CoT Reasoning

CoT: Chain-of-Thought

Chain-of-thought reasoning studies how language models break complex problems into steps, use intermediate information, and produce answers that can be checked or acted on. Approaches include prompting models to show their work, training on reasoning traces with supervised learning or reinforcement learning, and using structured methods such as code, symbolic rules, trees, or latent reasoning states. Researchers test these methods on tasks including mathematics, multi-hop question answering, coding, clinical decisions, and visual or spatial understanding, while developing verification and adaptive-compute methods to control cost. A central tension is that longer or more detailed traces do not reliably improve accuracy or explain how a model reached its answer; they can also amplify errors, bias, or unsupported claims.

Latest papers

228 papers25 papers in the last four weeks, up 56% on the four weeks before. 0.1% of all new papers.

VIRAASAT: Traversing Novel Paths for Indian Cultural Reasoning
Feb 20, 2026Harshul Raj Surana, Arijit Maji, Aryan Vats +3Cultural Knowledge in Language Models Multi-Hop QA
AI Institute, University of South Carolina, USA · Indian Institute of Technology Patna, India
Benchmarking Zero-Shot Reasoning Approaches for Error Detection in Solidity Smart Contracts
Feb 17, 2026Eduardo Sardenberg, Antonio José Grandson Busson, Daniel de Sousa Moraes +2Software Vulnerability Detection LLMs for Cybersecurity
TeleMídia Lab - PUC-Rio, Rio de Janeiro, RJ - Brazil · BTG Pactual, São Paulo, SP - Brazil · Instituto Militar de Engenharia, Rio de Janeiro, RJ - Brazil
The Potential of CoT for Reasoning: A Closer Look at Trace Dynamics
Feb 16, 2026Gregor Bachmann, Yichen Jiang, Seyed Mohsen Moosavi Dezfooli +1CoT Reasoning LLM Reasoning
Apple
Beyond the Prompt in Large Language Models: Comprehension, In-Context Learning, and Chain-of-Thought
Feb 16, 2026Yuling Jiao, Yanming Lai, Huazhen Lin +3LLM Prompting In-Context Learning
School of Artificial Intelligence, Wuhan University · National Center for Applied Mathematics in Hubei, Wuhan University · Hubei Key Laboratory of Computational Science, Wuhan University +6
Measuring and Mitigating Post-hoc Rationalization in Reverse Chain-of-Thought Generation
Feb 16, 2026Guangyue Peng, Zongchao Chen, Wen Luo +9CoT Distillation CoT Reasoning
State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University · Nanbeige Lab, BOSS Zhipin · University of Electronic Science and Technology of China
Broken Chains: The Cost of Incomplete Reasoning in LLMs
Feb 16, 2026Ian Su, Gaurav Purushothaman, Jey Narayan +5LLM Reasoning CoT Reasoning
Divide-and-Conquer CoT: RL for Reducing Latency via Parallel Reasoning
Jan 30, 2026Arvind Mahankali, Kaiyue Wen, Tengyu MaMulti-Agent LLM Systems CoT Reasoning
Stanford University
Triggering Chain-of-Thought via Latent Feature Interventions in Large Language Models
Jan 12, 2026Zhenghao He, Guangzhi Xiong, Bohan Liu +2LLM Interpretability CoT Reasoning
University of Virginia, USA
Stepwise Think-Critique: Interleaved Reasoning and Self-Critique in a Single LLM
Dec 17, 2025Jiaqi Xu, Cuiling Lan, Xuejin Chen +1LLM Self-Correction CoT Reasoning
University of Science and Technology of China · ⋆This work was done when Jiaqi was an intern at MSRA. · Microsoft Research Asia
Deductive Logic in Language Models: Horizontal vs Vertical Reasoning
Oct 10, 2025Davide Maltoni, Matteo FerraraTransformer Interpretability CoT Reasoning
Department of Computer Science and Engineering, University of Bologna, Italy
$π$ -CoT: Prolog-Initialized Chain-of-Thought Prompting for Multi-Hop Question-Answering
Jun 25, 2025Chao Wan, Albert Gong, Mihir Mishra +3Multi-Hop QA LLM Prompting
Cornell University.
Learning Composable Chains-of-Thought
May 28, 2025Fangcong Yin, Zeyu Leo Liu, Liu Leqi +2LLM Fine-Tuning CoT Reasoning
The University of Texas at Austin · Princeton University
Reasoning Beyond Language: A Comprehensive Survey on Latent Chain-of-Thought Reasoning
May 22, 2025Xinghao Chen, Anhao Zhao, Heming Xia +7CoT Reasoning LLM Reasoning
Department of Computing, The Hong Kong Polytechnic University · Ningbo Digital Twin Institute, Eastern Institute of Technology, Ningbo, China
HoT: Highlighted Chain of Thought for Referencing Supporting Facts from Inputs
Mar 3, 2025Tin Nguyen, Logan Bolton, Mohammad Reza Taesiri +2LLM Grounding LLM Answer Verification
Auburn University · University of Alberta
The relationship between reasoning and performance in large language models--o3 (mini) thinks harder, not longer
Feb 21, 2025Marthe Ballon, Andres Algaba, Vincent GinisCoT Reasoning Efficient Language Model Reasoning
Data Analytics Lab, Vrije Universiteit Brussel, Brussel 1050, Belgium · School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
Thought-Like-Pro: Enhancing Reasoning of Large Language Models through Self-Bootstrapped Prolog-based Chain-of-Thought
Jul 18, 2024Xihe Qiu, Yongxin Deng, Xiaoyu Tan +2Symbolic Reasoning in Language Models LLM Training
Shanghai University of Engineering Science · National University of Singapore · University of Technology Sydney
Few shot chain-of-thought driven reasoning to prompt LLMs for open ended medical question answering
Mar 7, 2024Saeel Sandeep Nachane, Ojas Gramopadhye, Prateek Chanda +5Healthcare Medical Diagnosis
2Indian Institute of Technology Bombay · 1IBM Research
Amortizing intractable inference in large language models
Oct 6, 2023Edward J. Hu, Moksh Jain, Eric Elmoznino +4Amortized Inference LLM Fine-Tuning
Mila – Quebec AI Institute, Universit´e de Montr´eal · University of Oxford

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VIRAASAT: Traversing Novel Paths for Indian Cultural Reasoning

Harshul Raj Surana, Arijit Maji, Aryan Vats, Akash Ghosh+2

AI Institute, University of South Carolina, USA · Indian Institute of Technology Patna, India

Cultural Knowledge in Language Models Multi-Hop QA CoT Reasoning Knowledge Graph Question Answering

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Large Language Models (LLMs) have made significant progress in reasoning tasks across various domains such as mathematics and coding. However, their performance deteriorates in tasks requiring rich socio-cultural knowledge and diverse local contexts, particularly those involving Indian Culture. Existing Cultural benchmarks are (i) Manually crafted, (ii) contain single-hop questions testing factual recall, and (iii) prohibitively costly to scale, leaving this deficiency largely unmeasured. To address this, we introduce VIRAASAT, a novel, semi-automated multi-hop approach for generating cultural specific multi-hop Question-Answering dataset for Indian culture. VIRAASAT leverages a Knowledge Graph comprising more than 700 expert-curated cultural artifacts, covering 13 key attributes of Indian culture (history, festivals, etc). VIRAASAT spans all 28 states and 8 Union Territories, yielding more than 3,200 multi-hop questions that necessitate chained cultural reasoning. We evaluate current State-of-the-Art (SOTA) LLMs on VIRAASAT and identify key limitations in reasoning wherein fine-tuning on Chain-of-Thought(CoT) traces fails to ground and synthesize low-probability facts. To bridge this gap, we propose a novel framework named Symbolic Chain-of-Manipulation (SCoM). Adapting the Chain-of-Manipulation paradigm, we train the model to simulate atomic Knowledge Graph manipulations internally. SCoM teaches the model to reliably traverse the topological structure of the graph. Experiments on Supervised Fine-Tuning (SFT) demonstrate that SCoM outperforms standard CoT baselines by up to 20%. We release the VIRAASAT dataset along with our findings, laying a strong foundation towards building Culturally Aware Reasoning Models.