SolarChain-Eval: A Physics-Constrained Benchmark for Trustworthy Economic Agents in Decentralized Energy Markets
SolarChain-Eval: 分散型エネルギー市場における信頼できる経済エージェントのための物理制約付きベンチマーク (AI 翻訳)
Shilin Ou, Yifan Xu, Luyao Zhang
🤖 gxceed AI 要約
日本語
分散型エネルギー市場におけるAIエージェントの信頼性評価用ベンチマーク「SolarChain-Eval」を提案。物理制約とLLMによる監査層を組み込み、RLエージェントとLLM監査の性能を評価。ユーティリティと安全性のトレードオフを明らかにし、信頼性向上には物理制約と透明な介入記録が必要と結論。
English
This paper introduces SolarChain-Eval, a physics-constrained benchmark for evaluating trustworthy economic agents in decentralized energy markets. It integrates an LLM-based Planner/Auditor layer to supervise RL agents, revealing a utility-safety trade-off: RL agents improve market utility but can produce unsafe behaviors. The results underscore the need for physical constraints and transparent intervention traces in trustworthy agentic AI evaluation.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本のGX政策では、分散型エネルギーリソース（DER）の活用やデジタル技術による需給調整が進められている。本ベンチマークは、AIエージェントのエネルギー市場参入に伴う安全性・信頼性評価の枠組みを提供し、日本のバーチャルパワープラント（VPP）やエネルギーマネジメントシステム（EMS）の高度化にも示唆を与える。
In the global GX context
This benchmark addresses the emerging need for trustworthy AI agents in decentralized energy markets, which are central to global energy transition. It provides a framework combining physical constraints and LLM-based auditability, relevant to regulatory discussions on AI safety in energy systems (e.g., EU AI Act, US DOE guidelines).
👥 読者別の含意
🔬研究者:Provides a benchmark for evaluating RL and LLM agents in energy markets, highlighting the utility-safety trade-off and the role of auditability.
🏢実務担当者:Energy market operators and VPP aggregators can use this framework to assess the trustworthiness of autonomous trading agents before deployment.
🏛政策担当者:Regulators can consider the need for physical constraints and transparent intervention logs in AI governance for decentralized energy systems.
📄 Abstract（原文）
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.
🔗 Provenance — このレコードを発見したソース
- arXiv https://arxiv.org/abs/2607.08681first seen 2026-07-13 04:11:03
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