Tommi Niemi
176164815b
Tommi Niemi / Rotko Networks Hidden-state episodic memory for frozen transformers. No gradients. Teach via one forward pass, recall via cosine similarity + logit injection. 200-line Python reproduction included. pip install transformers torch numpy && python python/epimem.py
17 lines
574 B
Plaintext
17 lines
574 B
Plaintext
=== Sleep Cycle Report ===
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NREM: 8 synapse updates
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syn_input_attn: residual=0.0133
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syn_attn_output: residual=0.0333
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syn_basal_ganglia: residual=0.0605
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syn_hippocampus: residual=0.0347
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syn_insula: residual=0.0118
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syn_motor_input: residual=0.0136
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syn_cerebellum: residual=0.0161
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syn_output_motor: residual=0.0446
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Graduated: 0 episodes → semantic
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REM: 3 replayed, 0 fears treated
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Health: 1.00/1.00
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=== Angeris Bounds Analysis ===
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Neurons: 64 total, dead: input=0 attn=0 output=0 motor=0
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ALL SYNAPSES AT OPTIMUM — sleep consolidation won't improve further
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