In University of California admissions, up is down: “Today, the more successful a public high school is at preparing its students, the lower its graduates’ chances of getting into top UC campuses like Berkeley and San Diego”

Oh @grok, stop being such a conspiracy MAGA extremist!

Critically important @RyanDEnos - I share his fear that any setback for Trump will lead to triumphalism about how U.S. democracy was never threatened in the first place. I invite anyone to apply this logic to attempted murder.

Assume Pratt's support in that precinct = 1%. Odds of going Z ballots with no Pratt vote: Z = 1: 99% 2: 99%^2 = 98.01% 3: 99%^3 = 97.03% 4: 99%^4 = 96.05% . . . 24,000: 99%^24,000 = 1.75659 × 10⁻¹⁰⁵ = .[105 zeros followed by] 17% "So you're saying there's a chance?"😂🤣

Grad school indoctrination camps

Join us in celebrating the 2025 Cozzarelli Prize Class V: Behavioral and Social Sciences winning paper, “The potential existential threat of large language models to online survey research.” Read the article here: ow.ly/ZVO050YKxZG

I’m proud of the work and testimony I provided in the Eastman proceedings. In two rounds of testimony we demonstrated that his empirical claims about manipulation in the 2020 election were false. This took a lot of time ( and lack of sleep) but it demonstrates the importance of careful quantitative social science for the “real world” politico.com/news/2026/04/15…

🚨SHOCKING: Apple just proved that AI models cannot do math. Not advanced math. Grade school math. The kind a 10-year-old solves. And the way they proved it is devastating. Apple researchers took the most popular math benchmark in AI — GSM8K, a set of grade-school math problems — and made one change. They swapped the numbers. Same problem. Same logic. Same steps. Different numbers. Every model's performance dropped. Every single one. 25 state-of-the-art models tested. But that wasn't the real experiment. The real experiment broke everything. They added one sentence to a math problem. One sentence that is completely irrelevant to the answer. It has nothing to do with the math. A human would read it and ignore it instantly. Here's the actual example from the paper: "Oliver picks 44 kiwis on Friday. Then he picks 58 kiwis on Saturday. On Sunday, he picks double the number of kiwis he did on Friday, but five of them were a bit smaller than average. How many kiwis does Oliver have?" The correct answer is 190. The size of the kiwis has nothing to do with the count. A 10-year-old would ignore "five of them were a bit smaller" because it's obviously irrelevant. It doesn't change how many kiwis there are. But o1-mini, OpenAI's reasoning model, subtracted 5. It got 185. Llama did the same thing. Subtracted 5. Got 185. They didn't reason through the problem. They saw the number 5, saw a sentence that sounded like it mattered, and blindly turned it into a subtraction. The models do not understand what subtraction means. They see a pattern that looks like subtraction and apply it. That is all. Apple tested this across all models. They call the dataset "GSM-NoOp" — as in, the added clause is a no-operation. It does nothing. It changes nothing. The results are catastrophic. Phi-3-mini dropped over 65%. More than half of its "math ability" vanished from one irrelevant sentence. GPT-4o dropped from 94.9% to 63.1%. o1-mini dropped from 94.5% to 66.0%. o1-preview, OpenAI's most advanced reasoning model at the time, dropped from 92.7% to 77.4%. Even giving the models 8 examples of the exact same question beforehand, with the correct solution shown each time, barely helped. The models still fell for the irrelevant clause. This means it's not a prompting problem. It's not a context problem. It's structural. The Apple researchers also found that models convert words into math operations without understanding what those words mean. They see the word "discount" and multiply. They see a number near the word "smaller" and subtract. Regardless of whether it makes any sense. The paper's exact words: "current LLMs are not capable of genuine logical reasoning; instead, they attempt to replicate the reasoning steps observed in their training data." And: "LLMs likely perform a form of probabilistic pattern-matching and searching to find closest seen data during training without proper understanding of concepts." They also tested what happens when you increase the number of steps in a problem. Performance didn't just decrease. The rate of decrease accelerated. Adding two extra clauses to a problem dropped Gemma2-9b from 84.4% to 41.8%. Phi-3.5-mini from 87.6% to 44.8%. The more thinking required, the more the models collapse. A real reasoner would slow down and work through it. These models don't slow down. They pattern-match. And when the pattern becomes complex enough, they crash. This paper was published at ICLR 2025, one of the most prestigious AI conferences in the world. You are using AI to help you make financial decisions. To check legal documents. To solve problems at work. To help your children with homework. And Apple just proved that the AI is not thinking about any of it. It is pattern matching. And the moment something unexpected shows up in your question, it breaks. It does not tell you it broke. It just quietly gives you the wrong answer with full confidence.

1/ Happy to release StatsClaw — an open-source multi-agent workflow for building statistical software with AI. w/ @Maple_Optboy Site: statsclaw.ai Paper: bit.ly/statsclaw

New preprint out today (osf.io/preprints/psyarxiv/pv…). We tested whether AI agents are actually infiltrating online surveys. Spoiler alert: they aren't Thread 🧵 [1/9]