Advanced neural binary analysis and decompilation

This project introduces a paradigm shift in binary code analysis by transferring the concept of semantic embeddings from natural language processing to compiled software. While traditional binary analysis relies on rule-based decompilers and lossy intermediate representations, the proposed approach treats compilation as a translation problem between two representations of the same program: source code and binary code. An end-to-end neural architecture directly processes raw binaries and learns a fully optimizable internal representation from a dataset of large-scale source - binary pairs, avoiding structural information loss inherent to classical pipelines. As a result, compiled software becomes machine-understandable and semantically analyzable for the first time. We aim to train a binary space-foundation model that enables semantic search, automated reconstruction of readable source code, and precise software composition analysis, including vulnerability and component detection. By prioritizing general machine learning methods over handcrafted heuristics, the approach overcomes fundamental limitations of existing tools and establishes the foundation for a sovereign European AI model for binary file analysis.