Suche: chain of thought

520 Paper gefunden.

• Integrating Chain-of-Thought and Retrieval Augmented Generation Enhances Rare Disease Diagnosis from Clinical Notes

  Zhanliang Wang, Da Wu, Quan Nguyen, Kai Wang · 19. Februar 2026 · Genomics and Rare Diseases

  Background: Several studies show that large language models (LLMs) struggle with phenotype-driven gene prioritization for rare diseases. These studies typically use Human Phenotype Ontology (HPO) terms to prompt foundation models like GPT and LLaMA to predict candidate genes. However, in real-world …

• SurgRAW: Multi-Agent Workflow with Chain of Thought Reasoning for Robotic Surgical Video Analysis

  Chang Han Low, Ziyue Wang, Tianyi Zhang, Zhu Zhuo, Zhitao Zeng, Evangelos B. Mazomenos, Yueming Jin · 19. Februar 2026 · Multimodal Machine Learning Applications

  Robotic-assisted surgery (RAS) is central to modern surgery, driving the need for intelligent systems with accurate scene understanding. Most existing surgical AI methods rely on isolated, task-specific models, leading to fragmented pipelines with limited interpretability and no unified understandin…

• NPG-Muse: Scaling Long Chain-of-Thought Reasoning with NP-Hard Graph Problems

  Yuyao Wang, Bowen Liu, Jianheng Tang, Nuo Chen, Yuhan Li, Qifan Zhang, Chenyi Zi, Chen Zhang, Jia Li · 18. Februar 2026 · Natural Language Processing Techniques

  Reasoning Large Language Models (RLLMs) have recently achieved remarkable progress on complex reasoning tasks, largely enabled by their long chain-of-thought (Long CoT) capabilities. However, developing these Long CoT behaviors relies heavily on post-training with high-quality datasets, which are ty…

• Chain of Thought in Order: Discovering Learning-Friendly Orders for Arithmetic

  Yuta Sato, Kazuhiko Kawamoto, Hiroshi Kera · 17. Februar 2026 · Cognitive and developmental aspects of mathematical skills

  The chain of thought, i.e., step-by-step reasoning, is one of the fundamental mechanisms of Transformers. While the design of intermediate reasoning steps has been extensively studied and shown to critically influence performance on mathematical, multi-step reasoning tasks, the ordering of these ste…

• Making Slow Thinking Faster: Compressing LLM Chain-of-Thought via Step Entropy

  Zeju Li, Jianyuan Zhong, Ziyang Zheng, Xiangyu Wen, Zhijian Xu, Yingying Cheng, Fan Zhang, Qiang Xu · 17. Februar 2026 · Large Language Models

  Large Language Models (LLMs) using Chain-of-Thought (CoT) prompting excel at complex reasoning but generate verbose thought processes with considerable redundancy, leading to increased inference costs and reduced efficiency. We introduce a novel CoT compression framework based on step entropy, a met…

• Diagnosing Pathological Chain-of-Thought in Reasoning Models

  Manqing Liu, David Williams-King, Ida Caspary, Linh Le, Hannes Whittingham, Puria Radmard, Cameron Tice, Edward James Young · 17. Februar 2026 · Explainable Artificial Intelligence (XAI)

  Chain-of-thought (CoT) reasoning is fundamental to modern LLM architectures and represents a critical intervention point for AI safety. However, CoT reasoning may exhibit failure modes that we note as pathologies, which prevent it from being useful for monitoring. Prior work has identified three dis…

• Measuring and Mitigating Post-hoc Rationalization in Reverse Chain-of-Thought Generation

  Guangyue Peng, Zongchao Chen, Wen Luo, Yuntao Wen, Wei Li, Ruixiang Feng, Ran Le, Chen Yang, Zhenwei An, Yang Song, Tao Zhang, Houfeng Wang · 17. Februar 2026 · Large Language Models

  Reverse Chain-of-Thought Generation (RCG) synthesizes reasoning traces from query-answer pairs, but answer-visible generation can justify a pre-committed answer rather than derive it. This post-hoc rationalization creates a train-inference mismatch because student models are trained on answer-condit…

• LogitsCoder: Towards Efficient Chain-of-Thought Path Search via Logits Preference Decoding for Code Generation

  Jizheng Chen, Weiming Zhang, Xinyi Dai, Weiwen Liu, Kounianhua Du, Yasheng Wang, Ruiming Tang, Yong Yu, Weinan Zhang · 17. Februar 2026 · Software Testing and Debugging Techniques

  Code generation remains a challenging task that requires precise and structured reasoning. Existing Test Time Scaling (TTS) methods, including structured tree search, have made progress in exploring reasoning paths but still face two major challenges: (1) underthinking, where reasoning chains tend t…

• Chain-of-Thought Reasoning with Large Language Models for Clinical Alzheimer's Disease Assessment and Diagnosis

  Tongze Zhang, Jun-En Ding, Melik Ozolcer, Fang-Ming Hung, Albert Chih-Chieh Yang, Feng Liu, Yi-Rou Ji, Sang Won Bae · 17. Februar 2026 · Machine Learning in Healthcare

  Alzheimer's disease (AD) has become a prevalent neurodegenerative disease worldwide. Traditional diagnosis still relies heavily on medical imaging and clinical assessment by physicians, which is often time-consuming and resource-intensive in terms of both human expertise and healthcare resources. In…

• On Learning Verifiers and Implications to Chain-of-Thought Reasoning

  Maria-Florina Balcan, Avrim Blum, Zhiyuan Li, Dravyansh Sharma · 16. Februar 2026 · Logic, Reasoning, and Knowledge

  Chain-of-Thought reasoning has emerged as a powerful approach for solving complex mathematical and logical problems. However, it can often veer off track through incorrect or unsubstantiated inferences. Formal mathematical reasoning, which can be checked with a formal verifier, is one approach to ad…

• Constraint-Rectified Training for Efficient Chain-of-Thought

  Qinhang Wu, Sen Lin, Ming Zhang, Yingbin Liang, Ness B. Shroff · 16. Februar 2026 · Large Language Models

  Chain-of-Thought (CoT) has significantly enhanced the reasoning capabilities of Large Language Models (LLMs), especially when combined with reinforcement learning (RL) based post-training methods. While longer reasoning traces can improve answer quality and unlock abilities such as self-correction, …

• On Robustness and Chain-of-Thought Consistency of RL-Finetuned VLMs

  Rosie Zhao, Anshul Shah, Xiaoyu Zhu, Xinke Deng, Zhongyu Jiang, Yang Yang, Joerg Liebelt, Arnab Mondal · 16. Februar 2026 · Multimodal Machine Learning Applications

  Reinforcement learning (RL) fine-tuning has become a key technique for enhancing large language models (LLMs) on reasoning-intensive tasks, motivating its extension to vision language models (VLMs). While RL-tuned VLMs improve on visual reasoning benchmarks, they remain vulnerable to weak visual gro…

• Understanding Chain-of-Thought in Large Language Models via Topological Data Analysis

  Chenghao Li, Chaoning Zhang, Yi Lu, Shuxu Chen, Xudong Wang, Jiaquan Zhang, Zhicheng Wang, Zhengxun Jin, Kuien Liu, Sung-Ho Bae, Guoqing Wang, Yang Yang, Heng Tao Shen · 16. Februar 2026 · Topological and Geometric Data Analysis

  With the development of large language models (LLMs), particularly with the introduction of the long reasoning chain technique, the reasoning ability of LLMs in complex problem-solving has been significantly enhanced. While acknowledging the power of long reasoning chains, we cannot help but wonder:…

• Retrieval-Augmented Self-Taught Reasoning Model with Adaptive Chain-of-Thought for ASR Named Entity Correction

  Junjie An, Jingguang Tian, Tianyi Wang, Yu Gao, Xiaofeng Mou, Yi Xu · 16. Februar 2026 · Large Language Models

  End-to-end automatic speech recognition (ASR) systems frequently misrecognize domain-specific phrases like named entities, which can cause catastrophic failures in downstream tasks. A new family of named entity correction methods based on large language models (LLMs) has recently emerged. However, t…

• UniT: Unified Multimodal Chain-of-Thought Test-time Scaling

  Leon Liangyu Chen, Haoyu Ma, Zhipeng Fan, Ziqi Huang, Animesh Sinha, Xiaoliang Dai, Jialiang Wang, Zecheng He, Jianwei Yang, Chunyuan Li, Junzhe Sun, Chu Wang, Serena Yeung-Levy, Felix Juefei-Xu · 13. Februar 2026 · Multimodal Machine Learning Applications

  Unified models can handle both multimodal understanding and generation within a single architecture, yet they typically operate in a single pass without iteratively refining their outputs. Many multimodal tasks, especially those involving complex spatial compositions, multiple interacting objects, o…

• Spatial Chain-of-Thought: Bridging Understanding and Generation Models for Spatial Reasoning Generation

  Wei Chen, Yancheng Long, Mingqiao Liu, Haojie Ding, Yankai Yang, Hongyang Wei, Yi-Fan Zhang, Bin Wen, Fan Yang, Tingting Gao, Han Li, Long Chen · 13. Februar 2026 · Generative Adversarial Networks and Image Synthesis

  While diffusion models have shown exceptional capabilities in aesthetic image synthesis, they often struggle with complex spatial understanding and reasoning. Existing approaches resort to Multimodal Large Language Models (MLLMs) to enhance this capability. However, they either incur high computatio…

• Expanding Reasoning Potential in Foundation Model by Learning Diverse Chains of Thought Patterns

  Xuemiao Zhang, Can Ren, Chengying Tu, Rongxiang Weng, Shuo Wang, Hongfei Yan, Jingang Wang, Xunliang Cai · 12. Februar 2026 · Semantic Web and Ontologies

  Recent progress in large reasoning models for challenging mathematical reasoning has been driven by reinforcement learning (RL). Incorporating long chain-of-thought (CoT) data during mid-training has also been shown to substantially improve reasoning depth. However, current approaches often utilize …

• Reinforcing Chain-of-Thought Reasoning with Self-Evolving Rubrics

  Leheng Sheng, Wenchang Ma, Ruixin Hong, Xiang Wang, An Zhang, Tat-Seng Chua · 12. Februar 2026 · Embodied and Extended Cognition

  Despite chain-of-thought (CoT) playing crucial roles in LLM reasoning, directly rewarding it is difficult: training a reward model demands heavy human labeling efforts, and static RMs struggle with evolving CoT distributions and reward hacking. These challenges motivate us to seek an autonomous CoT …

• Long Chain-of-Thought Compression via Fine-Grained Group Policy Optimization

  Xinchen Han, Hossam Afifi, Michel Marot, Xilu Wang, Lu Yin · 11. Februar 2026 · Large Language Models

  Large Language Models (LLMs) often generate unnecessarily verbose Chain-of-Thought (CoT) reasoning that increases computational costs and latency without proportional performance gains. In this paper, we propose \textbf{F}ine-grained \textbf{G}roup policy \textbf{O}ptimization (\textbf{FGO}), a Rein…

• Towards Efficient Large Language Reasoning Models via Extreme-Ratio Chain-of-Thought Compression

  Yuntian Tang, Bohan Jia, Wenxuan Huang, Lianyue Zhang, Jiao Xie, Wenxi Li, Wei Li, Jie Hu, Xinghao Chen, Rongrong Ji, Shaohui Lin · 10. Februar 2026 · Large Language Models

  Chain-of-Thought (CoT) reasoning successfully enhances the reasoning capabilities of Large Language Models (LLMs), yet it incurs substantial computational overhead for inference. Existing CoT compression methods often suffer from a critical loss of logical fidelity at high compression ratios, result…

• SPD-Faith Bench: Diagnosing and Improving Faithfulness in Chain-of-Thought for Multimodal Large Language Models

  Weijiang Lv, Yaoxuan Feng, Xiaobo Xia, Jiayu Wang, Yan Jing, Wenchao Chen, Bo Chen · 10. Februar 2026 · Multimodal Machine Learning Applications

  Chain-of-Thought reasoning is widely used to improve the interpretability of multimodal large language models (MLLMs), yet the faithfulness of the generated reasoning traces remains unclear. Prior work has mainly focused on perceptual hallucinations, leaving reasoning level unfaithfulness underexplo…

• Fine-R1: Make Multi-modal LLMs Excel in Fine-Grained Visual Recognition by Chain-of-Thought Reasoning

  Hulingxiao He, Zijun Geng, Yuxin Peng · 10. Februar 2026 · Multimodal Machine Learning Applications

  Any entity in the visual world can be hierarchically grouped based on shared characteristics and mapped to fine-grained sub-categories. While Multi-modal Large Language Models (MLLMs) achieve strong performance on coarse-grained visual tasks, they often struggle with Fine-Grained Visual Recognition …

• Dynamics Within Latent Chain-of-Thought: An Empirical Study of Causal Structure

  Zirui Li, Xuefeng Bai, Kehai Chen, Yizhi Li, Jian Yang, Chenghua Lin, Min Zhang · 10. Februar 2026 · Child and Animal Learning Development

  Latent or continuous chain-of-thought methods replace explicit textual rationales with a number of internal latent steps, but these intermediate computations are difficult to evaluate beyond correlation-based probes. In this paper, we view latent chain-of-thought as a manipulable causal process in r…

• Joint Reward Modeling: Internalizing Chain-of-Thought for Efficient Visual Reward Models

  Yankai Yang, Yancheng Long, Hongyang Wei, Wei Chen, Tianke Zhang, Kaiyu Jiang, Haonan Fan, Changyi Liu, Jiankang Chen, Kaiyu Tang, Bin Wen, Fan Yang, Tingting Gao, Han Li, Shuo Yang · 10. Februar 2026 · Multimodal Machine Learning Applications

  Reward models are critical for reinforcement learning from human feedback, as they determine the alignment quality and reliability of generative models. For complex tasks such as image editing, reward models are required to capture global semantic consistency and implicit logical constraints beyond …

• Are Reasoning LLMs Robust to Interventions on Their Chain-of-Thought?

  Alexander von Recum, Leander Girrbach, Zeynep Akata · 10. Februar 2026 · Advanced Graph Neural Networks

  Reasoning LLMs (RLLMs) generate step-by-step chains of thought (CoTs) before giving an answer, which improves performance on complex tasks and makes reasoning more transparent. But how robust are these reasoning traces to disruptions that occur within them? To address this question, we introduce a c…