Mathematical guarantee against overfitting: at each training step,
project out gradient components that contradict a held-out validation
batch. The optimizer can only move in directions where both train and
val agree — overfitting gradients are surgically removed.
Complementary to the Temporal Causal Bottleneck (#31):
- Bottleneck constrains REPRESENTATION (100 bits, no memorization room)
- Vaccine constrains OPTIMIZATION (only generalizing gradients survive)
- Together: "you can only remember 2 numbers, and those 2 numbers must
work on data you haven't trained on"
Implementation: dot(g_train, g_val) reduction + conditional SAXPY
projection between replay_forward() and replay_adam(). Two kernel
launches inside the CUDA Graph.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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