AI BriefingKuna: Building a Decompiler for the Coding Agent Era
Key point
An experimental decompiler called Kuna was unveiled that uses LLMs to autonomously improve its own performance.
Details
The experimental decompiler Kuna, whose code was almost entirely written by an LLM, has been unveiled. Going beyond simple auto-generation, this project adopts an Autonomous Refinement approach in which the LLM compares its performance against existing decompilers (IDA Pro, Ghidra, etc.) and improves itself.
Key features include:
- Performance metrics: On a control flow structuring benchmark for C programs, it achieves perfect structuring for 44.4% of functions, a figure close to the industry standard IDA Pro (45.7%).
- Autonomous learning: The LLM learns from cases where it underperforms IDA Pro on specific metrics, and improves through trial and error to solve the problems.
- Technical foundation: It is built on a Rust port of NSA's Ghidra, and about 20 core features of the existing
angrdecompiler have been reimplemented via the LLM.
This project takes an experimental approach to verifying whether, through a feedback loop designed based on human scientific insight, an LLM can learn high-level scientific metrics and automatically improve a tool.
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