title: Boosting Multimodal Reasoning with MCTS-Automated Structured Thinking
summary: Multimodal large language models (MLLMs) exhibit impressive capabilities but
  still face challenges in complex visual reasoning. While recent efforts attempt
  to enhance MLLMs' reasoning by incorporating OpenAI o1-like structured thinking
  through explicit search structures or teacher-guided distillation, they often
  struggle to balance performance and efficiency. A critical limitation is their
  heavy reliance on extensive data and search spaces, resulting in low-efficiency
  implicit insight extraction and data utilization. To address this, we propose
  AStar, an Automated Structured thinking paradigm for multimodal reasoning via
  Monte Carlo Tree Search (MCTS). AStar automatically derives high-level
  cognitive reasoning patterns from limited data using MCTS-powered hierarchical
  structures. Building on these explicit patterns, we design a unified reasoning
  framework that seamlessly integrates models' internal reasoning capabilities
  and external reasoning guidelines, enabling efficient inference with minimal
  tree iterations. This novel paradigm strikes a compelling balance between
  performance and efficiency. Extensive experiments demonstrate AStar's
  effectiveness, achieving superior accuracy (54.0%) on the MathVerse
  benchmark with a 7B backbone, surpassing GPT-4o (50.2%) while maintaining
  substantial data and computational efficiency.
paper_authors._id: 67a3262873bdaf626f1e9eab
paper_authors.name: Jinyang Wu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-06T14:15:16.426Z
paper_authors._id: 6747de57f8cab58c22ec94a2
paper_authors.avatarUrl: /avatars/5bae0341862fac24564781c0fa32aac5.svg
paper_authors.fullname: Jinyang Wu
paper_authors.type: user
paper_authors.user: Jinyang23
paper_authors._id: 67a3262873bdaf626f1e9eac
paper_authors.name: Mingkuan Feng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-06T14:37:21.287Z
paper_authors._id: 660d13b85e00095e45ee28e0
paper_authors.avatarUrl: /avatars/8f06c01edc2a791266feadc775acb901.svg
paper_authors.fullname: FengMingkuan
paper_authors.type: user
paper_authors.user: fmk345
paper_authors._id: 67a3262873bdaf626f1e9ead
paper_authors.name: Shuai Zhang
paper_authors._id: 67a3262873bdaf626f1e9eae
paper_authors.name: Ruihan Jin
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-06T14:37:48.323Z
paper_authors._id: 64be16d8ef8c0e42bf3d27f6
paper_authors.avatarUrl: /avatars/6ae308088f6f196d9f470655dae0c14d.svg
paper_authors.fullname: Ruihan Jin
paper_authors.type: user
paper_authors.user: RuihanJin
paper_authors._id: 67a3262873bdaf626f1e9eaf
paper_authors.name: Feihu Che
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-06T14:37:55.510Z
paper_authors._id: 63ef2de81e695b35aa4813a2
paper_authors.avatarUrl: /avatars/6abd1918c1b94d927c7c976054e16322.svg
paper_authors.fullname: feihu
paper_authors.type: user
paper_authors.user: feihuchen
paper_authors._id: 67a3262873bdaf626f1e9eb0
paper_authors.name: Zengqi Wen
paper_authors._id: 67a3262873bdaf626f1e9eb1
paper_authors.name: Jianhua Tao
paper_title: Boosting Multimodal Reasoning with MCTS-Automated Structured Thinking
paper_summary: Multimodal large language models (MLLMs) exhibit impressive capabilities but
  still face challenges in complex visual reasoning. While recent efforts attempt
  to enhance MLLMs' reasoning by incorporating OpenAI o1-like structured thinking
  through explicit search structures or teacher-guided distillation, they often
  struggle to balance performance and efficiency. A critical limitation is their
  heavy reliance on extensive data and search spaces, resulting in low-efficiency
  implicit insight extraction and data utilization. To address this, we propose
  AStar, an Automated Structured thinking paradigm for multimodal reasoning via
  Monte Carlo Tree Search (MCTS). AStar automatically derives high-level
  cognitive reasoning patterns from limited data using MCTS-powered hierarchical
  structures. Building on these explicit patterns, we design a unified reasoning
  framework that seamlessly integrates models' internal reasoning capabilities
  and external reasoning guidelines, enabling efficient inference with minimal
  tree iterations. This novel paradigm strikes a compelling balance between
  performance and efficiency. Extensive experiments demonstrate AStar's
  effectiveness, achieving superior accuracy (54.0%) on the MathVerse
  benchmark with a 7B backbone, surpassing GPT-4o (50.2%) while maintaining
  substantial data and computational efficiency.
paper_ai_summary: AStar leverages MCTS to enhance multimodal large language models' visual reasoning from limited data, achieving superior accuracy while maintaining efficiency.
paper_ai_keywords: Multimodal large language models OpenAI o1 structured thinking explicit search structures teacher-guided distillation Monte Carlo Tree Search MCTS hierarchical structures unified reasoning framework internal reasoning capabilities external reasoning guidelines MathVerse benchmark GPT-4o