diff --git a/docs/superpowers/specs/2026-04-15-supervised-architecture-transfer-design.md b/docs/superpowers/specs/2026-04-15-supervised-architecture-transfer-design.md
index fb411bcf6..1127500a2 100644
--- a/docs/superpowers/specs/2026-04-15-supervised-architecture-transfer-design.md
+++ b/docs/superpowers/specs/2026-04-15-supervised-architecture-transfer-design.md
@@ -324,22 +324,93 @@ trajectory_backtracking(context_divergence)                   [CPBI backtrack]
 
 ---
 
+## Component 8: Q-Mean Drift Correction
+
+### Problem (observed in train-vpb4w)
+Q-mean drifts 0.0 → +0.47 over 18 epochs. All Q-values shift positive due to bootstrapping bias — the DDQN target overestimates returns. This causes the model to become optimistically biased and eventually degrades action selection quality (val_Sharpe 54→31 at epoch 16-18).
+
+### Solution
+Mean-centering Q-values per training step. After `compute_expected_q` produces Q_raw[B, 12]:
+
+```
+Q_mean_all = mean(Q_raw)                     // scalar mean across all actions and samples
+Q_centered[b, a] = Q_raw[b, a] - Q_mean_all  // zero-mean Q-values
+```
+
+This removes the global bias while preserving relative Q-value ordering (which action is best). The centering is applied BEFORE Q-attention, graph message passing, and action selection — so all downstream components see zero-mean Q-values.
+
+### Properties
+- Zero parameters
+- One reduction kernel (mean over B×12) + one element-wise subtract
+- Preserves action ranking (subtracting a constant doesn't change argmax/Boltzmann)
+- Prevents bootstrapping drift from accumulating across epochs
+- The C51 loss still uses the ORIGINAL Q-values (centering is for action selection only, not for the Bellman target)
+
+### Implementation
+- New CUDA kernel `q_mean_center`: compute mean of Q_raw[B, 12], subtract from all elements. One block for reduction, ceil(B*12/256) blocks for subtraction. Two-phase kernel or fused.
+- Runs AFTER `compute_expected_q`, BEFORE `cross_branch_q_attention`
+- Q-stats monitoring reads from ORIGINAL Q_raw (not centered) to track true Q-mean drift
+
+---
+
+## Component 9: Atom Utilization Recovery
+
+### Problem (observed in train-vpb4w)
+Atom utilization drops 100% → 20% over 18 epochs. The C51 distribution collapses onto a few atoms — most atoms carry negligible probability mass. This degrades the distributional representation: less uncertainty information for variance sizing, quantile extraction, and diffusion denoising.
+
+### Solution
+Atom entropy regularization with adaptive weight. Add a small loss term that penalizes low atom entropy:
+
+```
+// Per-sample, per-action atom entropy (already computed in compute_expected_q)
+H = -sum(p[j] * log(p[j]))                    // atom entropy
+H_max = log(num_atoms)                         // maximum entropy (uniform)
+utilization = H / H_max                        // normalized [0, 1]
+
+// Entropy bonus in the C51 loss (gradient pushes toward higher entropy)
+L_entropy = -entropy_weight * mean(H)          // negative = maximize entropy
+entropy_weight = base_weight * (1 - utilization_ema)  // adaptive: high weight when util is low
+```
+
+When utilization is high (atoms well-used), `entropy_weight ≈ 0` (no interference with C51 learning). When utilization drops below a threshold, entropy weight increases to push the distribution back toward using more atoms.
+
+### Properties
+- Zero extra parameters
+- Atom entropy and utilization already computed in `compute_expected_q` (atom_stats buffer)
+- Entropy gradient already exists in `c51_grad_kernel` (the `entropy_coeff` parameter)
+- The fix is to make `entropy_coeff` ADAPTIVE based on utilization, not fixed
+- The adaptive entropy_weight is computed on CPU from the Q-stats readback (utilization value) and passed as a kernel argument
+
+### Implementation
+- In `reduce_current_q_stats`: read atom_utilization from q_stats readback
+- Track `utilization_ema` with adaptive alpha (same pattern as all other EMAs)
+- Compute `entropy_weight = base_entropy_coeff * max(0, 1 - utilization_ema / 0.5)` — ramps up when utilization drops below 50%, zero above 50%
+- Pass adaptive `entropy_weight` to `c51_grad_kernel` instead of the fixed `entropy_coeff` hyperparameter
+- No new CUDA kernels — uses existing `entropy_coeff` parameter in c51_grad_kernel
+
+---
+
 ## Implementation Order
 
-1. Component 6 (Quantile Q-select) — zero params, uses existing atoms, highest OOS impact
-2. Component 4 (Graph message passing) — 60 params, one kernel, structural coordination
-3. Component 1 (KAN spline gates) — replaces sigmoid in existing GLU, localized change
-4. Component 3 (Diffusion refinement) — 2,772 params, separate Adam, post-Q-pipeline
-5. Component 7 (TLOB injection) — widens 4 weight tensors, same pattern as Layer 3
-6. Component 2 (xLSTM context) — CPU-only, feeds into Components 5 and existing systems
-7. Component 5 (RK4 ODE) — depends on Component 2, pure algorithm change
+1. **Component 8 (Q-mean centering)** — zero params, one kernel, directly fixes the Q-mean drift
+2. **Component 9 (Atom entropy recovery)** — zero params, adaptive existing param, directly fixes atom collapse
+3. Component 6 (Quantile Q-select) — zero params, uses existing atoms, highest OOS impact
+4. Component 4 (Graph message passing) — 60 params, one kernel, structural coordination
+5. Component 1 (KAN spline gates) — replaces sigmoid in existing GLU, localized change
+6. Component 3 (Diffusion refinement) — 2,772 params, separate Adam, post-Q-pipeline
+7. Component 7 (TLOB injection) — widens 4 weight tensors, same pattern as Layer 3
+8. Component 2 (xLSTM context) — GPU kernels, feeds into Components 5 and existing systems
+9. Component 5 (RK4 ODE) — depends on Component 2, GPU kernel
 
 ---
 
 ## Success Criteria
 
-| Metric | Current (CPBI baseline) | Target | Source |
-|--------|------------------------|--------|--------|
+| Metric | Current (train-vpb4w ep 18) | Target | Source |
+|--------|---------------------------|--------|--------|
+| Q-mean drift | +0.47 (unbounded growth) | ±0.05 (centered) | Component 8 |
+| Atom utilization | 20% (collapsing) | >50% sustained | Component 9 |
+| val_Sharpe sustained | 54 peak → 31 decay | >40 sustained 50+ epochs | Components 8, 9 |
 | val_Sharpe plateau | Epoch 22 freeze (train-w6qfd) | No freeze through 200 epochs | All components |
 | OOS Sharpe | ~0 oscillating | >0 sustained 20+ epochs | Components 1, 3, 6 |
 | Exploration quality | Epsilon-greedy / Boltzmann | Distributional (no epsilon) | Component 6 |
@@ -353,6 +424,8 @@ trajectory_backtracking(context_divergence)                   [CPBI backtrack]
 
 | Risk | Mitigation |
 |------|-----------|
+| Q-mean centering removes useful signal | Centering preserves relative ordering; C51 loss uses original Q-values |
+| Adaptive entropy too aggressive | Ramps linearly, zero above 50% utilization — no interference when atoms are healthy |
 | KAN spline overflow | Clamp gate output to [0, 1] |
 | xLSTM C matrix divergence | Forget gate f_t ∈ (0,1) prevents unbounded growth |
 | Diffusion refinement distorts Q ordering | Small step (alpha=0.1) + residual preserves ranking |