# AGNOMIN -- Architecture Agnostic Multi-Label Function Name Prediction

arXiv: https://arxiv.org/abs/2509.25514 · HTML: https://mlsanity.com/paper/2509.25514/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 52 references that are in the map.
- First posted on arXiv 29 Sep 2025.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [DiSMEC - Distributed Sparse Machines for Extreme Multi-label Classification](https://mlsanity.com/paper/1609.02521/) (2016)
- [Neural Network-based Graph Embedding for Cross-Platform Binary Code Similarity Detection](https://mlsanity.com/paper/1708.06525/) (2017)
- [A no-regret generalization of hierarchical softmax to extreme multi-label classification](https://mlsanity.com/paper/1810.11671/) (2018)
- [DeepXML: A Deep Extreme Multi-Label Learning Framework Applied to Short Text Documents](https://mlsanity.com/paper/2111.06685/) (2021)
- [DIRE: A Neural Approach to Decompiled Identifier Naming](https://mlsanity.com/paper/1909.09029/) (2019)
- [An Observational Investigation of Reverse Engineers' Processes](https://mlsanity.com/paper/1912.00317/) (2019)
- [Probabilistic Label Trees for Extreme Multi-label Classification](https://mlsanity.com/paper/2009.11218/) (2020)
- [Simultaneous Learning of Trees and Representations for Extreme Classification and Density Estimation](https://mlsanity.com/paper/1610.04658/) (2016)
- [CFG2VEC: Hierarchical Graph Neural Network for Cross-Architectural Software Reverse Engineering](https://mlsanity.com/paper/2301.02723/) (2023)
- [ICSREF: A Framework for Automated Reverse Engineering of Industrial Control Systems Binaries](https://mlsanity.com/paper/1812.03478/) (2018)
- [XFL: Naming Functions in Binaries with Extreme Multi-label Learning](https://mlsanity.com/paper/2107.13404/) (2021)

## Built on by (most cited first, 1 of 1)

- [HELIOS: Hierarchical Graph Abstraction for Structure-Aware LLM Decompilation](https://mlsanity.com/paper/2601.14598/) (2026)

## Abstract (arXiv)

Function name prediction is crucial for understanding stripped binaries in software reverse engineering, a key step for enabling subsequent vulnerability analysis and patching. However, existing approaches often struggle with architecture-specific limitations, data scarcity, and diverse naming conventions. We present AGNOMIN, a novel architecture-agnostic approach for multi-label function name prediction in stripped binaries. AGNOMIN builds Feature-Enriched Hierarchical Graphs (FEHGs), combining Control Flow Graphs, Function Call Graphs, and dynamically learned textttPCode features. A hierarchical graph neural network processes this enriched structure to generate consistent function representations across architectures, vital for scalable security assessments. For function name prediction, AGNOMIN employs a Ren'ee-inspired decoder, enhanced with an attention-based head layer and algorithmic improvements. We evaluate AGNOMIN on a comprehensive dataset of 9,000 ELF executable binaries across three architectures, demonstrating its superior performance compared to state-of-the-art approaches, with improvements of up to 27.17% in precision and 55.86% in recall across the testing dataset. Moreover, AGNOMIN generalizes well to unseen architectures, achieving 5.89% higher recall than the closest baseline. AGNOMIN's practical utility has been validated through security hackathons, where it successfully aided reverse engineers in analyzing and patching vulnerable binaries across different architectures.