🔗 Who cites «AEGIS2.0: A Diverse AI Safety Dataset and Risks Taxonomy for Alignment of LLM Guardrails»

39 papers of our library cite it (of the 149,439 whose bibliographies we read) · 10 with a page here, the most cited first · ← back to «AEGIS2.0: A Diverse AI Safety Dataset and Risks Taxonomy for Alignment of LLM Guardrails»

1. NVIDIA Nemotron Nano 2: An Accurate and Efficient Hybrid Mamba-Transformer Reasoning ModelarXiv:2508.14444NVIDIA: Aarti Basant, Abhijit Khairnar, Abhijit Paithankar, et al.2025 · 39 citations
2. Nemotron-H: A Family of Accurate and Efficient Hybrid Mamba-Transformer ModelsarXiv:2504.03624NVIDIA: Aaron Blakeman, Aarti Basant, Abhinav Khattar, et al.2025 · 38 citations
3. Qwen3Guard Technical ReportarXiv:2510.14276Haiquan Zhao, Chenhan Yuan, Fei Huang, et al.2025 · 33 citations
4. Nemotron 3 Nano: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic ReasoningarXiv:2512.20848NVIDIA: Aaron Blakeman, Aaron Grattafiori, Aarti Basant, et al.2025 · 27 citations
5. Nemotron-Cascade 2: Post-Training LLMs with Cascade RL and Multi-Domain On-Policy DistillationarXiv:2603.19220Zhuolin Yang, Zihan Liu, Yang Chen, et al.2026 · 16 citations
6. Rethinking Jailbreak Detection of Large Vision Language Models with Representational Contrastive ScoringAnnual Meeting of the Association for Computational Linguistics (ACL)Peichun Hua, Hao Li, Shanghao Shi, et al.2026 · 10 citations
7. False Sense of Security: Why Probing-based Malicious Input Detection Fails to GeneralizeFindings of the Association for Computational LinguisticsCheng Wang, Zeming Wei, Qin Liu, et al.2026 · 5 citations
8. CodeGuard: Improving LLM Guardrails in CS EducationFindings of the Association for Computational LinguisticsNishat Raihan, Noah Erdachew, Jayoti Devi, et al.2026
9. SEA-SafeguardBench: Culturally Grounded Safety Benchmark for Southeast Asian LanguagesFindings of the Association for Computational LinguisticsPanuthep Tasawong, Jian Gang Ngui, Alham Fikri Aji, et al.2026
10. SGT: Securing Open-Source LLMs Against Malicious Fine-tuning via Safety Guidance TriggerAnnual Meeting of the Association for Computational Linguistics (ACL)Sunguk Shin, Fangzhao Wu, Byung-Jun Lee, et al.2026

29 more papers of our library cite it; they have no page here yet.

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