Two more deep dives:
- Dreaming as diffusion: the dream loop IS a generative process.
Memory graph as latent space, temperature as noise level, training
as denoising. Connects to policy gradient / filtered behavioral
cloning. The dream loop generates scenarios at the edge of the
model's capability — the boundary where learning happens.
- Hippocampal replay: our architecture converges with the brain's
two-stage memory system. Fast learning (context window) → slow
learning (weights) via compressed replay (context-frozen training)
with emotional prioritization (training-signal agent) and
interleaved replay (diverse training data prevents forgetting).
We didn't design from neuroscience — we converged on it.
