Add Phase 10A: no prefit threshold — even random Vec blend beats DFA by +1.3%

E_prefit=0 (random Vec) + blend(0.75): 32.4% vs DFA 31.1% (+1.3%)
E_prefit=15: 32.3% (+1.2%)
E_prefit=60: 32.5% (+1.4%)

Frozen Gamma/rho near zero at all prefit levels. The Phase 9A success was NOT
from Vec learning useful credit — it was from the blend mechanism itself providing
regularization/diversification over pure DFA.

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

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+# Phase 10A Memo: Prefit Threshold Curve
+
+**Date**: 2026-03-26
+**Config**: CIFAR-10, L=4, d=256, t0=5, blend_075, seed=42
+
+## Question
+How much offline prefit does Vec need before blend handoff helps?
+
+## Answer: NONE. Even random Vec with blend(0.75) outperforms DFA by +1.3%.
+
+| E_prefit | Gamma_frozen | rho_frozen | final acc | diff vs DFA |
+|----------|-------------|-----------|-----------|-------------|
+| 0 (random) | -0.005 | 0.014 | 0.324 | **+1.3%** |
+| 15 | 0.002 | 0.011 | 0.323 | **+1.2%** |
+| 60 | -0.001 | -0.009 | 0.325 | **+1.4%** |
+
+**This is Case C**: very weak (or zero) prefit suffices for blend to beat DFA.
+
+## Critical Reinterpretation
+
+The Phase 9A success was NOT due to Vec learning useful credit. The frozen Gamma/rho are near zero at all prefit levels. The benefit comes from **blending DFA with any additional signal** — even random noise through a VectorCreditNet provides a regularization/diversification effect that improves over pure DFA.
+
+This means:
+1. The "cold-start paradox" narrative was partially wrong — Vec doesn't need to be good to help
+2. The blend mechanism itself is the active ingredient, not Vec's credit quality
+3. Phase 9A's +1.5% was not evidence that "Vec credit is useful online" — it was evidence that "blended updates regularize better than pure DFA"
+
+## Implication
+
+The next question is: is this just a regularization artifact (any noise helps), or does Vec's structure matter? This should be tested by comparing blend with random Vec vs blend with random noise of same magnitude.