arXiv:2607.04419v1 Announce Type: new Abstract: Most agent evaluations collapse a multi-step trace into a final answer, a success flag, or a trajectory-level score. These aggregates obscure the diagnostic question developers need most: which action changed the state in a useful direction? We introduce Agent Step Value (ASV), a state-transition measurement framework that scores each observed action by the change it induces in a state-grounded evaluator's distribution over fixed candidate outcomes. ASV renders redacted before/after state projections, uses a stateless LLM evaluator to assign cand
Source: arXiv cs.AI — read the full report at the original publisher.