Khan, Akbir

Multi-Agent Risks from Advanced AI

Hammond, L., Chan, A., Clifton, J., Hoelscher-Obermaier, J., Khan, A., McLean, E., Smith, C., Barfuss, W., Foerster, J., Gavenčiak, T., et al. (2025) · arXiv

Ethics and Social Impacts of AIMulti-Agent Systems and Negotiation

Cited by 305

The Goldilocks of Pragmatic Understanding: Fine-Tuning Strategy Matters for Implicature Resolution by LLMs

Ruis, L., Khan, A., Biderman, S., Hooker, S., Rocktäschel, T., & Grefenstette, E. (2023) · Advances in Neural Information Processing Systems 36

Natural Language Processing TechniquesSyntax, Semantics, Linguistic Variation

Cited by 47

Alignment faking in large language models

Greenblatt, R., Denison, C., Wright, B., Roger, F., MacDiarmid, M., Marks, S., Treutlein, J., Belonax, T., Chen, J., Duvenaud, D., et al. (2024) · arXiv

Ethics and Social Impacts of AIAdversarial Robustness in Machine Learning

Cited by 30

Debating with More Persuasive LLMs Leads to More Truthful Answers

Khan, A., Hughes, J., Valentine, D., Ruis, L., Sachan, K., Radhakrishnan, A., Grefenstette, E., Bowman, S. R., Rocktäschel, T., & Perez, E. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 13

Considering Race a Problem of Transfer Learning

Khan, A., & Mahmoud, M. (2019) · 2019 IEEE Winter Applications of Computer Vision Workshops (WACVW)

Face recognition and analysisDomain Adaptation and Few-Shot Learning

Cited by 12

MAESTRO: Open-Ended Environment Design for Multi-Agent Reinforcement Learning

Samvelyan, M., Khan, A., Dennis, M., Jiang, M., Parker-Holder, J., Foerster, J., Raileanu, R., & Rocktäschel, T. (2023) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 8

Language Models Learn to Mislead Humans via RLHF

Wen, J., Zhong, R., Khan, A., Perez, E., Steinhardt, J., Huang, M., Bowman, S. R., He, H., & Feng, S. (2024) · arXiv

Adversarial Robustness in Machine LearningTopic Modeling

Cited by 4

Auditing language models for hidden objectives

Marks, S., Treutlein, J., Bricken, T., Lindsey, J., Marcus, J., Mishra-Sharma, S., Ziegler, D., Ameisen, E., Batson, J., Belonax, T., et al. (2025) · arXiv

Explainable Artificial Intelligence (XAI)Ethics and Social Impacts of AI

Cited by 4

BALROG: Benchmarking Agentic LLM and VLM Reasoning On Games

Paglieri, D., Cupiał, B., Coward, S., Piterbarg, U., Wolczyk, M., Khan, A., Pignatelli, E., Kuciński, Ł., Pinto, L., Fergus, R., et al. (2024) · arXiv

Artificial Intelligence in GamesMultimodal Machine Learning Applications

Cited by 2

Adaptive Deployment of Untrusted LLMs Reduces Distributed Threats

Wen, J., Hebbar, V., Larson, C., Bhatt, A., Radhakrishnan, A., Sharma, M., Sleight, H., Feng, S., He, H., Perez, E., et al. (2024) · arXiv

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 1

Ctrl-Z: Controlling AI Agents via Resampling

Bhatt, A., Rushing, C., Kaufman, A., Tracy, T., Georgiev, V., Matolcsi, D., Khan, A., & Shlegeris, B. (2025) · arXiv

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 0