Can Rager
Independent
Independent
Can is currently developing SAE Bench, an evaluation suite for interpretability techniques, in collaboration with Neel Nanda, Adam Karvonen and Decode Research. They launched the project during the MATS 2024 summer cohort. Can, a physics graduate, transitioned to language model interpretability through ARENA and AI Safety Camp. His previous work on Sparse Feature Circuits and Edge Attribution Patching focused on circuit discovery in language models.
The Summer 2024 cohort marked a significant expansion, supporting approximately 90 scholars with 40 mentors—the broadest mentor selection in MATS history. This cohort incorporated MATS as a 501(c)(3) nonprofit organization, formalizing its institutional structure. The program expanded its research portfolio to include at least four governance mentors alongside technical research streams, reflecting growing interest in AI policy and technical governance work. The 10-week research phase continued in Berkeley, with scholars conducting work across mechanistic interpretability, evaluations, scalable oversight, and governance research.Notable outputs from this cohort include research on targeted manipulation and deception in LLMs trained on user feedback, which was accepted to NeurIPS workshops, and contributions to an AI safety via debate paper that won best paper at ICML 2024. One scholar co-founded Decode Research, a new AI safety organization focused on building interpretability tools.
Can Rager
