MageBench: Bridging Large Multimodal Models to Agents

TL;DR

MageBench introduces a new multimodal agent benchmark with diverse environments to evaluate reasoning, visual understanding, and interaction skills, revealing current models' significant limitations compared to humans.

Contribution

This work presents MageBench, a novel benchmark for assessing multimodal reasoning and interaction in agents, emphasizing visual feedback and imagination capabilities.

Findings

Current models perform near random in the benchmark.

Models lack ability to adapt plans based on visual feedback.

Models are far below human-level performance.

Abstract

LMMs have shown impressive visual understanding capabilities, with the potential to be applied in agents, which demand strong reasoning and planning abilities. Nevertheless, existing benchmarks mostly assess their reasoning abilities in language part, where the chain-of-thought is entirely composed of text.We consider the scenario where visual signals are continuously updated and required along the decision making process. Such vision-in-the-chain reasoning paradigm is more aligned with the needs of multimodal agents, while being rarely evaluated. In this paper, we introduce MageBench, a reasoning capability oriented multimodal agent benchmark that, while having light-weight environments, poses significant reasoning challenges and holds substantial practical value. This benchmark currently includes three types of environments: WebUI, Sokoban, and Football, comprising a total of 483 different scenarios. It thoroughly validates the agent's knowledge and engineering capabilities, visual intelligence, and interaction skills. The results show that only a few product-level models are better than random acting, and all of them are far inferior to human-level. More specifically, we found current models severely lack the ability to modify their planning based on visual feedback, as well as visual imagination, interleaved image-text long context handling, and other abilities. We hope that our work will provide optimization directions for LMM from the perspective of being an agent. We release our code and data at https://github.com/microsoft/MageBench.