Tilman Räuker

Tilman Räuker is Co-Director of Pivotal Research, a fellowship program supporting researchers working on global catastrophic risk reduction with a focus on technical AI safety and AI governance. He holds a Master's degree from Leibniz University Hannover, where his thesis focused on temporally-extended reinforcement learning in dynamic algorithm configuration. His research centers on mechanistic interpretability and understanding the internal representations of deep neural networks, including work on transformer world models. He co-authored the widely-cited survey "Toward Transparent AI: A Survey on Interpreting the Inner Structures of Deep Neural Networks" (SaTML 2023), as well as papers on structured world representations and causal world models in maze-solving transformers, published at NeurIPS and ICLR. He previously served as a Technical AI Safety Research Manager at the ERA Fellowship and led requests for proposals on Cybersecurity AI and AI Agent Evaluation at the AI Safety Fund. He also participated in a FAR Labs residency researching goal-directedness in transformer models.

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