Add Phase 10A.5: blend gain is implicit regularization, not learned credit

Dissection of 6 branches from same DFA checkpoint:
- blend_random_frozen: 12.6% (CATASTROPHIC — frozen noise destroys training)
- blend_random_trainable: 32.2% (+1.2% — trainable network helps)
- blend_shuffled_trainable: 32.5% (+1.4% — even wrong targets work!)
- blend_gaussian_noise: 30.8% (neutral)
- scaled_DFA_norm_match: 31.0% (neutral)

The gain comes from implicit regularization through a co-optimized auxiliary
network, NOT from learned credit quality. Phase 9A's +1.5% was an optimization
dynamics effect, not evidence of useful credit assignment.

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

1 files changed, 38 insertions, 0 deletions

diff --git a/report_explore/MEMO_10A5_blend_dissection.md b/report_explore/MEMO_10A5_blend_dissection.md
new file mode 100644
index 0000000..3634cba
--- /dev/null
+++ b/report_explore/MEMO_10A5_blend_dissection.md
@@ -0,0 +1,38 @@
+# Phase 10A.5 Memo: Blend Mechanism Dissection
+
+**Date**: 2026-03-26
+
+## Question
+Phase 10A's gain from blend(random Vec, DFA) — is it learned correction or blend mechanism?
+
+## Answer: Neither. It's implicit regularization from a trainable auxiliary network.
+
+| Branch | final acc | diff vs DFA |
+|--------|-----------|-------------|
+| continue_DFA | 0.311 | baseline |
+| blend_random_**frozen** | **0.126** | **-18.5%** |
+| blend_random_**trainable** | 0.322 | +1.2% |
+| blend_shuffled_trainable | 0.325 | +1.4% |
+| blend_gaussian_noise | 0.308 | -0.3% |
+| scaled_DFA_norm_match | 0.310 | -0.0% |
+
+## Key findings
+
+1. **Frozen random Vec destroys training** (12.6%). A fixed random direction blended at 75% is catastrophic. This rules out "any signal diversification helps."
+
+2. **Trainable Vec helps** even from random init (+1.2%), even with shuffled targets (+1.4%). The Vec network doesn't need to learn correct credit — it just needs to be trainable.
+
+3. **Gaussian noise and norm scaling are neutral.** The mechanism is NOT noise injection or step-size calibration.
+
+4. **Gamma/rho stay near zero** for all trainable branches throughout training. The Vec never learns semantically correct credit.
+
+## Mechanism
+
+The gain comes from **implicit regularization through a co-optimized auxiliary network**. The Vec network, even with wrong training targets, adjusts its outputs during training in a way that smoothly regularizes the block-local updates. This is analogous to how auxiliary tasks in multi-task learning can improve main task performance even when the auxiliary task is unrelated — the shared optimization dynamics provide implicit regularization.
+
+## Implications
+
+1. **The Phase 9A narrative was wrong**: the +1.5% was NOT from Vec learning useful credit
+2. **The credit bridge hypothesis is not validated by online results**: the gain has nothing to do with credit quality
+3. **The gain is real but has a different cause**: it's an optimization dynamics phenomenon, not a credit assignment phenomenon
+4. **This does NOT invalidate the frozen CIFAR results**: Vec truly learns better credit on frozen features. But that credit quality doesn't transfer to online improvement — the online improvement comes from a different mechanism entirely.