# Beyond the C: Retargetable Decompilation using Neural Machine Translation

arXiv: https://arxiv.org/abs/2212.08950 · HTML: https://mlsanity.com/paper/2212.08950/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 41 references that are in the map.
- First posted on arXiv 17 Dec 2022.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [The Curious Case of Neural Text Degeneration](https://mlsanity.com/paper/1904.09751/) (2019)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [Beam Search Strategies for Neural Machine Translation](https://mlsanity.com/paper/1702.01806/) (2017)
- [Does BLEU Score Work for Code Migration?](https://mlsanity.com/paper/1906.04903/) (2019)
- [PalmTree: Learning an Assembly Language Model for Instruction Embedding](https://mlsanity.com/paper/2103.03809/) (2021)
- [Trex: Learning Execution Semantics from Micro-Traces for Binary Similarity](https://mlsanity.com/paper/2012.08680/) (2020)
- [Describing Multimedia Content using Attention-based Encoder--Decoder Networks](https://mlsanity.com/paper/1507.01053/) (2015)
- [XDA: Accurate, Robust Disassembly with Transfer Learning](https://mlsanity.com/paper/2010.00770/) (2020)
- [Towards Neural Decompilation](https://mlsanity.com/paper/1905.08325/) (2019)
- [Building Program Vector Representations for Deep Learning](https://mlsanity.com/paper/1409.3358/) (2014)

## Built on by (most cited first, 15 of 15)

- [Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Code](https://mlsanity.com/paper/2311.07989/) (2023)
- [Large Language Models for Compiler Optimization](https://mlsanity.com/paper/2309.07062/) (2023)
- [Meta Large Language Model Compiler: Foundation Models of Compiler Optimization](https://mlsanity.com/paper/2407.02524/) (2024)
- [SLaDe: A Portable Small Language Model Decompiler for Optimized Assembly](https://mlsanity.com/paper/2305.12520/) (2023)
- [How Does Code Pretraining Affect Language Model Task Performance?](https://mlsanity.com/paper/2409.04556/) (2024)
- [Nova: Generative Language Models for Assembly Code with Hierarchical Attention and Contrastive Learning](https://mlsanity.com/paper/2311.13721/) (2023)
- [ShieldedCode: Learning Robust Representations for Virtual Machine Protected Code](https://mlsanity.com/paper/2601.20679/) (2026)
- [DecompileBench: A Comprehensive Benchmark for Evaluating Decompilers in Real-World Scenarios](https://mlsanity.com/paper/2505.11340/) (2025)
- [Forklift: An Extensible Neural Lifter](https://mlsanity.com/paper/2404.16041/) (2024)
- [Deconstructing Obfuscation: A four-dimensional framework for evaluating Large Language Models assembly code deobfuscation capabilities](https://mlsanity.com/paper/2505.19887/) (2025)
- [Decaf: Improving Neural Decompilation with Automatic Feedback and Search](https://mlsanity.com/paper/2605.11501/) (2026)
- [Can Neural Decompilation Assist Vulnerability Prediction on Binary Code?](https://mlsanity.com/paper/2412.07538/) (2024)
- [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)
- [Echo: Learning-based Matching Decompilation using Trusted Back Translation](https://mlsanity.com/paper/2609.18706/) (2026)

## Abstract (arXiv)

The problem of reversing the compilation process, decompilation, is an important tool in reverse engineering of computer software. Recently, researchers have proposed using techniques from neural machine translation to automate the process in decompilation. Although such techniques hold the promise of targeting a wider range of source and assembly languages, to date they have primarily targeted C code. In this paper we argue that existing neural decompilers have achieved higher accuracy at the cost of requiring language-specific domain knowledge such as tokenizers and parsers to build an abstract syntax tree (AST) for the source language, which increases the overhead of supporting new languages. We explore a different tradeoff that, to the extent possible, treats the assembly and source languages as plain text, and show that this allows us to build a decompiler that is easily retargetable to new languages. We evaluate our prototype decompiler, Beyond The C (BTC), on Go, Fortran, OCaml, and C, and examine the impact of parameters such as tokenization and training data selection on the quality of decompilation, finding that it achieves comparable decompilation results to prior work in neural decompilation with significantly less domain knowledge. We will release our training data, trained decompilation models, and code to help encourage future research into language-agnostic decompilation.