ING-VP: MLLMs cannot Play Easy Vision-based Games Yet
<|reference_start|>ING-VP: MLLMs cannot Play Easy Vision-based Games Yet: As multimodal large language models (MLLMs) continue to demonstrate increasingly competitive performance across a broad spectrum of tasks, more intricate and comprehensive benchmarks have been developed to assess these cutting-edge models. These benchmarks introduce new challenges to core capabilities such as perception, reasoning, and planning. However, existing multimodal benchmarks fall short in providing a focused evaluation of multi-step planning based on spatial relationships in images. To bridge this gap, we present ING-VP, the first INteractive Game-based Vision Planning benchmark, specifically designed to evaluate the spatial imagination and multi-step reasoning abilities of MLLMs. ING-VP features 6 distinct games, encompassing 300 levels, each with 6 unique configurations. A single model engages in over 60,000 rounds of interaction. The benchmark framework allows for multiple comparison settings, including image-text vs. text-only inputs, single-step vs. multi-step reasoning, and with-history vs. without-history conditions, offering valuable insights into the model's capabilities. We evaluated numerous state-of-the-art MLLMs, with the highest-performing model, Claude-3.5 Sonnet, achieving an average accuracy of only 3.37%, far below the anticipated standard. This work aims to provide a specialized evaluation framework to drive advancements in MLLMs' capacity for complex spatial reasoning and planning. The code is publicly available at https://github.com/Thisisus7/ING-VP.git.<|reference_end|>
@article{zhang2024ing-vp:,
    title={ING-VP: MLLMs cannot Play Easy Vision-based Games Yet},
    author={Haoran Zhang, Hangyu Guo, Shuyue Guo, Meng Cao, Wenhao Huang, Jiaheng
  Liu, Ge Zhang},
    journal={arXiv preprint arXiv:2410.06555},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.06555},
    primaryClass={cs.CL}
}