---
title: "Selective Knowledge Distillation — paper by Zhou et al. (2026)"
description: "The authors propose BinSKD, a relational knowledge distillation framework. 36 references."
authors: ["Shize Zhou", "Peiyu Liu", "Lirong Fu", "Tong Ye", "Wenhai Wang"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.1193/"
doi: "10.18653/v1/2026.acl-long.1193"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/zhou-2026-selective-knowledge-distillation-fusing-llm/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/zhou-2026-selective-knowledge-distillation-fusing-llm/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2026](https://md.research.elamur.ai/free/en/papers/2026/page/17/) / Selective Knowledge Distillation: Fusing LLM Semantic Strengths with DNN Efficiency for Binary Code Similarity Detection

# Selective Knowledge Distillation: Fusing LLM Semantic Strengths with DNN Efficiency for Binary Code Similarity Detection

**Authors:** Shize Zhou, [Peiyu Liu](https://md.research.elamur.ai/free/en/authors/peiyu-liu/), Lirong Fu, Tong Ye and [Wenhai Wang](https://md.research.elamur.ai/free/en/authors/wenhai-wang/) · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.acl-long.1193/) · [PDF](https://aclanthology.org/2026.acl-long.1193.pdf) · [DOI 10.18653/v1/2026.acl-long.1193](https://doi.org/10.18653/v1/2026.acl-long.1193) · [OpenAlex](https://openalex.org/W7166821160)

**Citations:** 1 citations (OpenAlex, 2026-10-14)

## Summary
The paper tackles a trade-off in Binary Code Similarity Detection (BCSD), which supports tasks such as vulnerability search, malware analysis and plagiarism detection. DNN-based detectors are fast but depend on low-level lexical, control-flow or data-flow features. These features change across compiler optimization levels, so the detectors miss higher-level program meaning. LLM-based detectors capture that meaning but are about two orders of magnitude larger and slower.

The authors propose BinSKD, a relational knowledge distillation framework. A pre-trained LLM-based BCSD model acts as teacher, and its embeddings are computed offline. Various DNN-based BCSD models act as students. Each model builds a z-score-normalized matrix of pairwise cosine similarities. Two mechanisms shape the distillation:
- **Selective distillation:** keeps only the queries where the teacher ranks true matches above the student's ranking.
- **Discrepancy-weighted sampling:** gives more weight to candidates whose reciprocal ranks differ most between the two models.

A Huber-based distillation loss is then combined with the student's own task loss. Because the student absorbs the teacher's semantics during training, the gains come without any extra cost at inference time.

## Key points
- Across students with different architectures, BinSKD raises Recall@1 by 14.5%–91.2%.
- Applied to HermesSim, the student reaches accuracy comparable to the LLM teacher while running 107× faster with 290× fewer parameters.
- Motivating analysis of 3,000 manually inspected queries: in 47.3% of cases, the teacher did not place true positives ahead of where the student placed them. This is the justification for filtering unreliable teacher knowledge.
- Vulnerability-search case study, top-50 retrievals: for CVE-2016-5195, SAFE found 5 vulnerable functions before distillation and 12 after.
- Limitations: evaluation covers only cross-optimization settings on x64, because the teacher supports only that architecture. Code is released at github.com/todoni123/BinSKD.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Zhejiang University](https://md.research.elamur.ai/free/en/universities/zhejiang-university/) — University, Hangzhou, China · 172 works · 12,988 citations
- [Hangzhou Dianzi University](https://md.research.elamur.ai/free/en/universities/hangzhou-dianzi-university/) — University, Hangzhou, China · 7 works · 1,061 citations

📚 Shelves (4): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 16,667 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 16,194 works · [🗄️ Information Systems](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/) · 1,437 works · [🔊 Signal Processing](https://md.research.elamur.ai/free/en/shelves/computer-science/signal-processing/) · 452 works

🧭 Topics (1): [🪶 Efficient models & compression](https://md.research.elamur.ai/free/en/topics/efficient-models/) · 242 works

## Cite

Cite as: Shize Zhou, Peiyu Liu, Lirong Fu, Tong Ye and Wenhai Wang. 2026. Selective Knowledge Distillation: Fusing LLM Semantic Strengths with DNN Efficiency for Binary Code Similarity Detection. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{zhou2026selective,
  title = {{Selective Knowledge Distillation: Fusing LLM Semantic Strengths with DNN Efficiency for Binary Code Similarity Detection}},
  author = {Shize Zhou, Peiyu Liu, Lirong Fu, Tong Ye and Wenhai Wang},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.1193},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1193}
}
```

- [← previous Selected-Layer Codec Compression for Compact Speech Translation Models: An IWSLT 2026 English-to-Chinese Submission](https://md.research.elamur.ai/free/en/papers/palomino-2026-selected-layer-codec-compression-compact/)
- [next → Sema System for the DMR 2026 Shared Task: Multistage UMR Parsing with Qwen3-4B](https://md.research.elamur.ai/free/en/papers/vergnette-2026-sema-system-dmr-2026-shared/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/17/)

## References

36 references, 2 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Yiyi Chen, Heather Lent, and Johannes Bjerva. 2024. Text embedding inversion security for multilingual language models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7808–7827, Bangkok, Thailand. Association for Computationa… [find](https://scholar.google.com/scholar?q=Yiyi+Chen%2C+Heather+Lent%2C+and+Johannes+Bjerva%2E+2024%2E+Text+embedding+inversion+security+for+multilingual+language+models%2E+In+Proceedings+of+the+62nd+Annual+Meeting+of+the+Association+for+Computational+L)
2. Chaopeng Dong, Siyuan Li, Shouguo Yang, Yang Xiao, Yongpan Wang, Hong Li, Zhi Li, and Limin Sun. 2024. Libvdiff: Library version difference guided oss version identification in binaries. In 2024 IEEE/ACM 46th International Conference on Software Engineering (ICSE), pages 791–802. [find](https://scholar.google.com/scholar?q=Chaopeng+Dong%2C+Siyuan+Li%2C+Shouguo+Yang%2C+Yang+Xiao%2C+Yongpan+Wang%2C+Hong+Li%2C+Zhi+Li%2C+and+Limin+Sun%2E+2024%2E+Libvdiff%3A+Library+version+difference+guided+oss+version+identification+in+binaries%2E+In+2024+IEEE%2F)
3. Lirong Fu, Shouling Ji, Changchang Liu, Peiyu Liu, Fuzheng Duan, Zonghui Wang, Whenzhi Chen, and Ting Wang. 2022. Focus: Function clone identification on cross-platform. International Journal of Intelligent Systems, 37(8):5082–5112. [find](https://scholar.google.com/scholar?q=Lirong+Fu%2C+Shouling+Ji%2C+Changchang+Liu%2C+Peiyu+Liu%2C+Fuzheng+Duan%2C+Zonghui+Wang%2C+Whenzhi+Chen%2C+and+Ting+Wang%2E+2022%2E+Focus%3A+Function+clone+identification+on+cross%2Dplatform%2E+International+Journal+of+Intel)
4. Lirong Fu, Peiyu Liu, Wenlong Meng, Kangjie Lu, Shize Zhou, Xuhong Zhang, Wenzhi Chen, and Shouling Ji. 2024. Understanding the ai-powered binary code similarity detection. Preprint, arXiv:2410.07537. — [arXiv:2410.07537](https://arxiv.org/abs/2410.07537) [find](https://scholar.google.com/scholar?q=Lirong+Fu%2C+Peiyu+Liu%2C+Wenlong+Meng%2C+Kangjie+Lu%2C+Shize+Zhou%2C+Xuhong+Zhang%2C+Wenzhi+Chen%2C+and+Shouling+Ji%2E+2024%2E+Understanding+the+ai%2Dpowered+binary+code+similarity+detection%2E+Preprint%2C+arXiv%3A2410%2E07537%2E)
5. Jian Gao, Xin Yang, Ying Fu, Yu Jiang, and Jiaguang Sun. 2018. VulSeeker: A semantic learning based vulnerability seeker for cross-platform binary. In 2018 33rd IEEE/ACM International Conference on Automated Software Engineering (ASE), pages 896– 899. ISSN: 2643-1572. [find](https://scholar.google.com/scholar?q=Jian+Gao%2C+Xin+Yang%2C+Ying+Fu%2C+Yu+Jiang%2C+and+Jiaguang+Sun%2E+2018%2E+VulSeeker%3A+A+semantic+learning+based+vulnerability+seeker+for+cross%2Dplatform+binary%2E+In+2018+33rd+IEEE%2FACM+International+Conference+on+Au)
6. Zeyu Gao, Hao Wang, Yuanda Wang, and Chao Zhang. 2024. Virtual compiler is all you need for assembly code search. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3040–3051, Bangkok, Thailand. Association for Computational Ling… [find](https://scholar.google.com/scholar?q=Zeyu+Gao%2C+Hao+Wang%2C+Yuanda+Wang%2C+and+Chao+Zhang%2E+2024%2E+Virtual+compiler+is+all+you+need+for+assembly+code+search%2E+In+Proceedings+of+the+62nd+Annual+Meeting+of+the+Association+for+Computational+Linguis)
7. Yixin Guo, Pengcheng Li, Yingwei Luo, Xiaolin Wang, and Zhenlin Wang. 2022. Exploring gnn based program embedding technologies for binary related tasks. In Proceedings of the 30th IEEE/ACM International Conference on Program Comprehension, ICPC ’22, page 366–377, New York, NY, USA. Association for C… [find](https://scholar.google.com/scholar?q=Yixin+Guo%2C+Pengcheng+Li%2C+Yingwei+Luo%2C+Xiaolin+Wang%2C+and+Zhenlin+Wang%2E+2022%2E+Exploring+gnn+based+program+embedding+technologies+for+binary+related+tasks%2E+In+Proceedings+of+the+30th+IEEE%2FACM+Internation)
8. Haojie He, Xingwei Lin, Ziang Weng, Ruijie Zhao, Shuitao Gan, Libo Chen, Yuede Ji, Jiashui Wang, and Zhi Xue. 2024. Code is not natural language: Unlock the power of Semantics-Oriented graph representation for binary code similarity detection. In 33rd USENIX Security Symposium (USENIX Security 24),… [find](https://scholar.google.com/scholar?q=Haojie+He%2C+Xingwei+Lin%2C+Ziang+Weng%2C+Ruijie+Zhao%2C+Shuitao+Gan%2C+Libo+Chen%2C+Yuede+Ji%2C+Jiashui+Wang%2C+and+Zhi+Xue%2E+2024%2E+Code+is+not+natural+language%3A+Unlock+the+power+of+Semantics%2DOriented+graph+represent)
9. Shengxiang Hu, Guobing Zou, Song Yang, Shiyi Lin, Yanglan Gan, Bofeng Zhang, and Yixin Chen. 2025. Large language model meets graph neural network in knowledge distillation. Proceedings of the AAAI Conference on Artificial Intelligence, 39(16):17295– 17304. [find](https://scholar.google.com/scholar?q=Shengxiang+Hu%2C+Guobing+Zou%2C+Song+Yang%2C+Shiyi+Lin%2C+Yanglan+Gan%2C+Bofeng+Zhang%2C+and+Yixin+Chen%2E+2025%2E+Large+language+model+meets+graph+neural+network+in+knowledge+distillation%2E+Proceedings+of+the+AAAI+Co)
10. Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár. 2017. [Focal loss for dense object detection](https://md.research.elamur.ai/free/en/papers/lin-2017-focal-loss-dense-object-detection/). In 2017 IEEE International Conference on Computer Vision (ICCV), pages 2999–3007.
11. Zhijie Liu, Qiyi Tang, Sen Nie, Shi Wu, Liang Feng Zhang, and Yutian Tang. 2025. Keenhash: Hashing programs into function-aware embeddings for large-scale binary code similarity analysis. Proc. ACM Softw. Eng., 2(ISSTA). [find](https://scholar.google.com/scholar?q=Zhijie+Liu%2C+Qiyi+Tang%2C+Sen+Nie%2C+Shi+Wu%2C+Liang+Feng+Zhang%2C+and+Yutian+Tang%2E+2025%2E+Keenhash%3A+Hashing+programs+into+function%2Daware+embeddings+for+large%2Dscale+binary+code+similarity+analysis%2E+Proc%2E+ACM+So)
12. Zhenhao Luo, Pengfei Wang, Baosheng Wang, Yong Tang, Wei Xie, Xu Zhou, Danjun Liu, and Kai Lu. 2023. Vulhawk: Cross-architecture vulnerability detection with entropy-based binary code search. In NDSS. [find](https://scholar.google.com/scholar?q=Zhenhao+Luo%2C+Pengfei+Wang%2C+Baosheng+Wang%2C+Yong+Tang%2C+Wei+Xie%2C+Xu+Zhou%2C+Danjun+Liu%2C+and+Kai+Lu%2E+2023%2E+Vulhawk%3A+Cross%2Darchitecture+vulnerability+detection+with+entropy%2Dbased+binary+code+search%2E+In+NDSS%2E)
13. Andrea Marcelli, Mariano Graziano, Xabier Ugarte-Pedrero, Yanick Fratantonio, Mohamad Mansouri, and Davide Balzarotti. 2022. How machine learning is solving the binary function similarity problem. In 31st USENIX Security Symposium (USENIX Security 22), pages 2099–2116, Boston, MA. USENIX Association… [find](https://scholar.google.com/scholar?q=Andrea+Marcelli%2C+Mariano+Graziano%2C+Xabier+Ugarte%2DPedrero%2C+Yanick+Fratantonio%2C+Mohamad+Mansouri%2C+and+Davide+Balzarotti%2E+2022%2E+How+machine+learning+is+solving+the+binary+function+similarity+problem%2E+In+)
14. Luca Massarelli, Giuseppe Antonio Di Luna, Fabio Petroni, Roberto Baldoni, and Leonardo Querzoni. 2019. Safe: Self-attentive function embeddings for binary similarity. In Detection of Intrusions and Malware, and Vulnerability Assessment: 16th International Conference, DIMVA 2019, Gothenburg, Sweden,… [find](https://scholar.google.com/scholar?q=Luca+Massarelli%2C+Giuseppe+Antonio+Di+Luna%2C+Fabio+Petroni%2C+Roberto+Baldoni%2C+and+Leonardo+Querzoni%2E+2019%2E+Safe%3A+Self%2Dattentive+function+embeddings+for+binary+similarity%2E+In+Detection+of+Intrusions+and+M)
15. Gregory P. Meyer. 2021. An alternative probabilistic interpretation of the huber loss. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 5257–5265. [find](https://scholar.google.com/scholar?q=Gregory+P%2E+Meyer%2E+2021%2E+An+alternative+probabilistic+interpretation+of+the+huber+loss%2E+In+2021+IEEE%2FCVF+Conference+on+Computer+Vision+and+Pattern+Recognition+%28CVPR%29%2C+pages+5257%E2%80%935265%2E)
16. John Morris, Volodymyr Kuleshov, Vitaly Shmatikov, and Alexander Rush. 2023. [Text embeddings reveal (almost) as much as text](https://md.research.elamur.ai/free/en/papers/morris-2023-text-embeddings-reveal-almost-as/). In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 12448–12460, Singapore. Association for Computational Linguistics.
17. Wonpyo Park, Dongju Kim, Yan Lu, and Minsu Cho. 2019. Relational knowledge distillation. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 3962–3971. [find](https://scholar.google.com/scholar?q=Wonpyo+Park%2C+Dongju+Kim%2C+Yan+Lu%2C+and+Minsu+Cho%2E+2019%2E+Relational+knowledge+distillation%2E+In+2019+IEEE%2FCVF+Conference+on+Computer+Vision+and+Pattern+Recognition+%28CVPR%29%2C+pages+3962%E2%80%933971%2E)
18. Abdullah Qasem, Mourad Debbabi, Bernard Lebel, and Marthe Kassouf. 2023. Binary function clone search in the presence of code obfuscation and optimization over multi-cpu architectures. In Proceedings of the 2023 ACM Asia Conference on Computer and Communications Security, ASIA CCS ’23, page 443–456,… [find](https://scholar.google.com/scholar?q=Abdullah+Qasem%2C+Mourad+Debbabi%2C+Bernard+Lebel%2C+and+Marthe+Kassouf%2E+2023%2E+Binary+function+clone+search+in+the+presence+of+code+obfuscation+and+optimization+over+multi%2Dcpu+architectures%2E+In+Proceedings+)
19. August See, Moritz Mönnich, and Mathias Fischer. 2025. Enhancing binary code similarity analysis for software updates: A contextual diffing framework. In Proceedings of the 20th ACM Asia Conference on Computer and Communications Security, ASIA CCS ’25, page 1724–1740, New York, NY, USA. Association… [find](https://scholar.google.com/scholar?q=August+See%2C+Moritz+M%C3%B6nnich%2C+and+Mathias+Fischer%2E+2025%2E+Enhancing+binary+code+similarity+analysis+for+software+updates%3A+A+contextual+diffing+framework%2E+In+Proceedings+of+the+20th+ACM+Asia+Conference+on)
20. Jayakrishna Vadayath, Moritz Eckert, Kyle Zeng, Nicolaas Weideman, Gokulkrishna Praveen Menon, Yanick Fratantonio, Davide Balzarotti, Adam Doupé, Tiffany Bao, Ruoyu Wang, Christophe Hauser, and Yan Shoshitaishvili. 2022. Arbiter: Bridging the static and dynamic divide in vulnerability discovery on b… [find](https://scholar.google.com/scholar?q=Jayakrishna+Vadayath%2C+Moritz+Eckert%2C+Kyle+Zeng%2C+Nicolaas+Weideman%2C+Gokulkrishna+Praveen+Menon%2C+Yanick+Fratantonio%2C+Davide+Balzarotti%2C+Adam+Doup%C3%A9%2C+Tiffany+Bao%2C+Ruoyu+Wang%2C+Christophe+Hauser%2C+and+Yan+Sh)
21. Laurens van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-sne. Journal of Machine Learning Research, 9(86):2579–2605. [find](https://scholar.google.com/scholar?q=Laurens+van+der+Maaten+and+Geoffrey+Hinton%2E+2008%2E+Visualizing+data+using+t%2Dsne%2E+Journal+of+Machine+Learning+Research%2C+9%2886%29%3A2579%E2%80%932605%2E)
22. Hao Wang, Zeyu Gao, Chao Zhang, Zihan Sha, Mingyang Sun, Yuchen Zhou, Wenyu Zhu, Wenju Sun, Han Qiu, and Xi Xiao. 2024a. Clap: Learning transferable binary code representations with natural language supervision. In Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and A… [find](https://scholar.google.com/scholar?q=Hao+Wang%2C+Zeyu+Gao%2C+Chao+Zhang%2C+Zihan+Sha%2C+Mingyang+Sun%2C+Yuchen+Zhou%2C+Wenyu+Zhu%2C+Wenju+Sun%2C+Han+Qiu%2C+and+Xi+Xiao%2E+2024a%2E+Clap%3A+Learning+transferable+binary+code+representations+with+natural+language+s)
23. Hao Wang, Zeyu Gao, Chao Zhang, Mingyang Sun, Yuchen Zhou, Han Qiu, and Xi Xiao. 2024b. Ce-bin: A cost-effective framework for large-scale binary code similarity detection. In Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis, ISSTA 2024, page 149–161, New… [find](https://scholar.google.com/scholar?q=Hao+Wang%2C+Zeyu+Gao%2C+Chao+Zhang%2C+Mingyang+Sun%2C+Yuchen+Zhou%2C+Han+Qiu%2C+and+Xi+Xiao%2E+2024b%2E+Ce%2Dbin%3A+A+cost%2Deffective+framework+for+large%2Dscale+binary+code+similarity+detection%2E+In+Proceedings+of+the+33rd+)
24. Hao Wang, Wenjie Qu, Gilad Katz, Wenyu Zhu, Zeyu Gao, Han Qiu, Jianwei Zhuge, and Chao Zhang. 2022. Jtrans: Jump-aware transformer for binary code similarity detection. In Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis, ISSTA 2022, page 1–13, New York, N… [find](https://scholar.google.com/scholar?q=Hao+Wang%2C+Wenjie+Qu%2C+Gilad+Katz%2C+Wenyu+Zhu%2C+Zeyu+Gao%2C+Han+Qiu%2C+Jianwei+Zhuge%2C+and+Chao+Zhang%2E+2022%2E+Jtrans%3A+Jump%2Daware+transformer+for+binary+code+similarity+detection%2E+In+Proceedings+of+the+31st+ACM+)
25. Huaijin Wang, Pingchuan Ma, Yuanyuan Yuan, Zhibo Liu, Shuai Wang, Qiyi Tang, Sen Nie, and Shi Wu. 2023. Enhancing DNN-based binary code function search with low-cost equivalence checking. IEEE Transactions on Software Engineering, 49(1):226– 250. [find](https://scholar.google.com/scholar?q=Huaijin+Wang%2C+Pingchuan+Ma%2C+Yuanyuan+Yuan%2C+Zhibo+Liu%2C+Shuai+Wang%2C+Qiyi+Tang%2C+Sen+Nie%2C+and+Shi+Wu%2E+2023%2E+Enhancing+DNN%2Dbased+binary+code+function+search+with+low%2Dcost+equivalence+checking%2E+IEEE+Transac)
26. Yongpan Wang, Hong Li, Xiaojie Zhu, Siyuan Li, Chaopeng Dong, Shouguo Yang, and Kangyuan Qin. 2025. Binenhance: An enhancement framework based on external environment semantics for binary code search. In 32nd Annual Network and Distributed System Security Symposium, NDSS 2025, San Diego, California,… [find](https://scholar.google.com/scholar?q=Yongpan+Wang%2C+Hong+Li%2C+Xiaojie+Zhu%2C+Siyuan+Li%2C+Chaopeng+Dong%2C+Shouguo+Yang%2C+and+Kangyuan+Qin%2E+2025%2E+Binenhance%3A+An+enhancement+framework+based+on+external+environment+semantics+for+binary+code+search%2E)
27. Hongwei Wu, Jianliang Wu, Ruoyu Wu, Ayushi Sharma, Aravind Kumar Machiry, and Antonio Bianchi. 2025. Veribin: Adaptive verification of patches at the binary level. Proceedings 2025 Network and Distributed System Security Symposium. [find](https://scholar.google.com/scholar?q=Hongwei+Wu%2C+Jianliang+Wu%2C+Ruoyu+Wu%2C+Ayushi+Sharma%2C+Aravind+Kumar+Machiry%2C+and+Antonio+Bianchi%2E+2025%2E+Veribin%3A+Adaptive+verification+of+patches+at+the+binary+level%2E+Proceedings+2025+Network+and+Distrib)
28. Xiangzhe Xu, Shiwei Feng, Yapeng Ye, Guangyu Shen, Zian Su, Siyuan Cheng, Guanhong Tao, Qingkai Shi, Zhuo Zhang, and Xiangyu Zhang. 2023a. Improving binary code similarity transformer models by semantics-driven instruction deemphasis. In Proceedings of the 32nd ACM SIGSOFT International Symposium on… [find](https://scholar.google.com/scholar?q=Xiangzhe+Xu%2C+Shiwei+Feng%2C+Yapeng+Ye%2C+Guangyu+Shen%2C+Zian+Su%2C+Siyuan+Cheng%2C+Guanhong+Tao%2C+Qingkai+Shi%2C+Zhuo+Zhang%2C+and+Xiangyu+Zhang%2E+2023a%2E+Improving+binary+code+similarity+transformer+models+by+semant)
29. Xiangzhe Xu, Zhou Xuan, Shiwei Feng, Siyuan Cheng, Yapeng Ye, Qingkai Shi, Guanhong Tao, Le Yu, Zhuo Zhang, and Xiangyu Zhang. 2023b. Pem: Representing binary program semantics for similarity analysis via a probabilistic execution model. In Proceedings of the 31st ACM Joint European Software Enginee… [find](https://scholar.google.com/scholar?q=Xiangzhe+Xu%2C+Zhou+Xuan%2C+Shiwei+Feng%2C+Siyuan+Cheng%2C+Yapeng+Ye%2C+Qingkai+Shi%2C+Guanhong+Tao%2C+Le+Yu%2C+Zhuo+Zhang%2C+and+Xiangyu+Zhang%2E+2023b%2E+Pem%3A+Representing+binary+program+semantics+for+similarity+analysis)
30. Shouguo Yang, Long Cheng, Yicheng Zeng, Zhe Lang, Hongsong Zhu, and Zhiqiang Shi. 2021. Asteria: Deep learning-based ast-encoding for cross-platform binary code similarity detection. In 2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), pages 224–236. [find](https://scholar.google.com/scholar?q=Shouguo+Yang%2C+Long+Cheng%2C+Yicheng+Zeng%2C+Zhe+Lang%2C+Hongsong+Zhu%2C+and+Zhiqiang+Shi%2E+2021%2E+Asteria%3A+Deep+learning%2Dbased+ast%2Dencoding+for+cross%2Dplatform+binary+code+similarity+detection%2E+In+2021+51st+Annu)
31. Shouguo Yang, Chaopeng Dong, Yang Xiao, Yiran Cheng, Zhiqiang Shi, Zhi Li, and Limin Sun. 2023a. Asteria-pro: Enhancing deep learning-based binary code similarity detection by incorporating domain knowledge. ACM Trans. Softw. Eng. Methodol., 33(1). [find](https://scholar.google.com/scholar?q=Shouguo+Yang%2C+Chaopeng+Dong%2C+Yang+Xiao%2C+Yiran+Cheng%2C+Zhiqiang+Shi%2C+Zhi+Li%2C+and+Limin+Sun%2E+2023a%2E+Asteria%2Dpro%3A+Enhancing+deep+learning%2Dbased+binary+code+similarity+detection+by+incorporating+domain+kno)
32. Shouguo Yang, Zhengzi Xu, Yang Xiao, Zhe Lang, Wei Tang, Yang Liu, Zhiqiang Shi, Hong Li, and Limin Sun. 2023b. Towards practical binary code similarity detection: Vulnerability verification via patch semantic analysis. ACM Trans. Softw. Eng. Methodol., 32(6). [find](https://scholar.google.com/scholar?q=Shouguo+Yang%2C+Zhengzi+Xu%2C+Yang+Xiao%2C+Zhe+Lang%2C+Wei+Tang%2C+Yang+Liu%2C+Zhiqiang+Shi%2C+Hong+Li%2C+and+Limin+Sun%2E+2023b%2E+Towards+practical+binary+code+similarity+detection%3A+Vulnerability+verification+via+patch)
33. Zeping Yu, Rui Cao, Qiyi Tang, Sen Nie, Junzhou Huang, and Shi Wu. 2020. Order matters: Semantic-aware neural networks for binary code similarity detection. Proceedings of the AAAI Conference on Artificial Intelligence, 34(01):1145–1152. [find](https://scholar.google.com/scholar?q=Zeping+Yu%2C+Rui+Cao%2C+Qiyi+Tang%2C+Sen+Nie%2C+Junzhou+Huang%2C+and+Shi+Wu%2E+2020%2E+Order+matters%3A+Semantic%2Daware+neural+networks+for+binary+code+similarity+detection%2E+Proceedings+of+the+AAAI+Conference+on+Artif)
34. Qi Zhan, Xing Hu, Zhiyang Li, Xin Xia, David Lo, and Shanping Li. 2024. Ps3: Precise patch presence test based on semantic symbolic signature. In Proceedings of the IEEE/ACM 46th International Conference on Software Engineering, ICSE ’24, New York, NY, USA. Association for Computing Machinery. [find](https://scholar.google.com/scholar?q=Qi+Zhan%2C+Xing+Hu%2C+Zhiyang+Li%2C+Xin+Xia%2C+David+Lo%2C+and+Shanping+Li%2E+2024%2E+Ps3%3A+Precise+patch+presence+test+based+on+semantic+symbolic+signature%2E+In+Proceedings+of+the+IEEE%2FACM+46th+International+Confere)
35. Bolun Zhang, Zeyu Gao, Hao Wang, Yuxin Cui, Siliang Qin, Chao Zhang, Kai Chen, and Beibei Zhao. 2025. Binquery: A novel framework for natural language-based binary code retrieval. Proc. ACM Softw. Eng., 2(ISSTA). [find](https://scholar.google.com/scholar?q=Bolun+Zhang%2C+Zeyu+Gao%2C+Hao+Wang%2C+Yuxin+Cui%2C+Siliang+Qin%2C+Chao+Zhang%2C+Kai+Chen%2C+and+Beibei+Zhao%2E+2025%2E+Binquery%3A+A+novel+framework+for+natural+language%2Dbased+binary+code+retrieval%2E+Proc%2E+ACM+Softw%2E+Eng)
36. Shize Zhou, Lirong Fu, Peiyu Liu, and Wenhai Wang. 2025. Binega: Enhancing dnn-based binary code similarity detection through efficient graph alignment. In 2025 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), pages 488–499. [find](https://scholar.google.com/scholar?q=Shize+Zhou%2C+Lirong+Fu%2C+Peiyu+Liu%2C+and+Wenhai+Wang%2E+2025%2E+Binega%3A+Enhancing+dnn%2Dbased+binary+code+similarity+detection+through+efficient+graph+alignment%2E+In+2025+IEEE+International+Conference+on+Softwa)

*Updated 1 November 2026*