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

arXiv: https://arxiv.org/abs/1812.09652 · HTML: https://mlsanity.com/paper/1812.09652/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Dec 2018.

## Built on by (most cited first, 5 of 5)

- [Semantic-aware Binary Code Representation with BERT](https://mlsanity.com/paper/2106.05478/) (2021)
- [VEXIR2Vec: An Architecture-Neutral Embedding Framework for Binary Similarity](https://mlsanity.com/paper/2312.00507/) (2023)
- [Binary Diffing as a Network Alignment Problem via Belief Propagation](https://mlsanity.com/paper/2112.15337/) (2021)
- [On the Role of Pre-trained Embeddings in Binary Code Analysis](https://mlsanity.com/paper/2502.08682/) (2025)
- [Complexity-based code embeddings](https://mlsanity.com/paper/2601.00924/) (2026)

## Abstract (arXiv)

Given a closed-source program, such as most of proprietary software and viruses, binary code analysis is indispensable for many tasks, such as code plagiarism detection and malware analysis. Today, source code is very often compiled for various architectures, making cross-architecture binary code analysis increasingly important. A binary, after being disassembled, is expressed in an assembly languages. Thus, recent work starts exploring Natural Language Processing (NLP) inspired binary code analysis. In NLP, words are usually represented in high-dimensional vectors (i.e., embeddings) to facilitate further processing, which is one of the most common and critical steps in many NLP tasks. We regard instructions as words in NLP-inspired binary code analysis, and aim to represent instructions as embeddings as well. To facilitate cross-architecture binary code analysis, our goal is that similar instructions, regardless of their architectures, have embeddings close to each other. To this end, we propose a joint learning approach to generating instruction embeddings that capture not only the semantics of instructions within an architecture, but also their semantic relationships across architectures. To the best of our knowledge, this is the first work on building cross-architecture instruction embedding model. As a showcase, we apply the model to resolving one of the most fundamental problems for binary code similarity comparison---semantics-based basic block comparison, and the solution outperforms the code statistics based approach. It demonstrates that it is promising to apply the model to other cross-architecture binary code analysis tasks.