Búsqueda: chain of thought

521 artículos encontrados.

• Chain Of Thought Compression: A Theoritical Analysis

  Juncai Li, Ru Li, Yuxiang Zhou, Boxiang Ma, Jeff Z. Pan · 30 de enero de 2026 · Large Language Models

  Chain-of-Thought (CoT) has unlocked advanced reasoning abilities of Large Language Models (LLMs) with intermediate steps, yet incurs prohibitive computational costs due to generation of extra tokens. Recent studies empirically show that compressing reasoning steps into latent states, or implicit CoT…

• Latent Chain-of-Thought as Planning: Decoupling Reasoning from Verbalization

  Jiecong Wang, Hao Peng, Chunyang Liu · 30 de enero de 2026 · Multimodal Machine Learning Applications

  Chain-of-Thought (CoT) empowers Large Language Models (LLMs) to tackle complex problems, but remains constrained by the computational cost and reasoning path collapse when grounded in discrete token spaces. Recent latent reasoning approaches attempt to optimize efficiency by performing reasoning wit…

• Compositional Reasoning with Transformers, RNNs, and Chain of Thought

  Gilad Yehudai, Noah Amsel, Joan Bruna · 29 de enero de 2026 · Logic, Reasoning, and Knowledge

  It is well understood that different neural network architectures are suited to different tasks, but is there always a single best architecture for a given task? We compare the expressive power of transformers, RNNs, and transformers with chain of thought tokens on a simple and natural class of task…

• CtrlCoT: Dual-Granularity Chain-of-Thought Compression for Controllable Reasoning

  Zhenxuan Fan, Jie Cao, Yang Dai, Zheqi Lv, Wenqiao Zhang, Zhongle Xie, Peng LU, Beng Chin Ooi · 29 de enero de 2026 · Large Language Models

  Chain-of-thought (CoT) prompting improves LLM reasoning but incurs high latency and memory cost due to verbose traces, motivating CoT compression with preserved correctness. Existing methods either shorten CoTs at the semantic level, which is often conservative, or prune tokens aggressively, which c…

• TRACER: Texture-Robust Affordance Chain-of-Thought for Deformable-Object Refinement

  Wanjun Jia, Kang Li, Fan Yang, Mengfei Duan, Wenrui Chen, Yiming Jiang, Hui Zhang, Kailun Yang, Zhiyong Li, Yaonan Wang · 29 de enero de 2026 · Robot Manipulation and Learning

  The central challenge in robotic manipulation of deformable objects lies in aligning high-level semantic instructions with physical interaction points under complex appearance and texture variations. Due to near-infinite degrees of freedom, complex dynamics, and heterogeneous patterns, existing visi…

• Resisting Manipulative Bots in Meme Coin Copy Trading: A Multi-Agent Approach with Chain-of-Thought Reasoning

  Yichen Luo, Yebo Feng, Jiahua Xu, Yang Liu · 27 de enero de 2026 · Stock Market Forecasting Methods

  Copy trading has become the dominant entry strategy in meme coin markets. However, due to the market's extreme illiquid and volatile nature, the strategy exposes an exploitable attack surface: adversaries deploy manipulative bots to front-run trades, conceal positions, and fabricate sentiment, syste…

• Crystal-KV: Efficient KV Cache Management for Chain-of-Thought LLMs via Answer-First Principle

  Zihan Wang, Cheng Tang, Lei Gong, Cheng Li, Chao Wang, teng wang, Wenqi Lou, Xuehai Zhou · 27 de enero de 2026 · Large Language Models

  Chain-of-Thought (CoT) reasoning in large language models (LLMs) significantly improves accuracy on complex tasks, yet incurs excessive memory overhead due to the long think-stage sequences stored in the Key-Value (KV) cache. Unlike traditional generation tasks where all tokens are uniformly importa…

• EFT-CoT: A Multi-Agent Chain-of-Thought Framework for Emotion-Focused Therapy

  Lanqing Du, Yunong Li, YuJie Long, Shihong Chen · 27 de enero de 2026 · Mental Health via Writing

  The use of large language models (LLMs) for Mental Health Question Answering (MHQA) offers a promising way to alleviate shortages in mental health resources. However, prior work has mainly relied on Cognitive Behavioral Therapy (CBT) and predominantly follows a top-down strategy centered on rational…

• CoT-Seg: Rethinking Segmentation with Chain-of-Thought Reasoning and Self-Correction

  Shiu-hong Kao, Chak Ho Huang, Huaiqian Liu, Yu-Wing Tai, Chi-Keung Tang · 27 de enero de 2026 · Multimodal Machine Learning Applications

  Existing works of reasoning segmentation often fall short in complex cases, particularly when addressing complicated queries and out-of-domain images. Inspired by the chain-of-thought reasoning, where harder problems require longer thinking steps/time, this paper aims to explore a system that can th…

• Modern Hopfield Networks Require Chain-of-Thought to Solve $\mathsf{NC}^1$-Hard Problems

  Yang Cao, Xiaoyu Li, Yuanpeng Li, Yingyu Liang, Zhenmei Shi, Zhao Song · 26 de enero de 2026 · Cellular Automata and Applications

  Modern Hopfield Networks (MHNs) have emerged as powerful components in deep learning, serving as effective replacements for pooling layers, LSTMs, and attention mechanisms. While recent advancements have significantly improved their storage capacity and retrieval efficiency, their fundamental theore…

• Beyond Single-Granularity Prompts: A Multi-Scale Chain-of-Thought Prompt Learning for Graph

  Ziyu Zheng, Yaming Yang, Ziyu Guan, Wei Zhao, Xinyan Huang, Weigang Lu · 22 de enero de 2026 · Advanced Graph Neural Networks

  The ``pre-train, prompt" paradigm, designed to bridge the gap between pre-training tasks and downstream objectives, has been extended from the NLP domain to the graph domain and has achieved remarkable progress. Current mainstream graph prompt-tuning methods modify input or output features using lea…

• Gaming the Judge: Unfaithful Chain-of-Thought Can Undermine Agent Evaluation

  Muhammad Khalifa, Lajanugen Logeswaran, Jaekyeom Kim, Sungryull Sohn, Yunxiang Zhang, Moontae Lee, Hao Peng, Lu Wang, Honglak Lee · 22 de enero de 2026 · Explainable Artificial Intelligence (XAI)

  Large language models (LLMs) are increasingly used as judges to evaluate agent performance, particularly in non-verifiable settings where judgments rely on agent trajectories including chain-of-thought (CoT) reasoning. This paradigm implicitly assumes that the agent's CoT faithfully reflects both it…

• RCP-Merging: Merging Long Chain-of-Thought Models with Domain-Specific Models by Considering Reasoning Capability as Prior

  Junyao Yang, Jianwei Wang, Huiping Zhuang, Cen Chen, Ziqian Zeng · 21 de enero de 2026 · Large Language Models

  Large Language Models (LLMs) with long chain-of-thought (CoT) capability, termed Reasoning Models, demonstrate superior intricate problem-solving abilities through multi-step long CoT reasoning. To create a dual-capability model with long CoT capability and domain-specific knowledge without substant…

• UniMo: Unified Motion Generation and Understanding with Chain of Thought

  Guocun Wang, Kenkun Liu, Jing Lin, Guorui Song, Jian Li, Xiaoguang Han · 21 de enero de 2026 · Human Motion and Animation

  Existing 3D human motion generation and understanding methods often exhibit limited interpretability, restricting effective mutual enhancement between these inherently related tasks. While current unified frameworks based on large language models (LLMs) leverage linguistic priors, they frequently en…

• Thinking Traps in Long Chain-of-Thought: A Measurable Study and Trap-Aware Adaptive Restart

  Kang Chen, Fan Yu, Junjie Nian, Shihan Zhao, Zhuoka Feng, Zijun Yao, Heng Wang, Minshen Yu, Yixin Cao · 21 de enero de 2026 · Software System Performance and Reliability

  Scaling test-time compute via Long Chain-of-Thought (Long-CoT) significantly enhances reasoning capabilities, yet extended generation does not guarantee correctness: after an early wrong commitment, models may keep elaborating a self-consistent but incorrect prefix. Through fine-grained trajectory a…

• FantasyVLN: Unified Multimodal Chain-of-Thought Reasoning for Vision-Language Navigation

  Jing Zuo, Lingzhou Mu, Fan Jiang, Chengcheng Ma, Mu Xu, Yonggang Qi · 21 de enero de 2026 · Multimodal Machine Learning Applications

  Achieving human-level performance in Vision-and-Language Navigation (VLN) requires an embodied agent to jointly understand multimodal instructions and visual-spatial context while reasoning over long action sequences. Recent works, such as NavCoT and NavGPT-2, demonstrate the potential of Chain-of-T…

• Chain-of-Thought Compression Should Not Be Blind: V-Skip for Efficient Multimodal Reasoning via Dual-Path Anchoring

  Dongxu Zhang, Yiding Sun, Cheng Tan, Wenbiao Yan, Ning Yang, Jihua Zhu, Haijun Zhang · 21 de enero de 2026 · Multimodal Machine Learning Applications

  While Chain-of-Thought (CoT) reasoning significantly enhances the performance of Multimodal Large Language Models (MLLMs), its autoregressive nature incurs prohibitive latency constraints. Current efforts to mitigate this via token compression often fail by blindly applying text-centric metrics to m…

• Neural Chain-of-Thought Search: Searching the Optimal Reasoning Path to Enhance Large Language Models

  Guoming Ling, Zhongzhan Huang, Yupei Lin, Junxin Li, Shanshan Zhong, Hefeng Wu, Liang Lin · 19 de enero de 2026 · Large Language Models

  Chain-of-Thought reasoning has significantly enhanced the problem-solving capabilities of Large Language Models. Unfortunately, current models generate reasoning steps sequentially without foresight, often becoming trapped in suboptimal reasoning paths with redundant steps. In contrast, we introduce…

• Improving Chain-of-Thought for Logical Reasoning via Attention-Aware Intervention

  Nguyen Minh Phuong, Dang Huu Tien, Naoya Inoue · 16 de enero de 2026 · Large Language Models

  Modern logical reasoning with LLMs primarily relies on employing complex interactive frameworks that decompose the reasoning process into subtasks solved through carefully designed prompts or requiring external resources (e.g., symbolic solvers) to exploit their strong logical structures. While inte…

• Slang Context-based Inference Enhancement via Greedy Search-Guided Chain-of-Thought Prompting

  Jinghan Cao, Qingyang Ren, Xiangyun Chen, Xinjin Li, Haoxiang Gao, Yu Zhao · 16 de enero de 2026 · Natural Language Processing Techniques

  Slang interpretation has been a challenging downstream task for Large Language Models (LLMs) as the expressions are inherently embedded in contextual, cultural, and linguistic frameworks. In the absence of domain-specific training data, it is difficult for LLMs to accurately interpret slang meaning …

• GeoSteer: Faithful Chain-of-Thought Steering via Latent Manifold Gradients

  Kentaro Kazama, Daiki Shirafuji, Tatsuhiko Saito · 16 de enero de 2026 · Large Language Models

  Recent advances in Large Language Models (LLMs) have demonstrated remarkable progress in their reasoning capabilities, such as Chain-of-Thought (CoT). Most approaches rely on CoT rationales. Previous studies have shown that LLMs often generate logically inconsistent reasoning steps even when their f…

• The Hypocrisy Gap: Quantifying Divergence Between Internal Belief and Chain-of-Thought Explanation via Sparse Autoencoders

  Shikhar Shiromani, Archie Chaudhury, Sri Pranav Kunda · 15 de enero de 2026 · Explainable Artificial Intelligence (XAI)

  Large Language Models (LLMs) frequently exhibit unfaithful behavior, producing a final answer that differs significantly from their internal chain of thought (CoT) reasoning in order to appease the user they are conversing with. In order to better detect this behavior, we introduce the Hypocrisy Gap…

• GTR-CoT: Graph Traversal as Visual Chain of Thought for Molecular Structure Recognition

  Jingchao Wang, Yifan He, Haote Yang, Jiang Wu, Lingli Ge, Xingjian Wei, Yinfan Wang, Linye Li, Huijie Ao, Chengjin Liu, Bin Wang, Lijun Wu, Conghui He · 14 de enero de 2026 · Multimodal Machine Learning Applications

  Optical Chemical Structure Recognition (OCSR) is essential for converting molecular images into machine-readable formats. While recent vision-language models (VLMs) have shown promise, their image-captioning approach often struggles with complex molecular structures and inconsistent annotations. To …

• Reasoning Beyond Chain-of-Thought: A Latent Computational Mode in Large Language Models

  Zhenghao He, Guangzhi Xiong, Bohan Liu, Sanchit Sinha, Aidong Zhang · 14 de enero de 2026 · Large Language Models

  Chain-of-Thought (CoT) prompting has improved the reasoning performance of large language models (LLMs), but it remains unclear why it works and whether it is the unique mechanism for triggering reasoning in large language models. In this work, we study this question by directly analyzing and interv…

• Resisting Manipulative Bots in Memecoin Copy Trading: A Multi-Agent Approach with Chain-of-Thought Reasoning

  Yichen Luo, Yebo Feng, Jiahua Xu, Yang Liu · 14 de enero de 2026 · Stock Market Forecasting Methods

  The launch of \$Trump coin ignited a wave in meme coin investment. Copy trading, as a strategy-agnostic approach that eliminates the need for deep trading knowledge, quickly gains widespread popularity in the meme coin market. However, copy trading is not a guarantee of profitability due to the prev…