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Who cites Jan Brauner

468 citations of their works from 418 papers of our library (of the 149,439 whose bibliographies we read) · 113 with a page here, the most cited first · page 1 of 2 · 1–100 · ← Jan Brauner

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  1. GPT-4o System CardarXiv:2410.21276OpenAI, Aaron Hurst, Adam Lerer, et al.2024 · 2,304 citations
  2. OpenAI o1 System CardarXiv:2412.16720OpenAI, Aaron Jaech, Adam Kalai, et al.2024 · 1,110 citations
  3. SWE-bench: Can Language Models Resolve Real-World GitHub Issues?arXiv:2310.06770Carlos E. Jimenez, John Yang, Alexander Wettig, et al.2024 · 704 citations
  4. Towards Understanding Sycophancy in Language ModelsarXiv:2310.13548Mrinank Sharma, Meg Tong, Tomasz Korbak, et al.2023 · 384 citations
  5. Chain-of-Verification Reduces Hallucination in Large Language ModelsarXiv:2309.11495Shehzaad Dhuliawala, Mojtaba Komeili, Jing Xu, et al.2023 · 305 citations
  6. Measuring Faithfulness in Chain-of-Thought ReasoningarXiv:2307.13702Tamera Lanham, Anna Chen, Ansh Radhakrishnan, et al.2023 · 262 citations
  7. Humanity's Last ExamNatureLong Phan, Alice Gatti, Ziwen Han, et al.2025 · 256 citations
  8. XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language ModelsarXiv:2308.01263Paul Rottger, Hannah Rose Kirk, Bertie Vidgen, et al.2023 · 244 citations
  9. ELEPHANT: Measuring and understanding social sycophancy in LLMsScienceMyra Cheng, Sunny Yu, Cinoo Lee, et al.2025 · 188 citations
  10. AI Alignment: A Comprehensive SurveyarXiv:2310.19852Jiaming Ji, Tianyi Qiu, Boyuan Chen, et al.2023 · 173 citations
  11. Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak SupervisionarXiv:2312.09390Collin Burns, Pavel Izmailov, Jan Hendrik Kirchner, et al.2023 · 172 citations
  12. rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep ThinkingarXiv:2501.04519Xinyu Guan, Li Lyna Zhang, Yifei Liu, et al.2025 · 146 citations
  13. To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoningarXiv:2409.12183Zayne Sprague, Fangcong Yin, Juan Diego Rodriguez, et al.2024 · 138 citations
  14. SOTOPIA: Interactive Evaluation for Social Intelligence in Language AgentsarXiv:2310.11667Xuhui Zhou, Hao Zhu, Leena Mathur, et al.2023 · 138 citations
  15. Simple synthetic data reduces sycophancy in large language modelsarXiv:2308.03958Jerry Wei, Da Huang, Yifeng Lu, et al.2023 · 134 citations
  16. Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and FuturearXiv:2309.15402Zheng Chu, Jingchang Chen, Qianglong Chen, et al.2023 · 125 citations
  17. Sleeper Agents: Training Deceptive LLMs that Persist Through Safety TrainingarXiv:2401.05566Evan Hubinger, Carson Denison, Jesse Mu, et al.2024 · 123 citations
  18. The Alignment Problem from a Deep Learning PerspectivearXiv:2209.00626Richard Ngo, Lawrence Chan, Soren Mindermann2022 · 121 citations
  19. Best Practices and Lessons Learned on Synthetic DataarXiv:2404.07503Ruibo Liu, Jerry Wei, Fangyu Liu, et al.2024 · 119 citations
  20. Mechanistic Interpretability for AI Safety — A ReviewarXiv:2404.14082Leonard Bereska, Efstratios Gavves2024 · 108 citations
  21. Reasoning Models Don't Always Say What They ThinkarXiv:2505.05410Yanda Chen, Joe Benton, Ansh Radhakrishnan, et al.2025 · 106 citations
  22. Multi-Step Reasoning with Large Language Models, a SurveyACM Computing SurveysAske Plaat, Annie Wong, Suzan Verberne, et al.2024 · 106 citations
  23. Debating with More Persuasive LLMs Leads to More Truthful AnswersarXiv:2402.06782Akbir Khan, John Hughes, Dan Valentine, et al.2024 · 101 citations
  24. Alignment for HonestyConference on Neural Information Processing Systems (NeurIPS)Yuqing Yang, Ethan Chern, Xipeng Qiu, et al.2023 · 90 citations
  25. Automated Design of Agentic SystemsarXiv:2408.08435Shengran Hu, Cong Lu, Jeff Clune2024 · 89 citations
  26. Foundational Challenges in Assuring Alignment and Safety of Large Language ModelsarXiv:2404.09932Usman Anwar, Abulhair Saparov, Javier Rando, et al.2024 · 87 citations
  27. Rethinking Interpretability in the Era of Large Language ModelsarXiv:2402.01761Chandan Singh, Jeevana Priya Inala, Michel Galley, et al.2024 · 87 citations
  28. A Primer on the Inner Workings of Transformer-based Language ModelsarXiv:2405.00208Javier Ferrando, Gabriele Sarti, Arianna Bisazza, et al.2024 · 72 citations
  29. Monitoring Reasoning Models for Misbehavior and the Risks of Promoting ObfuscationarXiv:2503.11926Bowen Baker, Joost Huizinga, Leo Gao, et al.2025 · 63 citations
  30. Compressed Chain of Thought: Efficient Reasoning Through Dense RepresentationsarXiv:2412.13171Jeffrey Cheng, Benjamin Van Durme2024 · 60 citations
  31. A Survey of Efficient Reasoning for Large Reasoning Models: Language, Multimodality, and BeyondarXiv:2503.21614Xiaoye Qu, Yafu Li, Zhao-Chen Su, et al.2025 · 55 citations
  32. Tokenization counts: the impact of tokenization on arithmetic in frontier LLMsarXiv:2402.14903Aaditya K. Singh, DJ Strouse2024 · 55 citations
  33. In-context Learning with Retrieved Demonstrations for Language Models: A SurveyarXiv:2401.11624Man Luo, Xin Xu, Yue Liu, et al.2024 · 54 citations
  34. Introducing v0.5 of the AI Safety Benchmark from MLCommonsarXiv:2404.12241Bertie Vidgen, Adarsh Agrawal, Ahmed M. Ahmed, et al.2024 · 38 citations
  35. Towards LogiGLUE: A Brief Survey and A Benchmark for Analyzing Logical Reasoning Capabilities of Language ModelsarXiv:2310.00836Man Luo, Shrinidhi Kumbhar, Ming shen, et al.2023 · 27 citations
  36. Language Model Alignment in Multilingual Trolley ProblemsarXiv:2407.02273Zhijing Jin, Max Kleiman-Weiner, Giorgio Piatti, et al.2024 · 23 citations
  37. Argument-Based Consistency in Toxicity Explanations of LLMsFindings of the Association for Computational LinguisticsRamaravind Kommiya Mothilal, Joanna Roy, Syed Ishtiaque Ahmed, et al.2026 · 2 citations
  38. Training Language Models to Use Prolog as a ToolFindings of the Association for Computational LinguisticsNiklas Mellgren, Peter Schneider-Kamp, Lukas Galke Poech2026 · 1 citation

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