Today, we’re releasing SpeedrunBench: the first benchmark that measures how fast agents can beat video games. We asked frontier models not to simply complete video games, but to speedrun them - across 10 titles like Mario Kart and Pokemon. On simpler games, agents get close to the human world record. However, on more complicated games like Pokemon Blue, the best models Kimi-K3 and Opus 5 are ~4x off the world record. Benchmark, paper, and demo below. @PatronusAI

Today, we're open sourcing the first computer-use dataset for design. We captured 200+ hours and 3400+ trajectories of real design work in Figma, recording step by step how designers execute complex, long-horizon tasks. FigmaTrace dataset, paper, and blog below :) @PatronusAI

We just hired the creator of Fruit Ninja and Jetpack Joyride. Excited to share that Duncan Curtis is joining us as SVP of Product and Operations at @PatronusAI. Previously, Duncan was Head of Product at Zoox, SVP at Sama, and Product Lead at Google Play. He’s also the one to blame if you lost time playing Fruit Ninja back in the day… Designing LLM post-training data is oddly similar to designing games. A great game has simple rules, but creates a huge space of possible strategies. It has to be challenging without being impossible, and the challenge has to actually reveal skill. You can tell the difference between someone who has memorized a trick and someone who has actually mastered the system. Great RL tasks work the same way. You’re designing a simulation world with rules, tools, and feedback, and you’re testing whether agents can complete tasks through good decision making and NOT reward hacking. As models get better, I think agent task design will increasingly look like game design... the same principles that make games compelling can make RL envs powerful.