MAPS: A Multi-Agent Framework Based on Big Seven Personality and
  Socratic Guidance for Multimodal Scientific Problem Solving
Authors: name: Jian Zhang, name: Zhiyuan Wang, name: Zhangqi Wang, name: Xinyu Zhang, name: Fangzhi Xu, name: Qika Lin, name: Rui Mao, name: Erik Cambria, name: Jun Liu

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.