arXiv Machine Learning
FLARE: Physics-Grounded Evaluation of Language-Model Agents for Feeder Restoration with Degraded Observations
Read the original on arXiv Machine Learning →FLARE is a physics‑grounded benchmark for evaluating language‑model agents on feeder restoration tasks after distribution‑grid faults. It includes 800 AC‑feasible cases across five observation settings, allowing agents to gather evidence, evaluate switches, and perform restoration actions under fixed operational limits. In tests of five instruction‑tuned models over 78,750 episodes, mean normalized regret ranged from 0.082 to 0.148, with a high P95 regret indicating a significant failure tail, and 96.4% of guard‑rejected actions would have caused physical violations.
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