Multimodal CoT Reasoning

CoT: Chain-of-Thought

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Critical or Compliant? The Double-Edged Sword of Reasoning in Chain-of-Thought Explanations
Nov 15, 2025Eunkyu Park, Wesley Hanwen Deng, Vasudha Varadarajan +4VLM Reasoning Multimodal CoT Reasoning
Seoul National University · Human-Computer Interaction Institute, Carnegie Mellon University · Language Technologies Institute, Carnegie Mellon University
ChainReaction: Causal Chain-Guided Reasoning for Modular and Explainable Causal-Why Video Question Answering
Aug 28, 2025Paritosh Parmar, Eric Peh, Basura FernandoVideo QA Multimodal CoT Reasoning
Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore · Centre for Frontier AI Research, Agency for Science, Technology and Research, Singapore · College of Computing and Data Science, Nanyang Technological University, Singapore
Explain Before You Answer: A Survey on Compositional Visual Reasoning
Aug 24, 2025Fucai Ke, Joy Hsu, Zhixi Cai +10Compositional Reasoning Visual Reasoning
Monash University · Stanford University · University of Washington +3
Bridging the Gap in Ophthalmic AI: MM-Retinal-Reason Dataset and OphthaReason Model toward Dynamic Multimodal Reasoning
Aug 22, 2025Ruiqi Wu, Yuang Yao, Na Su +11Medical Image Analysis Multimodal Reasoning
School of Computer Science and Engineering, Southeast University, Nanjing, China · Department of Ophthalmology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China · School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China
Resource-Limited Joint Multimodal Sentiment Reasoning and Classification via Chain-of-Thought Enhancement and Distillation
Aug 7, 2025Haonan Shangguan, Xiaocui Yang, Shi Feng +3Efficient Multimodal Inference Sentiment Analysis
School of Computer Science and Engineering, Northeastern University, Shenyang, China
Multimodal Mathematical Reasoning with Diverse Solving Perspective
Jul 3, 2025Wenhao Shi, Zhiqiang Hu, Yi Bin +4Multimodal Reasoning Multimodal CoT Reasoning
Tongji University, Shanghai, China · National University of Singapore, Singapore · Tencent, Beijing, China +2
SurgRAW: Multi-Agent Workflow with Chain of Thought Reasoning for Robotic Surgical Video Analysis
Mar 13, 2025Chang Han Low, Ziyue Wang, Tianyi Zhang +4Surgical Video Understanding Multimodal CoT Reasoning
National University of Singapore, Singapore · Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), Singapore · Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) and the Department of Medical Physics and Biomedical Engineering, University College London, UK
V-Retrver: Evidence-Driven Agentic Reasoning for Universal Multimodal Retrieval
Date pendingDongyang Chen, Chaoyang Wang, Dezhao Su +6Multimodal IR Multimodal Grounding
Tsinghua University · University of Central Florida · Fudan University +4
Learning to Think Like a Cartoon Captionist: Incongruity-Resolution Supervision for Multimodal Humor Understanding
Date pendingHatice Merve Vural, Doga Kukul, Ege Erdem Ozlu +4VLM Reasoning Multimodal CoT Reasoning
Koç University, Istanbul, Turkey · KUIS AI Center, Istanbul, Turkey · Air Mail and Cartoon Collections +1

Nov 15, 2025, cs.CLJ/K move · Enter open · S save

Critical or Compliant? The Double-Edged Sword of Reasoning in Chain-of-Thought Explanations

Eunkyu Park, Wesley Hanwen Deng, Vasudha Varadarajan, Mingxi Yan+3

Seoul National University · Human-Computer Interaction Institute, Carnegie Mellon University · Language Technologies Institute, Carnegie Mellon University

VLM Reasoning Multimodal CoT Reasoning Trust in AI Explainability Evaluation

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Explanations are often promoted as tools for transparency, but they can also foster confirmation bias; users may assume reasoning is correct whenever outputs appear acceptable. We study this double-edged role of Chain-of-Thought (CoT) explanations in multimodal moral scenarios by systematically perturbing reasoning chains and manipulating delivery tones. Specifically, we analyze reasoning errors in vision language models (VLMs) and how they impact user trust and the ability to detect errors. Our findings reveal two key effects: (1) users often equate trust with outcome agreement, sustaining reliance even when reasoning is flawed, and (2) the confident tone suppresses error detection while maintaining reliance, showing that delivery styles can override correctness. These results highlight how CoT explanations can simultaneously clarify and mislead, underscoring the need for NLP systems to provide explanations that encourage scrutiny and critical thinking rather than blind trust. All code will be released publicly.

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