Mind the Gap No More: Achieving Zero-Gap Multimodal Integration via One Tokenizer
arXiv:2602.12286v2 Announce Type: replace-cross
Abstract: A central challenge in developing Multimodal Large Language Models (MLLMs) is effectively integrating heterogeneous inputs into a cohesive reasoning engine. Current paradigms predominantly rely…