Universal AI–Based Data Compressor via Algorithmic Reconstruction: The Principled Path Toward Near-Optimal Compression

This paper explores the concept of an intelligent, algorithm aware compression system powered by large-scale neural models. The central hypothesis is that all data with finite structure can be significantly compressed if its generating algorithm can be inferred. We propose an architecture where a neural network, trained on trillions of data (data, algorithm) pairs, learns to reverse-engineer the generative process underlying any given dataset. Such a system would represent a practical approximation to Solomonoff induction and could achieve compression ratios far beyond conventional statistical methods, limited only by the fundamental boundaries of Kolmogorov complexity. Proof-of-concept interactive demo available at https://algorithm-compress.streamlit.app