Empowering Multimodal LLMs with External Tools: A Comprehensive Survey

• URL: http://arxiv.org/abs/2508.10955v1
• Date: Thu, 14 Aug 2025 07:25:45 GMT
• Title: Empowering Multimodal LLMs with External Tools: A Comprehensive Survey
• Authors: Wenbin An, Jiahao Nie, Yaqiang Wu, Feng Tian, Shijian Lu, Qinghua Zheng,
• Abstract summary: Multimodal Large Language Models (MLLMs) have achieved great success in various multimodal tasks, pointing toward a promising pathway to artificial general intelligence.<n>Lack of multimodal data, poor performance on many complex downstream tasks, and inadequate evaluation protocols hinder the reliability and broader applicability of MLLMs.<n>Inspired by the human ability to leverage external tools for enhanced reasoning and problem-solving, augmenting MLLMs with external tools offers a promising strategy to overcome these challenges.
• Score: 61.66069828956139
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: By integrating the perception capabilities of multimodal encoders with the generative power of Large Language Models (LLMs), Multimodal Large Language Models (MLLMs), exemplified by GPT-4V, have achieved great success in various multimodal tasks, pointing toward a promising pathway to artificial general intelligence. Despite this progress, the limited quality of multimodal data, poor performance on many complex downstream tasks, and inadequate evaluation protocols continue to hinder the reliability and broader applicability of MLLMs across diverse domains. Inspired by the human ability to leverage external tools for enhanced reasoning and problem-solving, augmenting MLLMs with external tools (e.g., APIs, expert models, and knowledge bases) offers a promising strategy to overcome these challenges. In this paper, we present a comprehensive survey on leveraging external tools to enhance MLLM performance. Our discussion is structured along four key dimensions about external tools: (1) how they can facilitate the acquisition and annotation of high-quality multimodal data; (2) how they can assist in improving MLLM performance on challenging downstream tasks; (3) how they enable comprehensive and accurate evaluation of MLLMs; (4) the current limitations and future directions of tool-augmented MLLMs. Through this survey, we aim to underscore the transformative potential of external tools in advancing MLLM capabilities, offering a forward-looking perspective on their development and applications. The project page of this paper is publicly available athttps://github.com/Lackel/Awesome-Tools-for-MLLMs.

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