# Refining Decompiled C Code with Large Language Models

arXiv: https://arxiv.org/abs/2310.06530 · HTML: https://mlsanity.com/paper/2310.06530/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Oct 2023.

## Built on by (most cited first, 24 of 24)

- [Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Code](https://mlsanity.com/paper/2311.07989/) (2023)
- [How Far Are We From AGI: Are LLMs All We Need?](https://mlsanity.com/paper/2405.10313/) (2024)
- [Testing and Understanding Erroneous Planning in LLM Agents through Synthesized User Inputs](https://mlsanity.com/paper/2404.17833/) (2024)
- [Large Language Models (LLMs) for Source Code Analysis: applications, models and datasets](https://mlsanity.com/paper/2503.17502/) (2025)
- [IP Leakage Attacks Targeting LLM-Based Multi-Agent Systems](https://mlsanity.com/paper/2505.12442/) (2025)
- [EditLord: Learning Code Transformation Rules for Code Editing](https://mlsanity.com/paper/2504.15284/) (2025)
- [DecompileBench: A Comprehensive Benchmark for Evaluating Decompilers in Real-World Scenarios](https://mlsanity.com/paper/2505.11340/) (2025)
- [Reasoning as a Resource: Optimizing Fast and Slow Thinking in Code Generation Models](https://mlsanity.com/paper/2506.09396/) (2025)
- [HELIOS: Hierarchical Graph Abstraction for Structure-Aware LLM Decompilation](https://mlsanity.com/paper/2601.14598/) (2026)
- [VulCatch: Enhancing Binary Vulnerability Detection through CodeT5 Decompilation and KAN Advanced Feature Extraction](https://mlsanity.com/paper/2408.07181/) (2024)
- [StackSight: Unveiling WebAssembly through Large Language Models and Neurosymbolic Chain-of-Thought Decompilation](https://mlsanity.com/paper/2406.04568/) (2024)
- [Towards LLM-based optimization compilers. Can LLMs learn how to apply a single peephole optimization? Reasoning is all LLMs need!](https://mlsanity.com/paper/2412.12163/) (2024)
- [Can Large Language Models Understand Intermediate Representations in Compilers?](https://mlsanity.com/paper/2502.06854/) (2025)
- [TREAT: A Code LLMs Trustworthiness / Reliability Evaluation and Testing Framework](https://mlsanity.com/paper/2510.17163/) (2025)
- [CoDe-R: Refining Decompiler Output with LLMs via Rationale Guidance and Adaptive Inference](https://mlsanity.com/paper/2604.12913/) (2026)
- [NeuroDeX: Unlocking Diverse Support in Decompiling Deep Neural Network Executables](https://mlsanity.com/paper/2509.06402/) (2025)
- [QiMeng-NeuComBack: Self-Evolving Translation from IR to Assembly Code](https://mlsanity.com/paper/2511.01183/) (2025)
- [Asm2SrcEval: Evaluating Large Language Models for Assembly-to-Source Code Translation](https://mlsanity.com/paper/2512.00134/) (2025)
- [The Periodic Table of LLM Reasoning: A Structured Survey of Reasoning Paradigms, Methods, and Failure Modes](https://mlsanity.com/paper/2606.11470/) (2026)
- [Decomposer: Learning to Decompile Symbolic Music to Programs](https://mlsanity.com/paper/2607.01849/) (2026)
- [Evaluating Neural Decompilation of Dart AOT Binaries: Fine-Tuning, Metric Validity, Specification Leakage, and Reliability](https://mlsanity.com/paper/2607.06125/) (2026)
- [Beyond Text Matching: Towards Reference-Free Evaluation for Human-Oriented Binary Reverse Engineering](https://mlsanity.com/paper/2608.07038/) (2026)
- [Instruction Alignment for Binary Code Representation Learning](https://mlsanity.com/paper/2608.11766/) (2026)
- [Echo: Learning-based Matching Decompilation using Trusted Back Translation](https://mlsanity.com/paper/2609.18706/) (2026)

## Abstract (arXiv)

A C decompiler converts an executable into source code. The recovered C source code, once re-compiled, is expected to produce an executable with the same functionality as the original executable. With over twenty years of development, C decompilers have been widely used in production to support reverse engineering applications. Despite the prosperous development of C decompilers, it is widely acknowledged that decompiler outputs are mainly used for human consumption, and are not suitable for automatic recompilation. Often, a substantial amount of manual effort is required to fix the decompiler outputs before they can be recompiled and executed properly. This paper is motived by the recent success of large language models (LLMs) in comprehending dense corpus of natural language. To alleviate the tedious, costly and often error-prone manual effort in fixing decompiler outputs, we investigate the feasibility of using LLMs to augment decompiler outputs, thus delivering recompilable decompilation. Note that different from previous efforts that focus on augmenting decompiler outputs with higher readability (e.g., recovering type/variable names), we focus on augmenting decompiler outputs with recompilability, meaning to generate code that can be recompiled into an executable with the same functionality as the original executable. We conduct a pilot study to characterize the obstacles in recompiling the outputs of the de facto commercial C decompiler -- IDA-Pro. We then propose a two-step, hybrid approach to augmenting decompiler outputs with LLMs. We evaluate our approach on a set of popular C test cases, and show that our approach can deliver a high recompilation success rate to over 75% with moderate effort, whereas none of the IDA-Pro's original outputs can be recompiled. We conclude with a discussion on the limitations of our approach and promising future research directions.