Datasets:

launch
/

LudoBench

dataset_info:
  features:
    - name: ID
      dtype: int64
    - name: Game
      dtype: string
    - name: tier
      dtype: int64
    - name: Question
      dtype: string
    - name: Answer
      dtype: string
    - name: game_state_url
      dtype: string
configs:
  - config_name: default
    data_files:
      - split: test
        path: ludobench.parquet
license: mit
task_categories:
  - visual-question-answering
  - question-answering
language:
  - en
size_categories:
  - n<1K
pretty_name: LudoBench
tags:
  - board-games
  - multimodal-reasoning
  - benchmark

LudoBench

LLMs as Rules Oracles: Exploring Real-World Multimodal Reasoning in Tabletop Strategy Game Environments

ICLR 2026

A multimodal board-game reasoning benchmark evaluating LLM/VLM reasoning across 5 strategy games and 3 difficulty tiers.

Dataset Description

638 annotated question-answer pairs
5 games: Kingdomino, Res Arcana, Pax Renaissance, Carcassonne, Catan
3 tiers: Environment Perception (T1), Rules Integration (T2), Short-Horizon Optimization (T3)
3 rules modalities: None (parametric), Text (text rulebook), Image (image rulebook)

Fields

Field	Description
`ID`	Unique question identifier
`Game`	Board game name
`tier`	Difficulty tier (1, 2, or 3)
`Question`	The question text
`Answer`	Expected answer
`game_state_url`	Path(s) to game state image(s), semicolon-separated if multiple

Benchmark Results

See benchmark_results.csv for accuracy scores of 9 models across all game/tier/modality splits.

Citation

@inproceedings{peper2026ludobench,
  title={{LLMs} as Rules Oracles: Exploring Real-World Multimodal Reasoning in Tabletop Strategy Game Environments},
  author={Peper, Joseph J. and Gandra, Sai Krishna and Zhang, Yunxiang and Chennareddy, Vaibhav and Jha, Shloki and Payani, Ali and Wang, Lu},
  booktitle={Proceedings of the Fourteenth International Conference on Learning Representations (ICLR)},
  year={2026},
  address={Rio de Janeiro, Brazil}
}