# 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.