AIcell/war-test-dataset

War Forecast Bench

Dataset for the paper "When AI Navigates the Fog of War" (arXiv:2603.16642).

Website: war-forecast-arena.com

Overview

A temporally grounded benchmark for evaluating LLM reasoning during an ongoing geopolitical conflict. The dataset covers the early stages of the 2026 Middle East conflict, which unfolded after the training cutoff of current frontier models, substantially mitigating training-data leakage concerns.

Temporal Nodes

Node	Date	Event	Theme	Theme Description
T0	Feb 27	Operation Epic Fury	I	Initial Outbreak
T1	Feb 28	Israeli-US Strikes	I	Initial Outbreak
T2	Feb 28	Iranian Strikes	I	Initial Outbreak
T3	Mar 1	Two Missiles towards British Bases on Cyprus	II	Threshold Crossings
T4	Mar 1	Oil Refiner and Oil Tanker Was Attacked	III	Economic Shockwaves
T5	Mar 2	Qatar Halts Energy Production	III	Economic Shockwaves
T6	Mar 2	Natanz Nuclear Facility Damaged	II	Threshold Crossings
T7	Mar 3	U.S. Begins Evacuation of Citizens from the Middle East	II	Threshold Crossings
T8	Mar 3	Nine Countries Involved and Israeli Ground Invasion	II	Threshold Crossings
T9	Mar 3	Mojtaba Khamenei Becomes Supreme Leader	IV	Political Signaling
T10	Mar 6	Iranian Apology to Neighboring Countries	IV	Political Signaling

Files

test_dataset.json

The benchmark questions and ground truth. Contains:

• 11 critical temporal nodes (T0--T10, Feb 27 -- Mar 6, 2026)
• 42 node-specific verifiable Yes/No questions
• 5 general exploratory questions
• Event timestamps, bilingual questions (EN/CN), and ground truth answers

articles_clean.json

The news article corpus used as context for LLM predictions. Contains:

• ~1,685 news articles from 12+ sources
• Coverage: Feb 1 -- Mar 5, 2026
• Sources include Reuters, AP News, Al Jazeera, BBC, Bloomberg, The Guardian, and more
• Each article has: title, body text, publication timestamp, source name

Usage

from datasets import load_dataset

dataset = load_dataset("AIcell/war-test-dataset")

Or use with the evaluation pipeline:

git clone https://github.com/xirui-li/war-test.git
cd war-test
pip install -r requirements.txt
python run_predictions.py

The pipeline automatically downloads the data from this HuggingFace repo.

Citation

@misc{li2026ainavigatesfogwar,
      title={When AI Navigates the Fog of War},
      author={Ming Li and Xirui Li and Tianyi Zhou},
      year={2026},
      eprint={2603.16642},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2603.16642},
}