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cs.CV2026
Encoded but Unused: Bounding the Sources of VLM Errors
Anat Veikherman, Mor Ventura, Nitay Calderon +2
In this paper, we investigate why vision-language models (VLMs) produce errors by decomposing them into two sources: visual-representation failures, in which the visual evidence re…
cs.LG2026
MulTaBench: Benchmarking Multimodal Tabular Learning with Text and Image
Alan Arazi, Eilam Shapira, Shoham Grunblat +8
Tabular Foundation Models have recently established the state of the art in supervised tabular learning, by leveraging pretraining to learn generalizable representations of numeric…