AGP / MLX / ANE Research Track

Current Apple-local language model execution treats the model as a monolith. A single host loads the full graph, every token pays for roughly the same depth, hidden states are transient internals, and hardware engines are mostly passive containers. That leaves three opportunities underexploited on the current `Mac4 + Mac5` setup: 1. intermediate hidden states may already contain enough information for early acceptance, correction, or transfer 2. different Apple engines are better suited to different classes of work 3. Thunderbolt 5 makes inter-host state transfer practical if the transferred object is compact and semantically meaningful The research track therefore asks whether a transformer can be wrapped in a learned partitioning architecture where hidden states become routable artifacts rather than opaque byproducts. The goal is to prove that a `transformer-based model running natively in MLX` can be extended into a `hierarchical Apple-local compute fabric` where: - shallow inference, routing, and semantic inspection happen cheaply and frequently - deeper correction and continuation happen conditionally - hidden states are compressed, typed, and transferred across hosts only when justified - ANE, GPU, CPU, and Thunderbolt each own the work they are structurally best at