@inproceedings{warrier-etal-2026-lara,
    title = "{LARA}: {LLM}-based Agile Power Distribution Network Restoration from Disastrous Events",
    author = "Warrier, Jishnu  and
      Huang, Heqing  and
      Lin, Yuzhang  and
      Zhang, Sai Qian",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.findings-eacl.321/",
    pages = "6108--6116",
    ISBN = "979-8-89176-386-9",
    abstract = "Restoring power distribution networks after disruptions demands rapid, reliable coordination across repair crews, mobile power sources, and switching actions under strict constraints. Classical optimization yields high-quality plans but can be slow, while reinforcement learning often requires feeder-specific training and careful reward shaping. We recast restoration as language-conditioned planning: a large language model generates high-level restoration plans over a compact pre-validated catalogue of feasible actions. This constrained generation design makes decisions reliably, scalably, and interpretably, and allows for real-time human-in-the-loop decision-making while requiring no topology-specific setup or retraining. Our method achieves near-mixed-integer-linear programming performance on the IEEE 13-node standard power distribution feeder and outperforms a time-capped MILP solver on the IEEE 33-node standard feeder by around 13{\%}, while using less than 1{\%} of its wall-clock runtime."
}