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