Summary of Magebench: Bridging Large Multimodal Models to Agents, by Miaosen Zhang et al.
MageBench: Bridging Large Multimodal Models to Agents
by Miaosen Zhang, Qi Dai, Yifan Yang, Jianmin Bao, Dongdong Chen, Kai Qiu, Chong Luo, Xin Geng, Baining Guo
First submitted to arxiv on: 5 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces MageBench, a multimodal agent benchmark that evaluates reasoning capabilities in scenarios where visual signals are continuously updated. The benchmark includes three types of environments (WebUI, Sokoban, and Football) with 483 scenarios, testing agents’ knowledge, engineering capabilities, visual intelligence, and interaction skills. While current product-level models perform better than random acting, they are far inferior to human-level performance. The paper highlights the limitations of these models in modifying their planning based on visual feedback and other abilities. To address this, the authors release their code and data, providing optimization directions for Language-Model-based Models (LMMs) from an agent’s perspective. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new benchmark to test language model-based models’ ability to reason with visual information. This is important because current benchmarks only focus on language understanding. The new benchmark has three types of environments and many scenarios that require the models to use their knowledge, engineering capabilities, and interaction skills. While some models do better than random acting, they are not as good as humans. The paper shows what these models struggle with, such as using visual feedback to adjust their plans. |
Keywords
» Artificial intelligence » Language model » Language understanding » Optimization
