Can Atomic Step Decomposition Enhance the Self-structured Reasoning of Multimodal Large Models?

• URL: http://arxiv.org/abs/2503.06252v1
• Date: Sat, 08 Mar 2025 15:23:47 GMT
• Title: Can Atomic Step Decomposition Enhance the Self-structured Reasoning of Multimodal Large Models?
• Authors: Kun Xiang, Zhili Liu, Zihao Jiang, Yunshuang Nie, Kaixin Cai, Yiyang Yin, Runhui Huang, Haoxiang Fan, Hanhui Li, Weiran Huang, Yihan Zeng, Yu-Jie Yuan, Jianhua Han, Lanqing Hong, Hang Xu, Xiaodan Liang,
• Abstract summary: We propose a paradigm of Self-structured Chain of Thought (SCoT), which is composed of minimal semantic atomic steps.<n>Our method can not only generate cognitive CoT structures for various complex tasks but also mitigates the phenomenon of overthinking.<n>We conduct extensive experiments to show that the proposed AtomThink significantly improves the performance of baseline MLLMs.
• Score: 68.72260770171212
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we address the challenging task of multimodal mathematical reasoning by incorporating the ability of "slow thinking" into multimodal large language models (MLLMs). Our core idea is that different levels of reasoning abilities can be combined dynamically to tackle questions with different complexity. To this end, we propose a paradigm of Self-structured Chain of Thought (SCoT), which is composed of minimal semantic atomic steps. Different from existing methods that rely on structured templates or free-form paradigms, our method can not only generate cognitive CoT structures for various complex tasks but also mitigates the phenomenon of overthinking. To introduce structured reasoning capabilities into visual understanding models, we further design a novel AtomThink framework with four key modules, including (i) a data engine to generate high-quality multimodal reasoning paths; (ii) a supervised fine-tuning process with serialized inference data; (iii) a policy-guided multi-turn inference method; and (iv) an atomic capability metric to evaluate the single step utilization rate. We conduct extensive experiments to show that the proposed AtomThink significantly improves the performance of baseline MLLMs, achieving more than 10\% average accuracy gains on MathVista and MathVerse. Compared to state-of-the-art structured CoT approaches, our method not only achieves higher accuracy but also improves data utilization by 5 times and boosts inference efficiency by 85.3\%. Our code is now public available in https://github.com/Quinn777/AtomThink.

Related papers

• Boosting Multimodal Reasoning with MCTS-Automated Structured Thinking [24.416534698362643]
  Multimodal large language models (MLLMs) exhibit impressive capabilities but still face challenges in complex visual reasoning. 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.
  arXiv  Detail & Related papers  (2025-02-04T14:18:29Z)
• BRiTE: Bootstrapping Reinforced Thinking Process to Enhance Language Model Reasoning [78.63421517563056]
  Large Language Models (LLMs) have demonstrated remarkable capabilities in complex reasoning tasks.<n>We present a unified probabilistic framework that formalizes LLM reasoning through a novel graphical model.<n>We introduce the Bootstrapping Reinforced Thinking Process (BRiTE) algorithm, which works in two steps.
  arXiv  Detail & Related papers  (2025-01-31T02:39:07Z)
• Decompose-ToM: Enhancing Theory of Mind Reasoning in Large Language Models through Simulation and Task Decomposition [2.089191490381739]
  Theory of Mind (ToM) is the ability to understand and reflect on the mental states of others.<n>Large Language Models (LLMs) possess only a rudimentary understanding of ToM.<n>We propose Decompose-ToM'': an LLM-based inference algorithm that improves model performance on complex ToM tasks.
  arXiv  Detail & Related papers  (2025-01-15T18:44:01Z)
• TACO: Learning Multi-modal Action Models with Synthetic Chains-of-Thought-and-Action [103.5952731807559]
  We present TACO, a family of multi-modal large action models designed to improve performance on complex, multi-step, and multi-modal tasks. During inference, TACO produces chains-of-thought-and-action (CoTA), executes intermediate steps by invoking external tools such as OCR, depth estimation and calculator. This dataset enables TACO to learn complex reasoning and action paths, surpassing existing models trained on instruction tuning data with only direct answers.
  arXiv  Detail & Related papers  (2024-12-07T00:42:04Z)
• MAmmoTH-VL: Eliciting Multimodal Reasoning with Instruction Tuning at Scale [66.73529246309033]
  multimodal large language models (MLLMs) have shown significant potential in a broad range of multimodal tasks. Existing instruction-tuning datasets only provide phrase-level answers without any intermediate rationales. We introduce a scalable and cost-effective method to construct a large-scale multimodal instruction-tuning dataset with rich intermediate rationales.
  arXiv  Detail & Related papers  (2024-12-06T18:14:24Z)
• AtomThink: A Slow Thinking Framework for Multimodal Mathematical Reasoning [70.95645743670062]
  AtomThink is a framework for constructing long chains of thought (CoT) in a step-by-step manner, guiding MLLMs to perform complex reasoning.<n>AtomMATH is a large-scale multimodal dataset of long CoTs, and an atomic capability evaluation metric for mathematical tasks.<n>AtomThink significantly improves the performance of baseline MLLMs, achieving approximately 50% relative accuracy gains on MathVista and 120% on MathVerse.
  arXiv  Detail & Related papers  (2024-11-18T11:54:58Z)
• Guiding Language Model Reasoning with Planning Tokens [122.43639723387516]
  Large language models (LLMs) have recently attracted considerable interest for their ability to perform complex reasoning tasks. We propose a hierarchical generation scheme to encourage a more structural generation of chain-of-thought steps. Our approach requires a negligible increase in trainable parameters (0.001%) and can be applied through either full fine-tuning or a more parameter-efficient scheme.
  arXiv  Detail & Related papers  (2023-10-09T13:29:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.