MAPS：基於大七人格與蘇格拉底引導的多智能體框架，用於多模態科學問題解決

MAPS: A Multi-Agent Framework Based on Big Seven Personality and Socratic Guidance for Multimodal Scientific Problem Solving

March 21, 2025

作者: Jian Zhang, Zhiyuan Wang, Zhangqi Wang, Xinyu Zhang, Fangzhi Xu, Qika Lin, Rui Mao, Erik Cambria, Jun Liu

cs.AI

摘要

多模態科學問題（MSPs）涉及需要整合多種模態（如文本和圖表）的複雜問題，這在人工智慧領域提出了重大挑戰。儘管在解決傳統科學問題方面已取得進展，但MSPs仍面臨兩個主要問題：科學問題解決中的多模態綜合推理挑戰，以及缺乏反思與再思考能力。為應對這些問題，我們引入了一個基於大七人格特質與蘇格拉底引導的多代理框架（MAPS）。該框架利用七個不同的代理，通過反饋機制和蘇格拉底方法來指導MSPs的解決。針對第一個問題，我們提出了一個漸進式的四代理解決策略，每個代理專注於問題解決過程的特定階段。對於第二個問題，我們引入了一個受蘇格拉底提問啟發的批評代理，它促進了批判性思維並激發了自主學習。我們在EMMA、奧林匹克和MathVista數據集上進行了廣泛的實驗，在所有任務中取得了比當前SOTA模型高出15.84%的優異結果。同時，額外的分析實驗也驗證了模型的進步及其泛化能力。

English

Multimodal scientific problems (MSPs) involve complex issues that require the integration of multiple modalities, such as text and diagrams, presenting a significant challenge in artificial intelligence. While progress has been made in addressing traditional scientific problems, MSPs still face two primary issues: the challenge of multi-modal comprehensive reasoning in scientific problem-solving and the lack of reflective and rethinking capabilities. To address these issues, we introduce a Multi-Agent framework based on the Big Seven Personality and Socratic guidance (MAPS). This framework employs seven distinct agents that leverage feedback mechanisms and the Socratic method to guide the resolution of MSPs. To tackle the first issue, we propose a progressive four-agent solving strategy, where each agent focuses on a specific stage of the problem-solving process. For the second issue, we introduce a Critic agent, inspired by Socratic questioning, which prompts critical thinking and stimulates autonomous learning. We conduct extensive experiments on the EMMA, Olympiad, and MathVista datasets, achieving promising results that outperform the current SOTA model by 15.84% across all tasks. Meanwhile, the additional analytical experiments also verify the model's progress as well as generalization ability.