CORE-Bench: Fostering the Credibility of Published Research Through a Computational Reproducibility Agent Benchmark. - Zachary S. Siegel; Sayash Kapoor; Nitya Nagdir; Benedikt Stroebl; Arvind Narayanan (CoRR, 2024)

Inference Scaling fLaws: The Limits of LLM Resampling with Imperfect Verifiers. - Benedikt Stroebl; Sayash Kapoor; Arvind Narayanan (CoRR, 2024)

CORE-Bench: Fostering the Credibility of Published Research Through a Computational Reproducibility Agent Benchmark. - Zachary S. Siegel; Sayash Kapoor; Nitya Nadgir; Benedikt Stroebl; Arvind Narayanan (Trans. Mach. Learn. Res., 2024)

Hindsight Merging: Diverse Data Generation with Language Models. - Veniamin Veselovsky; Benedikt Stroebl; Gianluca M. Bencomo; Dilip Arumugam; Lisa Schut; Arvind Narayanan; Thomas L. Griffiths 001 (UAI, 2025)

AI Agents That Matter. - Sayash Kapoor; Benedikt Stroebl; Zachary S. Siegel; Nitya Nadgir; Arvind Narayanan (Trans. Mach. Learn. Res., 2025)

Localized Cultural Knowledge is Conserved and Controllable in Large Language Models. - Veniamin Veselovsky; Berke Argin; Benedikt Stroebl; Chris Wendler; Robert West 001; James Evans; Thomas L. Griffiths 001; Arvind Narayanan (CoRR, 2025)

Safety Devolution in AI Agents. - Cheng Yu; Benedikt Stroebl; Diyi Yang; Orestis Papakyriakopoulos (CoRR, 2025)

Dynamic Risk Assessments for Offensive Cybersecurity Agents. - Boyi Wei; Benedikt Stroebl; Jiacen Xu; Joie Zhang; Zhou Li 001; Peter Henderson 002 (CoRR, 2025)

The Book of Life approach: Enabling richness and scale for life course research. - Mark D. Verhagen; Benedikt Stroebl; Tiffany Liu; Lydia T. Liu; Matthew J. Salganik (CoRR, 2025)