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.