Erik Jenner

According to our database, Erik Jenner authored at least 18 papers between 2021 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Frontier Models Can Take Actions at Low Probabilities.

Alex Serrano, Wen Xing, David Lindner, Erik Jenner

CoRR, March, 2026

2025

Can Reasoning Models Obfuscate Reasoning? Stress-Testing Chain-of-Thought Monitorability.

Artur Zolkowski, Wen Xing, David Lindner, Florian Tramèr, Erik Jenner

CoRR, October, 2025

Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safety.

Tomek Korbak, Mikita Balesni, Elizabeth Barnes, Yoshua Bengio, Joe Benton, Joseph Bloom, Mark Chen, Alan Cooney, Allan Dafoe, Anca D. Dragan, Scott Emmons, Owain Evans, David Farhi, Ryan Greenblatt, Dan Hendrycks, Marius Hobbhahn, Evan Hubinger, Geoffrey Irving, Erik Jenner, Daniel Kokotajlo, Victoria Krakovna, Shane Legg, David Lindner, David Luan, Aleksander Madry, Julian Michael, Neel Nanda, Dave Orr, Jakub Pachocki, Ethan Perez, Mary Phuong, Fabien Roger, Joshua Saxe, Buck Shlegeris, Martín Soto, Eric Steinberger, Jasmine Wang, Wojciech Zaremba, Bowen Baker, Rohin Shah, Vladimir Mikulik

CoRR, July, 2025

When Chain of Thought is Necessary, Language Models Struggle to Evade Monitors.

Scott Emmons, Erik Jenner, David K. Elson, Rif A. Saurous, Senthooran Rajamanoharan, Heng Chen, Irhum Shafkat, Rohin Shah

CoRR, July, 2025

RL-Obfuscation: Can Language Models Learn to Evade Latent-Space Monitors?

Rohan Gupta, Erik Jenner

CoRR, June, 2025

Diffusion On Syntax Trees For Program Synthesis.

Shreyas Kapur, Erik Jenner, Stuart Russell

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

Foundational Challenges in Assuring Alignment and Safety of Large Language Models.

Usman Anwar, Abulhair Saparov, Javier Rando, Daniel Paleka, Miles Turpin, Peter Hase, Ekdeep Singh Lubana, Erik Jenner, Stephen Casper, Oliver Sourbut, Benjamin L. Edelman, Zhaowei Zhang, Mario Günther, Anton Korinek, José Hernández-Orallo, Lewis Hammond, Eric J. Bigelow, Alexander Pan, Lauro Langosco, Tomasz Korbak, Heidi Chenyu Zhang, Ruiqi Zhong, Seán Ó hÉigeartaigh, Gabriel Recchia, Giulio Corsi, Alan Chan, Markus Anderljung, Lilian Edwards, Aleksandar Petrov, Christian Schröder de Witt, Sumeet Ramesh Motwani, Yoshua Bengio, Danqi Chen, Philip Torr, Samuel Albanie, Tegan Maharaj, Jakob Nicolaus Foerster, Florian Tramèr, He He, Atoosa Kasirzadeh, Yejin Choi, David Krueger

Trans. Mach. Learn. Res., 2024

Obfuscated Activations Bypass LLM Latent-Space Defenses.

Luke Bailey, Alex Serrano, Abhay Sheshadri, Mikhail Seleznyov, Jordan Taylor, Erik Jenner, Jacob Hilton, Stephen Casper, Carlos Guestrin, Scott Emmons

CoRR, 2024

Foundational Challenges in Assuring Alignment and Safety of Large Language Models.

Usman Anwar, Abulhair Saparov, Javier Rando, Daniel Paleka, Miles Turpin, Peter Hase, Ekdeep Singh Lubana, Erik Jenner, Stephen Casper, Oliver Sourbut, Benjamin L. Edelman, Zhaowei Zhang, Mario Günther, Anton Korinek, José Hernández-Orallo, Lewis Hammond, Eric J. Bigelow, Alexander Pan, Lauro Langosco, Tomasz Korbak, Heidi C. Zhang, Ruiqi Zhong, Seán Ó hÉigeartaigh, Gabriel Recchia, Giulio Corsi, Alan Chan, Markus Anderljung, Lilian Edwards, Yoshua Bengio, Danqi Chen, Samuel Albanie, Tegan Maharaj, Jakob N. Foerster, Florian Tramèr, He He, Atoosa Kasirzadeh, Yejin Choi, David Krueger

CoRR, 2024

When Your AIs Deceive You: Challenges with Partial Observability of Human Evaluators in Reward Learning.

Leon Lang, Davis Foote, Stuart Russell, Anca D. Dragan, Erik Jenner, Scott Emmons

CoRR, 2024

When Your AIs Deceive You: Challenges of Partial Observability in Reinforcement Learning from Human Feedback.

Leon Lang, Davis Foote, Stuart J. Russell, Anca D. Dragan, Erik Jenner, Scott Emmons

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

Evidence of Learned Look-Ahead in a Chess-Playing Neural Network.

Erik Jenner, Shreyas Kapur, Vasil Georgiev, Cameron Allen, Scott Emmons, Stuart J. Russell

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

STARC: A General Framework For Quantifying Differences Between Reward Functions.

Joar Max Viktor Skalse, Lucy Farnik, Sumeet Ramesh Motwani, Erik Jenner, Adam Gleave, Alessandro Abate

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2022

imitation: Clean Imitation Learning Implementations.

Adam Gleave, Mohammad Taufeeque, Juan Rocamonde, Erik Jenner, Steven H. Wang, Sam Toyer, Maximilian Ernestus, Nora Belrose, Scott Emmons, Stuart Russell

CoRR, 2022

Calculus on MDPs: Potential Shaping as a Gradient.

Erik Jenner, Herke van Hoof, Adam Gleave

CoRR, 2022

Preprocessing Reward Functions for Interpretability.

Erik Jenner, Adam Gleave

CoRR, 2022

Steerable Partial Differential Operators for Equivariant Neural Networks.

Erik Jenner, Maurice Weiler

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Extensions of Karger's Algorithm: Why They Fail in Theory and How They Are Useful in Practice.

Erik Jenner, Enrique Fita Sanmartín, Fred A. Hamprecht

Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021