A Generalized-Zero-Preserving Method for Compact Encoding of Concept Lattices. - Matthew Skala; Victoria Krakovna; János Kramár; Gerald Penn (ACL, 2010)

Memory-Bounded Left-Corner Unsupervised Grammar Induction on Child-Directed Input. - Cory Shain; William Bryce; Lifeng Jin; Victoria Krakovna; Finale Doshi-Velez; Timothy Miller 002; William Schuler; Lane Schwartz (COLING, 2016)

Reinforcement Learning with a Corrupted Reward Channel. - Tom Everitt; Victoria Krakovna; Laurent Orseau; Shane Legg (IJCAI, 2017)

Reinforcement Learning with a Corrupted Reward Channel. - Tom Everitt; Victoria Krakovna; Laurent Orseau; Marcus Hutter; Shane Legg (CoRR, 2017)

AI Safety Gridworlds. - Jan Leike; Miljan Martic; Victoria Krakovna; Pedro A. Ortega; Tom Everitt; Andrew Lefrancq; Laurent Orseau; Shane Legg (CoRR, 2017)

Measuring and avoiding side effects using relative reachability. - Victoria Krakovna; Laurent Orseau; Miljan Martic; Shane Legg (CoRR, 2018)

Modeling AGI Safety Frameworks with Causal Influence Diagrams. - Tom Everitt; Ramana Kumar; Victoria Krakovna; Shane Legg (AISafety@IJCAI, 2019)

Penalizing Side Effects using Stepwise Relative Reachability. - Victoria Krakovna; Laurent Orseau; Miljan Martic; Shane Legg (AISafety@IJCAI, 2019)

Avoiding Side Effects By Considering Future Tasks. - Victoria Krakovna; Laurent Orseau; Richard Ngo; Miljan Martic; Shane Legg (NeurIPS, 2020)

REALab: An Embedded Perspective on Tampering. - Ramana Kumar; Jonathan Uesato; Richard Ngo; Tom Everitt; Victoria Krakovna; Shane Legg (CoRR, 2020)

Avoiding Tampering Incentives in Deep RL via Decoupled Approval. - Jonathan Uesato; Ramana Kumar; Victoria Krakovna; Tom Everitt; Richard Ngo; Shane Legg (CoRR, 2020)

Reward tampering problems and solutions in reinforcement learning: a causal influence diagram perspective. - Tom Everitt; Marcus Hutter; Ramana Kumar; Victoria Krakovna (Synth., 2021)

Goal Misgeneralization: Why Correct Specifications Aren't Enough For Correct Goals. - Rohin Shah; Vikrant Varma; Ramana Kumar; Mary Phuong; Victoria Krakovna; Jonathan Uesato; Zac Kenton (CoRR, 2022)

Power-seeking can be probable and predictive for trained agents. - Victoria Krakovna; János Kramár (CoRR, 2023)

Quantifying stability of non-power-seeking in artificial agents. - Evan Ryan Gunter; Yevgeny Liokumovich; Victoria Krakovna (CoRR, 2024)

Limitations of Agents Simulated by Predictive Models. - Raymond Douglas; Jacek Karwowski; Chan Bae; Andis Draguns; Victoria Krakovna (CoRR, 2024)

Evaluating Frontier Models for Dangerous Capabilities. - Mary Phuong; Matthew Aitchison; Elliot Catt; Sarah Cogan; Alexandre Kaskasoli; Victoria Krakovna; David Lindner; Matthew Rahtz; Yannis Assael; Sarah Hodkinson; Heidi Howard; Tom Lieberum; et al. (CoRR, 2024)

The Ethics of Advanced AI Assistants. - Iason Gabriel; Arianna Manzini; Geoff Keeling; Lisa Anne Hendricks; Verena Rieser; Hasan Iqbal; Nenad Tomasev; Ira Ktena; Zachary Kenton; Mikel Rodriguez; Seliem El-Sayed; Sasha Brown; et al. (CoRR, 2024)

An Approach to Technical AGI Safety and Security. - Rohin Shah; Alex Irpan; Alexander Matt Turner; Anna Wang; Arthur Conmy; David Lindner; Jonah Brown-Cohen; Lewis Ho; Neel Nanda; Raluca Ada Popa; Rishub Jain; Rory Greig; et al. (CoRR, 2025)

Evaluating Frontier Models for Stealth and Situational Awareness. - Mary Phuong; Roland S. Zimmermann; Ziyue Wang; David Lindner; Victoria Krakovna; Sarah Cogan; Allan Dafoe; Lewis Ho; Rohin Shah (CoRR, 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 003; Alan Cooney; Allan Dafoe; Anca D. Dragan; Scott Emmons; Owain Evans; et al. (CoRR, 2025)