SoK: Are LLMs Reliable at Source Code Recovery? A Taxonomy and Empirical Evaluation
The work introduces a Systematization of Knowledge (SoK) framework to systematically evaluate how Large Language Models (LLMs) can recover source code from binary files. By defining four research questions and six specific metrics, the study compares various state-of-the-art decompilation methods across different architectures and languages. Findings show that iterative, multi-role agents like Agent4Decompile perform best for byte-level accuracy.