I built an architecture that enables continuous learning for open-weight models.

The "curse" of LLMs is the knowledge cut-off. RAG is just a band-aid. Google's "Titans" memory solves this, but costs millions to train.

I built a "Grafted" version on a single desk-side GPU (€4.4k). I attached a trainable memory adapter to a frozen Qwen-2.5-0.5B model.

The Result: It beat the vanilla model even when the model had the full context on the challenging Babilong benchmark.

Vanilla Qwen (seeing context): 34.0% Accuracy

Grafted Titans (memory retrieval): 44.7% Accuracy

My memory module effectively acts as a denoising filter, outperforming the model's native attention mechanisms.

The Specs:

Hardware: Single Nvidia DGX Spark Blackwell.

Training Time: ~7 days.

Output: Plug-and-play adapter for open weights.

This proves we don't need industrial clusters to build AI that remembers.