Looped Transformers Could Buy Models More Thinking Time
The intriguing shift is not another benchmark score, but the possibility that a model can spend extra compute by cycling through the same transformer blocks. Sebastian Raschka uses GPT-6 Astra rumors as a jumping-off point, then explains recurrent depth, why repeated passes might improve hard reasoning, and why that is not the same thing as hiding a chain of thought. The piece also maps the idea to recent research on looping architectures.