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20a3a74 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | # 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)
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