# ReCopilot: Reverse Engineering Copilot in Binary Analysis

arXiv: https://arxiv.org/abs/2505.16366 · HTML: https://mlsanity.com/paper/2505.16366/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2025.

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

- [Cross-modal Retrieval Models for Stripped Binary Analysis](https://mlsanity.com/paper/2512.10393/) (2025)
- [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)
- [Beyond Text Matching: Towards Reference-Free Evaluation for Human-Oriented Binary Reverse Engineering](https://mlsanity.com/paper/2608.07038/) (2026)
- [Humans vs. Machines: A Comparison With AI Agents in Software Reverse Engineering](https://mlsanity.com/paper/2610.14960/) (2026)

## Abstract (arXiv)

Binary analysis plays a pivotal role in security domains such as malware detection and vulnerability discovery, yet it remains labor-intensive and heavily reliant on expert knowledge. General-purpose large language models (LLMs) perform well in programming analysis on source code, while binaryspecific LLMs are underexplored. In this work, we present ReCopilot, an expert LLM designed for binary analysis tasks. ReCopilot integrates binary code knowledge through a meticulously constructed dataset, encompassing continue pretraining (CPT), supervised fine-tuning (SFT), and direct preference optimization (DPO) stages. It leverages variable data flow and call graph to enhance context awareness and employs test-time scaling to improve reasoning capabilities. Evaluations on a comprehensive binary analysis benchmark demonstrate that ReCopilot achieves state-of-the-art performance in tasks such as function name recovery and variable type inference on the decompiled pseudo code, outperforming both existing tools and LLMs by 13%. Our findings highlight the effectiveness of domain-specific training and context enhancement, while also revealing challenges in building super long chain-of-thought. ReCopilot represents a significant step toward automating binary analysis with interpretable and scalable AI assistance in this domain.