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papersSEP 9 11:14 UTC

GPT-6 Astra spurs research interest in looped transformers and hidden reasoning

A new analysis examines the ideas behind GPT-6 Astra, focusing on transformer architectures that reuse the same blocks across multiple passes rather than adding more layers. It reviews recent work on recurrent depth, where looping blocks can increase effective model depth and enable internal computation that is not exposed in the visible output. The piece also discusses hidden chains of thought and what this implies for interpreting model reasoning.

research/transformers/reasoning/interpretability