ai4fire

AI4Fire

Task metadata for a five-task wildfire benchmark for language models and agents.

This package carries the benchmark's task inventory so it can be queried without cloning the repository. It does not run the evaluation. The prompts, the stored model responses, the scoring code, and the offline reproduction live at github.com/yzhao062/AI4Fire.

What the Benchmark Is

Five wildfire tasks that score without a human in the loop, against a released answer key. Every model answers every item twice, once from the task prompt alone and once with that task's grounding material added. Every task is scored beside at least one non-LLM comparator.

The five grounding interventions are not comparable to each other, because each appears on exactly one task. Results are therefore task-conditional, and there is no single average grounding effect to report.

Usage

pip install ai4fire
ai4fire            # print the task inventory
ai4fire --json     # the same, as JSON

from ai4fire import TASKS, REPOSITORY

for task in TASKS:
    print(task["key"], task["items"], task["comparator"])

Reproducing the Results

Clone the repository. One command rebuilds every primary table from the stored responses, offline, with no credentials and no network.

git clone https://github.com/yzhao062/AI4Fire.git
cd AI4Fire
pip install -r requirements.txt
python reproduce_tables.py

Data Licensing

Each upstream source keeps its own terms, which this package does not replace. ICS-209-PLUS and Mesogeos are CC BY 4.0. FIgLib is CC BY-NC-ND 4.0, whose NoDerivatives term is why neither the camera frames nor features derived from them are redistributed. FPA-FOD is US Government public domain. WildFireVQA carries an unresolved discrepancy between Apache-2.0 metadata and a CC BY 4.0 dataset card. The repository documents each in full.

License

BSD 2-Clause for the code and our original contributions.