OpenAI’s faster Fourier transform is less a mathematical breakthrough and more a malicious exploit of mathematical traditions. argmin.net/p/faster-fourier-…

Mike was my colleague at Berkeley, and he's been fairly skeptical of deep learning's trajectory for years. Most scientists are skeptics, and it can take a big shock to get someone to consider a possibility that goes against the anchoring effect coded in our DNA. I too was somewhat of a skeptic until ~6 months ago, and people ask me all the time why I joined Anthropic. It started in April, when a friend I've known for 20 yrs messaged me saying he'd be in the Bay Area in a week, and he suggested we meet up. I invited him to my house for dinner, and he told me he had just joined Anthropic. He said the models were becoming increasingly more capable, and he believed both that they'd soon be smarter than him and also that all intellectual work would be highly devalued in the near future, and lastly that society was not at all ready for what's coming. I'd of course heard claims similar to this before, but not from a friend I'd known for this long, and who I trusted, at my dining room table. It was the initial shock needed to destabilize my own anchoring. Also, I've been fortunate to work with and/or mentor many smart people in my life (e.g. Fields medalists, a 4x Putnam Fellow, IOI first place winners, etc.), so I feel pretty calibrated in recognizing very smart people, and this was someone who is definitely very smart and with impressive such credentials. So, it was hard to ignore this friend saying the models would be smarter than even him in particular. After that dinner, I decided to do an informal survey, calling and meeting up with a lot of my friends who now work on improving model capabilities at these frontier labs. They all agreed with that first friend. These were all people I'd known 10, 15, 20 years, who mentored me, or I mentored them, or we met professionally in academia. These are people I trust, and their beliefs on this topic were consistent. I then spent a whole lot of time understanding the missions of different companies, and talking to current and former employees and people in leadership. I read Dario's (long) essays and watched videos on YouTube of company leaders being interviewed. Anthropic's stated mission "to ensure that the world safely makes the transition through transformative AI" felt like exactly the right guiding principle to handle such a society-disrupting technology, and everyone I spoke to at the company was very aligned with this mission. It's easily the most mission-focused place I've ever been at, and everyone takes the work extremely seriously and with a profound sense of responsibility. And now here I am. And being on the inside, the pace of progress has surprised me more than I first imagined. The moment that pure mathematicians are in, to me, is just a microcosm of what I predict a much larger of society will be grappling with in the not too distant future, and I really hope our leaders and the general public internalize sooner than later just how significant a moment we're in.

We’re releasing a broad range of new mathematical results produced by an internal frontier model. We’ve been consulting with the independent Advisory Group on Mathematics and Artificial Intelligence at the Institute for Advanced Study, and we have drawn on their advice and public recommendations to inform how we release these results. github.com/openai/math

evals ≫ harness optimization (fast learning) ≫ model post-training (slow learning) Most agentic tasks don't need slow learning loops at all. Even when the goal is post-training, you'd first need high SNR evals, then optimize the harness on the signal to get the performance up, and then "distill"* the resulting system behavior back into the weights. * Yes, I called it distill.

How much reasoning can GPT-6 Astra do without CoT? @AISecurityInst puts its no-CoT time horizon at ~30mins on math. We run the Think Fast benchmark to measure this on a wider range of tasks. Our *rough estimate* is that Astra’s 50% TH is in [8mins, 1 hour].

Training for counterfactual simulatability may help maintain monitorability. Earlier this year @Justinkangs @dswg97 @noahysiegel and I introduced a new simulatability evaluation to work towards this goal. Getting the metric design right (and hence incentivising the right behaviour in training) is hard. We discuss some of these challenges in our paper, and I'll be sharing more thoughts on things we missed soon. Our metric isn't perfect, and I'd be excited to see more work explore this space. arxiv.org/abs/2602.02639

Having reviewed the OpenAI Black Hat talk, I now suspect that CoT monitoring would have provided only low-precision, redundant signals of the attack on Hugging Face. I extended my post with this argument: web.stanford.edu/~cgpotts/bl…