Add Phase 5: vector field audit, frozen CIFAR transfer, online pilot

Phase 5A: Audit passes — shuffle control collapses, gains are real
Phase 5B: Transfer SUCCESS — vec_M4 beats scalar CB by +0.25 Gamma, +0.31 rho on frozen CIFAR
Phase 5C: Online FAILURE — vec does worse than scalar CB online despite better frozen credit
Core finding: bottleneck is in local surrogate / co-adaptation, not estimator quality

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

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+# Phase 5C Memo: Online Shallow CIFAR Vector Pilot
+
+**Date**: 2026-03-24
+**Config**: CIFAR-10, L=4, d=256, 100 epochs, seed=42
+
+## Question
+Does the vector field's frozen credit advantage translate to online training?
+
+## Results
+
+| Config | Acc | Gamma | rho | S1 vs DFA | S2 vs DFA |
+|--------|-----|-------|-----|-----------|-----------|
+| DFA | 0.312 | 0.101 | -0.005 | 0 | 0 |
+| vec wr=0.0 tw=1.0 | 0.159 | 0.007 | 0.005 | -0.094 | +0.010 |
+| vec wr=0.0 tw=4.0 | 0.155 | -0.004 | 0.007 | -0.105 | +0.012 |
+| vec wr=0.2 tw=1.0 | 0.243 | 0.001 | 0.000 | -0.100 | +0.005 |
+| vec wr=0.2 tw=4.0 | 0.199 | 0.004 | 0.001 | -0.097 | +0.006 |
+
+**No positive configs (S1 > 0 AND S2 > 0) found.**
+
+Recall from Phase 4: scalar CB (wr=0.2 tgw=1.0) achieved S1=+0.079 online. The vector field does *worse* (S1=-0.100) despite being *much better* on frozen features (Gamma: 0.364 vs 0.115).
+
+## Diagnosis
+
+The vector field's online failure is NOT an estimator problem — it excels at credit recovery on fixed representations. The failure is in **co-adaptation**: when the forward net changes, the perturbation-based directional targets become stale. The vector field is actually MORE sensitive to representation drift than scalar CB because:
+
+1. The perturbation target requires accurate tail-forward loss evaluation, which changes every epoch
+2. The vector net directly outputs d-dimensional credit, giving it more capacity to overfit to current representations
+3. Scalar CB's bridge consistency + EMA target provides implicit regularization against distribution shift
+
+## Conclusion
+
+The vector credit field is a **better estimator** (Phase 5B confirmed this clearly) but a **worse online learner** under the current local surrogate training framework. The bottleneck is definitively in **local exploitability / co-adaptation**, not in credit estimation quality.
+
+This is the core finding of Phases 4-5: **improving the credit estimator beyond scalar CB does not help online training because the forward net's local surrogate update cannot exploit even moderately good credit signals.**
+
+## Recommended Next Steps
+
+The path forward is NOT to keep improving the credit estimator. Instead:
+1. **Fix the local surrogate**: The inner product <F_l(h), a_l> may be too crude to exploit directional credit information
+2. **Investigate representation stabilization**: Techniques like periodic re-training, replay buffers for the credit estimator, or slower forward net updates
+3. **Consider hybrid approaches**: Use vector field credit for the first few layers only (where co-adaptation is less severe), DFA for deeper layers