Búsqueda: chain of thought

524 artículos encontrados.

• Evaluating GRPO and DPO for Faithful Chain-of-Thought Reasoning in LLMs

  Hadi Mohammadi, Tamas Kozak, Anastasia Giachanou · 30 de diciembre de 2025 · Explainable Artificial Intelligence (XAI)

  Chain-of-thought (CoT) reasoning has emerged as a powerful technique for improving the problem-solving capabilities of large language models (LLMs), particularly for tasks requiring multi-step reasoning. However, recent studies show that CoT explanations often fail to reflect the model's actual reas…

• Chain-of-thought Reviewing and Correction for Time Series Question Answering

  Chen Su, Yuanhe Tian, Yan Song · 30 de diciembre de 2025 · Large Language Models

  With the advancement of large language models (LLMs), diverse time series analysis tasks are reformulated as time series question answering (TSQA) through a unified natural language interface. However, existing LLM-based approaches largely adopt general natural language processing techniques and are…

• Conscious Data Contribution via Community-Driven Chain-of-Thought Distillation

  Lena Libon, Meghana Bhange, Rushabh Solanki, Elliot Creager, Ulrich A\"ivodji · 23 de diciembre de 2025 · Mobile Crowdsensing and Crowdsourcing

  The current era of AI development places a heavy emphasis on training large models on increasingly scaled-up datasets. This paradigm has catalyzed entirely new product categories, such as LLM chatbots, while also raising concerns about data privacy and consumer choice. In this paper, we consider que…

• Exploring Zero-Shot ACSA with Unified Meaning Representation in Chain-of-Thought Prompting

  Filippos Ventirozos, Peter Appleby, Matthew Shardlow · 23 de diciembre de 2025 · Sentiment Analysis and Opinion Mining

  Aspect-Category Sentiment Analysis (ACSA) provides granular insights by identifying specific themes within reviews and their associated sentiment. While supervised learning approaches dominate this field, the scarcity and high cost of annotated data for new domains present significant barriers. We a…

• Knowledge Distillation with Structured Chain-of-Thought for Text-to-SQL

  Khushboo Thaker, Yony Bresler · 22 de diciembre de 2025 · Scientific Computing and Data Management

  Deploying accurate Text-to-SQL systems at the enterprise level faces a difficult trilemma involving cost, security and performance. Current solutions force enterprises to choose between expensive, proprietary Large Language Models (LLMs) and low-performing Small Language Models (SLMs). Efforts to im…

• Copyright Infringement Risk Reduction via Chain-of-Thought and Task Instruction Prompting

  Neeraj Sarna, Yuanyuan Li, Michael von Gablenz · 18 de diciembre de 2025 · Generative Adversarial Networks and Image Synthesis

  Large scale text-to-image generation models can memorize and reproduce their training dataset. Since the training dataset often contains copyrighted material, reproduction of training dataset poses a copyright infringement risk, which could result in legal liabilities and financial losses for both t…

• EagleVision: A Dual-Stage Framework with BEV-grounding-based Chain-of-Thought for Spatial Intelligence

  Jiaxu Wan, Xu Wang, Mengwei Xie, Hang Zhang, Mu Xu, Yang Han, Hong Zhang, Ding Yuan, Yifan Yang · 18 de diciembre de 2025 · Multimodal Machine Learning Applications

  Video-based spatial reasoning -- such as estimating distances, judging directions, or understanding layouts from multiple views -- requires selecting informative frames and, when needed, actively seeking additional viewpoints during inference. Existing multimodal large language models (MLLMs) consum…

• Explainable Action Form Assessment by Exploiting Multimodal Chain-of-Thoughts Reasoning

  Mengshi Qi, Yeteng Wu, Wulian Yun, Xianlin Zhang, Huadong Ma · 18 de diciembre de 2025 · Human Pose and Action Recognition

  Evaluating whether human action is standard or not and providing reasonable feedback to improve action standardization is very crucial but challenging in real-world scenarios. However, current video understanding methods are mainly concerned with what and where the action is, which is unable to meet…

• TIBSTC-CoT: A Multi-Domain Instruction Dataset for Chain-of-Thought Reasoning in Language Models

  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 · 17 de diciembre de 2025 · Large Language Models

  To address the severe data scarcity in Tibetan, a low-resource language spoken by over six million people, we introduce TIBSTC-CoT, the large-scale, multi-domain Tibetan dataset automatically constructed via chain-of-thought prompting with large language models (LLMs). TIBSTC-CoT establishes a scala…

• OmniDrive-R1: Reinforcement-driven Interleaved Multi-modal Chain-of-Thought for Trustworthy Vision-Language Autonomous Driving

  Zhenguo Zhang, Haohan Zhen, Yishen Wang, Le Xu, Tianchen Deng, Xuefeng Chen, Qu Chen, Bo Zhang, Wuxiong Huang · 17 de diciembre de 2025 · Multimodal Machine Learning Applications

  The deployment of Vision-Language Models (VLMs) in safety-critical domains like autonomous driving (AD) is critically hindered by reliability failures, most notably object hallucination. This failure stems from their reliance on ungrounded, text-based Chain-of-Thought (CoT) reasoning.While existing …

• Intention Chain-of-Thought Prompting with Dynamic Routing for Code Generation

  Shen Li, Li Huang, Shaoxiong Zhan, Weifeng Sun, Tao Yin, Zhongxin Liu, Meng Yan · 17 de diciembre de 2025 · Large Language Models

  Large language models (LLMs) exhibit strong generative capabilities and have shown great potential in code generation. Existing chain-of-thought (CoT) prompting methods enhance model reasoning by eliciting intermediate steps, but suffer from two major limitations: First, their uniform application te…

• Content-Aware Ad Banner Layout Generation with Two-Stage Chain-of-Thought in Vision Language Models

  Kei Yoshitake, Kento Hosono, Ken Kobayashi, Kazuhide Nakata · 16 de diciembre de 2025 · Multimodal Machine Learning Applications

  In this paper, we propose a method for generating layouts for image-based advertisements by leveraging a Vision-Language Model (VLM). Conventional advertisement layout techniques have predominantly relied on saliency mapping to detect salient regions within a background image, but such approaches of…

• FutureX: Enhance End-to-End Autonomous Driving via Latent Chain-of-Thought World Model

  Hongbin Lin, Yiming Yang, Yifan Zhang, Chaoda Zheng, Jie Feng, Sheng Wang, Zhennan Wang, Shijia Chen, Boyang Wang, Yu Zhang, Xianming Liu, Shuguang Cui, Zhen Li · 15 de diciembre de 2025 · Autonomous Vehicle Technology and Safety

  In autonomous driving, end-to-end planners learn scene representations from raw sensor data and utilize them to generate a motion plan or control actions. However, exclusive reliance on the current scene for motion planning may result in suboptimal responses in highly dynamic traffic environments wh…

• OPV: Outcome-based Process Verifier for Efficient Long Chain-of-Thought Verification

  Zijian Wu, Lingkai Kong, Wenwei Zhang, Songyang Gao, Yuzhe Gu, Zhongrui Cai, Tianyou Ma, Yuhong Liu, Zhi Wang, Runyuan Ma, Guangyu Wang, Wei Li, Conghui He, Dahua Lin, Kai Chen · 12 de diciembre de 2025 · Large Language Models

  Large language models (LLMs) have achieved significant progress in solving complex reasoning tasks by Reinforcement Learning with Verifiable Rewards (RLVR). This advancement is also inseparable from the oversight automated by reliable verifiers. However, current outcome-based verifiers (OVs) are una…

• Latency-Response Theory Model: Evaluating Large Language Models via Response Accuracy and Chain-of-Thought Length

  Zhiyu Xu, Jia Liu, Yixin Wang, Yuqi Gu · 12 de diciembre de 2025 · Computational and Text Analysis Methods

  The proliferation of Large Language Models (LLMs) necessitates valid evaluation methods to guide downstream applications and actionable future improvements. The Item Response Theory (IRT) has recently emerged as a promising framework for evaluating LLMs via their response accuracy. Beyond simple res…

• Latent Chain-of-Thought World Modeling for End-to-End Driving

  Shuhan Tan, Kashyap Chitta, Yuxiao Chen, Ran Tian, Yurong You, Yan Wang, Wenjie Luo, Yulong Cao, Philipp Krahenbuhl, Marco Pavone, Boris Ivanovic · 12 de diciembre de 2025 · Autonomous Vehicle Technology and Safety

  Recent Vision-Language-Action (VLA) models for autonomous driving explore inference-time reasoning as a way to improve driving performance and safety in challenging scenarios. Most prior work uses natural language to express chain-of-thought (CoT) reasoning before producing driving actions. However,…

• Rethinking Chain-of-Thought Reasoning for Videos

  Yiwu Zhong, Zi-Yuan Hu, Yin Li, Liwei Wang · 11 de diciembre de 2025 · Multimodal Machine Learning Applications

  Chain-of-thought (CoT) reasoning has been highly successful in solving complex tasks in natural language processing, and recent multimodal large language models (MLLMs) have extended this paradigm to video reasoning. However, these models typically build on lengthy reasoning chains and large numbers…

• MM-CoT:A Benchmark for Probing Visual Chain-of-Thought Reasoning in Multimodal Models

  Jusheng Zhang, Kaitong Cai, Xiaoyang Guo, Sidi Liu, Qinhan Lv, Ruiqi Chen, Jing Yang, Yijia Fan, Xiaofei Sun, Jian Wang, Ziliang Chen, Liang Lin, Keze Wang · 10 de diciembre de 2025 · Multimodal Machine Learning Applications

  The ability to perform Chain-of-Thought (CoT) reasoning marks a major milestone for multimodal models (MMs), enabling them to solve complex visual reasoning problems. Yet a critical question remains: is such reasoning genuinely grounded in visual evidence and logically coherent? Existing benchmarks …

• Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safety

  Tomek Korbak, Mikita Balesni, Elizabeth Barnes, Yoshua Bengio, Joe Benton, Joseph Bloom, Mark Chen, Alan Cooney, Allan Dafoe, Anca Dragan, Scott Emmons, Owain Evans, David Farhi, Ryan Greenblatt, Dan Hendrycks, Marius Hobbhahn, Evan Hubinger, Geoffrey Irving, Erik Jenner, Daniel Kokotajlo, Victoria Krakovna, Shane Legg, David Lindner, David Luan, Aleksander M\k{a}dry, Julian Michael, Neel Nanda, Dave Orr, Jakub Pachocki, Ethan Perez, Mary Phuong, Fabien Roger, Joshua Saxe, Buck Shlegeris, Mart\'in Soto, Eric Steinberger, Jasmine Wang, Wojciech Zaremba, Bowen Baker, Rohin Shah, Vlad Mikulik · 9 de diciembre de 2025 · Anomaly Detection Techniques and Applications

  AI systems that "think" in human language offer a unique opportunity for AI safety: we can monitor their chains of thought (CoT) for the intent to misbehave. Like all other known AI oversight methods, CoT monitoring is imperfect and allows some misbehavior to go unnoticed. Nevertheless, it shows pro…

• CoT4Det: A Chain-of-Thought Framework for Perception-Oriented Vision-Language Tasks

  Yu Qi, Yumeng Zhang, Chenting Gong, Xiao Tan, Weiming Zhang, Wei Zhang, Jingdong Wang · 9 de diciembre de 2025 · Multimodal Machine Learning Applications

  Large Vision-Language Models (LVLMs) have demonstrated remarkable success in a broad range of vision-language tasks, such as general visual question answering and optical character recognition (OCR). However, their performance on perception-centric tasks -- such as object detection, semantic segment…

• Enhancing Clinical Note Generation with ICD-10, Clinical Ontology Knowledge Graphs, and Chain-of-Thought Prompting Using GPT-4

  Ivan Makohon, Mohamad Najafi, Jian Wu, Mathias Brochhausen, Yaohang Li · 8 de diciembre de 2025 · Machine Learning in Healthcare

  In the past decade a surge in the amount of electronic health record (EHR) data in the United States, attributed to a favorable policy environment created by the Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009 and the 21st Century Cures Act of 2016. Clinical notes…

• CoT-X: An Adaptive Framework for Cross-Model Chain-of-Thought Transfer and Optimization

  Ziqian Bi, Kaijie Chen, Tianyang Wang, Junfeng Hao, Benji Peng, Xinyuan Song · 3 de diciembre de 2025 · Machine Learning in Healthcare

  Chain-of-Thought (CoT) reasoning enhances the problem-solving ability of large language models (LLMs) but leads to substantial inference overhead, limiting deployment in resource-constrained settings. This paper investigates efficient CoT transfer across models of different scales and architectures …

• COPE: Chain-Of-Thought Prediction Engine for Open-Source Large Language Model Based Stroke Outcome Prediction from Clinical Notes

  Yongkai Liu, Helena Feng, Bin Jiang, Yixin Wang, Max Wintermark, David S. Liebeskind, Michael Moseley, Maarten Lansberg, Gregory Albers, Jeremy Heit, Greg Zaharchuk · 3 de diciembre de 2025 · Acute Ischemic Stroke Management

  Predicting outcomes in acute ischemic stroke (AIS) guides clinical decision-making, patient counseling, and resource allocation. Clinical notes contain rich contextual information, but their unstructured nature limits their use in traditional predictive models. We developed and evaluated the Chain-o…

• Reasoning Under Pressure: How do Training Incentives Influence Chain-of-Thought Monitorability?

  Matt MacDermott, Qiyao Wei, Rada Djoneva, Francis Rhys Ward · 2 de diciembre de 2025 · Adversarial Robustness in Machine Learning

  AI systems that output their reasoning in natural language offer an opportunity for safety -- we can \emph{monitor} their chain of thought (CoT) for undesirable reasoning, such as the pursuit of harmful objectives. However, the extent to which CoT faithfully reflects the underlying reasoning process…

• Revisiting the Necessity of Lengthy Chain-of-Thought in Vision-centric Reasoning Generalization

  Yifan Du, Kun Zhou, Yingqian Min, Yue Ling, Wayne Xin Zhao, Youbin Wu · 1 de diciembre de 2025 · Multimodal Machine Learning Applications

  We study how different Chain-of-Thought (CoT) designs affect the acquisition of the generalizable visual reasoning ability in vision-language models (VLMs). While CoT data, especially long or visual CoT such as "think with image", has been widely used to supervise intermediate reasoning, it remains …