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arXiv AI By Noam Mazor, Andrew Morgan, Rafael Pass

Language Modeling is Monotone Compression

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The paper investigates the relationship between large language models (LLMs) and compression, showing that LLMs, modeled as next‑token predictors, are equivalent to monotone (order‑preserving) compression algorithms with an error gap of at most 2. It further proves that this monotonicity is necessary for the equivalence only if cryptographic one‑way functions exist, and derives a cryptographic corollary linking next‑bit pseudoentropy to monotone incompressibility.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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