Búsqueda: chain of thought

524 artículos encontrados.

• S-Chain: Structured Visual Chain-of-Thought For Medicine

  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 · 28 de octubre de 2025 · Multimodal Machine Learning Applications

  Faithful reasoning in medical vision-language models (VLMs) requires not only accurate predictions but also transparent alignment between textual rationales and visual evidence. While Chain-of-Thought (CoT) prompting has shown promise in medical visual question answering (VQA), no large-scale expert…

• Causal Sufficiency and Necessity Improves Chain-of-Thought Reasoning

  Xiangning Yu, Zhuohan Wang, Linyi Yang, Haoxuan Li, Anjie Liu, Xiao Xue, Jun Wang, Mengyue Yang · 28 de octubre de 2025 · Philosophy and History of Science

  Chain-of-Thought (CoT) prompting plays an indispensable role in endowing large language models (LLMs) with complex reasoning capabilities. However, CoT currently faces two fundamental challenges: (1) Sufficiency, which ensures that the generated intermediate inference steps comprehensively cover and…

• Can Confidence Estimates Decide When Chain-of-Thought Is Necessary for LLMs?

  Samuel Lewis-Lim, Xingwei Tan, Zhixue Zhao, Nikolaos Aletras · 27 de octubre de 2025 · Large Language Models

  Chain-of-thought (CoT) prompting is a common technique for improving the reasoning abilities of large language models (LLMs). However, extended reasoning is often unnecessary and substantially increases token usage. As such, a key question becomes how to optimally allocate compute to when reasoning …

• Video-Skill-CoT: Skill-based Chain-of-Thoughts for Domain-Adaptive Video Reasoning

  Daeun Lee, Jaehong Yoon, Jaemin Cho, Mohit Bansal · 27 de octubre de 2025 · Multimodal Machine Learning Applications

• To CoT or To Loop? A Formal Comparison Between Chain-of-Thought and Looped Transformers

  Kevin Xu, Issei Sato · 27 de octubre de 2025 · Scientific Research and Philosophical Inquiry

• Chain of Spatial Thoughts: Modality-Agnostic Spatial Grounding for Vision Language Models

  Hunter Schofield, Mohammed Elmahgiubi, Mohammad Mahdavian, Richard Shi, Jinjun Shan, Amir Rasouli, Dongfeng Bai · 12 de agosto de 2026 · Spatial Cognition and Navigation

  Spatial understanding is fundamental to embodied intelligence, underpinning applications such as robotic manipulation, embodied navigation, and autonomous driving. Although recent vision-language models (VLMs) have achieved impressive performance on spatial reasoning benchmarks, state-of-the-art app…

• CoLT: Teaching Multi-Modal Models to Think with Chain of Latent Thoughts

  Lianyu Hu, Shengqian Qin, Zeqin Liao, Qing Guo, Liang Wan, Wei Feng, Yang Liu · 1 de julio de 2026 · Multimodal Machine Learning Applications

  Chain-of-thought (CoT) reasoning has enabled multi-modal large language models (MLLMs) to tackle complex visual reasoning tasks by generating explicit intermediate reasoning steps in natural language. However, this text-based reasoning paradigm is inherently slow at inference time with even thousand…

• Deep Thinking by Markov Chain of Continuous Thoughts

  Jiayu Liu, Zhenya Huang, Xuan Yang, Tianyun Ji, Anya Sims, Hao Xu, Enhong Chen, Yee Whye Teh, Ning Miao · 5 de mayo de 2026 · Anomaly Detection Techniques and Applications

  Transformer-based models can perform complicated reasoning by generating reasoning paths token by token. While effective, this approach often requires generating thousands of tokens to solve a single problem, which can be slow and computationally expensive. More importantly, it involves a discrete s…

• Long-Document QA with Chain-of-Structured-Thought and Fine-Tuned SLMs

  Zhuowen Liang, Xiaotian Lin, Zhengxuan Zhang, Yuyu Luo, Haixun Wang, Nan Tang · 1 de abril de 2026 · Large Language Models

  Large language models (LLMs) are widely applied to data analytics over documents, yet direct reasoning over long, noisy documents remains brittle and error-prone. Hence, we study document question answering (QA) that consolidates dispersed evidence into a structured output (e.g., a table, graph, or …

• Emergence of Superposition: Unveiling the Training Dynamics of Chain of Continuous Thought

  Hanlin Zhu, Shibo Hao, Zhiting Hu, Jiantao Jiao, Stuart Russell, Yuandong Tian · 3 de marzo de 2026 · Semantic Web and Ontologies

  Previous work shows that the chain of continuous thought (continuous CoT) improves the reasoning capability of large language models (LLMs) by enabling implicit parallel thinking, and a subsequent work provided theoretical insight by showing that a two-layer transformer equipped with continuous CoT …

• Chain-of-Sanitized-Thoughts: Plugging PII Leakage in CoT of Large Reasoning Models

  Arghyadeep Das, Sai Sreenivas Chintha, Rishiraj Girmal, Kinjal Pandey, Sharvi Endait · 9 de enero de 2026 · Explainable Artificial Intelligence (XAI)

  Large Reasoning Models (LRMs) improve performance, reliability, and interpretability by generating explicit chain-of-thought (CoT) reasoning, but this transparency introduces a serious privacy risk: intermediate reasoning often leaks personally identifiable information (PII) even when final answers …

• Pat-DEVAL: Chain-of-Legal-Thought Evaluation for Patent Description

  Yongmin Yoo, Kris W Pan · 2 de enero de 2026 · Intellectual Property and Patents

  Patent descriptions must deliver comprehensive technical disclosure while meeting strict legal standards such as enablement and written description requirements. Although large language models have enabled end-to-end automated patent drafting, existing evaluation approaches fail to assess long-form …

• Do Latent Tokens Think? A Causal and Adversarial Analysis of Chain-of-Continuous-Thought

  Yuyi Zhang, Boyu Tang, Tianjie Ju, Sufeng Duan, Gongshen Liu · 29 de diciembre de 2025 · Large Language Models

  Latent tokens are gaining attention for enhancing reasoning in large language models (LLMs), yet their internal mechanisms remain unclear. This paper examines the problem from a reliability perspective, uncovering fundamental weaknesses: latent tokens function as uninterpretable placeholders rather …

• Chain-of-Anomaly Thoughts with Large Vision-Language Models

  Pedro Domingos, João Pereira, Vasco Lopes, João Neves, David Semedo · 24 de diciembre de 2025 · Anomaly Detection Techniques and Applications

  Automated video surveillance with Large Vision-Language Models is limited by their inherent bias towards normality, often failing to detect crimes. While Chain-of-Thought reasoning strategies show significant potential for improving performance in language tasks, the lack of inductive anomaly biases…

• Chain-of-Visual-Thought: Teaching VLMs to See and Think Better with Continuous Visual Tokens

  Yiming Qin, Bomin Wei, Jiaxin Ge, Konstantinos Kallidromitis, Stephanie Fu, Trevor Darrell, Xudong Wang · 25 de noviembre de 2025 · Multimodal Machine Learning Applications

  Vision-Language Models (VLMs) excel at reasoning in linguistic space but struggle with perceptual understanding that requires dense visual perception, e.g., spatial reasoning and geometric awareness. This limitation stems from the fact that current VLMs have limited mechanisms to capture dense visua…

• Reasoning by Superposition: A Theoretical Perspective on Chain of Continuous Thought

  Hanlin Zhu, Shibo Hao, Zhiting Hu, Jiantao Jiao, Stuart Russell, Yuandong Tian · 4 de noviembre de 2025 · Advanced Graph Neural Networks

  Large Language Models (LLMs) have demonstrated remarkable performance in many applications, including challenging reasoning problems via chain-of-thoughts (CoTs) techniques that generate ``thinking tokens'' before answering the questions. While existing theoretical works demonstrate that CoTs with d…

• Cognitive Loop of Thought: Reversible Hierarchical Markov Chain for Efficient Mathematical Reasoning

  Jia-Chen Zhang, Yu-Jie Xiong, Zheng Zhou · 9 de abril de 2026 · Cognitive Science and Mapping

  Multi-step Chain-of-Thought (CoT) has significantly advanced the mathematical reasoning capabilities of LLMs by leveraging explicit reasoning steps. However, the widespread adoption of Long CoT often results in sequence lengths that exceed manageable computational limits. While existing approaches a…

• CoMAT: Chain of Mathematically Annotated Thought Improves Mathematical Reasoning

  Joshua Ong Jun Leang, Aryo Pradipta Gema, Shay B. Cohen · 16 de enero de 2026 · Intelligent Tutoring Systems and Adaptive Learning

  Mathematical reasoning remains a significant challenge for large language models (LLMs), despite progress in prompting techniques such as Chain-of-Thought (CoT). We present **Chain of Mathematically Annotated Thought (CoMAT)**, which enhances reasoning through two stages: *Symbolic Conversion* (conv…

• Thought-For-Food: Reasoning Chain Induced Food Visual Question Answering

  Riddhi Jain, Manasi Patwardhan, Parijat Deshpande, Venkataramana Runkana · 4 de noviembre de 2025 · Multimodal Machine Learning Applications

  The immense diversity in the culture and culinary of Indian cuisines calls attention to the major shortcoming of the existing Visual Question Answering(VQA) systems which are inclined towards the foods from Western region. Recent attempt towards building a VQA dataset for Indian food is a step towar…

• $AutoDrive\text{-}P^3$: Unified Chain of Perception-Prediction-Planning Thought via Reinforcement Fine-Tuning

  Yuqi Ye, Zijian Zhang, Junhong Lin, Shangkun Sun, Changhao Peng, Wei Gao · 31 de marzo de 2026 · Multimodal Machine Learning Applications

  Vision-language models (VLMs) are increasingly being adopted for end-to-end autonomous driving systems due to their exceptional performance in handling long-tail scenarios. However, current VLM-based approaches suffer from two major limitations: 1) Some VLMs directly output planning results without …

• Chain of Event-Centric Causal Thought for Physically Plausible Video Generation

  Zixuan Wang, Yixin Hu, Haolan Wang, Feng Chen, Yan Liu, Wen Li, Yinjie Lei · 11 de marzo de 2026 · Generative Adversarial Networks and Image Synthesis

  Physically Plausible Video Generation (PPVG) has emerged as a promising avenue for modeling real-world physical phenomena. PPVG requires an understanding of commonsense knowledge, which remains a challenge for video diffusion models. Current approaches leverage commonsense reasoning capability of la…

• Long Grounded Thoughts: Synthesizing Visual Problems and Reasoning Chains at Scale

  David Acuna, Chao-Han Huck Yang, Yuntian Deng, Jaehun Jung, Ximing Lu, Prithviraj Ammanabrolu, Hyunwoo Kim, Yuan-Hong Liao, Yejin Choi · 18 de febrero de 2026 · Multimodal Machine Learning Applications

  Despite rapid progress, multimodal reasoning still lacks a systematic approach to synthesize large-scale vision-centric datasets beyond visual math. We introduce a framework able to synthesize vision-centric problems spanning diverse levels of complexity, and the resulting dataset with over 1M high-…

• Quality-Driven Agentic Reasoning for LLM-Assisted Software Design: Questions-of-Thoughts (QoT) as a Time-Series Self-QA Chain

  Yen-Ku Liu, Yun-Cheng Tsai · 13 de marzo de 2026 · Software Engineering Research

  Recent advances in large language models (LLMs) have accelerated AI-assisted software development, yet practical deployment remains constrained by incomplete implementations, weak modularization, and inconsistent security practices. We introduce Questions-of-Thoughts (QoT), a quality-driven inferenc…

• Framework of Thoughts: A Foundation Framework for Dynamic and Optimized Reasoning based on Chains, Trees, and Graphs

  Felix Fricke, Simon Malberg, Georg Groh · 19 de febrero de 2026 · Constraint Satisfaction and Optimization

  Prompting schemes such as Chain of Thought, Tree of Thoughts, and Graph of Thoughts can significantly enhance the reasoning capabilities of large language models. However, most existing schemes require users to define static, problem-specific reasoning structures that lack adaptability to dynamic or…