Inverse-LLaVA: Rethinking Multimodal Alignment via Text-to-Vision Mapping

arXiv:2508.12466v3 Announce Type: replace-cross Abstract: Connecting pretrained vision and language models usually involves projecting image features into the language model's input space. Inverse-LLaVA reverses this mapping within decoder attention: language states are projected to the visual feature dimension, and modality-specific maps produce residual query, key, and value updates. Fusion and low-rank adaptation (LoRA) learn jointly from 665K visual instructions, with frozen backbones and no separate alignment stage. Across nine primary benchmark evaluations, the final 7B model approaches

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