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22 JUL 2026 · Paper

ENTRAP-VL: A Taxonomic Probe for Dual Contextual Entrainment in Vision-Language Models

This paper introduces a new dataset and tool to study contextual entrainment in vision-language models, which is the tendency for models to respond to irrelevant or false context in their inputs. Practitioners in AI and ML might care about this because it can affect the accuracy and reliability of vision-language models in real-world applications.

vision-language models contextual entrainment multimodal learning taxonomies benchmarks

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ENTRAP-VL: A Taxonomic Probe for Dual Contextual Entrainment in Vision-Language Models