| # 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) | |