The Long Tail of LLM-Assisted Decompilation (blog.chrislewis.au)

In a recent exploration of using AI for decompiling Nintendo 64 games, a developer shared insights on his progress with the game Snowboard Kids 2, detailing how various techniques influenced the decompilation process. Initial attempts using a logistic regression model to prioritize easier functions led to rapid improvements, increasing matched code from 25% to 58%. However, as the project matured and harder functions remained, different strategies emerged, particularly the use of function similarity via text embeddings of assembly instructions, which proved instrumental in guiding decompilation efforts. This approach allowed for the identification and utilization of similar, previously unmatched functions. The significance of this work lies in its implications for the AI/ML community, particularly in the domain of code analysis and decompilation. By leveraging AI tools such as Claude and specialized skills for rendering graphics in the N64, the developer navigated a complex landscape characterized by the challenges of dynamic microcode and optimization issues. The exploration of composite similarity scores, automated cleanup processes, and strategic task orchestration also underscored the potential for tailored AI workflows in tackling difficult coding tasks. This project's findings offer valuable insights and techniques that could benefit future decompilation projects and enhance the capabilities of AI in similar contexts.