tl;dr I tested GPT-6 Astra on randomized Boolean logic problems. Astra can solve surprisingly complex logic problems without chain-of-thought, and its performance improves significantly with more filler tokens. Astra is also able to combine prior probabilities with constraints to find the most likely solution, and can output surprisingly accurate posterior...
tl;dr I have tested Astra's ability to complete various long multi-step tasks without using its chain-of-thought, and found that the number of sequential steps is a poor predictor of task success. Instead, Astra's ability to solve a task seems to correlate more strongly with what I will call the speculative...
Recent discussion of recurrent-depth/looped transformers (especially in the context of OpenAI's Astra model) has mostly focused on the number of recurrent passes per token. The usual reassurance is that if this number is small, the model only gains a bounded amount of opaque computation before it must emit another readable...