M³CoT: A Novel Benchmark for Multi-Domain Multi-step Multi-modal Chain-of-Thought
Qiguang Chen, Libo Qin, Jin Zhang, Zhi Chen, Xiao Xu, Wanxiang Che
Abstract
Multi-modal Chain-of-Thought (MCoT) requires models to leverage knowledge from both textual and visual modalities for step-bystep reasoning, which gains increasing attention. Nevertheless, the current MCoT benchmark still faces some challenges: (1) absence of visual modal reasoning, (2) single-step visual modal reasoning, and (3) Domain missing, thereby hindering the development of MCoT. Motivated by this, we introduce a novel benchmark (M 3 CoT) to address the above challenges, advancing the multi-domain, multi-step, and multi-modal CoT. Additionally, we conduct a thorough evaluation involving abundant MCoT approaches on Vision Large Language Models (VLLMs). In addition, we highlight that the current VLLMs still struggle to correctly reason in M 3 CoT and there remains a large gap between existing VLLMs and human performance in M 3 CoT, despite their superior results on previous MCoT benchmarks. To our knowledge, we take the first meaningful step toward the multi-domain, multi-step, and multi-modal scenario in MCoT. We hope that M 3 CoT can serve as a valuable resource, providing a pioneering foundation in multi-domain, multi-step, multi-modal chain-of-thought research. * Corresponding Author Q : … supports the plant … Which part do we usually eat? A: (B) the stem O: … (B) Only to indicate the time A: (B) soft A: (C) To indicate … R: … The feather is soft… (b) Single-step visual modal reasoning. (c) Multi-step visual modal reasoning. Q: Which property matches this object? O: … (B) soft O: …(B) stem R: Step 1: The wind vane on top … indicate the wind direction. Step 2: …. The clock on top …it is used to indicate the time. VLLM VLLM VLLM (a) Absence of visual modal reasoning. R: … we usually eat is the stem. It supports the plant … Single Step Missing Multi-Step 1 Multi-Step 2 Q : What is the purpose of the tower? (C) To indicate the time and wind direction…
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- Unlocking the Capabilities of Thought: A Reasoning Boundary Framework to Quantify and Optimize Chain-of-ThoughtQiguang Chen, Libo Qin, Jiaqi Wang, Jingxuan Zhou et al.NeurIPS 2024 · 104 citations
- Visual Planning: Let's Think Only with ImagesYi Xu, Chengzu Li, Han Zhou, Xingchen Wan et al.ICLR 2026 · 93 citations
- Visual Thoughts: A Unified Perspective of Understanding Multimodal Chain-of-ThoughtZihui Cheng, Qiguang Chen, Xiao Xu, Jiaqi Wang et al.NeurIPS 2025 · 38 citations
- What Factors Affect Multi-Modal In-Context Learning? An In-Depth ExplorationLibo Qin, Qiguang Chen, Hao Fei, Zhi Chen et al.NeurIPS 2024 · 37 citations
- From Narrow to Panoramic Vision: Attention-Guided Cold-Start Reshapes Multimodal ReasoningRuilin Luo, Chufan Shi, Yizhen Zhang, Cheng Yang et al.ICLR 2026 · 10 citations
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- Learning Transferable Visual Models From Natural Language SupervisionAlec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh et al.ICML 2021 · 47,906 citations
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- LoRA: Low-Rank Adaptation of Large Language ModelsEdward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu et al.ICLR 2022 · 18,833 citations
- Large Language Models are Zero-Shot ReasonersTakeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo et al.NeurIPS 2022 · 8,168 citations
- InstructBLIP: Towards General-purpose Vision-Language Models with Instruction TuningWenliang Dai, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong et al.NeurIPS 2023 · 4,013 citations
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