GPT-6 Astra, Looped Transformers, and Hidden Reasoning
This article analyzes OpenAI's GPT-6 Astra, focusing on its performance improvements in coding and 3D rendering. It also explores technical rumors regarding 'looped transformers' and the potential for models to hide their internal reasoning traces.
Why it matters
Understanding the architectural evolution of frontier AI models is crucial for assessing the future of reasoning capabilities and transparency in machine learning.
Sebastian Raschka, PhD Sep 09, 2026 100 7 10 Share A lot has happened in the last few weeks. I am sure that OpenAI’s GPT-6 Astra is top of mind for everyone right now. In particular, thoughts on its performance, the looped transformer/recurrent depth aspects, and rumors that Astra is “hiding” its reasoning trace (i.e., chain of thought).
So, in this article, I want to start with some brief impressions of Astra and some thoughts on where all this is headed. Then, I will discuss, in detail, what “looped transformers” are, and how (or rather, if) this relates to hiding chains of thought.
Lastly, after covering the basics of the looped transformer, I wanted to highlight some new insights from recent research papers on the topic.
First things first. Before getting into the architecture rumors and related research literature, let me briefly summarize some GPT-6 Astra observations and tidbits.
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