实证研究

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

Roger Grosse’s stream investigates how to improve influence functions and other training data attribution methods, and uses these tools to study alignment-related phenomena such as out-of-context reasoning and emergent misalignment. The ideal scholar has experience with LLM internals, strong statistics/applied math skills (especially numerical linear algebra), and can independently drive research from literature review through experimentation and analysis. Roger provides shovel-ready projects while giving exceptional scholars freedom to pursue their own ideas, and is open to scholars collaborating with others.

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.

I work on the science of evaluating advanced AI systems for biological and CBRN risks, with a particular interest in translating technical evidence into decisions by governments and frontier AI developers. In this stream, I’m interested in developing novel capability evaluations, studying how dangerous or dual-use capabilities diffuse into increasingly accessible models, and building scalable red-teaming methods that produce rigorous, decision-relevant evidence without requiring risky real-world demonstrations.

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.

We are interested in AI control and scalable oversight. I'm excited to work with scholars interested in empirical projects building and evaluating control measures and oversight techniques for LLM agents, especially those based on chain of thought monitoring. I'm also interested in the science of chain of thought monitorability, misalignment and control. An ideal project ends with a paper submitted to NeurIPS/ICML/ICLR.

We build scalable technology for AI understanding and oversight.

This stream will focus on evaluating dangerous capabilities in language models and detecting deception and dishonesty.