75% of the model is GDN layers. Behavioral training adjusts: projections
(what queries/updates the recurrent state), gating parameters (what
survives compression), A_log/dt_bias (baseline decay rates).
Key insight: GDN makes behavioral training DEEPER than full attention.
Full attention = 'I choose to look at direction' (deliberate). GDN =
'direction IS what I see' (structural — the compressed state is
direction-shaped). 48 GDN layers = disposition. 16 full attention =
procedure. The architecture IS disposition-over-procedure.
