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AI4Fire: Evaluating Large Language Models on Wildfire Tasks — arXiv:2610.10946

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.10946
License (as archived): CC0-1.0
Archived: 2026-10-09T06:04:03.629Z
SHA-256 of the archived text: 09bcc4e20ea8bac620e4803e22b0df816d00c2e71fd28ac9032bfc7bbc291d1a
Stable URL of this page: https://metraipolis.com/documento/dc_bf25ec0e01ec

How to cite

AI4Fire: Evaluating Large Language Models on Wildfire Tasks — arXiv:2610.10946 — arXiv (en), https://arxiv.org/abs/2610.10946 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_bf25ec0e01ec (Metraipolis archive dc_bf25ec0e01ec, sha256 09bcc4e20ea8bac620e4803e22b0df816d00c2e71fd28ac9032bfc7bbc291d1a)

BibTeX

@misc{dcbf25ec0e01ec,
  title = {AI4Fire: Evaluating Large Language Models on Wildfire Tasks — arXiv:2610.10946},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.10946},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_bf25ec0e01ec; sha256 09bcc4e20ea8bac620e4803e22b0df816d00c2e71fd28ac9032bfc7bbc291d1a},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.10946. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Large language models (LLMs) are entering wildfire management, where overstated evaluations can cost property and lives. How do they perform on wildfire tasks, with and without grounding? Bare means a model receives the task input alone. Grounded means it also receives one task-specific addition: for smoke detection, a smoke-free reference frame from the same camera. AI4Fire runs six core models bare and grounded on five wildfire tasks, zero-shot; a sweep adds 29 more. Our literature search on fire tasks found 138 works; none combines this roster, task coverage, and paired bare and grounded runs. We report three findings. (1) Grounding helped most where the addition carried the answer: a read-only SQL tool lifted every core model's database accuracy from at most 16 to at least 88 percent. (2) Simple rules were hard to beat: no core model outperformed repeating today's staffing count, and two open-weight models mostly copied the median of similar earlier fire-days, a worse forecast. (3) Public releases carry hazards: a fire-danger column separates the holdout perfectly, and 67 aerial fire frames carry smoldering or fire-free labels read from a clipped thermal maximum. We release prompts, responses, scores, code, and the survey record.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.CL, nuovi lavori del giorno).
Categorie arXiv: Computation and Language (cs.CL) ; Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2610.10946 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-09T05:47:15.103Z dalla pagina https://arxiv.org/list/cs.CL/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).