AGP / MLX / ANE Training Spec

The first trainable version of AGP is not a new foundation model. It is a `Gemma 4 E2B` decoder transformer wrapped in a small set of new trainable interfaces. The objective is to teach the system four things at once: how to speak in your distribution, how to estimate whether an intermediate state is alive enough to trust, how to project that state into a typed semantic layer, and how to compress or route that state without paying for full-depth inference every time. The right starting point is therefore not full-model training. The right starting point is `adapter-first curriculum training` on top of a mostly frozen base model in `MLX`. The architecture we are training has five trainable regions. The first region is the ordinary language-model adaptation layer, which is a LoRA or DoRA-style adapter on selected Gemma blocks so the base model moves toward your conversational, coding, and memory-grounded distribution. The second region is the `trajectory and vitality head`, which reads selected hidden layers and predicts the routing state of the current reasoning process. The third region is the `semantic projection head`, which turns dense hidden states into sparse activations over kernel-aligned primitives, invariants, and bundle neighborhoods. The fourth region is the `depth-routing residual module`, which learns whether earlier hidden states are sufficient and how much prior depth should still matter. The fifth region is the `transfer adapter`, which encodes a cross-host packet and then reconstructs a continuation-ready latent on the receiving side. The first thing we train is not the routing logic. It is not the transfer adapter either. We first train a `domain adapter` so the base Gemma hidden states become useful on your own distribution. If we skip that and train routing on generic hidden states, we end up learning a scheduler over the wrong representation manifold. So the actual order is strict. First produce a strong local LoRA-tuned Gemma 4 E2B on your data. Then use that tuned model as the teacher and backbone for every later AGP head. This matters because the architecture is trying to decide when a hidden state is sufficient. Sufficiency depends on the actual target workload. A hidden state that is sufficient for generic internet chat may not be sufficient for your prompt style, your code tasks, your memory-heavy requests, or your semantic-layer work. The hidden-state geometry has to be measured and trained on the real distribution. The canonical base for training is `Gemma 4 E2B`. The E2B model is small enough to iterate on two 16 GB Apple machines and large enough to expose meaningful intermediate-state structure. The next scale-up model is `Gemma 4 E4B`, but that is phase two only. We should not jump to E4B before the curriculum and