The long tail of LLM-assisted decompilation
blog.chrislewis.au
February 16, 2026
12 min read
Summary
Coding agents can effectively decompile Nintendo 64 games, achieving a one-shot decompilation success rate that increased code matching from 25% to 58% for Snowboard Kids 2. Workflow adjustments allowed further progress, raising the decompilation to approximately 75%, but subsequent advancements have stalled.
Key Takeaways
- One-shot decompilation significantly improved the progress of the Snowboard Kids 2 project, increasing matched code from 25% to 58% initially.
- A logistic regression model was used to prioritize decompilation tasks based on estimated difficulty, but this approach became less effective as only harder functions remained.
- A new method was developed to compute function similarity, which allowed the decompilation agent to prioritize functions with similar matched counterparts, enhancing its performance.
- The tool Coddog computes similarity scores based on opcode sequences and has shown comparable effectiveness to a more complex hand-built similarity scoring method.
Community Sentiment
MixedPositives
- Using AI for decompilation is a promising application, as it automates tedious tasks and allows for efficient problem-solving in generating human-readable C code.
- The potential of AI in recovering lost source code for games like Red Alert 2 showcases its value in preserving digital history and enhancing accessibility.
- Decompiling functions as individual problems allows for clear verification, making AI-assisted decompilation a practical and beneficial use case.
Concerns
- Claude's limitations with large functions highlight concerns about its effectiveness in complex decompilation tasks, which could hinder its practical application.
- Some users feel that the discussion around AI's role in decompilation underestimates the complexities of the job, leading to frustration and a sense of disrespect.
Source
blog.chrislewis.au
Published
February 16, 2026
Reading Time
12 minutes
Relevance Score
48/100
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