# μLattice: Breaking the Quadratic Memory Barrier in AI

**Tensorbend Research · Eric E. · October 2026**

## TL;DR

AI models today pay quadratically more to remember more. Attention scores every
stored token against every new query: doubling the context quadruples the cost.
μLattice breaks this barrier. Memory grows with the input — each token reads and
writes exactly 9 cells — so the cost per token stays flat no matter how much is
stored.

Measured on the released checkpoint: recall of 1.0000 at 8,192 stored pairs in a
133K-token sequence; 0.9980 on overwrite queries; 1.0000 on real-text retrieval
at 259K tokens; and per-token cost flat at ~10μs from 32K to 131K tokens while
the lattice grows to 37,000 cells.

## Try it

Use the **Your Own Facts** tab above. Paste any text, define facts as
name→value pairs, mark which get overwritten mid-stream, and watch the model
store, replace, and recall them — in one pass through your text.

## Links

- [Full paper](https://github.com/eelbaz/mLattice/blob/main/PAPER.md)
- [Code & tests](https://github.com/eelbaz/mLattice)
- [Results & figures](https://github.com/eelbaz/mLattice/tree/main/results)