Krakovna, Victoria

Gemini: A Family of Highly Capable Multimodal Models

Gemini Team, Anil, R., Borgeaud, S., Alayrac, J.-B., Yu, J., Soricut, R., Schalkwyk, J., Dai, A. M., Hauth, A., Millican, K., et al. (2023) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 990

AI Safety Gridworlds

Leike, J., Martic, M., Krakovna, V., Ortega, P. A., Everitt, T., Lefrancq, A., Orseau, L., & Legg, S. (2017) · arXiv

Reinforcement Learning in RoboticsEthics and Social Impacts of AI

Cited by 240

Reward tampering problems and solutions in reinforcement learning: a causal influence diagram perspective

Everitt, T., Hutter, M., Kumar, R., & Krakovna, V. (2021) · Synthese

Reinforcement Learning in RoboticsEthics and Social Impacts of AI

Cited by 80

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Comanici, G., Bieber, E., Schaekermann, M., Pasupat, I., Sachdeva, N., Dhillon, I., Blistein, M., Ram, O., Zhang, D., Rosen, E., et al. (2025) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 69

The Ethics of Advanced AI Assistants

Gabriel, I., Manzini, A., Keeling, G., Hendricks, L. A., Rieser, V., Iqbal, H., Tomašev, N., Ktena, I., Kenton, Z., Rodriguez, M., et al. (2024) · arXiv

Ethics and Social Impacts of AIArtificial Intelligence in Healthcare and Education

Cited by 57

Penalizing side effects using stepwise relative reachability

Krakovna, V., Orseau, L., Kumar, R., Martic, M., & Legg, S. (2018) · arXiv

Reinforcement Learning in RoboticsAI-based Problem Solving and Planning

Cited by 26

Goal Misgeneralization: Why Correct Specifications Aren't Enough For Correct Goals

Shah, R., Varma, V., Kumar, R., Phuong, M., Krakovna, V., Uesato, J., & Kenton, Z. (2022) · arXiv

Ethics and Social Impacts of AIAdversarial Robustness in Machine Learning

Cited by 16

Evaluating Frontier Models for Dangerous Capabilities

Phuong, M., Aitchison, M., Catt, E., Cogan, S., Kaskasoli, A., Krakovna, V., Lindner, D., Rahtz, M., Assael, Y., Hodkinson, S., et al. (2024) · arXiv

Ethics and Social Impacts of AIAdversarial Robustness in Machine Learning

Cited by 11

Power-seeking can be probable and predictive for trained agents

Krakovna, V., & Kramar, J. (2023) · arXiv

Ethics and Social Impacts of AIReinforcement Learning in Robotics

Cited by 2

Evaluating Frontier Models for Stealth and Situational Awareness

Phuong, M., Zimmermann, R. S., Wang, Z., Lindner, D., Krakovna, V., Cogan, S., Dafoe, A., Ho, L., & Shah, R. (2025) · arXiv

Ethics and Social Impacts of AIAdversarial Robustness in Machine Learning

Cited by 1