Replaces CfC's zero-initialised h_old at k=0 with the attention-pooled
context vector — a learned content-addressable summary over all K LN_b
output positions. The K-loop's recurrent semantics (h_old at k+1 = h_new
at k) are preserved; only the INITIAL state at k=0 changes from zero to
the pooled context.
Forward chain change:
... → m2 → LN_b → ln_out_d [B, K, HIDDEN_DIM]
→ attention_pool_fwd(Q, ln_out_d)
→ attn_context_d [B, HIDDEN_DIM] (used as h_old@k=0)
→ attn_weights_d [B, K] (saved for bwd)
→ K-loop CfC (h_old@k=0 = attn_context, not zero)
Backward chain change:
K-loop bwd ends with grad_h_carry_d holding the gradient that would
have flowed into the initial h_old = grad on attn_context.
attention_pool_bwd consumes:
Q, ln_out_d, attn_weights_d (forward state)
grad_h_carry_d = grad_context
Writes (BOTH `+=`):
grad_attn_q_d (accumulates Q gradient — pre-zeroed at step start)
grad_h_enriched_seq_d (ADDS attn-path contribution onto LN_b output
gradient — chains with K-loop contribution)
LN_b bwd then consumes the now-summed grad_h_enriched_seq_d.
Trainer state additions (8 fields):
attn_q_d, attn_context_d, attn_weights_d, grad_attn_q_d,
attn_fwd_fn, attn_bwd_fn, _attn_module, opt_attn_q
Q is tiny (HIDDEN_DIM=128 floats); initialised near zero so initial
attention ≈ uniform 1/K (context ≈ mean of LN_b output). Model learns
content addressing from a near-uniform starting point.
Eval path mirrors training: attn_pool_fwd runs after LN_b fwd,
attn_context_d feeds the eval K-loop at k=0.
Trainer now manages 22 AdamW: CfC×4 + GRN heads×10 + LN×2 + LN_a×2 +
VSN×2 + Attn Q×1 + Mamba2×2 grouped.
Synthetic overfit smoke: stride=1 0.29 → 0.0000 in 50 steps (faster
than pre-attn 0.30), stride=4 0.30 → 0.0000. All 8 perception_overfit
tests PASS. Demonstrates the full Phase 1+2+3 stack (VSN → m1 → LN_a →
m2 → LN_b → attn pool → CfC + GRN heads) is wired forward + backward
end-to-end with every gradient flowing through every learned param.
Phase 1+2+3 capacity scale-up complete. The cumulative architectural
lift over the 3-fix-stack baseline (496q7 mean_auc=0.716 / h6000=0.704)
will be measured by deploying this stack head-to-head against bsml6.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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