# EXHIB: A Benchmark for Realistic and Diverse Evaluation of Function Similarity in the Wild

arXiv: https://arxiv.org/abs/2604.01554 · HTML: https://mlsanity.com/paper/2604.01554/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 59 references that are in the map.
- First posted on arXiv 2 Apr 2026.

## 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)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Graph Matching Networks for Learning the Similarity of Graph Structured Objects](https://mlsanity.com/paper/1904.12787/) (2019)
- [Neural Network-based Graph Embedding for Cross-Platform Binary Code Similarity Detection](https://mlsanity.com/paper/1708.06525/) (2017)
- [jTrans: Jump-Aware Transformer for Binary Code Similarity](https://mlsanity.com/paper/2205.12713/) (2022)
- [Trex: Learning Execution Semantics from Micro-Traces for Binary Similarity](https://mlsanity.com/paper/2012.08680/) (2020)
- [SAFE: Self-Attentive Function Embeddings for Binary Similarity](https://mlsanity.com/paper/1811.05296/) (2018)
- [Graph Matching with Bi-level Noisy Correspondence](https://mlsanity.com/paper/2212.04085/) (2022)
- [A Survey of Binary Code Similarity](https://mlsanity.com/paper/1909.11424/) (2019)
- [CLAP: Learning Transferable Binary Code Representations with Natural Language Supervision](https://mlsanity.com/paper/2402.16928/) (2024)
- [BinPro: A Tool for Binary Source Code Provenance](https://mlsanity.com/paper/1711.00830/) (2017)
- [BinMatch: A Semantics-based Hybrid Approach on Binary Code Clone Analysis](https://mlsanity.com/paper/1808.06216/) (2018)

## Abstract (arXiv)

Binary Function Similarity Detection (BFSD) is a core problem in software security, supporting tasks such as vulnerability analysis, malware classification, and patch provenance. In the past few decades, numerous models and tools have been developed for this application; however, due to the lack of a comprehensive universal benchmark in this field, researchers have struggled to compare different models effectively. Existing datasets are limited in scope, often focusing on a narrow set of transformations or types of binaries, and fail to reflect the full diversity of real-world applications. We introduce EXHIB, a benchmark comprising five realistic datasets collected from the wild, each highlighting a distinct aspect of the BFSD problem space. We evaluate 9 representative models spanning multiple BFSD paradigms on EXHIB and observe performance degradations of up to 30% on firmware and semantic datasets compared to standard settings, revealing substantial generalization gaps. Our results show that robustness to low- and mid-level binary variations does not generalize to high-level semantic differences, underscoring a critical blind spot in current BFSD evaluation practices.