Neural Fractal Reconstruction (NFR): Breaking the Shannon Barrier with Deterministic Neural Context

Neural Fractal Reconstruction (NFR), developed by Philippe-Antoine Robert, represents a paradigm shift in data compression that challenges traditional Shannon entropy limits by treating data as a deterministic logic stream rather than a sequence of random variables. The protocol fuses a deep autoregressive LSTM neural network—which learns the specific structure and syntax of a file to predict byte probabilities—with a custom 32-bit high-precision Integer Arithmetic Coder that allocates fractional bits based on those predictions. By effectively converting model intelligence into storage efficiency, NFR approaches the theoretical bounds of Kolmogorov Complexity, achieving bit-perfect reconstruction where the storage cost of information decreases in direct proportion to the predictive confidence of the neural context.This page will be updated overtime throughout my research. [email protected]