de Witt, Christian Schroeder

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

Open Challenges in Multi-Agent Security: Towards Secure Systems of Interacting AI Agents

de Witt, C. S., Krawiecka, K., Krawczuk, I., Hagag, B., Anderson, W. L., Belcak, P., Bucknall, B., Cai, X., Chopra, A., Cohen, D., et al. (2025) · arXiv

Adversarial Robustness in Machine LearningAccess Control and Trust

Cited by 108

Humanity's Last Exam

Phan, L., Gatti, A., Han, Z., Li, N., Hu, J., Zhang, H., Zhang, C. B. C., Shaaban, M., Ling, J., Shi, S., et al. (2025) · arXiv

Topic ModelingMultimodal Machine Learning Applications

Cited by 65

Comparative Global AI Regulation: Policy Perspectives from the EU, China, and the US

Chun, J., de Witt, C. S., & Elkins, K. (2024) · arXiv

Digitalization, Law, and RegulationEthics and Social Impacts of AI

Cited by 44

Stratospheric Aerosol Injection as a Deep Reinforcement Learning Problem

de Witt, C. S., & Hornigold, T. (2019) · arXiv

Climate Change and GeoengineeringReinforcement Learning in Robotics

Cited by 7

Architecting Resilient LLM Agents: A Guide to Secure Plan-then-Execute Implementations

Del Rosario, R. F., Krawiecka, K., & de Witt, C. S. (2025) · arXiv

Adversarial Robustness in Machine LearningAI-based Problem Solving and Planning

Cited by 1

Architecture Matters for Multi-Agent Security

Hagag, B., Anderson, W. L., de Witt, C. S., & Scheffler, S. (2026) · arXiv

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 0

Hidden in Plain Text: Emergence & Mitigation of Steganographic Collusion in LLMs

Mathew, Y., Matthews, O., McCarthy, R., Velja, J., de Witt, C. S., Cope, D., & Schoots, N. (2024) · arXiv

Adversarial Robustness in Machine LearningAdvanced Steganography and Watermarking Techniques

Cited by 0