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ai-agents code-generation game-development

The long tail of LLM-assisted decompilation

blog.chrislewis.au

February 16, 2026

12 min read

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48/100

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.

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Community Sentiment

Mixed

Positives

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.