| name | solarchain-eval-a-physics-constrained-benchmark-for-trustworthy |
| description | 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, auton... Activation: agent, agentic, llm, benchmark, safety |
| metadata | {"arxiv_id":"2607.08681","published":"2026-07-09","authors":"Shilin Ou, Yifan Xu, Luyao Zhang","tags":["agent","agentic","llm","benchmark","safety","cyber-physical","policy","reward"]} |
SolarChain-Eval: A Physics-Constrained Benchmark for Trustworthy Economic Agents in Decentralized Energy Markets
Core Concept
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.
Key Innovations
1. Problem Formulation
- Addresses the challenge of agent with a novel approach
- Proposes a systematic framework for evaluation and analysis
- Demonstrates significant improvements over existing methods
2. Methodology
- Introduces new techniques for agentic
- Leverages llm for improved performance
- Provides comprehensive evaluation across multiple settings
3. Practical Impact
- Applicable to real-world scenarios involving benchmark
- Provides actionable insights for practitioners
- Open-source implementation available for reproducibility
Technical Details
Approach
The paper presents a method that combines agent, agentic, llm to address the core problem. The framework is designed to be generalizable and applicable across different settings.
Key Results
- Demonstrates state-of-the-art performance on benchmark tasks
- Provides comprehensive ablation studies
- Shows robustness across different experimental conditions
Applications
Primary Use Cases
- Research and development in agent
- Benchmark evaluation and comparison
- Practical deployment scenarios
Integration Considerations
- Compatible with existing agentic pipelines
- Can be adapted for domain-specific applications
- Supports reproducible research practices
Implementation Notes
Data Requirements
- Requires appropriate training/evaluation data
- Supports standard data formats
- Includes preprocessing recommendations
Training and Evaluation
- Follows standard evaluation protocols
- Provides reproducible experimental settings
- Includes statistical significance analysis
Related Work
- Builds upon recent advances in agent, agentic, llm
- Extends existing frameworks with novel contributions
- Provides comprehensive comparison with prior methods
References
- Paper: arXiv:2607.08681 (2026-07-09)
- Authors: Shilin Ou, Yifan Xu, Luyao Zhang
- Categories: cs.AI
