Yeah. Have a multimodal parser model that can decompose prompts into pieces, generate embeddings for each of them and route those embeddings to the correct mod

Yeah. Have a multimodal parser model that can decompose prompts into pieces, generate embeddings for each of them and route those embeddings to the correct model based on the location of the embedding in latent space. Then have a "combiner/resolver" model that is trained to take answer embeddings from multiple models and render it in one of a variety of human readable formats. Eventually there is going to be a model catalog that describes model inputs/outputs in a machine parseable format, all models will use a unified interface (embedding in -> embedding out, with adapters for different latent spaces), and we will have "agent" models designed to be rapidly fine tuned in an online manner that act as glue between all these different models.