Echo: Learning-based Matching Decompilation using Trusted Back Translation
Researchers introduce Echo, a matching decompiler using trusted back-translation that roughly doubles exact-match rates and outperforms GPT-5.6 and Codex on Mirai.
Echo performs matching decompilation by using compilation as trusted feedback for iterative search: a domain-specific model generates candidate code and compilation configurations, which are recompiled, compared at assembly level, and repaired via rule-based rewriting, neural refinement, and reasoning-based refinement. On function-level benchmarks, Echo produces 2.43x more exact matches than the strongest baseline and the highest structural similarity to ground truth. On the Mirai malware binary, it matches 2.75x and 7.4x as many functions as GPT-5.6 and Codex, respectively.
- Uses compilation as trusted feedback to guide iterative decompilation search
- Combines rule-based rewriting, neural refinement, and reasoning-based repair
- 2.43x more exact matches than the strongest baseline on benchmarks
- Outperforms GPT-5.6 and Codex on the Mirai malware binary
From the original article
1 min read at arxiv.orgNeural decompilers can recover readable and recompilable source code from binaries, but their predictions remain difficult to trust. Matching decompilation addresses this problem by searching for source code whose recompiled assembly exactly matches the target, providing stronger evidence of correctness. However, exact matching remains challenging for optimized binaries under unknown compilation configurations.
By Jun Bi · arxiv.org
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