This is a link post for https://rohansubramani.github.io/astra-no-cot. I recommend reading there for the best experience because it's easier to engage with this post when you can read the correct reasoning and answers to the multi-hop questions. I didn't want to include those here in order to avoid LLMs being trained on them. Some parts of the post also respond to users hovering over bars in graphs, which isn't supported in LessWrong (as far as I know).
TLDR
GPT-6 Astra is much better than GPT-5.6 Sol at solving multihop reasoning questions without using chain of thought. The table below shows some examples I find particularly instructive. Sol is bad at all the selected problems; Astra is good at some, ok at others, and bad at others, so the table gives some flavor of the limits of Astra's no-CoT serial reasoning ability. I'm pretty sure Astra actually isn't doing any chain of thought because it sometimes gets the hardest of these questions wrong, the API says reasoning_tokens=0, and there's no reasoning in the output. It's possible Astra can correctly answer some of these questions without internally doing every step in the chain of reasoning, but for most of them, I think it's unlikely.
This is a less systematic study than no-CoT time horizons, and the tasks are very unrealistic/unimportant, but I find them quite useful for intuition-building. I also didn't do a very systematic version with these prompts; maybe someone else should add error bars, try more models, etc.
This study yields a few takeaways for me:
1. Astra is capable of much more hidden serial reasoning than Sol (and probably most/all other models), even on a dataset where I'm very confident there was no contamination.
2. One frame for thinking about the limits of Astra's hidden serial reasoning capabilities, given by the table below, is roughly "4-5 hop reasoning questions where one or more hops require knowledge of somewhat rare facts." Sol was at something more like 2-3 hops.
3. One hypothes