VS-Bench: Evaluating VLMs for Strategic Reasoning and Decision-Making in
  Multi-Agent Environments
Authors: name: Zelai Xu, name: Zhexuan Xu, name: Xiangmin Yi, name: Huining Yuan, name: Xinlei Chen, name: Yi Wu, name: Chao Yu, name: Yu Wang

Recent advancements in Vision Language Models (VLMs) have expanded their
capabilities to interactive agent tasks, yet existing benchmarks remain limited
to single-agent or text-only environments. In contrast, real-world scenarios
often involve multiple agents interacting within rich visual and linguistic
contexts, posing challenges with both multimodal observations and strategic
interactions. To bridge this gap, we introduce Visual Strategic Bench
(VS-Bench), a multimodal benchmark that evaluates VLMs for strategic reasoning
and decision-making in multi-agent environments. VS-Bench comprises eight
vision-grounded environments spanning cooperative, competitive, and
mixed-motive interactions, designed to assess agents' ability to predict
others' future moves and optimize for long-term objectives. We consider two
complementary evaluation dimensions, including offline evaluation of strategic
reasoning by next-action prediction accuracy and online evaluation of
decision-making by normalized episode return. Extensive experiments of fourteen
leading VLMs reveal a significant gap between current models and optimal
performance, with the best models attaining 47.8% prediction accuracy and 24.3%
normalized return. We further conduct in-depth analyses on multimodal
observations, test-time scaling, social behaviors, and failure cases of VLM
agents. By standardizing the evaluation and highlighting the limitations of
existing models, we envision VS-Bench as a foundation for future research on
strategic multimodal agents. Code and data are available at
https://vs-bench.github.io.