The Complete Algorithm
Two descriptions of one pattern
The Deeper Law
The GPT Architecture
DISSIPATION
Differences even out
RANDOM NOISE
Gaussian initialization
NEGENTROPY
Order from randomness
TRAINING
Loss falls, patterns emerge
COORDINATION
Selective amplification
ATTENTION
Tokens weight all others
OPTIONALITY
Many possible futures
SOFTMAX
Temperature: order ↔ chaos
EMERGENCE
Coherent wholes
GENERATION
Language from learned patterns
This file is the complete algorithm. Everything else is just efficiency.
From Bénard cells to language models — the same pattern at different scales
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