# A Cross-Architecture Instruction Embedding Model for Natural Language Processing-Inspired Binary Code Analysis

> The paper learns instruction embeddings that capture semantic relationships within and across architectures. It evaluates them on semantic basic-block comparison, outperforming a code-statistics approach.

- Authors: Kimberly Redmond; Lannan Luo; Qiang Zeng
- Venue: arXiv
- Status: Preprint
- Publication / posting date: December 23, 2018
- Date source: First arXiv submission date.
- First arXiv submission: December 23, 2018
- arXiv: 1812.09652
- Reviewed arXiv version: v1
- Sources reviewed: 2026-11-06
- Added to the catalog: 2026-11-06
- Original paper: <https://arxiv.org/abs/1812.09652>
- PDF: <https://arxiv.org/pdf/1812.09652>
- BibTeX: <https://awesomerea.org/papers/citations/paper-cross-arch-instruction-embedding.bib>
- Catalog page: <https://awesomerea.org/papers/paper-cross-arch-instruction-embedding/>
- Record JSON: <https://awesomerea.org/papers/paper-cross-arch-instruction-embedding/record.json>
- Chinese version: [简体中文](https://awesomerea.org/zh/papers/paper-cross-arch-instruction-embedding/index.md)

## Scope and limitations

Evaluates semantic basic-block comparison across architectures on disassembled binary code.

## Why it fits this collection

It addresses cross-architecture binary code analysis through learned instruction representations.

## Publication and review notes

The date and venue come from the arXiv record.

## Primary sources

1. <https://arxiv.org/abs/1812.09652>

## About this entry

Reviewed against the paper and its primary sources. Cite the original authors using the linked paper or BibTeX.
Original editorial content: MIT. Linked projects, papers and assets retain their own rights and licenses.