Computer Science · Literature

Research papers on Reasoning in large language models

Recent and highly-cited academic work on reasoning in large language models, gathered from Semantic Scholar, CrossRef and OpenAlex.

1. LLaMA: Open and Efficient Foundation Language Models

   Hugo Touvron, Thibaut Lavril, Gautier Izacard, et al. · 2023 · arXiv (Cornell University) · 3,926 citations

   We introduce LLaMA, a collection of foundation language models ranging from 7B to 65B parameters. We train our models on trillions of tokens, and show that it is possible to train state-of-the-art models using publicly available datasets exclusively, without resorting to proprietary and inaccessible datasets. In particular, LLaMA-13B outperforms GPT-3 (175B) on most benchmarks, and LLaMA-65B is competitive with the best models, Chinchilla-70B and PaLM-540B. We release all our models to the research community.

2. Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models

   Tiffany H. Kung, Morgan Cheatham, Arielle Medenilla, et al. · 2023 · PLOS Digital Health · 3,638 citations

   We evaluated the performance of a large language model called ChatGPT on the United States Medical Licensing Exam (USMLE), which consists of three exams: Step 1, Step 2CK, and Step 3. ChatGPT performed at or near the passing threshold for all three exams without any specialized training or reinforcement. Additionally, ChatGPT demonstrated a high level of concordance and insight in its explanations. These results suggest that large language models may have the potential to assist with medical education, and potentially, clinical decision-making.

3. Large language models encode clinical knowledge

   Karan Singhal, Shekoofeh Azizi, Tao Tu, et al. · 2023 · Nature · 3,347 citations

   Abstract Large language models (LLMs) have demonstrated impressive capabilities, but the bar for clinical applications is high. Attempts to assess the clinical knowledge of models typically rely on automated evaluations based on limited benchmarks. Here, to address these limitations, we present MultiMedQA, a benchmark combining six existing medical question answering datasets spanning professional medicine, research and consumer queries and a new dataset of medical questions searched online, HealthSearchQA. We propose a human evaluation framework for model answers along multiple axes including factuality, comprehension, reasoning, possible harm and bias. In addition, we evaluate Pathways Lan

4. Evaluating Large Language Models Trained on Code

   Mark Chen, Jerry Tworek, Heewoo Jun, et al. · 2021 · arXiv (Cornell University) · 1,449 citations

   I created a basic proof of concept of LMVM (Language Model Virtual Machine), a command line toolchain that English instructions into native binaries without intermediary high-level language compilation. The command line uses a large language model (LLM) as IR generation backend, producing LLVM Intermediate Representation (IR) directly from user instructions coding. The IR is compiled and linked to machine code (-o files) via `clang` (with an `llc`/`lld` fallback on POSIX hosts) and executed on bare metal, within Docker sandboxes, on AWS Lambda, or as standalone HTTP servers. How It Works (5 Steps) 1. You type an instruction - e.g. "Create a rest api running on localhost port 3000 and return

5. A Survey of Large Language Models

   Wayne Xin Zhao, Kun Zhou, Junyi Li, et al. · 2026 · Frontiers of Computer Science · 1,420 citations

   Abstract The rapid evolution of large language models (LLMs) has driven a transformative shift in artificial intelligence (AI), reshaping both research paradigms and practical applications. Distinguished from their predecessors by unprecedented scale and advanced capabilities, LLMs necessitate new frameworks for understanding their development, behavior, and societal impact. This survey systematically reviews recent advancements in LLM techniques across four key dimensions: (1) pre-training methodologies, which establish core model capabilities through large-scale self-supervised training, architectural innovations, and data curation strategies; (2) post-training techniques, including superv

6. Large Language Models are Zero-Shot Reasoners

   Takeshi Kojima, Shixiang Gu, Machel Reid, et al. · 2022 · arXiv (Cornell University) · 1,112 citations

   Pretrained large language models (LLMs) are widely used in many sub-fields of natural language processing (NLP) and generally known as excellent few-shot learners with task-specific exemplars. Notably, chain of thought (CoT) prompting, a recent technique for eliciting complex multi-step reasoning through step-by-step answer examples, achieved the state-of-the-art performances in arithmetics and symbolic reasoning, difficult system-2 tasks that do not follow the standard scaling laws for LLMs. While these successes are often attributed to LLMs' ability for few-shot learning, we show that LLMs are decent zero-shot reasoners by simply adding "Let's think step by step" before each answer. Experi

7. Chain-Of-Thought Prompting Elicits Reasoning in Large Language Models

   Maarten Bosma, Ed Chi, Brian Ichter, et al. · 2022 · 994 citations

8. Self-Consistency Improves Chain of Thought Reasoning in Language Models

   Xuezhi Wang, Wei, Jason, Dale Schuurmans, et al. · 2022 · arXiv (Cornell University) · 704 citations

   Chain-of-thought prompting combined with pre-trained large language models has achieved encouraging results on complex reasoning tasks. In this paper, we propose a new decoding strategy, self-consistency, to replace the naive greedy decoding used in chain-of-thought prompting. It first samples a diverse set of reasoning paths instead of only taking the greedy one, and then selects the most consistent answer by marginalizing out the sampled reasoning paths. Self-consistency leverages the intuition that a complex reasoning problem typically admits multiple different ways of thinking leading to its unique correct answer. Our extensive empirical evaluation shows that self-consistency boosts the

9. A survey on multimodal large language models

   Shukang Yin, Chaoyou Fu, Sirui Zhao, et al. · 2024 · National Science Review · 602 citations

   Recently, the multimodal large language model (MLLM) represented by GPT-4V has been a new rising research hotspot, which uses powerful large language models (LLMs) as a brain to perform multimodal tasks. The surprising emergent capabilities of the MLLM, such as writing stories based on images and optical character recognition-free math reasoning, are rare in traditional multimodal methods, suggesting a potential path to artificial general intelligence. To this end, both academia and industry have endeavored to develop MLLMs that can compete with or even outperform GPT-4V, pushing the limit of research at a surprising speed. In this paper, we aim to trace and summarize the recent progress of

10. Graph of Thoughts: Solving Elaborate Problems with Large Language Models

    Maciej Besta, Nils Blach, Ales Kubicek, et al. · 2024 · Proceedings of the AAAI Conference on Artificial Intelligence · 426 citations

    We introduce Graph of Thoughts (GoT): a framework that advances prompting capabilities in large language models (LLMs) beyond those offered by paradigms such as Chain-of-Thought or Tree of Thoughts (ToT). The key idea and primary advantage of GoT is the ability to model the information generated by an LLM as an arbitrary graph, where units of information ("LLM thoughts") are vertices, and edges correspond to dependencies between these vertices. This approach enables combining arbitrary LLM thoughts into synergistic outcomes, distilling the essence of whole networks of thoughts, or enhancing thoughts using feedback loops. We illustrate that GoT offers advantages over state of the art on diffe

11. Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models

    Lei Wang, Wanyu Xu, Yihuai Lan, et al. · 2023 · 277 citations

    Lei Wang, Wanyu Xu, Yihuai Lan, Zhiqiang Hu, Yunshi Lan, Roy Ka-Wei Lee, Ee-Peng Lim. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.

12. Automatic Chain of Thought Prompting in Large Language Models

    Zhuosheng Zhang, Aston Zhang, Mu Li, et al. · 2022 · arXiv (Cornell University) · 235 citations

    Large language models (LLMs) can perform complex reasoning by generating intermediate reasoning steps. Providing these steps for prompting demonstrations is called chain-of-thought (CoT) prompting. CoT prompting has two major paradigms. One leverages a simple prompt like "Let's think step by step" to facilitate step-by-step thinking before answering a question. The other uses a few manual demonstrations one by one, each composed of a question and a reasoning chain that leads to an answer. The superior performance of the second paradigm hinges on the hand-crafting of task-specific demonstrations one by one. We show that such manual efforts may be eliminated by leveraging LLMs with the "Let's

13. 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 · 11 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

14. 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 · 9 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”.

15. Length Instruction Fine-Tuning with Chain-of-Thought (LIFT-COT): Enhancing Length Control and Reasoning in Edge-Deployed Large Language Models

    Pinzhe Chen, Zhen Li · 2025 · Electronics · 4 citations

    This paper investigates the effectiveness of Chain-of-Thought (COT) reasoning in addressing length bias when deploying Large Language Models (LLMs) on the device side within wireless network environments. By conducting systematic experiments on two representative datasets, we perform a comparative evaluation of LLM performance under standard prompting versus COT-augmented prompting, using two benchmark tests. The study emphasizes how the COT strategy regulates response length and improves compliance with predefined maximum length (max_len) constraints. Experimental results demonstrate that COT reasoning significantly mitigates length bias, enhances output length control, and improves overall

16. MS-CoTF: Multi-scale chain-of-thought fusion for interpretable biological reasoning with large language models

    Zeyuan Song, Xiao-Cong Zhen · 2026 · Computers in Biology and Medicine · 1 citations

17. BadThink: Triggered Overthinking Attacks on Chain-of-Thought Reasoning in Large Language Models

    Shuaitong Liu, Renjue Li, Lijia Yu, et al. · 2026 · Proceedings of the AAAI Conference on Artificial Intelligence · 1 citations

    Recent advances in Chain-of-Thought (CoT) prompting have substantially improved the reasoning capabilities of large language models (LLMs), but have also introduced their computational efficiency as a new attack surface. In this paper, we propose BadThink, the first backdoor attack designed to deliberately induce "overthinking" behavior in CoT-enabled LLMs while ensuring stealth. When activated by carefully crafted trigger prompts, BadThink manipulates the model to generate inflated reasoning traces—producing unnecessarily redundant thought processes while preserving the consistency of final outputs. This subtle attack vector creates a covert form of performance degradation that significantl

18. Enhancing Logical Reasoning of Large Language Models through Chain-of-Thought and Logical Fallacy

    Fucheng Wan, Bin Liu, Xinpeng Lei, et al. · 2025 · Data Intelligence

19. 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

20. Methods for Enhancing the Stability and Robustness of Chain-of-Thought Reasoning in Large Language Models

    Zhen Xu · 2026 · Journal of Computer Technology and Electronic Research

    Chain-of-Thought reasoning in large language models enhances the ability to solve complex tasks by generating intermediate steps; however, its stability and robustness face significant challenges. The reasoning process is susceptible to internal randomness, knowledge ambiguity, and error propagation, while existing architectures lack effective mechanisms for process constraint and regulation. This paper systematically analyzes the inherent vulnerabilities of such reasoning and proposes stability optimization methods, including structured iterative refinement, multi-granularity consistency constraints, and dynamic uncertainty regulation. It constructs a robustness enhancement framework encomp

21. Feature-Assisted Thought Chain Generation and Adaptive Feature Enhancement for Logical Reasoning in Large Language Models

    Chenglin Jiang, Qidong Yan, Yingjie Li, et al. · 2026 · Data Intelligence

22. An Empirical Investigation of Adaptive Cognitive Guidance Strategy Optimization Driven by Hierarchical Chain‐of‐Thought Reasoning in Large Language Models

    Jiaxin Sun · 2026 · Engineering Reports

    ABSTRACT As large‐scale language models (LLMs) gain traction in education, the core challenge for intelligent tutoring has shifted from static knowledge transfer to dynamic cognitive companionship. This study proposes a novel adaptive cognitive tutoring strategy driven by a hierarchical chain‐of‐reasoning. The strategy constructs a three‐layer reasoning framework—strategic, tactical, and detailed—allowing complex problems to be decomposed into manageable cognitive units so learners can progressively deepen their understanding and problem‐solving. This study also designs a cognitive‐state diagnosis module that extracts and quantifies learners' multi‐dimensional features in r

23. COTVD: A function-level vulnerability detection framework using chain-of-thought reasoning with large language models

    Yinan Chen, Xiangping Chen, Yuan Huang, et al. · 2026 · Information and Software Technology

24. Enhancing the Mathematical Reasoning Ability of Small Language Models through Thought Chain Distillation

    Xiangyu Shu · 2026 · Mathematical Modeling and Algorithm Application

    Large language models (LLMs) have demonstrated strong capabilities in reasoning tasks through the Chain of Thought (CoT) prompting technology, but their large scale makes it difficult to deploy them in resource-constrained environments. This paper explores the transfer of the reasoning capabilities of large models to small models through CoT Distillation to enhance the complex reasoning performance of the small models. The study uses the OpenR1-Math-220k dataset generated by DeepSeek R1 as the "teacher reasoning process" source, and employs the QLoRA parameter-efficient fine-tuning technique to train on the Qwen3-8B small model. Experimental results show that the fine-tuned model achieves an