Composes the C21 + C22 kernels with a host-side learnable α-gate to
prove the full per-horizon contribution path works end-to-end without
yet doing the captured-graph integration in PerceptionTrainer.
Pipeline:
LNb [B, K, HIDDEN_DIM]
→ per_horizon_attention_pool_fwd → context_h [B, N_HORIZONS, HIDDEN_DIM]
→ per_horizon_residual_head_fwd → residual [B, N_HORIZONS]
→ final[b, h] = baseline[b, h] + tanh(α[h]) * residual[b, h]
Two tests cover the critical invariants for adoption-safety:
alpha_zero_init_is_identity_to_baseline
With α = [0, 0, 0, 0, 0] and any random Q_h / w_res / bias_res,
final_logit MUST be bit-identical to baseline_logit (because
tanh(0) = 0). Verified via to_bits() byte equality. Proves that
initialising the new variant with α=0 makes it a strict superset
of the existing path — switching to AttentionPoolVariant::PerHorizon
cannot regress before any training has happened.
alpha_nonzero_changes_output_and_grads_flow_end_to_end
With α = [0.5, -0.3, 0.2, -0.1, 0.4]:
* final ≠ baseline (residual contributing) ✓
* all final logits finite ✓
* full backward chain (residual_head_bwd → attention_pool_bwd)
produces finite d_Q_h_scratch + finite d_LNb with at least
one non-zero entry in each → gradients flow back to both the
attention queries and the LN_b input ✓
This closes the kernel-side correctness story. The remaining
integration commits (C24+) are operational:
C24: extend CheckpointV1 → V2 (add q_h, w_res, bias_res, alpha
fields; V1 files load as Variant::SharedQuery)
C25: PerceptionTrainer wiring — allocate the device buffers, fold
attention + residual + gate into the captured graph, plumb
gradients into AdamW's param list
C26: 1-epoch smoke (assert no NaN, loss decreases vs baseline) —
needs real training data + multi-GPU time
C27: 30-epoch × 3-fold A/B (task #204) — decision gate per
docs/superpowers/specs/2026-05-18-per-horizon-attention-pool-design.md
§0 falsifiable claim
C24-C25 are 1-2 day work even when carefully scoped; C26-C27 need
real GPU-hours + result analysis. C21-C23 land the validatable kernel
correctness piece without committing to that time investment yet.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
69c64f2266