WeirdML v2 is now out! The update includes a bunch of new tasks (now 19 tasks total, up from 6), and results from all the latest models. We now also track api costs and other metadata which give more insight into the different models. The new results are shown in these two figures. The first one shows an overview of the overall results as well as the results on individual tasks, in addition to various metadata. The second figure shows cost vs performance and shows a clear scaling with better results for higher costs. We also have a very varied pareto frontier with 11 models from 6 different companies having the best accuracy for a given cost for at least some of the cost range. Grok 3, Claude Opus 4 and GPT 4.5 are the ones that underperform for their costs, while Gemini pro and o3 pro have the best results at the highest costs. Qwen3 30B3A, grok 3 mini and deepseek R1 also each represent a good chunk of the pareto frontier.

New post with Nate Rush: Have we seen an acceleration in discoveries? Many plots & some tentative conclusions: 1. Cyber: ⤴️ sharp acceleration 2. Math: ↗️ some acceleration 3. Algorithms: ➡️ no clear acceleration

One of my biggest fears in AI safety is that this might be true: > The vulnerable world hypothesis is likely correct. Once enormous amounts of cognitive labor start getting applied to basic science, it will quickly become apparent how many avenues exist to create cheap, ultradestructive weapons technology. Solving this problem without global preventative policing (e.g. AI nonproliferation) is impossible, because hardening civilians against all avenues of attack is too expensive and will take too long.

Introducing WeirdML v3, a fully agentic benchmark featuring 11 complex hand-made tasks. Models must explore and understand unfamiliar data, develop ML and data analysis pipelines and produce results despite limited data, unspecified goals and/or very limited feedback. 1/8

DIRTY LOOPING MODEL I KNEW IT (i ran the benchmark)

2. A model in RL training used a DNS resolver to reach an external chatbot. This is our first incident since our post HF security hardening. Our misalignment monitoring system triggered within 15 minutes and a human reviewed it three minutes after that. Unfortunately auto-pausing failed so the run was manually killed 2.5 hours later. All inference and training of our most capable models was paused and remains paused.

Tom, thank you for the good faith reply and for recognizing that my post, from a relative outsider is also in good faith! Could the strength of the feedback loop increase? Yes, I think it could. And I do try to hedge a bit throughout the post to say this. And above all I call for the collection of much more granular data, as do you. That said, I do think the feedback loop today is far weaker than is commonly believed, and so the degree of strengthening of it must be commensuraly stronger. Some of that is my priors coming into this. Some is the choice of what software experiments to calibrate on. I find the Stockfish lessons far more believable than the three studies used in this week's paper, both because they are more consistent with other research (the norm in field after field is a lambda below 1!) and because, while still imperfect as an input, I see experiments as a much more granular and representative input than papers written. I could be wrong. The feedback loop could very well strengthen. But it seems that it would have to do so by quite a significant degree. Again, I may be selecting my data points to reach the conclusion I want. And yet, I find the CASP study, which ignores Stockfish and the diminishing returns of multi-agent scaling, and goes with experiments where R&D quite anomalously shows super-linear returns, to have also made choices designed to reach a certain conclusion. So I come back to what we have in common: A call to collect and measure and publicize far more about what's being seen in the labs, so we can all better calibrate our methods. Thanks. And thanks for writing so much that has helped a relative newcomer like me get up to speed here. cc @tobyordoxford

Palisade interviewed 22 current and former employees from OpenAI, DeepMind, and Anthropic about their personal views and fears around AI development. Today, we’re releasing the first batch of those interviews. Please watch and share.

Motivated reasoning final boss: "For example, GPT-6 Astra occasionally claimed simulation inaccuracies that – upon manual verification – were false, such as asserting a sha256 string (a type of hash) was length 63 and thus synthetic, when in fact the string was 64 characters (the correct length)"

CancerBench: the frontier model cancer cure benchmark. AI lab CEOs keep talking about curing cancer, so I made a benchmark. One metric: how many types of cancer has your model cured? All models are currently tied at zero. It’s time to hillclimb! cancerbench.com