Agent Step Value: Probing the Observer Effect in Black-Box Traces

Abstract

Final-answer scores hide which agent transitions helped or harmed a trace. We introduce Agent Step Value (ASV), a replay framework that scores before/after states with a stateless LLM evaluator over a fixed candidate set. ASV reports entropy movement and Bayesian surprise measure belief movement, while offline gold-margin gain measures movement toward a reviewed target. It also quantifies evaluator-channel sensitivity by replaying the same frozen transitions under changed projection, rationale, prompt, or scoring rules. In a 100-question open-QA study with live PubMed retrieval and DeepSeek log-probability scoring, ASV evaluates 1,100 transitions. Entropy movement is 0.000 while mean Bayesian surprise is 2.693, exposing near-one-hot belief pivots. Under a 128-token rationale-conditioned protocol, mean gold-margin gain is -2.335 (95\% CI [-3.395, -1.272]); direct one-token scoring on the same traces gives +4.033. A 100-transition component audit traces the reversal to short generated rationales over full states. ASV turns prompt sensitivity into a measured channel effect and localizes the largest rationale-conditioned losses to extraction and audit.