Marius Hobbhahn
We will continue working on black-box monitors for scheming in complex agentic settings, building on the success of the previous stream.
See here for details.
Stream overview
The entire stream will be dedicated to building high-quality black-box scheming monitors.
In a previous stream, we have had great initial success designing black box monitors for scheming and we've made substantial progress since then.
We will continue to work on black box scheming monitors. Potential directions for this version of the stream could include: a) fine-tuning monitors, b) improving monitors through adversarial training, c) more "science of monitoring", d) harder and more complex datasets for scheming to improve monitor training/selection and testing.
Mentors
Marius Hobbhahn is the CEO of Apollo Research, where he also leads the monitoring team. Apollo is an AI safety research organization focused on scheming, evals and control/monitoring. He is a TIME100 in AI2025 recipient. Prior to starting Apollo, Marius did a PhD in Bayesian ML and worked on AI forecasting at Epoch.
Mentorship style
I want you to grow as a researcher/engineer as fast as possible. Therefore, the goal is always for scholars to write a paper and submit it to a top ML conference before the end of the extension. We will also try to produce other outputs, such as intermediate blog posts.
We have a large compute budget available for the stream, allowing us to run numerous experiments for fine-tuning or generating big data pipelines.
We have two weekly 60-minute calls by default. Since everyone will work on the same project, these calls will be with all participants of the stream. I respond on slack on a daily basis for asynchronous messages. Scholars will have a lot of freedom for day-to-day decisions and direction setting. In the best case, you will understand the project better than me after a few weeks and have a clear vision for where it should be heading. I recommend scholars focus 100% of their work time on the project and not pursue anything on the side. I think this way people will learn the most in MATS.
Fellows we are looking for
- You are interested in this project in particular, i.e. working on black-box monitors for scheming in agentic settings. I will only focus on this project and no other research.
- You enjoy tinkering with LLMs, e.g. prompting, building basic LM agents, and fine-tuning.
- You are happy to build datasets and synthetic data generation pipelines. We found that a substantial amount of initial work is required for the data generation process.
- You like quick empirical iteration and direct feedback loops.
- I expect that you will spend 20% on conceptual work (e.g., think about which environments could work or what techniques to try) and 80% on hands-on empirical work (e.g., implementing and running experiments).
- I prefer that scholars focus 100% of their work time on the project and not pursue any side projects. In general, I’m happy to support highly ambitious scholars who want to make a lot of progress during MATS. In the past, people have described my stream as "intense, but in a good way".
You will work with other scholars on the stream. The exact level of collaboration depends on your preferences and which exact subproject you're working on. You may also work with scholars from previous streams who are still working on the project.
Project selection
You will work on subprojects of black box monitoring. See here for details.
The Winter 2027 cohort offers a wide range of research streams led by experts across AI alignment, interpretability, governance, and safety. Each stream provides its own research agenda, methodology, and mentorship focus.
