Large Multimodal Agents: A Survey Summary

Introduction

This summary provides an overview of a survey on Large Multimodal Agents (LMAs) driven by Large Language Models (LLMs), highlighting their components, classification, evaluation methods, applications, and future research directions. The agents' ability to perceive, plan, act, and remember across various modalities, particularly visual data, is crucial for evolving AI to mimic human-like intelligence.

Core Components of LMAs

LMAs consist of four critical elements: perception, planning, action, and memory. Perception focuses on processing multimodal information from the environment. Planning involves deep reasoning and constructing plans for current tasks. Action executes these plans through interactions or physical movements. Memory, both short-term and long-term, stores relevant task information to guide future decisions.

Classification of LMAs

The survey categorizes existing research into four types based on whether they use closed-source LLMs as planners without long-term memory, fine-tuned LLMs without long-term memory, planners with indirect long-term memory, or planners with local long-term memory. This classification shows the evolution towards more integrated and capable LMAs.

Collaboration of Multiple Agents

Frameworks involving multiple LMAs working collaboratively are discussed, highlighting their roles and responsibilities in collective task accomplishment. These structures enhance task performance by distributing the workload among different agents.

Evaluation

The survey emphasizes the need for systematic and standardized evaluation frameworks for LMAs. It suggests that an ideal evaluation framework should include a range of tasks with clear metrics designed to assess LMAs' capabilities comprehensively and datasets that reflect real-world scenarios.

Applications

LMAs excel at processing diverse modalities, outperforming language-only agents in decision-making and response generation in various real-world multisensory environments. Applications span across GUI automation, robotics, gaming, autonomous driving, video understanding, visual generation and editing, and complex visual reasoning tasks.

Conclusion

Despite significant advances, many challenges remain in the field of LMAs, and there is substantial room for improvement. Future research directions include developing unified frameworks for single agents, enhancing collaboration between multiple multimodal agents, and expanding practical applications in real-world scenarios.

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查看原文：一文详解多模态智能体（LMAs）最新进展（核心组件/分类/评估/应用）

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