epimem: One-shot gradient-free learning on frozen transformers

- paper.md: full paper (Tommi Niemi / Rotko Networks)
- python/epimem.py: standalone Python reproduction
- export_onnx.py: ONNX export from HuggingFace (generates model files)
- results/memory_bank.json: example hidden-state vectors (896-dim)
- schema/: FlatBuffer schemas for memory bank + organism
- models/tokenizer/: Qwen 2.5 tokenizer files

Run: pip install transformers torch && python python/epimem.py
(Downloads Qwen 2.5 automatically from HuggingFace)

results/e2e_output.log

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+=== Sleep Cycle Report ===
+NREM: 8 synapse updates
+  syn_input_attn: residual=0.0133
+  syn_attn_output: residual=0.0333
+  syn_basal_ganglia: residual=0.0605
+  syn_hippocampus: residual=0.0347
+  syn_insula: residual=0.0118
+  syn_motor_input: residual=0.0136
+  syn_cerebellum: residual=0.0161
+  syn_output_motor: residual=0.0446
+Graduated: 0 episodes → semantic
+REM: 3 replayed, 0 fears treated
+Health: 1.00/1.00
+=== Angeris Bounds Analysis ===
+Neurons: 64 total, dead: input=0 attn=0 output=0 motor=0
+  ALL SYNAPSES AT OPTIMUM — sleep consolidation won't improve further