Scott Emmons I research AI safety and alignment. Most recently, I was a research scientist at Google DeepMind. I completed my PhD at UC Berkeley's Center for Human-Compatible AI, advised by Stuart Russell. I previously cofounded FAR.AI, a research and education nonprofit advancing the global field of trustworthy and secure AI. I develop AI alignment frameworks, stress-test their limits, and turn insights into methodology adopted across the field. I have established that chain-of-thought monitoring is a substantial defense when reasoning is necessary for misalignment, designed practical metrics for model developers to preserve chain-of-thought monitorability, shown that obfuscated activations can bypass latent-space defenses, and developed StrongREJECT, a jailbreak benchmark now used by OpenAI, US/UK AISI, Amazon, and others. Curriculum Vitae scott at scottemmons dot com

Publications

• A Pragmatic Way to Measure Chain-of-Thought Monitorability
  Scott Emmons^*, Roland S. Zimmermann^*, David K. Elson, and Rohin Shah
  arXiv, 2025
  [arXiv] [BibTeX]
• Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safety
  Tomek Korbak^*, Mikita Balesni^*, ..., Scott Emmons^†, ..., Bowen Baker^‡, Rohin Shah^‡, and Vlad Mikulik^‡
  arXiv, 2025
  [arXiv] [BibTeX]
• 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, and Rohin Shah
  arXiv, 2025
  [arXiv] [BibTeX]
• An Approach to Technical AGI Safety and Security
  Rohin Shah, ..., Scott Emmons^*, ..., and Anca Dragan
  arXiv, 2025
  [arXiv] [BibTeX]
• Observation Interference in Partially Observable Assistance Games
  Scott Emmons^*, Caspar Oesterheld^*, Vincent Conitzer, and Stuart Russell
  International Conference on Machine Learning, 2025
  [arXiv] [BibTeX]
• Failures to Find Transferable Image Jailbreaks Between Vision-Language Models
  Rylan Schaeffer, Dan Valentine, Luke Bailey, James Chua, Cristobal Eyzaguirre, Zane Durante, Joe Benton, Brando Miranda, Henry Sleight, Tony Tong Wang, John Hughes, Rajashree Agrawal, Mrinank Sharma, Scott Emmons, Sanmi Koyejo, and Ethan Perez
  International Conference on Learning Representations, 2025
  [code] [arXiv] [BibTeX]
• The Partially Observable Off-Switch Game
  Andrew Garber^*, Rohan Subramani^*, Linus Luu^*, Mark Bedaywi, Stuart Russell, and Scott Emmons
  Association for the Advancement of Artificial Intelligence, 2025
  [arXiv] [BibTeX]
• 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, and Scott Emmons
  arXiv, 2024
  [project page] [code] [arXiv] [BibTeX]
• When Your AIs Deceive You: Challenges of Partial Observability in Reinforcement Learning from Human Feedback
  Leon Lang^*, Davis Foote^*, Stuart Russell, Anca Dragan, Erik Jenner, and Scott Emmons^*
  Neural Information Processing Systems, 2024
  [arXiv] [BibTeX]
• A StrongREJECT for Empty Jailbreaks
  Alexandra Souly^*, Qingyuan Lu^*, Dillon Bowen^*, Tu Trinh^†, Elvis Hsieh^†, Sana Pandey, Pieter Abbeel, Justin Svegliato, Scott Emmons^‡, Olivia Watkins^‡, and Sam Toyer^‡
  Neural Information Processing Systems, 2024
  [code] [arXiv] [BibTeX]
• Evidence of Learned Look-Ahead in a Chess-Playing Neural Network
  Erik Jenner, Shreyas Kapur, Vasil Georgiev, Cameron Allen, Scott Emmons, and Stuart Russell
  Neural Information Processing Systems, 2024
  [project page] [code] [arXiv] [BibTeX]
• Image Hijacks: Adversarial Images can Control Generative Models at Runtime
  Luke Bailey^*, Euan Ong^*, Stuart Russell, and Scott Emmons
  International Conference on Machine Learning, 2024
  [project page] [code] [arXiv] [BibTeX]
• ALMANACS: A Simulatability Benchmark for Language Model Explainability
  Edmund Mills, Shiye Su, Stuart Russell, and Scott Emmons
  arXiv, 2023
  [code] [arXiv] [BibTeX]
• Do the Rewards Justify the Means? Measuring Trade-Offs Between Rewards and Ethical Behavior in the MACHIAVELLI Benchmark
  Alexander Pan^*, Chan Jun Shern^*, Andy Zou^*, Nathaniel Li, Steven Basart, Thomas Woodside, Jonathan Ng, Hanlin Zhang, Scott Emmons, and Dan Hendrycks
  International Conference on Machine Learning, 2023
  [project page] [code] [arXiv] [BibTeX]
• For Learning in Symmetric Teams, Local Optima are Global Nash Equilibria
  Scott Emmons, Caspar Oesterheld, Andrew Critch, Vincent Conitzer, and Stuart Russell
  International Conference on Machine Learning, 2022
  [code] [arXiv] [BibTeX]
• RvS: What is Essential for Offline RL via Supervised Learning?
  Scott Emmons, Benjamin Eysenbach, Ilya Kostrikov, and Sergey Levine
  International Conference on Learning Representations, 2022
  [code] [arXiv] [BibTeX]
• An Empirical Investigation of Representation Learning for Imitation
  Xin Chen^*, Sam Toyer^*, Cody Wild^*, Scott Emmons, Ian Fischer, Kuang-Huei Lee, Neel Alex, Steven H. Wang, Ping Luo, Stuart Russell, Pieter Abbeel, and Rohin Shah
  Neural Information Processing Systems, 2021
  [code] [arXiv] [BibTeX]
• Sparse Graphical Memory for Robust Planning
  Scott Emmons^*, Ajay Jain^*, Michael Laskin^*, Thanard Kurutach, Pieter Abbeel, and Deepak Pathak
  Neural Information Processing Systems, 2020
  [video] [code] [arXiv] [BibTeX]
• Concurrency and Reachability in Treelike Temporal Networks
  Eun Lee, Scott Emmons, Ryan Gibson, James Moody, and Peter J. Mucha
  Physical Review E, 2019
  [arXiv] [BibTeX]
• A Map Equation with Metadata: Varying the Role of Attributes in Community Detection
  Scott Emmons and Peter J. Mucha
  Physical Review E, 2019
  [code] [arXiv] [BibTeX]
• Global Redundancy Resolution via Continuous Pseudoinversion of the Forward Kinematic Map
  Kris Hauser and Scott Emmons
  IEEE Transactions on Automation Science and Engineering, 2018
  [project page] [code] [preprint] [BibTeX]
• MOOC Visual Analytics: Empowering Students, Teachers, Researchers, and Platform Developers of Massively Open Online Courses
  Scott Emmons, Robert Light, and Katy Börner
  Journal of the Association for Information Science and Technology (JASIST), 2017
  [project page] [code] [preprint] [BibTeX]
• Post-Processing Partitions to Identify Domains of Modularity Optimization
  William H. Weir, Scott Emmons, Ryan Gibson, Dane Taylor, and Peter J. Mucha
  Algorithms, 2017
  [code] [arXiv] [BibTeX]
• Analysis of Network Clustering Algorithms and Cluster Quality Metrics at Scale
  Scott Emmons, Stephen Kobourov, Mike Gallant, and Katy Börner
  PLoS ONE, 2016
  [project page] [code] [arXiv] [BibTeX]

Open-Source Software

• imitation: Clean Imitation Learning Implementations
  Adam Gleave, Mohammad Taufeeque, Juan Rocamonde, Erik Jenner, Steven H. Wang, Sam Toyer, Maximilian Ernestus, Nora Belrose, Scott Emmons, and Stuart Russell
  arXiv, 2022
  [code] [arXiv] [documentation] [BibTeX]