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)

README.md

@@ -139,12 +139,12 @@ Key files:
 ## Citation
 
 ```bibtex
-@article{niemi2026clivewearing,
+@article{niemi2026epimem,
   title={Clive Wearing: One-Shot Learning on Frozen Transformers via Hidden-State Episodic Memory},
   author={Tommi Niemi},
   year={2026},
   organization={Rotko Networks},
-  url={https://git.rotko.net/rotko/clivewearing},
+  url={https://git.rotko.net/rotko/epimem},
 }
 ```