Investigating Inference-time Scaling for Chain of Multi-modal Thought: A Preliminary Study (2025.findings-acl)
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| Challenge: | Inference-time scaling of chain-of-thought (CoT) has been demonstrated as a promising approach for addressing multi-modal reasoning tasks. |
| Approach: | They propose to integrate visual and textual modalities within the reasoning process . they adopt a consistency-enhanced verifier to ensure effective guidance for both methods across different thought paradigms. |
| Outcome: | The proposed method outperforms text-only reasoning on 10 tasks spanning diverse domains and requires higher token consumption for processing richer visual inputs. |
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| Challenge: | In inference-time scaling, Chain-of-Thought (CoT) data is scarce or even unavailable. |
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Beyond Chain-of-Thought: A Survey of Chain-of-X Paradigms for LLMs (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) have shown impressive reasoning abilities when prompted with Chain-of-Thought (CoT). |
| Approach: | They propose to categorize Chain-of-X methods by taxonomies of nodes, i.e., the X in CoX, and application tasks, and then categorise them by taxanomies and discuss potential future directions. |
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Look Light, Think Heavy: What Multimodal Chain-of-Thought Reasoning Can and Cannot Do (2026.acl-long)
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| Challenge: | Existing open-source models often yield only marginal overall improvements, possibly due to an overemphasis on mathematical reasoning at the expense of broader capabilities. |
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Explainable Chain-of-Thought Reasoning: An Empirical Analysis on State-Aware Reasoning Dynamics (2025.findings-emnlp)
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Sheldon Yu, Yuxin Xiong, Junda Wu, Xintong Li, Tong Yu, Xiang Chen, Ritwik Sinha, Jingbo Shang, Julian McAuley
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Chengshuai Zhao, Zhen Tan, Pingchuan Ma, Dawei Li, Bohan Jiang, Yancheng Wang, Yingzhen Yang, Huan Liu
| Challenge: | Chain-of-Thought (CoT) prompting has been shown to be effective in eliciting structured reasoning from large language models (LLMs). |
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Recursion of Thought: A Divide-and-Conquer Approach to Multi-Context Reasoning with Language Models (2023.findings-acl)
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| Challenge: | Existing methods to generate intermediate steps (CoT) are limited by the maximum context size due to various reasons. |
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Test-Time Scaling in Multimodal Foundation Models: A Comprehensive Survey of Generation and Reasoning (2026.findings-acl)
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| Challenge: | Recent advances have adapted this paradigm to Multimodal Foundation Models (MFMs), unlocking their potential in multimodal reasoning and generation. |
| Approach: | They propose a taxonomy framework that categorizes existing methodologies into three distinct strategies: sampling-based, feedback-based and search-based approaches. |
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Decoupling the Effect of Chain-of-Thought Reasoning: A Human Label Variation Perspective (2026.findings-acl)
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| Challenge: | Reasoning-tuned large language models (LLMs) with long Chain-of-Thought excel at single-answer tasks, yet their ability to model Human Label Variation remains underexplored. |
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From Long to Lean: Performance-aware and Adaptive Chain-of-Thought Compression via Multi-round Refinement (2025.emnlp-main)
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| Challenge: | Chain-of-Thought reasoning introduces significant inference latency due to its verbosity. |
| Approach: | They propose a framework that leverages token elasticity phenomenon to progressively compress CoTs via multiround refinement. |
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SpecCoT: Accelerating Chain-of-Thought Reasoning through Speculative Exploration (2025.findings-emnlp)
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| Challenge: | Large Reasoning Models suffer from high inference latency due to lengthy reasoning chains. |
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