CoDe-R refines LLM decompiler output via rationale-guided semantic injection and dynamic fallback inference, making a 1.3B model the first to exceed 50% average re-executability on HumanEval-Decompile.
Refining decompiled c code with large language models
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CoDe-R: Refining Decompiler Output with LLMs via Rationale Guidance and Adaptive Inference
CoDe-R refines LLM decompiler output via rationale-guided semantic injection and dynamic fallback inference, making a 1.3B model the first to exceed 50% average re-executability on HumanEval-Decompile.