VLM Reasoning

VLM: Vision-Language Model

Follow this topic Ask about this topic

Momentum

70 papers in the last four weeks, up 250% on the four weeks before. 0.7% of all new papers.

Jul 13Week of Sep 28

Latest papers 541

All topics

Chartographer: Counterfactual Chart Generation for Evaluating Vision-Language Models
May 26, 2026Yifan Jiang, Dae Yon Hwang, Jesse C. Cresswell +1VLM Evaluation Data Visualization
University of Waterloo · Vector Institute · Layer 6 AI
Scaling, Benchmarking, and Reasoning of Vision-Language Agents for Mobile GUI Navigation
May 26, 2026Heng Qu, Yike Liu, Renren Jin +4VLM Evaluation Vision-Language Navigation
1Wuhan University · 2Xiaomi
InterSketch: An Interleaved Reasoning Model with Self-correcting Visual Sketch and Stepwise Reward
May 26, 2026Zhiwei Ning, Wenwen Tong, Xiangli Kong +12Multimodal Reasoning VLM Reasoning
Shanghai Jiao Tong University · SenseTime Research · Shandong Normal University
OmniGF: A Dual-Branch Vision-Language Framework for Unified Gaze Following
May 26, 2026Qiaomu Miao, Haoyu Wu, Jingyi Xu +2Gaze Estimation Vision-Language Grounding
Stony Brook University · The University of Adelaide
DRScaffold: Boosting Dense-Scene Reasoning in Lightweight Vision Language Models
May 25, 2026Xinrui Shi, Kai Liu, Ziqing Zhang +3Visual Spatial Reasoning Vision-Language Grounding
1Shanghai Jiao Tong University
From Data to Insights: Exploring Program-of-Thoughts Prompting for Chart Summarization
May 25, 2026Yutong Qu, Wei ZhangData Visualization VLM Reasoning
Adelaide University · Adelaide, SA, Australia
Extending Embodied Question Answering from Perception to Decision
May 25, 2026Xicheng Gong, Qiwei Li, Peiran Xu +1Embodied QA VLM Reasoning
Peking University
Beyond Appearance: Can Multimodal Large Language Models Exploit Vertical Structure for Remote Sensing Natural Scene Understanding?
May 25, 2026Jing Huang, Duanchu Wang, Junjie Yang +5VLM Evaluation Remote Sensing Image Understanding
School of Software Engineering, Xi’an Jiaotong University, Xi’an 710049, China · Shenyang Institute of Automation, University of Chinese Academy of Sciences, Shenyang, China · State Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Xi’an Jiaotong University
ProSR: Process-Shaped Spatial Reasoning for Reliable Chain-of-Thought in VLMs
May 25, 2026Jiangyang Li, Cong Wan, Changjie Wu +8Visual Spatial Reasoning VLM Reasoning
1Xi’an Jiaotong University · 2Amap, Alibaba Group · 4Shenzhen University +1
Can MLLMs Reason Beyond Language? VisReason: A Comprehensive Benchmark for Vision-Centric Reasoning
May 25, 2026Longteng Guo, Yifan Wang, Pengkang Huo +4Visual Reasoning VLM Reasoning
Institute of Automation, Chinese Academy of Sciences · School of Artificial Intelligence, University of Chinese Academy of Sciences
DUEL: Adversarial Self-Play for Multimodal Reasoning
May 24, 2026Lin Qiu, Hanqing Zeng, Yao Liu +3VLM Robustness VLM Reasoning
Meta AI
IQA-Spider: Unifying Multi-Granularity Image Quality Assessment with Reasoning, Grounding and Referring
May 23, 2026Xinge Peng, Yiting Lu, Xin Li +1VLM Reasoning Image Quality Assessment
University of Science and Technology of China, Hefei, China.
Med-R2: An Adversarial Benchmark for Evidence-Grounded Reasoning in Medical VLMs
May 23, 2026Wen Ma, Fucheng Niu, Zhiting Fan +3Adversarial Attacks on VLMs Healthcare
Zhejiang University, Zhejiang, China · Guangdong Institute of Intelligence Science and Technology
D2-V2X: Depth-Driven Cooperative V2X Reasoning for Autonomous Driving
May 22, 2026Kevin Richard, Alphin Varghese, Colin Pham +2VLMs for Autonomous Driving Cross-Modal Alignment
University of North Carolina at Charlotte
DRIVESPATIAL: A Benchmark for Spatiotemporal Intelligence in VLMs for Autonomous Driving
May 22, 2026Hao Vo, Khoa Vo, Phu Loc Nguyen +10VLM Evaluation Autonomous Driving Datasets
University of Arkansas, USA · Google Research, Google · University of Liverpool, UK +1
CoReVAD: A Contextual Reasoning Framework for Training-Free Video Anomaly Detection
May 22, 2026Hyeongmuk Lim, Youngbum HurInterpretable Anomaly Detection VLM Reasoning
Department of Industrial Engineering, Inha University, Incheon, Republic of Korea
AwareVLN: Reasoning with Self-awareness for Vision-Language Navigation
May 21, 2026Wenxuan Guo, Xiuwei Xu, Yichen Liu +7Vision-Language Navigation VLM Reasoning
Tsinghua University, China
GeoWeaver: Grounding Visual Tokens with Geometric Evidence before Scene Reasoning
May 21, 2026Deshui Miao, Xingsen Huang, Yameng Gu +3Visual Spatial Reasoning Spatiotemporal Reasoning
Harbin Institute of Technology, Shenzhen · Hainan University · Pengcheng Laboratory +2
Interpreting and Enhancing Emotional Circuits in Large Vision-Language Models via Cross-Modal Information Flow
May 21, 2026Chengsheng Zhang, Chenghao Sun, Zhining Xie +1VLM Hallucination VLM Interpretability
MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition, University of Science and Technology of China · AIPD, Tencent
Foresee-to-Ground: From Predictive Temporal Perception to Evidence-Driven Reasoning for Video Temporal Grounding
May 21, 2026Zelin Zheng, Xinyan Liu, Ruixin Li +4Temporal Video Grounding Video-Language Models
School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China · Beijing Key Laboratory of Embodied Intelligence Computing, Beijing, China · Faculty of Computing, Harbin Institute of Technology, Weihai, China +1
Visual-Advantage On-Policy Distillation for Vision-Language Models
May 21, 2026Ruiqi Liu, Xiaolei Lv, Gengsheng Li +8Vision-Language Models VLM Distillation
Institute of Automation, CAS · School of Advanced Interdisciplinary Sciences, UCAS · 3Hello +1
SDGBiasBench: Benchmarking and Mitigating Vision--Language Models' Biases in Sustainable Development Goals
May 21, 2026Zihang Lin, Huaiyuan Qin, Muli Yang +1VLM Evaluation Vision-Language Models
1Nanyang Technological University · Institute for Infocomm Research (I2R), A*STAR, Singapore
Ablate-to-Validate: Are Vision-Language Models Really Using Continuous Thought Tokens?
May 20, 2026Tianyi Zhang, Mahtab Bigverdi, Ranjay KrishnaVLM Evaluation VLM Interpretability
University of Washington
Lost in Fog: Sensor Perturbations Expose Reasoning Fragility in Driving VLAs
May 20, 2026Abhinaw Priyadershi, Jelena FrtunikjVLM Robustness VLMs for Autonomous Driving
NVIDIA Corporation, USA · NVIDIA GmbH, Germany
VersusQ: Pairwise Margin Reasoning for Generalizable Video Quality Assessment
May 20, 2026Shibei Meng, Binxin Yang, Yuan Liu +4Pairwise Comparison Learning to Rank
The Chinese University of Hong Kong · Beijing Normal University · WeChat Vision, Tencent Inc. +2
Bridging Structure and Language: Graph-Based Visual Reasoning for Autonomous Road Understanding
May 20, 2026Lena Wild, Katie Z Luo, Marco PavoneVisual Reasoning Autonomous Driving
KTH Royal Institute of Technology · TRATON · Stanford University +1
RISE: Reliable Improvement in Self-Evolving Vision-Language Models
May 20, 2026Chaoran Xu, Yingmao Miao, Pengfei Zhang +3Vision-Language Models VLM Adaptation
AMAP, Alibaba Group
$Δ$ ynamics: Language-Based Representation for Inferring Rigid-Body Dynamics From Videos
May 20, 2026Chia-Hsiang Kao, Cong Phuoc Huynh, Chien-Yi Wang +5VLM Reasoning Physical Reasoning in Video
1Cornell University · 2Amazon
Miller-Index-Based Latent Crystallographic Fracture Plane Reasoning and generation with Vision-Language Models
May 19, 2026Qinwu Xu, Xiaofu Ma, Yifan JiangMultimodal Large Language Models Multimodal Reasoning
Independent research
From Seeing to Thinking: Decoupling Perception and Reasoning Improves Post-Training of Vision-Language Models
May 19, 2026Juncheng Wu, Hardy Chen, Haoqin Tu +6Vision-Language Models VLM Reasoning
Amazon · UC Santa Cruz · University of Waterloo +1

May 26, 2026, cs.CLJ/K move · Enter open · S save

Chartographer: Counterfactual Chart Generation for Evaluating Vision-Language Models

Yifan Jiang, Dae Yon Hwang, Jesse C. Cresswell, Freda Shi

University of Waterloo · Vector Institute · Layer 6 AI

VLM Evaluation Data Visualization Counterfactual Evaluation of VLMs Chart QA VLM Reasoning Visually Grounded Reasoning

Open paper AskPDF arXiv

Chart question-answering (QA) benchmarks aim to pose questions that require visual reasoning to correctly answer, but models can often reach solutions through shortcuts or prior familiarity with a chart based on their own background knowledge. To strictly evaluate visual reasoning, we propose counterfactual charts where the chart-question task remains fixed, but underlying chart and the corresponding answer are varied. We introduce Chartographer, a framework to reverse engineer charts into executable code, validate reconstruction fidelity, generate seed-controlled counterfactual variants, and derive new answers from executable QA logic. We apply this framework to existing chart QA datasets and evaluate proprietary and open-source vision-language models (VLMs), measuring variation sensitivity and generalizability. Counterfactual charts reveal failures hidden by single-chart performance: VLMs often fail to generalize after answering the original chart correctly. We find failures are most prevalent when updated charts require novel visual reasoning pathways.

Momentum

Related topics

Latest papers 541