| | --- |
| | 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](mailto:zelai.eecs@gmail.com) |
| |
|
| |
|
| | ### 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. |
| |
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| |
|
| | ### 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} |
| | } |
| | ``` |
| |
|
