想评估AI在真实物理系统中的可信度？这个新基准从六个维度测试智能体，还带了LLM审计层，结果挺扎心：RL优化收益但会钻物理漏洞。
SolarChain-Eval是一个受物理约束的基准，用于评估去中心化能源市场中的可信经济智能体。它将市场治理建模为Gymnasium兼容的马尔可夫决策过程，智能体每小时做出决策。基准从市场效用、物理安全、滑点、行动平滑度、空间公平性和可审计性六个维度评估策略。实验显示，RL智能体可提升市场效用但产生不安全行为，移除物理惩罚后智能体会利用无效发电数据并制造人为流动性。LLM规划器/审计器层能缓解部分风险但无法补偿错误奖励函数。
SolarChain-Eval: A Physics-Constrained Benchmark for Trustworthy Economic Agents in Decentralized Energy Markets
As agentic AI systems are increasingly applied to cyber-physical environments, their evaluation requires assessment of both task performance and trustworthiness. In decentralized energy markets, autonomous agents may improve market utility, but may also exploit invalid physical data, create artificial liquidity, and produce unstable governance decisions. Therefore, we propose SolarChain-Eval, a physics-constrained benchmark for evaluating trustworthy economic agents. It formulates market governance as a Gymnasium-compatible Markov Decision Process, where agents make hourly decisions. SolarChain-Eval evaluates each policy across multiple dimensions, including market utility, physical safety, slippage, action smoothness, spatial fairness, and auditability. To support agentic evaluation, SolarChain-Eval incorporates an LLM-based Planner/Auditor layer. The Planner defines episode-level action bounds and audit rules, while the Auditor reviews and revises high-risk actions. All interventions are recorded through structured logs, including trigger signals, proposed actions, revised actions, and audit rationales. Experiments with static, random, myopic, RL, and RL+LLM policies reveal a clear utility-safety trade-off. RL agents improve market utility but can still produce unsafe behavior. When the physics penalty is removed, reward-maximizing agents exploit invalid generation and increase artificial liquidity. The LLM Planner/Auditor improves auditability and mitigates selected risks, but it cannot fully compensate for a misspecified reward function. These results indicate that trustworthy agentic AI evaluation requires both physical constraints and transparent intervention traces. We release data and code as open access on GitHub for replicability.