title: MAPS: A Multi-Agent Framework Based on Big Seven Personality and
    Socratic Guidance for Multimodal Scientific Problem Solving
summary: 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.
paper_authors._id: 67e0c13fe5fa0da84e134581
paper_authors.name: Jian Zhang
paper_authors.status: claimed_verified
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paper_authors.avatarUrl: /avatars/830e5cec4565efdc23226a86a0fcef0e.svg
paper_authors.fullname: Jian Zhang
paper_authors.type: user
paper_authors.user: VentureZJ
paper_authors._id: 67e0c13fe5fa0da84e134582
paper_authors.name: Zhiyuan Wang
paper_authors._id: 67e0c13fe5fa0da84e134583
paper_authors.name: Zhangqi Wang
paper_authors._id: 67e0c13fe5fa0da84e134584
paper_authors.name: Xinyu Zhang
paper_authors._id: 67e0c13fe5fa0da84e134585
paper_authors.name: Fangzhi Xu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-24T12:33:42.732Z
paper_authors._id: 64e6cf78ecce34cb442dc889
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64e6cf78ecce34cb442dc889/qVZFiUEpBpSkmH8SQeinm.jpeg
paper_authors.fullname: Fangzhi Xu
paper_authors.type: user
paper_authors.user: xufangzhi
paper_authors._id: 67e0c13fe5fa0da84e134586
paper_authors.name: Qika Lin
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-03-24T02:19:51.913Z
paper_authors._id: 66ac77011cfb12c087605acb
paper_authors.avatarUrl: /avatars/54c06bd1c4c9d491470ed4162c2301ae.svg
paper_authors.fullname: Lin
paper_authors.type: user
paper_authors.user: Qika
paper_authors._id: 67e0c13fe5fa0da84e134587
paper_authors.name: Rui Mao
paper_authors._id: 67e0c13fe5fa0da84e134588
paper_authors.name: Erik Cambria
paper_authors._id: 67e0c13fe5fa0da84e134589
paper_authors.name: Jun Liu
paper_title: MAPS: A Multi-Agent Framework Based on Big Seven Personality and
    Socratic Guidance for Multimodal Scientific Problem Solving
paper_summary: 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.
paper_ai_summary: A Multi-Agent framework with feedback mechanisms and Socratic guidance addresses multi-modal scientific problems and enhances reasoning and reflection, outperforming current models.
paper_ai_keywords: Multi-Agent framework Big Seven Personality Socratic guidance MSR EMMA Olympiad MathVista Critic agent