title: Progressive Multimodal Reasoning via Active Retrieval
summary: Multi-step multimodal reasoning tasks pose significant challenges for
  multimodal large language models (MLLMs), and finding effective ways to enhance
  their performance in such scenarios remains an unresolved issue. In this paper,
  we propose AR-MCTS, a universal framework designed to progressively improve the
  reasoning capabilities of MLLMs through Active Retrieval (AR) and Monte Carlo
  Tree Search (MCTS). Our approach begins with the development of a unified
  retrieval module that retrieves key supporting insights for solving complex
  reasoning problems from a hybrid-modal retrieval corpus. To bridge the gap in
  automated multimodal reasoning verification, we employ the MCTS algorithm
  combined with an active retrieval mechanism, which enables the automatic
  generation of step-wise annotations. This strategy dynamically retrieves key
  insights for each reasoning step, moving beyond traditional beam search
  sampling to improve the diversity and reliability of the reasoning space.
  Additionally, we introduce a process reward model that aligns progressively to
  support the automatic verification of multimodal reasoning tasks. Experimental
  results across three complex multimodal reasoning benchmarks confirm the
  effectiveness of the AR-MCTS framework in enhancing the performance of various
  multimodal models. Further analysis demonstrates that AR-MCTS can optimize
  sampling diversity and accuracy, yielding reliable multimodal reasoning.
paper_authors._id: 6764d821772d134796df3f97
paper_authors.name: Guanting Dong
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paper_authors.user: dongguanting
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paper_authors.name: Chenghao Zhang
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paper_authors.fullname: Chenghao Zhang
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paper_authors.user: Snow-Nation
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paper_authors.name: Mengjie Deng
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paper_authors.fullname: dengmengjie
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paper_authors.user: dengmengjie
paper_authors._id: 6764d821772d134796df3f9a
paper_authors.name: Yutao Zhu
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paper_authors.fullname: Yutao Zhu
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paper_authors.user: yutaozhu94
paper_authors._id: 6764d821772d134796df3f9b
paper_authors.name: Zhicheng Dou
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paper_authors._id: 66f0bf59e9d50ec57febf751
paper_authors.avatarUrl: /avatars/be97941e60064e5dd806c6fe9db3c537.svg
paper_authors.fullname: Zhicheng Dou
paper_authors.type: user
paper_authors.user: douzc
paper_authors._id: 6764d821772d134796df3f9c
paper_authors.name: Ji-Rong Wen
paper_authors.status: admin_assigned
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paper_authors.avatarUrl: /avatars/cd59fffe81f6b07b4519540b8ff3d95f.svg
paper_authors.fullname: Ji-Rong Wen
paper_authors.type: user
paper_authors.user: jrwen
paper_title: Progressive Multimodal Reasoning via Active Retrieval
paper_summary: Multi-step multimodal reasoning tasks pose significant challenges for
  multimodal large language models (MLLMs), and finding effective ways to enhance
  their performance in such scenarios remains an unresolved issue. In this paper,
  we propose AR-MCTS, a universal framework designed to progressively improve the
  reasoning capabilities of MLLMs through Active Retrieval (AR) and Monte Carlo
  Tree Search (MCTS). Our approach begins with the development of a unified
  retrieval module that retrieves key supporting insights for solving complex
  reasoning problems from a hybrid-modal retrieval corpus. To bridge the gap in
  automated multimodal reasoning verification, we employ the MCTS algorithm
  combined with an active retrieval mechanism, which enables the automatic
  generation of step-wise annotations. This strategy dynamically retrieves key
  insights for each reasoning step, moving beyond traditional beam search
  sampling to improve the diversity and reliability of the reasoning space.
  Additionally, we introduce a process reward model that aligns progressively to
  support the automatic verification of multimodal reasoning tasks. Experimental
  results across three complex multimodal reasoning benchmarks confirm the
  effectiveness of the AR-MCTS framework in enhancing the performance of various
  multimodal models. Further analysis demonstrates that AR-MCTS can optimize
  sampling diversity and accuracy, yielding reliable multimodal reasoning.
paper_ai_keywords: multimodal large language models (MLLMs) AR-MCTS (Active Retrieval Monte Carlo Tree Search) unified retrieval module hybrid-modal retrieval corpus Monte Carlo Tree Search (MCTS) Active Retrieval (AR) beam search sampling process reward model multimodal reasoning benchmarks