# Context-Guided Decompilation: A Step Towards Re-executability

arXiv: https://arxiv.org/abs/2511.01763 · HTML: https://mlsanity.com/paper/2511.01763/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 62 references that are in the map.
- First posted on arXiv 3 Nov 2025.

## Builds on (most cited first, 29 of 29)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [Program Synthesis with Large Language Models](https://mlsanity.com/paper/2108.07732/) (2021)
- [Self-Refine: Iterative Refinement with Self-Feedback](https://mlsanity.com/paper/2303.17651/) (2023)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [StarCoder: may the source be with you!](https://mlsanity.com/paper/2305.06161/) (2023)
- [A Survey on In-context Learning](https://mlsanity.com/paper/2301.00234/) (2022)
- [GraphCodeBERT: Pre-training Code Representations with Data Flow](https://mlsanity.com/paper/2009.08366/) (2020)
- [InCoder: A Generative Model for Code Infilling and Synthesis](https://mlsanity.com/paper/2204.05999/) (2022)
- [The Learnability of In-Context Learning](https://mlsanity.com/paper/2303.07895/) (2023)
- [LLM4Decompile: Decompiling Binary Code with Large Language Models](https://mlsanity.com/paper/2403.05286/) (2024)
- [DIRE: A Neural Approach to Decompiled Identifier Naming](https://mlsanity.com/paper/1909.09029/) (2019)
- [SLaDe: A Portable Small Language Model Decompiler for Optimized Assembly](https://mlsanity.com/paper/2305.12520/) (2023)
- [Towards Neural Decompilation](https://mlsanity.com/paper/1905.08325/) (2019)
- [Decompile-Bench: Million-Scale Binary-Source Function Pairs for Real-World Binary Decompilation](https://mlsanity.com/paper/2505.12668/) (2025)
- [kTrans: Knowledge-Aware Transformer for Binary Code Embedding](https://mlsanity.com/paper/2308.12659/) (2023)
- [Source Code Foundation Models are Transferable Binary Analysis Knowledge Bases](https://mlsanity.com/paper/2405.19581/) (2024)
- [Studying How Configurations Impact Code Generation in LLMs: the Case of ChatGPT](https://mlsanity.com/paper/2502.17450/) (2025)
- [BinMetric: A Comprehensive Binary Analysis Benchmark for Large Language Models](https://mlsanity.com/paper/2505.07360/) (2025)
- [DecompileBench: A Comprehensive Benchmark for Evaluating Decompilers in Real-World Scenarios](https://mlsanity.com/paper/2505.11340/) (2025)
- [How do Developers Improve Code Readability? An Empirical Study of Pull Requests](https://mlsanity.com/paper/2309.02594/) (2023)
- [Idioms: Neural Decompilation With Joint Code and Type Definition Prediction](https://mlsanity.com/paper/2502.04536/) (2025)
- [ReF Decompile: Relabeling and Function Call Enhanced Decompile](https://mlsanity.com/paper/2502.12221/) (2025)
- [Reassessing Java Code Readability Models with a Human-Centered Approach](https://mlsanity.com/paper/2401.14936/) (2024)
- [Revisiting Deep Learning for Variable Type Recovery](https://mlsanity.com/paper/2304.03854/) (2023)
- [Revisiting Lightweight Compiler Provenance Recovery on ARM Binaries](https://mlsanity.com/paper/2305.03934/) (2023)

## Built on by (most cited first, 1 of 1)

- [PCodeTrans: Translate Decompiled Pseudocode to Compilable and Executable Equivalent](https://mlsanity.com/paper/2603.14855/) (2026)

## Abstract (arXiv)

Binary decompilation plays an important role in software security analysis, reverse engineering, and malware understanding when source code is unavailable. However, existing decompilation techniques often fail to produce source code that can be successfully recompiled and re-executed, particularly for optimized binaries. Recent advances in large language models (LLMs) have enabled neural approaches to decompilation, but the generated code is typically only semantically plausible rather than truly executable, limiting their practical reliability. These shortcomings arise from compiler optimizations and the loss of semantic cues in compiled code, which LLMs struggle to recover without contextual guidance. To address this challenge, we propose ICL4Decomp, a hybrid decompilation framework that leverages in-context learning (ICL) to guide LLMs toward generating re-executable source code. We evaluate our method across multiple datasets, optimization levels, and compilers, demonstrating around 40% improvement in re-executability over state-of-the-art decompilation methods while maintaining robustness.