NanoLM: word prediction as low-energy bonding

FUN Keyboard uses NanoLM, a deterministic on-device language architecture built around a simple idea:

Words that naturally belong together should require less computation to connect.

Think of each word as an atom. Context creates an energy field around it and likely next words occupy the lowest-energy “orbitals.” Like atoms forming a stable molecule, the most natural continuation should fall into place without searching a huge probability space.

In NanoLM:

WordID → atom
WordBond Tape → bonding structure
Sentence → molecule
Next-word candidates → energy landscape

Instead of neural inference or corpus-trained weights, NanoLM uses compiled deterministic structures, linguistic classes, word shape and local personalization to reduce the candidate space.

The stronger the natural word attraction, the less compute required to reach it.

That gives us a prediction engine designed for very low latency, low power consumption, offline operation and privacy by architecture.

The question we’re exploring is: how much language prediction can be expressed as structure and attraction before a conventional neural model is actually necessary?