title: MINT-CoT: Enabling Interleaved Visual Tokens in Mathematical
    Chain-of-Thought Reasoning
summary: Chain-of-Thought (CoT) has widely enhanced mathematical reasoning in Large
  Language Models (LLMs), but it still remains challenging for extending it to
  multimodal domains. Existing works either adopt a similar textual reasoning for
  image input, or seek to interleave visual signals into mathematical CoT.
  However, they face three key limitations for math problem-solving: reliance on
  coarse-grained box-shaped image regions, limited perception of vision encoders
  on math content, and dependence on external capabilities for visual
  modification. In this paper, we propose MINT-CoT, introducing Mathematical
  INterleaved Tokens for Chain-of-Thought visual reasoning. MINT-CoT adaptively
  interleaves relevant visual tokens into textual reasoning steps via an
  Interleave Token, which dynamically selects visual regions of any shapes within
  math figures. To empower this capability, we construct the MINT-CoT dataset,
  containing 54K mathematical problems aligning each reasoning step with visual
  regions at the token level, accompanied by a rigorous data generation pipeline.
  We further present a three-stage MINT-CoT training strategy, progressively
  combining text-only CoT SFT, interleaved CoT SFT, and interleaved CoT RL, which
  derives our MINT-CoT-7B model. Extensive experiments demonstrate the
  effectiveness of our method for effective visual interleaved reasoning in
  mathematical domains, where MINT-CoT-7B outperforms the baseline model by
  +34.08% on MathVista, +28.78% on GeoQA, and +23.2% on MMStar, respectively. Our
  code and data are available at https://github.com/xinyan-cxy/MINT-CoT
paper_authors._id: 684260765bfed1b94a9cc307
paper_authors.name: Xinyan Chen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-06T07:40:42.546Z
paper_authors._id: 647c7a4ed412b3b376572a00
paper_authors.avatarUrl: /avatars/9cc310fd3f9e3f211475816ed9b0cdaa.svg
paper_authors.fullname: Xinyan Chen
paper_authors.type: user
paper_authors.user: xy06
paper_authors._id: 684260765bfed1b94a9cc308
paper_authors.name: Renrui Zhang
paper_authors._id: 684260765bfed1b94a9cc309
paper_authors.name: Dongzhi Jiang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-06T07:40:40.308Z
paper_authors._id: 6349214f8146350b3a4c5cdf
paper_authors.avatarUrl: /avatars/cfd24caac9a87efb528d0f4c375932bc.svg
paper_authors.fullname: Dongzhi Jiang
paper_authors.type: user
paper_authors.user: CaraJ
paper_authors._id: 684260765bfed1b94a9cc30a
paper_authors.name: Aojun Zhou
paper_authors._id: 684260765bfed1b94a9cc30b
paper_authors.name: Shilin Yan
paper_authors._id: 684260765bfed1b94a9cc30c
paper_authors.name: Weifeng Lin
paper_authors._id: 684260765bfed1b94a9cc30d
paper_authors.name: Hongsheng Li
paper_title: MINT-CoT: Enabling Interleaved Visual Tokens in Mathematical
    Chain-of-Thought Reasoning
paper_summary: Chain-of-Thought (CoT) has widely enhanced mathematical reasoning in Large
  Language Models (LLMs), but it still remains challenging for extending it to
  multimodal domains. Existing works either adopt a similar textual reasoning for
  image input, or seek to interleave visual signals into mathematical CoT.
  However, they face three key limitations for math problem-solving: reliance on
  coarse-grained box-shaped image regions, limited perception of vision encoders
  on math content, and dependence on external capabilities for visual
  modification. In this paper, we propose MINT-CoT, introducing Mathematical
  INterleaved Tokens for Chain-of-Thought visual reasoning. MINT-CoT adaptively
  interleaves relevant visual tokens into textual reasoning steps via an
  Interleave Token, which dynamically selects visual regions of any shapes within
  math figures. To empower this capability, we construct the MINT-CoT dataset,
  containing 54K mathematical problems aligning each reasoning step with visual
  regions at the token level, accompanied by a rigorous data generation pipeline.
  We further present a three-stage MINT-CoT training strategy, progressively
  combining text-only CoT SFT, interleaved CoT SFT, and interleaved CoT RL, which
  derives our MINT-CoT-7B model. Extensive experiments demonstrate the
  effectiveness of our method for effective visual interleaved reasoning in
  mathematical domains, where MINT-CoT-7B outperforms the baseline model by
  +34.08% on MathVista, +28.78% on GeoQA, and +23.2% on MMStar, respectively. Our
  code and data are available at https://github.com/xinyan-cxy/MINT-CoT
paper_ai_summary: MINT-CoT enhances multimodal mathematical reasoning by interleaving visual tokens into textual chain-of-thought steps, enabling flexible visual perception and improved problem-solving.
paper_ai_keywords: Chain-of-Thought Large Language Models multimodal domains textual reasoning visual signals image input vision encoders math content visual modification Mathematical INterleaved Tokens Interleave Token visual regions token level MINT-CoT dataset text-only CoT SFT interleaved CoT SFT interleaved CoT RL MINT-CoT-7B MathVista GeoQA MMStar