we dug into those sorts of questions with hypertokens, a robust hash for lines, code, tables/rows or any in-context token tagging to give models photographic memory

one mechanism we establish is that each model has a fidelity window, i.e., r tokens of content for s tag tokens; each tag token adds extra GUID-like marker capacity via its embedding vector; since 1,2,3 digit numbers only one token in top models, a single hash token lacks enough capacity & separation in latent space

we also show hash should be properly prefix-free, or unique symbols perp digit, e.g., if using A-K & L-Z to hash then A,R is legal hash whereas M,C is not permitted hash

we can do all this & more rather precisely as we show in our arXiv paper on same; next update goes deeper into group theory, info theory, etc. on boosting model recall, reasoning, tool calls, etc. by way of robust hashing

For others, here's the paper: https://arxiv.org/abs/2507.00002