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Credit Without Ground Truth: Auditing Step-Level Credit Assignment in LLM Agents Against Executed Replay
arXiv cs.LG — Machine Learning
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.
Open sourceOneBench 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.