We found evidence of specific layer-localized "reasoning" circuits in a few models last year too! A very much work-in-progress paper is here: https://openreview.net/forum?id=mTjGBrkdtz
Huh there is so much limonene in Coca Cola?! Limonene works as a very good…pesticide and herbicide! I did a research project on limonene like 10 years ago with my mentor and it outperformed most commercial pesticides in controlled settings. It really can't be that great to ingest.
These metaphorical database analogies bug me, and from what it seems like, a lot of other people in comments! So far some of the most reasonable explanations I have found that take training dynamics into account are from Lenka Zdeborova's lab (albeit in toy, linear attention settings but it's easy to see why they generalize to practical ones). For instance, this is a lovely paper: https://arxiv.org/abs/2509.24914
Hi! Did you ever end up running this reproduction? If yes, could you also check if the Putnam/IMO problems are in the training data perhaps by trying to have it complete the problems n times? I would totally do this myself if I weren’t GPU poor!
If anyone else is curious about which ARC-AGI public eval puzzles o3 got right vs wrong (and its attempts at the ones it did get right), here's a quick visualization: https://arcagi-o3-viz.netlify.app