XekRung Technical Report
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
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| _version_ | 1866914523579940864 |
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| author | Zeng, Jiutian Li, Junjie Dai, Chengwei Liang, Jie Hu, Zhaoyu Zhang, Yiliang Weng, Ziang Huang, Longtao Zhang, Dongjie Dong, Libin Ge, Yang Wang, Yuanda Kacuila, Kaiwen Lv Zhu, Bingyu Wang, Jing Xu, Jin |
| author_facet | Zeng, Jiutian Li, Junjie Dai, Chengwei Liang, Jie Hu, Zhaoyu Zhang, Yiliang Weng, Ziang Huang, Longtao Zhang, Dongjie Dong, Libin Ge, Yang Wang, Yuanda Kacuila, Kaiwen Lv Zhu, Bingyu Wang, Jing Xu, Jin |
| contents | We present XekRung, a frontier large language model for cybersecurity, designed to provide comprehensive security capabilities. To achieve this, we develop diverse data synthesis pipelines tailored to the cybersecurity domain, enabling the scalable construction of high-quality training data and providing a strong foundation for cybersecurity knowledge and understanding. Building on this foundation, we establish a complete training pipeline spanning continued pre-training (CPT), supervised fine-tuning (SFT), and reinforcement learning (RL) to further extend the model's capabilities. We further introduce a multi-dimensional evaluation system to guide the iterative improvement of both domain-specific and general-purpose abilities. Extensive experiments demonstrate that XekRung achieves state-of-the-art performance on cybersecurity-specific benchmarks among models of the same scale, while maintaining strong performance on general benchmarks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_00072 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | XekRung Technical Report Zeng, Jiutian Li, Junjie Dai, Chengwei Liang, Jie Hu, Zhaoyu Zhang, Yiliang Weng, Ziang Huang, Longtao Zhang, Dongjie Dong, Libin Ge, Yang Wang, Yuanda Kacuila, Kaiwen Lv Zhu, Bingyu Wang, Jing Xu, Jin Cryptography and Security Artificial Intelligence We present XekRung, a frontier large language model for cybersecurity, designed to provide comprehensive security capabilities. To achieve this, we develop diverse data synthesis pipelines tailored to the cybersecurity domain, enabling the scalable construction of high-quality training data and providing a strong foundation for cybersecurity knowledge and understanding. Building on this foundation, we establish a complete training pipeline spanning continued pre-training (CPT), supervised fine-tuning (SFT), and reinforcement learning (RL) to further extend the model's capabilities. We further introduce a multi-dimensional evaluation system to guide the iterative improvement of both domain-specific and general-purpose abilities. Extensive experiments demonstrate that XekRung achieves state-of-the-art performance on cybersecurity-specific benchmarks among models of the same scale, while maintaining strong performance on general benchmarks. |
| title | XekRung Technical Report |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2605.00072 |