Búsqueda: chain of thought

520 artículos encontrados.

• MCoT-MVS: Multi-level Vision Selection by Multi-modal Chain-of-Thought Reasoning for Composed Image Retrieval

  Xuri Ge, Chunhao Wang, Xindi Wang, Zheyun Qin, Zhumin Chen, Xin Xin · 19 de marzo de 2026 · Multimodal Machine Learning Applications

  Composed Image Retrieval (CIR) aims to retrieve target images based on a reference image and modified texts. However, existing methods often struggle to extract the correct semantic cues from the reference image that best reflect the user's intent under textual modification prompts, resulting in int…

• Eliciting Chain-of-Thought Reasoning for Time Series Analysis using Reinforcement Learning

  Felix Parker, Nimeesha Chan, Chi Zhang, Kimia Ghobadi · 17 de marzo de 2026 · Time Series Analysis and Forecasting

  Complex numerical time series analysis often demands multi-step reasoning capabilities beyond current models' reach. Tasks like medical diagnosis and weather forecasting require sequential reasoning processes - including counterfactual analysis, logical deduction, knowledge application, and multi-mo…

• Rationale-Enhanced Decoding for Multi-modal Chain-of-Thought

  Shin'ya Yamaguchi, Kosuke Nishida, Daiki Chijiwa · 17 de marzo de 2026 · Multimodal Machine Learning Applications

  Large vision-language models (LVLMs) have demonstrated remarkable capabilities by integrating pre-trained vision encoders with large language models (LLMs). Similar to single-modal LLMs, chain-of-thought (CoT) prompting has been adapted for LVLMs to enhance multi-modal reasoning by generating interm…

• Chart-R1: Chain-of-Thought Supervision and Reinforcement for Advanced Chart Reasoner

  Lei Chen, Xuanle Zhao, Zhixiong Zeng, Jing Huang, Yufeng Zhong, Lin Ma · 17 de marzo de 2026 · Reinforcement Learning in Robotics

  Chart reasoning presents unique challenges due to its inherent complexity -- requiring precise numerical comprehension, multi-level visual understanding, and logical inference across interconnected data elements. Existing vision-language models often struggle with such reasoning tasks, particularly …

• SFCoT: Safer Chain-of-Thought via Active Safety Evaluation and Calibration

  Yu Pan, Wenlong Yu, Tiejun Wu, Xiaohu Ye, Qiannan Si, Guangquan Xu, Bin Wu · 17 de marzo de 2026 · Adversarial Robustness in Machine Learning

  Large language models (LLMs) have demonstrated remarkable capabilities in complex reasoning tasks. However, they remain highly susceptible to jailbreak attacks that undermine their safety alignment. Existing defense mechanisms typically rely on post hoc filtering applied only to the final output, le…

• Nudging Hidden States: Training-Free Model Steering for Chain-of-Thought Reasoning in Large Audio-Language Models

  Lok-Lam Ieong, Chia-Chien Chen, Chih-Kai Yang, Yu-Han Huang, An-Yu Cheng, Hung-yi Lee · 17 de marzo de 2026 · Multimodal Machine Learning Applications

  Chain-of-thought (CoT) prompting has been extended to large audio-language models (LALMs) to elicit reasoning, yet enhancing its effectiveness without training remains challenging. We study inference-time model steering as a training-free approach to improve LALM reasoning. We introduce three strate…

• $PA^3$: $\textbf{P}$olicy-$\textbf{A}$ware $\textbf{A}$gent $\textbf{A}$lignment through Chain-of-Thought

  Shubhashis Roy Dipta, Daniel Bis, Kun Zhou, Lichao Wang, Benjamin Z. Yao, Chenlei Guo, Ruhi Sarikaya · 17 de marzo de 2026 · AI in Service Interactions

  Conversational assistants powered by large language models (LLMs) excel at tool-use tasks but struggle with adhering to complex, business-specific rules. While models can reason over business rules provided in context, including all policies for every query introduces high latency and wastes compute…

• Step-CoT: Stepwise Visual Chain-of-Thought for Medical Visual Question Answering

  Lin Fan, Yafei Ou, Zhipeng Deng, Pengyu Dai, Hou Chongxian, Jiale Yan, Yaqian Li, Kaiwen Long, Xun Gong, Masayuki Ikebe, Yefeng Zheng · 17 de marzo de 2026 · Multimodal Machine Learning Applications

  Chain-of-thought (CoT) reasoning has advanced medical visual question answering (VQA), yet most existing CoT rationales are free-form and fail to capture the structured reasoning process clinicians actually follow. This work asks: Can traceable, multi-step reasoning supervision improve reasoning acc…

• Learning from Partial Chain-of-Thought via Truncated-Reasoning Self-Distillation

  Gianluigi Silvestri, Edoardo Cetin · 17 de marzo de 2026 · Large Language Models

  Reasoning-oriented language models achieve strong performance by generating long chain-of-thought traces at inference time. However, this capability comes with substantial and often excessive computational cost, which can materialize in redundant or inefficient reasoning. We study this setting and i…

• TERMINATOR: Learning Optimal Exit Points for Early Stopping in Chain-of-Thought Reasoning

  Alliot Nagle, Jakhongir Saydaliev, Dhia Garbaya, Michael Gastpar, Ashok Vardhan Makkuva, Hyeji Kim · 16 de marzo de 2026 · Multimodal Machine Learning Applications

  Large Reasoning Models (LRMs) achieve impressive performance on complex reasoning tasks via Chain-of-Thought (CoT) reasoning, which enables them to generate intermediate thinking tokens before arriving at the final answer. However, LRMs often suffer from significant overthinking, spending excessive …

• EndoCoT: Scaling Endogenous Chain-of-Thought Reasoning in Diffusion Models

  Xuanlang Dai, Yujie Zhou, Long Xing, Jiazi Bu, Xilin Wei, Yuhong Liu, Beichen Zhang, Kai Chen, Yuhang Zang · 13 de marzo de 2026 · Multimodal Machine Learning Applications

  Recently, Multimodal Large Language Models (MLLMs) have been widely integrated into diffusion frameworks primarily as text encoders to tackle complex tasks such as spatial reasoning. However, this paradigm suffers from two critical limitations: (i) MLLMs text encoder exhibits insufficient reasoning …

• Fuel Gauge: Estimating Chain-of-Thought Length Ahead of Time in Large Multimodal Models

  Yuedong Yang, Xiwen Wei, Mustafa Munir, Radu Marculescu · 12 de marzo de 2026 · Multimodal Machine Learning Applications

  Reasoning Large Multi-modality Models (LMMs) have become the de facto choice for many applications. However, these models rely on a Chain-of-Thought (CoT) process that is lengthy and unpredictable at runtime, often resulting in inefficient use of computational resources (due to memory fragmentation)…

• Beyond the Prompt in Large Language Models: Comprehension, In-Context Learning, and Chain-of-Thought

  Yuling Jiao, Yanming Lai, Huazhen Lin, Wensen Ma, Houduo Qi, Defeng Sun · 12 de marzo de 2026 · Large Language Models

  Large Language Models (LLMs) have demonstrated remarkable proficiency across diverse tasks, exhibiting emergent properties such as semantic prompt comprehension, In-Context Learning (ICL), and Chain-of-Thought (CoT) reasoning. Despite their empirical success, the theoretical mechanisms driving these…

• Context Over Compute Human-in-the-Loop Outperforms Iterative Chain-of-Thought Prompting in Interview Answer Quality

  Kewen Zhu, Zixi Liu, Yanjing Li · 12 de marzo de 2026 · Deception detection and forensic psychology

  Behavioral interview evaluation using large language models presents unique challenges that require structured assessment, realistic interviewer behavior simulation, and pedagogical value for candidate training. We investigate chain of thought prompting for interview answer evaluation and improvemen…

• Evolving Demonstration Optimization for Chain-of-Thought Feature Transformation

  Xinyuan Wang, Kunpeng Liu, Arun Vignesh Malarkkan, Yanjie Fu · 12 de marzo de 2026 · Domain Adaptation and Few-Shot Learning

  Feature Transformation (FT) is a core data-centric AI task that improves feature space quality to advance downstream predictive performance. However, discovering effective transformations remains challenging due to the large space of feature-operator combinations. Existing solutions rely on discrete…

• Learning When to Sample: Confidence-Aware Selective Sampling for Efficient Chain-of-Thought Reasoning

  Juming Xiong, Kevin Guo, Congning Ni, Weixin Liu, Chao Yan, Katherine Brown, Avinash Baidya, Xiang Gao, Bradley Malin, Zhijun Yin · 11 de marzo de 2026 · Large Language Models

  Large language models (LLMs) can achieve strong reasoning performance through chain-of-thought (CoT) reasoning, yet they often generate unnecessarily long reasoning paths that incur high inference cost. Self-consistency-based approaches push accuracy higher still, but they require sampling and aggre…

• Reasoning Efficiently Through Adaptive Chain-of-Thought Compression: A Self-Optimizing Framework

  Kerui Huang, Shuhan Liu, Xing Hu, Tongtong Xu, Lingfeng Bao, Xin Xia · 11 de marzo de 2026 · Semantic Web and Ontologies

  Chain-of-Thought (CoT) reasoning enhances Large Language Models (LLMs) by prompting intermediate steps, improving accuracy and robustness in arithmetic, logic, and commonsense tasks. However, this benefit comes with high computational costs: longer outputs increase latency, memory usage, and KV-cach…

• Quantifying the Necessity of Chain of Thought through Opaque Serial Depth

  Jonah Brown-Cohen, David Lindner, Rohin Shah · 11 de marzo de 2026 · Explainable Artificial Intelligence (XAI)

  Large language models (LLMs) tend to externalize their reasoning in their chain of thought, making the chain of thought a good target for monitoring. This is partially an inherent feature of the Transformer architecture: sufficiently long serial cognition must pass through the chain of thought (Korb…

• FreeFly-Thinking : Aligning Chain-of-Thought Reasoning with Continuous UAV Navigation

  Jiaxu Zhou, Shaobo Wang, Zhiyuan Yang, Zhenjun Yu, Tao Li · 10 de marzo de 2026 · Multimodal Machine Learning Applications

  Vision-Language Navigation aims to enable agents to understand natural language instructions and carry out appropriate navigation actions in real-world environments. Most work focuses on indoor settings, with little research in complex outdoor scenes. Current UAV Vision-and-Language Navigation model…

• CoTJudger: A Graph-Driven Framework for Automatic Evaluation of Chain-of-Thought Efficiency and Redundancy in LRMs

  Siyi Li, Jiajun Shi, Shiwen Ni, Ge Zhang, Shuaimin Li, Shijian Wang, Zhoufutu Wen, Yizhi Li, Hamid Alinejad-Rokny, Jiaheng Liu, Min Yang, Wenhao Huang · 10 de marzo de 2026 · Explainable Artificial Intelligence (XAI)

  Large Reasoning Models (LRMs) have demonstrated strong performance by producing extended Chain-of-Thought (CoT) traces before answering. However, this paradigm often induces over-reasoning: redundant calculations and circular self-verification that increase computational cost without improving outco…

• SmartThinker: Progressive Chain-of-Thought Length Calibration for Efficient Large Language Model Reasoning

  Chenzhi Hu, Qinzhe Hu, Yuhang Xu, Junyi Chen, Ruijie Wang, Shengzhong Liu, Jianxin Li, Fan Wu, Guihai Chen · 10 de marzo de 2026 · Large Language Models

  Large reasoning models (LRMs) like OpenAI o1 and DeepSeek-R1 achieve high accuracy on complex tasks by adopting long chain-of-thought (CoT) reasoning paths. However, the inherent verbosity of these processes frequently results in redundancy and overthinking. To address this issue, existing works lev…

• TumorChain: Interleaved Multimodal Chain-of-Thought Reasoning for Traceable Clinical Tumor Analysis

  Sijing Li, Zhongwei Qiu, Jiang Liu, Wenqiao Zhang, Tianwei Lin, Yihan Xie, Jianxiang An, Boxiang Yun, Chenglin Yang, Jun Xiao, Guangyu Guo, Jiawen Yao, Wei Liu, Yuan Gao, Ke Yan, Weiwei Cao, Zhilin Zheng, Tony C. W. Mok, Kai Cao, Yu Shi, Jiuyu Zhang, Jian Zhou, Beng Chin Ooi, Yingda Xia, Ling Zhang · 9 de marzo de 2026 · Multimodal Machine Learning Applications

  Accurate tumor analysis is central to clinical radiology and precision oncology, where early detection, reliable lesion characterization, and pathology-level risk assessment guide diagnosis and treatment planning. Chain-of-Thought (CoT) reasoning is particularly important in this setting because it …

• Safer Reasoning Traces: Measuring and Mitigating Chain-of-Thought Leakage in LLMs

  Patrick Ahrend, Tobias Eder, Xiyang Yang, Zhiyi Pan, Georg Groh · 9 de marzo de 2026 · Privacy-Preserving Technologies in Data

  Chain-of-Thought (CoT) prompting improves LLM reasoning but can increase privacy risk by resurfacing personally identifiable information (PII) from the prompt into reasoning traces and outputs, even under policies that instruct the model not to restate PII. We study such direct, inference-time PII l…

• Reasoning Models Struggle to Control their Chains of Thought

  Chen Yueh-Han, Robert McCarthy, Bruce W. Lee, He He, Ian Kivlichan, Bowen Baker, Micah Carroll, Tomek Korbak · 9 de marzo de 2026 · Memory and Neural Mechanisms

  Chain-of-thought (CoT) monitoring is a promising tool for detecting misbehaviors and understanding the motivations of modern reasoning models. However, if models can control what they verbalize in their CoT, it could undermine CoT monitorability. To measure this undesirable capability -- CoT control…

• Continuous Chain of Thought Enables Parallel Exploration and Reasoning

  Halil Alperen Gozeten, M. Emrullah Ildiz, Xuechen Zhang, Hrayr Harutyunyan, Ankit Singh Rawat, Samet Oymak · 6 de marzo de 2026 · Large Language Models

  Modern language models generate chain-of-thought traces by autoregressively sampling tokens from a finite vocabulary. While this discrete sampling has achieved remarkable success, conducting chain-of-thought with continuously-valued tokens (CoT2) offers a richer and more expressive alternative. Our …