MAPS: A Multi-Agent Framework Based on Big Seven Personality and
  Socratic Guidance for Multimodal Scientific Problem Solving
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.
67e0c13fe5fa0da84e134581
Jian Zhang
claimed_verified
2025-03-24T08:07:08.476Z
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Jian Zhang
user
VentureZJ
67e0c13fe5fa0da84e134582
Zhiyuan Wang
67e0c13fe5fa0da84e134583
Zhangqi Wang
67e0c13fe5fa0da84e134584
Xinyu Zhang
67e0c13fe5fa0da84e134585
Fangzhi Xu
admin_assigned
2025-03-24T12:33:42.732Z
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Fangzhi Xu
user
xufangzhi
67e0c13fe5fa0da84e134586
Qika Lin
extracted_pending
2025-03-24T02:19:51.913Z
66ac77011cfb12c087605acb
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Lin
user
Qika
67e0c13fe5fa0da84e134587
Rui Mao
67e0c13fe5fa0da84e134588
Erik Cambria
67e0c13fe5fa0da84e134589
Jun Liu
MAPS: A Multi-Agent Framework Based on Big Seven Personality and
  Socratic Guidance for Multimodal Scientific Problem Solving
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.
A Multi-Agent framework with feedback mechanisms and Socratic guidance addresses multi-modal scientific problems and enhances reasoning and reflection, outperforming current models.
Multi-Agent framework Big Seven Personality Socratic guidance MSR EMMA Olympiad MathVista Critic agent