InfantAgent-Next: A Multimodal Generalist Agent for Automated Computer Interaction

Bin Lei, Weitai Kang, Zijian Zhang, Winson Chen, Xi Xie, Shan Zuo, Mimi Xie, Ali Payani, Mingyi Hong, Yan Yan, Caiwen Ding

University of Minnesota· University of Illinois Chicago· University of Connecticut· University of Texas at San Antonio· Cisco Research

LLM based Agent Multimodal Generalist Agent Automated Computer Interaction Infrastructure

⋅ NeurIPS ⋅ OpenReview

Abstract

This paper introduces InfantAgent-Next, a generalist agent capable of interacting with computers in a multimodal manner, encompassing text, images, audio, and video.

Unlike existing approaches that either build intricate workflows around a single large model or only provide workflow modularity, our agent integrates tool-based and pure vision agents within a highly modular architecture, enabling different models to collaboratively solve decoupled tasks in a step-by-step manner.

Our generality is demonstrated by our ability to evaluate not only pure vision-based real-world benchmarks (i.e., OSWorld), but also more general or tool-intensive benchmarks (e.g., GAIA and SWE-Bench). Specifically, we achieve a

\mathbf{7.27\\%}

accuracy gain over Claude-Computer-Use on OSWorld.

Codes and evaluation scripts are included in the supplementary material and will be released as open-source.