发表机构
University of Michigan Law School（密歇根大学法学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现有智能体评估将终点分数视为最终结果的问题，该研究提出结果终局性与跨单元分离两个条件，通过实验和协议审查指出当前评估的不足，并提出开放效应记录方案以完善评估。
AI 中文摘要
当前的智能体评估在已停止的运行结束时，基于可见状态对模型打分，并将其计为一次试验。然而，将该分数解读为最终结果需要两个条件，而终点本身未必能确立这两个条件：结果终局性与跨单元分离。这两个条件相互独立，因为协调延迟结果可确定标签，同时运行仍共享状态；而隔离运行可防止状态延续，同时待评分结果仍未完成。我们提出一种完成论证，明确每项决策所需的证据，并认为仅当所有可能改变所主张结果的事项均已解决、受限或作为不确定性保留时，最终标签才合理。首先，在受控重放中，为展示智能体动作固定时的机制，我们发现对于每项延迟操作，终点与终端标签均不同；当服务状态在运行间持续存在时，延迟写入会改变下一次运行的分数，但在隔离或经验证的重置后则不会。其次，在对10个公开协议的审查中，我们发现所有协议均明确运行何时停止及评分内容，而未完成操作与将运行视为独立试验的证据则记录得较不一致。最后，我们提出一种开放效应记录，列出终点后仍可能相关的操作或资源、其当前状态，以及它们是否可能改变待评分结果或影响其他运行。
英文摘要
Agent evaluations commonly score the state observed when a run stops and count the run as one trial. Interpreting that score as a final result from a separate trial requires outcome finality and cross-unit separation. Outcome finality requires that later events cannot change the claimed result, while cross-unit separation requires that earlier runs cannot change the relevant conditions of later ones. The endpoint establishes neither condition by itself, and the two can hold independently. Waiting for a delayed outcome may settle the label even though its state remains available to another run. Isolation may prevent carryover even though the scored outcome remains unresolved. We develop a completion argument that identifies the evidence needed for each decision. A final success or failure label is justified only when every relevant effect is resolved or bounded tightly enough to fix the outcome. Any remaining uncertainty must be reported. First, in a controlled replay with fixed agent actions, we find that endpoint and terminal labels differ for every nonzero-delay operation and that a delayed write changes the next run's score under shared state but has no such effect after namespacing or verified reset. Second, in a review of ten public protocols, we find that reset or deliberate retention is documented explicitly more often than unfinished operations or evidence for separate scoring. Finally, we propose an open-effects record for operations and resources that may remain relevant after the endpoint, their status, and their possible effects on the scored outcome or another run.
