MATS Summer 2026

Implementing SL4/5 and searching for differentially defense-favored security tools.

This stream will pursue research on securing and hardening AI systems through rigorous testing, provable defenses, and formal specification, including improving benchmarks for agentic security, scaling mathematically-grounded robustness techniques like randomized smoothing and Lipschitz-constrained training, and developing formal methods for specifying safe agent behaviors.

We are excited to supervise projects that fall within the two following categories:

1. Studying the causes, implications, and mitigations of [instances of] situational awareness;
2. Contributing directly to LawZero's Scientist AI. 

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For 1., we are particularly interested in:

• Evaluation / monitorability awareness;
• Self-awareness, in an introspective sense.

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For 2., we are especially interested in:

• Testing if  "truth-ification" (a process that, given a corpus of text, augments it so as to make sources of information explicit) allows language models to generalize better;
• Developing amortized inference methods to estimate the uncertainty of a predictor (such as an autoregressive model). 

Lee's stream will focus primarily on improving mechanistic interpretability methods for reverse-engineering neural networks.  

This stream will work on projects that empirically assess national security threats of AI misuse (CBRN terrorism and cyberattacks) and improve dangerous capability evaluations. Threat modeling applicants should have a skeptical mindset, enjoy case study work, and be strong written communicators. Eval applicants should be able and excited to help demonstrate concepts like sandbagging elicitation gaps in an AI misuse context.

Priority directions:

• Risks from automating AI research
• Automating safety and alignment research
• AGI privacy
• Measuring long-horizon agentic capabilities
• New alignment methods
• Science of post-training

We will continue working on black-box monitors for scheming in complex agentic settings, building on the success of the previous stream.

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See here for details.

AI control focussed stream, probably running in-person in London.

Escalation risks from state perceptions of AI capability, AI-enabled targeting, AI-enabled decision manipulation, and the impact of AI integration into nuclear command and control. 

This stream focuses on AI policy, especially technical governance topics. Tentative project options include: technical projects for verifying AI treaties, metascience for AI safety and governance, and proposals for tracking AI-caused job loss. Scholars can also propose their own projects.

This stream will focus on the science and development of model evaluations, especially monitorability and alignment evals.

Research papers (technical governance or ML) related to evaluating and mitigating dangerous AI capabilities, with a focus on what's actionable and relevant for AGI companies

This stream will focus on projects to better understand the capabilities of the model on dangerous capabilities specially more related to security. 

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Also finding better ways to evaluate the safety and robustness of the models.

I'm interested in empirical projects that improve our ability to evaluate model capabilities or enable to understand or evaluate model monitorability. An ideal project culminates in a research output (conference/Arxiv paper or research blogpost with artifacts).

Making society safe from AI doesn't just mean making safe AI: we're figuring out how to uplift human collective intelligence, manage a highly multiagent world, improve foresight and institutional competence, ideally learning how to make best positive use of frontier AI systems as we go. FLF has a small, sharp team of researchers with a wide network, and we're looking to nurture new and missing approaches to minimising large-scale risks while steering to a flourishing future.

Projects in this stream will be on AI welfare and moral status; more specifically, on what it takes to be a moral patient and how we can determine whether AI systems meet the conditions. I'm looking for applicants who have ideas about these topics and are motivated to explore them in more detail.

In this stream we will explore extensions and implications of our discovery that neural networks pretrained on next-token prediction represent belief-state geometry in their activations. We will build on this fundamental theory of neural network representations in order to discover what AI systems are thinking, and understand their emergent behaviors.

Peter Henderson’s stream focuses on developing safe, aligned AI agents, with projects on scalable oversight rules informed by law and game theory, safe long-horizon exploration, and measuring “jagged” capability/safety frontiers. Scholars will join an independently driven, engineering-heavy research environment, collaborating with other MATS scholars and PhD students, with weekly 1:1s and active async mentorship.

The Redwood Research stream is looking for fast empirical iterators and strategists to work on control research.

My MATS fellows will do philosophical thinking about multi-agent intelligence and how agents change their values. This will likely involve trying to explore and synthesize ideas from game theory, signaling theory, reinforcement learning, and other related domains.