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cs.CV2026
SPELUNKA: A Framework for Finding Unanticipated Associations in Vision-Language Models
Cassandra Parent, Isha Agarwal, Kumail Alhamoud +4
Vision-Language Models (VLMs) are the backbone of many state-of-the-art multimodal systems, but often encode spurious and harmful associations from their training data. While prior…
cs.CV2025
SpaceVLM: Sub-Space Modeling of Negation in Vision-Language Models
Sepehr Kazemi Ranjbar, Kumail Alhamoud, Marzyeh Ghassemi
Vision-Language Models (VLMs) struggle with negation. Given a prompt like "retrieve (or generate) a street scene without pedestrians," they often fail to respect the "not." Existin…