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Research papers on Reasoning in large language models

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  4. Towards reasoning era: a survey of long chain-of-thought for reasoning large language models

    Qiguang Chen, Libo Qin, Jinhao Liu, et al. · 2026 · Science China Information Sciences · 33 citations

    Abstract Recent advancements in reasoning with large language models (RLLMs), such as OpenAI-o1 and DeepSeek-R1, have demonstrated their impressive capabilities in complex domains like mathematics and coding. A central factor in their success lies in the application of long chain-of-thought (Long CoT) characteristics, which enhance reasoning abilities and enable the solution of intricate problems. However, despite these developments, a comprehensive survey on Long CoT is still lacking, limiting our understanding of its distinctions from traditional short chain-of-thought (Short CoT) and complicating ongoing debates on issues like “overthinking” and “inference-time scaling”. This survey seeks

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  5. ChatCoT: Tool-Augmented Chain-of-Thought Reasoning on Chat-based Large Language Models

    Zhipeng Chen, Kun Zhou, Beichen Zhang, et al. · 2023 · 30 citations

    Although large language models (LLMs) have achieved excellent performance in a variety of evaluation benchmarks, they still struggle in complex reasoning tasks which require specific knowledge and multi-hop reasoning.To improve the reasoning abilities, we propose Chat-CoT, a tool-augmented chain-of-thought reasoning framework for chat-based LLMs (e.g., ChatGPT).In ChatCoT, we model the chainof-thought (CoT) reasoning as multi-turn conversations, to utilize tools in a more natural way through chatting.At each turn, LLMs can either interact with tools or perform the reasoning.Our approach can effectively leverage the multi-turn conversation ability of chatbased LLMs, and integrate the thought

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  6. Enhancing Zero-Shot Chain-of-Thought Reasoning in Large Language Models through Logic

    Xufeng Zhao, Mengdi Li, Wenhao Lu, et al. · 2024 · Language Resources and Evaluation Conference (LREC) · 26 citations

    Recent advancements in large language models have showcased their remarkable generalizability across various domains. However, their reasoning abilities still have significant room for improvement, especially when confronted with scenarios requiring multi-step reasoning. Although large language models possess extensive knowledge, their reasoning often fails to effectively utilize this knowledge to establish a coherent thinking paradigm. These models sometimes show hallucinations as their reasoning procedures are unconstrained by logical principles. Aiming at improving the zero-shot chain-of-thought reasoning ability of large language models, we propose LoT (Logical Thoughts), a self-improvem

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  7. Aligning Large and Small Language Models via Chain-of-Thought Reasoning

    Leonardo Ranaldi, André Freitas · 2024 · Conference of the European Chapter of the Association for Computational Linguistics · 20 citations

    Chain-of-Thought (CoT) prompting empowers the reasoning abilities of Large Language Models (LLMs), eliciting them to solve complex reasoning tasks in a step-wise manner.However, these abilities appear only in models with billions of parameters, which represent an entry barrier for many users who are constrained to operate on a smaller model scale, i.e., Small Language Models (SLMs).Although many companies are releasing LLMs of the same family with fewer parameters, these models tend not to preserve all the reasoning capabilities of the original models, including CoT reasoning.In this paper, we propose a method for aligning and transferring reasoning abilities between larger to smaller Langua

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  8. Enhancing Chain of Thought Prompting in Large Language Models via Reasoning Patterns

    Yufeng Zhang, Xuepeng Wang, Lingxiang Wu, et al. · 2025 · Proceedings of the AAAI Conference on Artificial Intelligence · 17 citations

    Chain of Thought (CoT) prompting can encourage language models to engage in multi-step logical reasoning. The quality of the provided demonstrations significantly influences the success of downstream inference tasks. Current unsupervised CoT methods primarily select examples based on the semantics of the questions, which can introduce noise and lack interpretability. In this paper, we propose leveraging reasoning patterns to enhance CoT prompting effectiveness. Reasoning patterns represent the process by which language models arrive at their final results. By utilizing prior knowledge and prompt-based methods from large models, we first construct task-specific pattern sets. We then select di

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  9. Interactive Reasoning: Visualizing and Controlling Chain-of-Thought Reasoning in Large Language Models

    Rock Yuren Pang, K. J. Kevin Feng, Shangbin Feng, et al. · 2026 · ACM International Conference on Intelligent User Interfaces (IUI) · 3 citations

    The output quality of large language models (LLMs) can be improved via “reasoning”: generating segments of chain-of-thought (CoT) content to further condition the model prior to producing user-facing output. While these chains contain valuable information, they are verbose and lack explicit organization, making them tedious to review. Moreover, they lack opportunities for user feedback, such as removing unwanted considerations, adding desired ones, or clarifying unclear assumptions. We introduce Interactive Reasoning, an interaction design that visualizes chain-of-thought outputs as a hierarchy of topics and enables user review and modification. We implement interactive reasoning in Hippo, a

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  10. On the Hardness of Faithful Chain-of-Thought Reasoning in Large Language Models

    Sree Harsha Tanneru, Dan Ley, Chirag Agarwal, et al. · 2024 · arXiv (Cornell University) · 1 citations

    As Large Language Models (LLMs) are increasingly being employed in real-world applications in critical domains such as healthcare, it is important to ensure that the Chain-of-Thought (CoT) reasoning generated by these models faithfully captures their underlying behavior. While LLMs are known to generate CoT reasoning that is appealing to humans, prior studies have shown that these explanations do not accurately reflect the actual behavior of the underlying LLMs. In this work, we explore the promise of three broad approaches commonly employed to steer the behavior of LLMs to enhance the faithfulness of the CoT reasoning generated by LLMs: in-context learning, fine-tuning, and activation editi

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  11. Optimizing Chain-of-Thought Reasoning in Large Language Models for Complex Reasoning Tasks

    Amelia Moreno, Julian Harper, Samuel Bennett · 2026 · Global Media and Social Sciences Research Journal

    The advent of large language models has fundamentally transformed the landscape of natural language processing, bringing unprecedented capabilities in text generation, summarization, and translation. However, these models often struggle with complex multi-step reasoning tasks, leading to the development of chain-of-thought prompting strategies. While chain-of-thought approaches significantly improve performance by eliciting intermediate reasoning steps, they are frequently constrained by inefficiencies such as error accumulation, high computational latency, and the generation of hallucinated reasoning paths. This paper presents a comprehensive framework for optimizing chain-of-thought reason

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  12. Scaling Laws of Chain-of-Thought Reasoning in Large Language Models

    Assignee Research · 2026 · Zenodo (CERN European Organization for Nuclear Research)

    This report synthesises findings from 18 peer-reviewed papers addressing the following research question: What are the scaling laws for chain-of-thought reasoning in large language models v6. 7 claims were extracted from source literature; 7 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 7.6/10. This report is a machine-generated literature synthesis and does not constitute original research. Research goal: What are the scaling laws for chain-of-thought reasoning in large language models v6 Autonomous literature synthesis. Automated review score: 7.6/10. Full text and citation available at Assignee Research.

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