# RestoreBench: Can AI Agents Restore Power Flow Convergence?

arXiv: https://arxiv.org/abs/2609.00384 · HTML: https://mlsanity.com/paper/2609.00384/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Financial trading and forecasting agents, one of 16 directions in LLM agents and tool use.
- Builds on 6 of its 15 references that are in the map.
- First posted on arXiv 31 Aug 2026.

## Placed in

- LLM agents and tool use / [Financial trading and forecasting agents](https://mlsanity.com/agents/financial-trading-and-forecasting-agents/)

## Builds on (most cited first, 6 of 6)

- [pandapower - an Open Source Python Tool for Convenient Modeling, Analysis and Optimization of Electric Power Systems](https://mlsanity.com/paper/1709.06743/) (2017)
- [PowerGraph-LLM: Novel Power Grid Graph Embedding and Optimization with Large Language Models](https://mlsanity.com/paper/2501.07639/) (2025)
- [PowerChain: A Verifiable Agentic AI System for Automating Distribution Grid Analyses](https://mlsanity.com/paper/2508.17094/) (2025)
- [Competition and Cooperation of LLM Agents in Games](https://mlsanity.com/paper/2604.00487/) (2026)
- [PowerAgentBench-SS: A Benchmark for Agentic AI in Power System Steady-State Studies](https://mlsanity.com/paper/2606.18789/) (2026)
- [PowerAgentBench-Dyn: A Benchmark for Agentic AI in Power System Dynamic Studies](https://mlsanity.com/paper/2606.20401/) (2026)

## Abstract (arXiv)

Large Language Model (LLM) agents increasingly automate multi-step engineering workflows through tool use, interpretation of intermediate results, and iterative planning. Diagnosing and resolving non-convergent power flow cases is a promising yet largely unexplored application, as it requires engineering judgment, experimentation, and decision-making within constrained action spaces. We introduce a benchmark that evaluates these capabilities across multiple LLMs and three architectures: chatbot, single agent, and multi-agent systems. The evaluation covers two power grids and 46 cases per grid, each requiring one or more corrective actions to restore convergence. The benchmark defines the simulation environment, observation and action spaces, and evaluation metrics, providing a reproducible foundation for developing agentic AI systems for power system planning and operation. The code is available at https://github.com/Mansutti081/RestoreBench