Large language model designed for reasoning tasks
Its quietest recent week still sits 27% above the old baseline. A news spike leaves the floor where it was.
Share of the last four months of extra attention that landed in its busiest seven days. High means a single story, not a trend.
How much of this topic's shape repeats every year. The annual cycle is estimated per topic and divided out before anything is measured.
Bot and scraper traffic is flat across the week. Anything with implausibly low day-to-day variation, or with no weekday/weekend pattern at all, is dropped before it reaches this page.
Long enough to separate this year's move from last year's level.
No second source yet. Common for consumer and culture topics, which do not show up in developer data.
Reasoning language models (RLMs) or large reasoning models (LRMs) are large language models that are trained further to solve tasks that take several steps of reasoning. They tend to do better on logic, math, and programming tasks than standard LLMs, can revisit and revise earlier steps, and make use of extra computation while answering as another way to scale performance, alongside the number of training examples, parameters, and training…
Excerpt from Reasoning model on Wikipedia, by its contributors, licensed CC BY-SA 4.0. Trimmed for length.