CoDe-R: Refining Decompiler Output with LLMs via Rationale Guidance and Adaptive Inference
Binary decompilation is a critical reverse engineering task aimed at reconstructing high-level source code from stripped executables. Although Large Language Models (LLMs) have recently shown promise, they often suffer from "logical hallucinations" and "semantic misalignment" due to the irreversible semantic loss during compilation, resulting in generated code that fails to re-execute. In this study, we propose Cognitive Decompiler Refinement with Robustness (CoDe-R), a lightweight two-stage code refinement framework. The first stage introduces Semantic Cognitive Enhancement (SCE), a Rationale-Guided Semantic Injection strategy that trains the model to recover high-level algorithmic inten...
| Authors | Qiang Zhang, Zhongnian Li |
|---|---|
| Primary category | cs.SE |
| Published | 14 April 2026 |
| Last updated at source | 22 June 2026 |
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