Suche: chain of thought

524 Paper gefunden.

• 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. Januar 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. Januar 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. Januar 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…

• M3CoTBench: Benchmark Chain-of-Thought of MLLMs in Medical Image Understanding

  Juntao Jiang, Jiangning Zhang, Yali Bi, Jinsheng Bai, Weixuan Liu, Weiwei Jin, Zhucun Xue, Yong Liu, Xiaobin Hu, Shuicheng Yan · 14. Januar 2026 · Multimodal Machine Learning Applications

  Chain-of-Thought (CoT) reasoning has proven effective in enhancing large language models by encouraging step-by-step intermediate reasoning, and recent advances have extended this paradigm to Multimodal Large Language Models (MLLMs). In the medical domain, where diagnostic decisions depend on nuance…

• How well can off-the-shelf LLMs elucidate molecular structures from mass spectra using chain-of-thought reasoning?

  Yufeng Wang, Lu Wei, Lin Liu, Hao Xu, Haibin Ling · 13. Januar 2026 · Machine Learning in Materials Science

  Mass spectrometry (MS) is a powerful analytical technique for identifying small molecules, yet determining complete molecular structures directly from tandem mass spectra (MS/MS) remains a long-standing challenge due to complex fragmentation patterns and the vast diversity of chemical space. Recent …

• Is Chain-of-Thought Reasoning of LLMs a Mirage? A Data Distribution Lens

  Chengshuai Zhao, Zhen Tan, Pingchuan Ma, Dawei Li, Bohan Jiang, Yancheng Wang, Yingzhen Yang, Huan Liu · 13. Januar 2026 · Large Language Models

  Chain-of-Thought (CoT) prompting has been shown to be effective in eliciting structured reasoning (i.e., CoT reasoning) from large language models (LLMs). Regardless of its popularity, recent studies expose its failures in some reasoning tasks, raising fundamental questions about the nature of CoT r…

• Reinforcement Learning for Chain of Thought Compression with One-Domain-to-All Generalization

  Hanyu Li, Jiangshan Duo, Bofei Gao, Hailin Zhang, Sujian Li, Xiaotie Deng, Liang Zhao · 13. Januar 2026 · Large Language Models

  Chain-of-thought reasoning in large language models often creates an "overthinking trap," leading to excessive computational cost and latency for unreliable accuracy gains. Prior work has typically relied on global, static controls that risk penalizing necessary reasoning. We introduce a sample-leve…

• Automatic Question Generation for Intuitive Learning Utilizing Causal Graph Guided Chain of Thought Reasoning

  Nicholas X. Wang, Neel V. Parpia, Aaryan D. Parikh, Aggelos K. Katsaggelos · 13. Januar 2026 · Intelligent Tutoring Systems and Adaptive Learning

  Intuitive learning is crucial for developing deep conceptual understanding, especially in STEM education, where students often struggle with abstract and interconnected concepts. Automatic question generation has become an effective strategy for personalized and adaptive learning. However, its effec…

• Let Me Think! A Long Chain-of-Thought Can Be Worth Exponentially Many Short Ones

  Parsa Mirtaheri, Ezra Edelman, Samy Jelassi, Eran Malach, Enric Boix-Adsera · 12. Januar 2026 · Large Language Models

  Inference-time computation has emerged as a promising scaling axis for improving large language model reasoning. However, despite yielding impressive performance, the optimal allocation of inference-time computation remains poorly understood. A central question is whether to prioritize sequential sc…

• Revisiting Chain-of-Thought Prompting: Zero-shot Can Be Stronger than Few-shot

  Xiang Cheng, Chengyan Pan, Minjun Zhao, Deyang Li, Fangchao Liu, Xinyu Zhang, Xiao Zhang, Yong Liu · 9. Januar 2026 · Mental Health Research Topics

  In-Context Learning (ICL) is an essential emergent ability of Large Language Models (LLMs), and recent studies introduce Chain-of-Thought (CoT) to exemplars of ICL to enhance the reasoning capability, especially in mathematics tasks. However, given the continuous advancement of model capabilities, i…

• PCoT: Persuasion-Augmented Chain of Thought for Detecting Fake News and Social Media Disinformation

  Arkadiusz Modzelewski, Witold Sosnowski, Tiziano Labruna, Adam Wierzbicki, Giovanni Da San Martino · 9. Januar 2026 · Misinformation and Its Impacts

  Disinformation detection is a key aspect of media literacy. Psychological studies have shown that knowledge of persuasive fallacies helps individuals detect disinformation. Inspired by these findings, we experimented with large language models (LLMs) to test whether infusing persuasion knowledge enh…

• FinChain: A Symbolic Benchmark for Verifiable Chain-of-Thought Financial Reasoning

  Zhuohan Xie, Daniil Orel, Rushil Thareja, Dhruv Sahnan, Hachem Madmoun, Fan Zhang, Debopriyo Banerjee, Georgi Georgiev, Xueqing Peng, Lingfei Qian, Jimin Huang, Jinyan Su, Aaryamonvikram Singh, Rui Xing, Rania Elbadry, Chen Xu, Haonan Li, Fajri Koto, Ivan Koychev, Tanmoy Chakraborty, Yuxia Wang, Salem Lahlou, Veselin Stoyanov, Sophia Ananiadou, Preslav Nakov · 9. Januar 2026 · Financial Reporting and XBRL

  Multi-step symbolic reasoning is essential for robust financial analysis; yet, current benchmarks largely overlook this capability. Existing datasets such as FinQA and ConvFinQA emphasize final numerical answers while neglecting the intermediate reasoning required for transparency and verification. …

• EntroCoT: Enhancing Chain-of-Thought via Adaptive Entropy-Guided Segmentation

  Zihang Li, Yuhang Wang, Yikun Zong, Wenhan Yu, Xiaokun Yuan, Runhan Jiang, Zirui Liu, Tong Yang, Arthur Jiang · 9. Januar 2026 · Large Language Models

  Chain-of-Thought (CoT) prompting has significantly enhanced the mathematical reasoning capabilities of Large Language Models. We find existing fine-tuning datasets frequently suffer from the "answer right but reasoning wrong" probelm, where correct final answers are derived from hallucinated, redund…

• ConMax: Confidence-Maximizing Compression for Efficient Chain-of-Thought Reasoning

  Minda Hu, Zexuan Qiu, Zenan Xu, Kun Li, Bo Zhou, Irwin King · 9. Januar 2026 · Machine Learning in Healthcare

  Recent breakthroughs in Large Reasoning Models (LRMs) have demonstrated that extensive Chain-of-Thought (CoT) generation is critical for enabling intricate cognitive behaviors, such as self-verification and backtracking, to solve complex tasks. However, this capability often leads to ``overthinking'…

• ThinkDrive: Chain-of-Thought Guided Progressive Reinforcement Learning Fine-Tuning for Autonomous Driving

  Chang Zhao, Zheming Yang, Yunqing Hu, Qi Guo, Zijian Wang, Pengcheng Li, Wen Ji · 9. Januar 2026 · Reinforcement Learning in Robotics

  With the rapid advancement of large language models (LLMs) technologies, their application in the domain of autonomous driving has become increasingly widespread. However, existing methods suffer from unstructured reasoning, poor generalization, and misalignment with human driving intent. While Chai…

• Know Thy Enemy: Securing LLMs Against Prompt Injection via Diverse Data Synthesis and Instruction-Level Chain-of-Thought Learning

  Zhiyuan Chang, Mingyang Li, Yuekai Huang, Ziyou Jiang, Xiaojun Jia, Qian Xiong, Junjie Wang, Zhaoyang Li, Qing Wang · 9. Januar 2026 · Adversarial Robustness in Machine Learning

  Large language model (LLM)-integrated applications have become increasingly prevalent, yet face critical security vulnerabilities from prompt injection (PI) attacks. Defending against PI attacks faces two major issues: malicious instructions can be injected through diverse vectors, and injected inst…

• DiffCoT: Diffusion-styled Chain-of-Thought Reasoning in LLMs

  Shidong Cao, Hongzhan Lin, Yuxuan Gu, Ziyang Luo, Jing Ma · 8. Januar 2026 · Constraint Satisfaction and Optimization

  Chain-of-Thought (CoT) reasoning improves multi-step mathematical problem solving in large language models but remains vulnerable to exposure bias and error accumulation, as early mistakes propagate irreversibly through autoregressive decoding. In this work, we propose DiffCoT, a diffusion-styled Co…

• Watch Wider and Think Deeper: Collaborative Cross-modal Chain-of-Thought for Complex Visual Reasoning

  Wenting Lu, Didi Zhu, Tao Shen, Donglin Zhu, Ayong Ye, Chao Wu · 7. Januar 2026 · Multimodal Machine Learning Applications

  Multi-modal reasoning requires the seamless integration of visual and linguistic cues, yet existing Chain-of-Thought methods suffer from two critical limitations in cross-modal scenarios: (1) over-reliance on single coarse-grained image regions, and (2) semantic fragmentation between successive reas…

• Decoupling the Effect of Chain-of-Thought Reasoning: A Human Label Variation Perspective

  Beiduo Chen, Tiancheng Hu, Caiqi Zhang, Robert Litschko, Anna Korhonen, Barbara Plank · 7. Januar 2026 · Large Language Models

  Reasoning-tuned LLMs utilizing long Chain-of-Thought (CoT) excel at single-answer tasks, yet their ability to model Human Label Variation--which requires capturing probabilistic ambiguity rather than resolving it--remains underexplored. We investigate this through systematic disentanglement experime…

• MemeMind: A Large-Scale Multimodal Dataset with Chain-of-Thought Reasoning for Harmful Meme Detection

  Hexiang Gu, Qifan Yu, Yuan Liu, Zikang Li, Saihui Hou, Jian Zhao, Zhaofeng He · 6. Januar 2026 · Misinformation and Its Impacts

  As a multimodal medium combining images and text, memes frequently convey implicit harmful content through metaphors and humor, rendering the detection of harmful memes a complex and challenging task. Although recent studies have made progress in detection accuracy and interpretability, large-scale,…

• GRACE: Discriminator-Guided Chain-of-Thought Reasoning

  Muhammad Khalifa, Lajanugen Logeswaran, Moontae Lee, Honglak Lee, Lu Wang · 6. Januar 2026 · Large Language Models

  In the context of multi-step reasoning, e.g., with chain-of-thought, language models (LMs) can easily assign a high likelihood to incorrect steps. As a result, decoding strategies that optimize for solution likelihood often yield incorrect solutions. To address this issue, we propose Guiding chain-o…

• Streaming Hallucination Detection in Long Chain-of-Thought Reasoning

  Haolang Lu, Minghui Pan, Ripeng Li, Guoshun Nan, Jialin Zhuang, Zijie Zhao, Zhongxiang Sun, Kun Wang, Yang Liu · 6. Januar 2026 · Embodied and Extended Cognition

  Long chain-of-thought (CoT) reasoning improves the performance of large language models, yet hallucinations in such settings often emerge subtly and propagate across reasoning steps. We suggest that hallucination in long CoT reasoning is better understood as an evolving latent state rather than a on…

• Can We Trust AI Explanations? Evidence of Systematic Underreporting in Chain-of-Thought Reasoning

  Deep Pankajbhai Mehta · 6. Januar 2026 · Explainable Artificial Intelligence (XAI)

  When AI systems explain their reasoning step-by-step, practitioners often assume these explanations reveal what actually influenced the AI's answer. We tested this assumption by embedding hints into questions and measuring whether models mentioned them. In a study of over 9,000 test cases across 11 …

• A Chain-of-Thought Approach to Semantic Query Categorization in e-Commerce Taxonomies

  Jetlir Duraj, Ishita Khan, Kilian Merkelbach, Mehran Elyasi · 5. Januar 2026 · Information Retrieval and Search Behavior

  Search in e-Commerce is powered at the core by a structured representation of the inventory, often formulated as a category taxonomy. An important capability in e-Commerce with hierarchical taxonomies is to select a set of relevant leaf categories that are semantically aligned with a given user quer…

• Generating Verifiable Chain of Thoughts from Exection-Traces

  Shailja Thakur, Vaibhav Saxena, Rohan Kulkarni, Shivdeep Singh, Parameswaran Selvam, Hima Patel, Hiroshi Kanayama · 30. Dezember 2025 · Software Engineering Research

  Teaching language models to reason about code execution remains a fundamental challenge. While Chain-of-Thought (CoT) prompting has shown promise, current synthetic training data suffers from a critical weakness: the reasoning steps are often plausible-sounding explanations generated by teacher mode…