🧑‍🔬 Arvind Narayanan

🏛️ Affiliations

🎓Princeton UniversityPrinceton · United States2010–2026 · 98 papersEmployment · Professor, Computer Sciencecurrent🎓Stanford UniversityStanford · United States2010–2012 · 6 papers🎓The University of Texas at AustinAustin · United States2005–2009 · 6 papersEducation🎓Indian Institute of Technology MadrasChennai · India2003–2006 · 4 papersEducation

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 more institutions on single bylines are not listed.

🧭 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📅 Works by year

2020s

📄 Papers

The most cited first (OpenAlex and our library).

1. AI Agents That Matter (2024 · 55 citations)
   Benchmarks steer the development of LLM-based agents. The Princeton authors argue that current evaluation habits reward topping leaderboards rather than building systems that work in practice. They identify four problems:
2. Localized Cultural Knowledge is Conserved and Controllable in Large Language Models (2026 · 7 citations)
   Large language models (LLMs) often answer non-English prompts as if the user lived in an English-speaking, largely American setting, even when they hold the relevant local knowledge.

All works

19 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

1. On the Feasibility of Internet-Scale Author Identification ↗Proceedings - IEEE Symposium on Security and Privacy/Proceedings of the ... IEEE Symposium on Security and Privacy · no summary yetArvind Narayanan et al.2012 · 275 citations
2. AI Agents That MatterarXiv:2407.01502Sayash Kapoor, Benedikt Stroebl, Zachary S. Siegel, et al.2024 · 55 citations
3. Foundation Model Transparency Reports ↗Proceedings of the AAAI/ACM Conference on AI Ethics and Society · no summary yetRishi Bommasani et al.2024 · 53 citations
4. Promises and pitfalls of artificial intelligence for legal applications ↗arXiv:2402.01656 · no summary yetSayash Kapoor, Peter Henderson, Arvind Narayanan2024 · 40 citations
5. CORE-Bench: Fostering the Credibility of Published Research Through a Computational Reproducibility Agent Benchmark ↗arXiv:2409.11363 · no summary yetZachary S. Siegel et al.2024 · 39 citations
6. Considerations for governing open foundation models ↗Science · no summary yetRishi Bommasani et al.2024 · 36 citations
7. On the Societal Impact of Open Foundation Models ↗arXiv (Cornell University) · no summary yetSayash Kapoor et al.2024 · 30 citations
8. Advancing science- and evidence-based AI policy ↗Science · no summary yetRishi Bommasani et al.2025 · 25 citations
9. A Safe Harbor for AI Evaluation and Red Teaming ↗arXiv (Cornell University) · no summary yetShayne Longpre et al.2024 · 22 citations
10. The Limits of Inference Scaling Through Resampling ↗arXiv:2411.17501 · no summary yetBenedikt Stroebl, Sayash Kapoor, Arvind Narayanan2024 · 21 citations
11. REFORMS: Reporting Standards for Machine Learning Based Science ↗arXiv:2308.07832 · no summary yetSayash Kapoor et al.2023 · 19 citations
12. Holistic Agent Leaderboard: The Missing Infrastructure for AI Agent Evaluation ↗arXiv:2510.11977 · no summary yetSayash Kapoor et al.2025 · 11 citations
13. The Responsible Foundation Model Development Cheatsheet: A Review of Tools & Resources ↗arXiv (Cornell University) · no summary yetShayne Longpre et al.2024 · 11 citations
14. Localized Cultural Knowledge is Conserved and Controllable in Large Language ModelsFindings of the Association for Computational LinguisticsVeniamin Veselovsky, Berke Argın, Benedikt Stroebl, et al.2026 · 7 citations
15. Position: On the Societal Impact of Open Foundation Models ↗ANU Open Research (Australian National University) · no summary yetSayash Kapoor et al.2024 · 6 citations
16. Establishing Best Practices in Building Rigorous Agentic Benchmarks ↗neural information processing systems · no summary yetYuxuan Zhu et al.2025 · 4 citations
17. AI Risk Management Should Incorporate Both Safety and Security ↗arXiv (Cornell University) · no summary yetXiangyu Qi et al.2024 · 3 citations
18. Log analysis is necessary for credible evaluation of AI agents ↗arXiv (Cornell University) · no summary yetPeter Kirgis et al.2026 · 1 citation
19. FLARE-AI: Flaw Reporting for AI ↗arXiv (Cornell University) · no summary yetShayne Longpre et al.2026

🤝 Co-authors here

6 co-authors with a page, the most papers together first.

• Benedikt Stroebl ×2
• Chris Wendler
• Robert West
• Thomas L. Griffiths
• Veniamin Veselovsky
• Zachary S. Siegel

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

• Science ×2
• Findings of the Association for Computational Linguistics: Findings of the Association for Computational Linguistics 2026
• ANU Open Research (Australian National University)
• Proceedings - IEEE Symposium on Security and Privacy/Proceedings of the ... IEEE Symposium on Security and Privacy
• Proceedings of the AAAI/ACM Conference on AI Ethics and Society
• neural information processing systems

🔗 Cited by

Papers of our library cite their works 226 times (of the 149,439 papers whose bibliographies we read); 26 citing papers with a page here →.

Updated 2 November 2026

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