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 governa
Record details
Published: 9 July 2026
Source: arXiv
Category: Research
Topics: Regulation · Military & security · Agents & autonomy · Environment
Retrieved: 14 July 2026
Related evidence
These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.
Beyond Component Testing: Validating Agentic AI Systems
arXiv cs.AI · 31 July 2026
WP 1: CYBER RANGE SCENARIOS - Enterprise Networks
UK Contracts Finder — AI procurement · 26 June 2025
Cyber Evaluations - WP1 - Cyber Range Scenarios
UK Contracts Finder — AI procurement · 6 March 2025
Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets
arXiv · 6 July 2026
Agentic-V2X: Small Language Model Agents for Deadline-Aware V2X Scheduling in 5G/6G Networks
arXiv · 5 July 2026
Internet of Agentic Things: Networked AI Agents for Closed-Loop IoT Orchestration
arXiv · 14 July 2026
How to cite this record
ethics.ai (9 July 2026), “SolarChain-Eval: A Physics-Constrained Benchmark for Trustworthy Economic Agents in Decentralized Energy Markets,” evidence record 81, https://ethics.ai/record/81 (originally published by arXiv).
Use and limitations
This page is a stable index and citation surface for a source record. ethics.ai did not author the underlying report and has not independently verified every claim. Automatic topics may be imperfect. For consequential use, quote and cite the original publisher.