  As the AI community increasingly adopts large-scale models, it is crucial to
develop general and flexible tools to integrate them. We introduce
Gather-Attend-Scatter (GATS), a novel module that enables seamless combination
of pretrained foundation models, both trainable and frozen, into larger
multimodal networks. GATS empowers AI systems to process and generate
information across multiple modalities at different rates. In contrast to
traditional fine-tuning, GATS allows for the original component models to
remain frozen, avoiding the risk of them losing important knowledge acquired
during the pretraining phase. We demonstrate the utility and versatility of
GATS with a few experiments across games, robotics, and multimodal input-output
systems.
