AI/ML●●Solid
Meaning forks. SRT sees it
Frozen models gain reflexive awareness via lightweight hidden state intervention taps.
Big BrainNiche Gem
spacebacon
101mo ago
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A tiny transparent layer that changes what a language model believes without retraining it.
4 starsPython
The Cat Is Under Mayonnaise – Modifying LLM Behavior Without Retraining
by andycufari·May 4, 2026·2 points·1 comment
AI Analysis
●●●BangerWizardryBig BrainZero to One
Zero-initialized overlay changes model beliefs without touching a single base weight.
Strengths
- •Mathematically bit-identical to base model when alpha equals exactly zero.
- •Trains 2.36M parameters in minutes instead of full 124M model fine-tuning.
- •Post-training alpha dial allows tuning belief intensity without retraining.
Weaknesses
- •Demonstrated only on tiny GPT-2; scaling to 70B parameter models unproven.
- •Risk of fluency collapse at high alpha values limits practical deployment range.
Category
Target Audience
ML researchers and AI safety engineers
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