CASA: Cross-Attention over Self-Attention for Efficient Vision-Language Fusion

ArXi:2512.19535v2 Announce Type: replace-cross Vision-language models (VLMs) are commonly trained by directly inserting image tokens from a pretrained vision encoder into the text stream of a language model. This allows text and image information to fully attend to one another within the model, but becomes rapidly costly for long multi-image conversations or streaming video applications, both in terms of memory and compute. VLMs leveraging cross-attention (CA) are an efficient alternative to token insertion as image tokens are not added to the KV cache. Despite being.