# ALT4Decompile: Inferring C-aligned Abstract Loop Tree for LLM-Based Binary Decompilation

arXiv: https://arxiv.org/abs/2509.14646 · HTML: https://mlsanity.com/paper/2509.14646/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2025.

## Built on by (most cited first, 4 of 4)

- [CoDe-R: Refining Decompiler Output with LLMs via Rationale Guidance and Adaptive Inference](https://mlsanity.com/paper/2604.12913/) (2026)
- [Challenges and Future Directions in Agentic Reverse Engineering Systems](https://mlsanity.com/paper/2604.14317/) (2026)
- [PCodeTrans: Translate Decompiled Pseudocode to Compilable and Executable Equivalent](https://mlsanity.com/paper/2603.14855/) (2026)
- [Constraint-Guided Multi-Agent Decompilation for Executable Binary Recovery](https://mlsanity.com/paper/2604.23940/) (2026)

## Abstract (arXiv)

Decompilation refers to the process of recovering high-level (C) language code from low-level (assembly) code. Recent Large Language Model (LLM)-based methods can generate re-executable code but struggle with the enormous syntax gap between assembly and C languages. Loop structures in C are fragmented into complex jumps within assembly languages, which poses a challenge to LLMs decompiling assembly language, which is mainly pre-trained on C. To bridge this gap, we propose ALT4Decompile, which refactors assembly to closely resemble C language structure, thereby preventing hallucinations in LLMs caused by complex jumps. Specifically, we construct a C-aligned underlineAbstract underlineLoop underlineTree (ALT) based on specific jump patterns of Assembly that explicitly aggregates fragmented Assembly blocks into corresponding high-level loop structures (e.g., nested loops). Finally, we fine-tune an LLM adapted to ALT to generate decompiled code and then improve its output by fixing specific errors and restoring symbols. Evaluated against 12 baselines (including rule-based SAILR and LLM-based LLM4Decompile) on Decompile-Eval, MBPP, and ExeBench, ALT4Decompile achieves state-of-the-art correct re-executable results (even under four commonly used obfuscation techniques): i.e., a 70.4% test case pass rate on Decompile-Eval, a 10.6% improvement over prior best work. Furthermore, analyses on real-world software and a user study further show its practicability in understanding binary functions. The datasets and source code used in this paper are available at https://github.com/wang-yongpan/ALT4Decompile.