# Datalog Disassembly

arXiv: https://arxiv.org/abs/1906.03969 · HTML: https://mlsanity.com/paper/1906.03969/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2019.

## Built on by (most cited first, 7 of 7)

- [SLaDe: A Portable Small Language Model Decompiler for Optimized Assembly](https://mlsanity.com/paper/2305.12520/) (2023)
- [Boosting Neural Networks to Decompile Optimized Binaries](https://mlsanity.com/paper/2301.00969/) (2023)
- [Decompiling x86 Deep Neural Network Executables](https://mlsanity.com/paper/2210.01075/) (2022)
- [Tady: A Neural Disassembler without Structural Constraint Violations](https://mlsanity.com/paper/2506.13323/) (2025)
- [Marvolo: Programmatic Data Augmentation for Practical ML-Driven Malware Detection](https://mlsanity.com/paper/2206.03265/) (2022)
- [Echo: Learning-based Matching Decompilation using Trusted Back Translation](https://mlsanity.com/paper/2609.18706/) (2026)
- [XLOG: A CUDA-Native Engine for Neurosymbolic Integration](https://mlsanity.com/paper/2609.27203/) (2026)

## Abstract (arXiv)

Disassembly is fundamental to binary analysis and rewriting. We present a novel disassembly technique that takes a stripped binary and produces reassembleable assembly code. The resulting assembly code has accurate symbolic information, providing cross-references for analysis and to enable adjustment of code and data pointers to accommodate rewriting. Our technique features multiple static analyses and heuristics in a combined Datalog implementation. We argue that Datalog's inference process is particularly well suited for disassembly and the required analyses. Our implementation and experiments support this claim. We have implemented our approach into an open-source tool called Ddisasm. In extensive experiments in which we rewrite thousands of x64 binaries we find Ddisasm is both faster and more accurate than the current state-of-the-art binary reassembling tool, Ramblr.