Online adaptive lossless compression with lockstep-trained predictive models

Abstract

A method and system are disclosed for adaptive lossless data compression using a predictive model that is updated deterministically in lockstep at both encoder and decoder. An encoder generates symbol probability distributions from a frozen base model with a small set of updatable parameters, encodes input blocks via entropy coding, and applies deterministic parameter updates using the observed data. A decoder entropy-decodes the compressed bitstream, reconstructs the same blocks, and applies identical updates, thereby maintaining synchronization without transmission of model parameters. Optional features include periodic beacons for state verification and resynchronization, error-control metadata, and modality-specific tokenization. In preferred embodiments, parameter adaptation is confined to adapter modules within a largely fixed neural network, ensuring computational efficiency and reproducibility across platforms. Over time, the model specializes to the stream, reducing average coding rate while guaranteeing exact reconstruction of the original data.

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