arXiv Machine Learning August 21, 2026 By Rongyu Yu, Ke Niu, Fengxiang He

Answer-Level Trust Selection for Physical Vision-Language Reasoning

Read the original on arXiv Machine Learning →

arXiv:2608. 19807v1 Announce Type: new Abstract: Vision-language models (VLMs) can estimate physical quantities such as duration, speed, and acceleration from visual observations, but existing benchmarks primarily assess overall model performance against annotated ground truth.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv AI

Aug 14

How Do VLMs Behave When Blind or Misled? Behavioral Evaluation of VLMs on Scientific Figures

arXiv:2608. 13267v1 Announce Type: cross Abstract: Existing vision-language model (VLM) benchmarks emphasize perception and reasoning accuracy (how well VLMs describe and reason about what they see in an image), with limited attention to behavioral reliability under uncertainty (how they behave when visual evidence is missing or misleading).

By Paul Osemudiame Oamen, Owusu-Banahene Osei, Ananya Mukherjee, Christian Greisinger, Steffen Eger, Pius Onobhayedo, Wei Zhao

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arXiv Machine Learning

Jul 14

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

arXiv:2603. 16728v2 Announce Type: replace Abstract: Vision-language models (VLMs) are increasingly deployed in high-stakes settings where reliable uncertainty quantification (UQ) is as important as predictive accuracy.

By Robert Welch, Emir Konuk, Kevin Smith

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arXiv AI

Aug 3

Step-Level Visual Grounding Faithfulness Predicts Out-of-Distribution Generalization in Long-Horizon Vision-Language Models

arXiv:2603. 06828v2 Announce Type: replace-cross Abstract: We uncover a behavioral law of long-horizon vision-language models: models that maintain temporally grounded beliefs generalize better.

By Md Ashikur Rahman, Md Arifur Rahman, Niamul Hassan Samin, Abdullah Ibne Hanif Arean, Juena Ahmed Noshin

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arXiv AI

Jul 17

Just Keep Prompting: Evaluating Repetitive Socratic Prompting in VLMs

arXiv:2607. 14099v1 Announce Type: cross Abstract: Deploying Vision-Language Models (VLMs) in real-world settings requires not only strong visual reasoning but also stability under sustained conversational pressure.

By Shayda Moezzi, Bishoy Galoaa, Lorena Genua, Taskin Padir, Sarah Ostadabbas

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arXiv Machine Learning

Jul 27

Small Vision-Language Models Know When They Are Wrong But Cannot Say So: A Two-Model Study of Stated versus Internal Confidence Under Realistic Image Degradation

arXiv:2607. 22034v1 Announce Type: cross Abstract: Vision-language models (VLMs) are increasingly deployed on consumer hardware where input images are degraded by compression, camera shake, and poor lighting.

By M M Asif Ferdous

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arXiv AI

Jun 2

To See or To Please: Uncovering Visual Sycophancy and Split Beliefs in VLMs

arXiv:2603. 18373v4 Announce Type: replace-cross Abstract: When VLMs answer correctly, do they genuinely rely on visual information?

By Rui Hong, Shuxue Quan

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How Do VLMs Behave When Blind or Misled? Behavioral Evaluation of VLMs on Scientific Figures

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