Aussie AI

LLM Reasoning Research

  • Last Updated 1 January, 2026
  • by David Spuler, Ph.D.

Reasoning is a key part of intelligence, and much work is ongoing to improve higher-level reasoning of AI models. Examples include solving mathematical problems or performing multi-step planning such as booking a holiday.

There are two main categories of methods to improve reasoning ability:

  • Training methods ("white box reasoning")
  • Multi-step inference methods ("black box reasoning")

You may also be interested in our recent research and blog articles:

Training-Based Reasoning

White Box Reasoning is the training of the weights internal to an LLM so that it performs better on reasoning tasks. Historically, the first idea to create smarter models was always to train an LLM using better data and better techniques. This has improved raw results on "reasoning" and "generalization" tasks.

Lately, this has given rise to the Large Reasoner Model (LRM) architectures, in two main types. There are the trained reasoning models that still give an answer in one step, and there are the multi-step inference models that use multiple steps and "test time compute" to give better answers to complex questions.

The single-shot inference types of reasoning models do rely on prompt engineering to get the LLM to do its reasoning steps. Many of the basic prmpt engineering ideas are applicable here:

  • Basic step prompting ("Let's think step by step")
  • Emotional prompting
  • Roles/personas
  • CoT prompting
  • Zero-shot CoT prompting
  • Echo prompting ("Let's repeat the question")
  • Self-consistency
  • Self-ask (followup questions)
  • Exemplars (In-Content Learning)

The major LRMs are using more advanced meta-prompts for reasoning, for either single-step or multi-step reasoning, but these prompts are commercially sensitive and not usually available. Interestingly, the meta-prompt for the single-step DeepSeek R1 reasoning model was disclosed in their paper (https://arxiv.org/abs/2501.12948): 

    A conversation between User and Assistant. The user asks a question, and the Assistant solves it.
    The assistant first thinks about the reasoning process in the mind and then provides the user
    with the answer. The reasoning process and answer are enclosed within <think> </think> and
    <answer> </answer> tags, respectively, i.e., <think> reasoning process here </think>
    <answer> answer here </answer>. User: PROMPT. Assistant:

Fine-tuning on a more specialized subset of relevant data is a particular submethod of this area. There has been much improvement in this area, in both the capabilities of high-end large SOTA models and also at the other end of the spectrum with Small Language Models (SLMs). See more about training methods, but note that there hasn't yet been much research about fine-tuning of reasoning capabilities.

Inference-Based Reasoning

Black Box Reasoning is the use of multiple steps of inference, wrapped around an LLM. The second idea is to treat the LLM as a "black box" and try to use more LLM calls to improve its reasoning abilities. These are called "few-shot" or "many-shot" or "multi-step" reasoning methods.

Chain-of-thought is the best known of these methods, having been adopted by OpenAI for the "o1" models released in September, 2024. However, multi-step reasoning is a longstanding area of research, with much overlap with prompt engineering techniques. There are numerous methods of doing this type of multiple calls to LLMs in the literature:

  • Chain-of-thought (CoT)
  • Self-reflection
  • Skeleton-of-thought
  • Best-of-N (BoN) method
  • Majority voting
  • Self-consistency decoding
  • Programmatic prompting
  • Tree-of-Thoughts (ToT) prompting
  • Chain-of-Symbols (CoS) prompting
  • Graph-of-Thoughts (GoT)
  • Algorithm-of-Thoughts (AoT)
  • Buffer of Thoughts
  • Least-to-Most prompting
  • Chain-of-Table prompting
  • Thread-of-Thought (ThoT) prompting
  • System 2 Attention (S2A) prompting
  • Chain-of-Verification (CoVe) prompting
  • ReAct prompting (reason-and-act)
  • Rephrase-and-Respond (RaR) prompting
  • Chain-of-Knowledge (CoK) prompting
  • Contrastive Chain-of-Thought (CCoT) prompting
  • Program of Thoughts (PoT) prompting
  • Structured Chain-of-Thought (SCoT) prompting
  • Chain-of-Code (CoC) prompting
  • Take a Step Back prompting

Also related to these areas are the various other ways to have the LLM give a "better" answer, even if it's not really using improved reasoning. The simplest ideas include prompt engineering techniques to give the LLM a better query, RAG architectures and Retrieval Augmented Language Models (RALM) to give an LLM more relevant source data, and also dynamic tool usage integrations to generalize the LLM's capabilities to handle answers that require computations. Also relevant is the research on improving answers by fixing specific LLM limitations such as hallucinations, mathematical problem solving difficulties, and language wordplay (in)abilities.

Long Answers versus Multiple Inference Steps

One of the nuances in the distinction between zero-shot reasoner models and multiple steps of inference is the simplest of ideas: output longer answers. Large Reasoner Models with a single-step architecture, such as DeepSeek R1, mimic the steps of reasoning by repeatedly extending the answers with re-phrased reasoning steps about the problem. This is analogous to multi-step inference reasoning, but the model is "talking to itself" about how to reason through the problem, all in one step of inference.

In effect, the sequence of multiple outputs in chained multi-step reasoning is merged into a single output stream of text. The model is deciding whether or not another step is required as part of the normal decoding phase. The output from these types of single-step reasoner models is a readable sequence showing how the model thought through a problem. Hence, the output to achieve a final answer can be a very long token sequence, which can be costly, and it's important to not restrict the "max tokens" settings in these cases.

Inference costs are obviously higher for producing an extended answer with many of the intermediate thoughts written to the answer. However, the number of tokens in multi-step inference is also high. Whether a single-inference model's long answer will be more or less tokens than a multi-step implementation of Chain-of-Thought is not really clear (need some papers!), but the reasoning ability is high for either approach.

Survey Papers on LLM Reasoning

Survey and review papers on reasoning:

  • 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.)
  • 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
  • Alhassan Mumuni, Fuseini Mumuni, 6 Jan 2025, Large language models for artificial general intelligence (AGI): A survey of foundational principles and approaches, https://arxiv.org/abs/2501.03151
  • 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
  • Violet Xiang, Charlie Snell, Kanishk Gandhi, Alon Albalak, Anikait Singh, Chase Blagden, Duy Phung, Rafael Rafailov, Nathan Lile, Dakota Mahan, Louis Castricato, Jan-Philipp Franken, Nick Haber, Chelsea Finn, 8 Jan 2025, Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-Thought, https://arxiv.org/abs/2501.04682
  • Fengli Xu, Qianyue Hao, Zefang Zong, Jingwei Wang, Yunke Zhang, Jingyi Wang, Xiaochong Lan, Jiahui Gong, Tianjian Ouyang, Fanjin Meng, Chenyang Shao, Yuwei Yan, Qinglong Yang, Yiwen Song, Sijian Ren, Xinyuan Hu, Yu Li, Jie Feng, Chen Gao, Yong Li, 17 Jan 2025 (v2), Towards Large Reasoning Models: A Survey on Scaling LLM Reasoning Capabilities, https://arxiv.org/abs/2501.09686
  • Jie Huang and Kevin Chen-Chuan Chang. July 2023. Towards Reasoning in Large Language Models: A Survey. In Findings of the Association for Computational Linguistics: ACL 2023, pages 1049–1065, Toronto, Canada. Association for Computational Linguistics. https://aclanthology.org/2023.findings-acl.67/
  • Seungpil Lee, Woochang Sim, Donghyeon Shin, Wongyu Seo, Jiwon Park, Seokki Lee, Sanha Hwang, Sejin Kim, and Sundong Kim. Jan 2025. Reasoning Abilities of Large Language Models: In-Depth Analysis on the Abstraction and Reasoning Corpus. ACM Trans. Intell. Syst. Technol. https://doi.org/10.1145/3712701 https://dl.acm.org/doi/10.1145/3712701 https://dl.acm.org/doi/pdf/10.1145/3712701
  • 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.)
  • Mohit Sewak, Ph.D., January 29, 2025, Achieving General Intelligence (AGI) and Super Intelligence (ASI): Pathways, Uncertainties, and Ethical Concerns, https://towardsai.net/p/l/achieving-general-intelligence-agi-and-super-intelligence-asi-pathways-uncertainties-and-ethical-concerns
  • Avinash Patil, 5 Feb 2025, Advancing Reasoning in Large Language Models: Promising Methods and Approaches, https://arxiv.org/abs/2502.03671
  • 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
  • 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
  • Fengxiang Cheng, Haoxuan Li, Fenrong Liu, Robert van Rooij, Kun Zhang, Zhouchen Lin, 24 Feb 2025 (v2), Empowering LLMs with Logical Reasoning: A Comprehensive Survey, https://arxiv.org/abs/2502.15652
  • Cameron R. Wolfe, Feb 18, 2025, Demystifying Reasoning Models: Understanding reasoning models and their relation to standard LLMs... https://cameronrwolfe.substack.com/p/demystifying-reasoning-models
  • 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
  • Komal Kumar, Tajamul Ashraf, Omkar Thawakar, Rao Muhammad Anwer, Hisham Cholakkal, Mubarak Shah, Ming-Hsuan Yang, Phillip H.S. Torr, Salman Khan, Fahad Shahbaz Khan, 28 Feb 2025, LLM Post-Training: A Deep Dive into Reasoning Large Language Models, https://arxiv.org/abs/2502.21321 https://github.com/mbzuai-oryx/Awesome-LLM-Post-training
  • Guiyao Tie, Zeli Zhao, Dingjie Song, Fuyang Wei, Rong Zhou, Yurou Dai, Wen Yin, Zhejian Yang, Jiangyue Yan, Yao Su, Zhenhan Dai, Yifeng Xie, Yihan Cao, Lichao Sun, Pan Zhou, Lifang He, Hechang Chen, Yu Zhang, Qingsong Wen, Tianming Liu, Neil Zhenqiang Gong, Jiliang Tang, Caiming Xiong, Heng Ji, Philip S. Yu, Jianfeng Gao, 8 Mar 2025, A Survey on Post-training of Large Language Models, https://arxiv.org/abs/2503.06072
  • 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.)
  • Yaoting Wang, Shengqiong Wu, Yuecheng Zhang, William Wang, Ziwei Liu, Jiebo Luo, Hao Fei, 16 Mar 2025, Multimodal Chain-of-Thought Reasoning: A Comprehensive Survey, https://arxiv.org/abs/2503.12605
  • Dibyanayan Bandyopadhyay, Soham Bhattacharjee, Asif Ekbal, 13 Mar 2025, Thinking Machines: A Survey of LLM based Reasoning Strategies, https://arxiv.org/abs/2503.10814
  • Xiaoye Qu, Yafu Li, Zhaochen Su, Weigao Sun, Jianhao Yan, Dongrui Liu, Ganqu Cui, Daizong Liu, Shuxian Liang, Junxian He, Peng Li, Wei Wei, Jing Shao, Chaochao Lu, Yue Zhang, Xian-Sheng Hua, Bowen Zhou, Yu Cheng, 27 Mar 2025, A Survey of Efficient Reasoning for Large Reasoning Models: Language, Multimodality, and Beyond, https://arxiv.org/abs/2503.21614
  • Ali Forootani, 22 Mar 2025, A Survey on Mathematical Reasoning and Optimization with Large Language Models, https://arxiv.org/abs/2503.17726
  • Qianjun Pan, Wenkai Ji, Yuyang Ding, Junsong Li, Shilian Chen, Junyi Wang, Jie Zhou, Qin Chen, Min Zhang, Yulan Wu, Liang He, 8 May 2025 (v2), A Survey of Slow Thinking-based Reasoning LLMs using Reinforced Learning and Inference-time Scaling Law, https://arxiv.org/abs/2505.02665
  • Zixuan Ke, Fangkai Jiao, Yifei Ming, Xuan-Phi Nguyen, Austin Xu, Do Xuan Long, Minzhi Li, Chengwei Qin, Peifeng Wang, Silvio Savarese, Caiming Xiong, Shafiq Joty, 5 Aug 2025 (v3), A Survey of Frontiers in LLM Reasoning: Inference Scaling, Learning to Reason, and Agentic Systems, https://arxiv.org/abs/2504.09037

Reasoning Theory

Papers about the deeper theory of what "reasoning" means:

  • Eghbal Hosseini, Colton Casto, Noga Zaslavsky, Colin Conwell, Mark Richardson, Evelina Fedorenko, Dec 2024, Universality of representation in biological and artificial neural networks, bioRxiv 2024.12.26.629294; doi: https://doi.org/10.1101/2024.12.26.629294 https://www.biorxiv.org/content/10.1101/2024.12.26.629294
  • 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.)
  • G Bao, H Zhang, C Wang, L Yang, Y Zhang, Jan 2025, How Likely Do LLMs with CoT Mimic Human Reasoning? Proceedings of the 31st International Conference on Computational Linguistics, pages 7831–7850, January 19–24, 2025, https://aclanthology.org/2025.coling-main.524.pdf
  • Santosh Kumar Radha, Oktay Goktas, 23 Jan 2025, On the Reasoning Capacity of AI Models and How to Quantify It, https://arxiv.org/abs/2501.13833
  • Alireza Amiri, Xinting Huang, Mark Rofin, Michael Hahn, 4 Feb 2025, Lower Bounds for Chain-of-Thought Reasoning in Hard-Attention Transformers, https://arxiv.org/abs/2502.02393
  • 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.)
  • Xinhao Yao, Ruifeng Ren, Yun Liao, Yong Liu, 7 Feb 2025, Unveiling the Mechanisms of Explicit CoT Training: How Chain-of-Thought Enhances Reasoning Generalization, https://arxiv.org/abs/2502.04667
  • 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
  • Kechen Li, Wenqi Zhu, Coralia Cartis, Tianbo Ji, Shiwei Liu, 27 Feb 2025, SoS1: O1 and R1-Like Reasoning LLMs are Sum-of-Square Solvers, https://arxiv.org/abs/2502.20545
  • Yijiong Yu, 16 Jan 2025 (v4), Do LLMs Really Think Step-by-step In Implicit Reasoning? https://arxiv.org/abs/2411.15862 https://github.com/yuyijiong/if_step_by_step_implicit_CoT
  • Marius Jahrens, Thomas Martinetz, 12 Mar 2025, Why LLMs Cannot Think and How to Fix It, https://arxiv.org/abs/2503.09211
  • Pengcheng Wen, Jiaming Ji, Chi-Min Chan, Juntao Dai, Donghai Hong, Yaodong Yang, Sirui Han, Yike Guo, 17 Mar 2025, ThinkPatterns-21k: A Systematic Study on the Impact of Thinking Patterns in LLMs, https://arxiv.org/abs/2503.12918
  • Dibyanayan Bandyopadhyay, Soham Bhattacharjee, Asif Ekbal, 13 Mar 2025, Thinking Machines: A Survey of LLM based Reasoning Strategies, https://arxiv.org/abs/2503.10814
  • Jiaran Ye, Zijun Yao, Zhidian Huang, Liangming Pan, Jinxin Liu, Yushi Bai, Amy Xin, Liu Weichuan, Xiaoyin Che, Lei Hou, Juanzi Li, 29 May 2025, How does Transformer Learn Implicit Reasoning? https://arxiv.org/abs/2505.23653
  • Róbert Csordás, Christopher D. Manning, Christopher Potts, 30 May 2025 (v2), Do Language Models Use Their Depth Efficiently? https://arxiv.org/abs/2505.13898

Reasoning Model Evaluation

Papers about testing LLMs (and overall systems) for their reasoning abilities:

  • 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
  • Fengli Xu, Qianyue Hao, Zefang Zong, Jingwei Wang, Yunke Zhang, Jingyi Wang, Xiaochong Lan, Jiahui Gong, Tianjian Ouyang, Fanjin Meng, Chenyang Shao, Yuwei Yan, Qinglong Yang, Yiwen Song, Sijian Ren, Xinyuan Hu, Yu Li, Jie Feng, Chen Gao, Yong Li, 17 Jan 2025 (v2), Towards Large Reasoning Models: A Survey on Scaling LLM Reasoning Capabilities, https://arxiv.org/abs/2501.09686
  • 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.)
  • Santosh Kumar Radha, Oktay Goktas, 23 Jan 2025, On the Reasoning Capacity of AI Models and How to Quantify It, https://arxiv.org/abs/2501.13833
  • Ben Dickson, January 31, 2025, Beyond benchmarks: How DeepSeek-R1 and o1 perform on real-world tasks, https://venturebeat.com/ai/beyond-benchmarks-how-deepseek-r1-and-o1-perform-on-real-world-tasks/
  • Guizhen Chen, Weiwen Xu, Hao Zhang, Hou Pong Chan, Chaoqun Liu, Lidong Bing, Deli Zhao, Anh Tuan Luu, Yu Rong, 27 Feb 2025, FINEREASON: Evaluating and Improving LLMs' Deliberate Reasoning through Reflective Puzzle Solving, https://arxiv.org/abs/2502.20238
  • Avinash Patil, 5 Feb 2025, Advancing Reasoning in Large Language Models: Promising Methods and Approaches, https://arxiv.org/abs/2502.03671
  • Komal Kumar, Tajamul Ashraf, Omkar Thawakar, Rao Muhammad Anwer, Hisham Cholakkal, Mubarak Shah, Ming-Hsuan Yang, Phillip H.S. Torr, Salman Khan, Fahad Shahbaz Khan, 28 Feb 2025, LLM Post-Training: A Deep Dive into Reasoning Large Language Models, https://arxiv.org/abs/2502.21321 https://github.com/mbzuai-oryx/Awesome-LLM-Post-training
  • 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.)
  • Yansheng Qiu, Li Xiao, Zhaopan Xu, Pengfei Zhou, Zheng Wang, Kaipeng Zhang, 16 May 2025, Human-Aligned Bench: Fine-Grained Assessment of Reasoning Ability in MLLMs vs. Humans, https://arxiv.org/abs/2505.11141
  • Michael Nuñez, July 15, 2025, OpenAI, Google DeepMind and Anthropic sound alarm: ‘We may be losing the ability to understand AI’, https://venturebeat.com/ai/openai-google-deepmind-and-anthropic-sound-alarm-we-may-be-losing-the-ability-to-understand-ai/ (Monitoring the text-based interim "thinking-out-loud" reasoning of models in CoT.)
  • Tomek Korbak, Mikita Balesni, (and many more authors) July 2025, Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safety, https://tomekkorbak.com/cot-monitorability-is-a-fragile-opportunity/cot_monitoring.pdf
  • Shima Imani, Liang Du, Harsh Shrivastava, 4 Mar 2023, MathPrompter: Mathematical Reasoning using Large Language Models, https://arxiv.org/abs/2303.05398 (Assess confidence of math reasoning and also modify the prompting, uses a CoT style reasoning that generates multiple equations and Python scripts to solve a problem, and then evaluates them.)

Large Reasoning Models (LRMs)

Large Reasoning Models (LRMs) are large-scale LLMs that have been trained on advanced reasoning capabilities. Their architecture may be training-only, but increasingly the architectures include multi-step inference or "test time compute" reasoning capabilities such as Chain-of-Thought.

Papers on large reasoning models:

  • Ignacio de Gregorio, Dec 2024, Uncovering OpenAI’s Frontier AI Strategy, https://medium.com/@ignacio.de.gregorio.noblejas/uncovering-openais-frontier-ai-strategy-a02e0aa5320e
  • 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.)
  • Fengli Xu, Qianyue Hao, Zefang Zong, Jingwei Wang, Yunke Zhang, Jingyi Wang, Xiaochong Lan, Jiahui Gong, Tianjian Ouyang, Fanjin Meng, Chenyang Shao, Yuwei Yan, Qinglong Yang, Yiwen Song, Sijian Ren, Xinyuan Hu, Yu Li, Jie Feng, Chen Gao, Yong Li, 17 Jan 2025 (v2), Towards Large Reasoning Models: A Survey on Scaling LLM Reasoning Capabilities, https://arxiv.org/abs/2501.09686
  • OpenAI, September 12, 2024 Learning to reason with LLMs. We are introducing OpenAI o1, a new large language model trained with reinforcement learning to perform complex reasoning. o1 thinks before it answers—it can produce a long internal chain of thought before responding to the user. https://openai.com/index/learning-to-reason-with-llms/
  • 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
  • Jie Huang and Kevin Chen-Chuan Chang. July 2023. Towards Reasoning in Large Language Models: A Survey. In Findings of the Association for Computational Linguistics: ACL 2023, pages 1049–1065, Toronto, Canada. Association for Computational Linguistics. https://aclanthology.org/2023.findings-acl.67/
  • Seungpil Lee, Woochang Sim, Donghyeon Shin, Wongyu Seo, Jiwon Park, Seokki Lee, Sanha Hwang, Sejin Kim, and Sundong Kim. Jan 2025. Reasoning Abilities of Large Language Models: In-Depth Analysis on the Abstraction and Reasoning Corpus. ACM Trans. Intell. Syst. Technol. https://doi.org/10.1145/3712701 https://dl.acm.org/doi/10.1145/3712701 https://dl.acm.org/doi/pdf/10.1145/3712701
  • Demis Hassabis, Jan 2025, X post: Announcing Gemini 2.0 Flash https://x.com/demishassabis/status/1881844417746632910 (Gemini 2.0 Flash from Google is a Large Reasoning Model with a 1M ultra-long context.)
  • 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.)
  • Alberto Romero, Jan 2025, DeepSeek, a little-known Chinese startup, released R1 yesterday, https://substack.com/@thealgorithmicbridge/note/c-87664591-
  • DeepSeek-AI, Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, Xiaokang Zhang, Xingkai Yu, Yu Wu, Z.F. Wu, Zhibin Gou, Zhihong Shao, Zhuoshu Li, Ziyi Gao, Aixin Liu, Bing Xue, Bingxuan Wang, Bochao Wu, Bei Feng, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chenyu Zhang, Chong Ruan, Damai Dai, Deli Chen, Dongjie Ji, Erhang Li, Fangyun Lin, Fucong Dai, Fuli Luo, Guangbo Hao, Guanting Chen, Guowei Li, H. Zhang, Han Bao, Hanwei Xu, Haocheng Wang, Honghui Ding, Huajian Xin, Huazuo Gao, Hui Qu, Hui Li, Jianzhong Guo, et al. (100+ additional authors not shown), 22 Jan 2025, DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning, https://arxiv.org/abs/2501.12948 (The DeepSeek R1 large reasoning model.)
  • 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
  • Ben Dickson, January 31, 2025, Beyond benchmarks: How DeepSeek-R1 and o1 perform on real-world tasks, https://venturebeat.com/ai/beyond-benchmarks-how-deepseek-r1-and-o1-perform-on-real-world-tasks/
  • Deqian Kong, Minglu Zhao, Dehong Xu, Bo Pang, Shu Wang, Edouardo Honig, Zhangzhang Si, Chuan Li, Jianwen Xie, Sirui Xie, Ying Nian Wu, 3 Feb 2025, Scalable Language Models with Posterior Inference of Latent Thought Vectors, https://arxiv.org/abs/2502.01567
  • 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.)
  • DiJia Su, Hanlin Zhu, Yingchen Xu, Jiantao Jiao, Yuandong Tian, Qinqing Zheng, 5 Feb 2025. Token Assorted: Mixing Latent and Text Tokens for Improved Language Model Reasoning, https://arxiv.org/abs/2502.03275
  • Cameron R. Wolfe, Feb 18, 2025, Demystifying Reasoning Models: Understanding reasoning models and their relation to standard LLMs... https://cameronrwolfe.substack.com/p/demystifying-reasoning-models
  • Jeremy Kahn, February 28, 2025, OpenAI launches long-awaited GPT-4.5 — but ‘Orion’s’ capabilities already lag competitors, https://fortune.com/2025/02/27/openai-gpt-4-5-orion-launch-sam-altman-benchmarks/
  • Komal Kumar, Tajamul Ashraf, Omkar Thawakar, Rao Muhammad Anwer, Hisham Cholakkal, Mubarak Shah, Ming-Hsuan Yang, Phillip H.S. Torr, Salman Khan, Fahad Shahbaz Khan, 28 Feb 2025, LLM Post-Training: A Deep Dive into Reasoning Large Language Models, https://arxiv.org/abs/2502.21321 https://github.com/mbzuai-oryx/Awesome-LLM-Post-training
  • Asif Razzaq, March 5, 2025, Qwen Releases QwQ-32B: A 32B Reasoning Model that Achieves Significantly Enhanced Performance in Downstream Task, https://www.marktechpost.com/2025/03/05/qwen-releases-qwq-32b-a-32b-reasoning-model-that-achieves-significantly-enhanced-performance-in-downstream-task/ (Features 32B parameters, 32K context length, 64 layers, RoPE, SwiGLU, RMSNorm, and attention enhancements.)
  • Parshin Shojaee, Maxwell Horton, Iman Mirzadeh, Samy Bengio, Keivan Alizadeh, June 2025, The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity, Apple, https://machinelearning.apple.com/research/illusion-of-thinking https://ml-site.cdn-apple.com/papers/the-illusion-of-thinking.pdf
  • Dr. Ashish Bamania, June 2025, Apple’s New Research Shows That LLM Reasoning Is Completely Broken: A deep dive into Apple research that exposes the flawed thinking process in state-of-the-art Reasoning LLMs, https://ai.gopubby.com/apples-new-research-shows-that-llm-reasoning-is-completely-broken-47b5be71a06a
  • Ryan Browne, Jun 10 2025, Microsoft-backed AI lab Mistral is launching its first reasoning model in challenge to OpenAI, https://www.cnbc.com/2025/06/10/microsoft-backed-ai-lab-mistral-debuts-reasoning-model-to-rival-openai.html (Mistral's new LRM has multilingual reasoning.)
  • Bowen Ding, Yuhan Chen, Futing Wang, Lingfeng Ming, Tao Lin, 30 Jun 2025, Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model, https://arxiv.org/abs/2506.23840
  • Bin Hong, Jiayu Liu, Zhenya Huang, Kai Zhang, Mengdi Zhang, 13 Aug 2025, Pruning Long Chain-of-Thought of Large Reasoning Models via Small-Scale Preference Optimization, https://arxiv.org/abs/2508.10164
  • Zhipeng Chen, Xiaobo Qin, Youbin Wu, Yue Ling, Qinghao Ye, Wayne Xin Zhao, Guang Shi, 14 Aug 2025, Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models, https://arxiv.org/abs/2508.10751
  • Datta Nimmaturi, Vaishnavi Bhargava, Rajat Ghosh, Johnu George, Debojyoti Dutta, 24 Jul 2025, Predictive Scaling Laws for Efficient GRPO Training of Large Reasoning Models, https://arxiv.org/abs/2507.18014
  • Kaiwen Chen, Xin Tan, Minchen Yu, Hong Xu, 29 Jul 2025, MemShare: Memory Efficient Inference for Large Reasoning Models through KV Cache Reuse, https://arxiv.org/abs/2507.21433
  • Tao He, Rongchuan Mu, Lizi Liao, Yixin Cao, Ming Liu, and Bing Qin, 31 Jul 2025, Good Learners Think Their Thinking: Generative PRM Makes Large Reasoning Model More Efficient Math Learner, https://arxiv.org/abs/2507.23317
  • Dadi Guo, Jiayu Liu, Zhiyuan Fan, Zhitao He, Haoran Li, Yumeng Wang, Yi R. Fung, 31 Jul 2025, Mathematical Proof as a Litmus Test: Revealing Failure Modes of Advanced Large Reasoning Models, https://arxiv.org/abs/2506.17114
  • Linan Yue, Yichao Du, Yizhi Wang, Weibo Gao, Fangzhou Yao, Li Wang, Ye Liu, Ziyu Xu, Qi Liu, Shimin Di, Min-Ling Zhang, 4 Aug 2025, Don't Overthink It: A Survey of Efficient R1-style Large Reasoning Models, https://arxiv.org/abs/2508.02120
  • Yule Liu, Jingyi Zheng, Zhen Sun, Zifan Peng, Wenhan Dong, Zeyang Sha, Shiwen Cui, Weiqiang Wang, Xinlei He, 4 Aug 2025, Thought Manipulation: External Thought Can Be Efficient for Large Reasoning Models, https://arxiv.org/abs/2504.13626
  • Junhong Wu, Jinliang Lu, Zixuan Ren, Ganqiang Hu, Zhi Wu, Dai Dai, Hua Wu, 5 Aug 2025, LLMs Have a Heart of Stone: Demystifying the Soft Thinking Ability of Large Reasoning Models, https://arxiv.org/abs/2508.03440
  • Yuan Xun, Xiaojun Jia, Xinwei Liu, Hua Zhang, 6 Aug 2025, The Emotional Baby Is Truly Deadly: Does your Multimodal Large Reasoning Model Have Emotional Flattery towards Humans?, https://arxiv.org/abs/2508.03986
  • Rui Ha, Chaozhuo Li, Rui Pu, Sen Su, 6 Aug 2025, From "Aha Moments" to Controllable Thinking: Toward Meta-Cognitive Reasoning in Large Reasoning Models via Decoupled Reasoning and Control, https://arxiv.org/abs/2508.04460
  • Thilo Hagendorff, Erik Derner, Nuria Oliver, 4 Aug 2025, Large Reasoning Models Are Autonomous Jailbreak Agents, https://arxiv.org/abs/2508.04039
  • Yuquan Wang, Mi Zhang, Yining Wang, Geng Hong, Xiaoyu You, Min Yang, 6 Aug 2025, ReasoningGuard: Safeguarding Large Reasoning Models with Inference-time Safety Aha Moments, https://arxiv.org/abs/2508.04204
  • Yongjiang Liu, Haoxi Li, Xiaosong Ma, Jie Zhang, Song Guo, 6 Aug 2025, Think How to Think: Mitigating Overthinking with Autonomous Difficulty Cognition in Large Reasoning Models, https://arxiv.org/abs/2507.02663
  • Youcheng Huang, Bowen Qin, Chen Huang, Duanyu Feng, Xi Yang, Wenqiang Lei, 15 Aug 2025, Beyond Solving Math Quiz: Evaluating the Ability of Large Reasoning Models to Ask for Information, https://arxiv.org/abs/2508.11252
  • Nuo Chen, Zhiyuan Hu, Qingyun Zou, Jiaying Wu, Qian Wang, Bryan Hooi, Bingsheng He, 20 Aug 2025, JudgeLRM: Large Reasoning Models as a Judge, https://arxiv.org/abs/2504.00050
  • Haonan Dong, Haoran Ye, Wenhao Zhu, Kehan Jiang, Guojie Song, 24 Aug 2025, Meta-R1: Empowering Large Reasoning Models with Metacognition, https://arxiv.org/abs/2508.17291
  • Yi Liu and Xiangyu Liu and Zequn Sun and Wei Hu, 26 Aug 2025, Answering the Unanswerable Is to Err Knowingly: Analyzing and Mitigating Abstention Failures in Large Reasoning Models, https://arxiv.org/abs/2508.18760
  • Microsoft, 17 Sep, 2025, GPT-5 vs GPT-4.1: choosing the right model for your use case https://learn.microsoft.com/en-us/azure/ai-foundry/foundry-models/how-to/model-choice-guide
  • Zhengxiang Cheng, Dongping Chen, Mingyang Fu, Tianyi Zhou, 11 Sep 2025, Optimizing Length Compression in Large Reasoning Models, https://arxiv.org/abs/2506.14755
  • Kaiyan Zhang, Yuxin Zuo, Bingxiang He, Youbang Sun, Runze Liu, Che Jiang, Yuchen Fan, Kai Tian, Guoli Jia, Pengfei Li, Yu Fu, Xingtai Lv, Yuchen Zhang, Sihang Zeng, Shang Qu, Haozhan Li, Shijie Wang, Yuru Wang, Xinwei Long, Fangfu Liu, Xiang Xu, Jiaze Ma, Xuekai Zhu, Ermo Hua, Yihao Liu, Zonglin Li, Huayu Chen, Xiaoye Qu, Yafu Li, Weize Chen, Zhenzhao Yuan, Junqi Gao, Dong Li, Zhiyuan Ma, Ganqu Cui, Zhiyuan Liu, Biqing Qi, Ning Ding, Bowen Zhou, 18 Sep 2025, A Survey of Reinforcement Learning for Large Reasoning Models, https://arxiv.org/abs/2509.08827
  • Yanlong Wang, Jian Xu, Fei Ma, Hongkang Zhang, Hang Yu, Tiantian Gao, Yu Wang, Haochen You, Shao-Lun Huang, Danny Dongning Sun, Xiao-Ping Zhang, 10 Sep 2025, FinZero: Launching Multi-modal Financial Time Series Forecast with Large Reasoning Model, https://arxiv.org/abs/2509.08742
  • Nan Zhang, Eugene Kwek, Yusen Zhang, Ngoc-Hieu Nguyen, Prasenjit Mitra, Rui Zhang, 2 Oct 2025, When Reasoning Meets Compression: Understanding the Effects of LLMs Compression on Large Reasoning Models, https://arxiv.org/abs/2504.02010
  • Jingcong Liang, Shijun Wan, Xuehai Wu, Siyuan Wang, Yitong Li, Qianglong Chen, Duyu Tang, Zhongyu Wei, 14 Oct 2025, HardcoreLogic: Challenging Large Reasoning Models with Long-tail Logic Puzzle Games, https://arxiv.org/abs/2510.12563
  • Yujian Zhang, Keyu Chen, Zhifeng Shen, Ruizhi Qiao, Xing Sun, 14 Oct 2025, Adaptive Dual Reasoner: Large Reasoning Models Can Think Efficiently by Hybrid Reasoning, https://arxiv.org/abs/2510.10207
  • Bowen Qin, Chen Yue, Fang Yin, Hui Wang, JG Yao, Jiakang Liu, Jing-Shu Zheng, Miguel Hu Chen, Richeng Xuan, Shibei Meng, Shiqi Zhou, Teng Dai, Tong-Shuai Ren, Wei Cui, Xi Yang, Xialin Du, Xiaojing Xu, Xue Sun, Xuejing Li, Yaming Liu, Yesheng Liu, Ying Liu, Yonghua Lin, Yu Zhao, Yunduo Zhang, Yuwen Luo, Zheqi He, Zhiyuan He, Zhongyuan Wang, 14 Oct 2025, FlagEval Findings Report: A Preliminary Evaluation of Large Reasoning Models on Automatically Verifiable Textual and Visual Questions, https://arxiv.org/abs/2509.17177
  • Tsung-Han Wu, Mihran Miroyan, David M. Chan, Trevor Darrell, Narges Norouzi, Joseph E. Gonzalez, 14 Oct 2025, Are Large Reasoning Models Interruptible?, https://arxiv.org/abs/2510.11713
  • ShengYun Peng, Eric Smith, Ivan Evtimov, Song Jiang, Pin-Yu Chen, Hongyuan Zhan, Haozhu Wang, Duen Horng Chau, Mahesh Pasupuleti, Jianfeng Chi, 1 Oct 2025, Large Reasoning Models Learn Better Alignment from Flawed Thinking, https://arxiv.org/abs/2510.00938
  • Gouki Minegishi, Hiroki Furuta, Takeshi Kojima, Yusuke Iwasawa, Yutaka Matsuo, 1 Oct 2025, Topology of Reasoning: Understanding Large Reasoning Models through Reasoning Graph Properties, https://arxiv.org/abs/2506.05744
  • Hans Peter Lynsg{\o}e Raaschou-jensen and Constanza Fierro and Anders S{\o}gaard, 1 Oct 2025, Towards a Progress Bar for Reasoning: Progress Prediction in Large Reasoning Models, https://arxiv.org/abs/2506.23274
  • Tommaso Green, Martin Gubri, Haritz Puerto, Sangdoo Yun, Seong Joon Oh, 1 Oct 2025, Leaky Thoughts: Large Reasoning Models Are Not Private Thinkers, https://arxiv.org/abs/2506.15674
  • Runzhe Zhan, Zhihong Huang, Xinyi Yang, Lidia S. Chao, Min Yang, Derek F. Wong, 23 Oct 2025, Are Large Reasoning Models Good Translation Evaluators? Analysis and Performance Boost, https://arxiv.org/abs/2510.20780
  • Zhehao Zhang, Weijie Xu, Shixian Cui, Chandan K. Reddy, 17 Oct 2025, Distractor Injection Attacks on Large Reasoning Models: Characterization and Defense, https://arxiv.org/abs/2510.16259
  • Mohan Zhang, Yihua Zhang, Jinghan Jia, Zhangyang Wang, Sijia Liu, Tianlong Chen, 12 Oct 2025, One Token Embedding Is Enough to Deadlock Your Large Reasoning Model, https://arxiv.org/abs/2510.15965
  • Giacomo Camposampiero, Michael Hersche, Roger Wattenhofer, Abu Sebastian, Abbas Rahimi, 20 Oct 2025, I-RAVEN-X: Benchmarking Generalization and Robustness of Analogical and Mathematical Reasoning in Large Language and Reasoning Models, https://arxiv.org/abs/2510.17496
  • G M Shahariar, Ali Nazari, Erfan Shayegani, Nael Abu-Ghazaleh, 25 Oct 2025, Modeling Hierarchical Thinking in Large Reasoning Models, https://arxiv.org/abs/2510.22437
  • Hoang Phan, Xianjun Yang, Kevin Yao, Jingyu Zhang, Shengjie Bi, Xiaocheng Tang, Madian Khabsa, Lijuan Liu, Deren Lei, 24 Oct 2025, Beyond Reasoning Gains: Mitigating General Capabilities Forgetting in Large Reasoning Models, https://arxiv.org/abs/2510.21978
  • Changyi Li, Jiayi Wang, Xudong Pan, Geng Hong, Min Yang, 15 Oct 2025, ReasoningShield: Safety Detection over Reasoning Traces of Large Reasoning Models, https://arxiv.org/abs/2505.17244
  • Rubing Yang, Huajun Bai, Song Liu, Guanghua Yu, Runzhi Fan, Yanbin Dang, Jiejing Zhang, Kai Liu, Jianchen Zhu, Peng Chen, 21 Oct 2025, SpecExit: Accelerating Large Reasoning Model via Speculative Exit, https://arxiv.org/abs/2509.24248
  • Yi Lu, Jianing Wang, Linsen Guo, Wei He, Hongyin Tang, Tao Gui, Xuanjing Huang, Xuezhi Cao, Wei Wang, Xunliang Cai, 21 Oct 2025, R-Horizon: How Far Can Your Large Reasoning Model Really Go in Breadth and Depth?, https://arxiv.org/abs/2510.08189
  • Junjie Zhang, Guozheng Ma, Shunyu Liu, Haoyu Wang, Jiaxing Huang, Ting-En Lin, Fei Huang, Yongbin Li, Dacheng Tao, 25 Sep 2025, A Simple "Motivation" Can Enhance Reinforcement Finetuning of Large Reasoning Models, https://arxiv.org/abs/2506.18485
  • Jinyi Han, Ying Huang, Ying Liao, Zishang Jiang, Xikun Lu, Haiquan Zhao, Xinyi Wang, Guanghao Zhou, Sihang Jiang, Jiaqing Liang, Weikang Zhou, Zeye Sun, Fei Yu, Yanghua Xiao, 27 Sep 2025, Your Models Have Thought Enough: Training Large Reasoning Models to Stop Overthinking, https://arxiv.org/abs/2509.23392
  • Guanxu Chen, Yafu Li, Yuxian Jiang, Chen Qian, Qihan Ren, Jingyi Yang, Yu Cheng, Dongrui Liu, Jing Shao, 28 Sep 2025, Conditional Advantage Estimation for Reinforcement Learning in Large Reasoning Models, https://arxiv.org/abs/2509.23962
  • Yuhui Wang, Changjiang Li, Guangke Chen, Jiacheng Liang, Ting Wang, 29 Sep 2025, Reasoning or Retrieval? A Study of Answer Attribution on Large Reasoning Models, https://arxiv.org/abs/2509.24156
  • Zihao Zhu, Xinyu Wu, Gehan Hu, Siwei Lyu, Ke Xu, Baoyuan Wu, 29 Sep 2025, AdvChain: Adversarial Chain-of-Thought Tuning for Robust Safety Alignment of Large Reasoning Models, https://arxiv.org/abs/2509.24269
  • Yichi Zhang, Yue Ding, Jingwen Yang, Tianwei Luo, Dongbai Li, Ranjie Duan, Qiang Liu, Hang Su, Yinpeng Dong, Jun Zhu, 29 Sep 2025, Towards Safe Reasoning in Large Reasoning Models via Corrective Intervention, https://arxiv.org/abs/2509.24393
  • Qingjie Zhang, Yujia Fu, Yang Wang, Liu Yan, Tao Wei, Ke Xu, Minlie Huang, Han Qiu, 29 Sep 2025, On the Self-awareness of Large Reasoning Models' Capability Boundaries, https://arxiv.org/abs/2509.24711
  • Yuyang Sha, Hongxin Pan, Gang Luo, Caijuan Shi, Jing Wang, Kefeng Li, 29 Sep 2025, MDD-Thinker: Towards Large Reasoning Models for Major Depressive Disorder Diagnosis, https://arxiv.org/abs/2509.24217
  • Yuxian Jiang, Yafu Li, Guanxu Chen, Dongrui Liu, Yu Cheng, Jing Shao, 29 Sep 2025, Rethinking Entropy Regularization in Large Reasoning Models, https://arxiv.org/abs/2509.25133
  • Yongchan Kwon, Shang Zhu, Federico Bianchi, Kaitlyn Zhou, James Zou, 17 Oct 2025, ReasonIF: Large Reasoning Models Fail to Follow Instructions During Reasoning, https://arxiv.org/abs/2510.15211
  • Mingkang Zhu, Xi Chen, Bei Yu, Hengshuang Zhao, Jiaya Jia, 6 Oct 2025, From Noisy Traces to Stable Gradients: Bias-Variance Optimized Preference Optimization for Aligning Large Reasoning Models, https://arxiv.org/abs/2510.05095
  • Yingzhi Mao (1 and 2), Chunkang Zhang (1 and 2), Junxiang Wang (1), Xinyan Guan (1 and 2), Boxi Cao (1), Yaojie Lu (1), Hongyu Lin (1), Xianpei Han (1 and 2), Le Sun (1 and 2) ((1) Chinese Information Processing Laboratory, Institute of Software, Chinese Academy of Sciences, (2) University of Chinese Academy of Sciences), 24 Oct 2025, When Models Outthink Their Safety: Mitigating Self-Jailbreak in Large Reasoning Models with Chain-of-Guardrails, https://arxiv.org/abs/2510.21285
  • Xiaoxi Li, Jiajie Jin, Guanting Dong, Hongjin Qian, Yongkang Wu, Ji-Rong Wen, Yutao Zhu, Zhicheng Dou, 13 Oct 2025, WebThinker: Empowering Large Reasoning Models with Deep Research Capability, https://arxiv.org/abs/2504.21776
  • Sheikh Shafayat, Fahim Tajwar, Ruslan Salakhutdinov, Jeff Schneider, Andrea Zanette, 8 Oct 2025, Can Large Reasoning Models Self-Train?, https://arxiv.org/abs/2505.21444
  • Adarsha Balaji and Le Chen and Rajeev Thakur and Franck Cappello and Sandeep Madireddy, 22 Sep 2025, Evaluating the Safety and Skill Reasoning of Large Reasoning Models Under Compute Constraints, https://arxiv.org/abs/2509.18382
  • Fr\'ed\'eric Berdoz, Luca A. Lanzend\"orfer, Nick Tuninga, Roger Wattenhofer, 30 Sep 2025, Text-to-Scene with Large Reasoning Models, https://arxiv.org/abs/2509.26091
  • Jiacheng Liang, Tanqiu Jiang, Yuhui Wang, Rongyi Zhu, Fenglong Ma, Ting Wang, 29 Sep 2025, AutoRAN: Automated Hijacking of Safety Reasoning in Large Reasoning Models, https://arxiv.org/abs/2505.10846
  • Gang Li, Ming Lin, Tomer Galanti, Zhengzhong Tu, Tianbao Yang, 30 Sep 2025, DisCO: Reinforcing Large Reasoning Models with Discriminative Constrained Optimization, https://arxiv.org/abs/2505.12366
  • Zhangyue Yin, Qiushi Sun, Zhiyuan Zeng, Zhiyuan Yu, Qipeng Guo, Xuanjing Huang, Xipeng Qiu, 7 Oct 2025, ARISE: An Adaptive Resolution-Aware Metric for Test-Time Scaling Evaluation in Large Reasoning Models, https://arxiv.org/abs/2510.06014

Open Source Reasoning

Open source reasoning projects are those that either: (a) use open-source code to implement multi-step inference-based reasoning algorithms such as Chain-of-Thought (on any underlying model), or (b) Large Reasoning Models where the model weights and architectural details have been open-sourced, such as Deepseek R3.

General Research on Intelligence

What does it mean to be smart? There are various answers to this, and it's a very nuanced question.

Research on intelligence or "smartness" of AI systems:

Chain-of-Thought (CoT) Reasoning

Research papers on chain-of-thought (CoT) for reasoning:

  • Maciej Besta, Florim Memedi, Zhenyu Zhang, Robert Gerstenberger, Guangyuan Piao, Nils Blach, Piotr Nyczyk, Marcin Copik, Grzegorz Kwaśniewski, Jürgen Müller, Lukas Gianinazzi, Ales Kubicek, Hubert Niewiadomski, Aidan O'Mahony, Onur Mutlu, Torsten Hoefler, 5 Apr 2024, Demystifying Chains, Trees, and Graphs of Thoughts, https://arxiv.org/abs/2401.14295 http://htor.ethz.ch/publications/img/besta-topologies.pdf
  • Jacob Pfau, William Merrill, Samuel R. Bowman, 24 Apr 2024, Let's Think Dot by Dot: Hidden Computation in Transformer Language Models, https://arxiv.org/abs/2404.15758
  • Hongxuan Zhang, Zhining Liu, Jiaqi Zheng, Chenyi Zhuang, Jinjie Gu, Guihai Chen, Nov 2023, Fast Chain-of-Thought: A Glance of Future from Parallel Decoding Leads to Answers Faster, https://arxiv.org/abs/2311.08263
  • Hunter Lightman, Vineet Kosaraju, Yura Burda, Harri Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, Karl Cobbe, May 2023, Let's Verify Step by Step, https://arxiv.org/abs/2305.20050
  • 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
  • kipply's blog, 2023-03-30, Transformer Taxonomy (the last lit review), https://kipp.ly/transformer-taxonomy/ (Papers for all the Transformer architectures and milestone papers for the major optimization improvements on them.)
  • Daniel Lopes, June 21, 2024, A Comprehensive Guide to Text Prompt Engineering Techniques, https://journal.daniellopes.dev/p/practical-prompt-engineering-notes
  • Wenxiao Wang, Wei Chen, Yicong Luo, Yongliu Long, Zhengkai Lin, Liye Zhang, Binbin Lin, Deng Cai, Xiaofei He, 15 Feb 2024, Model Compression and Efficient Inference for Large Language Models: A Survey, https://arxiv.org/abs/2402.09748
  • Hao Zhou, Chengming Hu, Ye Yuan, Yufei Cui, Yili Jin, Can Chen, Haolun Wu, Dun Yuan, Li Jiang, Di Wu, Xue Liu, Charlie Zhang, Xianbin Wang, Jiangchuan Liu, 17 May 2024, Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities, https://arxiv.org/abs/2405.10825
  • Yu Wang, Shiwan Zhao, Zhihu Wang, Heyuan Huang, Ming Fan, Yubo Zhang, Zhixing Wang, Haijun Wang, Ting Liu, 5 Sep 2024, Strategic Chain-of-Thought: Guiding Accurate Reasoning in LLMs through Strategy Elicitation, https://arxiv.org/abs/2409.03271
  • Asankhaya Sharma (codelion), Sep 2024, Optillm: Optimizing inference proxy for LLMs, https://github.com/codelion/optillm
  • Ziqi Jin, Wei Lu, 6 Sep 2024, Self-Harmonized Chain of Thought, https://arxiv.org/abs/2409.04057
  • Pranab Sahoo, Ayush Kumar Singh, Sriparna Saha, Vinija Jain, Samrat Mondal, Aman Chadha, 5 Feb 2024, A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications, https://arxiv.org/abs/2402.07927
  • Shizhe Diao, Pengcheng Wang, Yong Lin, Rui Pan, Xiang Liu, Tong Zhang, 21 Jul 2024 (v5), Active Prompting with Chain-of-Thought for Large Language Models, https://arxiv.org/abs/2302.12246 https://github.com/shizhediao/active-prompt
  • Zhuosheng Zhang, Aston Zhang, Mu Li, Alex Smola, 7 Oct 2022, Automatic Chain of Thought Prompting in Large Language Models, https://arxiv.org/abs/2210.03493 https://github.com/amazon-research/auto-cot
  • 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, Learning to Reason with LLMs, https://openai.com/index/learning-to-reason-with-llms/
  • Emilia David, September 12, 2024, How to prompt on OpenAI’s new o1 models, https://venturebeat.com/ai/how-to-prompt-on-openai-o1/ (Prompt engineering is different for o1, such as "don't use chain of thought.")
  • Du Phan, Matthew D. Hoffman, David Dohan, Sholto Douglas, Tuan Anh Le, Aaron Parisi, Pavel Sountsov, Charles Sutton, Sharad Vikram, Rif A. Saurous, 28 Nov 2023, Training Chain-of-Thought via Latent-Variable Inference, https://arxiv.org/abs/2312.02179
  • 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
  • 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
  • Artem Shelamanov, Sep 2024, Why OpenAI’s o1 Model Is A Scam, https://pub.towardsai.net/why-openais-o1-model-is-a-scam-eb3356c3d70e
  • Chung-Yu Wang, Alireza DaghighFarsoodeh, Hung Viet Pham, 24 Sep 2024, Task-oriented Prompt Enhancement via Script Generation, https://arxiv.org/abs/2409.16418
  • Cassandra A. Cohen, William W. Cohen, 17 Sep 2024, Watch Your Steps: Observable and Modular Chains of Thought, https://arxiv.org/abs/2409.15359
  • Tongxuan Liu, Wenjiang Xu, Weizhe Huang, Xingyu Wang, Jiaxing Wang, Hailong Yang, Jing Li, 26 Sep 2024, Logic-of-Thought: Injecting Logic into Contexts for Full Reasoning in Large Language Models, https://arxiv.org/abs/2409.17539
  • 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
  • Qiguang Chen, Libo Qin, Jiaqi Wang, Jinxuan Zhou, Wanxiang Che, 8 Oct 2024, Unlocking the Boundaries of Thought: A Reasoning Granularity Framework to Quantify and Optimize Chain-of-Thought, https://arxiv.org/abs/2410.05695 https://github.com/LightChen233/reasoning-granularity
  • 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
  • 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
  • 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
  • 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
  • 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
  • Junda Wu, Xintong Li, Ruoyu Wang, Yu Xia, Yuxin Xiong, Jianing Wang, Tong Yu, Xiang Chen, Branislav Kveton, Lina Yao, Jingbo Shang, Julian McAuley, 31 Oct 2024, OCEAN: Offline Chain-of-thought Evaluation and Alignment in Large Language Models, https://arxiv.org/abs/2410.23703
  • 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/
  • Yu Zhao, Huifeng Yin, Bo Zeng, Hao Wang, Tianqi Shi, Chenyang Lyu, Longyue Wang, Weihua Luo, Kaifu Zhang, 21 Nov 2024, Marco-o1: Towards Open Reasoning Models for Open-Ended Solutions, https://arxiv.org/abs/2411.14405
  • Jun Gao, Yongqi Li, Ziqiang Cao, Wenjie Li, 29 Nov 2024, Interleaved-Modal Chain-of-Thought, https://arxiv.org/abs/2411.19488 (Using CoT on a multimodal/vision model.)
  • 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
  • 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
  • Ben Dickson, December 10, 2024, OpenAI’s o1 model doesn’t show its thinking, giving open source an advantage, https://venturebeat.com/ai/heres-how-openai-o1-might-lose-ground-to-open-source-models/
  • 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
  • Jiaqi Zhang, Chen Gao, Liyuan Zhang, Yong Li, Hongzhi Yin, 10 Dec 2024, SmartAgent: Chain-of-User-Thought for Embodied Personalized Agent in Cyber World, https://arxiv.org/abs/2412.07472 https://github.com/tsinghua-fib-lab/SmartAgent
  • 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/
  • 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/
  • Tyler McDonald, Anthony Colosimo, Yifeng Li, Ali Emami, 2 Dec 2024, Can We Afford The Perfect Prompt? Balancing Cost and Accuracy with the Economical Prompting Index, https://arxiv.org/abs/2412.01690
  • Jiaxiang Liu, Yuan Wang, Jiawei Du, Joey Tianyi Zhou, Zuozhu Liu, 18 Dec 2024, MedCoT: Medical Chain of Thought via Hierarchical Expert, https://arxiv.org/abs/2412.13736
  • Changyue Wang, Weihang Su, Qingyao Ai, Yiqun Liu, 23 Dec 2024, Knowledge Editing through Chain-of-Thought, https://arxiv.org/abs/2412.17727 https://github.com/bebr2/EditCoT
  • 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
  • Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc Le, Denny Zhou, 10 Jan 2023 (v6), Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. https://arxiv.org/abs/2201.11903
  • Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, Yusuke Iwasawa, 29 Jan 2023 (v4), Large Language Models are Zero-Shot Reasoners, https://arxiv.org/abs/2205.11916 https://github.com/kojima-takeshi188/zero_shot_cot ("Let's think step by step" prepended to every prompt for a type of zero-shot CoT.)
  • Xuezhi Wang, Denny Zhou, 23 May 2024 (v2), Chain-of-Thought Reasoning Without Prompting, https://arxiv.org/abs/2402.10200 ("CoT decoding" is examining the alternative paths in the decoding algorithm, which is somewhat similar to Chain-of-Thought reasoning.)
  • xjdr-alt, Dec 2024, entropix: Entropy Based Sampling and Parallel CoT Decoding, https://github.com/xjdr-alt/entropix (Parallel decoding attempts to get something similar to CoT.)
  • 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.)
  • Jiaan Wang, Fandong Meng, Yunlong Liang, Jie Zhou, 23 Dec 2024, DRT-o1: Optimized Deep Reasoning Translation via Long Chain-of-Thought, https://arxiv.org/abs/2412.17498 https://github.com/krystalan/DRT-o1 (Examines similes and metaphors in literature using long CoT.)
  • Jiacheng Ye, Shansan Gong, Liheng Chen, Lin Zheng, Jiahui Gao, Han Shi, Chuan Wu, Xin Jiang, Zhenguo Li, Wei Bi, Lingpeng Kong, 5 Dec 2024 (v3), Diffusion of Thoughts: Chain-of-Thought Reasoning in Diffusion Language Models, https://arxiv.org/abs/2402.07754
  • Shiv Sakhuja, 25 Sep 2024, Chain-of-Thought (CoT) Prompting Explained: 7 Techniques for Optimizing AI Performance, https://hub.athina.ai/athina-originals/guides-chain-of-thought-cot-prompting-explained-7-techniques-for-optimizing-ai-performance/
  • Aryasomayajula Ram Bharadwaj, 5 Dec 2024, Understanding Hidden Computations in Chain-of-Thought Reasoning, https://arxiv.org/abs/2412.04537
  • 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
  • Cheng Yang, Chufan Shi, Siheng Li, Bo Shui, Yujiu Yang, Wai Lam, 29 Dec 2024, LLM2: Let Large Language Models Harness System 2 Reasoning, https://arxiv.org/abs/2412.20372
  • 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
  • Violet Xiang, Charlie Snell, Kanishk Gandhi, Alon Albalak, Anikait Singh, Chase Blagden, Duy Phung, Rafael Rafailov, Nathan Lile, Dakota Mahan, Louis Castricato, Jan-Philipp Franken, Nick Haber, Chelsea Finn, 8 Jan 2025, Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-Thought, https://arxiv.org/abs/2501.04682
  • 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.)
  • Ziyang Ma, Zhuo Chen, Yuping Wang, Eng Siong Chng, Xie Chen, 13 Jan 2025, Audio-CoT: Exploring Chain-of-Thought Reasoning in Large Audio Language Model, https://arxiv.org/abs/2501.07246
  • 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.)
  • G Bao, H Zhang, C Wang, L Yang, Y Zhang, Jan 2025, How Likely Do LLMs with CoT Mimic Human Reasoning? Proceedings of the 31st International Conference on Computational Linguistics, pages 7831–7850, January 19–24, 2025, https://aclanthology.org/2025.coling-main.524.pdf
  • Son, M., Won, Y.-J., & Lee, S. (2025). Optimizing Large Language Models: A Deep Dive into Effective Prompt Engineering Techniques. Applied Sciences, 15(3), 1430. https://doi.org/10.3390/app15031430 https://www.mdpi.com/2076-3417/15/3/1430
  • 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
  • Jianfeng Pan, Senyou Deng, Shaomang Huang, 4 Feb 2025, CoAT: Chain-of-Associated-Thoughts Framework for Enhancing Large Language Models Reasoning, https://arxiv.org/abs/2502.02390 (Integrating results from an "associative memory" in CoT reasoning paths at inference time.)
  • Avinash Patil, 5 Feb 2025, Advancing Reasoning in Large Language Models: Promising Methods and Approaches, https://arxiv.org/abs/2502.03671
  • 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
  • Komal Kumar, Tajamul Ashraf, Omkar Thawakar, Rao Muhammad Anwer, Hisham Cholakkal, Mubarak Shah, Ming-Hsuan Yang, Phillip H.S. Torr, Salman Khan, Fahad Shahbaz Khan, 28 Feb 2025, LLM Post-Training: A Deep Dive into Reasoning Large Language Models, https://arxiv.org/abs/2502.21321 https://github.com/mbzuai-oryx/Awesome-LLM-Post-training
  • Bin Hong, Jiayu Liu, Zhenya Huang, Kai Zhang, Mengdi Zhang, 13 Aug 2025, Pruning Long Chain-of-Thought of Large Reasoning Models via Small-Scale Preference Optimization, https://arxiv.org/abs/2508.10164
  • Ke Niu, Haiyang Yu, Zhuofan Chen, Mengyang Zhao, Teng Fu, Bin Li, Xiangyang Xue, 13 Aug 2025, From Intent to Execution: Multimodal Chain-of-Thought Reinforcement Learning for Precise CAD Code Generation, https://arxiv.org/abs/2508.10118
  • Ziyu Guo, Renrui Zhang, Chengzhuo Tong, Zhizheng Zhao, Rui Huang, Haoquan Zhang, Manyuan Zhang, Jiaming Liu, Shanghang Zhang, Peng Gao, Hongsheng Li, Pheng-Ann Heng, 23 Jul 2025, Can We Generate Images with CoT? Let's Verify and Reinforce Image Generation Step by Step, https://arxiv.org/abs/2501.13926
  • Ang Li, Charles Wang, Kaiyu Yue, Zikui Cai, Ollie Liu, Deqing Fu, Peng Guo, Wang Bill Zhu, Vatsal Sharan, Robin Jia, Willie Neiswanger, Furong Huang, Tom Goldstein, Micah Goldblum, 22 Jul 2025, Zebra-CoT: A Dataset for Interleaved Vision Language Reasoning, https://arxiv.org/abs/2507.16746
  • Hulayyil Alshammari, Praveen Rao, 23 Jul 2025, Evaluating the Performance of AI Text Detectors, Few-Shot and Chain-of-Thought Prompting Using DeepSeek Generated Text, https://arxiv.org/abs/2507.17944
  • Binbin Ji, Siddharth Agrawal, Qiance Tang, and Yvonne Wu, 6 Jul 2025, Enhancing Spatial Reasoning in Vision-Language Models via Chain-of-Thought Prompting and Reinforcement Learning, https://arxiv.org/abs/2507.13362
  • Qiguang Chen, Libo Qin, Jinhao Liu, Dengyun Peng, Jiannan Guan, Peng Wang, Mengkang Hu, Yuhang Zhou, Te Gao, Wanxiang Che, 18 Jul 2025, Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models, https://arxiv.org/abs/2503.09567
  • Lei Chen, Xuanle Zhao, Zhixiong Zeng, Jing Huang, Yufeng Zhong, Lin Ma, 21 Jul 2025, Chart-R1: Chain-of-Thought Supervision and Reinforcement for Advanced Chart Reasoner, https://arxiv.org/abs/2507.15509
  • Luyi Ma, Wanjia Zhang, Kai Zhao, Abhishek Kulkarni, Lalitesh Morishetti, Anjana Ganesh, Ashish Ranjan, Aashika Padmanabhan, Jianpeng Xu, Jason Cho, Praveen Kanumala, Kaushiki Nag, Sumit Dutta, Kamiya Motwani, Malay Patel, Evren Korpeoglu, Sushant Kumar, Kannan Achan, 19 Jul 2025, GRACE: Generative Recommendation via Journey-Aware Sparse Attention on Chain-of-Thought Tokenization, https://arxiv.org/abs/2507.14758
  • Hao Yang, Qinghua Zhao, Lei Li, 28 Jul 2025, How Chain-of-Thought Works? Tracing Information Flow from Decoding, Projection, and Activation, https://arxiv.org/abs/2507.20758
  • Eunkyu Park, Wesley Hanwen Deng, Gunhee Kim, Motahhare Eslami, Maarten Sap, 27 Jul 2025, Cognitive Chain-of-Thought: Structured Multimodal Reasoning about Social Situations, https://arxiv.org/abs/2507.20409
  • Xiangning Yu, Zhuohan Wang, Linyi Yang, Haoxuan Li, Anjie Liu, Xiao Xue, Jun Wang, Mengyue Yang, 26 Jul 2025, Causal Sufficiency and Necessity Improves Chain-of-Thought Reasoning, https://arxiv.org/abs/2506.09853
  • Ping Yu, Jack Lanchantin, Tianlu Wang, Weizhe Yuan, Olga Golovneva, Ilia Kulikov, Sainbayar Sukhbaatar, Jason Weston, Jing Xu, 31 Jul 2025, CoT-Self-Instruct: Building high-quality synthetic prompts for reasoning and non-reasoning tasks, https://arxiv.org/abs/2507.23751
  • Xi Chen, Aske Plaat, Niki van Stein, 24 Jul 2025, How does Chain of Thought Think? Mechanistic Interpretability of Chain-of-Thought Reasoning with Sparse Autoencoding, https://arxiv.org/abs/2507.22928
  • Shixin Yi, Lin Shang, 1 Aug 2025, CoRGI: Verified Chain-of-Thought Reasoning with Visual Grounding, https://arxiv.org/abs/2508.00378
  • Jianwei Wang, Ziming Wu, Fuming Lai, Shaobing Lian, Ziqian Zeng, 1 Aug 2025, SynAdapt: Learning Adaptive Reasoning in Large Language Models via Synthetic Continuous Chain-of-Thought, https://arxiv.org/abs/2508.00574
  • Chengshuai Zhao, Zhen Tan, Pingchuan Ma, Dawei Li, Bohan Jiang, Yancheng Wang, Yingzhen Yang, Huan Liu, 2 Aug 2025, Is Chain-of-Thought Reasoning of LLMs a Mirage? A Data Distribution Lens, https://arxiv.org/abs/2508.01191
  • Jialiang Hong, Taihang Zhen, Kai Chen, Jiaheng Liu, Wenpeng Zhu, Jing Huo, Yang Gao, Depeng Wang, Haitao Wan, Xi Yang, Boyan Wang, Fanyu Meng, 4 Aug 2025, Reconsidering Overthinking: Penalizing Internal and External Redundancy in CoT Reasoning, https://arxiv.org/abs/2508.02178
  • Chloe Li, Mary Phuong, Noah Y. Siegel, 31 Jul 2025, LLMs Can Covertly Sandbag on Capability Evaluations Against Chain-of-Thought Monitoring, https://arxiv.org/abs/2508.00943
  • Weibo Zhou, Lingbo Li, Shangsong Liang, 2 Aug 2025, D-SCoRE: Document-Centric Segmentation and CoT Reasoning with Structured Export for QA-CoT Data Generation, https://arxiv.org/abs/2508.01309
  • Fan Gao, Cheng Huang, Nyima Tashi, Yutong Liu, Xiangxiang Wang, Thupten Tsering, Ban Ma-bao, Renzeg Duojie, Gadeng Luosang, Rinchen Dongrub, Dorje Tashi, Xiao Feng, Hao Wang, Yongbin Yu, 4 Aug 2025, TIBSTC-CoT: A Multi-Domain Instruction Dataset for Chain-of-Thought Reasoning in Language Models, https://arxiv.org/abs/2508.01977
  • Huihan Li, You Chen, Siyuan Wang, Yixin He, Ninareh Mehrabi, Rahul Gupta, Xiang Ren, 4 Aug 2025, Diagnosing Memorization in Chain-of-Thought Reasoning, One Token at a Time, https://arxiv.org/abs/2508.02037
  • Hongbo Jin, Ruyang Liu, Wenhao Zhang, Guibo Luo, Ge Li, 3 Aug 2025, CoT-Vid: Dynamic Chain-of-Thought Routing with Self Verification for Training-Free Video Reasoning, https://arxiv.org/abs/2505.11830
  • Zeju Li, Jianyuan Zhong, Ziyang Zheng, Xiangyu Wen, Zhijian Xu, Yingying Cheng, Fan Zhang, Qiang Xu, 5 Aug 2025, Compressing Chain-of-Thought in LLMs via Step Entropy, https://arxiv.org/abs/2508.03346
  • Jueon Park, Yein Park, Minju Song, Soyon Park, Donghyeon Lee, Seungheun Baek and Jaewoo Kang, 5 Aug 2025, CoTox: Chain-of-Thought-Based Molecular Toxicity Reasoning and Prediction, https://arxiv.org/abs/2508.03159
  • Junyao Yang, Jianwei Wang, Huiping Zhuang, Cen Chen, Ziqian Zeng, 5 Aug 2025, RCP-Merging: Merging Long Chain-of-Thought Models with Domain-Specific Models by Considering Reasoning Capability as Prior, https://arxiv.org/abs/2508.03140
  • Weihua Zheng, Xin Huang, Zhengyuan Liu, Tarun Kumar Vangani, Bowei Zou, Xiyan Tao, Yuhao Wu, Ai Ti Aw, Nancy F. Chen, Roy Ka-Wei Lee, 5 Aug 2025, AdaMCoT: Rethinking Cross-Lingual Factual Reasoning through Adaptive Multilingual Chain-of-Thought, https://arxiv.org/abs/2501.16154
  • Xingyu Chen, Junxiu An, Jun Guo, Li Wang, Jingcai Guo, 6 Aug 2025, KG-Augmented Executable CoT for Mathematical Coding, https://arxiv.org/abs/2508.04072
  • Xiao Wang, Liye Jin, Xufeng Lou, Shiao Wang, Lan Chen, Bo Jiang, Zhipeng Zhang, 7 Aug 2025, ReasoningTrack: Chain-of-Thought Reasoning for Long-term Vision-Language Tracking, https://arxiv.org/abs/2508.05221
  • Haonan Shangguan, Xiaocui Yang, Shi Feng, Daling Wang, Yifei Zhang, and Ge Yu, 7 Aug 2025, Resource-Limited Joint Multimodal Sentiment Reasoning and Classification via Chain-of-Thought Enhancement and Distillation, https://arxiv.org/abs/2508.05234
  • Tianyun Yang, Yunwen Li, Ziniu Li, Zhihang Lin, Ruoyu Sun, Tian Ding, 12 Aug 2025, Bridging Formal Language with Chain-of-Thought Reasoning to Geometry Problem Solving, https://arxiv.org/abs/2508.09099
  • Haiyun Guo, ZhiYan Hou, Yu Chen, Jinghan He, Yandu Sun, Yuzhe Zhou, Shujing Guo, Kuan Zhu, Jinqiao Wang, 31 Jul 2025, MLLM-CBench:A Comprehensive Benchmark for Continual Instruction Tuning of Multimodal LLMs with Chain-of-Thought Reasoning Analysis, https://arxiv.org/abs/2508.08275
  • Axel Delaval, Shujian Yang, Haicheng Wang, Han Qiu, Jialiang Lu, 15 Aug 2025, ToxiFrench: Benchmarking and Enhancing Language Models via CoT Fine-Tuning for French Toxicity Detection, https://arxiv.org/abs/2508.11281
  • Phuong Minh Nguyen, Tien Huu Dang, Naoya Inoue, 17 Aug 2025, Non-Iterative Symbolic-Aided Chain-of-Thought for Logical Reasoning, https://arxiv.org/abs/2508.12425
  • Zhifeng Kong, Arushi Goel, Joao Felipe Santos, Sreyan Ghosh, Rafael Valle, Wei Ping, Bryan Catanzaro, 15 Aug 2025, Audio Flamingo Sound-CoT Technical Report: Improving Chain-of-Thought Reasoning in Sound Understanding, https://arxiv.org/abs/2508.11818
  • Ruheng Wang, Hang Zhang, Trieu Nguyen, Shasha Feng, Hao-Wei Pang, Xiang Yu, Li Xiao, Peter Zhiping Zhang, 20 Aug 2025, PepThink-R1: LLM for Interpretable Cyclic Peptide Optimization with CoT SFT and Reinforcement Learning, https://arxiv.org/abs/2508.14765
  • Josh Barua, Seun Eisape, Kayo Yin, Alane Suhr, 20 Aug 2025, Long Chain-of-Thought Reasoning Across Languages, https://arxiv.org/abs/2508.14828
  • Wenqiao Zhu, Ji Liu, Rongjuncheng Zhang, Haipang Wu, Yulun Zhang, 21 Aug 2025, CARFT: Boosting LLM Reasoning via Contrastive Learning with Annotated Chain-of-Thought-based Reinforced Fine-Tuning, https://arxiv.org/abs/2508.15868
  • Jeremy Berman, Sep 17, 2025, How I got the highest score on ARC-AGI again swapping Python for English: Using Multi-Agent Collaboration with Evolutionary Test-Time Compute, https://jeremyberman.substack.com/p/how-i-got-the-highest-score-on-arc-agi-again (Generates multiple solutions then prunes them with "evolution" and iterates in multi-step inference.)
  • Zeyu Gan, Hao Yi, Yong Liu, 4 Sep 2025, CoT-Space: A Theoretical Framework for Internal Slow-Thinking via Reinforcement Learning, https://arxiv.org/abs/2509.04027
  • Sunguk Choi, Yonghoon Kwon, Heondeuk Lee, 26 Aug 2025, CAC-CoT: Connector-Aware Compact Chain-of-Thought for Efficient Reasoning Data Synthesis Across Dual-System Cognitive Tasks, https://arxiv.org/abs/2508.18743
  • Xinglong Yang, Quan Feng, Zhongying Pan, Xiang Chen, Yu Tian, Wentong Li, Shuofei Qiao, Yuxia Geng, Xingyu Zhao, Sheng-Jun Huang, 26 Aug 2025, Tailored Teaching with Balanced Difficulty: Elevating Reasoning in Multimodal Chain-of-Thought via Prompt Curriculum, https://arxiv.org/abs/2508.18673
  • Rushitha Santhoshi Mamidala, Anshuman Chhabra, Ankur Mali, 22 Aug 2025, Rethinking Reasoning in LLMs: Neuro-Symbolic Local RetoMaton Beyond ICL and CoT, https://arxiv.org/abs/2508.19271
  • Haimei Pan, Jiyun Zhang, Qinxi Wei, Xiongnan Jin, Chen Xinkai, Jie Cheng, 25 Aug 2025, Robotic Fire Risk Detection based on Dynamic Knowledge Graph Reasoning: An LLM-Driven Approach with Graph Chain-of-Thought, https://arxiv.org/abs/2509.00054
  • Sheldon Yu, Yuxin Xiong, Junda Wu, Xintong Li, Tong Yu, Xiang Chen, Ritwik Sinha, Jingbo Shang, Julian McAuley, 29 Aug 2025, Explainable Chain-of-Thought Reasoning: An Empirical Analysis on State-Aware Reasoning Dynamics, https://arxiv.org/abs/2509.00190
  • Hao Yang, Zhiyu Yang, Yunjie Zhang, Shanyi Zhu, Lin Yang, 1 Sep 2025, Rethinking the Chain-of-Thought: The Roles of In-Context Learning and Pre-trained Priors, https://arxiv.org/abs/2509.01236
  • Ziyun Zeng, Junhao Zhang, Wei Li, Mike Zheng Shou, 2 Sep 2025, Draw-In-Mind: Learning Precise Image Editing via Chain-of-Thought Imagination, https://arxiv.org/abs/2509.01986
  • Xingyue Huang, Rishabh, Gregor Franke, Ziyi Yang, Jiamu Bai, Weijie Bai, Jinhe Bi, Zifeng Ding, Yiqun Duan, Chengyu Fan, Wendong Fan, Xin Gao, Ruohao Guo, Yuan He, Zhuangzhuang He, Xianglong Hu, Neil Johnson, Bowen Li, Fangru Lin, Siyu Lin, Tong Liu, Yunpu Ma, Hao Shen, Hao Sun, Beibei Wang, Fangyijie Wang, Hao Wang, Haoran Wang, Yang Wang, Yifeng Wang, Zhaowei Wang, Ziyang Wang, Yifan Wu, Zikai Xiao, Chengxing Xie, Fan Yang, Junxiao Yang, Qianshuo Ye, Ziyu Ye, Guangtao Zeng, Yuwen Ebony Zhang, Zeyu Zhang, Zihao Zhu, Bernard Ghanem, Philip Torr, Guohao Li, 3 Sep 2025, Loong: Synthesize Long Chain-of-Thoughts at Scale through Verifiers, https://arxiv.org/abs/2509.03059
  • Haoyang He, Zihua Rong, Kun Ji, Chenyang Li, Qing Huang, Chong Xia, Lan Yang, Honggang Zhang, 7 Sep 2025, Rethinking Reasoning Quality in Large Language Models through Enhanced Chain-of-Thought via RL, https://arxiv.org/abs/2509.06024
  • Yihong Luo, Wenwu He, Zhuo-Xu Cui, Dong Liang, 8 Sep 2025, Teaching AI Stepwise Diagnostic Reasoning with Report-Guided Chain-of-Thought Learning, https://arxiv.org/abs/2509.06409
  • Vardhan Palod, Karthik Valmeekam, Kaya Stechly, Subbarao Kambhampati, 9 Sep 2025, Performative Thinking? The Brittle Correlation Between CoT Length and Problem Complexity, https://arxiv.org/abs/2509.07339
  • Sahiti Yerramilli, Nilay Pande, Rynaa Grover, Jayant Sravan Tamarapalli, 9 Sep 2025, GeoChain: Multimodal Chain-of-Thought for Geographic Reasoning, https://arxiv.org/abs/2506.00785
  • Jie Xiao, Qianyi Huang, Xu Chen and Chen Tian, 11 Sep 2025, Understanding Large Language Models in Your Pockets: Performance Study on COTS Mobile Devices, https://arxiv.org/abs/2410.03613
  • Ryan Lucas, Kayhan Behdin, Zhipeng Wang, Qingquan Song, Shao Tang, Rahul Mazumder, 15 Sep 2025, Reasoning Models Can be Accurately Pruned Via Chain-of-Thought Reconstruction, https://arxiv.org/abs/2509.12464
  • Anmol Singhal Navya Singhal, 16 Sep 2025, Analogy-Driven Financial Chain-of-Thought (AD-FCoT): A Prompting Approach for Financial Sentiment Analysis, https://arxiv.org/abs/2509.12611
  • Jinghua Zhao, Hang Su, Lichun Fan, Zhenbo Luo, Jian Luan, Hui Wang, Haoqin Sun, Yong Qin, 14 Sep 2025, Omni-CLST: Error-aware Curriculum Learning with guided Selective chain-of-Thought for audio questuin answering, https://arxiv.org/abs/2509.12275
  • Heming Xia, Chak Tou Leong, Wenjie Wang, Yongqi Li, Wenjie Li, 16 Sep 2025, TokenSkip: Controllable Chain-of-Thought Compression in LLMs, https://arxiv.org/abs/2502.12067
  • Song Xu, Yilun Liu, Minggui He, Mingchen Dai, Ziang Chen, Chunguang Zhao, Jingzhou Du, Shimin Tao, Weibin Meng, Shenglin Zhang, Yongqian Sun, Boxing Chen, Daimeng Wei, 18 Sep 2025, RationAnomaly: Log Anomaly Detection with Rationality via Chain-of-Thought and Reinforcement Learning, https://arxiv.org/abs/2509.14693
  • Feiyang Li, Peng Fang, Zhan Shi, Arijit Khan, Fang Wang, Weihao Wang, Xin Zhang, Yongjian Cui, 10 Sep 2025, CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models, https://arxiv.org/abs/2504.13534
  • Anand Swaroop, Akshat Nallani, Saksham Uboweja, Adiliia Uzdenova, Michael Nguyen, Kevin Zhu, Sunishchal Dev, Ashwinee Panda, Vasu Sharma, Maheep Chaudhary, 10 Sep 2025, FRIT: Using Causal Importance to Improve Chain-of-Thought Faithfulness, https://arxiv.org/abs/2509.13334
  • Pulkit Verma, Ngoc La, Anthony Favier, Swaroop Mishra, Julie A. Shah, 14 Sep 2025, Teaching LLMs to Plan: Logical Chain-of-Thought Instruction Tuning for Symbolic Planning, https://arxiv.org/abs/2509.13351
  • Kerui Huang, Shuhan Liu, Xing Hu, Tongtong Xu, Lingfeng Bao, Xin Xia, 17 Sep 2025, Reasoning Efficiently Through Adaptive Chain-of-Thought Compression: A Self-Optimizing Framework, https://arxiv.org/abs/2509.14093
  • Daniel Zhao, Abhilash Shankarampeta, Lanxiang Hu, Tajana Rosing, Hao Zhang, 2 Oct 2025, Towards Interpretable and Inference-Optimal COT Reasoning with Sparse Autoencoder-Guided Generation, https://arxiv.org/abs/2510.01528
  • Junyi Xie, Yuankun Jiao, Jina Kim, Yao-Yi Chiang, Lingyi Zhao, Khurram Shafique, 14 Oct 2025, HiCoTraj:Zero-Shot Demographic Reasoning via Hierarchical Chain-of-Thought Prompting from Trajectory, https://arxiv.org/abs/2510.12067
  • Zhongwei Yu, Wannian Xia, Xue Yan, Bo Xu, Haifeng Zhang, Yali Du, Jun Wang, 14 Oct 2025, Self-Verifying Reflection Helps Transformers with CoT Reasoning, https://arxiv.org/abs/2510.12157
  • Elija Perrier, 1 Oct 2025, Typed Chain-of-Thought: A Curry-Howard Framework for Verifying LLM Reasoning, https://arxiv.org/abs/2510.01069
  • Felix Parker, Nimeesha Chan, Chi Zhang, Kimia Ghobadi, 1 Oct 2025, Eliciting Chain-of-Thought Reasoning for Time Series Analysis using Reinforcement Learning, https://arxiv.org/abs/2510.01116
  • Eric Hanchen Jiang, Haozheng Luo, Shengyuan Pang, Xiaomin Li, Zhenting Qi, Hengli Li, Cheng-Fu Yang, Zongyu Lin, Xinfeng Li, Hao Xu, Kai-Wei Chang, Ying Nian Wu, 30 Sep 2025, Learning to Rank Chain-of-Thought: Using a Small Model, https://arxiv.org/abs/2505.14999
  • Xilin Wei, Xiaoran Liu, Yuhang Zang, Xiaoyi Dong, Yuhang Cao, Jiaqi Wang, Xipeng Qiu, Dahua Lin, 24 Sep 2025, SIM-CoT: Supervised Implicit Chain-of-Thought, https://arxiv.org/abs/2509.20317
  • Guohao Sun, Hang Hua, Jian Wang, Jiebo Luo, Sohail Dianat, Majid Rabbani, Raghuveer Rao, Zhiqiang Tao, 27 Oct 2025, Latent Chain-of-Thought for Visual Reasoning, https://arxiv.org/abs/2510.23925
  • Scott Emmons, Roland S. Zimmermann, David K. Elson, Rohin Shah, 28 Oct 2025, A Pragmatic Way to Measure Chain-of-Thought Monitorability, https://arxiv.org/abs/2510.23966
  • Bo Liu, Xiangyu Zhao, Along He, Yidi Chen, Huazhu Fu, Xiao-Ming Wu, 28 Oct 2025, GEMeX-RMCoT: An Enhanced Med-VQA Dataset for Region-Aware Multimodal Chain-of-Thought Reasoning, https://arxiv.org/abs/2506.17939
  • Artur Zolkowski, Wen Xing, David Lindner, Florian Tram\`er, Erik Jenner, 21 Oct 2025, Can Reasoning Models Obfuscate Reasoning? Stress-Testing Chain-of-Thought Monitorability, https://arxiv.org/abs/2510.19851
  • Wonje Jeung, Sangyeon Yoon, Minsuk Kahng, Albert No, 23 Oct 2025, SAFEPATH: Preventing Harmful Reasoning in Chain-of-Thought via Early Alignment, https://arxiv.org/abs/2505.14667
  • Xiongkun Linghu, Jiangyong Huang, Ziyu Zhu, Baoxiong Jia, Siyuan Huang, 19 Oct 2025, Eliciting Grounded Chain-of-Thought Reasoning in 3D Scenes, https://arxiv.org/abs/2510.16714
  • Yiqi Li, Yusheng Liao, Zhe Chen, Yanfeng Wang, Yu Wang, 20 Oct 2025, DICE: Structured Reasoning in LLMs through SLM-Guided Chain-of-Thought Correction, https://arxiv.org/abs/2510.09211
  • Yue Xin, Chen Shen, Shaotian Yan, Xiaosong Yuan, Yaoming Wang, Xiaofeng Zhang, Chenxi Huang, Jieping Ye, 20 Sep 2025, SalaMAnder: Shapley-based Mathematical Expression Attribution and Metric for Chain-of-Thought Reasoning, https://arxiv.org/abs/2509.16561
  • Haojun Yu, Youcheng Li, Zihan Niu, Nan Zhang, Xuantong Gong, Huan Li, Zhiying Zou, Haifeng Qi, Zhenxiao Cao, Zijie Lan, Xingjian Yuan, Jiating He, Haokai Zhang, Shengtao Zhang, Zicheng Wang, Dong Wang, Ziwei Zhao, Congying Chen, Yong Wang, Wangyan Qin, and Qingli Zhu, 21 Sep 2025, A Chain-of-thought Reasoning Breast Ultrasound Dataset Covering All Histopathology Categories, https://arxiv.org/abs/2509.17046
  • Khai Le-Duc, Duy M. H. Nguyen, Phuong T. H. Trinh, Tien-Phat Nguyen, Nghiem T. Diep, An Ngo, Tung Vu, Trinh Vuong, Anh-Tien Nguyen, Mau Nguyen, Van Trung Hoang, Khai-Nguyen Nguyen, Hy Nguyen, Chris Ngo, Anji Liu, Nhat Ho, Anne-Christin Hauschild, Khanh Xuan Nguyen, Thanh Nguyen-Tang, Pengtao Xie, Daniel Sonntag, James Zou, Mathias Niepert, Anh Totti Nguyen, 26 Oct 2025, S-Chain: Structured Visual Chain-of-Thought For Medicine, https://arxiv.org/abs/2510.22728
  • Afrina Tabassum, Bin Guo, Xiyao Ma, Hoda Eldardiry, Ismini Lourentzou, 25 Sep 2025, MMPlanner: Zero-Shot Multimodal Procedural Planning with Chain-of-Thought Object State Reasoning, https://arxiv.org/abs/2509.21662
  • Jianzhi Yan, Le Liu, Youcheng Pan, Shiwei Chen, Zike Yuan, Yang Xiang, Buzhou Tang, 26 Sep 2025, From Long to Lean: Performance-aware and Adaptive Chain-of-Thought Compression via Multi-round Refinement, https://arxiv.org/abs/2509.22144
  • Qihua Dong, Luis Figueroa, Handong Zhao, Kushal Kafle, Jason Kuen, Zhihong Ding, Scott Cohen, Yun Fu, 3 Oct 2025, CoT Referring: Improving Referring Expression Tasks with Grounded Reasoning, https://arxiv.org/abs/2510.06243
  • Hadi Mohammadi, Anastasia Giachanou, and Ayoub Bagheri, 8 Oct 2025, EvalMORAAL: Interpretable Chain-of-Thought and LLM-as-Judge Evaluation for Moral Alignment in Large Language Models, https://arxiv.org/abs/2510.05942
  • Antonio-Gabriel Chac\'on Menke, Phan Xuan Tan, Eiji Kamioka, 20 Oct 2025, Annotating the Chain-of-Thought: A Behavior-Labeled Dataset for AI Safety, https://arxiv.org/abs/2510.18154
  • Shuxin Lin, Dhaval Patel, Christodoulos Constantinides, 21 Oct 2025, Fine-Tuned Thoughts: Leveraging Chain-of-Thought Reasoning for Industrial Asset Health Monitoring, https://arxiv.org/abs/2510.18817
  • Yongda Yu, Guohao Shi, Xianwei Wu, Haochuan He, XueMing Gu, Qianqian Zhao, Kui Liu, Qiushi Wang, Zhao Tian, Haifeng Shen, Guoping Rong, 25 Sep 2025, Fine-Tuning LLMs to Analyze Multiple Dimensions of Code Review: A Maximum Entropy Regulated Long Chain-of-Thought Approach, https://arxiv.org/abs/2509.21170
  • Zihao Zhu, Xinyu Wu, Gehan Hu, Siwei Lyu, Ke Xu, Baoyuan Wu, 29 Sep 2025, AdvChain: Adversarial Chain-of-Thought Tuning for Robust Safety Alignment of Large Reasoning Models, https://arxiv.org/abs/2509.24269
  • Chunxue Xu, Yiwei Wang, Yujun Cai, Bryan Hooi, Songze Li, 28 Sep 2025, Visual CoT Makes VLMs Smarter but More Fragile, https://arxiv.org/abs/2509.23789
  • Jianzhi Yan, Le Liu, Youcheng Pan, Shiwei Chen, Yang Xiang, Buzhou Tang, 28 Sep 2025, Towards Efficient CoT Distillation: Self-Guided Rationale Selector for Better Performance with Fewer Rationales, https://arxiv.org/abs/2509.23574
  • Haonan Ge, Yiwei Wang, Kai-Wei Chang, Hang Wu, Yujun Cai, 28 Sep 2025, FrameMind: Frame-Interleaved Chain-of-Thought for Video Reasoning via Reinforcement Learning, https://arxiv.org/abs/2509.24008
  • Wenquan Lu, Yuechuan Yang, Kyle Lee, Yanshu Li, Enqi Liu, 28 Sep 2025, Latent Chain-of-Thought? Decoding the Depth-Recurrent Transformer, https://arxiv.org/abs/2507.02199
  • Kumar Manas, Stefan Zwicklbauer and Adrian Paschke, 27 Sep 2025, CoT-TL: Low-Resource Temporal Knowledge Representation of Planning Instructions Using Chain-of-Thought Reasoning, https://arxiv.org/abs/2410.16207
  • Zhipeng Yang, Junzhuo Li, Siyu Xia and Xuming Hu, 28 Sep 2025, Internal Chain-of-Thought: Empirical Evidence for Layer-wise Subtask Scheduling in LLMs, https://arxiv.org/abs/2505.14530
  • Yuyao Zhang, Jinghao Li, Yu-Wing Tai, 17 Oct 2025, LayerCraft: Enhancing Text-to-Image Generation with CoT Reasoning and Layered Object Integration, https://arxiv.org/abs/2504.00010
  • Zhuohan Xie, Daniil Orel, Rushil Thareja, Dhruv Sahnan, Hachem Madmoun, Fan Zhang, Debopriyo Banerjee, Georgi Georgiev, Xueqing Peng, Lingfei Qian, Jimin Huang, Jinyan Su, Aaryamonvikram Singh, Rui Xing, Rania Elbadry, Chen Xu, Haonan Li, Fajri Koto, Ivan Koychev, Tanmoy Chakraborty, Yuxia Wang, Salem Lahlou, Veselin Stoyanov, Sophia Ananiadou, and Preslav Nakov, 17 Oct 2025, FinChain: A Symbolic Benchmark for Verifiable Chain-of-Thought Financial Reasoning, https://arxiv.org/abs/2506.02515
  • Xu Shen, Song Wang, Zhen Tan, Laura Yao, Xinyu Zhao, Kaidi Xu, Xin Wang, Tianlong Chen, 5 Oct 2025, FaithCoT-Bench: Benchmarking Instance-Level Faithfulness of Chain-of-Thought Reasoning, https://arxiv.org/abs/2510.04040
  • Soo Yong Kim, Suin Cho, Vincent-Daniel Yun, Gyeongyeon Hwang, 6 Oct 2025, MedCLM: Learning to Localize and Reason via a CoT-Curriculum in Medical Vision-Language Models, https://arxiv.org/abs/2510.04477
  • Imran Mansha, 6 Oct 2025, Resource-Efficient Fine-Tuning of LLaMA-3.2-3B for Medical Chain-of-Thought Reasoning, https://arxiv.org/abs/2510.05003
  • Yunfan Zhang, Kathleen McKeown, Smaranda Muresan, 5 Oct 2025, Exploring Chain-of-Thought Reasoning for Steerable Pluralistic Alignment, https://arxiv.org/abs/2510.04045
  • Zihao Xue and Zhen Bi and Long Ma and Zhenlin Hu and Yan Wang and Zhenfang Liu and Qing Sheng and Jie Xiao and Jungang Lou, 4 Oct 2025, Thought Purity: A Defense Framework For Chain-of-Thought Attack, https://arxiv.org/abs/2507.12314
  • Chengzhengxu Li, Xiaoming Liu, Zhaohan Zhang, Shaochu Zhang, Shengchao Liu, Guoxin Ma, Yu Lan, Chao Shen, 9 Oct 2025, Upfront Chain-of-Thought: A Cooperative Framework for Chain-of-Thought Compression, https://arxiv.org/abs/2510.08647
  • Zheng Zhao, Yeskendir Koishekenov, Xianjun Yang, Naila Murray, Nicola Cancedda, 10 Oct 2025, Verifying Chain-of-Thought Reasoning via Its Computational Graph, https://arxiv.org/abs/2510.09312
  • Ziyu Zheng, Yaming Yang, Ziyu Guan, Wei Zhao, Xinyan Huang and Weigang Lu, 10 Oct 2025, Beyond Single-Granularity Prompts: A Multi-Scale Chain-of-Thought Prompt Learning for Graph, https://arxiv.org/abs/2510.09394
  • Kevin Xu, Issei Sato, 24 Oct 2025, To CoT or To Loop? A Formal Comparison Between Chain-of-Thought and Looped Transformers, https://arxiv.org/abs/2505.19245
  • Daeun Lee, Jaehong Yoon, Jaemin Cho, Mohit Bansal, 24 Oct 2025, Video-Skill-CoT: Skill-based Chain-of-Thoughts for Domain-Adaptive Video Reasoning, https://arxiv.org/abs/2506.03525
  • Chengqi Duan, Kaiyue Sun, Rongyao Fang, Manyuan Zhang, Yan Feng, Ying Luo, Yufang Liu, Ke Wang, Peng Pei, Xunliang Cai, Hongsheng Li, Yi Ma, Xihui Liu, 13 Oct 2025, CodePlot-CoT: Mathematical Visual Reasoning by Thinking with Code-Driven Images, https://arxiv.org/abs/2510.11718
  • Thang Nguyen, Peter Chin, Yu-Wing Tai, 11 Oct 2025, MA-RAG: Multi-Agent Retrieval-Augmented Generation via Collaborative Chain-of-Thought Reasoning, https://arxiv.org/abs/2505.20096
  • Xiang Cheng, Chengyan Pan, Minjun Zhao, Deyang Li, Fangchao Liu, Xinyu Zhang, Xiao Zhang, Yong Liu, 13 Oct 2025, Revisiting Chain-of-Thought Prompting: Zero-shot Can Be Stronger than Few-shot, https://arxiv.org/abs/2506.14641
  • Yu Ti Huang, 20 Sep 2025, Conversational Orientation Reasoning: Egocentric-to-Allocentric Navigation with Multimodal Chain-of-Thought, https://arxiv.org/abs/2509.18200
  • Yunzhen Feng, Julia Kempe, Cheng Zhang, Parag Jain, Anthony Hartshorn, 23 Sep 2025, What Characterizes Effective Reasoning? Revisiting Length, Review, and Structure of CoT, https://arxiv.org/abs/2509.19284
  • Julian Schulz, 22 Oct 2025, A Concrete Roadmap towards Safety Cases based on Chain-of-Thought Monitoring, https://arxiv.org/abs/2510.19476
  • Kevin Xu and Issei Sato, 25 Sep 2025, A Formal Comparison Between Chain-of-Thought and Latent Thought, https://arxiv.org/abs/2509.25239
  • Raphael Schumann, Stefan Riezler, 30 Sep 2025, Boosting Process-Correct CoT Reasoning by Modeling Solvability of Multiple-Choice QA, https://arxiv.org/abs/2509.25941
  • Hongyu Chen, Guangrun Wang, 26 Sep 2025, UML-CoT: Structured Reasoning and Planning with Unified Modeling Language for Robotic Room Cleaning, https://arxiv.org/abs/2509.22628
  • Kaiwen Wang, Jin Peng Zhou, Jonathan Chang, Zhaolin Gao, Nathan Kallus, Kiant\'e Brantley, Wen Sun, 30 Sep 2025, Value-Guided Search for Efficient Chain-of-Thought Reasoning, https://arxiv.org/abs/2505.17373
  • Zeqi Gu, Markos Georgopoulos, Xiaoliang Dai, Marjan Ghazvininejad, Chu Wang, Felix Juefei-Xu, Kunpeng Li, Yujun Shi, Zecheng He, Zijian He, Jiawei Zhou, Abe Davis, Jialiang Wang, 7 Oct 2025, Improving Chain-of-Thought Efficiency for Autoregressive Image Generation, https://arxiv.org/abs/2510.05593
  • Haoran Zhang, Shuanghao Bai, Wanqi Zhou, Yuedi Zhang, Qi Zhang, Pengxiang Ding, Cheng Chi, Donglin Wang, Badong Chen, 7 Oct 2025, VCoT-Grasp: Grasp Foundation Models with Visual Chain-of-Thought Reasoning for Language-driven Grasp Generation, https://arxiv.org/abs/2510.05827

Advanced Chain-of-Thought

Some more research on advanced improvements to multi-step Chain-of-Thought are below. See also CoT efficiency optimizations.

  • Jiaan Wang, Fandong Meng, Yunlong Liang, Jie Zhou, 23 Dec 2024, DRT-o1: Optimized Deep Reasoning Translation via Long Chain-of-Thought, https://arxiv.org/abs/2412.17498 https://github.com/krystalan/DRT-o1 (Examines similes and metaphors in literature using long CoT.)
  • Violet Xiang, Charlie Snell, Kanishk Gandhi, Alon Albalak, Anikait Singh, Chase Blagden, Duy Phung, Rafael Rafailov, Nathan Lile, Dakota Mahan, Louis Castricato, Jan-Philipp Franken, Nick Haber, Chelsea Finn, 8 Jan 2025, Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-Thought, https://arxiv.org/abs/2501.04682
  • Haotian Xu, Xing Wu, Weinong Wang, Zhongzhi Li, Da Zheng, Boyuan Chen, Yi Hu, Shijia Kang, Jiaming Ji, Yingying Zhang, Zhijiang Guo, Yaodong Yang, Muhan Zhang, Debing Zhang, 20 Jan 2025, RedStar: Does Scaling Long-CoT Data Unlock Better Slow-Reasoning Systems? https://arxiv.org/abs/2501.11284 https://huggingface.co/RedStar-Reasoning
  • Yiyao Yu, Yuxiang Zhang, Dongdong Zhang, Xiao Liang, Hengyuan Zhang, Xingxing Zhang, Ziyi Yang, Mahmoud Khademi, Hany Awadalla, Junjie Wang, Yujiu Yang, Furu Wei, 19 Jan 2025, Chain-of-Reasoning: Towards Unified Mathematical Reasoning in Large Language Models via a Multi-Paradigm Perspective, https://arxiv.org/abs/2501.11110
  • Yuanheng Fang, Guoqing Chao, Wenqiang Lei, Shaobo Li, Dianhui Chu, 21 Jan 2025, CDW-CoT: Clustered Distance-Weighted Chain-of-Thoughts Reasoning, https://arxiv.org/abs/2501.12226 (CoT with integration of clustering and prompt optimization techniques.)
  • Jishnu Ray Chowdhury, Cornelia Caragea, 21 Jan 2025, Zero-Shot Verification-guided Chain of Thoughts, https://arxiv.org/abs/2501.13122
  • Ziyu Guo, Renrui Zhang, Chengzhuo Tong, Zhizheng Zhao, Peng Gao, Hongsheng Li, Pheng-Ann Heng, 23 Jan 2025, Can We Generate Images with CoT? Let's Verify and Reinforce Image Generation Step by Step, https://arxiv.org/abs/2501.13926 https://github.com/ZiyuGuo99/Image-Generation-CoT
  • Liang Wang, Haonan Chen, Nan Yang, Xiaolong Huang, Zhicheng Dou, Furu Wei, 24 Jan 2025, Chain-of-Retrieval Augmented Generation, https://arxiv.org/abs/2501.14342 (Combines RAG with multi-step reasoning such as Chain-of-Thought, with a method to control token cost.)
  • Zhenrui Yue, Honglei Zhuang, Aijun Bai, Kai Hui, Rolf Jagerman, Hansi Zeng, Zhen Qin, Dong Wang, Xuanhui Wang, Michael Bendersky, 6 Oct 2024, Inference Scaling for Long-Context Retrieval Augmented Generation, https://arxiv.org/abs/2410.04343 (Combine RAG and multi-step inference, controlling token cost via budgeting allocations.)
  • Jianfeng Pan, Senyou Deng, Shaomang Huang, 4 Feb 2025, CoAT: Chain-of-Associated-Thoughts Framework for Enhancing Large Language Models Reasoning, https://arxiv.org/abs/2502.02390 (Integrating results from an "associative memory" in CoT reasoning paths at inference time.)
  • Chen, H., Zhu, J., Wang, W. et al. Triplet-based contrastive method enhances the reasoning ability of large language models. J Supercomput 81, 555 (2025). https://doi.org/10.1007/s11227-025-07056-6 https://link.springer.com/article/10.1007/s11227-025-07056-6 (Providing prompt examples that contrast correct and incorrect results to improve CoT reasoning.)
  • Yexiang Liu, Zekun Li, Zhi Fang, Nan Xu, Ran He, Tieniu Tan, 16 May 2025, Rethinking the Role of Prompting Strategies in LLM Test-Time Scaling: A Perspective of Probability Theory, https://arxiv.org/abs/2505.10981
  • Halil Alperen Gozeten, M. Emrullah Ildiz, Xuechen Zhang, Hrayr Harutyunyan, Ankit Singh Rawat, Samet Oymak, 29 May 2025, Continuous Chain of Thought Enables Parallel Exploration and Reasoning, https://arxiv.org/abs/2505.23648
  • Chengshuai Zhao, Zhen Tan, Pingchuan Ma, Dawei Li, Bohan Jiang, Yancheng Wang, Yingzhen Yang, Huan Liu, 13 Aug 2025 (v3), Is Chain-of-Thought Reasoning of LLMs a Mirage? A Data Distribution Lens, https://arxiv.org/abs/2508.01191
  • Tiernan Ray, Sept. 6, 2025, AI's not 'reasoning' at all - how this team debunked the industry hype: Researchers just got very specific about what a language model's 'chain of thought' is actually doing, https://www.zdnet.com/article/ais-not-reasoning-at-all-how-this-team-debunked-the-industry-hype/
  • Jeremy Berman, Sep 17, 2025, How I got the highest score on ARC-AGI again swapping Python for English: Using Multi-Agent Collaboration with Evolutionary Test-Time Compute, https://jeremyberman.substack.com/p/how-i-got-the-highest-score-on-arc-agi-again (Generates multiple solutions then prunes them with "evolution" and iterates in multi-step inference.)

Tree-of-Thought (ToT)

Tree-of-thought is a tree-structured variant of multi-step Chain-of-Thought. Other tree-based versions of CoT are also examined below. Note that the "tree" structure also arises in "CoT decoding algorithms", which are single-step CoT-like inference optimizations that are based on the inherent tree hierarchy in beam search decoding.

Research papers on Tree-of-thought include:

Other Tree-Structured CoT Variants

Research papers on other tree-based CoT variants include:

  • Changcheng Li, Xiangyu Wang, Qiuju Chen, Xiren Zhou, Huanhuan Chen, 5 Dec 2024, MTMT: Consolidating Multiple Thinking Modes to Form a Thought Tree for Strengthening LLM, https://arxiv.org/abs/2412.03987
  • 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
  • Fengli Xu, Qianyue Hao, Zefang Zong, Jingwei Wang, Yunke Zhang, Jingyi Wang, Xiaochong Lan, Jiahui Gong, Tianjian Ouyang, Fanjin Meng, Chenyang Shao, Yuwei Yan, Qinglong Yang, Yiwen Song, Sijian Ren, Xinyuan Hu, Yu Li, Jie Feng, Chen Gao, Yong Li, 17 Jan 2025 (v2), Towards Large Reasoning Models: A Survey on Scaling LLM Reasoning Capabilities, https://arxiv.org/abs/2501.09686
  • Tiesunlong Shen, Jin Wang1, Xuejie Zhang, Erik Cambria, Jan 2025, Reasoning with Trees: Faithful Question Answering over Knowledge Graph, Proceedings of the 31st International Conference on Computational Linguistics, pages 3138–3157 January 19–24, 2025, Association for Computational Linguistics, https://aclanthology.org/2025.coling-main.211.pdf
  • 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.)
  • 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
  • Kun-Peng Ning, Jia-Yu Yao, Yu-Yang Liu, Mu-Nan Ning, Li Yuan, 13 Jan 2025, GPT as a Monte Carlo Language Tree: A Probabilistic Perspective, https://arxiv.org/abs/2501.07641
  • 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
  • Yang Li, 4 Feb 2025, Policy Guided Tree Search for Enhanced LLM Reasoning, https://arxiv.org/abs/2502.06813
  • Yifu Ding, Wentao Jiang, Shunyu Liu, Yongcheng Jing, Jinyang Guo, Yingjie Wang, Jing Zhang, Zengmao Wang, Ziwei Liu, Bo Du, Xianglong Liu, Dacheng Tao, 27 Feb 2025 (v2), Dynamic Parallel Tree Search for Efficient LLM Reasoning, https://arxiv.org/abs/2502.16235
  • Jeremy Berman, Sep 17, 2025, How I got the highest score on ARC-AGI again swapping Python for English: Using Multi-Agent Collaboration with Evolutionary Test-Time Compute, https://jeremyberman.substack.com/p/how-i-got-the-highest-score-on-arc-agi-again (Generates multiple solutions then prunes them with "evolution" and iterates in multi-step inference.)
  • Ahmed Bahloul, Simon Malberg, 18 Jul 2025, From Roots to Rewards: Dynamic Tree Reasoning with RL, https://arxiv.org/abs/2507.13142
  • Xin Wang, Jiyao Liu, Yulong Xiao, Junzhi Ning, Lihao Liu, Junjun He, Botian Shi, Kaicheng Yu, 21 Jul 2025, THE-Tree: Can Tracing Historical Evolution Enhance Scientific Verification and Reasoning?, https://arxiv.org/abs/2506.21763
  • Qi Peng, Jialin Cui, Jiayuan Xie, Yi Cai, Qing Li, 5 Aug 2025, Tree-of-Reasoning: Towards Complex Medical Diagnosis via Multi-Agent Reasoning with Evidence Tree, https://arxiv.org/abs/2508.03038
  • Pardis Moradbeiki, Nasser Ghadiri, Sayed Jalal Zahabi, Uffe Kock Wiil, Kristoffer Kittelmann Brockhattingen, Ali Ebrahimi, 26 Aug 2025, MedVQA-TREE: A Multimodal Reasoning and Retrieval Framework for Sarcopenia Prediction, https://arxiv.org/abs/2508.19319
  • Song Yu, Xiaofei Xu, Ke Deng, Li Li, Lin Tian, 8 Sep 2025, Tree of Agents: Improving Long-Context Capabilities of Large Language Models through Multi-Perspective Reasoning, https://arxiv.org/abs/2509.06436
  • Bingning Huang and Tu Nguyen and Matthieu Zimmer, 11 Sep 2025, Tree-OPO: Off-policy Monte Carlo Tree-Guided Advantage Optimization for Multistep Reasoning, https://arxiv.org/abs/2509.09284
  • Xinzhe Li, 1 Oct 2025, Chain-in-Tree: Back to Sequential Reasoning in LLM Tree Search, https://arxiv.org/abs/2509.25835
  • Shiqi He, Yue Cui, Xinyu Ma, Yaliang Li, Bolin Ding, Mosharaf Chowdhury, 18 Oct 2025, Branch-and-Browse: Efficient and Controllable Web Exploration with Tree-Structured Reasoning and Action Memory, https://arxiv.org/abs/2510.19838
  • Tristan Cinquin, Geoff Pleiss and Agustinus Kristiadi, 23 Oct 2025, Limits of PRM-Guided Tree Search for Mathematical Reasoning with LLMs, https://arxiv.org/abs/2510.20272
  • Fengxiao Tang, Yufeng Li, Zongzong Wu, Ming Zhao, 26 Sep 2025, Chain or tree? Re-evaluating complex reasoning from the perspective of a matrix of thought, https://arxiv.org/abs/2509.03918
  • Jiaxi Li, Yucheng Shi, Jin Lu, Ninghao Liu, 4 Oct 2025, MITS: Enhanced Tree Search Reasoning for LLMs via Pointwise Mutual Information, https://arxiv.org/abs/2510.03632
  • Yao Zhang, Yu Wu, Haowei Zhang, Weiguo Li, Haokun Chen, Jingpei Wu, Guohao Li, Zhen Han, Volker Tresp, 16 Oct 2025, GroundedPRM: Tree-Guided and Fidelity-Aware Process Reward Modeling for Step-Level Reasoning, https://arxiv.org/abs/2510.14942

Graph Reasoning

Graph reasoning is the use of a graph structure, such as a Knowledge Graph, as part of the reasoning algorithm. There is also a variant of Chain-of-Thought called "Graph-of-Thought" or GOT (dragons, anyone?). This is a further generalization of tree-based reasoning hierarchies.

Research papers on graph-based reasoning:

  • 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
  • Jiarui Ji, Runlin Lei, Jialing Bi, Zhewei Wei, Yankai Lin, Xuchen Pan, Yaliang Li, Bolin Ding, 13 Oct 2024, Dynamic and Textual Graph Generation Via Large-Scale LLM-based Agent Simulation, https://arxiv.org/abs/2410.09824
  • Yuwei Hu, Runlin Lei, Xinyi Huang, Zhewei Wei, Yongchao Liu, 7 Oct 2024, Scalable and Accurate Graph Reasoning with LLM-based Multi-Agents, https://arxiv.org/abs/2410.05130
  • Sambhav Khurana, Xiner Li, Shurui Gui, Shuiwang Ji, 29 Oct 2024, A Hierarchical Language Model For Interpretable Graph Reasoning, https://arxiv.org/abs/2410.22372
  • Haoyu Han, Yaochen Xie, Hui Liu, Xianfeng Tang, Sreyashi Nag, William Headden, Hui Liu, Yang Li, Chen Luo, Shuiwang Ji, Qi He, Jiliang Tang, 14 Jan 2025, Reasoning with Graphs: Structuring Implicit Knowledge to Enhance LLMs Reasoning, https://arxiv.org/abs/2501.07845
  • F. Alotaibi, A. Kulkarni and D. Zhou, "Graph of Logic: Enhancing LLM Reasoning with Graphs and Symbolic Logic," 2024 IEEE International Conference on Big Data (BigData), Washington, DC, USA, 2024, pp. 5926-5935, doi: 10.1109/BigData62323.2024.10825450. https://ieeexplore.ieee.org/abstract/document/10825450
  • 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.)
  • Xingtong Yu, Chang Zhou, Zhongwei Kuai, Xinming Zhang, Yuan Fang, 12 Feb 2025, GCoT: Chain-of-Thought Prompt Learning for Graphs, https://arxiv.org/abs/2502.08092
  • Han Zhang, Langshi Zhou, Hanfang Yang, 20 Feb 2025, Learning to Retrieve and Reason on Knowledge Graph through Active Self-Reflection, https://arxiv.org/abs/2502.14932
  • Anastasios Nentidis, Charilaos Akasiadis, Angelos Charalambidis, Alexander Artikis, 26 Feb 2025, Dealing with Inconsistency for Reasoning over Knowledge Graphs: A Survey, https://arxiv.org/abs/2502.19023
  • Avinash Patil, 5 Feb 2025, Advancing Reasoning in Large Language Models: Promising Methods and Approaches, https://arxiv.org/abs/2502.03671
  • Komal Kumar, Tajamul Ashraf, Omkar Thawakar, Rao Muhammad Anwer, Hisham Cholakkal, Mubarak Shah, Ming-Hsuan Yang, Phillip H.S. Torr, Salman Khan, Fahad Shahbaz Khan, 28 Feb 2025, LLM Post-Training: A Deep Dive into Reasoning Large Language Models, https://arxiv.org/abs/2502.21321 https://github.com/mbzuai-oryx/Awesome-LLM-Post-training
  • Wenjie Wu, Yongcheng Jing, Yingjie Wang, Wenbin Hu, Dacheng Tao, 3 Mar 2025, Graph-Augmented Reasoning: Evolving Step-by-Step Knowledge Graph Retrieval for LLM Reasoning, https://arxiv.org/abs/2503.01642

Skeleton-of-Thought

Skeleton-of-thought is a technique with dual aims of smarter reasoning and faster inference. The idea is to generate an outline that is a list of points, and then have the LLM process each sub-point in parallel. This allows both a more focused answer to that issue, and a faster parallelization of shorter token length answers.

Research on skeleton-of-thought reasoning includes:

  • L. Zheng, L. Yin, Z. Xie, J. Huang, C. Sun, C. H. Yu, S. Cao, C. Kozyrakis, I. Stoica, J. E. Gonzalez et al., Dec 2023, Efficiently programming large language models using SGLang, arXiv preprint arXiv:2312.07104, 2023, https://arxiv.org/abs/2312.07104 (Uses a radix attention method, a trie or prefix tree, for KV caching.)
  • Xuefei Ning , Zinan Lin , November 17, 2023 Skeleton-of-Thought: Parallel decoding speeds up and improves LLM output, Microsoft Research Blog, https://www.microsoft.com/en-us/research/blog/skeleton-of-thought-parallel-decoding-speeds-up-and-improves-llm-output/ Code: https://github.com/imagination-research/sot/
  • S. Jin, Y. Wu, H. Zheng, Q. Zhang, M. Lentz, Z. M. Mao, A. Prakash, F. Qian, and D. Zhuo, “Adaptive skeleton graph decoding,” arXiv preprint arXiv:2402.12280, 2024. https://arxiv.org/abs/2402.12280
  • M. Liu, A. Zeng, B. Wang, P. Zhang, J. Tang, and Y. Dong, “Apar: Llms can do auto-parallel auto-regressive decoding,” arXiv preprint arXiv:2401.06761, 2024. https://arxiv.org/abs/2401.06761
  • 8 Jun 2024 (v2), A Survey on Efficient Inference for Large Language Models, Zixuan Zhou, Xuefei Ning, Ke Hong, Tianyu Fu, Jiaming Xu, Shiyao Li, Yuming Lou, Luning Wang, Zhihang Yuan, Xiuhong Li, Shengen Yan, Guohao Dai, Xiao-Ping Zhang, Yuhan Dong, Yu Wang, https://arxiv.org/abs/2404.14294
  • Mahsa Khoshnoodi, Vinija Jain, Mingye Gao, Malavika Srikanth, Aman Chadha, 24 May 2024 (v2), A Comprehensive Survey of Accelerated Generation Techniques in Large Language Models, https://arxiv.org/abs/2405.13019
  • Steven Kolawole, KeshavSanthanam, Virginia Smith, Pratiksha Thaker, Nov 2024, Extracting Parallelism from LargeLanguageModelQueries, https://openreview.net/pdf?id=CZHt9kLS5S
  • Huiyou Zhan, Xuan Zhang, Haisheng Tan, Han Tian, Dongping Yong, Junyang Zhang, Xiang-Yang Li, 16 Jan 2025, PICE: A Semantic-Driven Progressive Inference System for LLM Serving in Cloud-Edge Networks, https://arxiv.org/abs/2501.09367 (Generate an outline in the cloud that is filled in by edge models, which is similar to Skeleton-of-Thought.)
  • Xuefei Ning, Zinan Lin, Zixuan Zhou, Zifu Wang, Huazhong Yang, Yu Wang, May 2024, Skeleton-of-Thought: Prompting LLMs for Efficient Parallel Generation, ICLR 2024, https://www.microsoft.com/en-us/research/publication/skeleton-of-thought-large-language-models-can-do-parallel-decoding/ https://neurips2023-enlsp.github.io/papers/paper_33.pdf Code: https://github.com/imagination-research/sot/
  • Ruibin Xiong, Yimeng Chen, Dmitrii Khizbullin, Jürgen Schmidhuber, 11 Mar 2025, Beyond Outlining: Heterogeneous Recursive Planning for Adaptive Long-form Writing with Language Models, https://arxiv.org/abs/2503.08275
  • Yijiong Yu, 26 Mar 2025, Accelerate Parallelizable Reasoning via Parallel Decoding within One Sequence, https://arxiv.org/abs/2503.20533 https://github.com/yuyijiong/parallel-decoding-in-one-sequence
  • Siqi Fan, Peng Han, Shuo Shang, Yequan Wang, Aixin Sun, 28 May 2025, CoThink: Token-Efficient Reasoning via Instruct Models Guiding Reasoning Models, https://arxiv.org/abs/2505.22017 (Generate an outline before reasoning.)
  • Ali Ismail-Fawaz and Maxime Devanne and Stefano Berretti and Jonathan Weber and Germain Forestier, 28 Jul 2025, Deep Learning for Skeleton Based Human Motion Rehabilitation Assessment: A Benchmark, https://arxiv.org/abs/2507.21018
  • Tiantian Liu, Xiao Li, Huan Li, Hua Lu, Christian S. Jensen, Jianliang Xu, 4 Aug 2025, Skeleton-Guided Learning for Shortest Path Search, https://arxiv.org/abs/2508.02270
  • Youwei Zhou and Tianyang Xu and Cong Wu and Xiaojun Wu and Josef Kittler, 4 Aug 2025, Adaptive Hyper-Graph Convolution Network for Skeleton-based Human Action Recognition with Virtual Connections, https://arxiv.org/abs/2411.14796
  • Devansh Arora, Nitin Kumar, Sukrit Gupta, 15 Aug 2025, Does the Skeleton-Recall Loss Really Work?, https://arxiv.org/abs/2508.11374
  • Maolin Sun, Yibiao Yang, Yuming Zhou, 28 Aug 2025, Boosting Skeleton-Driven SMT Solver Fuzzing by Leveraging LLM to Produce Formula Generators, https://arxiv.org/abs/2508.20340
  • Dongjingdin Liu, Pengpeng Chen, Miao Yao, Yijing Lu, Zijie Cai, Yuxin Tian, 12 Sep 2025, TSGCNeXt: Dynamic-Static Multi-Graph Convolution for Efficient Skeleton-Based Action Recognition with Long-term Learning Potential, https://arxiv.org/abs/2304.11631
  • Sanjeda Akter, Ibne Farabi Shihab, Anuj Sharma, 16 Sep 2025, Selective Risk Certification for LLM Outputs via Information-Lift Statistics: PAC-Bayes, Robustness, and Skeleton Design, https://arxiv.org/abs/2509.12527
  • Bo Wang, Tianyu Li, Ruishi Li, Umang Mathur, Prateek Saxena, 10 Apr 2025, Program Skeletons for Automated Program Translation, https://arxiv.org/abs/2504.07483
  • Feng Ding, Haisheng Fu, Soroush Oraki, Jie Liang, 18 Sep 2025, LSTC-MDA: A Unified Framework for Long-Short Term Temporal Convolution and Mixed Data Augmentation in Skeleton-Based Action Recognition, https://arxiv.org/abs/2509.14619
  • Liangjin Liu, Haoyang Zheng, Zhengzhong Zhu, Pei Zhou, 18 Sep 2025, Skeleton-based sign language recognition using a dual-stream spatio-temporal dynamic graph convolutional network, https://arxiv.org/abs/2509.08661
  • Wen-Bo Xie, Xun Fu, Bin Chen, Yan-Li Lee, Tao Deng, Tian Zou, Xin Wang, Zhen Liu, Jaideep Srivastavad, 10 Sep 2025, Data Skeleton Learning: Scalable Active Clustering with Sparse Graph Structures, https://arxiv.org/abs/2509.08530
  • Yewang Chen and Junfeng Li and Shuyin Xia and Qinghong Lai and Xinbo Gao and Guoyin Wang and Dongdong Cheng and Yi Liu and Yi Wang, 28 Sep 2025, GBSK: Skeleton Clustering via Granular-ball Computing and Multi-Sampling for Large-Scale Data, https://arxiv.org/abs/2509.23742
  • Yongqiang Wang, Weigang Li, Wenping Liu, Zhiqiang Tian, Jinling Li, 29 Sep 2025, Skeleton-based Robust Registration Framework for Corrupted 3D Point Clouds, https://arxiv.org/abs/2509.24273
  • Ziying Zhang, Yaqing Wang, Quanming Yao, 5 Oct 2025, Searching Meta Reasoning Skeleton to Guide LLM Reasoning, https://arxiv.org/abs/2510.04116
  • Suming Qiu, Jing Li, Zhicheng Zhou, Junjie Huang, Linyuan Qiu, Zhijie Sun, 10 Oct 2025, HES-SQL: Hybrid Reasoning for Efficient Text-to-SQL with Structural Skeleton Guidance, https://arxiv.org/abs/2510.08896
  • A. Candito (1), A. Dragan (1,2), R. Holbrey (1), A. Ribeiro (2), R. Donners (3), C. Messiou (1,2), N. Tunariu (1,2), D.-M. Koh (1,2), and M. D. Blackledge (1) ((1) The Institute of Cancer Research, London, United Kingdom (2) The Royal Marsden NHS Foundation Trust, London, United Kingdom (3) University Hospital Basel, Basel, Switzerland), 7 Oct 2025, A weakly-supervised deep learning model for fast localisation and delineation of the skeleton, internal organs, and spinal canal on Whole-Body Diffusion-Weighted MRI (WB-DWI), https://arxiv.org/abs/2503.20722

Reflection

Reflection, or self-reflection, is a type of reasoning where the LLM takes an extra step to "reflect" on its own answers. This is a type of multi-step reasoning method, where the LLM is admonished to improve its own answers. There are different variants of self-reflection for training improvement or inference improvement.

Research papers on reflection:

  • 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
  • Asankhaya Sharma (codelion), Sep 2024, Optillm: Optimizing inference proxy for LLMs, https://github.com/codelion/optillm
  • Pranab Sahoo, Ayush Kumar Singh, Sriparna Saha, Vinija Jain, Samrat Mondal, Aman Chadha, 5 Feb 2024, A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications, https://arxiv.org/abs/2402.07927
  • 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
  • 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
  • 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
  • Anita Kirkovska, David Vargas, Jul 11, 2024, Agentic Workflows in 2024: The ultimate guide, https://www.vellum.ai/blog/agentic-workflows-emerging-architectures-and-design-patterns
  • A. Singh, A. Ehtesham, S. Kumar and T. T. Khoei, "Enhancing AI Systems with Agentic Workflows Patterns in Large Language Model," 2024 IEEE World AI IoT Congress (AIIoT), Seattle, WA, USA, 2024, pp. 527-532, doi: 10.1109/AIIoT61789.2024.10578990. https://ieeexplore.ieee.org/abstract/document/10578990
  • Chawla, Chhavi; Chatterjee, Siddharth; Gadadinni, Sanketh Siddanna; Verma, Pulkit; Banerjee, Sourav, 2024, Agentic AI: The building blocks of sophisticated AI business applications, Journal of AI, Robotics & Workplace Automation, Volume 3 / Number 3 / Summer 2024, pp. 1-15(15), Henry Stewart Publications, DOI: https://doi.org/10.69554/XEHZ1946 https://www.ingentaconnect.com/content/hsp/airwa/2024/00000003/00000003/art00001
  • Yu Zhao, Huifeng Yin, Bo Zeng, Hao Wang, Tianqi Shi, Chenyang Lyu, Longyue Wang, Weihua Luo, Kaifu Zhang, 21 Nov 2024, Marco-o1: Towards Open Reasoning Models for Open-Ended Solutions, https://arxiv.org/abs/2411.14405
  • mshumer, Nov 2024, Open Reasoning Engine, https://github.com/mshumer/OpenReasoningEngine
  • Yaoke Wang, Yun Zhu, Xintong Bao, Wenqiao Zhang, Suyang Dai, Kehan Chen, Wenqiang Li, Gang Huang, Siliang Tang, Yueting Zhuang, 18 Dec 2024, Meta-Reflection: A Feedback-Free Reflection Learning Framework, https://arxiv.org/abs/2412.13781 (One-shot reflection by using a cache of prior reflection results.)
  • 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
  • Thomas Palmeira Ferraz, Kartik Mehta, Yu-Hsiang Lin, Haw-Shiuan Chang, Shereen Oraby, Sijia Liu, Vivek Subramanian, Tagyoung Chung, Mohit Bansal, Nanyun Peng, 9 Oct 2024, LLM Self-Correction with DeCRIM: Decompose, Critique, and Refine for Enhanced Following of Instructions with Multiple Constraints, https://arxiv.org/abs/2410.06458
  • 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
  • Ruwei Pan, Hongyu Zhang, Chao Liu, 14 Jan 2025, CodeCoR: An LLM-Based Self-Reflective Multi-Agent Framework for Code Generation, https://arxiv.org/abs/2501.07811
  • 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.)
  • Siyu Yuan, Zehui Chen, Zhiheng Xi, Junjie Ye, Zhengyin Du, Jiecao Chen, 20 Jan 2025, Agent-R: Training Language Model Agents to Reflect via Iterative Self-Training, https://arxiv.org/abs/2501.11425 (Iterative self-training using reflection.)
  • 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.)
  • 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
  • M. Renze and E. Guven, "Self-Reflection in Large Language Model Agents: Effects on Problem-Solving Performance," 2024 2nd International Conference on Foundation and Large Language Models (FLLM), Dubai, United Arab Emirates, 2024, pp. 516-525, doi: 10.1109/FLLM63129.2024.10852426. https://ieeexplore.ieee.org/abstract/document/10852426/ https://github.com/matthewrenze/self-reflection
  • 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
  • Komal Kumar, Tajamul Ashraf, Omkar Thawakar, Rao Muhammad Anwer, Hisham Cholakkal, Mubarak Shah, Ming-Hsuan Yang, Phillip H.S. Torr, Salman Khan, Fahad Shahbaz Khan, 28 Feb 2025, LLM Post-Training: A Deep Dive into Reasoning Large Language Models, https://arxiv.org/abs/2502.21321 https://github.com/mbzuai-oryx/Awesome-LLM-Post-training
  • Yichi Zhou, Jianqiu Zhao, Yongxin Zhang, Bohan Wang, Siran Wang, Luoxin Chen, Jiahui Wang, Haowei Chen, Allan Jie, Xinbo Zhang, Haocheng Wang, Luong Trung, Rong Ye, Phan Nhat Hoang, Huishuai Zhang, Peng Sun, Hang Li, 21 Jul 2025, Solving Formal Math Problems by Decomposition and Iterative Reflection, https://arxiv.org/abs/2507.15225
  • Yufan Song, Jiatao Zhang, Zeng Gu, Qingmiao Liang, Tuocheng Hu, Wei Song, Shiqiang Zhu, 20 Jul 2025, FCRF: Flexible Constructivism Reflection for Long-Horizon Robotic Task Planning with Large Language Models, https://arxiv.org/abs/2507.14975
  • Rui Lu and Jinhe Bi and Yunpu Ma and Feng Xiao and Yuntao Du and Yijun Tian, 10 Aug 2025, MV-Debate: Multi-view Agent Debate with Dynamic Reflection Gating for Multimodal Harmful Content Detection in Social Media, https://arxiv.org/abs/2508.05557
  • Shijie Cao, Yuan Yuan, 3 Aug 2025, ReflecSched: Solving Dynamic Flexible Job-Shop Scheduling via LLM-Powered Hierarchical Reflection, https://arxiv.org/abs/2508.01724
  • Abi Aryan, Zac Liu, 6 Aug 2025, Causal Reflection with Language Models, https://arxiv.org/abs/2508.04495
  • Vishnu Menon, Andy Cherney, Elizabeth B. Cloude, Li Zhang, Tiffany D. Do, 6 Aug 2025, Evaluating the Impact of LLM-guided Reflection on Learning Outcomes with Interactive AI-Generated Educational Podcasts, https://arxiv.org/abs/2508.04787
  • Jiameng Huang, Baijiong Lin, Guhao Feng, Jierun Chen, Di He, and Lu Hou, 7 Aug 2025, Efficient Reasoning for Large Reasoning Language Models via Certainty-Guided Reflection Suppression, https://arxiv.org/abs/2508.05337
  • Lingyuan Liu, Mengxiang Zhang, 8 Aug 2025, Less is More: Selective Reflection for Compatible and Efficient Knowledge Distillation in Large Language Models, https://arxiv.org/abs/2508.06135
  • Zeyu Tang, Alex John London, Atoosa Kasirzadeh, Sanmi Koyejo, Peter Spirtes, Kun Zhang, 10 Aug 2025, Algorithmic Fairness amid Social Determinants: Reflection, Characterization, and Approach, https://arxiv.org/abs/2508.08337
  • Jiawei Zhou, Amy Z. Chen, Darshi Shah, Laura M. Schwab Reese, and Munmun De Choudhury, 11 Aug 2025, A Risk Taxonomy and Reflection Tool for Large Language Model Adoption in Public Health, https://arxiv.org/abs/2411.02594
  • Katharina Stein, Nils Hodel, Daniel Fi\v{s}er, J\"org Hoffmann, Michael Katz and Alexander Koller, 19 Aug 2025, Improved Generalized Planning with LLMs through Strategy Refinement and Reflection, https://arxiv.org/abs/2508.13876
  • Feng Tian, Flora D. Salim, Hao Xue, 25 Aug 2025, TradingGroup: A Multi-Agent Trading System with Self-Reflection and Data-Synthesis, https://arxiv.org/abs/2508.17565
  • Fu-Chieh Chang, Yu-Ting Lee, Pei-Yuan Wu, 23 Aug 2025, Unveiling the Latent Directions of Reflection in Large Language Models, https://arxiv.org/abs/2508.16989
  • Melissa Kazemi Rad, Alberto Purpura, Himanshu Kumar, Emily Chen, Mohammad Shahed Sorower, 23 Aug 2025, GRAID: Synthetic Data Generation with Geometric Constraints and Multi-Agentic Reflection for Harmful Content Detection, https://arxiv.org/abs/2508.17057
  • Aswin RRV, Jacob Dineen, Divij Handa, Md Nayem Uddin, Mihir Parmar, Chitta Baral, Ben Zhou, 11 Aug 2025, ThinkTuning: Instilling Cognitive Reflections without Distillation, https://arxiv.org/abs/2508.07616
  • Chunlong Wu and Zhibo Qu, 26 Aug 2025, Reflection-Enhanced Meta-Optimization Integrating TextGrad-style Prompt Optimization with Memory-Driven Self-Evolution, https://arxiv.org/abs/2508.18749
  • Qiang Liu, Xinlong Chen, Yue Ding, Bowen Song, Weiqiang Wang, Shu Wu, Liang Wang, 3 Sep 2025, Attention-guided Self-reflection for Zero-shot Hallucination Detection in Large Language Models, https://arxiv.org/abs/2501.09997
  • Quan Chen, Chenrui Shi, Qi Chen, Yuwei Wu, Zhi Gao, Xintong Zhang, Rui Gao, Kun Wu, and Yunde Jia, 4 Sep 2025, Long-Horizon Visual Imitation Learning via Plan and Code Reflection, https://arxiv.org/abs/2509.05368
  • Qin Chen, Yuanyi Ren, Xiaojun Ma, Mugeng Liu, Han Shi, and Dongmei Zhang, 9 Sep 2025, SheetDesigner: MLLM-Powered Spreadsheet Layout Generation with Rule-Based and Vision-Based Reflection, https://arxiv.org/abs/2509.07473
  • Shicheng Ye, Chao Yu, Kaiqiang Ke, Chengdong Xu, Yinqi Wei, 16 Sep 2025, H$^2$R: Hierarchical Hindsight Reflection for Multi-Task LLM Agents, https://arxiv.org/abs/2509.12810
  • Hoang Phan, Victor Li, Qi Lei, 29 Sep 2025, Think Twice, Generate Once: Safeguarding by Progressive Self-Reflection, https://arxiv.org/abs/2510.01270
  • Zhongwei Yu, Wannian Xia, Xue Yan, Bo Xu, Haifeng Zhang, Yali Du, Jun Wang, 14 Oct 2025, Self-Verifying Reflection Helps Transformers with CoT Reasoning, https://arxiv.org/abs/2510.12157
  • Jack Butler, Nikita Kozodoi, Zainab Afolabi, Brian Tyacke, Gaiar Baimuratov, 23 Oct 2025, Finding the Sweet Spot: Trading Quality, Cost, and Speed During Inference-Time LLM Reflection, https://arxiv.org/abs/2510.20653
  • Sion Weatherhead, Flora Salim, Aaron Belbasis, 23 Oct 2025, Illusions of reflection: open-ended task reveals systematic failures in Large Language Models' reflective reasoning, https://arxiv.org/abs/2510.18254
  • Emily Alsentzer, Marie-Laure Charpignon, Bill Chen, Niharika D'Souza, Jason Fries, Yixing Jiang, Aparajita Kashyap, Chanwoo Kim, Simon Lee, Aishwarya Mandyam, Ashery Christopher Mbilinyi, Nikita Mehandru, Nitish Nagesh, Brighton Nuwagira, Emma Pierson, Arvind Pillai, Akane Sano, Tanveer Syeda-Mahmood, Shashank Yadav, Elias Adhanom, Muhammad Umar Afza, Amelia Archer, Suhana Bedi, Vasiliki Bikia, Trenton Chang, George H. Chen, Winston Chen, Erica Chiang, Edward Choi, Octavia Ciora, Paz Dozie-Nnamah, Shaza Elsharief, Matthew Engelhard, Ali Eshragh, Jean Feng, Josh Fessel, Scott Fleming, Kei Sen Fong, Thomas Frost, Soham Gadgil, Judy Gichoya, Leeor Hershkovich, Sujeong Im, Bhavya Jain, Vincent Jeanselme, Furong Jia, Qixuan Jin, Yuxuan Jin, Daniel Kapash, Geetika Kapoor, Behdokht Kiafar, Matthias Kleiner, et al. (41 additional authors not shown), 20 Oct 2025, Reflections from Research Roundtables at the Conference on Health, Inference, and Learning (CHIL) 2025, https://arxiv.org/abs/2510.15217
  • Jason Tsay, Zidane Wright, Gaodan Fang, Kiran Kate, Saurabh Jha, Yara Rizk, 17 Oct 2025, Repairing Tool Calls Using Post-tool Execution Reflection and RAG, https://arxiv.org/abs/2510.17874
  • Yubin Ge, Salvatore Romeo, Jason Cai, Monica Sunkara, Yi Zhang, 24 Sep 2025, SAMULE: Self-Learning Agents Enhanced by Multi-level Reflection, https://arxiv.org/abs/2509.20562
  • Junhao Su, Yuanliang Wan, Junwei Yang, Hengyu Shi, Tianyang Han, Junfeng Luo, Yurui Qiu, 25 Sep 2025, Failure Makes the Agent Stronger: Enhancing Accuracy through Structured Reflection for Reliable Tool Interactions, https://arxiv.org/abs/2509.18847
  • Mingfei Han, Haihong Hao, Jinxing Zhou, Zhihui Li, Yuhui Zheng, Xueqing Deng, Linjie Yang, Xiaojun Chang, 27 Sep 2025, Self-Consistency as a Free Lunch: Reducing Hallucinations in Vision-Language Models via Self-Reflection, https://arxiv.org/abs/2509.23236
  • Jeonghye Kim, Sojeong Rhee, Minbeom Kim, Dohyung Kim, Sangmook Lee, Youngchul Sung, Kyomin Jung, 28 Sep 2025, ReflAct: World-Grounded Decision Making in LLM Agents via Goal-State Reflection, https://arxiv.org/abs/2505.15182
  • Ruixuan Sun, Junyuan Wang, Sanjali Roy, and Joseph A. Konstan, 10 Oct 2025, Co-Authoring the Self: A Human-AI Interface for Interest Reflection in Recommenders, https://arxiv.org/abs/2510.08930
  • Yunlong Deng, Boyang Sun, Yan Li, Lingjing Kong, Zeyu Tang, Kun Zhang, Guangyi Chen, 9 Oct 2025, Selection, Reflection and Self-Refinement: Revisit Reasoning Tasks via a Causal Lens, https://arxiv.org/abs/2510.08222
  • Liwei Kang, Yue Deng, Yao Xiao, Zhanfeng Mo, Wee Sun Lee, Lidong Bing, 9 Oct 2025, First Try Matters: Revisiting the Role of Reflection in Reasoning Models, https://arxiv.org/abs/2510.08308

LLM as Judge

LLM as Judge is the method of improving outputs by having an LLM "judge" the correctness of another LLM's output, whether to evaluate it or make improvements. When the LLM judges its own output, this is known as "self-reflection." When an LLM judges a group of other LLM outputs from the same query, and chooses the best, this is called "Best-of-N."

Research papers on LLM-as-Judge areas:

  • Cameron R. Wolfe, Ph.D., Dec 02, 2024, Finetuning LLM Judges for Evaluation: The Prometheus suite, JudgeLM, PandaLM, AutoJ, and more..., https://cameronrwolfe.substack.com/p/finetuned-judge
  • Tom Schaul, 25 Nov 2024, Boundless Socratic Learning with Language Games, https://arxiv.org/abs/2411.16905
  • 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
  • Haitao Li, Qian Dong, Junjie Chen, Huixue Su, Yujia Zhou, Qingyao Ai, Ziyi Ye, Yiqun Liu, 10 Dec 2024 (v2), LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods, https://arxiv.org/abs/2412.05579 https://github.com/CSHaitao/Awesome-LLMs-as-Judges
  • 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.)
  • 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
  • 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
  • Zhenting Wang, Shuming Hu, Shiyu Zhao, Xiaowen Lin, Felix Juefei-Xu, Zhuowei Li, Ligong Han, Harihar Subramanyam, Li Chen, Jianfa Chen, Nan Jiang, Lingjuan Lyu, Shiqing Ma, Dimitris N. Metaxas, Ankit Jain, 31 Dec 2024, MLLM-as-a-Judge for Image Safety without Human Labeling, https://arxiv.org/abs/2501.00192
  • Zheqi Lv, Wenkai Wang, Jiawei Wang, Shengyu Zhang, Fei Wu, 10 Jan 2025, Cascaded Self-Evaluation Augmented Training for Efficient Multimodal Large Language Models, https://arxiv.org/abs/2501.05662 (Optimize multimodal CoT by breaking down prompts into smaller sub-goals.)
  • 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.)
  • Yafu Li, Zhilin Wang, Tingchen Fu, Ganqu Cui, Sen Yang, Yu Cheng, 21 Jan 2025, From Drafts to Answers: Unlocking LLM Potential via Aggregation Fine-Tuning, https://arxiv.org/abs/2501.11877 (Fine-tune an LLM to accept multiple candidate answers and output a final one.)
  • Swarnadeep Saha, Xian Li, Marjan Ghazvininejad, Jason Weston, Tianlu Wang, 30 Jan 2025, Learning to Plan & Reason for Evaluation with Thinking-LLM-as-a-Judge, https://arxiv.org/abs/2501.18099
  • Yubo Wang, Xiang Yue, Wenhu Chen, 30 Jan 2025 (v2), Critique Fine-Tuning: Learning to Critique is More Effective than Learning to Imitate, https://arxiv.org/abs/2501.17703
  • Gregor Bachmann, Sotiris Anagnostidis, Albert Pumarola, Markos Georgopoulos, Artsiom Sanakoyeu, Yuming Du, Edgar Schönfeld, Ali Thabet, Jonas Kohler, 31 Jan 2025, Judge Decoding: Faster Speculative Sampling Requires Going Beyond Model Alignment, https://arxiv.org/abs/2501.19309 (Using "LLM as Judge" methods to speed up speculative decoding via higher acceptance rates.)
  • Joshua Ong Jun Leang, Giwon Hong, Wenda Li, Shay B. Cohen, 18 Feb 2025, Theorem Prover as a Judge for Synthetic Data Generation, https://arxiv.org/abs/2502.13137
  • Avinash Patil, 5 Feb 2025, Advancing Reasoning in Large Language Models: Promising Methods and Approaches, https://arxiv.org/abs/2502.03671
  • Evangelia Spiliopoulou, Riccardo Fogliato, Hanna Burnsky, Tamer Soliman, Jie Ma, Graham Horwood, Miguel Ballesteros, 8 Aug 2025, Play Favorites: A Statistical Method to Measure Self-Bias in LLM-as-a-Judge, https://arxiv.org/abs/2508.06709
  • Zailong Tian, Zhuoheng Han, Yanzhe Chen, Haozhe Xu, Xi Yang, Richeng Xuan, Houfeng Wang, Lizi Liao, 11 Aug 2025, Overconfidence in LLM-as-a-Judge: Diagnosis and Confidence-Driven Solution, https://arxiv.org/abs/2508.06225
  • Asaf Yehudai, Lilach Eden, Yotam Perlitz, Roy Bar-Haim, Michal Shmueli-Scheuer, 24 Jul 2025, CLEAR: Error Analysis via LLM-as-a-Judge Made Easy, https://arxiv.org/abs/2507.18392
  • Nitay Calderon, Roi Reichart, Rotem Dror, 8 Aug 2025, The Alternative Annotator Test for LLM-as-a-Judge: How to Statistically Justify Replacing Human Annotators with LLMs, https://arxiv.org/abs/2501.10970
  • Francesco Fabbri, Gustavo Penha, Edoardo D'Amico, Alice Wang, Marco De Nadai, Jackie Doremus, Paul Gigioli, Andreas Damianou, Oskar Stal, and Mounia Lalmas, 12 Aug 2025, Evaluating Podcast Recommendations with Profile-Aware LLM-as-a-Judge, https://arxiv.org/abs/2508.08777
  • Yang Zhang, Cunxiang Wang, Lindong Wu, Wenbo Yu, Yidong Wang, Guangsheng Bao, Jie Tang, 13 Aug 2025, UDA: Unsupervised Debiasing Alignment for Pair-wise LLM-as-a-Judge, https://arxiv.org/abs/2508.09724
  • Hongchao Jiang, Yiming Chen, Yushi Cao, Hung-yi Lee, Robby T. Tan, 14 Aug 2025, CodeJudgeBench: Benchmarking LLM-as-a-Judge for Coding Tasks, https://arxiv.org/abs/2507.10535
  • Arduin Findeis, Floris Weers, Guoli Yin, Ke Ye, Ruoming Pang, Tom Gunter, 22 Jul 2025, Can External Validation Tools Improve Annotation Quality for LLM-as-a-Judge?, https://arxiv.org/abs/2507.17015
  • Luke Guerdan, Solon Barocas, Kenneth Holstein, Hanna Wallach, Zhiwei Steven Wu, Alexandra Chouldechova, 21 Aug 2025, Validating LLM-as-a-Judge Systems under Rating Indeterminacy, https://arxiv.org/abs/2503.05965
  • Jiawen Shi, Zenghui Yuan, Yinuo Liu, Yue Huang, Pan Zhou, Lichao Sun, Neil Zhenqiang Gong, 24 Aug 2025, Optimization-based Prompt Injection Attack to LLM-as-a-Judge, https://arxiv.org/abs/2403.17710
  • Xiaolong Wei, Bo Lu, Xingyu Zhang, Zhejun Zhao, Dongdong Shen, Long Xia, Dawei Yin, 29 Aug 2025, Igniting Creative Writing in Small Language Models: LLM-as-a-Judge versus Multi-Agent Refined Rewards, https://arxiv.org/abs/2508.21476
  • Chiyu Ma, Enpei Zhang, Yilun Zhao, Wenjun Liu, Yaning Jia, Peijun Qing, Lin Shi, Arman Cohan, Yujun Yan, Soroush Vosoughi, 17 Sep 2025, Judging with Many Minds: Do More Perspectives Mean Less Prejudice? On Bias Amplifications and Resistance in Multi-Agent Based LLM-as-Judge, https://arxiv.org/abs/2505.19477
  • Jiaxin Gao, Chen Chen, Yanwen Jia, Xueluan Gong, Kwok-Yan Lam, Qian Wang, 14 Oct 2025, Evaluating and Mitigating LLM-as-a-judge Bias in Communication Systems, https://arxiv.org/abs/2510.12462
  • Lucas Roberts, Denisa Roberts, 30 Sep 2025, Which Programming Language and Model Work Best With LLM-as-a-Judge For Code Retrieval?, https://arxiv.org/abs/2510.00324
  • Yidong Wang, Yunze Song, Tingyuan Zhu, Xuanwang Zhang, Zhuohao Yu, Hao Chen, Chiyu Song, Qiufeng Wang, Cunxiang Wang, Zhen Wu, Xinyu Dai, Yue Zhang, Wei Ye, Shikun Zhang, 26 Sep 2025, TrustJudge: Inconsistencies of LLM-as-a-Judge and How to Alleviate Them, https://arxiv.org/abs/2509.21117
  • Joseph Enguehard, Morgane Van Ermengem, Kate Atkinson, Sujeong Cha, Arijit Ghosh Chowdhury, Prashanth Kallur Ramaswamy, Jeremy Roghair, Hannah R Marlowe, Carina Suzana Negreanu, Kitty Boxall, Diana Mincu, 8 Oct 2025, LeMAJ (Legal LLM-as-a-Judge): Bridging Legal Reasoning and LLM Evaluation, https://arxiv.org/abs/2510.07243
  • Hadi Mohammadi, Anastasia Giachanou, and Ayoub Bagheri, 8 Oct 2025, EvalMORAAL: Interpretable Chain-of-Thought and LLM-as-Judge Evaluation for Moral Alignment in Large Language Models, https://arxiv.org/abs/2510.05942
  • Yoshinari Fujinuma, 21 Oct 2025, Contrastive Decoding Mitigates Score Range Bias in LLM-as-a-Judge, https://arxiv.org/abs/2510.18196
  • Luke Guerdan, Justin Whitehouse, Kimberly Truong, Kenneth Holstein, Zhiwei Steven Wu, 26 Sep 2025, Doubly-Robust LLM-as-a-Judge: Externally Valid Estimation with Imperfect Personas, https://arxiv.org/abs/2509.22957
  • Yulai Zhao, Haolin Liu, Dian Yu, Sunyuan Kung, Meijia Chen, Haitao Mi, Dong Yu, 26 Sep 2025, One Token to Fool LLM-as-a-Judge, https://arxiv.org/abs/2507.08794
  • Chenxi Whitehouse, Tianlu Wang, Ping Yu, Xian Li, Jason Weston, Ilia Kulikov, Swarnadeep Saha, 5 Oct 2025, J1: Incentivizing Thinking in LLM-as-a-Judge via Reinforcement Learning, https://arxiv.org/abs/2505.10320
  • Xin Zhou, Kisub Kim, Ting Zhang, Martin Weyssow, Luis F. Gomes, Guang Yang, Kui Liu, Xin Xia, David Lo, 10 Oct 2025, An LLM-as-Judge Metric for Bridging the Gap with Human Evaluation in SE Tasks, https://arxiv.org/abs/2505.20854
  • Jasmina Gajcin, Erik Miehling, Rahul Nair, Elizabeth Daly, Radu Marinescu, Seshu Tirupathi, 9 Oct 2025, Interpreting LLM-as-a-Judge Policies via Verifiable Global Explanations, https://arxiv.org/abs/2510.08120
  • Hong-Jie Dai, Zheng-Hao Li, An-Tai Lu, Bo-Tsz Shain, Ming-Ta Li, Tatheer Hussain Mir, Kuang-Te Wang, Min-I Su, Pei-Kang Liu, Ming-Ju Tsai, 23 Sep 2025, Model selection meets clinical semantics: Optimizing ICD-10-CM prediction via LLM-as-Judge evaluation, redundancy-aware sampling, and section-aware fine-tuning, https://arxiv.org/abs/2509.18846
  • Vanya Bannihatti Kumar, Divyanshu Goyal, Akhil Eppa, Neel Bhandari, 1 Oct 2025, Curiosity-Driven LLM-as-a-judge for Personalized Creative Judgment, https://arxiv.org/abs/2510.05135
  • Riccardo Cantini, Alessio Orsino, Massimo Ruggiero, Domenico Talia, 16 Oct 2025, Benchmarking Adversarial Robustness to Bias Elicitation in Large Language Models: Scalable Automated Assessment with LLM-as-a-Judge, https://arxiv.org/abs/2504.07887

System 2

System 2 is the slower reasoning mode of the human brain, which multi-step reasoning algorithms try to emulate. This is the conscious brain and its capability for rational reasoning, usually in a slow and step-by-step fashion, which reasoning algorithms such as Chain-of-Thought aim to copy. By comparison, System 1 is the sensory processing and intuitive type of brain functions, including the "subconscious" brain, which is massively parallel and innate, but also lacking in rationality and explainability, much like a raw neural network.

Research papers on LLMs and System 2 thinking modes:

  • Pranab Sahoo, Ayush Kumar Singh, Sriparna Saha, Vinija Jain, Samrat Mondal, Aman Chadha, 5 Feb 2024, A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications, https://arxiv.org/abs/2402.07927
  • Akash Bajwa, Oct 07, 2024, Inference Time Scaling Laws: AI Megacycle Of System 1 And System 2 Applications, https://akashbajwa.substack.com/p/inference-time-scaling-laws
  • 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
  • Ping Yu, Jing Xu, Jason Weston, Ilia Kulikov, 24 Jul 2024 (v3), Distilling System 2 into System 1, https://arxiv.org/abs/2407.06023
  • DiJia Su, Sainbayar Sukhbaatar, Michael Rabbat, Yuandong Tian, Qinqing Zheng, 13 Oct 2024, Dualformer: Controllable Fast and Slow Thinking by Learning with Randomized Reasoning Traces, https://arxiv.org/abs/2410.09918
  • Cheng Yang, Chufan Shi, Siheng Li, Bo Shui, Yujiu Yang, Wai Lam, 29 Dec 2024, LLM2: Let Large Language Models Harness System 2 Reasoning, https://arxiv.org/abs/2412.20372
  • 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
  • Scott C. Lowe, 29 Oct 2024 (v2), System 2 Reasoning Capabilities Are Nigh, https://arxiv.org/abs/2410.03662
  • 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
  • Violet Xiang, Charlie Snell, Kanishk Gandhi, Alon Albalak, Anikait Singh, Chase Blagden, Duy Phung, Rafael Rafailov, Nathan Lile, Dakota Mahan, Louis Castricato, Jan-Philipp Franken, Nick Haber, Chelsea Finn, 8 Jan 2025, Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-Thought, https://arxiv.org/abs/2501.04682
  • Fengli Xu, Qianyue Hao, Zefang Zong, Jingwei Wang, Yunke Zhang, Jingyi Wang, Xiaochong Lan, Jiahui Gong, Tianjian Ouyang, Fanjin Meng, Chenyang Shao, Yuwei Yan, Qinglong Yang, Yiwen Song, Sijian Ren, Xinyuan Hu, Yu Li, Jie Feng, Chen Gao, Yong Li, 17 Jan 2025 (v2), Towards Large Reasoning Models: A Survey on Scaling LLM Reasoning Capabilities, https://arxiv.org/abs/2501.09686
  • 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.)
  • Bilgehan Sel, Ruoxi Jia, Ming Jin, 23 Jan 2025, LLMs Can Plan Only If We Tell Them, https://arxiv.org/abs/2501.13545
  • Kounianhua Du, Hanjing Wang, Jianxing Liu, Jizheng Chen, Xinyi Dai, Yasheng Wang, Ruiming Tang, Yong Yu, Jun Wang, Weinan Zhang, 18 Feb 2025, Boost, Disentangle, and Customize: A Robust System2-to-System1 Pipeline for Code Generation, https://arxiv.org/abs/2502.12492
  • Alireza S. Ziabari, Nona Ghazizadeh, Zhivar Sourati, Farzan Karimi-Malekabadi, Payam Piray, Morteza Dehghani, 18 Feb 2025, Reasoning on a Spectrum: Aligning LLMs to System 1 and System 2 Thinking, https://arxiv.org/abs/2502.12470
  • 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
  • Pengcheng Wen, Jiaming Ji, Chi-Min Chan, Juntao Dai, Donghai Hong, Yaodong Yang, Sirui Han, Yike Guo, 17 Mar 2025, ThinkPatterns-21k: A Systematic Study on the Impact of Thinking Patterns in LLMs, https://arxiv.org/abs/2503.12918
  • Sapient Intelligence, 22/07/2025, Sapient Intelligence Open-Sources Hierarchical Reasoning Model, a Brain-Inspired Architecture That Solves Complex Reasoning Tasks With 27 Million Parameters, https://www.sapient.inc/blog/5
  • Sejin Kim, Sundong Kim, 13 Aug 2025, System 2 Reasoning for Human-AI Alignment: Generality and Adaptivity via ARC-AGI, https://arxiv.org/abs/2410.07866
  • Runqi Qiao and Qiuna Tan and Peiqing Yang and Yanzi Wang and Xiaowan Wang and Enhui Wan and Sitong Zhou and Guanting Dong and Yuchen Zeng and Yida Xu and Jie Wang and Chong Sun and Chen Li and Honggang Zhang, 14 Aug 2025, We-Math 2.0: A Versatile MathBook System for Incentivizing Visual Mathematical Reasoning, https://arxiv.org/abs/2508.10433
  • Jaeseong Lee, Dayoung Kwon, Seung-won Hwang, 8 Oct 2025, Gold-Switch: Training-Free Superposition of Slow- and Fast- Thinking LLMs, https://arxiv.org/abs/2510.06750
  • Julian Coda-Forno, Zhuokai Zhao, Qiang Zhang, Dipesh Tamboli, Weiwei Li, Xiangjun Fan, Lizhu Zhang, Eric Schulz, Hsiao-Ping Tseng, 1 Oct 2025, Exploring System 1 and 2 communication for latent reasoning in LLMs, https://arxiv.org/abs/2510.00494
  • Ashish Bhatia, Renato Cordeiro de Amorim, Vito De Feo, 15 Oct 2025, Hybrid Interval Type-2 Mamdani-TSK Fuzzy System for Regression Analysis, https://arxiv.org/abs/2510.13437
  • Core Francisco Park, Zechen Zhang, Hidenori Tanaka, 27 Sep 2025, $\textit{New News}$: System-2 Fine-tuning for Robust Integration of New Knowledge, https://arxiv.org/abs/2505.01812

Best of N Reasoning

Best of N is an LLM reasoning method where multiple answers are generated, and the best one is chosen. You can use Best of N (BoN) with multiple answers from a single LLM, or in an ensemble inference architecture with answers from multiple different LLMs. Usually, the last step is another LLM inference that performs "LLM as Judge" computations to choose the best answer. It is also possible to use other types of non-LLM ranking algorithms to choose the best one.

Research papers on Best-of-N reasoning:

  • 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
  • Hanshi Sun, Momin Haider, Ruiqi Zhang, Huitao Yang, Jiahao Qiu, Ming Yin, Mengdi Wang, Peter Bartlett, Andrea Zanette, 26 Oct 2024, Fast Best-of-N Decoding via Speculative Rejection, https://arxiv.org/abs/2410.20290
  • Do Xuan Long, Duong Ngoc Yen, Anh Tuan Luu, Kenji Kawaguchi, Min-Yen Kan, Nancy F. Chen, 1 Nov 2024, Multi-expert Prompting Improves Reliability, Safety, and Usefulness of Large Language Models, https://arxiv.org/abs/2411.00492
  • Yinlam Chow, Guy Tennenholtz, Izzeddin Gur, Vincent Zhuang, Bo Dai, Sridhar Thiagarajan, Craig Boutilier, Rishabh Agarwal, Aviral Kumar, Aleksandra Faust, 18 Dec 2024, Inference-Aware Fine-Tuning for Best-of-N Sampling in Large Language Models, https://arxiv.org/abs/2412.15287
  • Violet Xiang, Charlie Snell, Kanishk Gandhi, Alon Albalak, Anikait Singh, Chase Blagden, Duy Phung, Rafael Rafailov, Nathan Lile, Dakota Mahan, Louis Castricato, Jan-Philipp Franken, Nick Haber, Chelsea Finn, 8 Jan 2025, Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-Thought, https://arxiv.org/abs/2501.04682
  • 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.)
  • 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
  • Yafu Li, Zhilin Wang, Tingchen Fu, Ganqu Cui, Sen Yang, Yu Cheng, 21 Jan 2025, From Drafts to Answers: Unlocking LLM Potential via Aggregation Fine-Tuning, https://arxiv.org/abs/2501.11877 (Fine-tune an LLM to accept multiple candidate answers and output a final one.)
  • Weihua Du, Yiming Yang, Sean Welleck, 7 Feb 2025, Optimizing Temperature for Language Models with Multi-Sample Inference, https://arxiv.org/abs/2502.05234 https://github.com/StigLidu/TURN
  • Juntai Cao, Xiang Zhang, Raymond Li, Chuyuan Li, Shafiq Joty, Giuseppe Carenini, 27 Feb 2025, Multi2: Multi-Agent Test-Time Scalable Framework for Multi-Document Processing, https://arxiv.org/abs/2502.20592 (Test time computed applied to the multi-document summarization use case.)
  • Komal Kumar, Tajamul Ashraf, Omkar Thawakar, Rao Muhammad Anwer, Hisham Cholakkal, Mubarak Shah, Ming-Hsuan Yang, Phillip H.S. Torr, Salman Khan, Fahad Shahbaz Khan, 28 Feb 2025, LLM Post-Training: A Deep Dive into Reasoning Large Language Models, https://arxiv.org/abs/2502.21321 https://github.com/mbzuai-oryx/Awesome-LLM-Post-training
  • Chengsong Huang, Langlin Huang, Jixuan Leng, Jiacheng Liu, Jiaxin Huang, 25 Feb 2025, Efficient Test-Time Scaling via Self-Calibration, https://arxiv.org/abs/2503.00031
  • Yiming Wang, Pei Zhang, Siyuan Huang, Baosong Yang, Zhuosheng Zhang, Fei Huang, Rui Wang, 3 Mar 2025, Sampling-Efficient Test-Time Scaling: Self-Estimating the Best-of-N Sampling in Early Decoding, https://arxiv.org/abs/2503.01422
  • Yiwei Li, Jiayi Shi, Shaoxiong Feng, Peiwen Yuan, Xinglin Wang, Yueqi Zhang, Ji Zhang, Chuyi Tan, Boyuan Pan, Yao Hu, Kan Li, 7 Mar 2025, Speculative Decoding for Multi-Sample Inference, https://arxiv.org/abs/2503.05330 (Optimizing speculative decoding when generating multiple answers for a single query, such as for Best-of-N 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.)
  • Ningning Wang, Xavier Hu, Pai Liu, He Zhu, Yue Hou, Heyuan Huang, Shengyu Zhang, Jian Yang, Jiaheng Liu, Ge Zhang, Changwang Zhang, Jun Wang, Yuchen Eleanor Jiang, Wangchunshu Zhou, 24 Jul 2025, Efficient Agents: Building Effective Agents While Reducing Cost, https://arxiv.org/pdf/2508.02694 https://github.com/OPPO-PersonalAI/OAgents
  • Shubham Toshniwal, Ivan Sorokin, Aleksander Ficek, Ivan Moshkov, Igor Gitman, 23 Jul 2025, GenSelect: A Generative Approach to Best-of-N, https://arxiv.org/abs/2507.17797
  • Jizhou Guo, Zhaomin Wu, Hanchen Yang, Philip S. Yu, 29 Jul 2025, Mining Intrinsic Rewards from LLM Hidden States for Efficient Best-of-N Sampling, https://arxiv.org/abs/2505.12225
  • Jiahao Qiu, Yifu Lu, Yifan Zeng, Jiacheng Guo, Jiayi Geng, Chenhao Zhu, Xinzhe Juan, Ling Yang, Huazheng Wang, Kaixuan Huang, Yue Wu, Mengdi Wang, 3 Sep 2025, TreeBoN: Enhancing Inference-Time Alignment with Speculative Tree-Search and Best-of-N Sampling, https://arxiv.org/abs/2410.16033
  • Tomas Ruiz, Siyao Peng, Barbara Plank, Carsten Schwemmer, 14 Oct 2025, BoN Appetit Team at LeWiDi-2025: Best-of-N Test-time Scaling Can Not Stomach Annotation Disagreements (Yet), https://arxiv.org/abs/2510.12516
  • Farid Bagirov, Mikhail Arkhipov, Ksenia Sycheva, Evgeniy Glukhov, Egor Bogomolov, 27 Oct 2025, The Best of N Worlds: Aligning Reinforcement Learning with Best-of-N Sampling via max@k Optimisation, https://arxiv.org/abs/2510.23393
  • Vinod Raman, Hilal Asi, Satyen Kale, 29 Sep 2025, AdaBoN: Adaptive Best-of-N Alignment, https://arxiv.org/abs/2505.12050
  • Yung-Chen Tang, Pin-Yu Chen, Andrea Cavallaro, 17 Oct 2025, CarBoN: Calibrated Best-of-N Sampling Improves Test-time Reasoning, https://arxiv.org/abs/2510.15674
  • Hyung Gyu Rho, 5 Oct 2025, A Contextual Quality Reward Model for Reliable and Efficient Best-of-N Sampling, https://arxiv.org/abs/2510.04087

Program Synthesis

Program synthesis is the reasoning method whereby the LLM can synthesize program code that is then executed to solve a problem. Using a Python interpreter with an LLM is common, but any language can potentially be used, including more abstract mathematical symbolic languages. The virtually unlimited flexibility of programming languages, when combined with LLM pattern-matching power to create code, offers a fertile area for reasoning advancement.

Research papers related to program synthesis and similar symbolic reasoning approaches:

  • Guoxin Chen, Minpeng Liao, Chengxi Li, Kai Fan, 6 May 2024, AlphaMath Almost Zero: process Supervision without process, https://arxiv.org/abs/2405.03553 https://github.com/MARIO-Math-Reasoning/Super_MARIO
  • Wenhu Chen, Xueguang Ma, Xinyi Wang, and William W Cohen. Program of thoughts prompting: Disentangling computation from reasoning for numerical reasoning tasks. arXiv preprint arXiv:2211.12588, 2022. https://arxiv.org/abs/2211.12588 (Integrate a Python interpreter to execute the code generated by the LLM to answer the query.)
  • Luyu Gao, Aman Madaan, Shuyan Zhou, Uri Alon, Pengfei Liu, Yiming Yang, Jamie Callan, and Graham Neubig. Pal: Program-aided language models. In International Conference on Machine Learning, pages 10764–10799. PMLR, 2023. https://arxiv.org/abs/2211.10435 Code: http://reasonwithpal.com/ (Python interpreter integrated as a tool for LLMs.)
  • Long Hei Matthew Lam, Ehsan Shareghi, 1 Jun 2024, A Closer Look at Logical Reasoning with LLMs: The Choice of Tool Matters, https://arxiv.org/abs/2406.00284 (Using symbolic solvers with LLMs.)
  • M Keber, I Grubišic, A Barešic, A Jovic, 2024, A Review on Neuro-symbolic AI Improvements to Natural Language Processing, https://www.researchgate.net/profile/Alan-Jovic/publication/380911364_A_Review_on_Neuro-symbolic_AI_Improvements_to_Natural_Language_Processing/links/6655c0ec22a7f16b4f51fb2f/A-Review-on-Neuro-symbolic-AI-Improvements-to-Natural-Language-Processing.pdf
  • Joy He-Yueya, Gabriel Poesia, Rose E. Wang, and Noah D. Goodman. Solving math word problems by combining language models with symbolic solvers. ArXiv, abs/2304.09102, 2023. https://arxiv.org/abs/2304.09102
  • Owen Dugan, Donato Manuel Jimenez Beneto, Charlotte Loh, Zhuo Chen, Rumen Dangovski, Marin Soljačić, 4 Jun 2024, OccamLLM: Fast and Exact Language Model Arithmetic in a Single Step, https://arxiv.org/abs/2406.06576
  • 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
  • Yongchao Chen, Harsh Jhamtani, Srinagesh Sharma, Chuchu Fan, Chi Wang, 4 Oct 2024, Steering Large Language Models between Code Execution and Textual Reasoning, https://arxiv.org/abs/2410.03524 https://yongchao98.github.io/CodeSteer/
  • 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
  • Jiajun Chen, Yik-Cheung Tam, 5 Dec 2024, Enhancing Mathematical Reasoning in LLMs with Background Operators, https://arxiv.org/abs/2412.04110
  • 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
  • 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.)
  • 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.)
  • Ndea, Jan 16, 2025, Ndea is building frontier AI systems that blend intuitive pattern recognition and formal reasoning into a unified architecture., https://ndea.com/
  • François Chollet, 25 Nov 2019 (v2), On the Measure of Intelligence, https://arxiv.org/abs/1911.01547
  • Sumit Gulwani, Alex Polozov, Rishabh Singh, 2017, Program Synthesis, NOW, August 2017, Vol 4, https://www.microsoft.com/en-us/research/publication/program-synthesis/ https://www.microsoft.com/en-us/research/wp-content/uploads/2017/10/program_synthesis_now.pdf
  • Shraddha Barke, Emmanuel Anaya Gonzalez, Saketh Ram Kasibatla, Taylor Berg-Kirkpatrick, Nadia Polikarpova, 1 Nov 2024 (v2), HYSYNTH: Context-Free LLM Approximation for Guiding Program Synthesis, https://arxiv.org/abs/2405.15880
  • Stephen Mell, Steve Zdancewic, and Osbert Bastani. 2024. Optimal Program Synthesis via Abstract Interpretation. Proc. ACM Program. Lang. 8, POPL, Article 16 (January 2024), 25 pages. https://doi.org/10.1145/3632858 https://dl.acm.org/doi/abs/10.1145/3632858
  • Yixuan Li, Lewis Frampton, Federico Mora, Elizabeth Polgreen, 9 Jan 2025, Online Prompt and Solver Selection for Program Synthesis, https://arxiv.org/abs/2501.05247
  • Qikang Liu, Yang He, Yanwen Cai, Byeongguk Kwak, Yuepeng Wang, 8 Dec 2024, Synthesizing Document Database Queries using Collection Abstractions, https://arxiv.org/abs/2412.06102
  • F. Alotaibi, A. Kulkarni and D. Zhou, "Graph of Logic: Enhancing LLM Reasoning with Graphs and Symbolic Logic," 2024 IEEE International Conference on Big Data (BigData), Washington, DC, USA, 2024, pp. 5926-5935, doi: 10.1109/BigData62323.2024.10825450. https://ieeexplore.ieee.org/abstract/document/10825450
  • Yiyao Yu, Yuxiang Zhang, Dongdong Zhang, Xiao Liang, Hengyuan Zhang, Xingxing Zhang, Ziyi Yang, Mahmoud Khademi, Hany Awadalla, Junjie Wang, Yujiu Yang, Furu Wei, 19 Jan 2025, Chain-of-Reasoning: Towards Unified Mathematical Reasoning in Large Language Models via a Multi-Paradigm Perspective, https://arxiv.org/abs/2501.11110
  • Benjamin Callewaert, Simon Vandevelde, Joost Vennekens, 24 Jan 2025, VERUS-LM: a Versatile Framework for Combining LLMs with Symbolic Reasoning, https://arxiv.org/abs/2501.14540
  • 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
  • Mohit Sewak, Ph.D., January 29, 2025, Achieving General Intelligence (AGI) and Super Intelligence (ASI): Pathways, Uncertainties, and Ethical Concerns, https://towardsai.net/p/l/achieving-general-intelligence-agi-and-super-intelligence-asi-pathways-uncertainties-and-ethical-concerns
  • Yubin Ge, Salvatore Romeo, Jason Cai, Raphael Shu, Monica Sunkara, Yassine Benajiba, Yi Zhang, 3 Feb 2025, TReMu: Towards Neuro-Symbolic Temporal Reasoning for LLM-Agents with Memory in Multi-Session Dialogues, https://arxiv.org/abs/2502.01630
  • Avinash Patil, 5 Feb 2025, Advancing Reasoning in Large Language Models: Promising Methods and Approaches, https://arxiv.org/abs/2502.03671
  • Cheryl Li, Tianyuan Xu, Yiwen Guo, 5 Feb 2025, Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment, https://arxiv.org/abs/2502.07803
  • 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
  • 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
  • Ali Forootani, 22 Mar 2025, A Survey on Mathematical Reasoning and Optimization with Large Language Models, https://arxiv.org/abs/2503.17726
  • Siheng Xiong, Jieyu Zhou, Zhangding Liu, Yusen Su, 2 May 2025, SymPlanner: Deliberate Planning in Language Models with Symbolic Representation, https://arxiv.org/abs/2505.01479
  • Adam Stein, Aaditya Naik, Neelay Velingker, Mayur Naik, Eric Wong, 30 May 2025, The Road to Generalizable Neuro-Symbolic Learning Should be Paved with Foundation Models, https://arxiv.org/abs/2505.24874
  • Martin Berger, Nathanaël Fijalkow, Mojtaba Valizadeh, 26 Apr 2025, GPU accelerated program synthesis: Enumerate semantics, not syntax! https://arxiv.org/abs/2504.18943
  • Simon Ouellette, 17 Jul 2025, Out-of-Distribution Generalization in the ARC-AGI Domain: Comparing Execution-Guided Neural Program Synthesis and Test-Time Fine-Tuning, https://arxiv.org/abs/2507.15877
  • Noah van der Vleuten, 20 Jul 2025, Dr. Boot: Bootstrapping Program Synthesis Language Models to Perform Repairing, https://arxiv.org/abs/2507.15889
  • Busra Icoz, Goksel Biricik, 24 Jul 2025, Automated Code Review Using Large Language Models with Symbolic Reasoning, https://arxiv.org/abs/2507.18476
  • Julien Pourcel, C\'edric Colas, Pierre-Yves Oudeyer, 10 Jul 2025, Self-Improving Language Models for Evolutionary Program Synthesis: A Case Study on ARC-AGI, https://arxiv.org/abs/2507.14172
  • Luca Salvatore Lorello, Nikolaos Manginas, Marco Lippi, Stefano Melacci, 23 Jul 2025, LTLZinc: a Benchmarking Framework for Continual Learning and Neuro-Symbolic Temporal Reasoning, https://arxiv.org/abs/2507.17482
  • Gary Marcus, Jul 14, 2025, How o3 and Grok 4 Accidentally Vindicated Neurosymbolic AI, https://garymarcus.substack.com/p/how-o3-and-grok-4-accidentally-vindicated
  • Lin-Han Jia, Si-Yu Han, Wen-Chao Hu, Jie-Jing Shao, Wen-Da Wei, Zhi Zhou, Lan-Zhe Guo, Yu-Feng Li, 10 Aug 2025, When Is Prior Knowledge Helpful? Exploring the Evaluation and Selection of Unsupervised Pretext Tasks from a Neuro-Symbolic Perspective, https://arxiv.org/abs/2508.07299
  • Raffaele Pojer, Andrea Passerini, Kim G. Larsen, Manfred Jaeger, 29 Jul 2025, A Neuro-Symbolic Approach for Probabilistic Reasoning on Graph Data, https://arxiv.org/abs/2507.21873
  • Andrew Kiruluta, Andreas Lemos, and Priscilla Burity, 27 Jul 2025, Operator-Based Machine Intelligence: A Hilbert Space Framework for Spectral Learning and Symbolic Reasoning, https://arxiv.org/abs/2507.21189
  • Andrew Kiruluta, Andreas Lemos, and Priscilla Burity, 27 Jul 2025, Beyond Neural Networks: Symbolic Reasoning over Wavelet Logic Graph Signals, https://arxiv.org/abs/2507.21190
  • Wenkai Tan, Alvaro Velasquez, Houbing Song, 28 Jul 2025, DEM-NeRF: A Neuro-Symbolic Method for Scientific Discovery through Physics-Informed Simulation, https://arxiv.org/abs/2507.21350
  • Vasileios Manginas, Nikolaos Manginas, Edward Stevinson, Sherwin Varghese, Nikos Katzouris, Georgios Paliouras, Alessio Lomuscio, 29 Jul 2025, A Scalable Approach to Probabilistic Neuro-Symbolic Robustness Verification, https://arxiv.org/abs/2502.03274
  • Oren Sultan, Eitan Stern, Dafna Shahaf, 29 Jul 2025, Towards Reliable Proof Generation with LLMs: A Neuro-Symbolic Approach, https://arxiv.org/abs/2505.14479
  • Tilman Hinnerichs, Bart Swinkels, Jaap de Jong, Reuben Gardos Reid, Tudor Magirescu, Neil Yorke-Smith, Sebastijan Dumancic, 10 Jul 2025, Modelling Program Spaces in Program Synthesis with Constraints, https://arxiv.org/abs/2508.00005
  • Xinkai Zou, Xuan Jiang, Ruikai Huang, Haoze He, Parv Kapoor, Jiahua Zhao, 3 Aug 2025, CloudAnoAgent: Anomaly Detection for Cloud Sites via LLM Agent with Neuro-Symbolic Mechanism, https://arxiv.org/abs/2508.01844
  • Long S. T. Nguyen, Khang H. N. Vo, Thu H. A. Nguyen, Tuan C. Bui, Duc Q. Nguyen, Thanh-Tung Tran, Anh D. Nguyen, Minh L. Nguyen, Fabien Baldacci, Thang H. Bui, Emanuel Di Nardo, Angelo Ciaramella, Son H. Le, Ihsan Ullah, Lorenzo Di Rocco, and Tho T. Quan, 2 Aug 2025, Bridging LLMs and Symbolic Reasoning in Educational QA Systems: Insights from the XAI Challenge at IJCNN 2025, https://arxiv.org/abs/2508.01263
  • Zewen Liu, Juntong Ni, Xianfeng Tang, Max S.Y. Lau, Wei Jin, 5 Aug 2025, Can Large Language Models Adequately Perform Symbolic Reasoning Over Time Series?, https://arxiv.org/abs/2508.03963
  • Andrew Kiruluta, 7 Aug 2025, A Novel Architecture for Symbolic Reasoning with Decision Trees and LLM Agents, https://arxiv.org/abs/2508.05311
  • Anjiang Wei, Tarun Suresh, Jiannan Cao, Naveen Kannan, Yuheng Wu, Kai Yan, Thiago S. F. X. Teixeira, Ke Wang, Alex Aiken, 8 Aug 2025, CodeARC: Benchmarking Reasoning Capabilities of LLM Agents for Inductive Program Synthesis, https://arxiv.org/abs/2503.23145
  • Iman Sharifi, Mustafa Yildirim, Saber Fallah, 17 Aug 2025, Towards Safe Autonomous Driving Policies using a Neuro-Symbolic Deep Reinforcement Learning Approach, https://arxiv.org/abs/2307.01316
  • Ronit Virwani and Ruchika Suryawanshi, 18 Aug 2025, LOOP: A Plug-and-Play Neuro-Symbolic Framework for Enhancing Planning in Autonomous Systems, https://arxiv.org/abs/2508.13371
  • Xiao-Wen Yang, Jie-Jing Shao, Lan-Zhe Guo, Bo-Wen Zhang, Zhi Zhou, Lin-Han Jia, Wang-Zhou Dai and Yu-Feng Li, 19 Aug 2025, Neuro-Symbolic Artificial Intelligence: Towards Improving the Reasoning Abilities of Large Language Models, https://arxiv.org/abs/2508.13678
  • Andrew Kiruluta, 19 Aug 2025, A Fully Spectral Neuro-Symbolic Reasoning Architecture with Graph Signal Processing as the Computational Backbone, https://arxiv.org/abs/2508.14923
  • Xuan Zhang, Zhijian Zhou, Weidi Xu, Yanting Miao, Chao Qu, Yuan Qi, 22 Aug 2025, Constraints-Guided Diffusion Reasoner for Neuro-Symbolic Learning, https://arxiv.org/abs/2508.16524
  • Christopher J. Mungall and Adnan Malik and Daniel R. Korn and Justin T. Reese and Noel M. O'Boyle, Noel and Janna Hastings, 24 Aug 2025, Chemical classification program synthesis using generative artificial intelligence, https://arxiv.org/abs/2505.18470
  • Justin Chih-Yao Chen, Sukwon Yun, Elias Stengel-Eskin, Tianlong Chen, Mohit Bansal, 18 Jul 2025, Symbolic Mixture-of-Experts: Adaptive Skill-based Routing for Heterogeneous Reasoning, https://arxiv.org/abs/2503.05641
  • Tong Yang, Yu Huang, Yingbin Liang, Yuejie Chi, 11 Aug 2025, Multi-head Transformers Provably Learn Symbolic Multi-step Reasoning via Gradient Descent, https://arxiv.org/abs/2508.08222
  • Qiushi Sun, Jinyang Gong, Lei Li, Qipeng Guo, Fei Yuan, 25 Jul 2025, CodeEvo: Interaction-Driven Synthesis of Code-centric Data through Hybrid and Iterative Feedback, https://arxiv.org/abs/2507.22080
  • Gongyao Jiang, Qiong Luo, 16 Aug 2025, Chart-CoCa: Self-Improving Chart Understanding of Vision LMs via Code-Driven Synthesis and Candidate-Conditioned Answering, https://arxiv.org/abs/2508.11975
  • Phuong Minh Nguyen, Tien Huu Dang, Naoya Inoue, 17 Aug 2025, Non-Iterative Symbolic-Aided Chain-of-Thought for Logical Reasoning, https://arxiv.org/abs/2508.12425
  • Ryan Hare and Ying Tang, 25 Aug 2025, Toward Generalized Autonomous Agents: A Neuro-Symbolic AI Framework for Integrating Social and Technical Support in Education, https://arxiv.org/abs/2508.18406
  • Hung Ming Liu, 26 Aug 2025, Interpretable by AI Mother Tongue: Native Symbolic Reasoning in Neural Models, https://arxiv.org/abs/2508.18988
  • Rushitha Santhoshi Mamidala, Anshuman Chhabra, Ankur Mali, 22 Aug 2025, Rethinking Reasoning in LLMs: Neuro-Symbolic Local RetoMaton Beyond ICL and CoT, https://arxiv.org/abs/2508.19271
  • Marianne Defresne, Romain Gambardella, Sophie Barbe, Thomas Schiex, 28 Aug 2025, Efficient Neuro-Symbolic Learning of Constraints and Objective, https://arxiv.org/abs/2508.20978
  • Axel Mezini, Elena Umili, Ivan Donadello, Fabrizio Maria Maggi, Matteo Mancanelli, Fabio Patrizi, 31 Aug 2025, Neuro-Symbolic Predictive Process Monitoring, https://arxiv.org/abs/2509.00834
  • Jay Vaghasiya, Omkar Ghugarkar, Vishvesh Bhat, Vipul Dholaria, Julian McAuley, 31 Aug 2025, CoreThink: A Symbolic Reasoning Layer to reason over Long Horizon Tasks with LLMs, https://arxiv.org/abs/2509.00971
  • Madhav Kanda, Shubham Ugare, Sasa Misailovic, 1 Sep 2025, REFINESTAT: Efficient Exploration for Probabilistic Program Synthesis, https://arxiv.org/abs/2509.01082
  • Markus Reiter-Haas and Elisabeth Lex, 2 Sep 2025, Towards Multi-Aspect Diversification of News Recommendations Using Neuro-Symbolic AI for Individual and Societal Benefit, https://arxiv.org/abs/2509.02220
  • Kevin Alcedo and Pedro U. Lima and Rachid Alami, 2 Sep 2025, Perspective-Shifted Neuro-Symbolic World Models: A Framework for Socially-Aware Robot Navigation, https://arxiv.org/abs/2503.20425
  • Yousef Alhessi, S\'olr\'un Halla Einarsd\'ottir, George Granberry, Emily First, Moa Johansson, Sorin Lerner, Nicholas Smallbone, 1 Sep 2025, Lemmanaid: Neuro-Symbolic Lemma Conjecturing, https://arxiv.org/abs/2504.04942
  • Midhat Urooj, Ayan Banerjee, Farhat Shaikh, Kuntal Thakur, Sandeep Gupta, 3 Sep 2025, Single Domain Generalization in Diabetic Retinopathy: A Neuro-Symbolic Learning Approach, https://arxiv.org/abs/2509.02918
  • Kaizhi Zheng, Kaiwen Zhou, Jing Gu, Yue Fan, Jialu Wang, Zonglin Di, Xuehai He, Xin Eric Wang, 2 Sep 2025, JARVIS: A Neuro-Symbolic Commonsense Reasoning Framework for Conversational Embodied Agents, https://arxiv.org/abs/2208.13266
  • Safayat Bin Hakim, Muhammad Adil, Alvaro Velasquez, Shouhuai Xu, Houbing Herbert Song, 8 Sep 2025, Neuro-Symbolic AI for Cybersecurity: State of the Art, Challenges, and Opportunities, https://arxiv.org/abs/2509.06921
  • Andrew Kiruluta and Priscilla Burity, 7 Sep 2025, From Eigenmodes to Proofs: Integrating Graph Spectral Operators with Symbolic Interpretable Reasoning, https://arxiv.org/abs/2509.07017
  • Sania Sinha, Tanawan Premsri, Danial Kamali, Parisa Kordjamshidi, 8 Sep 2025, Neuro-Symbolic Frameworks: Conceptual Characterization and Empirical Comparative Analysis, https://arxiv.org/abs/2509.07122
  • Zhiwei Wang, Yunji Wang, Zhongwang Zhang, Zhangchen Zhou, Hui Jin, Tianyang Hu, Jiacheng Sun, Zhenguo Li, Yaoyu Zhang, Zhi-Qin John Xu, 9 Sep 2025, Understanding the Language Model to Solve the Symbolic Multi-Step Reasoning Problem from the Perspective of Buffer Mechanism, https://arxiv.org/abs/2405.15302
  • Antonin Sulc, Thorsten Hellert, 15 Sep 2025, Neuro-Symbolic Agents with Modal Logic for Autonomous Diagnostics, https://arxiv.org/abs/2509.11943
  • Prajit Sengupta and Islem Rekik, 2 Sep 2025, FireGNN: Neuro-Symbolic Graph Neural Networks with Trainable Fuzzy Rules for Interpretable Medical Image Classification, https://arxiv.org/abs/2509.10510
  • Ziwen He, Zhigang Wang, Yanlong Peng, Pengxu Chang, Hong Yang, and Ming Chen, 14 Sep 2025, Embodied Intelligence in Disassembly: Multimodal Perception Cross-validation and Continual Learning in Neuro-Symbolic TAMP, https://arxiv.org/abs/2509.11270
  • Xifeng Yao, Dongyu Lang, Wu Zhang, Xintong Guo, Huarui Xie, Yinhao Ni, Ping Liu, Guang Shen, Yi Bai, Dandan Tu, Changzheng Zhang, 16 Sep 2025, SCoGen: Scenario-Centric Graph-Based Synthesis of Real-World Code Problems, https://arxiv.org/abs/2509.14281
  • Jian Yao, Ran Cheng, and Kay Chen Tan, 2 Oct 2025, VAR-MATH: Probing True Mathematical Reasoning in LLMS via Symbolic Multi-Instance Benchmarks, https://arxiv.org/abs/2507.12885
  • Orestis Oikonomou, Levi Lingsch, Dana Grund, Siddhartha Mishra, Georgios Kissas, 1 Oct 2025, Neuro-Symbolic AI for Analytical Solutions of Differential Equations, https://arxiv.org/abs/2502.01476
  • Gokturk Aytug Akarlar, 27 Oct 2025, Beyond Prompt Engineering: Neuro-Symbolic-Causal Architecture for Robust Multi-Objective AI Agents, https://arxiv.org/abs/2510.23682
  • David Peer, Sebastian Stabinger, 18 Oct 2025, ATA: A Neuro-Symbolic Approach to Implement Autonomous and Trustworthy Agents, https://arxiv.org/abs/2510.16381
  • Kang-il Lee, Jahyun Koo, Seunghyun Yoon, Minbeom Kim, Hyukhun Koh, Dongryeol Lee, Kyomin Jung, 22 Sep 2025, Program Synthesis via Test-Time Transduction, https://arxiv.org/abs/2509.17393
  • Valentin Lacombe, Valentin Quesnel, Damien Sileo, 22 Sep 2025, Reasoning Core: A Scalable RL Environment for LLM Symbolic Reasoning, https://arxiv.org/abs/2509.18083
  • Yiting Wang, Wanghao Ye, Ping Guo, Yexiao He, Ziyao Wang, Bowei Tian, Shwai He, Guoheng Sun, Zheyu Shen, Sihan Chen, Ankur Srivastava, Qingfu Zhang, Gang Qu, Ang Li, 22 Sep 2025, SymRTLO: Enhancing RTL Code Optimization with LLMs and Neuron-Inspired Symbolic Reasoning, https://arxiv.org/abs/2504.10369
  • Chiara Bonfanti, Alessandro Druetto, Cataldo Basile, Tharindu Ranasinghe, Marcos Zampieri, 27 Oct 2025, A Neuro-Symbolic Multi-Agent Approach to Legal-Cybersecurity Knowledge Integration, https://arxiv.org/abs/2510.23443
  • Weixian Qian, Sebastian Schroder, Yao Deng, Jiaohong Yao, Linfeng Liang, Xiao Cheng, Richard Han and Xi Zheng, 25 Oct 2025, Bridging Perception and Reasoning: Dual-Pipeline Neuro-Symbolic Landing for UAVs in Cluttered Environments, https://arxiv.org/abs/2510.22204
  • Adam Stein, Neelay Velingker, Mayur Naik, Eric Wong, 26 Oct 2025, Once Upon an Input: Reasoning via Per-Instance Program Synthesis, https://arxiv.org/abs/2510.22849
  • Samuele Bortolotti, Emanuele Marconato, Paolo Morettin, Andrea Passerini, Stefano Teso, 27 Oct 2025, Shortcuts and Identifiability in Concept-based Models from a Neuro-Symbolic Lens, https://arxiv.org/abs/2502.11245
  • Manish Bhattarai, Miguel Cordova, Minh Vu, Javier Santos, Ismael Boureima, Dan O'Malley, 27 Oct 2025, ARCS: Agentic Retrieval-Augmented Code Synthesis with Iterative Refinement, https://arxiv.org/abs/2504.20434
  • Shichao Weng, Zhiqiang Wang, Yuhua Zhou, Rui Lu, Ting Liu, Zhiyang Teng, Xiaozhang Liu, Hanmeng Liu, 26 Sep 2025, GeoSketch: A Neural-Symbolic Approach to Geometric Multimodal Reasoning with Auxiliary Line Construction and Affine Transformation, https://arxiv.org/abs/2509.22460
  • Anjiang Wei, Tarun Suresh, Tianran Sun, Haoze Wu, Ke Wang, Alex Aiken, 25 Sep 2025, InvBench: Can LLMs Accelerate Program Verification with Invariant Synthesis?, https://arxiv.org/abs/2509.21629
  • Weiming Wu, Jin Ye, Zi-kang Wang, Zhi Zhou, Yu-Feng Li, Lan-Zhe Guo, 2 Oct 2025, NeSyGeo: A Neuro-Symbolic Framework for Multimodal Geometric Reasoning Data Generation, https://arxiv.org/abs/2505.17121
  • Yang Zhao, Chengxiao Dai, Wei Zhuo, Yue Xiu, Dusit Niyato, 25 Sep 2025, CLAUSE: Agentic Neuro-Symbolic Knowledge Graph Reasoning via Dynamic Learnable Context Engineering, https://arxiv.org/abs/2509.21035
  • Tian Qin, Yuhan Chen, Zhiwei Wang, Zhi-Qin John Xu, 27 Sep 2025, Limit Analysis for Symbolic Multi-step Reasoning Tasks with Information Propagation Rules Based on Transformers, https://arxiv.org/abs/2509.23178
  • Edward Kim, Daniel He, Jorge Chao, Wiktor Rajca, Mohammed Amin, Nishant Malpani, Ruta Desai, Antti Oulasvirta, Bjoern Hartmann, Sanjit Seshia, 29 Sep 2025, Interactive Program Synthesis for Modeling Collaborative Physical Activities from Narrated Demonstrations, https://arxiv.org/abs/2509.24250
  • Gauri Kholkar, Ratinder Ahuja, 28 Sep 2025, The AI Agent Code of Conduct: Automated Guardrail Policy-as-Prompt Synthesis, https://arxiv.org/abs/2509.23994
  • Sergiu Bursuc (BAIF), Theodore Ehrenborg (BAIF), Shaowei Lin (BAIF), Lacramioara Astefanoaei (BAIF), Ionel Emilian Chiosa (MIT), Jure Kukovec (BAIF), Alok Singh (BAIF), Oliver Butterley (BAIF), Adem Bizid (BAIF), Quinn Dougherty (BAIF), Miranda Zhao (MIT), Max Tan (MIT), Max Tegmark (MIT), 26 Sep 2025, A benchmark for vericoding: formally verified program synthesis, https://arxiv.org/abs/2509.22908
  • Shengyuan Chen, Zheng Yuan, Qinggang Zhang, Wen Hua, Jiannong Cao, Xiao Huang, 29 Sep 2025, Neuro-Symbolic Entity Alignment via Variational Inference, https://arxiv.org/abs/2410.04153
  • Zheng Zhang, 28 Sep 2025, Comprehension Without Competence: Architectural Limits of LLMs in Symbolic Computation and Reasoning, https://arxiv.org/abs/2507.10624
  • Zhuohan Xie, Daniil Orel, Rushil Thareja, Dhruv Sahnan, Hachem Madmoun, Fan Zhang, Debopriyo Banerjee, Georgi Georgiev, Xueqing Peng, Lingfei Qian, Jimin Huang, Jinyan Su, Aaryamonvikram Singh, Rui Xing, Rania Elbadry, Chen Xu, Haonan Li, Fajri Koto, Ivan Koychev, Tanmoy Chakraborty, Yuxia Wang, Salem Lahlou, Veselin Stoyanov, Sophia Ananiadou, and Preslav Nakov, 17 Oct 2025, FinChain: A Symbolic Benchmark for Verifiable Chain-of-Thought Financial Reasoning, https://arxiv.org/abs/2506.02515
  • Amir Sadikov, 4 Oct 2025, LLM-Guided Evolutionary Program Synthesis for Quasi-Monte Carlo Design, https://arxiv.org/abs/2510.03650
  • Chao Wen, Jacqueline Staub, Adish Singla, 6 Oct 2025, Program Synthesis Benchmark for Visual Programming in XLogoOnline Environment, https://arxiv.org/abs/2406.11334
  • Sanghyun Ahn, Wonje Choi, Junyong Lee, Jinwoo Park, Honguk Woo, 24 Oct 2025, Towards Reliable Code-as-Policies: A Neuro-Symbolic Framework for Embodied Task Planning, https://arxiv.org/abs/2510.21302
  • Xiangyu Wang, Haocheng Yang, Fengxiang Cheng, Fenrong Liu, 12 Oct 2025, Adaptive Selection of Symbolic Languages for Improving LLM Logical Reasoning, https://arxiv.org/abs/2510.10703
  • Tilman Hinnerichs, Reuben Gardos Reid, Jaap de Jong, Bart Swinkels, Pamela Wochner, Nicolae Filat, Tudor Magurescu, Issa Hanou, and Sebastijan Dumancic, 10 Oct 2025, Herb.jl: A Unifying Program Synthesis Library, https://arxiv.org/abs/2510.09726
  • Yibo Yang, 11 Oct 2025, CLMN: Concept based Language Models via Neural Symbolic Reasoning, https://arxiv.org/abs/2510.10063
  • Zhechong Huang, Zhao Zhang, Ruyi Ji, Tingxuan Xia, Qihao Zhu, Qinxiang Cao, Zeyu Sun, Yingfei Xiong, 11 Oct 2025, Learning to Guarantee Type Correctness in Code Generation through Type-Guided Program Synthesis, https://arxiv.org/abs/2510.10216
  • Prawaal Sharma, Poonam Goyal, Navneet Goyal, Vidisha Sharma, 12 Oct 2025, NIM: Neuro-symbolic Ideographic Metalanguage for Inclusive Communication, https://arxiv.org/abs/2510.10459
  • Weidi Xu, Jingwei Wang, Lele Xie, Jianshan He, Hongting Zhou, Taifeng Wang, Xiaopei Wan, Jingdong Chen, Chao Qu, Wei Chu, 9 Oct 2025, LogicMP: A Neuro-symbolic Approach for Encoding First-order Logic Constraints, https://arxiv.org/abs/2309.15458
  • Hanyuan Gao and Xiaoxuan Yang, 29 Sep 2025, Norm-Q: Effective Compression Method for Hidden Markov Models in Neuro-Symbolic Applications, https://arxiv.org/abs/2509.25439
  • Fadi Al Machot, Fidaa Al Machot, 6 Oct 2025, NASP-T: A Fuzzy Neuro-Symbolic Transformer for Logic-Constrained Aviation Safety Report Classification, https://arxiv.org/abs/2510.05451
  • Weichun Shi, Minghao Liu, Wanting Zhang, Langchen Shi, Fuqi Jia, Feifei Ma, Jian Zhang, 7 Oct 2025, ConstraintLLM: A Neuro-Symbolic Framework for Industrial-Level Constraint Programming, https://arxiv.org/abs/2510.05774
  • Emanuele Marconato, Samuele Bortolotti, Emile van Krieken, Paolo Morettin, Elena Umili, Antonio Vergari, Efthymia Tsamoura, Andrea Passerini, Stefano Teso, 16 Oct 2025, Symbol Grounding in Neuro-Symbolic AI: A Gentle Introduction to Reasoning Shortcuts, https://arxiv.org/abs/2510.14538

Reasoning Decoding Algorithms

Reasoning decoding algorithms, or Chain-of-Thought decoding algorithms, are methods of using the decoding phase of LLM inference rather than multiple steps. The idea is that the possible pathways based on logits can be similar to Chain-of-Thought reasoning, and these pathways can be explored and combined during inference. This yields an algorithm that is better at reasoning than simpler decoding algorithms, but is more efficient than Chain-of-Thought because it can examine multiple pathways in a single inference step.

Research papers on reasoning-decoding or CoT-decoding:

  • Xuezhi Wang, Denny Zhou, 23 May 2024 (v2), Chain-of-Thought Reasoning Without Prompting, https://arxiv.org/abs/2402.10200 ("CoT decoding" is examining the alternative paths in the decoding algorithm, which is somewhat similar to Chain-of-Thought reasoning.)
  • xjdr-alt, Dec 2024, entropix: Entropy Based Sampling and Parallel CoT Decoding, https://github.com/xjdr-alt/entropix (Parallel decoding attempts to get something similar to CoT.)
  • Hongxuan Zhang, Zhining Liu, Yao Zhao, Jiaqi Zheng, Chenyi Zhuang, Jinjie Gu, Guihai Chen, 4 Jun 2024 (v2), Fast Chain-of-Thought: A Glance of Future from Parallel Decoding Leads to Answers Faster, https://arxiv.org/abs/2311.08263 (Use of Jacobi parallel decoding with Chain-of-Thought.)
  • Renato Vukovic, David Arps, Carel van Niekerk, Benjamin Matthias Ruppik, Hsien-Chin Lin, Michael Heck, Milica Gašić, 5 Aug 2024, Dialogue Ontology Relation Extraction via Constrained Chain-of-Thought Decoding, https://arxiv.org/abs/2408.02361
  • Yuntian Deng, Kiran Prasad, Roland Fernandez, Paul Smolensky, Vishrav Chaudhary, Stuart Shieber, 2 Nov 2023, Implicit Chain of Thought Reasoning via Knowledge Distillation, https://arxiv.org/abs/2311.01460 (Knowledge distillation applied to optimizing the interim computations in Chain-of-Thought.)
  • Yuntian Deng, Yejin Choi, Stuart Shieber, 23 May 2024, From Explicit CoT to Implicit CoT: Learning to Internalize CoT Step by Step, https://arxiv.org/abs/2405.14838
  • Ping Yu, Jing Xu, Jason Weston, Ilia Kulikov, 24 Jul 2024 (v3), Distilling System 2 into System 1, https://arxiv.org/abs/2407.06023
  • Mehul Damani, Idan Shenfeld, Andi Peng, Andreea Bobu, Jacob Andreas, 7 Oct 2024, Learning How Hard to Think: Input-Adaptive Allocation of LM Computation, https://arxiv.org/abs/2410.04707
  • Pranjal Aggarwal, Aman Madaan, Yiming Yang, Mausam, 16 Nov 2023 (v2), Let's Sample Step by Step: Adaptive-Consistency for Efficient Reasoning and Coding with LLMs, EMNLP 2023, https://arxiv.org/abs/2305.11860 https://www.sample-step-by-step.info/
  • 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.)
  • Luyang Liu, Jonas Pfeiffer, Jiaxing Wu, Jun Xie, Arthur Szlam, 23 Dec 2024, Deliberation in Latent Space via Differentiable Cache Augmentation, https://arxiv.org/abs/2412.17747 (Augmenting the KV cache with reasoning information so that decoding will mimic multi-step reasoning with fewer tokens required for intermediate steps.)
  • Sachin Goyal, Ziwei Ji, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar, Vaishnavh Nagarajan, 21 Apr 2024 (v3), Think before you speak: Training Language Models With Pause Tokens, https://arxiv.org/abs/2310.02226 (Inserting extra "pause tokens" that trigger the LLM to perform extra reasoning during the decoding phase.)
  • Yuval Shalev, Amir Feder, Ariel Goldstein, 19 Jun 2024, Distributional reasoning in LLMs: Parallel reasoning processes in multi-hop reasoning, https://arxiv.org/abs/2406.13858 (Using embeddings from intermediate model layers in decoding to mimic reasoning pathways.)
  • Eden Biran, Daniela Gottesman, Sohee Yang, Mor Geva, Amir Globerson, 14 Oct 2024 (v2), Hopping Too Late: Exploring the Limitations of Large Language Models on Multi-Hop Queries, https://arxiv.org/abs/2406.12775 (Backpatching prior layers using embeddings from the current activations to mimic multi-step reasoning.)
  • Jacob Pfau, William Merrill, Samuel R. Bowman, 24 Apr 2024, Let's Think Dot by Dot: Hidden Computation in Transformer Language Models, https://arxiv.org/abs/2404.15758 (Use of dummy "filler tokens" similar to "pause tokens" or "reasoning tokens" to aid multi-step reasoning in decoding.)
  • Eric Zelikman, Georges Harik, Yijia Shao, Varuna Jayasiri, Nick Haber, Noah D. Goodman, 18 Mar 2024 (v2), Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking, https://arxiv.org/abs/2403.09629 (Introduces answers between a start-of-thought and end-of-thought meta-token for reasoning.)
  • Haoran Wang, Kai Shu, Jan 2025, MakeEveryTokenCount: ASystematic Survey on Decoding Methods for Foundation Model, https://www.researchgate.net/profile/Haoran-Wang-96/publication/387703971_Make_Every_Token_Count_A_Systematic_Survey_on_Decoding_Methods_for_Foundation_Models/links/67784c8ce74ca64e1f49eb15/Make-Every-Token-Count-A-Systematic-Survey-on-Decoding-Methods-for-Foundation-Models.pdf https://github.com/wang2226/Awesome-LLM-Decoding
  • Phuc Phan, Hieu Tran, Long Phan, 23 Aug 2024 (v2), Distillation Contrastive Decoding: Improving LLMs Reasoning with Contrastive Decoding and Distillation, https://arxiv.org/abs/2402.14874
  • Maxime Peyrard, Martin Josifoski, Robert West, 21 Mar 2024, The Era of Semantic Decoding, https://arxiv.org/abs/2403.14562
  • Fengli Xu, Qianyue Hao, Zefang Zong, Jingwei Wang, Yunke Zhang, Jingyi Wang, Xiaochong Lan, Jiahui Gong, Tianjian Ouyang, Fanjin Meng, Chenyang Shao, Yuwei Yan, Qinglong Yang, Yiwen Song, Sijian Ren, Xinyuan Hu, Yu Li, Jie Feng, Chen Gao, Yong Li, 17 Jan 2025 (v2), Towards Large Reasoning Models: A Survey on Scaling LLM Reasoning Capabilities, https://arxiv.org/abs/2501.09686
  • 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
  • Jonas Geiping, Sean McLeish, Neel Jain, John Kirchenbauer, Siddharth Singh, Brian R. Bartoldson, Bhavya Kailkhura, Abhinav Bhatele, Tom Goldstein, 7 Feb 2025, Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach, https://arxiv.org/abs/2502.05171
  • G Lu, L Peng, L Li, 2025, CoT-Decoding: Complex Reasoning via Chain-of-Thought Decoding, https://epubs.siam.org/doi/pdf/10.1137/1.9781611978520.44

Planning (as part of Reasoning)

Having an LLM know how to make a plan is part of intelligence. Here are some papers specifically on the aspect of "planning" as part of reasoning:

  • 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
  • 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
  • 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
  • Daniel Cao, Michael Katz, Harsha Kokel, Kavitha Srinivas, Shirin Sohrabi, 21 Aug 2024, Automating Thought of Search: A Journey Towards Soundness and Completeness, https://arxiv.org/abs/2408.11326
  • Vishal Rajput, Jul 8, 2024, Why LLMs Can’t Plan And Unlikely To Reach AGI? https://medium.com/aiguys/why-llms-cant-plan-and-unlikely-to-reach-agi-642bda3e0aa3
  • Evan Wang, Federico Cassano, Catherine Wu, Yunfeng Bai, Will Song, Vaskar Nath, Ziwen Han, Sean Hendryx, Summer Yue, Hugh Zhang, 5 Sep 2024, Planning In Natural Language Improves LLM Search For Code Generation, https://arxiv.org/abs/2409.03733
  • Yongjing Yin, Junran Ding, Kai Song, Yue Zhang, 17 Sep 2024, Semformer: Transformer Language Models with Semantic Planning, https://arxiv.org/abs/2409.11143
  • Chung-Yu Wang, Alireza DaghighFarsoodeh, Hung Viet Pham, 24 Sep 2024, Task-oriented Prompt Enhancement via Script Generation, https://arxiv.org/abs/2409.16418
  • LangChain, Jul 20, 2024, Planning for Agents, https://blog.langchain.dev/planning-for-agents/
  • A. Singh, A. Ehtesham, S. Kumar and T. T. Khoei, "Enhancing AI Systems with Agentic Workflows Patterns in Large Language Model," 2024 IEEE World AI IoT Congress (AIIoT), Seattle, WA, USA, 2024, pp. 527-532, doi: 10.1109/AIIoT61789.2024.10578990. https://ieeexplore.ieee.org/abstract/document/10578990
  • Chawla, Chhavi; Chatterjee, Siddharth; Gadadinni, Sanketh Siddanna; Verma, Pulkit; Banerjee, Sourav, 2024, Agentic AI: The building blocks of sophisticated AI business applications, Journal of AI, Robotics & Workplace Automation, Volume 3 / Number 3 / Summer 2024, pp. 1-15(15), Henry Stewart Publications, DOI: https://doi.org/10.69554/XEHZ1946 https://www.ingentaconnect.com/content/hsp/airwa/2024/00000003/00000003/art00001
  • Jian Xie, Kexun Zhang, Jiangjie Chen, Siyu Yuan, Kai Zhang, Yikai Zhang, Lei Li, Yanghua Xiao, 16 Oct 2024, Revealing the Barriers of Language Agents in Planning, https://arxiv.org/abs/2410.12409
  • Wenchao Xu, Jinyu Chen, Peirong Zheng, Xiaoquan Yi, Tianyi Tian, Wenhui Zhu, Quan Wan, Haozhao Wang, Yunfeng Fan, Qinliang Su, Xuemin Shen, https://arxiv.org/abs/2412.13437 18 Dec 2024, Deploying Foundation Model Powered Agent Services: A Survey, (A survey of not just deployment, but many inference optimization techniques.)
  • Gautier Dagan, Frank Keller, Alex Lascarides, 30 Dec 2024, Plancraft: an evaluation dataset for planning with LLM agents, https://arxiv.org/abs/2412.21033
  • 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.)
  • 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/
  • Ben Dickson, January 22, 2025, DeepMind’s new inference-time scaling technique improves planning accuracy in LLMs, https://venturebeat.com/ai/deepmind-new-inference-time-scaling-technique-improves-planning-accuracy-in-llms/
  • Xinzhe Li, Jan 2025, A Review of Prominent Paradigms for LLM-Based Agents: Tool Use (Including RAG), Planning, and Feedback Learning, Proceedings of the 31st International Conference on Computational Linguistics, pages 9760–9779, January 19–24, 2025. ©2025 Association for Computational Linguistics, https://aclanthology.org/2025.coling-main.652.pdf https://github.com/xinzhel/LLM-Agent-Survey
  • 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.)
  • Karthik Valmeekam, Kaya Stechly, Subbarao Kambhampati, 20 Sep 2024, LLMs Still Can't Plan; Can LRMs? A Preliminary Evaluation of OpenAI's o1 on PlanBench, https://arxiv.org/abs/2409.13373
  • Bilgehan Sel, Ruoxi Jia, Ming Jin, 23 Jan 2025, LLMs Can Plan Only If We Tell Them, https://arxiv.org/abs/2501.13545
  • Weimin Xiong, Yifan Song, Qingxiu Dong, Bingchan Zhao, Feifan Song, Xun Wang, Sujian Li, 4 Mar 2025, MPO: Boosting LLM Agents with Meta Plan Optimization, https://arxiv.org/abs/2503.02682
  • Yuqi Zhou, Shuai Wang, Sunhao Dai, Qinglin Jia, Zhaocheng Du, Zhenhua Dong, Jun Xu, 5 Mar 2025, CHOP: Mobile Operating Assistant with Constrained High-frequency Optimized Subtask Planning, https://arxiv.org/abs/2503.03743
  • P Verma, SP Midigeshi, G Sinha, A Solin, N Natarajan, Mar 2025, Plan *RAG: Efficient Test-Time Planning for Retrieval Augmented Generation, ICLR 2025 review, https://openreview.net/pdf?id=gi9aqlYdBk (Improve RAG reasoning efficiency via planning for parallel reasoning.)
  • Lutfi Eren Erdogan, Nicholas Lee, Sehoon Kim, Suhong Moon, Hiroki Furuta, Gopala Anumanchipalli, Kurt Keutzer, Amir Gholami, 12 Mar 2025, Plan-and-Act: Improving Planning of Agents for Long-Horizon Tasks, https://arxiv.org/abs/2503.09572
  • Siheng Xiong, Jieyu Zhou, Zhangding Liu, Yusen Su, 2 May 2025, SymPlanner: Deliberate Planning in Language Models with Symbolic Representation, https://arxiv.org/abs/2505.01479
  • Pengfei Cao, Tianyi Men, Wencan Liu, Jingwen Zhang, Xuzhao Li, Xixun Lin, Dianbo Sui, Yanan Cao, Kang Liu, Jun Zhao, 26 May 2025, Large Language Models for Planning: A Comprehensive and Systematic Survey, https://arxiv.org/abs/2505.19683
  • Kenneth Payne, Baptiste Alloui-Cros, 3 Jul 2025, Strategic Intelligence in Large Language Models: Evidence from evolutionary Game Theory, https://arxiv.org/abs/2507.02618
  • Guancheng Zeng, Xueyi Chen, Jiawang Hu, Shaohua Qi, Yaxuan Mao, Zhantao Wang, Yifan Nie, Shuang Li, Qiuyang Feng, Pengxu Qiu, Yujia Wang, Wenqiang Han, Linyan Huang, Gang Li, Jingjing Mo, Haowen Hu, 22 Jul 2025 (v2), Routine: A Structural Planning Framework for LLM Agent System in Enterprise, https://arxiv.org/abs/2507.14447
  • Sangwoo Jeon, Juchul Shin, Gyeong-Tae Kim, YeonJe Cho and Seongwoo Kim, 14 Aug 2025, Scaling Up without Fading Out: Goal-Aware Sparse GNN for RL-based Generalized Planning, https://arxiv.org/abs/2508.10747
  • Steven Klee and Yuntian Xia, 13 Aug 2025, Measuring Time Series Forecast Stability for Demand Planning, https://arxiv.org/abs/2508.10063
  • Anantha Narayanan, Battu Bhanu Teja, Pruthwik Mishra, 14 Aug 2025, TLE-Based A2C Agent for Terrestrial Coverage Orbital Path Planning, https://arxiv.org/abs/2508.10872
  • Rishi Parekh, Saisubramaniam Gopalakrishnan, Zishan Ahmad, Anirudh Deodhar, 23 Jul 2025, Leveraging Knowledge Graphs and LLM Reasoning to Identify Operational Bottlenecks for Warehouse Planning Assistance, https://arxiv.org/abs/2507.17273
  • Stefan Borgwardt, Duy Nhu, Gabriele R\"oger, 23 Jul 2025, Automated planning with ontologies under coherence update semantics (Extended Version), https://arxiv.org/abs/2507.15120
  • Muhayy Ud Din and Jan Rosell and Waseem Akram and Isiah Zaplana and Maximo A Roa and Irfan Hussain, 23 Jul 2025, Onto-LLM-TAMP: Knowledge-oriented Task and Motion Planning using Large Language Models, https://arxiv.org/abs/2412.07493
  • Zixiao Huang, Junhao Hu, Hao Lin, Chunyang Zhu, Yueran Tang, Quanlu Zhang, Zhen Guo, Zhenhua Li, Shengen Yan, Zhenhua Zhu, Guohao Dai, Yu Wang, 22 Jul 2025, Reducing GPU Memory Fragmentation via Spatio-Temporal Planning for Efficient Large-Scale Model Training, https://arxiv.org/abs/2507.16274
  • Chi-Pin Huang, Yueh-Hua Wu, Min-Hung Chen, Yu-Chiang Frank Wang, Fu-En Yang, 22 Jul 2025, ThinkAct: Vision-Language-Action Reasoning via Reinforced Visual Latent Planning, https://arxiv.org/abs/2507.16815
  • Dominic LaBella, Valeriia Abramova, Mehdi Astaraki, Andre Ferreira, Zhifan Jiang, Mason C. Cleveland, Ramandeep Kang, Uma M. Lal-Trehan Estrada, Cansu Yalcin, Rachika E. Hamadache, Clara Lisazo, Adri\`a Casamitjana, Joaquim Salvi, Arnau Oliver, Xavier Llad\'o, Iuliana Toma-Dasu, Tiago Jesus, Behrus Puladi, Jens Kleesiek, Victor Alves, Jan Egger, Daniel Capell\'an-Mart\'in, Abhijeet Parida, Austin Tapp, Xinyang Liu, Maria J. Ledesma-Carbayo, Jay B. Patel, Thomas N. McNeal, Maya Viera, Owen McCall, Albert E. Kim, Elizabeth R. Gerstner, Christopher P. Bridge, Katherine Schumacher, Michael Mix, Kevin Leu, Shan McBurney-Lin, Pierre Nedelec, Javier Villanueva-Meyer, David R. Raleigh, Jonathan Shapey, Tom Vercauteren, Kazumi Chia, Marina Ivory, Theodore Barfoot, Omar Al-Salihi, Justin Leu, Lia M. Halasz, et al. (57 additional authors not shown), 21 Jul 2025, Analysis of the 2024 BraTS Meningioma Radiotherapy Planning Automated Segmentation Challenge, https://arxiv.org/abs/2405.18383
  • Bofei Liu and Dong Ye and Zunhao Yao and Zhaowei Sun, 22 Jul 2025, A Goal-Oriented Reinforcement Learning-Based Path Planning Algorithm for Modular Self-Reconfigurable Satellites, https://arxiv.org/abs/2505.01966
  • Sunandita Patra, Mehtab Pathan, Mahmoud Mahfouz, Parisa Zehtabi, Wided Ouaja, Daniele Magazzeni, and Manuela Veloso, 21 Jul 2025, Capacity Planning and Scheduling for Jobs with Uncertainty in Resource Usage and Duration, https://arxiv.org/abs/2507.01225
  • Dario Della Monica, Angelo Montanari, Pietro Sala, 23 Jul 2025, Synthesis of timeline-based planning strategies avoiding determinization, https://arxiv.org/abs/2507.17988
  • Gilberto Cunha, Alexandra Ram\^oa, Andr\'e Sequeira, Michael de Oliveira, Lu\'is Barbosa, 24 Jul 2025, Hybrid quantum-classical algorithm for near-optimal planning in POMDPs, https://arxiv.org/abs/2507.18606
  • Andres M Bran, Theo A Neukomm, Daniel P Armstrong, Zlatko Jon\v{c}ev, Philippe Schwaller, 23 Jul 2025, Chemical reasoning in LLMs unlocks strategy-aware synthesis planning and reaction mechanism elucidation, https://arxiv.org/abs/2503.08537
  • Zhiwei Xu, 24 Jul 2025, DAA*: Deep Angular A Star for Image-based Path Planning, https://arxiv.org/abs/2507.09305
  • Genliang Li, Yaxin Cui, Jinyu Su, 18 Jul 2025, A multi-strategy improved snake optimizer for three-dimensional UAV path planning and engineering problems, https://arxiv.org/abs/2507.14043
  • Yuejiao Xie, Maonan Wang, Di Zhou, Man-On Pun, and Zhu Han, 18 Jul 2025, Real-Time Communication-Aware Ride-Sharing Route Planning for Urban Air Mobility: A Multi-Source Hybrid Attention Reinforcement Learning Approach, https://arxiv.org/abs/2507.14249
  • Yufan Song, Jiatao Zhang, Zeng Gu, Qingmiao Liang, Tuocheng Hu, Wei Song, Shiqiang Zhu, 20 Jul 2025, FCRF: Flexible Constructivism Reflection for Long-Horizon Robotic Task Planning with Large Language Models, https://arxiv.org/abs/2507.14975
  • Thanh Thi Nguyen, Saeid Nahavandi, Imran Razzak, Dung Nguyen, Nhat Truong Pham, Quoc Viet Hung Nguyen, 21 Jul 2025, The Emergence of Deep Reinforcement Learning for Path Planning, https://arxiv.org/abs/2507.15469
  • Alexandru Coca, Mark Gaynor, Zhenxing Zhang, Jianpeng Cheng, Bo-Hsiang Tseng, Pete Boothroyd, H\'ector Martinez Alonso, Diarmuid \'O S\'eaghdha, Anders Johannsen, 21 Jul 2025, ASPERA: A Simulated Environment to Evaluate Planning for Complex Action Execution, https://arxiv.org/abs/2507.15501
  • Jubin Abhishek Soni, Amit Anand, Rajesh Kumar Pandey, Aniket Abhishek Soni, 19 Jul 2025, Dynamic Context Tuning for Retrieval-Augmented Generation: Enhancing Multi-Turn Planning and Tool Adaptation, https://arxiv.org/abs/2506.11092
  • Giwon Lee, Wooseong Jeong, Daehee Park, Jaewoo Jeong, and Kuk-Jin Yoon, 21 Jul 2025, Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning, https://arxiv.org/abs/2507.04790
  • Abhinav Sagar, Sai Teja Gilukara, 20 Jul 2025, CBAGAN-RRT: Convolutional Block Attention Generative Adversarial Network for Sampling-Based Path Planning, https://arxiv.org/abs/2305.10442
  • Hayeon Oh, 21 Jul 2025, LaViPlan : Language-Guided Visual Path Planning with RLVR, https://arxiv.org/abs/2507.12911
  • Markus Fritzsche, Elliot Gestrin, Jendrik Seipp, 11 Aug 2025, Symmetry-Aware Transformer Training for Automated Planning, https://arxiv.org/abs/2508.07743
  • Alejandro Murillo-Gonzalez, Junhong Xu and Lantao Liu, 8 Aug 2025, Learning Causal Structure Distributions for Robust Planning, https://arxiv.org/abs/2508.06742
  • Naiyi Li, Zihui Ma, Runlong Yu, Lingyao Li, 9 Aug 2025, LSDTs: LLM-Augmented Semantic Digital Twins for Adaptive Knowledge-Intensive Infrastructure Planning, https://arxiv.org/abs/2508.06799
  • Alberto Pozanco, Marianela Morales, Daniel Borrajo, Manuela Veloso, 11 Aug 2025, A Planning Compilation to Reason about Goal Achievement at Planning Time, https://arxiv.org/abs/2503.09545
  • Yanchen Zhu, Honghui Zou, Chufan Liu, Yuyu Luo, Yuankai Wu, Yuxuan Liang, 10 Aug 2025, Reinforcement Learning for Hybrid Charging Stations Planning and Operation Considering Fixed and Mobile Chargers, https://arxiv.org/abs/2506.16764
  • Jaike van Twiller, Yossiri Adulyasak, Erick Delage, Djordje Grbic, Rune M{\o}ller Jensen, 11 Aug 2025, Navigating Demand Uncertainty in Container Shipping: Deep Reinforcement Learning for Enabling Adaptive and Feasible Master Stowage Planning, https://arxiv.org/abs/2502.12756
  • Vlad Sobal, Wancong Zhang, Kyunghyun Cho, Randall Balestriero, Tim G. J. Rudner, Yann LeCun, 10 Aug 2025, Learning from Reward-Free Offline Data: A Case for Planning with Latent Dynamics Models, https://arxiv.org/abs/2502.14819
  • Zhipeng Tang, Sha Zhang, Jiajun Deng, Chenjie Wang, Guoliang You, Yuting Huang, Xinrui Lin and Yanyong Zhang, 27 Jul 2025, VLMPlanner: Integrating Visual Language Models with Motion Planning, https://arxiv.org/abs/2507.20342
  • Sara Pohland and Claire Tomlin, 26 Jul 2025, Competency-Aware Planning for Probabilistically Safe Navigation Under Perception Uncertainty, https://arxiv.org/abs/2409.06111
  • Chang-Hun Ji, SiWoon Song, Youn-Hee Han, SungTae Moon, 29 Jul 2025, Decision Transformer-Based Drone Trajectory Planning with Dynamic Safety-Efficiency Trade-Offs, https://arxiv.org/abs/2507.21506
  • Tyler Han, Yanda Bao, Bhaumik Mehta, Gabriel Guo, Anubhav Vishwakarma, Emily Kang, Sanghun Jung, Rosario Scalise, Jason Zhou, Bryan Xu, Byron Boots, 29 Jul 2025, Model Predictive Adversarial Imitation Learning for Planning from Observation, https://arxiv.org/abs/2507.21533
  • Yi Kong, Dianxi Shi, Guoli Yang, Zhang ke-di, Chenlin Huang, Xiaopeng Li, Songchang Jin, 29 Jul 2025, MapAgent: Trajectory-Constructed Memory-Augmented Planning for Mobile Task Automation, https://arxiv.org/abs/2507.21953
  • Ratijit Mitra and Indranil Saha, 28 Jul 2025, Online Concurrent Multi-Robot Coverage Path Planning, https://arxiv.org/abs/2403.10460
  • Shengao Yi, Xiaojiang Li, Wei Tu, Tianhong Zhao, 30 Jul 2025, Planning for Cooler Cities: A Multimodal AI Framework for Predicting and Mitigating Urban Heat Stress through Urban Landscape Transformation, https://arxiv.org/abs/2507.23000
  • Mahmoud Ghorab and Matthias Lorenzen, 31 Jul 2025, Multi-Waypoint Path Planning and Motion Control for Non-holonomic Mobile Robots in Agricultural Applications, https://arxiv.org/abs/2507.23350
  • Yiyan Ji, Haoran Chen, Qiguang Chen, Chengyue Wu, Libo Qin, Wanxiang Che, 31 Jul 2025, MPCC: A Novel Benchmark for Multimodal Planning with Complex Constraints in Multimodal Large Language Models, https://arxiv.org/abs/2507.23382
  • Kai Goebel and Patrik Zips, 31 Jul 2025, Can LLM-Reasoning Models Replace Classical Planning? A Benchmark Study, https://arxiv.org/abs/2507.23589
  • Babak Esmaeili, Hamidreza Modares, Stefano Di Cairano, 31 Jul 2025, Data-Driven Motion Planning for Uncertain Nonlinear Systems, https://arxiv.org/abs/2508.00154
  • Milad Farjadnasab, Shahin Sirouspour, 31 Jul 2025, Cooperative and Asynchronous Transformer-based Mission Planning for Heterogeneous Teams of Mobile Robots, https://arxiv.org/abs/2410.06372
  • Yuanzhe Shen, Kaimin Wang, Changze Lv, Xiaoqing Zheng, Xuanjing Huang, 2 Aug 2025, TripTailor: A Real-World Benchmark for Personalized Travel Planning, https://arxiv.org/abs/2508.01432
  • Yinghao Zhu, Yifan Qi, Zixiang Wang, Lei Gu, Dehao Sui, Haoran Hu, Xichen Zhang, Ziyi He, Liantao Ma, Lequan Yu, 4 Aug 2025, HealthFlow: A Self-Evolving AI Agent with Meta Planning for Autonomous Healthcare Research, https://arxiv.org/abs/2508.02621
  • Enrique Valero-Leal, Pedro Larra\~naga and Concha Bielza, 4 Aug 2025, Actionable Counterfactual Explanations Using Bayesian Networks and Path Planning with Applications to Environmental Quality Improvement, https://arxiv.org/abs/2508.02634
  • Mikhail Andronov, Natalia Andronova, Michael Wand, J\"urgen Schmidhuber, Djork-Arn\'e Clevert, 2 Aug 2025, Fast and scalable retrosynthetic planning with a transformer neural network and speculative beam search, https://arxiv.org/abs/2508.01459
  • Krish Agarwal, Yuqian Jiang, Jiaheng Hu, Bo Liu, Peter Stone, 3 Aug 2025, L3M+P: Lifelong Planning with Large Language Models, https://arxiv.org/abs/2508.01917
  • Alexander Tuisov, Yonatan Vernik and Alexander Shleyfman, 3 Aug 2025, LLM-Generated Heuristics for AI Planning: Do We Even Need Domain-Independence Anymore?, https://arxiv.org/abs/2501.18784
  • Jungkoo Kang, 3 Aug 2025, Scaling LLM Planning: NL2FLOW for Parametric Problem Generation and Rigorous Evaluation, https://arxiv.org/abs/2507.02253
  • An T. Le, Khai Nguyen, Minh Nhat Vu, Jo\~ao Carvalho, Jan Peters, 2 Aug 2025, Model Tensor Planning, https://arxiv.org/abs/2505.01059
  • Zhichen Dong, Zhanhui Zhou, Zhixuan Liu, Chao Yang, Chaochao Lu, 4 Aug 2025, Emergent Response Planning in LLMs, https://arxiv.org/abs/2502.06258
  • Mikhail Soutchanski and Yongmei Liu, 26 Jul 2025, Planning with Dynamically Changing Domains, https://arxiv.org/abs/2508.02697
  • Michael Katz, Harsha Kokel, Sarath Sreedharan, 4 Aug 2025, Seemingly Simple Planning Problems are Computationally Challenging: The Countdown Game, https://arxiv.org/abs/2508.02900
  • Yutong Wang, Pengliang Ji, Kaixin Li, Baolong Bi, Tao Feng, and Guillaume Sartoretti, 5 Aug 2025, Beyond Policy Optimization: A Data Curation Flywheel for Sparse-Reward Long-Horizon Planning, https://arxiv.org/abs/2508.03018
  • Longling Geng and Edward Y. Chang, 5 Aug 2025, REALM-Bench: A Benchmark for Evaluating Multi-Agent Systems on Real-world, Dynamic Planning and Scheduling Tasks, https://arxiv.org/abs/2502.18836
  • Hamza El Alaoui, Atieh Taheri, Yi-Hao Peng, Jeffrey P. Bigham, 6 Aug 2025, StepWrite: Adaptive Planning for Speech-Driven Text Generation, https://arxiv.org/abs/2508.04011
  • Aniket Johri, Divyanshi Dwivedi, Mayukha Pal, 6 Aug 2025, Agentic-AI based Mathematical Framework for Commercialization of Energy Resilience in Electrical Distribution System Planning and Operation, https://arxiv.org/abs/2508.04170
  • Kim Hammar and Tansu Alpcan and Emil C. Lupu, 7 Aug 2025, Incident Response Planning Using a Lightweight Large Language Model with Reduced Hallucination, https://arxiv.org/abs/2508.05188
  • Hongyu Nie, Xu Liu, Zhaotong Tan, Sen Mei, and Wenbo Su, 7 Aug 2025, Unified Linear Parametric Map Modeling and Perception-aware Trajectory Planning for Mobile Robotics, https://arxiv.org/abs/2507.09340
  • Sahil Bansal, Sai Shruthi Sistla, Aarti Arikatala, Sebastian Schreiber, 7 Aug 2025, Planning Agents on an Ego-Trip: Leveraging Hybrid Ego-Graph Ensembles for Improved Tool Retrieval in Enterprise Task Planning, https://arxiv.org/abs/2508.05888
  • Michael Wehrli, Alicia Durrer, Paul Friedrich, Sidaty El Hadramy, Edwin Li, Luana Brahaj, Carol C. Hasler, Philippe C. Cattin, 8 Aug 2025, Towards MR-Based Trochleoplasty Planning, https://arxiv.org/abs/2508.06076
  • Yongchao Chen, Yilun Hao, Yang Zhang, Chuchu Fan, 7 Aug 2025, Code-as-Symbolic-Planner: Foundation Model-Based Robot Planning via Symbolic Code Generation, https://arxiv.org/abs/2503.01700
  • Masataro Asai, 11 Aug 2025, Bilevel MCTS for Amortized O(1) Node Selection in Classical Planning, https://arxiv.org/abs/2508.08385
  • Yuechen Wang, Yuming Qiao, Dan Meng, Jun Yang, Haonan Lu, Zhenyu Yang, Xudong Zhang, 12 Aug 2025, Efficient Agent: Optimizing Planning Capability for Multimodal Retrieval Augmented Generation, https://arxiv.org/abs/2508.08816
  • Maxence Boels, Harry Robertshaw, Thomas C Booth, Prokar Dasgupta, Alejandro Granados, Sebastien Ourselin, 12 Aug 2025, When Imitation Learning Outperforms Reinforcement Learning in Surgical Action Planning, https://arxiv.org/abs/2507.05011
  • Navin Sriram Ravie, Keerthi Vasan M, Asokan Thondiyath and Bijo Sebastian, 28 Apr 2025, QuickGrasp: Lightweight Antipodal Grasp Planning with Point Clouds, https://arxiv.org/abs/2504.19716
  • Kechen Li, Yaotian Tao, Ximing Wen, Quanwei Sun, Zifei Gong, Chang Xu, Xizhe Zhang, Tianbo Ji, 13 Aug 2025, GridRoute: A Benchmark for LLM-Based Route Planning with Cardinal Movement in Grid Environments, https://arxiv.org/abs/2505.24306
  • Qingqing Wang, Liqiang Xiao, Chang Chang, 14 Aug 2025, Learn to optimize for automatic proton PBS treatment planning for H&N cancers, https://arxiv.org/abs/2508.11085
  • David H. Chan, Mark Roberts, Dana S. Nau, 15 Aug 2025, Landmark-Assisted Monte Carlo Planning, https://arxiv.org/abs/2508.11493
  • Rowan Hodson, Bruce Bassett, Charel van Hoof, Benjamin Rosman, Mark Solms, Jonathan P. Shock, Ryan Smith, 14 Aug 2025, Sophisticated Learning: A novel algorithm for active learning during model-based planning, https://arxiv.org/abs/2308.08029
  • Yanming Liu, Xinyue Peng, Jiannan Cao, Yuwei Zhang, Xuhong Zhang, Sheng Cheng, Xun Wang, Jianwei Yin, Tianyu Du, 15 Aug 2025, Tool-Planner: Task Planning with Clusters across Multiple Tools, https://arxiv.org/abs/2406.03807
  • Michael Aichm\"uller, Hector Geffner, 15 Aug 2025, Sketch Decompositions for Classical Planning via Deep Reinforcement Learning, https://arxiv.org/abs/2412.08574
  • Kyle Brown, Dylan M. Asmar, Mac Schwager, and Mykel J. Kochenderfer, 15 Aug 2025, Large-Scale Multi-Robot Assembly Planning for Autonomous Manufacturing, https://arxiv.org/abs/2311.00192
  • Frazier N. Baker, Daniel Adu-Ampratwum, Reza Averly, Botao Yu, Huan Sun, Xia Ning, 16 Aug 2025, LARC: Towards Human-level Constrained Retrosynthesis Planning through an Agentic Framework, https://arxiv.org/abs/2508.11860
  • Chunliang Hua, Xiao Hu, Jiayang Sun, Zeyuan Yang, 18 Aug 2025, The Maximum Coverage Model and Recommendation System for UAV Vertiports Location Planning, https://arxiv.org/abs/2508.12651
  • Wenjie Chen, Wenbin Li, Di Yao, Xuying Meng, Chang Gong, Jingping Bi, 18 Aug 2025, GTool: Graph Enhanced Tool Planning with Large Language Model, https://arxiv.org/abs/2508.12725
  • Petr Anokhin, Roman Khalikov, Stefan Rebrikov, Viktor Volkov, Artyom Sorokin, Vincent Bissonnette, 18 Aug 2025, HeroBench: A Benchmark for Long-Horizon Planning and Structured Reasoning in Virtual Worlds, https://arxiv.org/abs/2508.12782
  • Giovanni Briglia, Francesco Fabiano, Stefano Mariani, 18 Aug 2025, Scaling Multi-Agent Epistemic Planning through GNN-Derived Heuristics, https://arxiv.org/abs/2508.12840
  • Qian Cao and Jielin Chen and Junchao Zhao and Rudi Stouffs, 15 Aug 2025, From Heuristics to Data: Quantifying Site Planning Layout Indicators with Deep Learning and Multi-Modal Data, https://arxiv.org/abs/2508.11723
  • Sangwoo Jeon, Juchul Shin, YeonJe Cho, Gyeong-Tae Kim and Seongwoo Kim, 16 Aug 2025, Integrating Symbolic RL Planning into a BDI-based Autonomous UAV Framework: System Integration and SIL Validation, https://arxiv.org/abs/2508.11890
  • Long Ma, Fangwei Zhong, Yizhou Wang, 18 Aug 2025, Reinforced Context Order Recovery for Adaptive Reasoning and Planning, https://arxiv.org/abs/2508.13070
  • Gokul Puthumanaillam, Aditya Penumarti, Manav Vora, Paulo Padrao, Jose Fuentes, Leonardo Bobadilla, Jane Shin, Melkior Ornik, 16 Aug 2025, Belief-Conditioned One-Step Diffusion: Real-Time Trajectory Planning with Just-Enough Sensing, https://arxiv.org/abs/2508.12166
  • Deqian Kong, Dehong Xu, Minglu Zhao, Bo Pang, Jianwen Xie, Andrew Lizarraga, Yuhao Huang, Sirui Xie, Ying Nian Wu, 18 Aug 2025, Latent Plan Transformer for Trajectory Abstraction: Planning as Latent Space Inference, https://arxiv.org/abs/2402.04647
  • Bernhard Jaeger and Daniel Dauner and Jens Bei{\ss}wenger and Simon Gerstenecker and Kashyap Chitta and Andreas Geiger, 18 Aug 2025, CaRL: Learning Scalable Planning Policies with Simple Rewards, https://arxiv.org/abs/2504.17838
  • Ronit Virwani and Ruchika Suryawanshi, 18 Aug 2025, LOOP: A Plug-and-Play Neuro-Symbolic Framework for Enhancing Planning in Autonomous Systems, https://arxiv.org/abs/2508.13371
  • Minh Hoang Nguyen, Van Dai Do, Dung Nguyen, Thin Nguyen, Hung Le, 19 Aug 2025, CausalPlan: Empowering Efficient LLM Multi-Agent Collaboration Through Causality-Driven Planning, https://arxiv.org/abs/2508.13721
  • Katharina Stein, Nils Hodel, Daniel Fi\v{s}er, J\"org Hoffmann, Michael Katz and Alexander Koller, 19 Aug 2025, Improved Generalized Planning with LLMs through Strategy Refinement and Reflection, https://arxiv.org/abs/2508.13876
  • Kim Hammar and Tao Li, 20 Aug 2025, Online Incident Response Planning under Model Misspecification through Bayesian Learning and Belief Quantization, https://arxiv.org/abs/2508.14385
  • Karin A. Olthof, Matteo Fusagli, Bianca G\"uttner, Tiziano Natali, Bram Westerink, Stefanie Speidel, Theo J.M. Ruers, Koert F.D. Kuhlmann, Andrey Zhylka, 19 Aug 2025, Automated surgical planning with nnU-Net: delineation of the anatomy in hepatobiliary phase MRI, https://arxiv.org/abs/2508.14133
  • Md Mainul Abrar, Xun Jia, Yujie Chi, 19 Aug 2025, New Insights into Automatic Treatment Planning for Cancer Radiotherapy Using Explainable Artificial Intelligence, https://arxiv.org/abs/2508.14229
  • Jo\~ao Vitor de Carvalho Silva and Douglas G. Macharet, 20 Aug 2025, Can LLM Agents Solve Collaborative Tasks? A Study on Urgency-Aware Planning and Coordination, https://arxiv.org/abs/2508.14635
  • Xiaowei Chi, Kuangzhi Ge, Jiaming Liu, Siyuan Zhou, Peidong Jia, Zichen He, Yuzhen Liu, Tingguang Li, Lei Han, Sirui Han, Shanghang Zhang, Yike Guo, 20 Aug 2025, MinD: Learning A Dual-System World Model for Real-Time Planning and Implicit Risk Analysis, https://arxiv.org/abs/2506.18897
  • Wei Yang, Jinwei Xiao, Hongming Zhang, Qingyang Zhang, Yanna Wang, Bo Xu, 21 Aug 2025, Coarse-to-Fine Grounded Memory for LLM Agent Planning, https://arxiv.org/abs/2508.15305
  • Bin Deng, Yizhe Feng, Zeming Liu, Qing Wei, Xiangrong Zhu, Shuai Chen, Yuanfang Guo, Yunhong Wang, 21 Aug 2025, RETAIL: Towards Real-world Travel Planning for Large Language Models, https://arxiv.org/abs/2508.15335
  • Alberto Pozanco, Marianela Morales, Daniel Borrajo, Manuela Veloso, 21 Aug 2025, Planning with Minimal Disruption, https://arxiv.org/abs/2508.15358
  • Deyu Zhang, Xicheng Zhang, Jiahao Li, Tingting Long, Xunhua Dai, Yongjian Fu, Jinrui Zhang, Ju Ren, and Yaoxue Zhang, 21 Aug 2025, LLM-Driven Self-Refinement for Embodied Drone Task Planning, https://arxiv.org/abs/2508.15501
  • Nikita Kachaev, Andrei Spiridonov, Andrey Gorodetsky, Kirill Muravyev, Nikita Oskolkov, Aditya Narendra, Vlad Shakhuro, Dmitry Makarov, Aleksandr I. Panov, Polina Fedotova, Alexey K. Kovalev, 21 Aug 2025, Mind and Motion Aligned: A Joint Evaluation IsaacSim Benchmark for Task Planning and Low-Level Policies in Mobile Manipulation, https://arxiv.org/abs/2508.15663
  • Yiheng Hu, Xiaoyang Wang, Qing Liu, Xiwei Xu, Qian Fu, Wenjie Zhang, Liming Zhu, 22 Aug 2025, MMAPG: A Training-Free Framework for Multimodal Multi-hop Question Answering via Adaptive Planning Graphs, https://arxiv.org/abs/2508.16051
  • Sijie Yang, Binyu Lei, Filip Biljecki, 22 Aug 2025, Urban Comfort Assessment in the Era of Digital Planning: A Multidimensional, Data-driven, and AI-assisted Framework, https://arxiv.org/abs/2508.16057
  • Hichem Cheriet, Khellat Kihel Badra, Chouraqui Samira, 22 Aug 2025, Comparative Analysis of UAV Path Planning Algorithms for Efficient Navigation in Urban 3D Environments, https://arxiv.org/abs/2508.16515
  • Bert de Vries, Wouter Nuijten, Thijs van de Laar, Wouter Kouw, Sepideh Adamiat, Tim Nisslbeck, Mykola Lukashchuk, Hoang Minh Huu Nguyen, Marco Hidalgo Araya, Raphael Tresor, Thijs Jenneskens, Ivana Nikoloska, Raaja Ganapathy Subramanian, Bart van Erp, Dmitry Bagaev and Albert Podusenko, 22 Aug 2025, Expected Free Energy-based Planning as Variational Inference, https://arxiv.org/abs/2504.14898
  • Yulison Herry Chrisnanto and Julian Evan Chrisnanto, 13 Aug 2025, Quantum-Inspired DRL Approach with LSTM and OU Noise for Cut Order Planning Optimization, https://arxiv.org/abs/2508.16611
  • Xing Wei, Yuqi Ouyang, 24 Aug 2025, GPG-HT: Generalized Policy Gradient with History-Aware Decision Transformer for Probabilistic Path Planning, https://arxiv.org/abs/2508.17218
  • Fan Ding, Xuewen Luo, Hwa Hui Tew, Ruturaj Reddy, Xikun Wang, Junn Yong Loo, 23 Aug 2025, Drive As You Like: Strategy-Level Motion Planning Based on A Multi-Head Diffusion Model, https://arxiv.org/abs/2508.16947
  • Jatin Nainani, Sankaran Vaidyanathan, Connor Watts, Andre N. Assis, Alice Rigg, 25 Aug 2025, Detecting and Characterizing Planning in Language Models, https://arxiv.org/abs/2508.18098
  • Arvi Jonnarth, Ola Johansson, Jie Zhao, Michael Felsberg, 23 Aug 2025, Sim-to-Real Transfer of Deep Reinforcement Learning Agents for Online Coverage Path Planning, https://arxiv.org/abs/2406.04920
  • Yongzhi Qi, Jiaheng Yin, Jianshen Zhang, Dongyang Geng, Zhengyu Chen, Hao Hu, Wei Qi, Zuo-Jun Max Shen, 4 Sep 2025, Leveraging LLM-Based Agents for Intelligent Supply Chain Planning, https://arxiv.org/abs/2509.03811
  • \'Angel Aso-Mollar and Diego Aineto and Enrico Scala and Eva Onaindia, 4 Sep 2025, Handling Infinite Domain Parameters in Planning Through Best-First Search with Delayed Partial Expansions, https://arxiv.org/abs/2509.03953
  • Alberto Luise, Michele Lombardi, Florent Teichteil Koenigsbuch, 4 Sep 2025, Hybrid Reinforcement Learning and Search for Flight Trajectory Planning, https://arxiv.org/abs/2509.04100
  • Antonio Guillen-Perez, 3 Sep 2025, Efficient Virtuoso: A Latent Diffusion Transformer Model for Goal-Conditioned Trajectory Planning, https://arxiv.org/abs/2509.03658
  • Lennart Clasmeier, Jan-Gerrit Habekost, Connor G\"ade, Philipp Allgeuer, and Stefan Wermter, 4 Sep 2025, Keypoint-based Diffusion for Robotic Motion Planning on the NICOL Robot, https://arxiv.org/abs/2509.04076
  • Yuan Zhao, Liu Lin, 4 Sep 2025, MEPG:Multi-Expert Planning and Generation for Compositionally-Rich Image Generation, https://arxiv.org/abs/2509.04126
  • Babak Esmaeili and Hamidreza Modares, 4 Sep 2025, SAFE--MA--RRT: Multi-Agent Motion Planning with Data-Driven Safety Certificates, https://arxiv.org/abs/2509.04413
  • Hang Zhao, Juzhan Xu, Kexiong Yu, Ruizhen Hu, Chenyang Zhu, Bo Du, Kai Xu, 4 Sep 2025, Deliberate Planning of 3D Bin Packing on Packing Configuration Trees, https://arxiv.org/abs/2504.04421
  • Kushan Mitra, Dan Zhang, Hannah Kim, Estevam Hruschka, 29 Aug 2025, RECAP: REwriting Conversations for Intent Understanding in Agentic Planning, https://arxiv.org/abs/2509.04472
  • Yilin Guan, Wenyue Hua, Qingfeng Lan, Sun Fei, Dujian Ding, Devang Acharya, Chi Wang, William Yang Wang, 5 Sep 2025, Dynamic Speculative Agent Planning, https://arxiv.org/abs/2509.01920
  • Yaniv Hassidof, Tom Jurgenson, Kiril Solovey, 5 Sep 2025, Train-Once Plan-Anywhere Kinodynamic Motion Planning via Diffusion Trees, https://arxiv.org/abs/2508.21001
  • Dillon Z. Chen and Johannes Zenn and Tristan Cinquin and Sheila A. McIlraith, 25 Aug 2025, Language Models For Generalised PDDL Planning: Synthesising Sound and Programmatic Policies, https://arxiv.org/abs/2508.18507
  • Dillon Z. Chen, 25 Aug 2025, Weisfeiler-Leman Features for Planning: A 1,000,000 Sample Size Hyperparameter Study, https://arxiv.org/abs/2508.18515
  • Lisai Zhang, Baohan Xu, Siqian Yang, Mingyu Yin, Jing Liu, Chao Xu, Siqi Wang, Yidi Wu, Yuxin Hong, Zihao Zhang, Yanzhang Liang, and Yudong Jiang, 26 Aug 2025, AniME: Adaptive Multi-Agent Planning for Long Animation Generation, https://arxiv.org/abs/2508.18781
  • Antonio Guillen-Perez, 25 Aug 2025, Mining the Long Tail: A Comparative Study of Data-Centric Criticality Metrics for Robust Offline Reinforcement Learning in Autonomous Motion Planning, https://arxiv.org/abs/2508.18397
  • Ziyue Li, Yuan Chang, Gaihong Yu, Xiaoqiu Le, 26 Aug 2025, HiPlan: Hierarchical Planning for LLM-Based Agents with Adaptive Global-Local Guidance, https://arxiv.org/abs/2508.19076
  • Christopher Chandler, Bernd Porr, Giulia Lafratta, Alice Miller, 26 Aug 2025, Real-Time Model Checking for Closed-Loop Robot Reactive Planning, https://arxiv.org/abs/2508.19186
  • Alex LaGrassa, Zixuan Huang, Dmitry Berenson, and Oliver Kroemer, 26 Aug 2025, Planning-Query-Guided Model Generation for Model-Based Deformable Object Manipulation, https://arxiv.org/abs/2508.19199
  • Maris F. L. Galesloot, Marnix Suilen, Thiago D. Sim\~ao, Steven Carr, Matthijs T. J. Spaan, Ufuk Topcu, Nils Jansen, 26 Aug 2025, Pessimistic Iterative Planning with RNNs for Robust POMDPs, https://arxiv.org/abs/2408.08770
  • Rajesh Mangannavar, Alan Fern, Prasad Tadepalli, 25 Aug 2025, Hierarchical Object-Oriented POMDP Planning for Object Rearrangement, https://arxiv.org/abs/2412.01348
  • Rajesh Mangannavar, Stefan Lee, Alan Fern, Prasad Tadepalli, 25 Aug 2025, Graph Neural Network Based Action Ranking for Planning, https://arxiv.org/abs/2412.04752
  • Zhiwei Li, Yong Hu, Wenqing Wang, 27 Aug 2025, Encouraging Good Processes Without the Need for Good Answers: Reinforcement Learning for LLM Agent Planning, https://arxiv.org/abs/2508.19598
  • Jinhao Liang, Sven Koenig, Ferdinando Fioretto, 27 Aug 2025, Discrete-Guided Diffusion for Scalable and Safe Multi-Robot Motion Planning, https://arxiv.org/abs/2508.20095
  • Wenfeng Feng and Chuzhan Hao and Yuewei Zhang and Guochao Jiang and Jingyi Song and Hao Wang, 27 Aug 2025, AirRAG: Autonomous Strategic Planning and Reasoning Steer Retrieval Augmented Generation, https://arxiv.org/abs/2501.10053
  • Qizhen Wu, Lei Chen, Kexin Liu, and Jinhu Lu, 27 Aug 2025, Bidirectional Task-Motion Planning Based on Hierarchical Reinforcement Learning for Strategic Confrontation, https://arxiv.org/abs/2504.15876
  • Nicola Gigante, Francesco Leofante, Andrea Micheli, 29 Aug 2025, Counterfactual Scenarios for Automated Planning, https://arxiv.org/abs/2508.21521
  • Saravanan Venkatachalam, 29 Aug 2025, Integrating Large Language Models with Network Optimization for Interactive and Explainable Supply Chain Planning: A Real-World Case Study, https://arxiv.org/abs/2508.21622
  • Usman A. Khan, Mouhacine Benosman, Wenliang Liu, Federico Pecora, Joseph W. Durham, 28 Aug 2025, Multi-robot Path Planning and Scheduling via Model Predictive Optimal Transport (MPC-OT), https://arxiv.org/abs/2508.21205
  • Bo Fu, Zhe Chen, Rahul Chandan, Alex Barbosa, Michael Caldara, Joey Durham, Federico Pecora, 31 Aug 2025, Symbolic Planning and Multi-Agent Path Finding in Extremely Dense Environments with Movable Obstacles, https://arxiv.org/abs/2509.01022
  • Huang Fang, Mengxi Zhang, Heng Dong, Wei Li, Zixuan Wang, Qifeng Zhang, Xueyun Tian, Yucheng Hu, Hang Li, 1 Sep 2025, Robix: A Unified Model for Robot Interaction, Reasoning and Planning, https://arxiv.org/abs/2509.01106
  • Riya Kinnarkar, Mansur Arief, 15 Aug 2025, Optimized Renewable Energy Planning MDP for Socially-Equitable Electricity Coverage in the US, https://arxiv.org/abs/2509.00008
  • Fulvio Mastrogiovanni and Antony Thomas, 30 Aug 2025, A Framework for Task and Motion Planning based on Expanding AND/OR Graphs, https://arxiv.org/abs/2509.00317
  • Andrea Eirale, Matteo Leonetti, Marcello Chiaberge, 2 Sep 2025, Learning Social Heuristics for Human-Aware Path Planning, https://arxiv.org/abs/2509.02134
  • Aline Dobrovsky, Konstantin Schekotihin, Christian Burmer, 2 Sep 2025, Intelligent Assistants for the Semiconductor Failure Analysis with LLM-Based Planning Agents, https://arxiv.org/abs/2506.15567
  • Shahab Shokouhi, Oguzhan Oruc, May-Win Thein, 1 Sep 2025, Self-Supervised Learning-Based Path Planning and Obstacle Avoidance Using PPO and B-Splines in Unknown Environments, https://arxiv.org/abs/2412.02176
  • Delong Chen, Theo Moutakanni, Willy Chung, Yejin Bang, Ziwei Ji, Allen Bolourchi, Pascale Fung, 2 Sep 2025, Planning with Reasoning using Vision Language World Model, https://arxiv.org/abs/2509.02722
  • Hankang Gu, Yuli Zhang, Chengming Wang, Ruiyuan Jiang, Ziheng Qiao, Pengfei Fan, Dongyao Jia, 3 Sep 2025, A Hierarchical Deep Reinforcement Learning Framework for Traffic Signal Control with Predictable Cycle Planning, https://arxiv.org/abs/2509.03118
  • Itai Zilberstein, Alberto Candela, Steve Chien, 3 Sep 2025, Real-Time Instrument Planning and Perception for Novel Measurements of Dynamic Phenomena, https://arxiv.org/abs/2509.03500
  • Yunzhe Wang, Volkan Ustun, Chris McGroarty, 8 Sep 2025, A data-driven discretized CS:GO simulation environment to facilitate strategic multi-agent planning research, https://arxiv.org/abs/2509.06355
  • Yanda Yang, Max Sokolich, Fatma Ceren Kirmizitas, Sambeeta Das, Andreas A. Malikopoulos, 5 Sep 2025, Microrobot Vascular Parkour: Analytic Geometry-based Path Planning with Real-time Dynamic Obstacle Avoidance, https://arxiv.org/abs/2509.05500
  • Jiahui Yang, Jason Jingzhou Liu, Yulong Li, Youssef Khaky, Kenneth Shaw, Deepak Pathak, 8 Sep 2025, Deep Reactive Policy: Learning Reactive Manipulator Motion Planning for Dynamic Environments, https://arxiv.org/abs/2509.06953
  • Matthew Lai, Keegan Go, Zhibin Li, Torsten Kroger, Stefan Schaal, Kelsey Allen, Jonathan Scholz, 5 Sep 2025, RoboBallet: Planning for Multi-Robot Reaching with Graph Neural Networks and Reinforcement Learning, https://arxiv.org/abs/2509.05397
  • Jie-Jing Shao, Bo-Wen Zhang, Xiao-Wen Yang, Baizhi Chen, Si-Yu Han, Wen-Da Wei, Guohao Cai, Zhenhua Dong, Lan-Zhe Guo, Yu-Feng Li, 6 Sep 2025, ChinaTravel: An Open-Ended Benchmark for Language Agents in Chinese Travel Planning, https://arxiv.org/abs/2412.13682
  • Yanwei Gong, Junchao Fan, Ruichen Zhang, Dusit Niyato, Yingying Yao, and Xiaolin Chang, 7 Sep 2025, Safe and Economical UAV Trajectory Planning in Low-Altitude Airspace: A Hybrid DRL-LLM Approach with Compliance Awareness, https://arxiv.org/abs/2506.08532
  • Mustafa Baniodeh, Kratarth Goel, Scott Ettinger, Carlos Fuertes, Ari Seff, Tim Shen, Cole Gulino, Chenjie Yang, Ghassen Jerfel, Dokook Choe, Rui Wang, Benjamin Charrow, Vinutha Kallem, Sergio Casas, Rami Al-Rfou, Benjamin Sapp, Dragomir Anguelov, 8 Sep 2025, Scaling Laws of Motion Forecasting and Planning - Technical Report, https://arxiv.org/abs/2506.08228
  • Yuan Pu, Yazhe Niu, Jia Tang, Junyu Xiong, Shuai Hu, Hongsheng Li, 9 Sep 2025, One Model for All Tasks: Leveraging Efficient World Models in Multi-Task Planning, https://arxiv.org/abs/2509.07945
  • Nasim Borazjanizadeh, Roei Herzig, Eduard Oks, Trevor Darrell, Rogerio Feris, Leonid Karlinsky, 9 Sep 2025, Visualizing Thought: Conceptual Diagrams Enable Robust Combinatorial Planning in LMMs, https://arxiv.org/abs/2503.11790
  • Yuxuan Li, Victor Zhong, 11 Sep 2025, How well can LLMs provide planning feedback in grounded environments?, https://arxiv.org/abs/2509.09790
  • Tamir Shazman, Idan Lev-Yehudi, Ron Benchetit, Vadim Indelman, 12 Sep 2025, Online Robust Planning under Model Uncertainty: A Sample-Based Approach, https://arxiv.org/abs/2509.10162
  • Alice Kate Li, Thales C Silva, Victoria Edwards, Vijay Kumar, M. Ani Hsieh, 11 Sep 2025, KoopMotion: Learning Almost Divergence Free Koopman Flow Fields for Motion Planning, https://arxiv.org/abs/2509.09074
  • Philipp Andelfinger, Jieyi Bi, Qiuyu Zhu, Jianan Zhou, Bo Zhang, Fei Fei Zhang, Chew Wye Chan, Boon Ping Gan, Wentong Cai, Jie Zhang, 19 Sep 2025, Learning to Optimize Capacity Planning in Semiconductor Manufacturing, https://arxiv.org/abs/2509.15767
  • Jane Luo, Xin Zhang, Steven Liu, Jie Wu, Yiming Huang, Yangyu Huang, Chengyu Yin, Ying Xin, Jianfeng Liu, Yuefeng Zhan, Hao Sun, Qi Chen, Scarlett Li, Mao Yang, 19 Sep 2025, RPG: A Repository Planning Graph for Unified and Scalable Codebase Generation, https://arxiv.org/abs/2509.16198
  • Liangqi Yuan and Dong-Jun Han and Christopher G. Brinton and Sabine Brunswicker, 14 Sep 2025, LLMAP: LLM-Assisted Multi-Objective Route Planning with User Preferences, https://arxiv.org/abs/2509.12273
  • Yarin Benyamin, Argaman Mordoch, Shahaf S. Shperberg, Roni Stern, 16 Sep 2025, Toward PDDL Planning Copilot, https://arxiv.org/abs/2509.12987
  • Kento Murata, Shoichi Hasegawa, Tomochika Ishikawa, Yoshinobu Hagiwara, Akira Taniguchi, Lotfi El Hafi, Tadahiro Taniguchi, 16 Sep 2025, Multi-Robot Task Planning for Multi-Object Retrieval Tasks with Distributed On-Site Knowledge via Large Language Models, https://arxiv.org/abs/2509.12838
  • Miroslav Cibula, Krist\'ina Malinovsk\'a, Matthias Kerzel, 16 Sep 2025, Towards Bio-Inspired Robotic Trajectory Planning via Self-Supervised RNN, https://arxiv.org/abs/2507.02171
  • Hamied Nabizada, Lasse Beers, Alain Chahine, Felix Gehlhoff, Oliver Niggemann, Alexander Fay, 15 Sep 2025, Bridging Engineering and AI Planning through Model-Based Knowledge Transformation for the Validation of Automated Production System Variants, https://arxiv.org/abs/2509.12091
  • Mianchu Wang and Giovanni Montana, 1 Sep 2025, Retrosynthesis Planning via Worst-path Policy Optimisation in Tree-structured MDPs, https://arxiv.org/abs/2509.10504
  • Junfeng Tang, Yuping Yan, Zihan Ye, Zhenshou, Song, Zeqi Zheng and Yaochu Jin, 14 Sep 2025, Think Small, Plan Smart: Minimalist Symbolic Abstraction and Heuristic Subspace Search for LLM-Guided Task Planning, https://arxiv.org/abs/2501.15214
  • Qihang Chen, 11 Sep 2025, Unified Crew Planning and Replanning Optimization in Multi-Line Metro Systems Considering Workforce Heterogeneity, https://arxiv.org/abs/2509.14251
  • Rohin Gillgallon, Giacomo Bergami, Reham Almutairi and Graham Morgan, 18 Sep 2025, AI-Driven Multi-Agent Vehicular Planning for Battery Efficiency and QoS in 6G Smart Cities, https://arxiv.org/abs/2509.14877
  • Yuxuan Jiang, Zehua Chen, Zeqian Ju, Chang Li, Weibei Dou, Jun Zhu, 18 Sep 2025, FreeAudio: Training-Free Timing Planning for Controllable Long-Form Text-to-Audio Generation, https://arxiv.org/abs/2507.08557
  • Zhuoyun Zhong, Seyedali Golestaneh, Constantinos Chamzas, 18 Sep 2025, ActivePusher: Active Learning and Planning with Residual Physics for Nonprehensile Manipulation, https://arxiv.org/abs/2506.04646
  • Wondmgezahu Teshome, Kian Behzad, Octavia Camps, Michael Everett, Milad Siami and Mario Sznaier, 18 Sep 2025, Real-Time Adaptive Motion Planning via Point Cloud-Guided, Energy-Based Diffusion and Potential Fields, https://arxiv.org/abs/2507.09383
  • Abigail Breitfeld, Alberto Candela, Juan Delfa, Akseli Kangaslahti, Itai Zilberstein, Steve Chien, David Wettergreen, 5 Sep 2025, Learning-Based Planning for Improving Science Return of Earth Observation Satellites, https://arxiv.org/abs/2509.07997
  • Minjong Yoo, Jinwoo Jang, Wei-jin Park, Honguk Woo, 10 Sep 2025, Exploratory Retrieval-Augmented Planning For Continual Embodied Instruction Following, https://arxiv.org/abs/2509.08222
  • Viraj Parimi and Brian C. Williams, 9 Sep 2025, Diffusion-Guided Multi-Arm Motion Planning, https://arxiv.org/abs/2509.08160
  • Thomas Bolander, Alessandro Burigana, Marco Montali, 10 Sep 2025, Depth-Bounded Epistemic Planning, https://arxiv.org/abs/2406.01139
  • Pulkit Verma, Ngoc La, Anthony Favier, Swaroop Mishra, Julie A. Shah, 14 Sep 2025, Teaching LLMs to Plan: Logical Chain-of-Thought Instruction Tuning for Symbolic Planning, https://arxiv.org/abs/2509.13351
  • Huilin Yin, Yiming Kan, Daniel Watzenig, 17 Sep 2025, MAP: End-to-End Autonomous Driving with Map-Assisted Planning, https://arxiv.org/abs/2509.13926
  • Mehran Behjati, Rosdiadee Nordin, Nor Fadzilah Abdullah, 11 Sep 2025, Maximizing UAV Cellular Connectivity with Reinforcement Learning for BVLoS Path Planning, https://arxiv.org/abs/2509.13336
  • Jun Wang, Jiaming Tong, Kaiyuan Tan, Yevgeniy Vorobeychik, Yiannis Kantaros, 17 Sep 2025, Conformal Temporal Logic Planning using Large Language Models, https://arxiv.org/abs/2309.10092
  • Minh Pham Dinh, Munira Syed, Michael G Yankoski, Trenton W. Ford, 17 Sep 2025, DAVIS: Planning Agent with Knowledge Graph-Powered Inner Monologue, https://arxiv.org/abs/2410.09252
  • Xiwen Liang, Min Lin, Weiqi Ruan, Rongtao Xu, Yuecheng Liu, Jiaqi Chen, Bingqian Lin, Yuzheng Zhuang, Xiaodan Liang, 17 Sep 2025, Structured Preference Optimization for Vision-Language Long-Horizon Task Planning, https://arxiv.org/abs/2502.20742
  • Zhihao Dou, Qinjian Zhao, Zhongwei Wan, Dinggen Zhang, Weida Wang, Towsif Raiyan, Benteng Chen, Qingtao Pan, Yang Ouyang, Zhiqiang Gao, Shufei Zhang, Sumon Biswas, 2 Oct 2025, Plan Then Action:High-Level Planning Guidance Reinforcement Learning for LLM Reasoning, https://arxiv.org/abs/2510.01833
  • Jaehyun Nam, Jinsung Yoon, Jiefeng Chen, Tomas Pfister, 2 Oct 2025, DS-STAR: Data Science Agent via Iterative Planning and Verification, https://arxiv.org/abs/2509.21825
  • Dongrong Yang, Xin Wu, Yibo Xie, Xinyi Li, Qiuwen Wu, Jackie Wu, Yang Sheng, 12 Oct 2025, Zero-Shot Large Language Model Agents for Fully Automated Radiotherapy Treatment Planning, https://arxiv.org/abs/2510.11754
  • Hang Yu, Julian Jordan, Julian Schmidt, Silvan Lindner, Alessandro Canevaro, and Wilhelm Stork, 14 Oct 2025, HYPE: Hybrid Planning with Ego Proposal-Conditioned Predictions, https://arxiv.org/abs/2510.12733
  • Yincen Qu, Huan Xiao, Feng Li, Gregory Li, Hui Zhou, Xiangying Dai, 14 Oct 2025, TripScore: Benchmarking and rewarding real-world travel planning with fine-grained evaluation, https://arxiv.org/abs/2510.09011
  • Saravanan Venkatachalam, 1 Oct 2025, Integrating AI and Ensemble Forecasting: Explainable Materials Planning with Scorecards and Trend Insights for a Large-Scale Manufacturer, https://arxiv.org/abs/2510.01006
  • Alexander Nasuta, Alessandro Cisi, Sylwia Olbrych, Gustavo Vieira, Rui Fernandes, Lucas Paletta, Marlene Mayr, Rishyank Chevuri, Robert Woitsch, Hans Aoyang Zhou, Anas Abdelrazeq, Robert H. Schmitt, 1 Oct 2025, Optimizing Fairness in Production Planning: A Human-Centric Approach to Machine and Workforce Allocation, https://arxiv.org/abs/2510.01094
  • Jorge Mendez-Mendez, 30 Sep 2025, A Systematic Study of Large Language Models for Task and Motion Planning With PDDLStream, https://arxiv.org/abs/2510.00182
  • Junhyeok Lee, Han Jang, and Kyu Sung Choi, 1 Oct 2025, Domain-Specialized Interactive Segmentation Framework for Meningioma Radiotherapy Planning, https://arxiv.org/abs/2510.00416
  • Elliot Gestrin, Marco Kuhlmann, Jendrik Seipp, 1 Oct 2025, NL2Plan: Robust LLM-Driven Planning from Minimal Text Descriptions, https://arxiv.org/abs/2405.04215
  • Ruiping Ren, Xing Yao, Shu Cole, Haining Wang, 1 Oct 2025, Whose Journey Matters? Investigating Identity Biases in Large Language Models (LLMs) for Travel Planning Assistance, https://arxiv.org/abs/2410.17333
  • Yichao Liang, Dat Nguyen, Cambridge Yang, Tianyang Li, Joshua B. Tenenbaum, Carl Edward Rasmussen, Adrian Weller, Zenna Tavares, Tom Silver, Kevin Ellis, 1 Oct 2025, ExoPredicator: Learning Abstract Models of Dynamic Worlds for Robot Planning, https://arxiv.org/abs/2509.26255
  • Li Zhou, Elvan Ceyhan, 23 Sep 2025, Stochastic Path Planning in Correlated Obstacle Fields, https://arxiv.org/abs/2509.19559
  • Ruibin Xiong, Yimeng Chen, Dmitrii Khizbullin, Mingchen Zhuge, J\"urgen Schmidhuber, 24 Sep 2025, Beyond Outlining: Heterogeneous Recursive Planning for Adaptive Long-form Writing with Language Models, https://arxiv.org/abs/2503.08275
  • Zhenyu Zhang, Tianyi Chen, Weiran Xu, Alex Pentland, Jiaxin Pei, 27 Oct 2025, ReCAP: Recursive Context-Aware Reasoning and Planning for Large Language Model Agents, https://arxiv.org/abs/2510.23822
  • Murad Ismayilov, Edwin Meriaux, Shuo Wen, Gregory Dudek, 27 Oct 2025, Decentralized Multi-Agent Goal Assignment for Path Planning using Large Language Models, https://arxiv.org/abs/2510.23824
  • Xin Yang, Yuhang Zhang, Wei Li, Xin Lin, Wenbin Zou, Chen Xu, 28 Oct 2025, UniPlanner: A Unified Motion Planning Framework for Autonomous Vehicle Decision-Making Systems via Multi-Dataset Integration, https://arxiv.org/abs/2510.24166
  • Shengjie Liu, Li Dong, Zhenyu Zhang, 28 Oct 2025, Bridging Tool Dependencies and Domain Knowledge: A Graph-Based Framework for In-Context Planning, https://arxiv.org/abs/2510.24690
  • Marianne Menglin Liu, Sai Ashish Somayajula, Syed Fahad Allam Shah, Sujith Ravi, and Dan Roth, 27 Oct 2025, OraPlan-SQL: A Planning-Centric Framework for Complex Bilingual NL2SQL Reasoning, https://arxiv.org/abs/2510.23870
  • Bita Banihashemi, Megh Patel and Yves Lesp\'erance, 23 Oct 2025, Using Large Language Models for Abstraction of Planning Domains - Extended Version, https://arxiv.org/abs/2510.20258
  • Mustafa F. Abdelwahed, Alice Toniolo, Joan Espasa, Ian P. Gent, 20 Oct 2025, Diverse Planning with Simulators via Linear Temporal Logic, https://arxiv.org/abs/2510.17418
  • Yueqian Lin, Zhengmian Hu, Jayakumar Subramanian, Qinsi Wang, Nikos Vlassis, Hai "Helen" Li, Yiran Chen, 17 Oct 2025, AsyncVoice Agent: Real-Time Explanation for LLM Planning and Reasoning, https://arxiv.org/abs/2510.16156
  • Tianyang Xu, Dan Zhang, Kushan Mitra, Estevam Hruschka, 20 Oct 2025, Verification-Aware Planning for Multi-Agent Systems, https://arxiv.org/abs/2510.17109
  • Cansu Erdogan, Cesar Alan Contreras, Alireza Rastegarpanah, Manolis Chiou, Rustam Stolkin, 20 Oct 2025, Intent-Driven LLM Ensemble Planning for Flexible Multi-Robot Disassembly: Demonstration on EV Batteries, https://arxiv.org/abs/2510.17576
  • Fernando Salanova, Jes\'us Roche, Cristian Mahuela, Eduardo Montijano, 20 Oct 2025, High-Level Multi-Robot Trajectory Planning And Spurious Behavior Detection, https://arxiv.org/abs/2510.17261
  • David Wang and Mohammad Abdulaziz, 19 Oct 2025, Formally Verified Certification of Unsolvability of Temporal Planning Problems, https://arxiv.org/abs/2510.10189
  • Hang Xu, Zang Yu, Yehui Tang, Pengbo Hu, Yuhao Tang, Hao Dong, 21 Sep 2025, MCTS-EP: Empowering Embodied Planning with Online Preference Optimization, https://arxiv.org/abs/2509.17116
  • Cheng Jiayang, Qianqian Zhuang, Haoran Li, Chunkit Chan, Xin Liu, Lin Qiu, Yangqiu Song, 20 Sep 2025, InteGround: On the Evaluation of Verification and Retrieval Planning in Integrative Grounding, https://arxiv.org/abs/2509.16534
  • Elton Pan, Soonhyoung Kwon, Sulin Liu, Mingrou Xie, Alexander J. Hoffman, Yifei Duan, Thorben Prein, Killian Sheriff, Yuriy Roman-Leshkov, Manuel Moliner, Rafael Gomez-Bombarelli, Elsa Olivetti, 21 Sep 2025, $\texttt{DiffSyn}$: A Generative Diffusion Approach to Materials Synthesis Planning, https://arxiv.org/abs/2509.17094
  • Jieren Deng, Zhizhang Hu, Ziyan He, Aleksandar Cvetkovic, Pak Kiu Chung, Dragomir Yankov and Chiqun Zhang, 20 Sep 2025, IMAIA: Interactive Maps AI Assistant for Travel Planning and Geo-Spatial Intelligence, https://arxiv.org/abs/2507.06993
  • Crimson Stambaugh and Rajesh P. N. Rao, 27 Oct 2025, Mixed Density Diffuser: Efficient Planning with Non-uniform Temporal Resolution, https://arxiv.org/abs/2510.23026
  • Anwesha Das, John Duff, J\"org Hoffmann and Vera Demberg, 27 Oct 2025, Planning Ahead with RSA: Efficient Signalling in Dynamic Environments by Projecting User Awareness across Future Timesteps, https://arxiv.org/abs/2510.23340
  • Hao Sun, Zile Qiao, Bo Wang, Guoxin Chen, Yingyan Hou, Yong Jiang, Pengjun Xie, Fei Huang, Yan Zhang, 7 Sep 2025, DecoupleSearch: Decouple Planning and Search via Hierarchical Reward Modeling, https://arxiv.org/abs/2510.21712
  • Shuning Zhang, 23 Oct 2025, A Physics-Informed Neural Network Approach for UAV Path Planning in Dynamic Environments, https://arxiv.org/abs/2510.21874
  • Moran Barenboim and Vadim Indelman, 27 Oct 2025, Online POMDP Planning with Anytime Deterministic Optimality Guarantees, https://arxiv.org/abs/2310.01791
  • Marcus Tantakoun, Xiaodan Zhu, Christian Muise, 25 Oct 2025, LLMs as Planning Formalizers: A Survey for Leveraging Large Language Models to Construct Automated Planning Models, https://arxiv.org/abs/2503.18971
  • Ruijia Liu, Ancheng Hou, Xiao Yu, Xiang Yin, 26 Oct 2025, Zero-Shot Trajectory Planning for Signal Temporal Logic Tasks, https://arxiv.org/abs/2501.13457
  • Yi Wang, Zeyu Xue, Mujie Liu, Tongqin Zhang, Yan Hu, Zhou Zhao, Chenguang Yang and Zhenyu Lu, 27 Oct 2025, Open-Vocabulary Spatio-Temporal Scene Graph for Robot Perception and Teleoperation Planning, https://arxiv.org/abs/2509.23107
  • Minglu Zhao, Dehong Xu, Deqian Kong, Wen-Hao Zhang, Ying Nian Wu, 24 Oct 2025, Place Cells as Multi-Scale Position Embeddings: Random Walk Transition Kernels for Path Planning, https://arxiv.org/abs/2505.14806
  • Wei Fan, Wenlin Yao, Zheng Li, Feng Yao, Xin Liu, Liang Qiu, Qingyu Yin, Yangqiu Song, Bing Yin, 14 Oct 2025, DeepPlanner: Scaling Planning Capability for Deep Research Agents via Advantage Shaping, https://arxiv.org/abs/2510.12979
  • Joy Jia Yin Lim, Ye He, Jifan Yu, Xin Cong, Daniel Zhang-Li, Zhiyuan Liu, Huiqin Liu, Lei Hou, Juanzi Li, Bin Xu, 15 Oct 2025, Personalized Learning Path Planning with Goal-Driven Learner State Modeling, https://arxiv.org/abs/2510.13215
  • Adnan Jafar, Xun Jia, 15 Oct 2025, Towards Human-Centric Intelligent Treatment Planning for Radiation Therapy, https://arxiv.org/abs/2510.13062
  • Taylor Webb, Shanka Subhra Mondal, Ida Momennejad, 14 Oct 2025, Improving Planning with Large Language Models: A Modular Agentic Architecture, https://arxiv.org/abs/2310.00194
  • Xuanjin Jin, Chendong Zeng, Shengfa Zhu, Chunxiao Liu, and Panpan Cai, 15 Oct 2025, Hi-Drive: Hierarchical POMDP Planning for Safe Autonomous Driving in Diverse Urban Environments, https://arxiv.org/abs/2409.18411
  • Yansong Ning, Rui Liu, Jun Wang, Kai Chen, Wei Li, Jun Fang, Kan Zheng, Naiqiang Tan, Hao Liu, 26 Sep 2025, DeepTravel: An End-to-End Agentic Reinforcement Learning Framework for Autonomous Travel Planning Agents, https://arxiv.org/abs/2509.21842
  • Hieu Tran, Zonghai Yao, Nguyen Luong Tran, Zhichao Yang, Feiyun Ouyang, Shuo Han, Razieh Rahimi, Hong Yu, 26 Sep 2025, PRIME: Planning and Retrieval-Integrated Memory for Enhanced Reasoning, https://arxiv.org/abs/2509.22315
  • Siwei Wang, Yifei Shen, Haoran Sun, Shi Feng, Shang-Hua Teng, Li Dong, Yaru Hao, Wei Chen, 26 Sep 2025, Benefits and Pitfalls of Reinforcement Learning for Language Model Planning: A Theoretical Perspective, https://arxiv.org/abs/2509.22613
  • Afrina Tabassum, Bin Guo, Xiyao Ma, Hoda Eldardiry, Ismini Lourentzou, 25 Sep 2025, MMPlanner: Zero-Shot Multimodal Procedural Planning with Chain-of-Thought Object State Reasoning, https://arxiv.org/abs/2509.21662
  • Lingguang Wang, \"Omer \c{S}ahin Ta\c{s}, Marlon Steiner, Christoph Stiller, 26 Sep 2025, FlowDrive: moderated flow matching with data balancing for trajectory planning, https://arxiv.org/abs/2509.21961
  • Sigmund Hennum H{\o}eg, Aksel Vaaler, Chaoqi Liu, Olav Egeland, and Yilun Du, 26 Sep 2025, Hybrid Diffusion for Simultaneous Symbolic and Continuous Planning, https://arxiv.org/abs/2509.21983
  • Divake Kumar, Sina Tayebati, Francesco Migliarba, Ranganath Krishnan, and Amit Ranjan Trivedi, 26 Sep 2025, Learnable Conformal Prediction with Context-Aware Nonconformity Functions for Robotic Planning and Perception, https://arxiv.org/abs/2509.21955
  • Manav Vora, Ilan Shomorony, Melkior Ornik, 26 Sep 2025, Capacity-Aware Planning and Scheduling in Budget-Constrained Multi-Agent MDPs: A Meta-RL Approach, https://arxiv.org/abs/2410.21249
  • Yitao Long, Yuru Jiang, Hongjun Liu, Yilun Zhao, Jingchen Sun, Yiqiu Shen, Chen Zhao, Arman Cohan, Dennis Shasha, 7 Oct 2025, PuzzlePlex: Benchmarking Foundation Models on Reasoning and Planning with Puzzles, https://arxiv.org/abs/2510.06475
  • Jingbo Yang, Bairu Hou, Wei Wei, Shiyu Chang, Yujia Bao, 8 Oct 2025, WebDART: Dynamic Decomposition and Re-planning for Complex Web Tasks, https://arxiv.org/abs/2510.06587
  • Baixuan Xu, Tianshi Zheng, Zhaowei Wang, Hong Ting Tsang, Weiqi Wang, Tianqing Fang, Yangqiu Song, 8 Oct 2025, The Cognitive Bandwidth Bottleneck: Shifting Long-Horizon Agent from Planning with Actions to Planning with Schemas, https://arxiv.org/abs/2510.07091
  • Tian Qin, Felix Bai, Ting-Yao Hu, Raviteja Vemulapalli, Hema Swetha Koppula, Zhiyang Xu, Bowen Jin, Mert Cemri, Jiarui Lu, Zirui Wang, Meng Cao, 8 Oct 2025, COMPASS: A Multi-Turn Benchmark for Tool-Mediated Planning & Preference Optimization, https://arxiv.org/abs/2510.07043
  • Grayson Byrd, Corban Rivera, Bethany Kemp, Meghan Booker, Aurora Schmidt, Celso M de Melo, Lalithkumar Seenivasan, Mathias Unberath, 7 Oct 2025, Constrained Natural Language Action Planning for Resilient Embodied Systems, https://arxiv.org/abs/2510.06357
  • Donald Pfaffmann, Matthias Klusch, Marcel Steinmetz, 8 Oct 2025, HyPlan: Hybrid Learning-Assisted Planning Under Uncertainty for Safe Autonomous Driving, https://arxiv.org/abs/2510.07210
  • Marlon M\"uller, Florian Finkeldei, Hanna Krasowski, Murat Arcak, Matthias Althoff, 8 Oct 2025, Falsification-Driven Reinforcement Learning for Maritime Motion Planning, https://arxiv.org/abs/2510.06970
  • Jo\~ao Pedro Gandarela and Thiago Rios and Stefan Menzel and Andr\'e Freitas, 8 Oct 2025, Controlled Agentic Planning & Reasoning for Mechanism Synthesis, https://arxiv.org/abs/2505.17607
  • Yu-Hong Shen, Chuan-Yu Wu, Yi-Ru Yang, Yen-Ling Tai, Yi-Ting Chen, 8 Oct 2025, Mitigating Cross-Modal Distraction and Ensuring Geometric Feasibility via Affordance-Guided and Self-Consistent MLLMs for Task Planning in Instruction-Following Manipulation, https://arxiv.org/abs/2503.13055
  • Emanuele Musumeci, Michele Brienza, Francesco Argenziano, Abdel Hakim Drid, Vincenzo Suriani, Daniele Nardi, Domenico D. Bloisi, 8 Oct 2025, Context Matters! Relaxing Goals with LLMs for Feasible 3D Scene Planning, https://arxiv.org/abs/2506.15828
  • Riqiang Gao, Mamadou Diallo, Han Liu, Anthony Magliari, Jonathan Sackett, Wilko Verbakel, Sandra Meyers, Rafe Mcbeth, Masoud Zarepisheh, Simon Arberet, Martin Kraus, Florin C. Ghesu, Ali Kamen, 8 Oct 2025, Automating RT Planning at Scale: High Quality Data For AI Training, https://arxiv.org/abs/2501.11803
  • Yunqi Huang, Nishith Chennakeshava, Alexis Carras, Vladislav Neverov, Wei Liu, Aske Plaat, Yingjie Fan, 2 Oct 2025, A Benchmark Study of Deep Reinforcement Learning Algorithms for the Container Stowage Planning Problem, https://arxiv.org/abs/2510.02589
  • Junlin Zeng, Xin Zhang, Xiang Zhao, and Yan Pan, 3 Oct 2025, A $1000\times$ Faster LLM-enhanced Algorithm For Path Planning in Large-scale Grid Maps, https://arxiv.org/abs/2510.02716
  • Yifan Liao and Zhen Sun and Xiaoyun Qiu and Zixiao Zhao and Wenbing Tang and Xinlei He and Xinhu Zheng and Tianwei Zhang and Xinyi Huang and Xingshuo Han, 3 Oct 2025, Work Zones challenge VLM Trajectory Planning: Toward Mitigation and Robust Autonomous Driving, https://arxiv.org/abs/2510.02803
  • Yilun Hao, Yongchao Chen, Chuchu Fan, Yang Zhang, 3 Oct 2025, Simulation to Rules: A Dual-VLM Framework for Formal Visual Planning, https://arxiv.org/abs/2510.03182
  • Larkin Liu, Shiqi Liu, Yinruo Hua, Matej Jusup, 3 Oct 2025, Improved Monte Carlo Planning via Causal Disentanglement for Structurally-Decomposed Markov Decision Processes, https://arxiv.org/abs/2406.16151
  • Nikhil Verma, Manasa Bharadwaj, Wonjun Jang, Harmanpreet Singh, Yixiao Wang, Homa Fashandi, Chul Lee, 20 Oct 2025, SMaRT: Select, Mix, and ReinvenT - A Strategy Fusion Framework for LLM-Driven Reasoning and Planning, https://arxiv.org/abs/2510.18095
  • Ziwei Deng, Mian Deng, Chenjing Liang, Zeming Gao, Chennan Ma, Chenxing Lin, Haipeng Zhang, Songzhu Mei, Cheng Wang, Siqi Shen, 21 Oct 2025, PlanU: Large Language Model Decision Making through Planning under Uncertainty, https://arxiv.org/abs/2510.18442
  • Xiaozhuang Song, Xuanhao Pan, Xinjian Zhao, Hangting Ye, Shufei Zhang, Jian Tang, Tianshu Yu, 25 Sep 2025, AOT*: Efficient Synthesis Planning via LLM-Empowered AND-OR Tree Search, https://arxiv.org/abs/2509.20988
  • Hanjiang Hu, Changliu Liu, Na Li, Yebin Wang, 24 Sep 2025, Training Task Reasoning LLM Agents for Multi-turn Task Planning via Single-turn Reinforcement Learning, https://arxiv.org/abs/2509.20616
  • Muhammad Fadhil Ginting, Dong-Ki Kim, Xiangyun Meng, Andrzej Reinke, Bandi Jai Krishna, Navid Kayhani, Oriana Peltzer, David D. Fan, Amirreza Shaban, Sung-Kyun Kim, Mykel J. Kochenderfer, Ali-akbar Agha-mohammadi, and Shayegan Omidshafiei, 25 Sep 2025, Enter the Mind Palace: Reasoning and Planning for Long-term Active Embodied Question Answering, https://arxiv.org/abs/2507.12846
  • Qimin Zhong, Hao Liao, Siwei Wang, Mingyang Zhou, Xiaoqun Wu, Rui Mao, Wei Chen, 27 Sep 2025, Understanding and Enhancing the Planning Capability of Language Models via Multi-Token Prediction, https://arxiv.org/abs/2509.23186
  • Shu Liu, Wenlin Chen, Weihao Li, Zheng Wang, Lijin Yang, Jianing Huang, Yipin Zhang, Zhongzhan Huang, Ze Cheng, Hao Yang, 28 Sep 2025, BridgeDrive: Diffusion Bridge Policy for Closed-Loop Trajectory Planning in Autonomous Driving, https://arxiv.org/abs/2509.23589
  • Shaobin Ling, Yun Wang, Chenyou Fan, Tin Lun Lam, and Junjie Hu, 29 Sep 2025, ELHPlan: Efficient Long-Horizon Task Planning for Multi-Agent Collaboration, https://arxiv.org/abs/2509.24230
  • Sai Wang, Yu Wu, Zhongwen Xu, 29 Sep 2025, Cogito, Ergo Ludo: An Agent that Learns to Play by Reasoning and Planning, https://arxiv.org/abs/2509.25052
  • Zezhong Fan, Xiaohan Li, Luyi Ma, Kai Zhao, Liang Peng, Topojoy Biswas, Evren Korpeoglu, Kaushiki Nag, Kannan Achan, 24 Sep 2025, LayoutAgent: A Vision-Language Agent Guided Compositional Diffusion for Spatial Layout Planning, https://arxiv.org/abs/2509.22720
  • Arman Barghi, Hamed Hosseini, Seraj Ghasemi, Mehdi Tale Masouleh, Ahmad Kalhor, 26 Sep 2025, Dynamic Buffers: Cost-Efficient Planning for Tabletop Rearrangement with Stacking, https://arxiv.org/abs/2509.22828
  • Athanasios Bacharis, Konstantinos D. Polyzos, Georgios B. Giannakis, and Nikolaos Papanikolopoulos, 28 Sep 2025, BOSfM: A View Planning Framework for Optimal 3D Reconstruction of Agricultural Scenes, https://arxiv.org/abs/2509.24126
  • Tomoyuki Kagaya, Subramanian Lakshmi, Yuxuan Lou, Thong Jing Yuan, Jayashree Karlekar, Sugiri Pranata, Natsuki Murakami, Akira Kinose, Yang You, 29 Sep 2025, Memory Transfer Planning: LLM-driven Context-Aware Code Adaptation for Robot Manipulation, https://arxiv.org/abs/2509.24160
  • Tomoyuki Kagaya, Subramanian Lakshmi, Anbang Ye, Thong Jing Yuan, Jayashree Karlekar, Sugiri Pranata, Natsuki Murakami, Akira Kinose, Yang You, 29 Sep 2025, ViReSkill: Vision-Grounded Replanning with Skill Memory for LLM-Based Planning in Lifelong Robot Learning, https://arxiv.org/abs/2509.24219
  • Jeongyong Yang, Seunghwan Jang, Soojean Han, 29 Sep 2025, SafeFlowMatcher: Safe and Fast Planning using Flow Matching with Control Barrier Functions, https://arxiv.org/abs/2509.24243
  • Kaustubh Vyas, Damien Graux, Yijun Yang, S\'ebastien Montella, Chenxin Diao, Wendi Zhou, Pavlos Vougiouklis, Ruofei Lai, Yang Ren, Keshuang Li, Jeff Z. Pan, 28 Sep 2025, From An LLM Swarm To A PDDL-Empowered HIVE: Planning Self-Executed Instructions In A Multi-Modal Jungle, https://arxiv.org/abs/2412.12839
  • Ziming Wei, Bingqian Lin, Zijian Jiao, Yunshuang Nie, Liang Ma, Yuecheng Liu, Yuzheng Zhuang, Xiaodan Liang, 28 Sep 2025, MineAnyBuild: Benchmarking Spatial Planning for Open-world AI Agents, https://arxiv.org/abs/2505.20148
  • Byeongchan Lee, Jonghoon Lee, Dongyoung Kim, Jaehyung Kim, Kyungjoon Park, Dongjun Lee, Jinwoo Shin, 27 Sep 2025, Efficient LLM Collaboration via Planning, https://arxiv.org/abs/2506.11578
  • Yi Xu, Chengzu Li, Han Zhou, Xingchen Wan, Caiqi Zhang, Anna Korhonen, Ivan Vuli\'c, 29 Sep 2025, Visual Planning: Let's Think Only with Images, https://arxiv.org/abs/2505.11409
  • Quentin Clark and Florian Shkurti, 29 Sep 2025, What Do You Need for Diverse Trajectory Composition in Diffusion Planning?, https://arxiv.org/abs/2505.18083
  • Kumar Manas, Stefan Zwicklbauer and Adrian Paschke, 27 Sep 2025, CoT-TL: Low-Resource Temporal Knowledge Representation of Planning Instructions Using Chain-of-Thought Reasoning, https://arxiv.org/abs/2410.16207
  • Guang Hu, Weijia Li, Yangmengfei Xu, 17 Oct 2025, Beyond Static Assumptions: the Predictive Justified Perspective Model for Epistemic Planning, https://arxiv.org/abs/2412.07941
  • Guang Hu, Tim Miller, Nir Lipovetzky, 17 Oct 2025, Where Common Knowledge Cannot Be Formed, Common Belief Can -- Planning with Multi-Agent Belief Using Group Justified Perspectives, https://arxiv.org/abs/2412.07981
  • Keshav Ramani, Vali Tawosi, Salwa Alamir, Daniel Borrajo, 3 Oct 2025, Bridging LLM Planning Agents and Formal Methods: A Case Study in Plan Verification, https://arxiv.org/abs/2510.03469
  • Xurui Song, Shuo Huai, JingJing Jiang, Jiayi Kong, Jun Luo, 6 Oct 2025, More Than Meets the Eye? Uncovering the Reasoning-Planning Disconnect in Training Vision-Language Driving Models, https://arxiv.org/abs/2510.04532
  • Hao Wu, Yuan Gao, Xingjian Shi, Shuaipeng Li, Fan Xu, Fan Zhang, Zhihong Zhu, Weiyan Wang, Xiao Luo, Kun Wang, Xian Wu, Xiaomeng Huang, 5 Oct 2025, Spatiotemporal Forecasting as Planning: A Model-Based Reinforcement Learning Approach with Generative World Models, https://arxiv.org/abs/2510.04020
  • Yifei Dong, Fengyi Wu, Guangyu Chen, Zhi-Qi Cheng, Qiyu Hu, Yuxuan Zhou, Jingdong Sun, Jun-Yan He, Qi Dai, Alexander G Hauptmann, 9 Oct 2025, Unified World Models: Memory-Augmented Planning and Foresight for Visual Navigation, https://arxiv.org/abs/2510.08713
  • Grace Ra Kim, Hailey Warner, Duncan Eddy, Evan Astle, Zachary Booth, Edward Balaban, Mykel J. Kochenderfer, 9 Oct 2025, Adaptive Science Operations in Deep Space Missions Using Offline Belief State Planning, https://arxiv.org/abs/2510.08812
  • Martin Schuck, Dinushka Orrin Dahanaggamaarachchi, Ben Sprenger, Vedant Vyas, Siqi Zhou, and Angela P. Schoellig, 10 Oct 2025, SwarmGPT: Combining Large Language Models with Safe Motion Planning for Drone Swarm Choreography, https://arxiv.org/abs/2412.08428
  • Sanghyun Ahn, Wonje Choi, Junyong Lee, Jinwoo Park, Honguk Woo, 24 Oct 2025, Towards Reliable Code-as-Policies: A Neuro-Symbolic Framework for Embodied Task Planning, https://arxiv.org/abs/2510.21302
  • Edward Holmberg, Elias Ioup, Mahdi Abdelguerfi, 24 Oct 2025, A Knowledge-Graph Translation Layer for Mission-Aware Multi-Agent Path Planning in Spatiotemporal Dynamics, https://arxiv.org/abs/2510.21695
  • Jaesik Yoon, Hyeonseo Cho, Sungjin Ahn, 24 Oct 2025, Compositional Monte Carlo Tree Diffusion for Extendable Planning, https://arxiv.org/abs/2510.21361
  • Priyanshu Karmakar (1), Soumyabrata Chaudhuri (1), Shubhojit Mallick (2), Manish Gupta (2), Abhik Jana (1), Shreya Ghosh (1) ((1) School of Electrical and Computer Sciences, IIT Bhubaneswar, India, (2) Microsoft, India), 24 Oct 2025, TripTide: A Benchmark for Adaptive Travel Planning under Disruptions, https://arxiv.org/abs/2510.21329
  • Augusto B. Corr\^ea and Andr\'e G. Pereira and Jendrik Seipp, 23 Oct 2025, Classical Planning with LLM-Generated Heuristics: Challenging the State of the Art with Python Code, https://arxiv.org/abs/2503.18809
  • Jaesik Yoon, Hyeonseo Cho, Yoshua Bengio, Sungjin Ahn, 24 Oct 2025, Fast Monte Carlo Tree Diffusion: 100x Speedup via Parallel Sparse Planning, https://arxiv.org/abs/2506.09498
  • Weikai Wang and Erick Delage, 24 Oct 2025, Planning and Learning in Average Risk-aware MDPs, https://arxiv.org/abs/2503.17629
  • Donghyeon Ki, JunHyeok Oh, Seong-Woong Shim, Byung-Jun Lee, 24 Oct 2025, Prior-Guided Diffusion Planning for Offline Reinforcement Learning, https://arxiv.org/abs/2505.10881
  • Amartya Chakraborty, Paresh Dashore, Nadia Bathaee, Anmol Jain, Anirban Das, Shi-Xiong Zhang, Sambit Sahu, Milind Naphade, Genta Indra Winata, 23 Oct 2025, T1: A Tool-Oriented Conversational Dataset for Multi-Turn Agentic Planning, https://arxiv.org/abs/2505.16986
  • Shiqi Zhang, Xinbei Ma, Yunqing Xu, Zouying Cao, Pengrui Lu, Haobo Yuan, Tiancheng Shen, Zhuosheng Zhang, Hai Zhao, Ming-Hsuan Yang, 13 Oct 2025, ParaCook: On Time-Efficient Planning for Multi-Agent Systems, https://arxiv.org/abs/2510.11608
  • Aditya Chakravarty, 8 Oct 2025, Spatial Uncertainty Quantification in Wildfire Forecasting for Climate-Resilient Emergency Planning, https://arxiv.org/abs/2510.09666
  • Dong Yan, Gaochen Wu, Bowen Zhou, 7 Oct 2025, Mission Impossible: Feedback-Guided Dynamic Interactive Planning for Improving Reasoning on LLMs, https://arxiv.org/abs/2510.05577
  • Rohan Gupta, Trevor Asbery, Zain Merchant, Abrar Anwar, Jesse Thomason, 12 Oct 2025, RobotFleet: An Open-Source Framework for Centralized Multi-Robot Task Planning, https://arxiv.org/abs/2510.10379
  • Ahmed Alanazi, Duy Ho, and Yugyung Lee, 13 Oct 2025, GRIP: A Unified Framework for Grid-Based Relay and Co-Occurrence-Aware Planning in Dynamic Environments, https://arxiv.org/abs/2510.10865
  • Subhransu S. Bhattacharjee, Hao Lu, Dylan Campbell, Rahul Shome, 13 Oct 2025, Into the Unknown: Towards using Generative Models for Sampling Priors of Environment Uncertainty for Planning in Configuration Spaces, https://arxiv.org/abs/2510.11014
  • Tianyi Tan, Yinan Zheng, Ruiming Liang, Zexu Wang, Kexin Zheng, Jinliang Zheng, Jianxiong Li, Xianyuan Zhan, Jingjing Liu, 13 Oct 2025, Flow Matching-Based Autonomous Driving Planning with Advanced Interactive Behavior Modeling, https://arxiv.org/abs/2510.11083
  • Hang Liu, Yuman Gao, Sangli Teng, Yufeng Chi, Yakun Sophia Shao, Zhongyu Li, Maani Ghaffari, Koushil Sreenath, 13 Oct 2025, Ego-Vision World Model for Humanoid Contact Planning, https://arxiv.org/abs/2510.11682
  • Zifeng Ding, Sikuan Yan, Zhangdie Yuan, Xianglong Hu, Fangru Lin, Andreas Vlachos, 13 Oct 2025, TCP: a Benchmark for Temporal Constraint-Based Planning, https://arxiv.org/abs/2505.19927
  • Nikolas Belle, Dakota Barnes, Alfonso Amayuelas, Ivan Bercovich, Xin Eric Wang, and William Wang, 13 Oct 2025, Agents of Change: Self-Evolving LLM Agents for Strategic Planning, https://arxiv.org/abs/2506.04651
  • Dongjie Yang, Chengqiang Lu, Qimeng Wang, Xinbei Ma, Yan Gao, Yao Hu, Hai Zhao, 12 Oct 2025, Wide-Horizon Thinking and Simulation-Based Evaluation for Real-World LLM Planning with Multifaceted Constraints, https://arxiv.org/abs/2506.12421
  • Binrong Zhu, Guiran Liu, and Nina Jiang, 8 Oct 2025, ExpertAgent: Enhancing Personalized Education through Dynamic Planning and Retrieval-Augmented Long-Chain Reasoning, https://arxiv.org/abs/2510.07456
  • Michal Koren, Or Peretz, Tai Dinh, Philip S. Yu, 9 Oct 2025, Reinforcement Learning from Probabilistic Forecasts for Safe Decision-Making via Conditional Value-at-Risk Planning, https://arxiv.org/abs/2510.08226
  • Jun Liu, 8 Oct 2025, Quantum Grid Path Planning Using Parallel QAOA Circuits Based on Minimum Energy Principle, https://arxiv.org/abs/2510.07413
  • Shiyuan Yin, Chenjia Bai, Zihao Zhang, Junwei Jin, Xinxin Zhang, Chi Zhang, and Xuelong Li, 9 Oct 2025, Towards Reliable LLM-based Robot Planning via Combined Uncertainty Estimation, https://arxiv.org/abs/2510.08044
  • Dongjie Wang, Chang-Tien Lu, Xinyue Ye, Tan Yigitcanlar, Yanjie Fu, 9 Oct 2025, Advancing Automated Urban Planning: Exploring Algorithmic Approaches with Generative Artificial Intelligence, https://arxiv.org/abs/2304.03892
  • Zikang Tian, Shaohui Peng, Du Huang, Jiaming Guo, Ruizhi Chen, Rui Zhang, Xishan Zhang, Yuxuan Guo, Zidong Du, Qi Guo, Ling Li, Yewen Pu, Xing Hu, Yunji Chen, 23 Sep 2025, Code Driven Planning with Domain-Adaptive Critic, https://arxiv.org/abs/2509.19077
  • Neel P. Bhatt, Yunhao Yang, Rohan Siva, Pranay Samineni, Daniel Milan, Zhangyang Wang, and Ufuk Topcu, 23 Sep 2025, VLN-Zero: Rapid Exploration and Cache-Enabled Neurosymbolic Vision-Language Planning for Zero-Shot Transfer in Robot Navigation, https://arxiv.org/abs/2509.18592
  • Ghulam Mudassir, Antinisca Di Marco and Giordano d'Aloisio, 21 Oct 2025, REPAIR Approach for Social-based City Reconstruction Planning in case of natural disasters, https://arxiv.org/abs/2510.19048
  • Mehran Ghafarian Tamizi, Homayoun Honari, Amir Mehdi Soufi Enayati, Aleksey Nozdryn-Plotnicki, Homayoun Najjaran, 21 Oct 2025, A Cross-Environment and Cross-Embodiment Path Planning Framework via a Conditional Diffusion Model, https://arxiv.org/abs/2510.19128
  • Zhida Zhao, Talas Fu, Yifan Wang, Lijun Wang, Huchuan Lu, 22 Oct 2025, From Forecasting to Planning: Policy World Model for Collaborative State-Action Prediction, https://arxiv.org/abs/2510.19654
  • Jinrui Liu, Bingyan Nie, Boyu Li, Yaran Chen, Yuze Wang, Shunsen He, Haoran Li, 22 Oct 2025, RoboGPT-R1: Enhancing Robot Planning with Reinforcement Learning, https://arxiv.org/abs/2510.14828
  • Palash Chatterjee and Roni Khardon, 21 Oct 2025, Improving planning and MBRL with temporally-extended actions, https://arxiv.org/abs/2505.15754
  • Muhammed Ustaomeroglu, Baris Askin, Gauri Joshi, Carlee Joe-Wong, Guannan Qu, 28 Sep 2025, Language Model Planning from an Information Theoretic Perspective, https://arxiv.org/abs/2509.25260
  • Jihye Choi, Jinsung Yoon, Jiefeng Chen, Somesh Jha and Tomas Pfister, 29 Sep 2025, ATLAS: Constraints-Aware Multi-Agent Collaboration for Real-World Travel Planning, https://arxiv.org/abs/2509.25586
  • Hongyu Chen, Guangrun Wang, 26 Sep 2025, UML-CoT: Structured Reasoning and Planning with Unified Modeling Language for Robotic Room Cleaning, https://arxiv.org/abs/2509.22628
  • Guancheng Chen, Sheng Yang, Tong Zhan, Jian Wang, 27 Sep 2025, BEV-VLM: Trajectory Planning via Unified BEV Abstraction, https://arxiv.org/abs/2509.25249
  • Zichao Shen, Chen Gao, Jiaqi Yuan, Tianchen Zhu, Xingcheng Fu, Qingyun Sun, 30 Sep 2025, SDA-PLANNER: State-Dependency Aware Adaptive Planner for Embodied Task Planning, https://arxiv.org/abs/2509.26375
  • Sangwon Ryu, Heejin Do, Yunsu Kim, Gary Geunbae Lee, Jungseul Ok, 30 Sep 2025, Adaptive Planning for Multi-Attribute Controllable Summarization with Monte Carlo Tree Search, https://arxiv.org/abs/2509.26435
  • Zhuofeng Li, Haoxiang Zhang, Seungju Han, Sheng Liu, Jianwen Xie, Yu Zhang, Yejin Choi, James Zou, and Pan Lu, 7 Oct 2025, In-the-Flow Agentic System Optimization for Effective Planning and Tool Use, https://arxiv.org/abs/2510.05592
  • Periklis Mantenoglou, Rishi Hazra, Pedro Zuidberg Dos Martires, Luc De Raedt, 7 Oct 2025, LexiCon: a Benchmark for Planning under Temporal Constraints in Natural Language, https://arxiv.org/abs/2510.05972
  • Hsien-Chin Lin, Benjamin Matthias Ruppik, Carel van Niekerk, Chia-Hao Shen, Michael Heck, Nurul Lubis, Renato Vukovic, Shutong Feng, Milica Ga\v{s}i\'c, 7 Oct 2025, Prompt reinforcing for long-term planning of large language models, https://arxiv.org/abs/2510.05921
  • Yiyang Ling, Karan Owalekar, Oluwatobiloba Adesanya, Erdem B{\i}y{\i}k, Daniel Seita, 6 Oct 2025, IMPACT: Intelligent Motion Planning with Acceptable Contact Trajectories via Vision-Language Models, https://arxiv.org/abs/2503.10110
  • Zachary Ravichandran, Ignacio Hounie, Fernando Cladera, Alejandro Ribeiro, George J. Pappas, Vijay Kumar, 6 Oct 2025, Distilling On-device Language Models for Robot Planning with Minimal Human Intervention, https://arxiv.org/abs/2506.17486
  • Xikai Zhang, Bo Wang, Likang Xiao, Yongzhi Li, Quan Chen, Wenju Wu, Liu Liu, 16 Oct 2025, IMAGINE: Integrating Multi-Agent System into One Model for Complex Reasoning and Planning, https://arxiv.org/abs/2510.14406
  • Luis Gonz\'alez-Gudi\~no, Mariona Jaramillo-Civill, Pau Closas, Tales Imbiriba, 16 Oct 2025, Active Jammer Localization via Acquisition-Aware Path Planning, https://arxiv.org/abs/2510.14790
  • Shuang Ao, Flora D. Salim, Simon Khan, 16 Oct 2025, EMAC+: Embodied Multimodal Agent for Collaborative Planning with VLM+LLM, https://arxiv.org/abs/2505.19905
  • Wanjing Huang, Weixiang Yan, Zhen Zhang and Ambuj Singh, 16 Oct 2025, APEX: Empowering LLMs with Physics-Based Task Planning for Real-time Insight, https://arxiv.org/abs/2505.13921

LLM Long Term Memory

LLM Long Term Memory refers to having the LLM "remember" things that it has learned during inference. By default, an LLM is "stateless" and does not recall facts between queries. Short-term memory can be given via tracking conversational history as "context" for a query, but long term memory is the aim of having an LLM "learn" or "memorize" new facts. Note that this research area is about accuracy of the output, not about the speed optimization of LLM inference memory efficiency.

Research on LLM long term memory:

Agentic Workflow

Agentic workflow has some aspects of reasoning (e.g., planning, multi-step execution) combined with agent technologies. Papers on agentic workflow include:

Temporal Reasoning (Time-Based Logic)

AI models struggle with the concept of time and any sort of "temporal reasoning" that is based on time progression or causation over time.

  • Jonas Wallat, Adam Jatowt, Avishek Anand, March 2024, Temporal Blind Spots in Large Language Models, WSDM '24: Proceedings of the 17th ACM International Conference on Web Search and Data Mining, Pages 683–692, https://arxiv.org/abs/2401.12078, https://doi.org/10.1145/3616855.3635818, https://dl.acm.org/doi/abs/10.1145/3616855.3635818
  • Siheng Xiong, Ali Payani, Ramana Kompella, Faramarz Fekri, 22 Apr 2024 (v3), Large Language Models Can Learn Temporal Reasoning, https://arxiv.org/abs/2401.06853
  • Bowen Zhao, Zander Brumbaugh, Yizhong Wang, Hannaneh Hajishirzi, Noah A. Smith, 26 Feb 2024, Set the Clock: Temporal Alignment of Pretrained Language Models, https://arxiv.org/abs/2402.16797 Code: https://github.com/yizhongw/llm-temporal-alignment
  • 16 Nov 2023, Towards Robust Temporal Reasoning of Large Language Models via a Multi-Hop QA Dataset and Pseudo-Instruction Tuning, Qingyu Tan, Hwee Tou Ng, Lidong Bing, https://arxiv.org/abs/2311.09821
  • Yifu Qiu, Zheng Zhao, Yftah Ziser, Anna Korhonen, Edoardo M. Ponti, Shay B. Cohen, 16 Nov 2023 (v2), Are Large Language Models Temporally Grounded? https://arxiv.org/abs/2311.08398 Code: https://github.com/yfqiu-nlp/temporal-llms
  • Raghav Jain, Daivik Sojitra, Arkadeep Acharya, Sriparna Saha, Adam Jatowt, Sandipan Dandapat, December 2023, Do Language Models Have a Common Sense regarding Time? Revisiting Temporal Commonsense Reasoning in the Era of Large Language Models, Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing https://aclanthology.org/2023.emnlp-main.418/ PDF: https://aclanthology.org/2023.emnlp-main.418.pdf
  • Yifan Wei, Yisong Su, Huanhuan Ma, Xiaoyan Yu, Fangyu Lei, Yuanzhe Zhang, Jun Zhao, Kang Liu, 8 Oct 2023, MenatQA: A New Dataset for Testing the Temporal Comprehension and Reasoning Abilities of Large Language Models, https://arxiv.org/abs/2310.05157
  • Himanshu Beniwal, Kowsik Nandagopan D, Mayank Singh, 19 Feb 2024, Remember This Event That Year? Assessing Temporal Information and Reasoning in Large Language Models, https://arxiv.org/abs/2402.11997
  • Bahare Fatemi, Mehran Kazemi, Anton Tsitsulin, Karishma Malkan, Jinyeong Yim, John Palowitch, Sungyong Seo, Jonathan Halcrow, Bryan Perozzi, 13 Jun 2024, Test of Time: A Benchmark for Evaluating LLMs on Temporal Reasoning, https://arxiv.org/abs/2406.09170
  • Irwin Deng, Kushagra Dixit, Vivek Gupta, Dan Roth, 22 Jul 2024, Enhancing Temporal Understanding in LLMs for Semi-structured Tables, https://arxiv.org/abs/2407.16030
  • Dimitris Spathis, Fahim Kawsar, The first step is the hardest: pitfalls of representing and tokenizing temporal data for large language models, Journal of the American Medical Informatics Association, Volume 31, Issue 9, September 2024, Pages 2151–2158, https://doi.org/10.1093/jamia/ocae090 https://academic.oup.com/jamia/advance-article-abstract/doi/10.1093/jamia/ocae090/7702405?redirectedFrom=fulltext
  • 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.)
  • Yubin Ge, Salvatore Romeo, Jason Cai, Raphael Shu, Monica Sunkara, Yassine Benajiba, Yi Zhang, 3 Feb 2025, TReMu: Towards Neuro-Symbolic Temporal Reasoning for LLM-Agents with Memory in Multi-Session Dialogues, https://arxiv.org/abs/2502.01630
  • Jongho Kim, Seung-won Hwang, 17 Feb 2025, Counterfactual-Consistency Prompting for Relative Temporal Understanding in Large Language Models, https://arxiv.org/abs/2502.11425
  • Ningke Li, Yahui Song, Kailong Wang, Yuekang Li, Ling Shi, Yi Liu, Haoyu Wang, 19 Feb 2025, Detecting LLM Fact-conflicting Hallucinations Enhanced by Temporal-logic-based Reasoning, https://arxiv.org/abs/2502.13416
  • Yuhan Xie, William Cappelletti, Mahsa Shoaran and Pascal Frossard, 13 Aug 2025, rETF-semiSL: Semi-Supervised Learning for Neural Collapse in Temporal Data, https://arxiv.org/abs/2508.10147
  • Xuanhao Mu, G\"okhan Demirel, Yuzhe Zhang, Jianlei Liu, Thorsten Schlachter and Veit Hagenmeyer, 14 Aug 2025, Self-Supervised Temporal Super-Resolution of Energy Data using Generative Adversarial Transformer, https://arxiv.org/abs/2508.10587
  • Qianru Zhang, Xinyi Gao, Haixin Wang, Dong Huang, Siu-Ming Yiu and Hongzhi Yin, 14 Aug 2025, HGAurban: Heterogeneous Graph Autoencoding for Urban Spatial-Temporal Learning, https://arxiv.org/abs/2410.10915
  • Chandra Raskoti, Iftekharul Islam, Xuan Wang, and Weizi Li, 13 Aug 2025, MIAT: Maneuver-Intention-Aware Transformer for Spatio-Temporal Trajectory Prediction, https://arxiv.org/abs/2504.05059
  • Luca Salvatore Lorello, Nikolaos Manginas, Marco Lippi, Stefano Melacci, 23 Jul 2025, LTLZinc: a Benchmarking Framework for Continual Learning and Neuro-Symbolic Temporal Reasoning, https://arxiv.org/abs/2507.17482
  • Shaohan Li, Hao Yang, Min Chen, Xiaolin Qin, 23 Jul 2025, Met$^2$Net: A Decoupled Two-Stage Spatio-Temporal Forecasting Model for Complex Meteorological Systems, https://arxiv.org/abs/2507.17189
  • Tobias Morocutti, Jonathan Greif, Paul Primus, Florian Schmid, Gerhard Widmer, 23 Jul 2025, On Temporal Guidance and Iterative Refinement in Audio Source Separation, https://arxiv.org/abs/2507.17297
  • Weihua Gao, Chunxu Ren, Wenlong Niu, Xiaodong Peng, 23 Jul 2025, Temporal Point-Supervised Signal Reconstruction: A Human-Annotation-Free Framework for Weak Moving Target Detection, https://arxiv.org/abs/2507.17334
  • Jianhao Chen, Junyang Ren, Wentao Ding, Haoyuan Ouyang, Wei Hu, Yuzhong Qu, 23 Jul 2025, Conflict Detection for Temporal Knowledge Graphs:A Fast Constraint Mining Algorithm and New Benchmarks, https://arxiv.org/abs/2312.11053
  • Guangqiang Li, M. Amine Atoui and Xiangshun Li, 23 Jul 2025, Attention-Based Multiscale Temporal Fusion Network for Uncertain-Mode Fault Diagnosis in Multimode Processes, https://arxiv.org/abs/2504.05172
  • Pascal K\"undig, Fabio Sigrist, 23 Jul 2025, A Spatio-Temporal Machine Learning Model for Mortgage Credit Risk: Default Probabilities and Loan Portfolios, https://arxiv.org/abs/2410.02846
  • Xi Yang, Jiachen Wang, Song Han, Suining He, 21 Jul 2025, Micromobility Flow Prediction: A Bike Sharing Station-level Study via Multi-level Spatial-Temporal Attention Neural Network, https://arxiv.org/abs/2507.16020
  • Chang Li, Yaren Zhang, Haoran Lv, Qiong Cao, Chao Xue, Xiaodong He, 22 Jul 2025, Learning Temporal Abstractions via Variational Homomorphisms in Option-Induced Abstract MDPs, https://arxiv.org/abs/2507.16473
  • Zixiao Huang, Junhao Hu, Hao Lin, Chunyang Zhu, Yueran Tang, Quanlu Zhang, Zhen Guo, Zhenhua Li, Shengen Yan, Zhenhua Zhu, Guohao Dai, Yu Wang, 22 Jul 2025, Reducing GPU Memory Fragmentation via Spatio-Temporal Planning for Efficient Large-Scale Model Training, https://arxiv.org/abs/2507.16274
  • Alireza Dizaji, Benedict Aaron Tjandra, Mehrab Hamidi, Shenyang Huang, Guillaume Rabusseau, 22 Jul 2025, T-GRAB: A Synthetic Diagnostic Benchmark for Learning on Temporal Graphs, https://arxiv.org/abs/2507.10183
  • Shiyuan Zhang, Tong Li, Zhu Xiao, Hongyang Du, Kaibin Huang, 23 Jul 2025, LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction, https://arxiv.org/abs/2507.17795
  • Jianchao Wang, Qingfeng Li, Pengcheng Zheng, Xiaorong Pu, Yazhou Ren, 24 Jul 2025, ChronoSelect: Robust Learning with Noisy Labels via Dynamics Temporal Memory, https://arxiv.org/abs/2507.18183
  • Ruizhe Chen, Zhiting Fan, Tianze Luo, Heqing Zou, Zhaopeng Feng, Guiyang Xie, Hansheng Zhang, Zhuochen Wang, Zuozhu Liu, Huaijian Zhang, 24 Jul 2025, Datasets and Recipes for Video Temporal Grounding via Reinforcement Learning, https://arxiv.org/abs/2507.18100
  • Edward Fish and Andrew Gilbert, 24 Jul 2025, PLOT-TAL: Prompt Learning with Optimal Transport for Few-Shot Temporal Action Localization, https://arxiv.org/abs/2403.18915
  • Haoyang Li, Yuming Xu, Yiming Li, Hanmo Liu, Darian Li, Chen Jason Zhang, Lei Chen, Qing Li, 18 Jul 2025, When Speed meets Accuracy: an Efficient and Effective Graph Model for Temporal Link Prediction, https://arxiv.org/abs/2507.13825
  • Pedro Cabalar, Mart\'in Di\'eguez, Fran\c{c}ois Olivier, Torsten Schaub and Igor St\'ephan, 18 Jul 2025, Towards Constraint Temporal Answer Set Programming, https://arxiv.org/abs/2507.13958
  • Itay Katav, Aryeh Kontorovich, 18 Jul 2025, ParallelTime: Dynamically Weighting the Balance of Short- and Long-Term Temporal Dependencies, https://arxiv.org/abs/2507.13998
  • Jianhong Chen, Meng Zhao, Mostafa Reisi Gahrooei, Xubo Yue, 18 Jul 2025, Toward Temporal Causal Representation Learning with Tensor Decomposition, https://arxiv.org/abs/2507.14126
  • Sirui Wang, Zhou Guan, Bingxi Zhao, Tongjia Gu, 17 Jul 2025, CaSTFormer: Causal Spatio-Temporal Transformer for Driving Intention Prediction, https://arxiv.org/abs/2507.13425
  • Garapati Keerthana, Manik Gupta, 18 Jul 2025, DENSE: Longitudinal Progress Note Generation with Temporal Modeling of Heterogeneous Clinical Notes Across Hospital Visits, https://arxiv.org/abs/2507.14079
  • Haoyu He, Haozheng Luo, Yan Chen, Qi R. Wang, 18 Jul 2025, Efficient Temporal Tokenization for Mobility Prediction with Large Language Models, https://arxiv.org/abs/2507.14017
  • Jiayu Song, Mahmud Elahi Akhter, Dana Atzil Slonim, Maria Liakata, 18 Jul 2025, Temporal reasoning for timeline summarisation in social media, https://arxiv.org/abs/2501.00152
  • Lingyu Li, Yang Yao, Yixu Wang, Chubo Li, Yan Teng, Yingchun Wang, 21 Jul 2025, The Other Mind: How Language Models Exhibit Human Temporal Cognition, https://arxiv.org/abs/2507.15851
  • Xuetao Lin (1 and 2), Tianhao Peng (1 and 2), Peihong Dai (1 and 2), Yu Liang (3), Wenjun Wu (1 and 2) ((1) Beihang University, Beijing, China, (2) SKLCCSE, Beijing, China, (3) Beijing University of Technology, Beijing, China), 19 Jul 2025, Spatial-Temporal Transformer with Curriculum Learning for EEG-Based Emotion Recognition, https://arxiv.org/abs/2507.14698
  • Mehak Arora, Ayman Ali, Kaiyuan Wu, Carolyn Davis, Takashi Shimazui, Mahmoud Alwakeel, Victor Moas, Philip Yang, Annette Esper, Rishikesan Kamaleswaran, 19 Jul 2025, CXR-TFT: Multi-Modal Temporal Fusion Transformer for Predicting Chest X-ray Trajectories, https://arxiv.org/abs/2507.14766
  • Rabia Latief Bhat and Iqra Altaf Gillani, 21 Jul 2025, Spatio-Temporal Demand Prediction for Food Delivery Using Attention-Driven Graph Neural Networks, https://arxiv.org/abs/2507.15246
  • Matthew J. Bryan, Felix Schwock, Azadeh Yazdan-Shahmorad, Rajesh P N Rao, 21 Jul 2025, Temporal Basis Function Models for Closed-Loop Neural Stimulation, https://arxiv.org/abs/2507.15274
  • Xinxin Dong, Baoyun Peng, Haokai Ma, Yufei Wang, Zixuan Dong, Fei Hu, Xiaodong Wang, 20 Jul 2025, LeAdQA: LLM-Driven Context-Aware Temporal Grounding for Video Question Answering, https://arxiv.org/abs/2507.14784
  • Shaohang Wei, Wei Li, Feifan Song, Wen Luo, Tianyi Zhuang, Haochen Tan, Zhijiang Guo, Houfeng Wang, 19 Jul 2025, TIME: A Multi-level Benchmark for Temporal Reasoning of LLMs in Real-World Scenarios, https://arxiv.org/abs/2505.12891
  • Duygu Sezen Islakoglu, Jan-Christoph Kalo, 21 Jul 2025, ChronoSense: Exploring Temporal Understanding in Large Language Models with Time Intervals of Events, https://arxiv.org/abs/2501.03040
  • Luo Ji, Gao Liu, Mingyang Yin, Hongxia Yang, Jingren Zhou, 19 Jul 2025, Hierarchical Reinforcement Learning for Temporal Abstraction of Listwise Recommendation, https://arxiv.org/abs/2409.07416
  • Yijing Lin, Mengqi Huang, Shuhan Zhuang, Zhendong Mao, 20 Jul 2025, RealGeneral: Unifying Visual Generation via Temporal In-Context Learning with Video Models, https://arxiv.org/abs/2503.10406
  • Bo-Cheng Chiu, Jen-Jee Chen, Yu-Chee Tseng and Feng-Chi Chen, 21 Jul 2025, DaMO: A Data-Efficient Multimodal Orchestrator for Temporal Reasoning with Video LLMs, https://arxiv.org/abs/2506.11558
  • Yiming Yang, Yueru Luo, Bingkun He, Hongbin Lin, Suzhong Fu, Chao Zheng, Zhipeng Cao, Erlong Li, Chao Yan, Shuguang Cui, Zhen Li, 20 Jul 2025, TopoStreamer: Temporal Lane Segment Topology Reasoning in Autonomous Driving, https://arxiv.org/abs/2507.00709
  • Agnideep Aich, Ashit Baran Aich, Dipak C. Jain, 21 Jul 2025, Temporal Conformal Prediction (TCP): A Distribution-Free Statistical and Machine Learning Framework for Adaptive Risk Forecasting, https://arxiv.org/abs/2507.05470
  • Zhaoyu Chen, Hongnan Lin, Yongwei Nie, Fei Ma, Xuemiao Xu, Fei Yu, Chengjiang Long, 10 Aug 2025, Invert4TVG: A Temporal Video Grounding Framework with Inversion Tasks for Enhanced Action Understanding, https://arxiv.org/abs/2508.07388
  • Jie Li, Haoye Dong, Zhengyang Wu, Zetao Zheng, Mingrong Lin, 11 Aug 2025, Disentangling Multiplex Spatial-Temporal Transition Graph Representation Learning for Socially Enhanced POI Recommendation, https://arxiv.org/abs/2508.07649
  • Yiran Huang, Amirhossein Nouranizadeh, Christine Ahrends, Mengjia Xu, 9 Aug 2025, BrainATCL: Adaptive Temporal Brain Connectivity Learning for Functional Link Prediction and Age Estimation, https://arxiv.org/abs/2508.07106
  • Sichen Zhao, Wei Shao, Jeffrey Chan, Ziqi Xu, Flora Salim, 11 Aug 2025, FairDRL-ST: Disentangled Representation Learning for Fair Spatio-Temporal Mobility Prediction, https://arxiv.org/abs/2508.07518
  • Mohammed-Khalil Ghali, Cecil Pang, Oscar Molina, Carlos Gershenson-Garcia, Daehan Won, 24 Jul 2025, Forecasting Commodity Price Shocks Using Temporal and Semantic Fusion of Prices Signals and Agentic Generative AI Extracted Economic News, https://arxiv.org/abs/2508.06497
  • Zihao Sheng, Zilin Huang, Yen-Jung Chen, Yansong Qu, Yuhao Luo, Yue Leng, Sikai Chen, 9 Aug 2025, SafePLUG: Empowering Multimodal LLMs with Pixel-Level Insight and Temporal Grounding for Traffic Accident Understanding, https://arxiv.org/abs/2508.06763
  • Yanru Sun, Emadeldeen Eldele, Zongxia Xie, Yucheng Wang, Wenzhe Niu, Qinghua Hu, Chee Keong Kwoh, Min Wu, 10 Aug 2025, Adapting LLMs to Time Series Forecasting via Temporal Heterogeneity Modeling and Semantic Alignment, https://arxiv.org/abs/2508.07195
  • Chaohong Guo, Xun Mo, Yongwei Nie, Xuemiao Xu, Chao Xu, Fei Yu, and Chengjiang Long, 11 Aug 2025, TAR-TVG: Enhancing VLMs with Timestamp Anchor-Constrained Reasoning for Temporal Video Grounding, https://arxiv.org/abs/2508.07683
  • Zhuqiang Lu, Zhenfei Yin, Mengwei He, Zhihui Wang, Zicheng Liu, Zhiyong Wang and Kun Hu, 11 Aug 2025, B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens, https://arxiv.org/abs/2412.09919
  • Jiawen Qi, Chang Gao, Zhaochun Ren, Qinyu Chen, 25 Jul 2025, DeltaLLM: A Training-Free Framework Exploiting Temporal Sparsity for Efficient Edge LLM Inference, https://arxiv.org/abs/2507.19608
  • Pritom Ray Nobin, Imran Ahammad Rifat, 28 Jul 2025, STARN-GAT: A Multi-Modal Spatio-Temporal Graph Attention Network for Accident Severity Prediction, https://arxiv.org/abs/2507.20451
  • Yongzheng Liu, Yiming Wang, Po Xu, Yingjie Xu, Yuntian Chen, Dongxiao Zhang, 28 Jul 2025, BuildSTG: A Multi-building Energy Load Forecasting Method using Spatio-Temporal Graph Neural Network, https://arxiv.org/abs/2507.20838
  • Gongli Xi, Ye Tian, Yannan Hu, Yuchao Zhang, Yapeng Niu and Xiangyang Gong, 27 Jul 2025, Packet-Level DDoS Data Augmentation Using Dual-Stream Temporal-Field Diffusion, https://arxiv.org/abs/2507.20115
  • Javier Sol\'is-Garc\'ia, Bel\'en Vega-M\'arquez, Juan A. Nepomuceno, Isabel A. Nepomuceno-Chamorro, 26 Jul 2025, CoSTI: Consistency Models for (a faster) Spatio-Temporal Imputation, https://arxiv.org/abs/2501.19364
  • Dyuman Aditya, Colton Payne, Mario Leiva, Paulo Shakarian, 27 Jul 2025, Machine Learning Model Integration with Open World Temporal Logic for Process Automation, https://arxiv.org/abs/2506.17776
  • Lei Zheng, Ning Li, Weinan Zhang, Yong Yu, 27 Jul 2025, Retrieval and Distill: A Temporal Data Shift-Free Paradigm for Online Recommendation System, https://arxiv.org/abs/2404.15678
  • Yu Tai, Xinglong Wu, Hongwei Yang, Hui He, Duanjing Chen, Yuanming Shao and Weizhe Zhang, 28 Jul 2025, How to Bridge Spatial and Temporal Heterogeneity in Link Prediction? A Contrastive Method, https://arxiv.org/abs/2411.00612
  • Pallavi Zambare, Venkata Nikhil Thanikella, Ying Liu, 25 Jul 2025, Seeing Beyond Frames: Zero-Shot Pedestrian Intention Prediction with Raw Temporal Video and Multimodal Cues, https://arxiv.org/abs/2507.21161
  • Jing Ren, Suyu Ma, Hong Jia, Xiwei Xu, Ivan Lee, Haytham Fayek, Xiaodong Li, Feng Xia, 29 Jul 2025, LiteFat: Lightweight Spatio-Temporal Graph Learning for Real-Time Driver Fatigue Detection, https://arxiv.org/abs/2507.21756
  • Zhengpeng Feng, Clement Atzberger, Sadiq Jaffer, Jovana Knezevic, Silja Sormunen, Robin Young, Madeline C Lisaius, Markus Immitzer, David A. Coomes, Anil Madhavapeddy, Andrew Blake and Srinivasan Keshav, 29 Jul 2025, TESSERA: Temporal Embeddings of Surface Spectra for Earth Representation and Analysis, https://arxiv.org/abs/2506.20380
  • Boyuan Zheng and Victor W. Chu, 30 Jul 2025, Multi-Hazard Early Warning Systems for Agriculture with Featural-Temporal Explanations, https://arxiv.org/abs/2507.22962
  • Sofiane Bouaziz, Adel Hafiane, Raphael Canals, Rachid Nedjai, 30 Jul 2025, FuseTen: A Generative Model for Daily 10 m Land Surface Temperature Estimation from Spatio-Temporal Satellite Observations, https://arxiv.org/abs/2507.23154
  • Molly Wang, Kin.K Leung, 31 Jul 2025, Spatial-Temporal Reinforcement Learning for Network Routing with Non-Markovian Traffic, https://arxiv.org/abs/2507.22174
  • Shahla John, 30 Jul 2025, Efficient Spatial-Temporal Modeling for Real-Time Video Analysis: A Unified Framework for Action Recognition and Object Tracking, https://arxiv.org/abs/2507.22421
  • Mohammed Kamran, Maria Bernathova, Raoul Varga, Christian Singer, Zsuzsanna Bago-Horvath, Thomas Helbich, Georg Langs, Philipp Seeb\"ock, 1 Aug 2025, LesiOnTime -- Joint Temporal and Clinical Modeling for Small Breast Lesion Segmentation in Longitudinal DCE-MRI, https://arxiv.org/abs/2508.00496
  • Camille Bourgaux, Anton Gnatenko, Micha\"el Thomazo, 1 Aug 2025, Analysing Temporal Reasoning in Description Logics Using Formal Grammars, https://arxiv.org/abs/2508.00575
  • Mingyu Kang, Duxin Chen, Ning Meng, Gang Yan and Wenwu Yu, 1 Aug 2025, Identifying Unique Spatial-Temporal Bayesian Network without Markov Equivalence, https://arxiv.org/abs/2211.10085
  • Yujing Ke and Kevin George and Kathan Pandya and David Blumenthal and Maximilian Sprang and Gerrit Gro{\ss}mann and Sebastian Vollmer and David Antony Selby, 2 Aug 2025, BioDisco: Multi-agent hypothesis generation with dual-mode evidence, iterative feedback and temporal evaluation, https://arxiv.org/abs/2508.01285
  • Jingtian Yan, Stephen F. Smith, Jiaoyang Li, 2 Aug 2025, WinkTPG: An Execution Framework for Multi-Agent Path Finding Using Temporal Reasoning, https://arxiv.org/abs/2508.01495
  • Zijian Guo, \.Ilker I\c{s}{\i}k, H. M. Sabbir Ahmad, Wenchao Li, 3 Aug 2025, One Subgoal at a Time: Zero-Shot Generalization to Arbitrary Linear Temporal Logic Requirements in Multi-Task Reinforcement Learning, https://arxiv.org/abs/2508.01561
  • Dong Li, Yichen Niu, Ying Ai, Xiang Zou, Biqing Qi, Jianxing Liu, 3 Aug 2025, T-GRAG: A Dynamic GraphRAG Framework for Resolving Temporal Conflicts and Redundancy in Knowledge Retrieval, https://arxiv.org/abs/2508.01680
  • Zhenan Lin, Yuni Lai, Wai Lun Lo, Richard Tai-Chiu Hsung, Harris Sik-Ho Tsang, Xiaoyu Xue, Kai Zhou, Yulin Zhu, 25 Jul 2025, Multi-Grained Temporal-Spatial Graph Learning for Stable Traffic Flow Forecasting, https://arxiv.org/abs/2508.00884
  • Bang Hu, Changze Lv, Mingjie Li, Yunpeng Liu, Xiaoqing Zheng, Fengzhe Zhang, Wei cao, Fan Zhang, 4 Aug 2025, SpikeSTAG: Spatial-Temporal Forecasting via GNN-SNN Collaboration, https://arxiv.org/abs/2508.02069
  • Wei Hao, Bin Chong, Ronghua Ji, and Chen Hou, 4 Aug 2025, User Trajectory Prediction Unifying Global and Local Temporal Information, https://arxiv.org/abs/2508.02161
  • Erhang Zhang, Junyi Ma, Yin-Dong Zheng, Yixuan Zhou, Hesheng Wang, 4 Jun 2025, Zero-Shot Temporal Interaction Localization for Egocentric Videos, https://arxiv.org/abs/2506.03662
  • Jose M. S\'anchez Vel\'azquez, Mingbo Cai, Andrew Coney, \'Alvaro J. Garc\'ia- Tejedor, Alberto Nogales, 28 Jul 2025, Benefits of Feature Extraction and Temporal Sequence Analysis for Video Frame Prediction: An Evaluation of Hybrid Deep Learning Models, https://arxiv.org/abs/2508.00898
  • Weihong Li, Shaohua Dong, Haonan Lu, Yanhao Zhang, Heng Fan, Libo Zhang, 3 Aug 2025, DMTrack: Spatio-Temporal Multimodal Tracking via Dual-Adapter, https://arxiv.org/abs/2508.01592
  • Zhaoyu Hu, Hao Guo, Yuan Tian, Erpeng Xue, Jianyang Wang, Xianyang Qi, Hongxiang Lin, Lei Wang, Sheng Chen, 4 Aug 2025, Dynamic Forgetting and Spatio-Temporal Periodic Interest Modeling for Local-Life Service Recommendation, https://arxiv.org/abs/2508.02451
  • Yixuan He, Aaron Sandel, David Wipf, Mihai Cucuringu, John Mitani, Gesine Reinert, 3 Aug 2025, Learning to Fuse Temporal Proximity Networks: A Case Study in Chimpanzee Social Interactions, https://arxiv.org/abs/2502.00302
  • Fengbin Zhu, Junfeng Li, Liangming Pan, Wenjie Wang, Fuli Feng, Chao Wang, Huanbo Luan, Tat-Seng Chua, 3 Aug 2025, Towards Temporal-Aware Multi-Modal Retrieval Augmented Generation in Finance, https://arxiv.org/abs/2503.05185
  • Yihe Wang, Nadia Mammone, Darina Petrovsky, Alexandros T. Tzallas, Francesco C. Morabito, Xiang Zhang, 4 Aug 2025, ADformer: A Multi-Granularity Spatial-Temporal Transformer for EEG-Based Alzheimer Detection, https://arxiv.org/abs/2409.00032
  • Osama Mohammed, Jiaxin Pan, Mojtaba Nayyeri, Daniel Hern\'andez and Steffen Staab, 5 Aug 2025, Full-History Graphs with Edge-Type Decoupled Networks for Temporal Reasoning, https://arxiv.org/abs/2508.03251
  • Irene Ferfoglia, Simone Silvetti, Gaia Saveri, Laura Nenzi, Luca Bortolussi, 5 Aug 2025, Towards Interpretable Concept Learning over Time Series via Temporal Logic Semantics, https://arxiv.org/abs/2508.03269
  • Evangelos Sariyanidi, John D. Herrington, Lisa Yankowitz, Pratik Chaudhari, Theodore D. Satterthwaite, Casey J. Zampella, Robert T. Schultz, Russell T. Shinohara, Birkan Tunc, 29 Jul 2025, Measuring Dependencies between Biological Signals with Temporal Self-supervision, and its Limitations, https://arxiv.org/abs/2508.02703
  • Yi Zhang, Nikolaos Farmakidis, Ioannis Roumpos, Miltiadis Moralis-Pegios, Apostolos Tsakyridis, June Sang Lee, Bowei Dong, Yuhan He, Samarth Aggarwal, Nikolaos Pleros and Harish Bhaskaran, 5 Aug 2025, All-optical temporal integration mediated by subwavelength heat antennas, https://arxiv.org/abs/2505.04405
  • Amin Farajzadeh, Hongzhao Zheng, Sarah Dumoulin, Trevor Ha, Halim Yanikomeroglu, Amir Ghasemi, 5 Aug 2025, Data-Driven Spectrum Demand Prediction: A Spatio-Temporal Framework with Transfer Learning, https://arxiv.org/abs/2508.03863
  • Krishnakanta Barik and Goutam Paul, 6 Aug 2025, Quantum Temporal Fusion Transformer, https://arxiv.org/abs/2508.04048
  • Xiangzhe Xu, Guangyu Shen, Zian Su, Siyuan Cheng, Hanxi Guo, Lu Yan, Xuan Chen, Jiasheng Jiang, Xiaolong Jin, Chengpeng Wang, Zhuo Zhang, Xiangyu Zhang, 5 Aug 2025, ASTRA: Autonomous Spatial-Temporal Red-teaming for AI Software Assistants, https://arxiv.org/abs/2508.03936
  • Zhihao Wen, Yuan Fang, Pengcheng Wei, Fayao Liu, Zhenghua Chen, Min Wu, 6 Aug 2025, Temporal and Heterogeneous Graph Neural Network for Remaining Useful Life Prediction, https://arxiv.org/abs/2405.04336
  • Keivan Faghih Niresi, Ismail Nejjar, Olga Fink, 6 Aug 2025, Efficient Unsupervised Domain Adaptation Regression for Spatial-Temporal Sensor Fusion, https://arxiv.org/abs/2411.06917
  • Chin-Chia Michael Yeh, Xiran Fan, Zhimeng Jiang, Yujie Fan, Huiyuan Chen, Uday Singh Saini, Vivian Lai, Xin Dai, Junpeng Wang, Zhongfang Zhuang, Liang Wang, Yan Zheng, 6 Aug 2025, UltraSTF: Ultra-Compact Model for Large-Scale Spatio-Temporal Forecasting, https://arxiv.org/abs/2502.20634
  • Luis Mandl and Dibyajyoti Nayak and Tim Ricken and Somdatta Goswami, 7 Aug 2025, Physics-Informed Time-Integrated DeepONet: Temporal Tangent Space Operator Learning for High-Accuracy Inference, https://arxiv.org/abs/2508.05190
  • Shuonan Yang, Tailin Chen, Rahul Singh, Jiangbei Yue, Jianbo Jiao, Zeyu Fu, 6 Aug 2025, Revealing Temporal Label Noise in Multimodal Hateful Video Classification, https://arxiv.org/abs/2508.04900
  • Zhu Xu, Ting Lei, Zhimin Li, Guan Wang, Qingchao Chen, Yuxin Peng, Yang liu, 7 Aug 2025, TRKT: Weakly Supervised Dynamic Scene Graph Generation with Temporal-enhanced Relation-aware Knowledge Transferring, https://arxiv.org/abs/2508.04943
  • Long Yang, Lianqing Zheng, Wenjin Ai, Minghao Liu, Sen Li, Qunshu Lin, Shengyu Yan, Jie Bai, Zhixiong Ma, Tao Huang and Xichan Zhu, 7 Aug 2025, MetaOcc: Spatio-Temporal Fusion of Surround-View 4D Radar and Camera for 3D Occupancy Prediction with Dual Training Strategies, https://arxiv.org/abs/2501.15384
  • Serkan Sulun, Paula Viana, Matthew E. P. Davies, 7 Aug 2025, Video Soundtrack Generation by Aligning Emotions and Temporal Boundaries, https://arxiv.org/abs/2502.10154
  • Wenhao Dong, Yueyang Li, Weiming Zeng, Lei Chen, Hongjie Yan, Wai Ting Siok, and Nizhuan Wang, 7 Aug 2025, STARFormer: A Novel Spatio-Temporal Aggregation Reorganization Transformer of FMRI for Brain Disorder Diagnosis, https://arxiv.org/abs/2501.00378
  • Barak Gahtan, Alex M. Bronstein, 8 Aug 2025, Architecture-Aware Generalization Bounds for Temporal Networks: Theory and Fair Comparison Methodology, https://arxiv.org/abs/2508.06066
  • Yidong Wang, Xin Wang, Cunxiang Wang, Junfeng Fang, Qiufeng Wang, Jianing Chu, Xuran Meng, Shuxun Yang, Libo Qin, Yue Zhang, Wei Ye, Shikun Zhang, 8 Aug 2025, Temporal Self-Rewarding Language Models: Decoupling Chosen-Rejected via Past-Future, https://arxiv.org/abs/2508.06026
  • Sofiane Bouaziz, Adel Hafiane, Raphael Canals, Rachid Nedjai, 8 Aug 2025, WGAST: Weakly-Supervised Generative Network for Daily 10 m Land Surface Temperature Estimation via Spatio-Temporal Fusion, https://arxiv.org/abs/2508.06485
  • Zibo Liu, Zhe Jiang, Zelin Xu, Tingsong Xiao, Zhengkun Xiao, Yupu zhang, Haibo Wang, and Shigang Chen, 8 Aug 2025, Spatio-Temporal Partial Sensing Forecast for Long-term Traffic, https://arxiv.org/abs/2408.02689
  • Ignatius Rollere, Caspian Hartsfield, Seraphina Courtenay, Lucian Fenwick, Aurelia Grunwald, 8 Aug 2025, Algorithmic Segmentation and Behavioral Profiling for Ransomware Detection Using Temporal-Correlation Graphs, https://arxiv.org/abs/2501.17429
  • Abhishek Rajgaria, Kushagra Dixit, Mayank Vyas, Harshavardhan Kalalbandi, Dan Roth, Vivek Gupta, 7 Aug 2025, No Universal Prompt: Unifying Reasoning through Adaptive Prompting for Temporal Table Reasoning, https://arxiv.org/abs/2506.11246
  • Ningning Fu, Shengheng Liu, Weiliang Xie, Yongming Huang, 1 Aug 2025, Multi-grained spatial-temporal feature complementarity for accurate online cellular traffic prediction, https://arxiv.org/abs/2508.08281
  • Milad Sabouri, Masoud Mansoury, Kun Lin, Bamshad Mobasher, 11 Aug 2025, Temporal User Profiling with LLMs: Balancing Short-Term and Long-Term Preferences for Recommendations, https://arxiv.org/abs/2508.08454
  • Milad Sabouri, Masoud Mansoury, Kun Lin, Bamshad Mobasher, 11 Aug 2025, Using LLMs to Capture Users' Temporal Context for Recommendation, https://arxiv.org/abs/2508.08512
  • Edith Elkind, Tzeh Yuan Neoh, Nicholas Teh, 12 Aug 2025, Not in My Backyard! Temporal Voting Over Public Chores, https://arxiv.org/abs/2508.08810
  • Ziyi Guo and Yan Wang, 12 Aug 2025, Urban-STA4CLC: Urban Theory-Informed Spatio-Temporal Attention Model for Predicting Post-Disaster Commercial Land Use Change, https://arxiv.org/abs/2508.08976
  • Maxim A. Patratskiy, Alexey K. Kovalev, Aleksandr I. Panov, 12 Aug 2025, Spatial Traces: Enhancing VLA Models with Spatial-Temporal Understanding, https://arxiv.org/abs/2508.09032
  • Wen Wang, Bozhen Fang, Chenchen Jing, Yongliang Shen, Yangyi Shen, Qiuyu Wang, Hao Ouyang, Hao Chen, Chunhua Shen, 12 Aug 2025, Time Is a Feature: Exploiting Temporal Dynamics in Diffusion Language Models, https://arxiv.org/abs/2508.09138
  • Yunhua Pei and John Cartlidge and Anandadeep Mandal and Daniel Gold and Enrique Marcilio and Riccardo Mazzon, 12 Aug 2025, Cross-Modal Temporal Fusion for Financial Market Forecasting, https://arxiv.org/abs/2504.13522
  • Victor Shea-Jay Huang, Le Zhuo, Yi Xin, Zhaokai Wang, Fu-Yun Wang, Yuchi Wang, Renrui Zhang, Peng Gao, Hongsheng Li, 12 Aug 2025, TIDE : Temporal-Aware Sparse Autoencoders for Interpretable Diffusion Transformers in Image Generation, https://arxiv.org/abs/2503.07050
  • Yanlai Yang and Mengye Ren, 11 Aug 2025, Memory Storyboard: Leveraging Temporal Segmentation for Streaming Self-Supervised Learning from Egocentric Videos, https://arxiv.org/abs/2501.12254
  • Yue Yao, Zhen Xu, Youzhu Liu, Kunyuan Ma, Yuxiu Lin, Mohan Jiang, 13 Aug 2025, Integrating Feature Attention and Temporal Modeling for Collaborative Financial Risk Assessment, https://arxiv.org/abs/2508.09399
  • Faruk Alpay, Bugra Kilictas, Hamdi Alakkad, 13 Aug 2025, Temporal Anchoring in Deepening Embedding Spaces: Event-Indexed Projections, Drift, Convergence, and an Internal Computational Architecture, https://arxiv.org/abs/2508.09693
  • Wouter M. Kouw, 13 Aug 2025, Bayesian autoregression to optimize temporal Mat\'ern kernel Gaussian process hyperparameters, https://arxiv.org/abs/2508.09792
  • Jihang Wang, Dongcheng Zhao, Ruolin Chen, Qian Zhang, Yi Zeng, 15 Aug 2025, Boosting the Robustness-Accuracy Trade-off of SNNs by Robust Temporal Self-Ensemble, https://arxiv.org/abs/2508.11279
  • Changhong Jing, Yan Liu, Shuqiang Wang, Bruce X.B. Yu, Gong Chen, Zhejing Hu, Zhi Zhang, Yanyan Shen, 15 Aug 2025, PTSM: Physiology-aware and Task-invariant Spatio-temporal Modeling for Cross-Subject EEG Decoding, https://arxiv.org/abs/2508.11357
  • Ahmad Mousavi, Yeganeh Abdollahinejad, Roberto Corizzo, Nathalie Japkowicz, and Zois Boukouvalas, 15 Aug 2025, E-CaTCH: Event-Centric Cross-Modal Attention with Temporal Consistency and Class-Imbalance Handling for Misinformation Detection, https://arxiv.org/abs/2508.11197
  • Rahmat K. Adesunkanmi, Ashfaq Khokhar, Goce Trajcevski, Sohail Murad, 17 Aug 2025, Root Cause Analysis of Hydrogen Bond Separation in Spatio-Temporal Molecular Dynamics using Causal Models, https://arxiv.org/abs/2508.12500
  • Xiangxiang Cui, Min Zhao, Dongmei Zhi, Shile Qi, Vince D Calhoun, Jing Sui, 15 Aug 2025, BRIEF: BRain-Inspired network connection search with Extensive temporal feature Fusion enhances disease classification, https://arxiv.org/abs/2508.11732
  • Sishun Liu, Ke Deng, Xiuzhen Zhang, Yan Wang, 16 Aug 2025, Learning Marked Temporal Point Process Explanations based on Counterfactual and Factual Reasoning, https://arxiv.org/abs/2508.11943
  • Haolong Chen, Liang Zhang, Zhengyuan Xin, Guangxu Zhu, 17 Aug 2025, STM3: Mixture of Multiscale Mamba for Long-Term Spatio-Temporal Time-Series Prediction, https://arxiv.org/abs/2508.12247
  • Ismail Lamaakal, Chaymae Yahyati, Khalid El Makkaoui, Ibrahim Ouahbi, Yassine Maleh, 18 Aug 2025, TCUQ: Single-Pass Uncertainty Quantification from Temporal Consistency with Streaming Conformal Calibration for TinyML, https://arxiv.org/abs/2508.12905
  • Alicja Ziarko, Michal Bortkiewicz, Michal Zawalski, Benjamin Eysenbach and Piotr Milos, 18 Aug 2025, Contrastive Representations for Temporal Reasoning, https://arxiv.org/abs/2508.13113
  • Yueyang Liu, Lance Kennedy, Ruochen Kong, Joon-Seok Kim, Andreas Z\"ufle, 18 Aug 2025, Training Machine Learning Models on Human Spatio-temporal Mobility Data: An Experimental Study [Experiment Paper], https://arxiv.org/abs/2508.13135
  • Friedhelm Hamann, Emil Mededovic, Fabian G\"ulhan, Yuli Wu, Johannes Stegmaier, Jing He, Yiqing Wang, Kexin Zhang, Lingling Li, Licheng Jiao, Mengru Ma, Hongxiang Huang, Yuhao Yan, Hongwei Ren, Xiaopeng Lin, Yulong Huang, Bojun Cheng, Se Hyun Lee, Gyu Sung Ham, Kanghan Oh, Gi Hyun Lim, Boxuan Yang, Bowen Du, Guillermo Gallego, 18 Aug 2025, SIS-Challenge: Event-based Spatio-temporal Instance Segmentation Challenge at the CVPR 2025 Event-based Vision Workshop, https://arxiv.org/abs/2508.12813
  • Yangchen Pan, Junfeng Wen, Chenjun Xiao, Philip Torr, 18 Aug 2025, An MRP Formulation for Supervised Learning: Generalized Temporal Difference Learning Models, https://arxiv.org/abs/2404.15518
  • Zhiyuan Zheng, Jianpeng Qi, Jiantao Li, Guoqing Chao, Junyu Dong, Yanwei Yu, 18 Aug 2025, Efficient Discovery of Motif Transition Process for Large-Scale Temporal Graphs, https://arxiv.org/abs/2504.15979
  • Jiayu Fang, Zhiqi Shao, S T Boris Choy, Junbin Gao, 19 Aug 2025, STPFormer: A State-of-the-Art Pattern-Aware Spatio-Temporal Transformer for Traffic Forecasting, https://arxiv.org/abs/2508.13433
  • Su Chen, Xiaohua Qi, Xixun Lin, Yanmin Shang, Xiaolin Xu and Yangxi Li, 17 Aug 2025, Deep Graph Neural Point Process For Learning Temporal Interactive Networks, https://arxiv.org/abs/2508.13219
  • Tinh-Anh Nguyen-Nhu, Triet Dao Hoang Minh, Dat To-Thanh, Phuc Le-Gia, Tuan Vo-Lan, Tien-Huy Nguyen, 19 Aug 2025, STER-VLM: Spatio-Temporal With Enhanced Reference Vision-Language Models, https://arxiv.org/abs/2508.13470
  • Zongyuan Huang, Weipeng Wang, Shaoyu Huang, Marta C. Gonzalez, Yaohui Jin, Yanyan Xu, 19 Aug 2025, Where to Go Next Day: Multi-scale Spatial-Temporal Decoupled Model for Mid-term Human Mobility Prediction, https://arxiv.org/abs/2501.06561
  • Qianang Zhou, Junhui Hou, Meiyi Yang, Yongjian Deng, Youfu Li, Junlin Xiong, 19 Aug 2025, Spatially-guided Temporal Aggregation for Robust Event-RGB Optical Flow Estimation, https://arxiv.org/abs/2501.00838
  • Songyu Ke and Chenyu Wu and Yuxuan Liang and Xiuwen Yi and Yanping Sun and Junbo Zhang and Yu Zheng, 13 Aug 2025, GeoMAE: Masking Representation Learning for Spatio-Temporal Graph Forecasting with Missing Values, https://arxiv.org/abs/2508.14083
  • Donghwa Kang, Doohyun Kim, Sang-Ki Ko, Jinkyu Lee, Brent ByungHoon Kang, Hyeongboo Baek, 19 Aug 2025, STAS: Spatio-Temporal Adaptive Computation Time for Spiking Transformers, https://arxiv.org/abs/2508.14138
  • Lian Lian, Yilin Li, Song Han, Renzi Meng, Sibo Wang, Ming Wang, 20 Aug 2025, Artificial Intelligence-Based Multiscale Temporal Modeling for Anomaly Detection in Cloud Services, https://arxiv.org/abs/2508.14503
  • Jiafeng Xiong and Rizos Sakellariou, 20 Aug 2025, Graph Structure Learning with Temporal Graph Information Bottleneck for Inductive Representation Learning, https://arxiv.org/abs/2508.14859
  • Anushka A. Kore, Frank G. te Nijenhuis, Matthijs van der Sluijs, Wim van Zwam, Charles Majoie, Geert Lycklama \`a Nijeholt, Danny Ruijters, Frans Vos, Sandra Cornelissen, Ruisheng Su, Theo van Walsum, 19 Aug 2025, OccluNet: Spatio-Temporal Deep Learning for Occlusion Detection on DSA, https://arxiv.org/abs/2508.14286
  • Peiming Li, Ziyi Wang, Yulin Yuan, Hong Liu, Xiangming Meng, Junsong Yuan, Mengyuan Liu, 20 Aug 2025, UST-SSM: Unified Spatio-Temporal State Space Models for Point Cloud Video Modeling, https://arxiv.org/abs/2508.14604
  • Yutian Liu, Zhengyi Yang, Jiancan Wu, Xiang Wang, 20 Aug 2025, Enhancing Temporal Sensitivity of Large Language Model for Recommendation with Counterfactual Tuning, https://arxiv.org/abs/2507.03047
  • Jiacheng Hu, Bo Zhang, Ting Xu, Haifeng Yang, Min Gao, 20 Aug 2025, Structure-Aware Temporal Modeling for Chronic Disease Progression Prediction, https://arxiv.org/abs/2508.14942
  • Haodi Zhong, Liuxin Zou, Di Wang, Bo Wang, Zhenxing Niu, Quan Wang, 21 Aug 2025, EvoFormer: Learning Dynamic Graph-Level Representations with Structural and Temporal Bias Correction, https://arxiv.org/abs/2508.15378
  • H. I. Nurdin and C. A. Nijhuis, 21 Aug 2025, A Solvable Molecular Switch Model for Stable Temporal Information Processing, https://arxiv.org/abs/2508.15451
  • Haibo Wang, Zhiyang Xu, Yu Cheng, Shizhe Diao, Yufan Zhou, Yixin Cao, Qifan Wang, Weifeng Ge, Lifu Huang, 21 Aug 2025, Grounded-VideoLLM: Sharpening Fine-grained Temporal Grounding in Video Large Language Models, https://arxiv.org/abs/2410.03290
  • Jihua Huang, Yi Yao and Ajay Divakaran, 21 Aug 2025, Transforming Causality: Transformer-Based Temporal Causal Discovery with Prior Knowledge Integration, https://arxiv.org/abs/2508.15928
  • Yujie Li, Zezhi Shao, Chengqing Yu, Tangwen Qian, Zhao Zhang, Yifan Du, Shaoming He, Fei Wang, Yongjun Xu, 22 Aug 2025, STA-GANN: A Valid and Generalizable Spatio-Temporal Kriging Approach, https://arxiv.org/abs/2508.16161
  • Nadia Asif and Zhiqing Hong and Shaogang Ren and Xiaonan Zhang and Xiaojun Shang and Yukun Yuan, 22 Aug 2025, MuST2-Learn: Multi-view Spatial-Temporal-Type Learning for Heterogeneous Municipal Service Time Estimation, https://arxiv.org/abs/2508.16503
  • Shunsuke Iwashita, Ning Ding, Keisuke Fujii, 25 Aug 2025, Evaluating Movement Initiation Timing in Ultimate Frisbee via Temporal Counterfactuals, https://arxiv.org/abs/2508.17611
  • Bangchao Deng, Lianhua Ji, Chunhua Chen, Xin Jing, Ling Ding, Bingqing QU, Pengyang Wang, Dingqi Yang, 14 Aug 2025, STRelay: A Universal Spatio-Temporal Relaying Framework for Location Prediction with Future Spatiotemporal Contexts, https://arxiv.org/abs/2508.16620
  • Weilin Ruan, Xilin Dang, Ziyu Zhou, Sisuo Lyu, Yuxuan Liang, 14 Aug 2025, A Retrieval Augmented Spatio-Temporal Framework for Traffic Prediction, https://arxiv.org/abs/2508.16623
  • Zhuding Liang, Jianxun Cui, Qingshuang Zeng, Feng Liu, Nenad Filipovic, Tijana Geroski, 21 Aug 2025, STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting, https://arxiv.org/abs/2508.16685
  • Bicheng Wang and Junping Wang and Yibo Xue, 22 Aug 2025, Physics-Inspired Spatial Temporal Graph Neural Networks for Predicting Industrial Chain Resilience, https://arxiv.org/abs/2508.16836
  • YongKyung Oh, Dong-Young Lim, Sungil Kim, Alex Bui, 24 Aug 2025, TANDEM: Temporal Attention-guided Neural Differential Equations for Missingness in Time Series Classification, https://arxiv.org/abs/2508.17519
  • Hoyoung Lee, Wonbin Ahn, Suhwan Park, Jaehoon Lee, Minjae Kim, Sungdong Yoo, Taeyoon Lim, Woohyung Lim, Yongjae Lee, 23 Aug 2025, THEME : Enhancing Thematic Investing with Semantic Stock Representations and Temporal Dynamics, https://arxiv.org/abs/2508.16936
  • Ziyao Shangguan, Chuhan Li, Yuxuan Ding, Yanan Zheng, Yilun Zhao, Tesca Fitzgerald, Arman Cohan, 25 Aug 2025, TOMATO: Assessing Visual Temporal Reasoning Capabilities in Multimodal Foundation Models, https://arxiv.org/abs/2410.23266
  • Xiuyuan Cheng, Zheng Dong, Yao Xie, 22 Aug 2025, Deep spatio-temporal point processes: Advances and new directions, https://arxiv.org/abs/2504.06364
  • Aarush Kumbhakern, Saransh Kumar Gupta, Lipika Dey, Partha Pratim Das, 4 Sep 2025, Towards an Action-Centric Ontology for Cooking Procedures Using Temporal Graphs, https://arxiv.org/abs/2509.04159
  • Yin Huang, Yongqi Dong, Youhua Tang, Li Li, 4 Sep 2025, Parking Availability Prediction via Fusing Multi-Source Data with A Self-Supervised Learning Enhanced Spatio-Temporal Inverted Transformer, https://arxiv.org/abs/2509.04362
  • Zhaoyan Gong, Juan Li, Zhiqiang Liu, Lei Liang, Huajun Chen, Wen Zhang, 4 Sep 2025, RTQA : Recursive Thinking for Complex Temporal Knowledge Graph Question Answering with Large Language Models, https://arxiv.org/abs/2509.03995
  • Brett Daley, Prabhat Nagarajan, Martha White, Marlos C. Machado, 4 Sep 2025, An Analysis of Action-Value Temporal-Difference Methods That Learn State Values, https://arxiv.org/abs/2507.09523
  • Zacharia A. Rudge, Dominik Dold, Moritz Fieback, Dario Izzo, Said Hamdioui, 2 Sep 2025, Memristor-Based Neural Network Accelerators for Space Applications: Enhancing Performance with Temporal Averaging and SIRENs, https://arxiv.org/abs/2509.04506
  • Konstantinos Drossos and Mikko Heikkinen and Paschalis Tsiaflakis, 5 Sep 2025, Lightweight DNN for Full-Band Speech Denoising on Mobile Devices: Exploiting Long and Short Temporal Patterns, https://arxiv.org/abs/2509.05079
  • Mahishanka Withanachchi, 23 Aug 2025, Learning Spatio-Temporal Dynamics via Operator-Valued RKHS and Kernel Koopman Methods, https://arxiv.org/abs/2508.18307
  • Yunyang Cao, Juekai Lin, Wenhao Li, Bo Jin, 26 Aug 2025, MOCHA: Discovering Multi-Order Dynamic Causality in Temporal Point Processes, https://arxiv.org/abs/2508.18873
  • Yao Wu, 26 Aug 2025, HierCVAE: Hierarchical Attention-Driven Conditional Variational Autoencoders for Multi-Scale Temporal Modeling, https://arxiv.org/abs/2508.18922
  • Hudson de Martim, 26 Aug 2025, An Ontology-Driven Graph RAG for Legal Norms: A Hierarchical, Temporal, and Deterministic Approach, https://arxiv.org/abs/2505.00039
  • Yongbin Lee, Ki H. Chon, 26 Aug 2025, Atrial Fibrillation Prediction Using a Lightweight Temporal Convolutional and Selective State Space Architecture, https://arxiv.org/abs/2508.19361
  • Haruki Yonekura, Ren Ozeki, Tatsuya Amano, Hamada Rizk, Hirozumi Yamaguchi, 27 Aug 2025, MobText-SISA: Efficient Machine Unlearning for Mobility Logs with Spatio-Temporal and Natural-Language Data, https://arxiv.org/abs/2508.19554
  • Chenghao Liu, Jiachen Zhang, Chengxuan Li, Zhimu Zhou, Shixin Wu, Songfang Huang and Huiling Duan, 15 Aug 2025, TTF-VLA: Temporal Token Fusion via Pixel-Attention Integration for Vision-Language-Action Models, https://arxiv.org/abs/2508.19257
  • Amirhossein Sohrabbeig, Omid Ardakanian, and Petr Musilek, 26 Aug 2025, Forecasting Multivariate Urban Data via Decomposition and Spatio-Temporal Graph Analysis, https://arxiv.org/abs/2505.22474
  • Zeyue Zhang, Lin Song, Erkang Bao, Xiaoling Lv, Xinyue Wang, 28 Aug 2025, ATM-GAD: Adaptive Temporal Motif Graph Anomaly Detection for Financial Transaction Networks, https://arxiv.org/abs/2508.20829
  • Chengjun Zhang, Yuhao Zhang, Jie Yang and Mohamad Sawan, 28 Aug 2025, Ultra-Low-Latency Spiking Neural Networks with Temporal-Dependent Integrate-and-Fire Neuron Model for Objects Detection, https://arxiv.org/abs/2508.20392
  • Yi Jiang, Malyaban Bal, Brian Matejek, Susmit Jha, Adam Cobb, Abhronil Sengupta, 23 Aug 2025, Spatio-Temporal Pruning for Compressed Spiking Large Language Models, https://arxiv.org/abs/2508.20122
  • Yunwoo Kim, Junhyuk Hwang, 29 Aug 2025, Predicting Social Media Engagement from Emotional and Temporal Features, https://arxiv.org/abs/2508.21650
  • Aditya Makineni, Baocheng Geng, Qing Tian, 28 Aug 2025, Full-Frequency Temporal Patching and Structured Masking for Enhanced Audio Classification, https://arxiv.org/abs/2508.21243
  • Shanshan Song, Hui Tang, Honglong Yang, Xiaomeng Li, 29 Aug 2025, DDaTR: Dynamic Difference-aware Temporal Residual Network for Longitudinal Radiology Report Generation, https://arxiv.org/abs/2505.03401
  • Zifeng Ding, Shenyang Huang, Zeyu Cao, Emma Kondrup, Zachary Yang, Xingyue Huang, Yuan Sui, Zhangdie Yuan, Yuqicheng Zhu, Xianglong Hu, Yuan He, Farimah Poursafaei, Michael Bronstein, Andreas Vlachos, 31 Aug 2025, Self-Exploring Language Models for Explainable Link Forecasting on Temporal Graphs via Reinforcement Learning, https://arxiv.org/abs/2509.00975
  • Giacomo Acciarini and Simone Mestici and Halil Kelebek and Linnea Wolniewicz and Michael Vergalla and Madhulika Guhathakurta and Umaa Rebbapragada and Bala Poduval and At{\i}l{\i}m G\"une\c{s} Baydin and Frank Soboczenski, 30 Aug 2025, Forecasting the Ionosphere from Sparse GNSS Data with Temporal-Fusion Transformers, https://arxiv.org/abs/2509.00631
  • Mengjie Zhao and Olga Fink, 30 Aug 2025, Disentangling Slow and Fast Temporal Dynamics in Degradation Inference with Hierarchical Differential Models, https://arxiv.org/abs/2509.00639
  • Binqing Wu, Jianlong Huang, Zongjiang Shang, Ling Chen, 2 Sep 2025, ST-Hyper: Learning High-Order Dependencies Across Multiple Spatial-Temporal Scales for Multivariate Time Series Forecasting, https://arxiv.org/abs/2509.02217
  • Jinzhou Tang, Jusheng zhang, Sidi Liu, Waikit Xiu, Qinhan Lv, Xiying Li, 29 Aug 2025, Beyond Pixels: Introducing Geometric-Semantic World Priors for Video-based Embodied Models via Spatio-temporal Alignment, https://arxiv.org/abs/2509.00210
  • Zhen Chen, Xingjian Luo, Kun Yuan, Jinlin Wu, Danny T.M. Chan, Nassir Navab, Hongbin Liu, Zhen Lei, Jiebo Luo, 30 Aug 2025, SurgLLM: A Versatile Large Multimodal Model with Spatial Focus and Temporal Awareness for Surgical Video Understanding, https://arxiv.org/abs/2509.00357
  • Junxiang Liu and Junming Lin and Jiangtong Li and Jie Li, 1 Sep 2025, DynaMind: Reconstructing Dynamic Visual Scenes from EEG by Aligning Temporal Dynamics and Multimodal Semantics to Guided Diffusion, https://arxiv.org/abs/2509.01177
  • James Amarel, Nicolas Hengartner, Robyn Miller, Kamaljeet Singh, Siddharth Mansingh, Arvind Mohan, Benjamin Migliori, Emily Casleton, Alexei Skurikhin, Earl Lawrence, Gerd J. Kunde, 18 Aug 2025, Generalization vs. Memorization in Autoregressive Deep Learning: Or, Examining Temporal Decay of Gradient Coherence, https://arxiv.org/abs/2509.00024
  • Jiawei Cao, Jie Ouyang, Zhaomeng Zhou, Mingyue Cheng, Yupeng Li, Jiaxian Yan, Qi Liu, 1 Sep 2025, Re3: Learning to Balance Relevance & Recency for Temporal Information Retrieval, https://arxiv.org/abs/2509.01306
  • Yves Stebler, Thomas M. Sutter, Ece Ozkan, Julia E. Vogt, 1 Sep 2025, Temporal Representation Learning for Real-Time Ultrasound Analysis, https://arxiv.org/abs/2509.01433
  • Chengyuan Ma, Peng Jia, Hongyue Guo, and Wenming Yang, 2 Sep 2025, ESTM: An Enhanced Dual-Branch Spectral-Temporal Mamba for Anomalous Sound Detection, https://arxiv.org/abs/2509.02471
  • Rui Li, Xiaohan Wang, Yuhui Zhang, Orr Zohar, Zeyu Wang, Serena Yeung-Levy, 1 Sep 2025, Temporal Preference Optimization for Long-Form Video Understanding, https://arxiv.org/abs/2501.13919
  • Minjung Park, Gyuyeon Na, Soyoun Kim, Sunyoung Moon, HyeonJeong Cha, Sangmi Chai, 3 Sep 2025, HyPV-LEAD: Proactive Early-Warning of Cryptocurrency Anomalies through Data-Driven Structural-Temporal Modeling, https://arxiv.org/abs/2509.03260
  • Kaustuv Mukherji, Jaikrishna Manojkumar Patil, Dyuman Aditya, Paulo Shakarian, Devendra Parkar, Lahari Pokala, Clark Dorman, Gerardo I. Simari, 3 Sep 2025, Lattice Annotated Temporal (LAT) Logic for Non-Markovian Reasoning, https://arxiv.org/abs/2509.02958
  • Huaicheng Zhang, Ruoxin Wang, Chenlian Zhou, Jiguang Shi, Yue Ge, Zhoutong Li, Sheng Chang, Hao Wang, Jin He and Qijun Huang, 3 Sep 2025, S2M2ECG: Spatio-temporal bi-directional State Space Model Enabled Multi-branch Mamba for ECG, https://arxiv.org/abs/2509.03066
  • Mattia Litrico and Francesco Guarnera and Mario Valerio Giuffrida and Daniele Rav\`i and Sebastiano Battiato, 3 Sep 2025, Temporally-Aware Diffusion Model for Brain Progression Modelling with Bidirectional Temporal Regularisation, https://arxiv.org/abs/2509.03141
  • Joel Jaskari, Chandreyee Roy, Fumiko Ogushi, Mikko Saukkoriipi, Jaakko Sahlsten, Kimmo Kaski, 3 Sep 2025, Temporal social network modeling of mobile connectivity data with graph neural networks, https://arxiv.org/abs/2509.03319
  • Naoufal El Bekri, Lucas Drumetz, Franck Vermet, 3 Sep 2025, FlowKac: An Efficient Neural Fokker-Planck solver using Temporal Normalizing Flows and the Feynman-Kac Formula, https://arxiv.org/abs/2503.11427
  • Wei Chen, Yuqian Wu, Yuanshao Zhu, Xixuan Hao, Shiyu Wang, Yuxuan Liang, 6 Sep 2025, Select, then Balance: A Plug-and-Play Framework for Exogenous-Aware Spatio-Temporal Forecasting, https://arxiv.org/abs/2509.05779
  • Shaoqi Wei, Senling Wang, Hiroshi Kai, Yoshinobu Higami, Ruijun Ma, Tianming Ni, Xiaoqing Wen and Hiroshi Takahashi, 8 Sep 2025, A Spatio-Temporal Graph Neural Networks Approach for Predicting Silent Data Corruption inducing Circuit-Level Faults, https://arxiv.org/abs/2509.06289
  • Guanjie Cheng, Boyi Li, Peihan Wu, Feiyi Chen, Xinkui Zhao, Mengying Zhu, Shuiguang Deng, 8 Sep 2025, DyC-STG: Dynamic Causal Spatio-Temporal Graph Network for Real-time Data Credibility Analysis in IoT, https://arxiv.org/abs/2509.06483
  • Henry Graf\'e, Hugo Van hamme, 5 Sep 2025, Graph Connectionist Temporal Classification for Phoneme Recognition, https://arxiv.org/abs/2509.05399
  • Aswini Kumar Patra, 8 Sep 2025, Improved Classification of Nitrogen Stress Severity in Plants Under Combined Stress Conditions Using Spatio-Temporal Deep Learning Framework, https://arxiv.org/abs/2509.06625
  • Jingyu Li, Tiehua Zhang, Jinze Wang, Yi Zhang, Yuhuan Li, Yifan Zhao, Zhishu Shen, Libing Wu, Jiannan Liu, 6 Sep 2025, MetaSTH-Sleep: Towards Effective Few-Shot Sleep Stage Classification for Health Management with Spatial-Temporal Hypergraph Enhanced Meta-Learning, https://arxiv.org/abs/2505.17142
  • Md. Kamrul Hasan, Guang Yang, Choon Hwai Yap, 6 Sep 2025, Motion-enhanced Cardiac Anatomy Segmentation via an Insertable Temporal Attention Module, https://arxiv.org/abs/2501.14929
  • Haruki Yokota, Koki Yamada, Yuichi Tanaka, Antonio Ortega, 8 Sep 2025, Time-Varying Graph Learning with Constraints on Graph Temporal Variation, https://arxiv.org/abs/2001.03346
  • Weichen Wu, Yuting Wei, Alessandro Rinaldo, 6 Sep 2025, Uncertainty quantification for Markov chain induced martingales with application to temporal difference learning, https://arxiv.org/abs/2502.13822
  • Xueyi Wang and Elisabeth Wilhelm, 28 Aug 2025, Individualized and Interpretable Sleep Forecasting via a Two-Stage Adaptive Spatial-Temporal Model, https://arxiv.org/abs/2509.06974
  • Yuxuan Bai, Yuxuan Sun, Tan Chen, Wei Chen, Sheng Zhou, Zhisheng Niu, 9 Sep 2025, FedTeddi: Temporal Drift and Divergence Aware Scheduling for Timely Federated Edge Learning, https://arxiv.org/abs/2509.07342
  • Seyd Teymoor Seydi, 9 Sep 2025, Deep Learning-Based Burned Area Mapping Using Bi-Temporal Siamese Networks and AlphaEarth Foundation Datasets, https://arxiv.org/abs/2509.07852
  • Ibne Farabi Shihab, Sanjeda Akter and Anuj Sharma, 9 Sep 2025, Enhancing Traffic Incident Response through Sub-Second Temporal Localization with HybridMamba, https://arxiv.org/abs/2504.03235
  • Ali Mazyaki, Mohammad Naghizadeh, Samaneh Ranjkhah Zonouzaghi, Hossein Setareh, 5 Sep 2025, Temporal Preferences in Language Models for Long-Horizon Assistance, https://arxiv.org/abs/2509.09704
  • Mingyu Kang and Duxin Chen and Ziyuan Pu and Jianxi Gao and Wenwu Yu, 12 Sep 2025, Spatio-Temporal Graphical Counterfactuals: An Overview, https://arxiv.org/abs/2407.01875
  • Hong Liu and Kerui Cen and Yanxing Chen and Zige Liu and Dong Chen and Zifeng Yang and Chitin Hon, 12 Sep 2025, MAESTRO: Multi-modal Adaptive Estimation for Temporal Respiratory Disease Outbreak, https://arxiv.org/abs/2509.08578
  • Jan V\'avra (1 and 2), Bettina Gr\"un (1) and Paul Hofmarcher (2) ((1) Vienna University of Economics and Business, (2) Paris-Lodron University of Salzburg), 12 Sep 2025, Evolving Voices Based on Temporal Poisson Factorisation, https://arxiv.org/abs/2410.18486
  • Julia Gastinger, Christian Meilicke, Heiner Stuckenschmidt, 11 Sep 2025, CountTRuCoLa: Rule Confidence Learning for Temporal Knowledge Graph Forecasting, https://arxiv.org/abs/2509.09474
  • Daria Laslo, Efthymios Georgiou, Marius George Linguraru, Andreas Rauschecker, Sabine Muller, Catherine R. Jutzeler, Sarah Bruningk, 11 Sep 2025, Mechanistic Learning with Guided Diffusion Models to Predict Spatio-Temporal Brain Tumor Growth, https://arxiv.org/abs/2509.09610
  • Shengsheng Lin, Haojun Chen, Haijie Wu, Chunyun Qiu, Weiwei Lin, 11 Sep 2025, Temporal Query Network for Efficient Multivariate Time Series Forecasting, https://arxiv.org/abs/2505.12917
  • Junhong Lin, Song Wang, Xiaojie Guo, Julian Shun, Yada Zhu, 18 Sep 2025, Temporal Reasoning with Large Language Models Augmented by Evolving Knowledge Graphs, https://arxiv.org/abs/2509.15464
  • M. Giselle Fern\'andez-Godino, Meir H. Shachar, Kevin Korner, Jonathan L. Belof, Mukul Kumar, Jonathan Lind, and William J. Schill, 19 Sep 2025, Spatio-temporal, multi-field deep learning of shock propagation in meso-structured media, https://arxiv.org/abs/2509.16139
  • Dylan L\'eveill\'e (Carleton University), 17 Sep 2025, Generating Plans for Belief-Desire-Intention (BDI) Agents Using Alternating-Time Temporal Logic (ATL), https://arxiv.org/abs/2509.15238
  • Mulugeta Weldezgina Asres, Christian Walter Omlin, Long Wang, David Yu, Pavel Parygin, Jay Dittmann, Georgia Karapostoli, Markus Seidel, Rosamaria Venditti, Luka Lambrecht, Emanuele Usai, Muhammad Ahmad, Javier Fernandez Menendez, Kaori Maeshima and the CMS-HCAL Collaboration, 19 Sep 2025, Spatio-Temporal Anomaly Detection with Graph Networks for Data Quality Monitoring of the Hadron Calorimeter, https://arxiv.org/abs/2311.04190
  • Qi Xiong, Kai Tang, Minbo Ma, Ji Zhang, Jie Xu, Tianrui Li, 19 Sep 2025, Modeling Temporal Dependencies within the Target for Long-Term Time Series Forecasting, https://arxiv.org/abs/2406.04777
  • Da Long, Shandian Zhe, Samuel Williams, Leonid Oliker, Zhe Bai, 18 Sep 2025, StFT: Spatio-temporal Fourier Transformer for Long-term Dynamics Prediction, https://arxiv.org/abs/2503.11899
  • Yubo Li, Xinyu Yao, Rema Padman, 19 Sep 2025, No Black Box Anymore: Demystifying Clinical Predictive Modeling with Temporal-Feature Cross Attention Mechanism, https://arxiv.org/abs/2503.19285
  • Michael Somma, 19 Sep 2025, Hybrid Temporal Differential Consistency Autoencoder for Efficient and Sustainable Anomaly Detection in Cyber-Physical Systems, https://arxiv.org/abs/2504.06320
  • Hyeonseok Jin, Geonmin Kim, Kyungbaek Kim, 19 Sep 2025, Deformable Dynamic Convolution for Accurate yet Efficient Spatio-Temporal Traffic Prediction, https://arxiv.org/abs/2507.11550
  • Jinmeiyang Wang, Jing Dong, Li Zhou, 13 Sep 2025, Research on Short-Video Platform User Decision-Making via Multimodal Temporal Modeling and Reinforcement Learning, https://arxiv.org/abs/2509.12269
  • Konstantinos Vasili, Zachery T. Dahm, Stylianos Chatzidakis, 15 Sep 2025, Explainable Unsupervised Multi-Anomaly Detection and Temporal Localization in Nuclear Times Series Data with a Dual Attention-Based Autoencoder, https://arxiv.org/abs/2509.12372
  • Pratik Nag, 16 Sep 2025, Spatio-temporal DeepKriging in PyTorch: A Supplementary Application to Precipitation Data for Interpolation and Probabilistic Forecasting, https://arxiv.org/abs/2509.12708
  • Francis Ndikum Nji, Vandana Janaja, Jianwu Wang, 16 Sep 2025, B-TGAT: A Bi-directional Temporal Graph Attention Transformer for Clustering Multivariate Spatiotemporal Data, https://arxiv.org/abs/2509.13202
  • Susanta Mitra, 15 Sep 2025, Agentic Temporal Graph of Reasoning with Multimodal Language Models: A Potential AI Aid to Healthcare, https://arxiv.org/abs/2509.11944
  • Amirhossein Ghaffari, Huong Nguyen, Lauri Lov\'en, Ekaterina Gilman, 4 Sep 2025, STM-Graph: A Python Framework for Spatio-Temporal Mapping and Graph Neural Network Predictions, https://arxiv.org/abs/2509.10528
  • Yuan Gao, Xuelong Wang, Zhenguo Dong and Yong Zhang, 15 Sep 2025, Dynamic Adaptive Parsing of Temporal and Cross-Variable Patterns for Network State Classification, https://arxiv.org/abs/2509.11601
  • Ocheme Anthony Ekle and William Eberle, 15 Sep 2025, Adaptive-GraphSketch: Real-Time Edge Anomaly Detection via Multi-Layer Tensor Sketching and Temporal Decay, https://arxiv.org/abs/2509.11633
  • Arash Peik, Mohammad Ali Zare Chahooki, Amin Milani Fard, Mehdi Agha Sarram, 6 Sep 2025, Adaptive Temporal Fusion Transformers for Cryptocurrency Price Prediction, https://arxiv.org/abs/2509.10542
  • Mika Sipil\"a, Klaus Nordhausen and Sara Taskinen, 15 Sep 2025, Identifiable Autoregressive Variational Autoencoders for Nonlinear and Nonstationary Spatio-Temporal Blind Source Separation, https://arxiv.org/abs/2509.11962
  • Shrey Ganatra, Spandan Anaokar, Pushpak Bhattacharyya, 15 Sep 2025, Timing Matters: Enhancing User Experience through Temporal Prediction in Smart Homes, https://arxiv.org/abs/2411.18719
  • Hyeju Shin, Vincent-Daniel, Kyudan Jung, Seongwon Yun, 13 Sep 2025, Fast Fourier Transform-Based Spectral and Temporal Gradient Filtering for Differential Privacy, https://arxiv.org/abs/2505.04468
  • Parv Kapoor, Kazuki Mizuta, Eunsuk Kang, Karen Leung, 15 Sep 2025, STLCG++: A Masking Approach for Differentiable Signal Temporal Logic Specification, https://arxiv.org/abs/2501.04194
  • Padmaksha Roy, Almuatazbellah Boker, Lamine Mili, 18 Sep 2025, Beyond Marginals: Learning Joint Spatio-Temporal Patterns for Multivariate Anomaly Detection, https://arxiv.org/abs/2509.15033
  • Dan Zhang, Min Cai, Jonathan Li, Ziniu Hu, Yisong Yue, Yuxiao Dong, Jie Tang, 18 Sep 2025, TDRM: Smooth Reward Models with Temporal Difference for LLM RL and Inference, https://arxiv.org/abs/2509.15110
  • Feng Ding, Haisheng Fu, Soroush Oraki, Jie Liang, 18 Sep 2025, LSTC-MDA: A Unified Framework for Long-Short Term Temporal Convolution and Mixed Data Augmentation in Skeleton-Based Action Recognition, https://arxiv.org/abs/2509.14619
  • Liangjin Liu, Haoyang Zheng, Zhengzhong Zhu, Pei Zhou, 18 Sep 2025, Skeleton-based sign language recognition using a dual-stream spatio-temporal dynamic graph convolutional network, https://arxiv.org/abs/2509.08661
  • Ali Amini, Mohammad Alijanpour, Behnam Latifi, and Ali Motie Nasrabadi, 10 Sep 2025, ADHDeepNet From Raw EEG to Diagnosis: Improving ADHD Diagnosis through Temporal-Spatial Processing, Adaptive Attention Mechanisms, and Explainability in Raw EEG Signals, https://arxiv.org/abs/2509.08779
  • Hyotaek Jeon, Hyunwook Lee, Juwon Kim and Sungahn Ko, 17 Sep 2025, ST-LINK: Spatially-Aware Large Language Models for Spatio-Temporal Forecasting, https://arxiv.org/abs/2509.13753
  • Ivana Kesi\'c, Alja\v{z} Blatnik, Carolina Fortuna, Bla\v{z} Bertalani\v{c}, 17 Sep 2025, Deep Temporal Graph Networks for Real-Time Correction of GNSS Jamming-Induced Deviations, https://arxiv.org/abs/2509.14000
  • Jun Wang, Jiaming Tong, Kaiyuan Tan, Yevgeniy Vorobeychik, Yiannis Kantaros, 17 Sep 2025, Conformal Temporal Logic Planning using Large Language Models, https://arxiv.org/abs/2309.10092
  • Xinran Zheng, Shuo Yang, Edith C.H. Ngai, Suman Jana, Lorenzo Cavallaro, 17 Sep 2025, Learning Temporal Invariance in Android Malware Detectors, https://arxiv.org/abs/2502.05098
  • Junwon Lee, Jaekwon Im, Dabin Kim, Juhan Nam, 17 Sep 2025, Video-Foley: Two-Stage Video-To-Sound Generation via Temporal Event Condition For Foley Sound, https://arxiv.org/abs/2408.11915
  • Antonio \'Alvarez-L\'opez and Marcos Matabuena, 17 Sep 2025, Continuous Temporal Learning of Probability Distributions via Neural ODEs with Applications in Continuous Glucose Monitoring Data, https://arxiv.org/abs/2505.08698
  • Koyena Chowdhury, Paramita Koley, Abhijnan Chakraborty, and Saptarshi Ghosh, 26 Sep 2025, RSTGCN: Railway-centric Spatio-Temporal Graph Convolutional Network for Train Delay Prediction, https://arxiv.org/abs/2510.01262
  • Thomas Gravier, Thomas Boyer, Auguste Genovesio, 2 Oct 2025, Multi-marginal temporal Schr\"odinger Bridge Matching for video generation from unpaired data, https://arxiv.org/abs/2510.01894
  • Shira Schiber, Ofir Lindenbaum, Idan Schwartz, 2 Oct 2025, TempoControl: Temporal Attention Guidance for Text-to-Video Models, https://arxiv.org/abs/2510.02226
  • Zixuan Xie, Xinyu Liu, Rohan Chandra, Shangtong Zhang, 2 Oct 2025, Finite Sample Analysis of Linear Temporal Difference Learning with Arbitrary Features, https://arxiv.org/abs/2505.21391
  • Tianyang Luo, Xikun Zhang, Dongjin Song, 2 Oct 2025, FTSCommDetector: Discovering Behavioral Communities through Temporal Synchronization, https://arxiv.org/abs/2510.00014
  • Junyi Xie, Jina Kim, Yao-Yi Chiang, Lingyi Zhao, Khurram Shafique, 14 Oct 2025, BeSTAD: Behavior-Aware Spatio-Temporal Anomaly Detection for Human Mobility Data, https://arxiv.org/abs/2510.12076
  • David Berghaus and Patrick Seifner and Kostadin Cvejoski and Ramses J. Sanchez, 14 Oct 2025, On Foundation Models for Temporal Point Processes to Accelerate Scientific Discovery, https://arxiv.org/abs/2510.12640
  • Youhao Si, Yuan Liao, Qiushi Han, Yuhang Yang, Rui Dai and Liya Huang, 14 Oct 2025, TFGA-Net: Temporal-Frequency Graph Attention Network for Brain-Controlled Speaker Extraction, https://arxiv.org/abs/2510.12275
  • Sebastian Griesbach, Carlo D'Eramo, 14 Oct 2025, Learning to Explore in Diverse Reward Settings via Temporal-Difference-Error Maximization, https://arxiv.org/abs/2506.13345
  • Tianxiang Xu, Zhichao Wen, Xinyu Zhao, Qi Hu, Yan Li and Chang Liu, 14 Oct 2025, GTCN-G: A Residual Graph-Temporal Fusion Network for Imbalanced Intrusion Detection (Preprint), https://arxiv.org/abs/2510.07285
  • Weilin Xin, Chenyu Huang, Peilin Li, Jing Zhong, Jiawei Yao, 1 Oct 2025, UrbanGraph: Physics-Informed Spatio-Temporal Dynamic Heterogeneous Graphs for Urban Microclimate Prediction, https://arxiv.org/abs/2510.00457
  • Yanbo Xu, Yu Wu, Sungjae Park, Zhizhuo Zhou, Shubham Tulsiani, 1 Oct 2025, Temporal Score Rescaling for Temperature Sampling in Diffusion and Flow Models, https://arxiv.org/abs/2510.01184
  • Chukwuemeka Ugwu and Oluwafemi Oyeleke, 26 Sep 2025, Temporal-Aware Iterative Speech Model for Dementia Detection, https://arxiv.org/abs/2510.00030
  • Yue Meng, Fei Chen, Chuchu Fan, 30 Sep 2025, TGPO: Temporal Grounded Policy Optimization for Signal Temporal Logic Tasks, https://arxiv.org/abs/2510.00225
  • Anja Adamov, Markus Chardonnet, Florian Krach, Jakob Heiss, Josef Teichmann and Nicholas A. Bokulich, 30 Sep 2025, Revealing the temporal dynamics of antibiotic anomalies in the infant gut microbiome with neural jump ODEs, https://arxiv.org/abs/2510.00087
  • Victor Brabant, Angela Bonifati, R\'emy Cazabet, 1 Oct 2025, Discovering Communities in Continuous-Time Temporal Networks by Optimizing L-Modularity, https://arxiv.org/abs/2510.00741
  • Md Hasan Shahriar, Md Mohaimin Al Barat, Harshavardhan Sundar, Ning Zhang, Naren Ramakrishnan, Y. Thomas Hou, Wenjing Lou, 1 Oct 2025, Temporal Misalignment Attacks against Multimodal Perception in Autonomous Driving, https://arxiv.org/abs/2507.09095
  • Wendong Yao, Binhua Huang and Soumyabrata Dev, 1 Oct 2025, Multi-modal Spatio-Temporal Transformer for High-resolution Land Subsidence Prediction, https://arxiv.org/abs/2509.25393
  • Kuiye Ding and Fanda Fan and Chunyi Hou and Zheya Wang and Lei Wang and Zhengxin Yang and Jianfeng Zhan, 23 Sep 2025, TimeMosaic: Temporal Heterogeneity Guided Time Series Forecasting via Adaptive Granularity Patch and Segment-wise Decoding, https://arxiv.org/abs/2509.19406
  • Kevin Garcia, Cassandra Garza, Brooklyn Berry, Yifeng Gao, 24 Sep 2025, Symbol-Temporal Consistency Self-supervised Learning for Robust Time Series Classification, https://arxiv.org/abs/2509.19654
  • Mohsen Nayebi Kerdabadi, William Andrew Byron, Xin Sun, Amirfarrokh Iranitalab, 24 Sep 2025, Spatio-Temporal Directed Graph Learning for Account Takeover Fraud Detection, https://arxiv.org/abs/2509.20339
  • Chang Wang, Ming Zhu, Shahram Latifi, Buddhadeb Dawn, and Shengjie Zhai, 5 Sep 2025, Graph-Based Spatio-temporal Attention and Multi-Scale Fusion for Clinically Interpretable, High-Fidelity Fetal ECG Extraction, https://arxiv.org/abs/2509.19308
  • Shangqing Yuan, Wenshuang Zhai, and Shengwen Guo, 15 Sep 2025, A Spatio-Temporal Feature Fusion EEG Virtual Channel Signal Generation Network and Its Application in Anxiety Assessment, https://arxiv.org/abs/2509.19334
  • Ranga Shaarad Ayyagari, Revanth Raj Eega, Ambedkar Dukkipati, 24 Sep 2025, Markov Decision Processes under External Temporal Processes, https://arxiv.org/abs/2305.16056
  • Yisong Fu, Fei Wang, Zezhi Shao, Boyu Diao, Lin Wu, Zhulin An, Chengqing Yu, Yujie Li, Yongjun Xu, 24 Sep 2025, On the Integration of Spatial-Temporal Knowledge: A Lightweight Approach to Atmospheric Time Series Forecasting, https://arxiv.org/abs/2408.09695
  • Xintong Wang, Haihan Nan, Ruidong Li and Huaming Wu, 24 Sep 2025, DP-LET: An Efficient Spatio-Temporal Network Traffic Prediction Framework, https://arxiv.org/abs/2504.03792
  • Siwei Zhang, Yun Xiong, Yateng Tang, Jiarong Xu, Xi Chen, Zehao Gu, Xuezheng Hao, Zian Jia, Jiawei Zhang, 24 Sep 2025, Unifying Text Semantics and Graph Structures for Temporal Text-attributed Graphs with Large Language Models, https://arxiv.org/abs/2503.14411
  • Ahmad Bin Rabiah, Julian McAuley, 24 Sep 2025, GSPRec: Temporal-Aware Graph Spectral Filtering for Recommendation, https://arxiv.org/abs/2505.11552
  • Gagan Bhatia, Maxime Peyrard, Wei Zhao, 24 Sep 2025, Date Fragments: A Hidden Bottleneck of Tokenization for Temporal Reasoning, https://arxiv.org/abs/2505.16088
  • Zibo Liu, Zhe Jiang, Zelin Xu, Tingsong Xiao, Yupu Zhang, Zhengkun Xiao, Haibo Wang, Shigang Chen, 28 Oct 2025, Spatio-temporal Multivariate Time Series Forecast with Chosen Variables, https://arxiv.org/abs/2510.24027
  • Edward Markai, Sina Molavipour, 28 Oct 2025, Temporal Knowledge Graph Hyperedge Forecasting: Exploring Entity-to-Category Link Prediction, https://arxiv.org/abs/2510.24240
  • Marco S\"alzer, Przemys{\l}aw Andrzej Wa{\l}\k{e}ga, Martin Lange, 28 Oct 2025, The Logical Expressiveness of Temporal GNNs via Two-Dimensional Product Logics, https://arxiv.org/abs/2505.11930
  • Keisuke Kawano, Takuro Kutsuna, Naoki Hayashi, Yasushi Esaki, Hidenori Tanaka, 28 Oct 2025, CT-OT Flow: Estimating Continuous-Time Dynamics from Discrete Temporal Snapshots, https://arxiv.org/abs/2505.17354
  • Yuting Huang, Ziquan Fang, Zhihao Zeng, Lu Chen, Yunjun Gao, 28 Oct 2025, Causal Spatio-Temporal Prediction: An Effective and Efficient Multi-Modal Approach, https://arxiv.org/abs/2505.17637
  • Vahid Jalili, 22 Oct 2025, The Temporal Graph of Bitcoin Transactions, https://arxiv.org/abs/2510.20028
  • Marin Bilo\v{s}, Anderson Schneider, Yuriy Nevmyvaka, 22 Oct 2025, Speculative Sampling for Parametric Temporal Point Processes, https://arxiv.org/abs/2510.20031
  • Qitai Tan, Yiyun Chen, Mo Li, Ruiwen Gu, Yilin Su, Xiao-Ping Zhang, 23 Oct 2025, SynTSBench: Rethinking Temporal Pattern Learning in Deep Learning Models for Time Series, https://arxiv.org/abs/2510.20273
  • Yuhang Wang, 23 Oct 2025, InvDec: Inverted Decoder for Multivariate Time Series Forecasting with Separated Temporal and Variate Modeling, https://arxiv.org/abs/2510.20302
  • Sishun Liu, Ke Deng, Xiuzhen Zhang, Yongli Ren, Yan Wang, 23 Oct 2025, Addressing Mark Imbalance in Integration-free Neural Marked Temporal Point Processes, https://arxiv.org/abs/2510.20414
  • Jiahao Meng, Xiangtai Li, Haochen Wang, Yue Tan, Tao Zhang, Lingdong Kong, Yunhai Tong, Anran Wang, Zhiyang Teng, Yujing Wang, Zhuochen Wang, 23 Oct 2025, Open-o3 Video: Grounded Video Reasoning with Explicit Spatio-Temporal Evidence, https://arxiv.org/abs/2510.20579
  • Minseok Kang, Minhyeok Lee, Minjung Kim, Donghyeong Kim, and Sangyoun Lee, 23 Oct 2025, Empower Words: DualGround for Structured Phrase and Sentence-Level Temporal Grounding, https://arxiv.org/abs/2510.20244
  • Luckeciano C. Melo, Alessandro Abate, Yarin Gal, 23 Oct 2025, Temporal-Difference Variational Continual Learning, https://arxiv.org/abs/2410.07812
  • Abdellah Rahmani, Pascal Frossard, 23 Oct 2025, Flow based approach for Dynamic Temporal Causal models with non-Gaussian or Heteroscedastic Noises, https://arxiv.org/abs/2506.17065
  • Ioan Hedea, 16 Oct 2025, Algorithms for dynamic scheduling in manufacturing, towards digital factories Improving Deadline Feasibility and Responsiveness via Temporal Networks, https://arxiv.org/abs/2510.16047
  • Mustafa F. Abdelwahed, Alice Toniolo, Joan Espasa, Ian P. Gent, 20 Oct 2025, Diverse Planning with Simulators via Linear Temporal Logic, https://arxiv.org/abs/2510.17418
  • Dongchan Cho, Jiho Han, Keumyeong Kang, Minsang Kim, Honggyu Ryu, Namsoon Jung, 18 Oct 2025, Structured Temporal Causality for Interpretable Multivariate Time Series Anomaly Detection, https://arxiv.org/abs/2510.16511
  • Shihao Ji, Zihui Song, 19 Oct 2025, Xiaoice: Training-Free Video Understanding via Self-Supervised Spatio-Temporal Clustering of Semantic Features, https://arxiv.org/abs/2510.16781
  • Panos Kalnis. Shuo Shang, Christian S. Jensen, 20 Oct 2025, Comprehending Spatio-temporal Data via Cinematic Storytelling using Large Language Models, https://arxiv.org/abs/2510.17301
  • Shengnan Guo, Tonglong Wei, Yiheng Huang, Yan Lin, Zekai Shen, Yujuan Dong, Junliang Lin, Youfang Lin, Huaiyu Wan, 17 Oct 2025, A Survey and Benchmarking of Spatial-Temporal Traffic Data Imputation Models, https://arxiv.org/abs/2412.04733
  • Haoyu He, Haozheng Luo, Yan Chen, Qi R. Wang, 20 Oct 2025, RHYTHM: Reasoning with Hierarchical Temporal Tokenization for Human Mobility, https://arxiv.org/abs/2509.23115
  • Danyang Li, Yixuan Wang, Matthew Cleaveland, Mingyu Cai, Roberto Tron, 17 Oct 2025, Conformal Prediction for Signal Temporal Logic Inference, https://arxiv.org/abs/2509.25473
  • Shahriar Noroozizadeh, Sayantan Kumar, Jeremy C. Weiss, 18 Oct 2025, Forecasting Clinical Risk from Textual Time Series: Structuring Narratives for Temporal AI in Healthcare, https://arxiv.org/abs/2504.10340
  • David Wang and Mohammad Abdulaziz, 19 Oct 2025, Formally Verified Certification of Unsolvability of Temporal Planning Problems, https://arxiv.org/abs/2510.10189
  • Yassir Lairgi, Ludovic Moncla, Khalid Benabdeslem, R\'emy Cazabet, Pierre Cl\'eau, 26 Oct 2025, ATOM: AdapTive and OptiMized dynamic temporal knowledge graph construction using LLMs, https://arxiv.org/abs/2510.22590
  • Crimson Stambaugh and Rajesh P. N. Rao, 27 Oct 2025, Mixed Density Diffuser: Efficient Planning with Non-uniform Temporal Resolution, https://arxiv.org/abs/2510.23026
  • Micha{\l} Bortkiewicz, W{\l}adys{\l}aw Pa{\l}ucki, Mateusz Ostaszewski, Benjamin Eysenbach, 24 Oct 2025, Is Temporal Difference Learning the Gold Standard for Stitching in RL?, https://arxiv.org/abs/2510.21995
  • Zhongyi Yu, Jianqiu Wu, Zhenghao Wu, Shuhan Zhong, Weifeng Su, Chul-Ho Lee, Weipeng Zhuo, 23 Oct 2025, TAMI: Taming Heterogeneity in Temporal Interactions for Temporal Graph Link Prediction, https://arxiv.org/abs/2510.23577
  • Mohammad Ali Etemadi Naeen, Hoda Mohammadzade, Saeed Bagheri Shouraki, 24 Oct 2025, Human-Centric Anomaly Detection in Surveillance Videos Using YOLO-World and Spatio-Temporal Deep Learning, https://arxiv.org/abs/2510.22056
  • Anooshka Bajaj, Deven Mahesh Mistry, Sahaj Singh Maini, Yash Aggarwal, Zoran Tiganj, 26 Oct 2025, Beyond Semantics: How Temporal Biases Shape Retrieval in Transformer and State-Space Models, https://arxiv.org/abs/2510.22752
  • Haruki Settai, Naoya Takeishi, and Takehisa Yairi, 26 Oct 2025, A Temporal Difference Method for Stochastic Continuous Dynamics, https://arxiv.org/abs/2505.15544
  • Kelvin J.L. Koa, Yunshan Ma, Yi Xu, Ritchie Ng, Huanhuan Zheng, Tat-Seng Chua, 25 Oct 2025, Temporal Relational Reasoning of Large Language Models for Detecting Stock Portfolio Crashes, https://arxiv.org/abs/2410.17266
  • Dayoung Baik, Jaejun Yoo, 25 Oct 2025, Dynamic-Aware Spatio-temporal Representation Learning for Dynamic MRI Reconstruction, https://arxiv.org/abs/2501.09049
  • Ruijia Liu, Ancheng Hou, Xiao Yu, Xiang Yin, 26 Oct 2025, Zero-Shot Trajectory Planning for Signal Temporal Logic Tasks, https://arxiv.org/abs/2501.13457
  • Nilava Metya and Ankit Shah and Arunesh Sinha, 24 Oct 2025, Temporal Robustness in Discrete Time Linear Dynamical Systems, https://arxiv.org/abs/2505.02347
  • Yi Wang, Zeyu Xue, Mujie Liu, Tongqin Zhang, Yan Hu, Zhou Zhao, Chenguang Yang and Zhenyu Lu, 27 Oct 2025, Open-Vocabulary Spatio-Temporal Scene Graph for Robot Perception and Teleoperation Planning, https://arxiv.org/abs/2509.23107
  • Qingyue Yang, Jie Wang, Xing Li, Zhihai Wang, Chen Chen, Lei Chen, Xianzhi Yu, Wulong Liu, Jianye Hao, Mingxuan Yuan, Bin Li, 26 Oct 2025, AttentionPredictor: Temporal Patterns Matter for KV Cache Compression, https://arxiv.org/abs/2502.04077
  • Cecilia Di Florio, Huimin Dong, Antonino Rotolo, 15 Oct 2025, A Modal Logic for Temporal and Jurisdictional Classifier Models, https://arxiv.org/abs/2510.13691
  • Muhammad Faraz Ul Abrar, Nicol\`o Michelusi, and Erik G. Larsson, 15 Oct 2025, Time-Varying Optimization for Streaming Data Via Temporal Weighting, https://arxiv.org/abs/2510.13052
  • Md. Joshem Uddin, Soham Changani, Baris Coskunuzer, 15 Oct 2025, T3former: Temporal Graph Classification with Topological Machine Learning, https://arxiv.org/abs/2510.13789
  • Jialong Zhou, Lichao Wang, Xiao Yang, 15 Oct 2025, GUARDIAN: Safeguarding LLM Multi-Agent Collaborations with Temporal Graph Modeling, https://arxiv.org/abs/2505.19234
  • Jan Corazza, Hadi Partovi Aria, Hyohun Kim, Daniel Neider, Zhe Xu, 14 Oct 2025, Decentralizing Multi-Agent Reinforcement Learning with Temporal Causal Information, https://arxiv.org/abs/2506.07829
  • Haozhen Zheng, Beitong Tian, Mingyuan Wu, Zhenggang Tang, Klara Nahrstedt, Alex Schwing, 15 Oct 2025, Spatio-Temporal LLM: Reasoning about Environments and Actions, https://arxiv.org/abs/2507.05258
  • Houliang Zhou, Rong Zhou, Yangying Liu, Kanhao Zhao, Li Shen, Brian Y. Chen, Yu Zhang, Lifang He, and Alzheimer's Disease Neuroimaging Initiative, 26 Sep 2025, Uncovering Alzheimer's Disease Progression via SDE-based Spatio-Temporal Graph Deep Learning on Longitudinal Brain Networks, https://arxiv.org/abs/2509.21735
  • Sai Varun Kodathala and Rakesh Vunnam, 25 Sep 2025, Temporal vs. Spatial: Comparing DINOv3 and V-JEPA2 Feature Representations for Video Action Analysis, https://arxiv.org/abs/2509.21595
  • Niharika Hegde, Subarnaduti Paul, Lars Joel-Frey, Manuel Brack, Kristian Kersting, Martin Mundt, Patrick Schramowski, 26 Sep 2025, CHRONOBERG: Capturing Language Evolution and Temporal Awareness in Foundation Models, https://arxiv.org/abs/2509.22360
  • Zhongbin Guo, Yuhao Wang, Ping Jian, Chengzhi Li, Xinyue Chen, Zhen Yang, Ertai E, 26 Sep 2025, TAMMs: Temporal-Aware Multimodal Model for Satellite Image Change Understanding and Forecasting, https://arxiv.org/abs/2506.18862
  • Cristian Meo, Varun Sarathchandran, Avijit Majhi, Shao Hung, Carlo Saccardi, Ruben Imhoff, Roberto Deidda, Remko Uijlenhoet, Justin Dauwels, 7 Oct 2025, BlockGPT: Spatio-Temporal Modelling of Rainfall via Frame-Level Autoregression, https://arxiv.org/abs/2510.06293
  • Zhipeng Liu and Peibo Duan and Xuan Tang and Baixin Li and Yongsheng Huang and Mingyang Geng and Changsheng Zhang and Bin Zhang and Binwu Wang, 8 Oct 2025, TimeFormer: Transformer with Attention Modulation Empowered by Temporal Characteristics for Time Series Forecasting, https://arxiv.org/abs/2510.06680
  • Krishna Sri Ipsit Mantri, Or Feldman, Moshe Eliasof, Chaim Baskin, 8 Oct 2025, Revisiting Node Affinity Prediction in Temporal Graphs, https://arxiv.org/abs/2510.06940
  • Laurent Brisson (IMT Atlantique - DSD), C\'ecile Bothorel (IMT Atlantique - DSD), Nicolas Duminy (IMT Atlantique, IMT Atlantique - DSD), 3 Oct 2025, DynBenchmark: Customizable Ground Truths to Benchmark Community Detection and Tracking in Temporal Networks, https://arxiv.org/abs/2510.06245
  • Avishree Khare, Hideki Okamoto, Bardh Hoxha, Georgios Fainekos, Rajeev Alur, 7 Oct 2025, LogSTOP: Temporal Scores over Prediction Sequences for Matching and Retrieval, https://arxiv.org/abs/2510.06512
  • Xing Han, Hsing-Huan Chung, Joydeep Ghosh, Paul Pu Liang, Suchi Saria, 8 Oct 2025, Guiding Mixture-of-Experts with Temporal Multimodal Interactions, https://arxiv.org/abs/2509.25678
  • David L\"udke, Marten Lienen, Marcel Kollovieh, Stephan G\"unnemann, 8 Oct 2025, Edit-Based Flow Matching for Temporal Point Processes, https://arxiv.org/abs/2510.06050
  • Yolo Yunlong Tang, Daiki Shimada, Jing Bi, Mingqian Feng, Hang Hua, Chenliang Xu, 8 Oct 2025, Empowering LLMs with Pseudo-Untrimmed Videos for Audio-Visual Temporal Understanding, https://arxiv.org/abs/2403.16276
  • Hang Hua, Yolo Yunlong Tang, Chenliang Xu, Jiebo Luo, 8 Oct 2025, V2Xum-LLM: Cross-Modal Video Summarization with Temporal Prompt Instruction Tuning, https://arxiv.org/abs/2404.12353
  • Nidhi Soley, Vishal M Patel, Casey O Taylor, 2 Oct 2025, AttentiveGRUAE: An Attention-Based GRU Autoencoder for Temporal Clustering and Behavioral Characterization of Depression from Wearable Data, https://arxiv.org/abs/2510.02558
  • Tarun Kumar Biswas, Ashrafun Zannat, Waqas Ishtiaq, Md. Alamgir Hossain, 3 Oct 2025, A Novel Unified Lightweight Temporal-Spatial Transformer Approach for Intrusion Detection in Drone Networks, https://arxiv.org/abs/2510.02711
  • Rina Foygel Barber, Ashwin Pananjady, 2 Oct 2025, Predictive inference for time series: why is split conformal effective despite temporal dependence?, https://arxiv.org/abs/2510.02471
  • Weichen Wu, Gen Li, Yuting Wei, Alessandro Rinaldo, 3 Oct 2025, Statistical Inference for Temporal Difference Learning with Linear Function Approximation, https://arxiv.org/abs/2410.16106
  • Francis Ndikum Nji, Vandana Janeja, Jianwu Wang, 20 Oct 2025, Attention-Guided Deep Adversarial Temporal Subspace Clustering (A-DATSC) Model for multivariate spatiotemporal data, https://arxiv.org/abs/2510.18004
  • Jay Phil Yoo, Kazuma Kobayashi, Souvik Chakraborty, Syed Bahauddin Alam, 20 Oct 2025, Cross-Domain Long-Term Forecasting: Radiation Dose from Sparse Neutron Sensor via Spatio-Temporal Operator Network, https://arxiv.org/abs/2510.18041
  • Zijian Li, Minghao Fu, Junxian Huang, Yifan Shen, Ruichu Cai, Yuewen Sun, Guangyi Chen, Kun Zhang, 21 Oct 2025, Towards Identifiability of Hierarchical Temporal Causal Representation Learning, https://arxiv.org/abs/2510.18310
  • Yili Wang, Tairan Huang, Changlong He, Qiutong Li, Jianliang Gao, 21 Oct 2025, Simple and Efficient Heterogeneous Temporal Graph Neural Network, https://arxiv.org/abs/2510.18467
  • Xiang Zhang, Suping Wu, Weibin Qiu, Zhaocheng Jin, Sheng Yang, 21 Oct 2025, Hyperbolic Space Learning Method Leveraging Temporal Motion Priors for Human Mesh Recovery, https://arxiv.org/abs/2510.18256
  • Xue Han, Qian Hu, Yitong Wang, Wenchun Gao, Lianlian Zhang, Qing Wang, Lijun Mei, Chao Deng, Junlan Feng, 21 Oct 2025, Temporal Alignment of LLMs through Cycle Encoding for Long-Range Time Representations, https://arxiv.org/abs/2503.04150
  • Yue Jiang, Jichu Li, Yang Liu, Dingkang Yang, Feng Zhou, Quyu Kong, 21 Oct 2025, DanmakuTPPBench: A Multi-modal Benchmark for Temporal Point Process Modeling and Understanding, https://arxiv.org/abs/2505.18411
  • Lutz Oettershagen, Othon Michail, 21 Oct 2025, Fair Minimum Labeling: Efficient Temporal Network Activations for Reachability and Equity, https://arxiv.org/abs/2510.03899
  • Christoph D\"using and Philipp Cimiano, 25 Sep 2025, Federated Markov Imputation: Privacy-Preserving Temporal Imputation in Multi-Centric ICU Environments, https://arxiv.org/abs/2509.20867
  • Xin Zhang, Qiyu Wei, Yingjie Zhu, Fanyi Wu, Sophia Ananiadou, 24 Sep 2025, THCM-CAL: Temporal-Hierarchical Causal Modelling with Conformal Calibration for Clinical Risk Prediction, https://arxiv.org/abs/2506.17844
  • Xiaowen Ma, Shuning Ge, Fan Yang, Xiangyu Li, Yun Chen, Mengting Ma, Wei Zhang, Zhipeng Liu, 27 Sep 2025, TimeExpert: Boosting Long Time Series Forecasting with Temporal Mix of Experts, https://arxiv.org/abs/2509.23145
  • Xvyuan Liu, Xiangfei Qiu, Hanyin Cheng, Xingjian Wu, Chenjuan Guo, Bin Yang, Jilin Hu, 27 Sep 2025, ASTGI: Adaptive Spatio-Temporal Graph Interactions for Irregular Multivariate Time Series Forecasting, https://arxiv.org/abs/2509.23313
  • Xiangchen Song, Jiaqi Sun, Zijian Li, Yujia Zheng, Kun Zhang, 27 Sep 2025, LLM Interpretability with Identifiable Temporal-Instantaneous Representation, https://arxiv.org/abs/2509.23323
  • Divyam Madaan, Sumit Chopra, Kyunghyun Cho, 27 Sep 2025, Temporal Generalization: A Reality Check, https://arxiv.org/abs/2509.23487
  • Xiangfei Qiu, Liu Yang, Hanyin Cheng, Xingjian Wu, Rongjia Wu, Zhigang Zhang, Ding Tu, Chenjuan Guo, Bin Yang, Christian S. Jensen, Jilin Hu, 28 Sep 2025, Multi-Scale Spatial-Temporal Hypergraph Network with Lead-Lag Structures for Stock Time Series Forecasting, https://arxiv.org/abs/2509.23668
  • Yao Luan, Ni Mu, Yiqin Yang, Bo Xu, Qing-Shan Jia, 28 Sep 2025, STAIR: Addressing Stage Misalignment through Temporal-Aligned Preference Reinforcement Learning, https://arxiv.org/abs/2509.23802
  • Tao Yin and Xiaohong Zhang and Shaochen Fu and Zhibin Zhang and Li Huang and Yiyuan Yang and Kaixiang Yang and Meng Yan, 29 Sep 2025, ScatterAD: Temporal-Topological Scattering Mechanism for Time Series Anomaly Detection, https://arxiv.org/abs/2509.24414
  • Sophia N. Wilson, Jens Hesselbjerg Christensen, Raghavendra Selvan, 29 Sep 2025, Trading Carbon for Physics: On the Resource Efficiency of Machine Learning for Spatio-Temporal Forecasting, https://arxiv.org/abs/2509.24517
  • David Berghaus, Patrick Seifner, Kostadin Cvejoski, C\'esar Ojeda, Rams\'es J. S\'anchez, 29 Sep 2025, In-Context Learning of Temporal Point Processes with Foundation Inference Models, https://arxiv.org/abs/2509.24762
  • Lingyu Zhang, Guobin Wu, Yan Wang, Pengfei Xu, Jian Liang, Xuan Song, Yunhai Wang, 29 Aug 2025, Next Point-of-interest (POI) Recommendation Model Based on Multi-modal Spatio-temporal Context Feature Embedding, https://arxiv.org/abs/2509.22661
  • Rohit Chowdhury, Aniruddha Bala, Rohan Jaiswal, Siddharth Roheda, 27 Sep 2025, Vid-Freeze: Protecting Images from Malicious Image-to-Video Generation via Temporal Freezing, https://arxiv.org/abs/2509.23279
  • Md. Saiful Bari Siddiqui and Utsab Saha, 27 Sep 2025, AudioFuse: Unified Spectral-Temporal Learning via a Hybrid ViT-1D CNN Architecture for Robust Phonocardiogram Classification, https://arxiv.org/abs/2509.23454
  • Gyuhyeon Seo, Jungwoo Yang, Junseong Pyo, Nalim Kim, Jonggeun Lee, Yohan Jo, 29 Sep 2025, SimuHome: A Temporal- and Environment-Aware Benchmark for Smart Home LLM Agents, https://arxiv.org/abs/2509.24282
  • Ibrahim Delibasoglu and Fredrik Heintz, 26 Sep 2025, Learning Temporal Saliency for Time Series Forecasting with Cross-Scale Attention, https://arxiv.org/abs/2509.22839
  • Emmanouil Angelis, Francesco Quinzan, Ashkan Soleymani, Patrick Jaillet, Stefan Bauer, 29 Sep 2025, Double Machine Learning Based Structure Identification from Temporal Data, https://arxiv.org/abs/2311.06012
  • Yan Ru Pei, Olivier Coenen, 27 Sep 2025, PLEIADES: Building Temporal Kernels with Orthogonal Polynomials, https://arxiv.org/abs/2405.12179
  • Panqi Chen, Lei Cheng, Jianlong Li, Weichang Li, Weiqing Liu, Jiang Bian, Shikai Fang, 29 Sep 2025, Functional Complexity-adaptive Temporal Tensor Decomposition, https://arxiv.org/abs/2502.06164
  • Longlong Li, Cunquan Qu, Guanghui Wang, 28 Sep 2025, TGT: A Temporal Gating Transformer for Smartphone App Usage Prediction, https://arxiv.org/abs/2502.16957
  • Yang Du, Yuqi Liu, Qin Jin, 28 Sep 2025, Reversed in Time: A Novel Temporal-Emphasized Benchmark for Cross-Modal Video-Text Retrieval, https://arxiv.org/abs/2412.19178
  • Kumar Manas, Stefan Zwicklbauer and Adrian Paschke, 27 Sep 2025, CoT-TL: Low-Resource Temporal Knowledge Representation of Planning Instructions Using Chain-of-Thought Reasoning, https://arxiv.org/abs/2410.16207
  • Jan Corazza, Hadi Partovi Aria, Daniel Neider, Zhe Xu, 17 Oct 2025, Expediting Reinforcement Learning by Incorporating Knowledge About Temporal Causality in the Environment, https://arxiv.org/abs/2510.15456
  • Yunyang Cao, Juekai Lin, Hongye Wang, Wenhao Li, Bo Jin, 17 Oct 2025, Interpretable Hybrid-Rule Temporal Point Processes, https://arxiv.org/abs/2504.11344
  • Jiaxiang Chen, Mingxi Zou, Zhuo Wang, Qifan Wang, Dongning Sun, Chi Zhang, Zenglin Xu, 17 Oct 2025, FinHEAR: Human Expertise and Adaptive Risk-Aware Temporal Reasoning for Financial Decision-Making, https://arxiv.org/abs/2506.09080
  • Ankit Bhardwaj, Ananth Balashankar, Lakshminarayanan Subramanian, 4 Oct 2025, FieldFormer: Physics-Informed Transformers for Spatio-Temporal Field Reconstruction from Sparse Sensors, https://arxiv.org/abs/2510.03589
  • Haotian Gao, Zheng Dong, Jiawei Yong, Shintaro Fukushima, Kenjiro Taura, Renhe Jiang, 6 Oct 2025, How Different from the Past? Spatio-Temporal Time Series Forecasting with Self-Supervised Deviation Learning, https://arxiv.org/abs/2510.04908
  • Yucheng Wang, Peiliang Gong, Min Wu, Felix Ott, Xiaoli Li, Lihua Xie, Zhenghua Chen, 4 Oct 2025, Temporal Source Recovery for Time-Series Source-Free Unsupervised Domain Adaptation, https://arxiv.org/abs/2409.19635
  • S M Rafiuddin, 9 Oct 2025, Edu-EmotionNet: Cross-Modality Attention Alignment with Temporal Feedback Loops, https://arxiv.org/abs/2510.08802
  • T. Ed Li, Junyu Ren, 9 Oct 2025, Time-Aware Feature Selection: Adaptive Temporal Masking for Stable Sparse Autoencoder Training, https://arxiv.org/abs/2510.08855
  • Haroon Gharwi, Kai Shu, 10 Oct 2025, Variability Aware Recursive Neural Network (VARNN): A Residual-Memory Model for Capturing Temporal Deviation in Sequence Regression Modeling, https://arxiv.org/abs/2510.08944
  • Jose Tupayachi, Mustafa C. Camur, Kevin Heaslip, Xueping Li, 10 Oct 2025, Spatio-Temporal Graph Convolutional Networks for EV Charging Demand Forecasting Using Real-World Multi-Modal Data Integration, https://arxiv.org/abs/2510.09048
  • Vu Duc Anh Nguyen, Ziyue Li, 10 Oct 2025, Deep Learning to Identify the Spatio-Temporal Cascading Effects of Train Delays in a High-Density Network, https://arxiv.org/abs/2510.09350
  • Abigail J. Hayes, Tobias Schumacher, Markus Strohmaier, 10 Oct 2025, What Do Temporal Graph Learning Models Learn?, https://arxiv.org/abs/2510.09416
  • Disharee Bhowmick, Ranjith Ramanathan, Sathyanarayanan N. Aakur, 10 Oct 2025, STaTS: Structure-Aware Temporal Sequence Summarization via Statistical Window Merging, https://arxiv.org/abs/2510.09593
  • Vishakha Lall, Yisi Liu, 2 Oct 2025, Dynamic Stress Detection: A Study of Temporal Progression Modelling of Stress in Speech, https://arxiv.org/abs/2510.08586
  • Beige Jerry Jin, Leila Wehbe, 10 Oct 2025, Estimating Brain Activity with High Spatial and Temporal Resolution using a Naturalistic MEG-fMRI Encoding Model, https://arxiv.org/abs/2510.09415
  • Harsh Poonia, Felix Divo, Kristian Kersting, Devendra Singh Dhami, 10 Oct 2025, Exploring Neural Granger Causality with xLSTMs: Unveiling Temporal Dependencies in Complex Data, https://arxiv.org/abs/2502.09981
  • Xiangrui Liu, Minghao Qin, Yan Shu, Zhengyang Liang, Yang Tian, Chen Jason Zhang, Bo Zhao, Zheng Liu, 10 Oct 2025, TimeScope: Towards Task-Oriented Temporal Grounding In Long Videos, https://arxiv.org/abs/2509.26360
  • Zhongyu Ouyang, Qianlong Wen, Chunhui Zhang, Yanfang Ye, Soroush Vosoughi, 10 Oct 2025, What Makes LLMs Effective Sequential Recommenders? A Study on Preference Intensity and Temporal Context, https://arxiv.org/abs/2506.02261
  • Zan Li, Rui Fan, 23 Oct 2025, Crisis-Resilient Portfolio Management via Graph-based Spatio-Temporal Learning, https://arxiv.org/abs/2510.20868
  • Yunuo Zhang, Baiting Luo, Ayan Mukhopadhyay, Gabor Karsai, Abhishek Dubey, 24 Oct 2025, ESCORT: Efficient Stein-variational and Sliced Consistency-Optimized Temporal Belief Representation for POMDPs, https://arxiv.org/abs/2510.21107
  • Lei Liu, Zhenxin Huang, Hong Wang, huanshuo dong, Haiyang Xin, Hongwei Zhao, Bin Li, 24 Oct 2025, Accelerating Data Generation for Nonlinear temporal PDEs via homologous perturbation in solution space, https://arxiv.org/abs/2510.21592
  • Lucas Maystre, Gabriel Barello, Tudor Berariu, Aleix Cambray, Rares Dolga, Alvaro Ortega Gonzalez, Andrei Nica, David Barber, 24 Oct 2025, Incremental Sequence Classification with Temporal Consistency, https://arxiv.org/abs/2505.16548
  • Brandon R. Feng, David Keetae Park, Xihaier Luo, Arantxa Urdangarin, Shinjae Yoo, Brian J. Reich, 23 Oct 2025, STACI: Spatio-Temporal Aleatoric Conformal Inference, https://arxiv.org/abs/2505.21658
  • Martina G. Vilas, Safoora Yousefi, Besmira Nushi, Eric Horvitz, Vidhisha Balachandran, 12 Oct 2025, Tracing the Traces: Latent Temporal Signals f