3 papers
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.CL2026
Disparities In Negation Understanding Across Languages In Vision-Language Models
Charikleia Moraitaki, Sarah Pan, Skyler Pulling +3
Vision-language models (VLMs) exhibit affirmation bias: a systematic tendency to select positive captions ("X is present") even when the correct description contains negation ("no…
cs.LG2025
Neuroprobe: Evaluating Intracranial Brain Responses to Naturalistic Stimuli
Andrii Zahorodnii, Christopher Wang, Geeling Chau +7
High-resolution neural datasets enable foundation models for the next generation of brain-computer interfaces and neurological treatments. The community requires rigorous benchmark…