A multimodal LLM trained with an auxiliary visual loss, input token masking, modality-disentangled weights, and synthetic spatial data shows spatial-reasoning gains that are not cleanly attributable to the proposed method.
Self-supervised learning from images with a joint-embedding predictive architecture
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Perceiving Beyond Language Priors: Enhancing Visual Comprehension and Attention in Multimodal Models
A multimodal LLM trained with an auxiliary visual loss, input token masking, modality-disentangled weights, and synthetic spatial data shows spatial-reasoning gains that are not cleanly attributable to the proposed method.