SolarChain-Eval: A Physics-Constrained Benchmark for Trustworthy Economic Agents in Decentralized Energy Markets
arXiv:2607.08681v1 Announce Type: new Abstract: 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 Proces
The increasing deployment of agentic AI in critical cyber-physical systems like energy markets necessitates robust evaluation frameworks to ensure safety and trustworthiness as these systems become more autonomous.
This benchmark addresses the critical need for trustworthy economic agents in decentralized energy markets, pre-empting potential systemic risks from AI exploitation or unstable governance decisions.
The development of physics-constrained benchmarks like SolarChain-Eval shifts the focus from mere task performance to incorporating trustworthiness and safety in AI agent evaluation for sensitive infrastructure.
- · Decentralized energy market operators
- · AI safety and ethics researchers
- · Energy grid stability
- · Malicious AI developers
- · Trust-agnostic AI deployment strategies
Improved reliability and security of AI-managed decentralized energy markets.
Accelerated adoption of AI agents in other cyber-physical infrastructure due to enhanced trust models.
New regulatory frameworks and certification standards for agentic AI in critical national infrastructure.
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Read at arXiv cs.AI