The BDH (Dragon Hatchling) paper (arXiv:2509.26507) describes a Hebbian synaptic plasticity mechanism where model weights update during inference. The released code computes the co-activation product and discards it, the write-back was never implemented publicly. I implemented it.
The model rewrites its own decoder weights during inference using sparse activation codes as addresses. Same token always produces the same code regardless of position.
Consolidation (v2): Once episodic fast weights work, the next question is whether you can write them back into slow weights without destroying the signal. Dense writeback degrades it. Selective writeback (top 10% of rows by episode activity) preserves most of it:
| n2 | n4 | n8 | |
|---|---|---|---|
| Control (no consolidation) | 97.2% | 95.5% | 97.4% |
| Dense writeback | 75.4% | 68.1% | 89.8% |
| Selective (rowtop10) | 97.5% | 97.1% | 96.2% |
Verified on independent hardware (H100) and seed. Counter-benchmarks stay in the 91–95% range.
Base mechanism: Baseline without write-back gets 1% (chance). Best Hebbian run hits 99.0 / 98.0 / 97.5 on n2/n4/n8. Reproduced across independent seeds. Five bugs had to be solved — all documented in the README.
Limitations: This is a mechanism proof on synthetic n-back associative recall. 25M parameter model. Not validated on natural language. Next step is FineWeb-Edu.
Repo (Apache 2.0): https://github.com/fleeb83/bdh-fast-weights
Independent researcher, no lab. Happy to answer any questions.
[link] [comments]
