Global Cache Workspace: Towards Robust Reasoning through Internal Computation

Language models are usually adapted by updating their weights, trading broad, out-of-domain competence from large pre-training runs for in-domain gains. We show that adaptation can instead live in a model's memory. We introduce the Global Cache Workspace (GCW), a module inspired by Global Workspace Theory that selects information from specialised heads across the layers of a frozen backbone, integrates it in a shared workspace, and broadcasts an update back into their memory, covering both attention and linear-attention layers. Trained with next-token prediction while every backbone parameter stays fixed, GCW reaches in-domain gains competitive with full fine-tuning and LoRA while forgetting far less: across every adaptation setting we study, it loses the least out-of-domain reasoning accuracy on average, and after mathematical adaptation its loss is less than half that of weight-space methods. Its adaptation is also selective. With thinking enabled, GCW outperforms full fine-tuning in domain while shortening in-domain reasoning by up to 82%, a saving comparable to weight-space methods, yet keeps reasoning on unfamiliar tasks close to its original length; weight-space methods shorten that reasoning too, and lose far more accuracy there. Adapting what a frozen model remembers, rather than its weights, thus adds capability while preserving far more of what the model already knows.