metadata
license: mit
language:
- en
tags:
- multi-agent
- multimodal
- strategic reasoning
Dataset Description
- Homepage: https://vs-bench.github.io
- Repository: https://github.com/zelaix/VS-Bench
- Paper: https://arxiv.org/abs/2506.02387
- Contact: Zelai Xu
Dataset Summary
VS-Bench is a multimodal benchmark for evaluating VLMs in multi-agent environments. We evaluate fourteen state-of-the-art models in eight vision-grounded environments with two complementary dimensions, including offline evaluation of strategic reasoning by next-action prediction accuracy and online evaluation of decision-making by normalized episode return.
Citation Information
@article{xu2025vs,
title={VS-Bench: Evaluating VLMs for Strategic Reasoning and Decision-Making in Multi-Agent Environments},
author={Xu, Zelai and Xu, Zhexuan and Yi, Xiangmin and Yuan, Huining and Chen, Xinlei and Wu, Yi and Yu, Chao and Wang, Yu},
journal={arXiv preprint arXiv:2506.02387},
year={2025}
}