AI 中文总结
提出一种基于证据的审计协议，通过评估证据而非分数来验证模块化智能体，能定位组件价值损失并测试验证器可靠性，应用于投资组合智能体揭示机制信息价值依赖动作集等发现。
AI 中文摘要
当开发者更改智能体的一个组件（例如其控制器、学习模型或验证器）时，他们通常通过一个总体任务分数来判断更改的效果。该分数无法说明改进是否可实现、哪个组件失去了价值，或者智能体自身的检查能证明什么。我们引入了一种针对模块化智能体的声明特定验证审计，这些智能体进行规划、行动、检查和改进。该审计不是对智能体进行评分，而是对证据进行评分：每个结论都记录其背后的证据、四种判定之一（支持、不支持、未解决或未评估）以及其成立的范围。三个工具提供这些证据。Oracle策略在明确规定的动作集下衡量可实现的改进，因此低价值可以追溯到评估而非环境。每次用一个完美对应物替换一个组件，可以定位失去的价值，当下游组件可能掩盖结果时，将空结果解读为未解决。一个单独的测试询问验证器的分数是否识别了它被解读为界限的数量。应用于合成市场中具有已知隐藏机制的受约束投资组合配置智能体，审计表明，完美机制信息的价值取决于用于衡量它的动作集，场景生成器丢弃了大部分机制信号，而更好的局部保真度并不改善决策，并且运行时验证器可以在结果没有可见变化的情况下被绕过。贡献在于协议及其强制执行的证据区分；实证发现特定于所研究的智能体和环境。
英文摘要
When developers change one component of an agent, such as its controller, a learned model or its verifier, they usually judge the change by an aggregate task score. That score cannot tell whether improvement was attainable, which component lost value, or what the agent's own checks certify. We introduce a claim-specific verification audit for modular agents that plan, act, check and refine. Instead of scoring the agent, the audit scores the evidence: each conclusion is recorded with the evidence behind it, one of four verdicts (supported, unsupported, unresolved or not evaluated) and the boundary within which it holds. Three tools supply that evidence. Oracle policies measure attainable improvement under an explicitly stated action set, so that a low value can be traced to the evaluation rather than to the environment. Replacing one component at a time with a perfect counterpart locates lost value, with null results read as unresolved whenever a downstream component could mask them. A separate test asks whether the verifier's score identifies the quantity it is read as bounding. Applied to a constrained portfolio-allocation agent in a synthetic market with known hidden regimes, the audit shows that the value of perfect regime information depends on the action set used to measure it, that the scenario generator discards most of the regime signal while better local fidelity does not improve decisions, and that the runtime verifier can be bypassed with no visible change in outcomes. The contribution is the protocol and the evidential distinctions it enforces; the empirical findings are specific to the agent and environment studied.
Comments32 pages, 4 figures, 15 tables
