Credit Without Ground Truth: Auditing Step-Level Credit Assignment in LLM Agents Against Executed Replay
Factual evidence
What the source reports
Study shows step-level AI agent credit signals (LLM-judge, logprobs, confidence) perform no better than chance against causal replay.
Inspect the evidence
- Inclusion basis
- AI in finance
- Publisher and source type
- arXiv cs.LG — Machine Learning · RESEARCH
- Published by source
- 27 October 2026
- Collected by OneBench
- 21 Aug 2026, 20:33 UK
Stored source excerpt
arXiv:2608.19760v1 Announce Type: new Abstract: Audited against causal ground truth from executed replay in a single-agent tool environment (ALFWorld), none of the step-level credit signals…
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OneBench interpretation
Institutional assessment
So what
Current audit methods for agentic workflows fail to causally attribute errors, invalidating common RLHF and step-wise compliance monitoring techniques.
Do what
Brief your model risk team to pause relying on LLM-as-a-judge for step-level audit trails in autonomous agent POCs.