Reasoning for VLMs

VLM: Vision-Language Model

Follow this topic Ask about this topic

Latest papers

39 papers7 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

The Cost of Reasoning: Chain-of-Thought Induces Overconfidence in Vision-Language Models
Mar 17, 2026Robert Welch, Emir Konuk, Kevin SmithConfidence Estimation for VLMs VLM Reasoning
KTH Royal Institute of Technology, Stockholm, Sweden · Science for Life Laboratory, Stockholm, Sweden
Multi-turn Physics-informed Vision-language Model for Physics-grounded Anomaly Detection
Mar 16, 2026Yao Gu, Xiaohao Xu, Yingna WuReasoning for VLMs Video Anomaly Detection
Shanghaitech University · University of Michigan, Ann Arbor
Vision Verification Enhanced Fusion of VLMs for Efficient Visual Reasoning
Mar 13, 2026Selim Furkan Tekin, Yichang Xu, Gaowen Liu +3VLM Hallucination Mitigation VLM Reasoning
Georgia Institute of Technology · Cisco Systems
Scale Can't Overcome Pragmatics: The Impact of Reporting Bias on Vision-Language Reasoning
Feb 26, 2026Amita Kamath, Jack Hessel, Khyathi Chandu +3VLM Reasoning Reasoning for VLMs
University of Washington · University of California, Los Angeles · Samaya AI +2
MindDriver: Introducing Progressive Multimodal Reasoning for Autonomous Driving
Feb 25, 2026Lingjun Zhang, Yujian Yuan, Changjie Wu +7Reasoning for VLMs VLMs for Autonomous Driving
Amap, Alibaba Group · The Hong Kong University of Science and Technology · The Chinese University of Hong Kong +1
Saliency-Aware Multi-Route Thinking: Revisiting Vision-Language Reasoning
Feb 18, 2026Mingjia Shi, Yinhan He, Yaochen Zhu +1VLM Reasoning Reasoning for VLMs
University of Virginia.
CARE Drive A Framework for Evaluating Reason-Responsiveness of Vision Language Models in Automated Driving
Feb 17, 2026Lucas Elbert Suryana, Farah Bierenga, Sanne van Buuren +6Reasoning for VLMs VLM Reasoning
Department of Transport & Planning, Faculty of Civil Engineering and Geosciences, Delft University of Technology · Centre for Meaningful Human Control, Delft University of Technology · Faculty of Mechanical Engineering, Delft University of Technology, Delft, The Netherlands +1
Understanding the Effects of Distractors on Reasoning Vision-Language Models
Nov 26, 2025Jiyun Bae, Hyunjong Ok, Sangwoo Mo +1VLM Reasoning Visual Question Answering
Pohang University of Science and Technology (POSTECH)
Position: Reasoning After Perception Means Reasoning Without Vision
Jul 21, 2025Hongcheng Gao, Zihao Huang, Jingyi Tang +12Multimodal Understanding Visual Reasoning
Tsinghua University · BIT · Peking University +6

Mar 17, 2026J/K move · Enter open · S save

The Cost of Reasoning: Chain-of-Thought Induces Overconfidence in Vision-Language Models

Robert Welch, Emir Konuk, Kevin Smith

KTH Royal Institute of Technology, Stockholm, Sweden · Science for Life Laboratory, Stockholm, Sweden

Confidence Estimation for VLMs VLM Reasoning Reasoning for VLMs

Open paper AskPDF arXiv

Vision-language models (VLMs) are increasingly deployed in high-stakes settings where reliable uncertainty quantification (UQ) is as important as predictive accuracy. Extended reasoning via chain-of-thought (CoT) prompting or reasoning-trained models has become ubiquitous in modern VLM pipelines, yet its effect on UQ reliability remains poorly understood. Our results show that reasoning tends to degrade the quality of many uncertainty estimates, even when it improves task accuracy. We identify implicit answer conditioning as the primary mechanism: as reasoning traces converge on a conclusion before the final answer is generated, token probabilities increasingly reflect consistency with the model's own reasoning trace rather than uncertainty about correctness. In effect, the model becomes overconfident in its answer. In contrast, agreement-based consistency remains robust and often improves under reasoning, making it a practical choice for uncertainty estimation in reasoning-enabled VLMs.