DoraemonGPT: Toward Understanding Dynamic Scenes with Large Language Models

The field of AI agents is advancing at an unprecedented rate due to the capabilities of large language models (LLMs). However, LLM-driven visual agents mainly focus on solving tasks for the image modality, which limits their ability to understand the dynamic nature of the real world, making it still far from real-life applications, e.g., guiding students in laboratory experiments and identifying their mistakes. Considering the video modality better reflects the ever-changing and perceptually intensive nature of real-world scenarios, we devise DoraemonGPT, a comprehensive and conceptually elegant system driven by LLMs to handle dynamic video tasks. Given a video with a question/task, DoraemonGPT begins by converting the input video with massive content into a symbolic memory that stores \textit{task-related} attributes. This structured representation allows for spatial-temporal querying and reasoning by sub-task tools, resulting in concise and relevant intermediate results. Recognizing that LLMs have limited internal knowledge when it comes to specialized domains (e.g., analyzing the scientific principles underlying experiments), we incorporate plug-and-play tools to assess external knowledge and address tasks across different domains. Moreover, we introduce a novel LLM-driven planner based on Monte Carlo Tree Search to efficiently explore the large planning space for scheduling various tools. The planner iteratively finds feasible solutions by backpropagating the result's reward, and multiple solutions can be summarized into an improved final answer. We extensively evaluate DoraemonGPT in dynamic scenes and provide in-the-wild showcases demonstrating its ability to handle more complex questions than previous studies.