Long List of LLM Reasoning Papers

Reasoning Research Papers

This page is a long list of paper citations in the area of LLM reasoning. For a more useful discussion and categorization of research papers, see:

• LLM Reasoning
• Multi-Step Inference-Based LLM Reasoning
• Chain-of-Thought optimization

Blog articles: You may also be interested in our recent blog articles related to reasoning:

• Reasoning inference optimization
• Reasoning is the New AI Middleware
• Reasoning Decoding Algorithms
• 500 LLM inference optimization techniques

Multi-Step Inference for Reasoning

A general list of multi-step reasoning or "test time compute" reasoning papers:

• Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L. Griffiths, Yuan Cao, Karthik Narasimhan, 3 Dec 2023 (v2), Tree of Thoughts: Deliberate Problem Solving with Large Language Models, https://arxiv.org/abs/2305.10601 Code: https://github.com/princeton-nlp/tree-of-thought-llm
• Zehui Chen, Kuikun Liu, Qiuchen Wang, Jiangning Liu, Wenwei Zhang, Kai Chen, Feng Zhao, 29 Jul 2024, MindSearch: Mimicking Human Minds Elicits Deep AI Searcher, https://arxiv.org/abs/2407.20183 Code: https://github.com/InternLM/MindSearch Project: https://mindsearch.netlify.app
• Bradley Brown, Jordan Juravsky, Ryan Ehrlich, Ronald Clark, Quoc V. Le, Christopher Ré, Azalia Mirhoseini, 31 Jul 2024, Large Language Monkeys: Scaling Inference Compute with Repeated Sampling, https://arxiv.org/abs/2407.21787 (Generating multiple answers by repeated inference queries, and then using a verifier to choose the best one, which is shown to greatly increase overall accuracy.)
• David Gu, July 18, 2024, Text Compression for Efficient Language Generation, Master’s Thesis, Distributed Computing Group, Computer Engineering and Networks Laboratory, ETH Zürich, https://pub.tik.ee.ethz.ch/students/2023-HS/MA-2023-19.pdf (Training and inference at the sentence level, including caching of embeddings per sentence, which also has the side-effect of compressing the input prompts and reducing computation analogously to token pruning.)
• Ignacio de Gregorio, Aug 2024, Grokking, a New Form of Reasoning, https://medium.com/@ignacio.de.gregorio.noblejas/grokking-a-new-form-of-reasoning-6785ea89d2ec
• Lingjiao Chen, Jared Quincy Davis, Boris Hanin, Peter Bailis, Ion Stoica, Matei Zaharia, James Zou, 4 Jun 2024 (v2), Are More LLM Calls All You Need? Towards Scaling Laws of Compound Inference Systems, https://arxiv.org/abs/2403.02419
• Asankhaya Sharma (codelion), Sep 2024, Optillm: Optimizing inference proxy for LLMs, https://github.com/codelion/optillm
• Justin Chih-Yao Chen, Archiki Prasad, Swarnadeep Saha, Elias Stengel-Eskin, Mohit Bansal, 18 Sep 2024, MAgICoRe: Multi-Agent, Iterative, Coarse-to-Fine Refinement for Reasoning, https://arxiv.org/abs/2409.12147 https://github.com/dinobby/MAgICoRe
• Xiaohan Xu, Chongyang Tao, Tao Shen, Can Xu, Hongbo Xu, Guodong Long, Jian-guang Lou, 29 Feb 2024 (v2), Re-Reading Improves Reasoning in Large Language Models, https://arxiv.org/abs/2309.06275
• Artem Shelamanov, Sep 2024, Why OpenAI’s o1 Model Is A Scam, https://pub.towardsai.net/why-openais-o1-model-is-a-scam-eb3356c3d70e
• Rinon Gal, Adi Haviv, Yuval Alaluf, Amit H. Bermano, Daniel Cohen-Or, Gal Chechik, 2 Oct 2024, ComfyGen: Prompt-Adaptive Workflows for Text-to-Image Generation, https://arxiv.org/abs/2410.01731 https://comfygen-paper.github.io/
• Yi Cheng, Xiao Liang, Yeyun Gong, Wen Xiao, Song Wang, Yuji Zhang, Wenjun Hou, Kaishuai Xu, Wenge Liu, Wenjie Li, Jian Jiao, Qi Chen, Peng Cheng, Wayne Xiong, Integrative Decoding: Improve Factuality via Implicit Self-consistency, 3 Oct 2024 (v2), https://arxiv.org/abs/2410.01556 (Prepends a previous response to improve decoding accuracy.)
• Zhenwen Liang, Ye Liu, Tong Niu, Xiangliang Zhang, Yingbo Zhou, Semih Yavuz, 5 Oct 2024, Improving LLM Reasoning through Scaling Inference Computation with Collaborative Verification, https://arxiv.org/abs/2410.05318
• Sonya Huang, Pat Grady, and o1, Sequoia, October 9, 2024 Generative AI’s Act o1, https://www.sequoiacap.com/article/generative-ais-act-o1/
• Yingqian Cui, Pengfei He, Xianfeng Tang, Qi He, Chen Luo, Jiliang Tang, Yue Xing, 21 Oct 2024, A Theoretical Understanding of Chain-of-Thought: Coherent Reasoning and Error-Aware Demonstration, https://arxiv.org/abs/2410.16540
• Jiangming Liu, Matt Gardner, Shay B. Cohen, Mirella Lapata, 7 Jun 2021 (v2), Multi-Step Inference for Reasoning Over Paragraphs, https://arxiv.org/abs/2004.02995
• Aditya Kalyanpur, Kailash Karthik Saravanakumar, Victor Barres, CJ McFate, Lori Moon, Nati Seifu, Maksim Eremeev, Jose Barrera, Abraham Bautista-Castillo, Eric Brown, David Ferrucci 24 Jul 2024 (v4), Multi-step Inference over Unstructured Data https://arxiv.org/abs/2406.17987
• Banghao Chen, Zhaofeng Zhang, Nicolas Langrené, Shengxin Zhu, 5 Sep 2024 (v5), Unleashing the potential of prompt engineering in Large Language Models: a comprehensive review, https://arxiv.org/abs/2310.14735
• Xiaodong Liu, Kevin Duh, Jianfeng Gao, 30 Mar 2019 (v2), Stochastic Answer Networks for Natural Language Inference, https://arxiv.org/abs/1804.07888
• TED, Oct 2024, Multi-Step Reasoning Agents, https://tedai-sanfrancisco.ted.com/glossary/multi-step-reasoning-agents/
• Yao Fu, Hao Peng, Ashish Sabharwal, Peter Clark, Tushar Khot, 30 Jan 2023 (v2), Complexity-Based Prompting for Multi-Step Reasoning, https://arxiv.org/abs/2210.00720
• Junting Lu, Oct 2024 (accessed), Awesome-LLM-Reasoning-Techniques, https://github.com/Junting-Lu/Awesome-LLM-Reasoning-Techniques
• Cameron R. Wolfe, Dec 23, 2023, Tree of Thoughts Prompting. Solving multi-step problems with LLMs via deliberate planning and exploration, https://towardsdatascience.com/tree-of-thoughts-prompting-65a3e51f9ac4
• Data Camp, Jul 10, 2024, Chain-of-Thought Prompting: Step-by-Step Reasoning with LLMs, https://www.datacamp.com/tutorial/chain-of-thought-prompting
• Pankaj, Dec 21, 2023, Chain of Thought Prompting: Guiding LLMs Step-by-Step, https://medium.com/@pankaj_pandey/chain-of-thought-prompting-guiding-llms-step-by-step-e6eac32d02d8
• Cobus Greyling, Aug 2, 2023, 12 Prompt Engineering Techniques, https://cobusgreyling.medium.com/12-prompt-engineering-techniques-644481c857aa
• Cameron R. Wolfe, Aug 21, 2023, Tree of Thoughts Prompting. Solving multi-step problems with LLMs via deliberate planning and exploration, https://cameronrwolfe.substack.com/p/tree-of-thoughts-prompting
• Cameron R. Wolfe, Jan 3, 2024, Graph-Based Prompting and Reasoning with Language Models. Understanding graph of thoughts prompting and several variants… https://towardsdatascience.com/graph-based-prompting-and-reasoning-with-language-models-d6acbcd6b3d8
• Jason Wei and Denny Zhou, May 11, 2022, Language Models Perform Reasoning via Chain of Thought, https://research.google/blog/language-models-perform-reasoning-via-chain-of-thought/
• Cameron R. Wolfe, Jul 24, 2023, Chain of Thought Prompting for LLMs: A practical and simple approach for “reasoning” with LLMs, https://towardsdatascience.com/chain-of-thought-prompting-for-llms-33c963eead38
• Siwei Wu, Zhongyuan Peng, Xinrun Du, Tuney Zheng, Minghao Liu, Jialong Wu, Jiachen Ma, Yizhi Li, Jian Yang, Wangchunshu Zhou, Qunshu Lin, Junbo Zhao, Zhaoxiang Zhang, Wenhao Huang, Ge Zhang, Chenghua Lin, J.H. Liu, 22 Oct 2024 (v2), A Comparative Study on Reasoning Patterns of OpenAI's o1 Model, https://arxiv.org/abs/2410.13639
• Arun Shankar, Oct 2024, Designing Cognitive Architectures: Agentic Workflow Patterns from Scratch, https://medium.com/google-cloud/designing-cognitive-architectures-agentic-workflow-patterns-from-scratch-63baa74c54bc
• Tanay Jaipuria, Oct 29, 2024, OpenAI's o-1 and inference-time scaling laws, https://www.tanayj.com/p/openais-o-1-and-inference-time-scaling
• Jinlin Wang, Suyuchen Wang, Ziwen Xia, Sirui Hong, Yun Zhu, Bang Liu, Chenglin Wu, 28 Oct 2024, FACT: Examining the Effectiveness of Iterative Context Rewriting for Multi-fact Retrieval, https://arxiv.org/abs/2410.21012
• Latent Space, Nov 05, 2024, Inference, Fast and Slow. When System 1/System 2 analogies are not enough: The 6 types of LLM inference https://www.latent.space/p/inference-fast-and-slow
• Shanghaoran Quan, Tianyi Tang, Bowen Yu, An Yang, Dayiheng Liu, Bofei Gao, Jianhong Tu, Yichang Zhang, Jingren Zhou, Junyang Lin, 31 Oct 2024, Language Models can Self-Lengthen to Generate Long Texts, https://arxiv.org/abs/2410.23933?
• LangChain, Nov 7, 2024. SCIPE - Systematic Chain Improvement and Problem Evaluation, https://blog.langchain.dev/scipe-systematic-chain-improvement-and-problem-evaluation/ https://github.com/garg-ankush/scipe/tree/main
• X Wang, L Mu, J Zhang, H Xu, 2024, Multi-pass Decoding for Grammatical Error Correction, Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 9904–9916, November 12-16, 2024, https://aclanthology.org/2024.emnlp-main.553.pdf
• Siyun Zhao, Yuqing Yang, Zilong Wang, Zhiyuan He, Luna K. Qiu, Lili Qiu, 23 Sep 2024, Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely, https://arxiv.org/abs/2409.14924
• Guowei Xu, Peng Jin, Li Hao, Yibing Song, Lichao Sun, Li Yuan, 15 Nov 2024, LLaVA-o1: Let Vision Language Models Reason Step-by-Step, https://arxiv.org/abs/2411.10440
• Carl Franzen, November 20, 2024, DeepSeek’s first reasoning model R1-Lite-Preview turns heads, beating OpenAI o1 performance, https://venturebeat.com/ai/deepseeks-first-reasoning-model-r1-lite-preview-turns-heads-beating-openai-o1-performance/
• mshumer, Nov 2024, Open Reasoning Engine, https://github.com/mshumer/OpenReasoningEngine
• Eric Horvitz , Harsha Nori , Naoto Usuyama , November 27, 2024 Advances in run-time strategies for next-generation foundation models, Microsoft Research Blog, https://www.microsoft.com/en-us/research/blog/advances-in-run-time-strategies-for-next-generation-foundation-models/
• Harsha Nori, Naoto Usuyama, Nicholas King, Scott Mayer McKinney, Xavier Fernandes, Sheng Zhang, Eric Horvitz, 6 Nov 2024, From Medprompt to o1: Exploration of Run-Time Strategies for Medical Challenge Problems and Beyond, https://arxiv.org/abs/2411.03590
• Hieu Tran, Zonghai Yao, Junda Wang, Yifan Zhang, Zhichao Yang, Hong Yu, 5 Dec 2024 (v2), RARE: Retrieval-Augmented Reasoning Enhancement for Large Language Models, https://arxiv.org/abs/2412.02830
• Zhe Chen, Weiyun Wang, Yue Cao, Yangzhou Liu, Zhangwei Gao, Erfei Cui, Jinguo Zhu, Shenglong Ye, Hao Tian, Zhaoyang Liu, Lixin Gu, Xuehui Wang, Qingyun Li, Yimin Ren, Zixuan Chen, Jiapeng Luo, Jiahao Wang, Tan Jiang, Bo Wang, Conghui He, Botian Shi, Xingcheng Zhang, Han Lv, Yi Wang, Wenqi Shao, Pei Chu, Zhongying Tu, Tong He, Zhiyong Wu, Huipeng Deng, Jiaye Ge, Kai Chen, Min Dou, Lewei Lu, Xizhou Zhu, Tong Lu, Dahua Lin, Yu Qiao, Jifeng Dai, Wenhai Wang, 6 Dec 2024, Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling, https://arxiv.org/abs/2412.05271
• Arda Sevinc, Abdurrahman Gumus, 9 Dec 2024, AutoReason: Automatic Few-Shot Reasoning Decomposition, https://arxiv.org/abs/2412.06975 https://github.com/miralab-ai/autoreason
• Mingchen Zhuge, Changsheng Zhao, Dylan Ashley, Wenyi Wang, Dmitrii Khizbullin, Yunyang Xiong, Zechun Liu, Ernie Chang, Raghuraman Krishnamoorthi, Yuandong Tian, Yangyang Shi, Vikas Chandra, Jürgen Schmidhuber, 16 Oct 2024 (v2), Agent-as-a-Judge: Evaluate Agents with Agents, https://arxiv.org/abs/2410.10934
• Kyle Wiggers, December 14, 2024, ‘Reasoning’ AI models have become a trend, for better or worse, https://techcrunch.com/2024/12/14/reasoning-ai-models-have-become-a-trend-for-better-or-worse/
• Ekin Akyürek, Mehul Damani, Linlu Qiu, Han Guo, Yoon Kim, Jacob Andreas, 11 Nov 2024, The Surprising Effectiveness of Test-Time Training for Abstract Reasoning, https://arxiv.org/abs/2411.07279
• Noam Brown, Tuomas Sandholm, 16 Nov 2017 (v3), Safe and Nested Subgame Solving for Imperfect-Information Games, https://arxiv.org/abs/1705.02955 (An early pre-AI paper on reasoning in multiple steps.)
• Maxwell Zeff, November 20, 2024, Current AI scaling laws are showing diminishing returns, forcing AI labs to change course, https://techcrunch.com/2024/11/20/ai-scaling-laws-are-showing-diminishing-returns-forcing-ai-labs-to-change-course/ ("at least 10 to 20x gains in model performance ...intelligent prompting, UX decisions, and passing context at the right time into the models...")
• Agnostiq, Dec 2024, multi-agent-llm: LLM based Multi-Agent methods: Lean implementation of various multi-agent LLM methods, including Iteration of Thought (IoT), https://github.com/AgnostiqHQ/multi-agent-llm
• Maxwell Nye, Anders Johan Andreassen, Guy Gur-Ari, Henryk Michalewski, Jacob Austin, David Bieber, David Dohan, Aitor Lewkowycz, Maarten Bosma, David Luan, Charles Sutton, Augustus Odena, 30 Nov 2021, Show Your Work: Scratchpads for Intermediate Computation with Language Models, https://arxiv.org/abs/2112.00114
• Xiangjue Dong, Maria Teleki, James Caverlee, 18 Dec 2024, A Survey on LLM Inference-Time Self-Improvement, https://arxiv.org/abs/2412.14352 https://github.com/dongxiangjue/Awesome-LLM-Self-Improvement (Broad survey of reasoning improvement methods from multi-step inference to RALM to decoding algorithms.)
• Xingyu Chen, Jiahao Xu, Tian Liang, Zhiwei He, Jianhui Pang, Dian Yu, Linfeng Song, Qiuzhi Liu, Mengfei Zhou, Zhuosheng Zhang, Rui Wang, Zhaopeng Tu, Haitao Mi, Dong Yu, 30 Dec 2024, Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs, https://arxiv.org/abs/2412.21187
• Rohin Manvi, Anikait Singh, Stefano Ermon, 3 Oct 2024, Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation, https://arxiv.org/abs/2410.02725
• Yiwei Li, Peiwen Yuan, Shaoxiong Feng, Boyuan Pan, Xinglin Wang, Bin Sun, Heda Wang, and Kan Li, 19 Jan 2024, Escape sky-high cost: Early-stopping self-consistency for multi-step reasoning. The Twelfth International Conference on Learning Representations, 2024, https://arxiv.org/abs/2401.10480 https://github.com/Yiwei98/ESC (Uses "early stopping" idea to improve CoT efficiency during inference.)
• Akash Bajwa, Jan 06, 2025, Test-Time Search: A Path To AGI: Stacking Scaling Laws And Reward Engineering, https://akashbajwa.substack.com/p/test-time-search-a-path-to-agi
• NovaSky, Jan 2025, Sky-T1: Train your own O1 preview model within $450, https://novasky-ai.github.io/posts/sky-t1/
• Tong Xiao, Jingbo Zhu, 16 Jan 2025, Foundations of Large Language Models, https://arxiv.org/abs/2501.09223 (Huge 230 page paper on many topics such as training, prompting, alignment, and long context.)
• Zekun Xi, Wenbiao Yin, Jizhan Fang, Jialong Wu, Runnan Fang, Ningyu Zhang, Jiang Yong, Pengjun Xie, Fei Huang, Huajun Chen, 16 Jan 2025, OmniThink: Expanding Knowledge Boundaries in Machine Writing through Thinking, https://arxiv.org/abs/2501.09751 (Iteratively going deeper into a topic while generating.)
• Siddharth Narayanan, James D. Braza, Ryan-Rhys Griffiths, Manu Ponnapati, Albert Bou, Jon Laurent, Ori Kabeli, Geemi Wellawatte, Sam Cox, Samuel G. Rodriques, Andrew D. White, 30 Dec 2024, Aviary: training language agents on challenging scientific tasks, https://arxiv.org/abs/2412.21154 (Using smaller models combined with multi-step reasoning to compete with big models with 100x less inference cost.)
• Kuang-Huei Lee, Ian Fischer, Yueh-Hua Wu, Dave Marwood, Shumeet Baluja, Dale Schuurmans, Xinyun Chen, 17 Jan 2025, Evolving Deeper LLM Thinking, https://arxiv.org/abs/2501.09891 (An alternative search strategy broad/deep, compared to CoT and reflection.)
• Edward Beeching, Lewis Tunstall, Sasha Rush Dec 16, 2024, Scaling Test Time Compute with Open Source Models, https://huggingface.co/spaces/HuggingFaceH4/blogpost-scaling-test-time-compute
• Maciej Besta, Julia Barth, Eric Schreiber, Ales Kubicek, Afonso Catarino, Robert Gerstenberger, Piotr Nyczyk, Patrick Iff, Yueling Li, Sam Houliston, Tomasz Sternal, Marcin Copik, Grzegorz Kwaśniewski, Jürgen Müller, Łukasz Flis, Hannes Eberhard, Hubert Niewiadomski, Torsten Hoefler, 23 Jan 2025 (v3), Reasoning Language Models: A Blueprint, https://arxiv.org/abs/2501.11223 (Survey and blueprint for how to build a Large Reasoning Model.)
• Enze Xie, Junsong Chen, Yuyang Zhao, Jincheng Yu, Ligeng Zhu, Yujun Lin, Zhekai Zhang, Muyang Li, Junyu Chen, Han Cai, Bingchen Liu, Daquan Zhou, Song Han, 30 Jan 2025, SANA 1.5: Efficient Scaling of Training-Time and Inference-Time Compute in Linear Diffusion Transformer, https://arxiv.org/abs/2501.18427 (Diffusion model optimization using block-level depth pruning and inference-time scaling.)
• S Wang, X Zhang, J Ma, A Hwang, Z Yu, Jan 2025, JumpStarter: Getting Started on Personal Goals with Adaptive Personal Context Curation, https://sitong-wang.github.io/data/JumpStarter.pdf (Long-term planning of goal-oriented long multi-step projects.)
• Niklas Muennighoff, Zitong Yang, Weijia Shi, Xiang Lisa Li, Li Fei-Fei, Hannaneh Hajishirzi, Luke Zettlemoyer, Percy Liang, Emmanuel Candès, Tatsunori Hashimoto, 3 Feb 2025 (v2), s1: Simple test-time scaling, https://arxiv.org/abs/2501.19393 https://github.com/simplescaling/s1 (Method of "budget forcing" that allows either shortening or lengthening multi-step reasoning sequences.)
• Manish Sanwal, 3 Feb 2025 (v2), Layered Chain-of-Thought Prompting for Multi-Agent LLM Systems: A Comprehensive Approach to Explainable Large Language Models, https://arxiv.org/abs/2501.18645
• Sebastian Raschka, PhD, Feb 05, 2025, Understanding Reasoning LLMs: Methods and Strategies for Building and Refining Reasoning Models https://magazine.sebastianraschka.com/p/understanding-reasoning-llms
• Ling Yang, Zhaochen Yu, Bin Cui, Mengdi Wang, 10 Feb 2025, ReasonFlux: Hierarchical LLM Reasoning via Scaling Thought Templates, https://arxiv.org/abs/2502.06772 https://github.com/Gen-Verse/ReasonFlux (RALM-like retrieval of reasoning prompt templates at inference time.)
• Hanmeng Liu, Zhizhang Fu, Mengru Ding, Ruoxi Ning, Chaoli Zhang, Xiaozhang Liu, Yue Zhang, 13 Feb 2025, Logical Reasoning in Large Language Models: A Survey, https://arxiv.org/abs/2502.09100
• Zeping Yu, Yonatan Belinkov, Sophia Ananiadou, 15 Feb 2025, Back Attention: Understanding and Enhancing Multi-Hop Reasoning in Large Language Models, https://arxiv.org/abs/2502.10835
• Dacheng Li, Shiyi Cao, Chengkun Cao, Xiuyu Li, Shangyin Tan, Kurt Keutzer, Jiarong Xing, Joseph E. Gonzalez, Ion Stoica, 20 Feb 2025, S*: Test Time Scaling for Code Generation, https://arxiv.org/abs/2502.14382 https://github.com/NovaSky-AI/SkyThought
• Ben Dickson, February 20, 2025, How test-time scaling unlocks hidden reasoning abilities in small language models (and allows them to outperform LLMs), https://venturebeat.com/ai/how-test-time-scaling-unlocks-hidden-reasoning-abilities-in-small-language-models-and-allows-them-to-outperform-llms/
• Zhiyuan Zeng, Qinyuan Cheng, Zhangyue Yin, Yunhua Zhou, Xipeng Qiu, 17 Feb 2025, Revisiting the Test-Time Scaling of o1-like Models: Do they Truly Possess Test-Time Scaling Capabilities? https://arxiv.org/abs/2502.12215
• Shubham Parashar, Blake Olson, Sambhav Khurana, Eric Li, Hongyi Ling, James Caverlee, Shuiwang Ji, 18 Feb 2025, Inference-Time Computations for LLM Reasoning and Planning: A Benchmark and Insights, https://arxiv.org/abs/2502.12521
• Zihao Zeng, Xuyao Huang, Boxiu Li, Zhijie Deng, 19 Feb 2025, SIFT: Grounding LLM Reasoning in Contexts via Stickers, https://arxiv.org/abs/2502.14922 https://github.com/zhijie-group/SIFT (Multi-step reasoning where the LLM first generates a modified prompt that summarizes the key points, and then does inference for both the original and modified prompts, then comparing results and adjusting forwards and backwards.)
• Marthe Ballon, Andres Algaba, Vincent Ginis, 21 Feb 2025, The Relationship Between Reasoning and Performance in Large Language Models -- o3 (mini) Thinks Harder, Not Longer, https://arxiv.org/abs/2502.15631
• Maxwell Zeff, February 24, 2025, Anthropic launches a new AI model that ‘thinks’ as long as you want, https://techcrunch.com/2025/02/24/anthropic-launches-a-new-ai-model-that-thinks-as-long-as-you-want/
• Kif Leswing, Feb 26 2025, Nvidia CEO Huang says AI has to do ’100 times more’ computation now than when ChatGPT was released, https://www.cnbc.com/2025/02/26/nvidia-ceo-huang-says-next-generation-ai-will-need-more-compute.html (The thesis that AI reasoning will need 100 times more compute, regardless of whether it is a single-step "long answers" model thinking out loud, or a multi-step test time compute model.)
• Zhong-Zhi Li, Duzhen Zhang, Ming-Liang Zhang, Jiaxin Zhang, Zengyan Liu, Yuxuan Yao, Haotian Xu, Junhao Zheng, Pei-Jie Wang, Xiuyi Chen, Yingying Zhang, Fei Yin, Jiahua Dong, Zhijiang Guo, Le Song, Cheng-Lin Liu, 25 Feb 2025 (v2), From System 1 to System 2: A Survey of Reasoning Large Language Models, https://arxiv.org/abs/2502.17419
• Wenkai Yang, Shuming Ma, Yankai Lin, Furu Wei, 25 Feb 2025, Towards Thinking-Optimal Scaling of Test-Time Compute for LLM Reasoning, https://arxiv.org/abs/2502.18080 (Trying to generate the "shortest correct response" by examining the lengths needed for CoT.)
• Yuchen Yan, Yongliang Shen, Yang Liu, Jin Jiang, Mengdi Zhang, Jian Shao, Yueting Zhuang, 13 Mar 2025 (v2), InftyThink: Breaking the Length Limits of Long-Context Reasoning in Large Language Models, https://arxiv.org/abs/2503.06692
• Qiguang Chen, Libo Qin, Jinhao Liu, Dengyun Peng, Jiannan Guan, Peng Wang, Mengkang Hu, Yuhang Zhou, Te Gao, Wanxiang Che, 13 Mar 2025 (v2), Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models, https://arxiv.org/abs/2503.09567 (Massive and broad survey of all types of reasoning.)
• Eric Zhao, Pranjal Awasthi, Sreenivas Gollapudi, 20 Feb 2025 (v2), Sample, Scrutinize and Scale: Effective Inference-Time Search by Scaling Verification https://arxiv.org/abs/2502.01839 (Wrapping a single model with a Best-of-N approach that self-selects the best answer can significantly improve reasoning rates.)

General Reasoning Papers

• Junlin Wang, Siddhartha Jain, Dejiao Zhang, Baishakhi Ray, Varun Kumar, Ben Athiwaratkun, 11 Jun 2024 (v2), Reasoning in Token Economies: Budget-Aware Evaluation of LLM Reasoning Strategies, https://arxiv.org/abs/2406.06461
• Xuan Zhang, Chao Du, Tianyu Pang, Qian Liu, Wei Gao, Min Lin, 13 Jun 2024, Chain of Preference Optimization: Improving Chain-of-Thought Reasoning in LLMs, https://arxiv.org/abs/2406.09136 Code: https://github.com/sail-sg/CPO
• Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L. Griffiths, Yuan Cao, Karthik Narasimhan, 3 Dec 2023 (v2), Tree of Thoughts: Deliberate Problem Solving with Large Language Models, https://arxiv.org/abs/2305.10601 Code: https://github.com/princeton-nlp/tree-of-thought-llm
• Hayden Field, June 20, 2024, OpenAI competitor Anthropic announces its most powerful AI yet, CNBC, https://www.cnbc.com/2024/06/20/anthropic-claude-3point5-sonnet-ai-announced.html
• Wai-Chung Kwan, Xingshan Zeng, Yuxin Jiang, Yufei Wang, Liangyou Li, Lifeng Shang, Xin Jiang, Qun Liu, Kam-Fai Wong, 30 Jan 2024, MT-Eval: A Multi-Turn Capabilities Evaluation Benchmark for Large Language Models, https://arxiv.org/abs/2401.16745 Code: https://github.com/KwanWaiChung/MT-Eval
• M.Besta, N. Blach, A. Kubicek, R. Gerstenberger, M. Podstawski, L. Gianinazzi, J. Gajda, T. Lehmann, H. Niewiadomski, P. Nyczyk et al., “Graph of thoughts: Solving elaborate problems with large language models,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, no. 16, 2024, pp. 17682–17690. https://arxiv.org/abs/2308.09687
• Q. Sun, Z. Yin, X. Li, Z. Wu, X. Qiu, and L. Kong, “Corex: Pushing the boundaries of complex reasoning through multi model collaboration,” arXiv preprint arXiv:2310.00280, 2023. https://arxiv.org/abs/2310.00280
• Tianle Li, Wei-Lin Chiang, Lisa Dunlap, May 20, 2024, Introducing Hard Prompts Category in Chatbot Arena, https://lmsys.org/blog/2024-05-17-category-hard/
• Myeonghwa Lee, Seonho An, Min-Soo Kim, 18 Jun 2024, PlanRAG: A Plan-then-Retrieval Augmented Generation for Generative Large Language Models as Decision Makers, https://arxiv.org/abs/2406.12430 Code: https://github.com/myeon9h/PlanRAG
• Rahul Verma, June 21, 2024, OpenAI's GPT-5 Pushed Back To Late 2025, But Promises Ph.D.-Level Abilities, https://in.mashable.com/tech/77593/openais-gpt-5-pushed-back-to-late-2025-but-promises-phd-level-abilities
• Krista Opsahl-Ong, Michael J Ryan, Josh Purtell, David Broman, Christopher Potts, Matei Zaharia, Omar Khattab, 17 Jun 2024, Optimizing Instructions and Demonstrations for Multi-Stage Language Model Programs, https://arxiv.org/abs/2406.11695
• Sachit Menon, Richard Zemel, Carl Vondrick, 20 Jun 2024, Whiteboard-of-Thought: Thinking Step-by-Step Across Modalities, https://arxiv.org/abs/2406.14562
• Lina M. Rojas-Barahona, 2024, Talking to Machines: do you read me?. Computation and Language, Universite de Lorraine, https://hal.science/tel-04620199/document
• Rafe Brena, May 24, 2024, 3 Key Differences Between Human and Machine Intelligence You Need to Know: AI is an alien intelligence https://pub.towardsai.net/3-key-differences-between-human-and-machine-intelligence-you-need-to-know-7a34dcee2cd3 (Good article about how LLMs don't have "emotions" or "intelligence" and they don't "pause".)
• Vishal Rajput, Apr 11, 2024, What’s next for AI: AI agentic workflows? https://medium.com/aiguys/next-for-llms-and-rag-ai-agentic-workflows-1869ba0a6796
• Rachel Metz, July 12, 2024, OpenAI Scale Ranks Progress Toward ‘Human-Level’ Problem Solving: The company believes its technology is approaching the second level of five on the path to artificial general intelligence, Bloomberg, https://www.bloomberg.com/news/articles/2024-07-11/openai-sets-levels-to-track-progress-toward-superintelligent-ai?sref=P6Q0mxvj
• Sirui Chen, Bo Peng, Meiqi Chen, Ruiqi Wang, Mengying Xu, Xingyu Zeng, Rui Zhao, Shengjie Zhao, Yu Qiao, Chaochao Lu, 1 May 2024, Causal Evaluation of Language Models, https://arxiv.org/abs/2405.00622 Project: https://opencausalab.github.io/CaLM
• Anna Tong and Katie Paul July 16, 2024, Exclusive: OpenAI working on new reasoning technology under code name ‘Strawberry’, https://www.reuters.com/technology/artificial-intelligence/openai-working-new-reasoning-technology-under-code-name-strawberry-2024-07-12/
• Zehui Chen, Kuikun Liu, Qiuchen Wang, Jiangning Liu, Wenwei Zhang, Kai Chen, Feng Zhao, 29 Jul 2024, MindSearch: Mimicking Human Minds Elicits Deep AI Searcher, https://arxiv.org/abs/2407.20183 Code: https://github.com/InternLM/MindSearch Project: https://mindsearch.netlify.app
• Ethan Mollick, May 12, 2024, Superhuman? What does it mean for AI to be better than a human? And how can we tell? https://www.oneusefulthing.org/p/superhuman
• Ignacio de Gregorio, Aug 2024, Grokking, a New Form of Reasoning, https://medium.com/@ignacio.de.gregorio.noblejas/grokking-a-new-form-of-reasoning-6785ea89d2ec
• Zarif Bin Akhtar, Mapping Generative Artificial Intelligence (GAI's) Exciting Future: From Gemini to Q* and Beyond, https://publications.eai.eu/index.php/airo/article/view/5962 https://doi.org/10.4108/airo.5962 PDF: https://publications.eai.eu/index.php/airo/article/view/5962/3329
• Guangyan Sun, Mingyu Jin, Zhenting Wang, Cheng-Long Wang, Siqi Ma, Qifan Wang, Ying Nian Wu, Yongfeng Zhang, Dongfang Liu, 16 Aug 2024, Visual Agents as Fast and Slow Thinkers, https://arxiv.org/abs/2408.08862
• Adam Zewe, June 14, 2024, Technique improves the reasoning capabilities of large language models: Combining natural language and programming, the method enables LLMs to solve numerical, analytical, and language-based tasks transparently, MIT News, https://news.mit.edu/2024/technique-improves-reasoning-capabilities-large-language-models-0614
• Pei Zhou, Jay Pujara, Xiang Ren, Xinyun Chen, Heng-Tze Cheng, Quoc V. Le, Ed H. Chi, Denny Zhou, Swaroop Mishra, Huaixiu Steven Zheng, 6 Feb 2024, Self-Discover: Large Language Models Self-Compose Reasoning Structures, https://arxiv.org/abs/2402.03620
• Tinghui Zhu, Kai Zhang, Jian Xie, Yu Su, 4 Feb 2024 (v2), Deductive Beam Search: Decoding Deducible Rationale for Chain-of-Thought Reasoning, https://arxiv.org/abs/2401.17686
• Xiaoyu Liu, Paiheng Xu, Junda Wu, Jiaxin Yuan, Yifan Yang, Yuhang Zhou, Fuxiao Liu, Tianrui Guan, Haoliang Wang, Tong Yu, Julian 14 Mar 2024, Large Language Models and Causal Inference in Collaboration: A Comprehensive Survey, McAuley, Wei Ai, Furong Huang, https://arxiv.org/abs/2403.09606
• Jiace Zhu, Yingtao Shen, Jie Zhao, An Zou, 25 Aug 2024, Path-Consistency: Prefix Enhancement for Efficient Inference in LLM, https://arxiv.org/abs/2409.01281
• Cogni Down Under, Sep 2024, Reflection 70B: The AI That Thinks Before It Speaks, https://medium.com/@cognidownunder/reflection-70b-the-ai-that-thinks-before-it-speaks-8a70d3a0e38a
• Anson Ho, Tamay Besiroglu, Ege Erdil, David Owen, Robi Rahman, Zifan Carl Guo, David Atkinson, Neil Thompson, Jaime Sevilla, 9 Mar 2024, Algorithmic progress in language models, https://arxiv.org/abs/2403.05812
• Alberto Romero. Sep 10, 2024, Big News: OpenAI to Launch AI Model That Can Reason in 2 Weeks, https://www.thealgorithmicbridge.com/p/big-news-openai-to-launch-ai-model
• Zifan Zheng, Yezhaohui Wang, Yuxin Huang, Shichao Song, Bo Tang, Feiyu Xiong, Zhiyu Li, 5 Sep 2024, Attention Heads of Large Language Models: A Survey, https://arxiv.org/abs/2409.03752 https://github.com/IAAR-Shanghai/Awesome-Attention-Heads (This survey is about making attention mechanisms more performant, accurate and intelligent, rather than improving efficiency.)
• Asankhaya Sharma (codelion), Sep 2024, Optillm: Optimizing inference proxy for LLMs, https://github.com/codelion/optillm
• Louis Bouchard, Sep 12, 2024, OpenAI's o1 Model: The Future of Reasoning AI? What Sets It Apart, How OpenAI's o1 Model Thinks Through Problems (And Why It's Slower), https://www.louisbouchard.ai/openai-o1/
• OpenAI, September 12, 2024, Introducing OpenAI o1-preview, A new series of reasoning models for solving hard problems. https://openai.com/index/introducing-openai-o1-preview/
• OpenAI, September 12, 2024, Learning to Reason with LLMs, https://openai.com/index/learning-to-reason-with-llms/
• Nathan Lambert, Sep 05, 2024, OpenAI’s Strawberry, LM self-talk, inference scaling laws, and spending more on inference, Whether or not scaling works, we should spend more on inference, https://www.interconnects.ai/p/openai-strawberry-and-inference-scaling-laws
• Ignacio de Gregorio Noblejas, September 15, 2024, OpenAI Launches o1. Here’s All You Need to Know, https://thetechoasis.beehiiv.com/p/openai-launches-o1-heres-need-know
• Trung Quoc Luong, Xinbo Zhang, Zhanming Jie, Peng Sun, Xiaoran Jin, Hang Li, 27 Jun 2024 (v2), ReFT: Reasoning with Reinforced Fine-Tuning, https://arxiv.org/abs/2401.08967
• Tianqiao Liu, Zui Chen, Zitao Liu, Mi Tian, Weiqi Luo, 13 Sep 2024, Expediting and Elevating Large Language Model Reasoning via Hidden Chain-of-Thought Decoding, https://arxiv.org/abs/2409.08561
• Michael Nuñez, September 16, 2024, SambaNova challenges OpenAI’s o1 model with Llama 3.1-powered demo on HuggingFace, https://venturebeat.com/ai/sambanova-challenges-openais-o1-model-with-llama-3-1-powered-demo-on-huggingface/
• Zayne Sprague, Fangcong Yin, Juan Diego Rodriguez, Dongwei Jiang, Manya Wadhwa, Prasann Singhal, Xinyu Zhao, Xi Ye, Kyle Mahowald, Greg Durrett, 18 Sep 2024, To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning, https://arxiv.org/abs/2409.12183
• Santosh Kumar Radha, Yasamin Nouri Jelyani, Ara Ghukasyan, Oktay Goktas, 19 Sep 2024, Iteration of Thought: Leveraging Inner Dialogue for Autonomous Large Language Model Reasoning, https://arxiv.org/abs/2409.12618
• Justin Chih-Yao Chen, Archiki Prasad, Swarnadeep Saha, Elias Stengel-Eskin, Mohit Bansal, 18 Sep 2024, MAgICoRe: Multi-Agent, Iterative, Coarse-to-Fine Refinement for Reasoning, https://arxiv.org/abs/2409.12147 https://github.com/dinobby/MAgICoRe
• Siyun Zhao, Yuqing Yang, Zilong Wang, Zhiyuan He, Luna K. Qiu, Lili Qiu, 23 Sep 2024, Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely, https://arxiv.org/abs/2409.14924
• Artem Shelamanov, Sep 2024, Why OpenAI’s o1 Model Is A Scam, https://pub.towardsai.net/why-openais-o1-model-is-a-scam-eb3356c3d70e
• Chloe Berger, October 2, 2024, Mark Cuban says his puppy is ‘smarter than AI is today’, https://fortune.com/2024/10/01/mark-cuban-dog-puppy-smarter-than-ai/
• Julia Love and Rachel Metz, October 2, 2024, Google Is Working on Reasoning AI, Chasing OpenAI’s Efforts, https://www.bloomberg.com/news/articles/2024-10-02/google-is-working-on-reasoning-ai-chasing-openai-s-efforts
• Zhenwen Liang, Ye Liu, Tong Niu, Xiangliang Zhang, Yingbo Zhou, Semih Yavuz, 5 Oct 2024, Improving LLM Reasoning through Scaling Inference Computation with Collaborative Verification, https://arxiv.org/abs/2410.05318
• Iman Mirzadeh, Keivan Alizadeh, Hooman Shahrokhi, Oncel Tuzel, Samy Bengio, Mehrdad Farajtabar, 7 Oct 2024, GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models, https://arxiv.org/abs/2410.05229
• Sonya Huang, Pat Grady, and o1, Sequoia, October 9, 2024 Generative AI’s Act o1, https://www.sequoiacap.com/article/generative-ais-act-o1/
• Ignacio de Gregorio Noblejas, October 20, 2024, The Anti-LLM Revolution Begins,https://thetechoasis.beehiiv.com/p/the-anti-llm-revolution-begins
• By Asif Razzaq, October 13, 2024, OpenR: An Open-Source AI Framework Enhancing Reasoning in Large Language Models, https://www.marktechpost.com/2024/10/13/openr-an-open-source-ai-framework-enhancing-reasoning-in-large-language-models/
• Siwei Wu, Zhongyuan Peng, Xinrun Du, Tuney Zheng, Minghao Liu, Jialong Wu, Jiachen Ma, Yizhi Li, Jian Yang, Wangchunshu Zhou, Qunshu Lin, Junbo Zhao, Zhaoxiang Zhang, Wenhao Huang, Ge Zhang, Chenghua Lin, J.H. Liu, 22 Oct 2024 (v2), A Comparative Study on Reasoning Patterns of OpenAI's o1 Model, https://arxiv.org/abs/2410.13639
• Latent Space, Nov 05, 2024, Inference, Fast and Slow. When System 1/System 2 analogies are not enough: The 6 types of LLM inference https://www.latent.space/p/inference-fast-and-slow
• Will Lockett Nov 2024, Apple Calls BS On The AI Revolution, They aren’t late to the AI game; they are just the only sceptical big tech company. https://medium.com/predict/apple-calls-bullshit-on-the-ai-revolution-ae38fdf83392
• Anthony Ha, Nov 2024, OpenAI reportedly developing new strategies to deal with AI improvement slowdown, https://techcrunch.com/2024/11/09/openai-reportedly-developing-new-strategies-to-deal-with-ai-improvement-slowdown/
• Michael Nuñez, November 11, 2024, AI’s math problem: FrontierMath benchmark shows how far technology still has to go, https://venturebeat.com/ai/ais-math-problem-frontiermath-benchmark-shows-how-far-technology-still-has-to-go/
• Kyle Orland, 13 Nov 2024, What if AI doesn’t just keep getting better forever? New reports highlight fears of diminishing returns for traditional LLM training. https://arstechnica.com/ai/2024/11/what-if-ai-doesnt-just-keep-getting-better-forever/
• Carl Franzen, November 20, 2024, DeepSeek’s first reasoning model R1-Lite-Preview turns heads, beating OpenAI o1 performance, https://venturebeat.com/ai/deepseeks-first-reasoning-model-r1-lite-preview-turns-heads-beating-openai-o1-performance/
• Zhi Rui Tam, Cheng-Kuang Wu, Yi-Lin Tsai, Chieh-Yen Lin, Hung-yi Lee, Yun-Nung Chen, 14 Oct 2024 (v3), Let Me Speak Freely? A Study on the Impact of Format Restrictions on Performance of Large Language Models, https://arxiv.org/abs/2408.02442
• Janelle Teng, Nov 26, 2024, AI's reasoning quandary, https://nextbigteng.substack.com/p/ais-reasoning-quandary
• Qwen Team, November 28, 2024, QwQ: Reflect Deeply on the Boundaries of the Unknown, https://qwenlm.github.io/blog/qwq-32b-preview/
• mshumer, Nov 2024, Open Reasoning Engine, https://github.com/mshumer/OpenReasoningEngine
• Tom Schaul, 25 Nov 2024, Boundless Socratic Learning with Language Games, https://arxiv.org/abs/2411.16905
• Alberto Romero, Dec 06, 2024, OpenAI Announces o1 Model And ChatGPT Pro ($200/Mo). OpenAI Christmas event: Day 1 of 12, https://www.thealgorithmicbridge.com/p/openai-announces-o1-model-and-chatgpt
• Justin Chih-Yao Chen, Zifeng Wang, Hamid Palangi, Rujun Han, Sayna Ebrahimi, Long Le, Vincent Perot, Swaroop Mishra, Mohit Bansal, Chen-Yu Lee, Tomas Pfister, 29 Nov 2024, Reverse Thinking Makes LLMs Stronger Reasoners, https://arxiv.org/abs/2411.19865
• Tiernan Ray, Dec. 10, 2024, How Cerebras boosted Meta's Llama to 'frontier model' performance The company also demonstrates initial training of a one-trillion-parameter AI model on a single machine using conventional DDR5 memory chips. https://www.zdnet.com/article/how-cerebras-boosted-metas-llama-to-frontier-model-performance/
• Shibo Hao, Sainbayar Sukhbaatar, DiJia Su, Xian Li, Zhiting Hu, Jason Weston, Yuandong Tian, 9 Dec 2024, Training Large Language Models to Reason in a Continuous Latent Space, https://arxiv.org/abs/2412.06769 (Performing reasoning in a model trained to operate in the embedding vector space, rather than more directly in the token space.)
• Arda Sevinc, Abdurrahman Gumus, 9 Dec 2024, AutoReason: Automatic Few-Shot Reasoning Decomposition, https://arxiv.org/abs/2412.06975 https://github.com/miralab-ai/autoreason
• Kyle Wiggers, December 14, 2024, ‘Reasoning’ AI models have become a trend, for better or worse, https://techcrunch.com/2024/12/14/reasoning-ai-models-have-become-a-trend-for-better-or-worse/
• Vincent-Pierre Berges, Barlas Oguz, December 12, 2024, Memory Layers at Scale, Meta, https://ai.meta.com/research/publications/memory-layers-at-scale/ https://github.com/facebookresearch/memory (Augmention of an LLM with an additional key-value associative memory, by replacing some FFNs with a "memory layer".)
• Ekin Akyürek, Mehul Damani, Linlu Qiu, Han Guo, Yoon Kim, Jacob Andreas, 11 Nov 2024, The Surprising Effectiveness of Test-Time Training for Abstract Reasoning, https://arxiv.org/abs/2411.07279
• Noam Brown, Tuomas Sandholm, 16 Nov 2017 (v3), Safe and Nested Subgame Solving for Imperfect-Information Games, https://arxiv.org/abs/1705.02955 (An early pre-AI paper on reasoning in multiple steps.)
• Maxwell Zeff, November 20, 2024, Current AI scaling laws are showing diminishing returns, forcing AI labs to change course, https://techcrunch.com/2024/11/20/ai-scaling-laws-are-showing-diminishing-returns-forcing-ai-labs-to-change-course/ ("at least 10 to 20x gains in model performance ...intelligent prompting, UX decisions, and passing context at the right time into the models...")
• Agnostiq, Dec 2024, multi-agent-llm: LLM based Multi-Agent methods: Lean implementation of various multi-agent LLM methods, including Iteration of Thought (IoT), https://github.com/AgnostiqHQ/multi-agent-llm
• Denise Holt, Dec 18, 2024, VERSES AI Crushes OpenAI o1 in Head to Head Competition: VERSES AI 's New Genius™ Platform Delivers Far More Performance than Open AI's Most Advanced Model at a Fraction of the Cost. https://deniseholt.substack.com/p/verses-ai-crushes-openai-o1-in-head
• Junnan Liu, Hongwei Liu, Linchen Xiao, Ziyi Wang, Kuikun Liu, Songyang Gao, Wenwei Zhang, Songyang Zhang, Kai Chen, 18 Dec 2024 (v2), Are Your LLMs Capable of Stable Reasoning? https://arxiv.org/abs/2412.13147 https://github.com/open-compass/GPassK
• Alberto Romero, Dec 21, 2024, OpenAI o3 Model Is a Message From the Future: Update All You Think You Know About AI. Incredible, a miracle, more than just a better state-of-the-art AI model. https://www.thealgorithmicbridge.com/p/openai-o3-model-is-a-message-from
• Sabrina Ortiz, Dec. 20, 2024, OpenAI unveils its most advanced o3 reasoning model on its last day of 'shipmas', https://www.zdnet.com/article/openai-unveils-its-most-advanced-o3-reasoning-model-on-its-last-day-of-shipmas/
• Jie He, Nan Hu, Wanqiu Long, Jiaoyan Chen, Jeff Z. Pan, 22 Dec 2024, MINTQA: A Multi-Hop Question Answering Benchmark for Evaluating LLMs on New and Tail Knowledge, https://arxiv.org/abs/2412.17032 https://github.com/probe2/multi-hop/ (Model evaluation of reasoning abilities.)
• Huanjin Yao, Jiaxing Huang, Wenhao Wu, Jingyi Zhang, Yibo Wang, Shunyu Liu, Yingjie Wang, Yuxin Song, Haocheng Feng, Li Shen, Dacheng Tao, 24 Dec 2024, Mulberry: Empowering MLLM with o1-like Reasoning and Reflection via Collective Monte Carlo Tree Search, https://arxiv.org/abs/2412.18319 https://github.com/HJYao00/Mulberry (Multimodal multi-step reasoning like CoT.)
• Xiangjue Dong, Maria Teleki, James Caverlee, 18 Dec 2024, A Survey on LLM Inference-Time Self-Improvement, https://arxiv.org/abs/2412.14352 https://github.com/dongxiangjue/Awesome-LLM-Self-Improvement (Broad survey of reasoning improvement methods from multi-step inference to RALM to decoding algorithms.)
• Junying Chen, Zhenyang Cai, Ke Ji, Xidong Wang, Wanlong Liu, Rongsheng Wang, Jianye Hou, Benyou Wang, 25 Dec 2024, HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs, https://arxiv.org/abs/2412.18925
• Lori Dajose, December 17, 2024, Thinking Slowly: The Paradoxical Slowness of Human Behavior, https://www.caltech.edu/about/news/thinking-slowly-the-paradoxical-slowness-of-human-behavior
• Aske Plaat, Annie Wong, Suzan Verberne, Joost Broekens, Niki van Stein, Thomas Back, 16 Jul 2024, Reasoning with Large Language Models, a Survey, https://arxiv.org/abs/2407.11511
• Mingyu Jin, Weidi Luo, Sitao Cheng, Xinyi Wang, Wenyue Hua, Ruixiang Tang, William Yang Wang, Yongfeng Zhang, 21 Nov 2024 (v2), Disentangling Memory and Reasoning Ability in Large Language Models, https://arxiv.org/abs/2411.13504 https://github.com/MingyuJ666/Disentangling-Memory-and-Reasoning
• Allen Nie, Yi Su, Bo Chang, Jonathan N. Lee, Ed H. Chi, Quoc V. Le, Minmin Chen, 8 Oct 2024, EVOLvE: Evaluating and Optimizing LLMs For Exploration, https://arxiv.org/abs/2410.06238
• Tianhao Wu, Janice Lan, Weizhe Yuan, Jiantao Jiao, Jason Weston, Sainbayar Sukhbaatar, 14 Oct 2024, Thinking LLMs: General Instruction Following with Thought Generation, https://arxiv.org/abs/2410.10630 (Training an LLM to reason by generating additional "thoughts" during training.)
• Xiang Huang, Jiayu Shen, Shanshan Huang, Sitao Cheng, Xiaxia Wang, Yuzhong Qu, 27 Dec 2024, TARGA: Targeted Synthetic Data Generation for Practical Reasoning over Structured Data, https://arxiv.org/abs/2412.19544?
• Xiaoxue Cheng, Junyi Li, Wayne Xin Zhao, Ji-Rong Wen, 2 Jan 2025, Think More, Hallucinate Less: Mitigating Hallucinations via Dual Process of Fast and Slow Thinking, https://arxiv.org/abs/2501.01306
• Mayi Xu, Yunfeng Ning, Yongqi Li, Jianhao Chen, Jintao Wen, Yao Xiao, Shen Zhou, Birong Pan, Zepeng Bao, Xin Miao, Hankun Kang, Ke Sun, Tieyun Qian, 2 Jan 2025, Reasoning based on symbolic and parametric knowledge bases: a survey, https://arxiv.org/abs/2501.01030 (Extensive survey of reasoning from CoT to knowledge graphs to table-based reasoning.)
• Yixin Ji, Juntao Li, Hai Ye, Kaixin Wu, Jia Xu, Linjian Mo, Min Zhang, 5 Jan 2025, Test-time Computing: from System-1 Thinking to System-2 Thinking, https://arxiv.org/abs/2501.02497
• Xiaoxi Li, Guanting Dong, Jiajie Jin, Yuyao Zhang, Yujia Zhou, Yutao Zhu, Peitian Zhang, Zhicheng Dou, 9 Jan 2025, Search-o1: Agentic Search-Enhanced Large Reasoning Models, https://arxiv.org/abs/2501.05366 https://github.com/sunnynexus/Search-o1 (RAG retrieval and agentic methods applied to Large Reasoning Models.)
• Yuhang Liu, Pengxiang Li, Zishu Wei, Congkai Xie, Xueyu Hu, Xinchen Xu, Shengyu Zhang, Xiaotian Han, Hongxia Yang, Fei Wu, 8 Jan 2025, InfiGUIAgent: A Multimodal Generalist GUI Agent with Native Reasoning and Reflection, https://arxiv.org/abs/2501.04575
• Andrea Matarazzo, Riccardo Torlone, 3 Jan 2025, A Survey on Large Language Models with some Insights on their Capabilities and Limitations, https://arxiv.org/abs/2501.04040 (Broad survey with many LLM topics covered from history to architectures to optimizations.)
• Ben Hylak and swyx & Alessio, Jan 12, 2025, o1 isn’t a chat model (and that’s the point): How Ben Hylak turned from ol pro skeptic to fan by overcoming his skill issue. https://www.latent.space/p/o1-skill-issue (Prompting reasoning models like "o1" is different from previous model generations.)
• NovaSky, Jan 2025, Sky-T1: Train your own O1 preview model within $450, https://novasky-ai.github.io/posts/sky-t1/
• Omkar Thawakar, Dinura Dissanayake, Ketan More, Ritesh Thawkar, Ahmed Heakl, Noor Ahsan, Yuhao Li, Mohammed Zumri, Jean Lahoud, Rao Muhammad Anwer, Hisham Cholakkal, Ivan Laptev, Mubarak Shah, Fahad Shahbaz Khan, Salman Khan, 10 Jan 2025, LlamaV-o1: Rethinking Step-by-step Visual Reasoning in LLMs, https://arxiv.org/abs/2501.06186
• François Chollet, 25 Nov 2019 (v2), On the Measure of Intelligence, https://arxiv.org/abs/1911.01547
• Siddharth Narayanan, James D. Braza, Ryan-Rhys Griffiths, Manu Ponnapati, Albert Bou, Jon Laurent, Ori Kabeli, Geemi Wellawatte, Sam Cox, Samuel G. Rodriques, Andrew D. White, 30 Dec 2024, Aviary: training language agents on challenging scientific tasks, https://arxiv.org/abs/2412.21154 (Using smaller models combined with multi-step reasoning to compete with big models with 100x less inference cost.)
• Di Zhang, Jianbo Wu, Jingdi Lei, Tong Che, Jiatong Li, Tong Xie, Xiaoshui Huang, Shufei Zhang, Marco Pavone, Yuqiang Li, Wanli Ouyang, Dongzhan Zhou, 21 Nov 2024 (v2), LLaMA-Berry: Pairwise Optimization for O1-like Olympiad-Level Mathematical Reasoning, https://arxiv.org/abs/2410.02884
• Dan Zhang, Sining Zhoubian, Ziniu Hu, Yisong Yue, Yuxiao Dong, Jie Tang, 18 Nov 2024 (v3), ReST-MCTS*: LLM Self-Training via Process Reward Guided Tree Search, https://arxiv.org/abs/2406.03816 https://github.com/THUDM/ReST-MCTS
• Jun Wang, Meng Fang, Ziyu Wan, Muning Wen, Jiachen Zhu, Anjie Liu, Ziqin Gong, Yan Song, Lei Chen, Lionel M. Ni, Linyi Yang, Ying Wen, Weinan Zhang, 12 Oct 2024, OpenR: An Open Source Framework for Advanced Reasoning with Large Language Models, https://arxiv.org/abs/2410.09671 https://openreasoner.github.io/
• Yiwei Qin, Xuefeng Li, Haoyang Zou, Yixiu Liu, Shijie Xia, Zhen Huang, Yixin Ye, Weizhe Yuan, Hector Liu, Yuanzhi Li, Pengfei Liu, 8 Oct 2024, O1 Replication Journey: A Strategic Progress Report -- Part 1. https://arxiv.org/abs/2410.18982
• Matthias Bastian, Oct 6, 2024, Study reveals major reasoning flaws in smaller AI language models, https://the-decoder.com/study-reveals-major-reasoning-flaws-in-smaller-ai-language-models/
• Paul Sawers, January 23, 2025, Meta’s Yann LeCun predicts a ‘new AI architectures paradigm’ within 5 years and ‘decade of robotics’, https://techcrunch.com/2025/01/23/metas-yann-lecun-predicts-a-new-ai-architectures-paradigm-within-5-years-and-decade-of-robotics/
• Latent Space, Jan 25, 2025, Why o3-mini *had* to be free: the coming DeepSeek R1, 2.0 Flash, and Sky-T1 Price War: 2025's biggest surprise so far: Reasoning is less of a moat than anyone thought. https://www.latent.space/p/reasoning-price-war
• Maciej Besta, Julia Barth, Eric Schreiber, Ales Kubicek, Afonso Catarino, Robert Gerstenberger, Piotr Nyczyk, Patrick Iff, Yueling Li, Sam Houliston, Tomasz Sternal, Marcin Copik, Grzegorz Kwaśniewski, Jürgen Müller, Łukasz Flis, Hannes Eberhard, Hubert Niewiadomski, Torsten Hoefler, 23 Jan 2025 (v3), Reasoning Language Models: A Blueprint, https://arxiv.org/abs/2501.11223 (Survey and blueprint for how to build a Large Reasoning Model.)
• Akash Bajwa Jan 27, 2025, The Post-R1 World: AI Economics Have Irreversibly Changed, https://akashbajwa.substack.com/p/the-post-r1-world
• G Wang, S Zhang, T Zhan, Z Shen, J Li, X Hu, X Sun, Jan 2025, Unlocking the Mysteries of OpenAI o1: A Survey of the Reasoning Abilities of Large Language Models, https://openreview.net/pdf?id=J0ADLa2rNp
• Lan Pan, Hanbo Xie, Robert C. Wilson, 29 Jan 2025, Large Language Models Think Too Fast To Explore Effectively, https://arxiv.org/abs/2501.18009
• Yue Wang, Qiuzhi Liu, Jiahao Xu, Tian Liang, Xingyu Chen, Zhiwei He, Linfeng Song, Dian Yu, Juntao Li, Zhuosheng Zhang, Rui Wang, Zhaopeng Tu, Haitao Mi, Dong Yu, 30 Jan 2025, Thoughts Are All Over the Place: On the Underthinking of o1-Like LLMs, https://arxiv.org/abs/2501.18585
• Han Zhong, Yutong Yin, Shenao Zhang, Xiaojun Xu, Yuanxin Liu, Yifei Zuo, Zhihan Liu, Boyi Liu, Sirui Zheng, Hongyi Guo, Liwei Wang, Mingyi Hong, Zhaoran Wang, 31 Jan 2025, BRiTE: Bootstrapping Reinforced Thinking Process to Enhance Language Model Reasoning, https://arxiv.org/abs/2501.18858
• Sebastian Raschka, PhD, Feb 05, 2025, Understanding Reasoning LLMs: Methods and Strategies for Building and Refining Reasoning Models https://magazine.sebastianraschka.com/p/understanding-reasoning-llms
• Ahmed El-Kishky, Alexander Wei, Andre Saraiva, Borys Minaev, Daniel Selsam, David Dohan, Francis Song, Hunter Lightman, Ignasi Clavera, Jakub Pachocki, Jerry Tworek, Lorenz Kuhn, Lukasz Kaiser, Mark Chen, Max Schwarzer, Mostafa Rohaninejad, Nat McAleese, o3 contributors, Oleg Mürk, Rhythm Garg, Rui Shu, Szymon Sidor, Vineet Kosaraju, Wenda Zhou, 3 Feb 2025, Competitive Programming with Large Reasoning Models, https://arxiv.org/abs/2502.06807 (OpenAI's paper on o3 that has similar conclusions to what DeepSeek showed about Reinforcement Learning for reasoning models, namely that "scaling general-purpose reinforcement learning" still works.)
• Hieu Minh "Jord" Nguyen, 10 Feb 2025, A Survey of Theory of Mind in Large Language Models: Evaluations, Representations, and Safety Risks, https://arxiv.org/abs/2502.06470
• Daniel Fleischer, Moshe Berchansky, Gad Markovits, Moshe Wasserblat, 13 Feb 2025, SQuARE: Sequential Question Answering Reasoning Engine for Enhanced Chain-of-Thought in Large Language Models, https://arxiv.org/abs/2502.09390 https://github.com/IntelLabs/RAG-FiT/tree/square
• Salvatore Raieli, Feb 2025, The LLMs’ Dilemma: Thinking Too Much OR Too Little? Exploring the fine line between deep reasoning and computational overkill in large language models., https://levelup.gitconnected.com/the-llms-dilemma-thinking-too-much-or-too-little-619a7532a47e
• Ben Dickson, February 20, 2025, How test-time scaling unlocks hidden reasoning abilities in small language models (and allows them to outperform LLMs), https://venturebeat.com/ai/how-test-time-scaling-unlocks-hidden-reasoning-abilities-in-small-language-models-and-allows-them-to-outperform-llms/
• Ali Razghandi, Seyed Mohammad Hadi Hosseini, Mahdieh Soleymani Baghshah, 20 Feb 2025, CER: Confidence Enhanced Reasoning in LLMs, https://arxiv.org/abs/2502.14634 (Using model confidence metrics, i.e., logits, to evaluate reasoning pathways.)
• Zhipeng Chen, Yingqian Min, Beichen Zhang, Jie Chen, Jinhao Jiang, Daixuan Cheng, Wayne Xin Zhao, Zheng Liu, Xu Miao, Yang Lu, Lei Fang, Zhongyuan Wang, Ji-Rong Wen, 6 Mar 2025, An Empirical Study on Eliciting and Improving R1-like Reasoning Models, https://arxiv.org/abs/2503.04548 https://github.com/RUCAIBox/Slow_Thinking_with_LLMs

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