MATS Summer 2026
The Summer 2026 program will run from June through August. It will be largest MATS program to date with 120 fellows and 100 mentors. Fellows will be connected with mentors or organizational research groups, such as Anthropic's Alignment Science team, UK AISI, Redwood Research, ARC, and LawZero, to collaborate on a research project over the summer. Some fellows will be offered a 6+ month extension to continue this collaboration.
Applications are now closed.
Program phases
Key dates for the application and admissions timeline
Applications typically open several months before the program begins. Applicants complete a multi-stage admissions process, beginning with a general application. Depending on the tracks, streams, and mentors they apply to, applicants may also complete additional evaluations such as interviews, work tests, coding assessments, or writing samples before final admissions decisions are made.
Each MATS stream brings together scholars and mentors around a shared research agenda. Streams vary in methodology and focus area, spanning topics such as interpretability, control, evaluations, governance, cybersecurity, and agent foundations.
We build scalable technology for AI understanding and oversight.
The stream will focus on conceptual, empirical, and theoretical work on scalable oversight and control. This includes but is not limited to creating model organisms for specific failure modes, designing training procedures against them, and making progress on subproblems involved in safety cases.
I (Cas) work on a range of projects from technical safeguards to technical governance. This stream follows an academic collaboration model and work will likely focus on technical topics in AI governance.
In the shard theory stream, we create qualitatively new methods and fields of inquiry, from steering vectors to gradient routing to unsupervised capability elicitation to robust unlearning. If you're theory-minded, maybe you'll help us formalize shard theory itself.
This stream is for the UK AISI Red-team. The team focuses on stress-testing mitigations for AI risk, including misuse safeguards, control techniques and model alignment red-teaming. We plan to work on projects building and improving methods for performing these kinds of evaluations and methods.
Conceptual research on deceptive alignment, designing scheming propensity evaluations and honeypots. The stream will run in person in London, with scholars working together in team(s).