NINMENI's MULTIPITA Tackles AI Compute Costs Without Merging Character Identities

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Most large language models compress text into tokens before heavy computation begins, but NINMENI takes a different approach by assigning each normalized character a fixed, unchangeable identity unit called an NMU. With a registry of 10,240 identity slots, every character retains its own state, position, output, and training target throughout processing. This design prevents sequence-length reduction through learned segmentation, making computational cost the key engineering challenge. MULTIPITA is NINMENI's proposed solution, aiming to reorganize how computation is performed around the character sequence rather than altering the identities within it.

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