by-channel interaction from a matched 2 × 2 design. Across frozen PubMed question-answering transitions, cross-channel gain signs disagree on 48.3% of comparisons, compared with 0.3% for same-channel retries. Transition interactions are nearly balanced in sign (median +0.014; 10% trimmed mean -0.033), but asymmetric negative magnitudes produce a positive direct mean and a negative generated-view mean under the empirical target mixture. Equal-target weighting preserves a negative mean interaction but not this strict mean reversal. Matched retrieval designs localize the negative component to the generated-view coordinate and retain its direction across three sign checks that vary readout and evaluator stack. In a matched 512-token source-contract follow-up, source-only generation restores the positive mean direction on artifact-bearing retrievals and removes parser-detected support claims from artifact-free before-state views. These results support an explanation in which generated support inflates the pre-retrieval baseline. The resulting audit can localize channel sensitivity and test an intervention before step rewards are reused.
%U https://aclanthology.org/2026.realm-1.12/
%P 161-176
Markdown (Informal)
[Agent Step Value: Auditing Evaluation-Channel Sensitivity in Black-Box Agent Traces](https://aclanthology.org/2026.realm-1.12/) (Zhang & Li, REALM 2026)
ACL