FlexUMM: flexible representation learning for unified multimodal models (arXiv preprint)
The authors introduce FlexUMM, a model-agnostic framework that dynamically decides when to preserve shared computation, route specialized capacity, and stop further processing in unified multimodal models. They present evidence that this flexible allocation reduces harmful competition between understanding and generation, improving accuracy and efficiency, and provide code on GitHub.