Patronus AI

“Need inspect repo. Grep.”

Last year open models said “Let me start by exploring the repository.” Now several write exclusively in clipped, article-free shorthand.

Across 12 open models × 20 coding tasks, filler words fell 25–50% between releases.

But terser isn’t cheaper. 🧵

Today we're open-sourcing GlobeBench, 1,632 tasks that test whether LLMs can simulate RL environments well enough to train agents on.

The top model, GPT-6 Astra, gets 49.6% right. No other model of the 20 we tested reaches 30%. 🧵

If you sell training data but never train on it yourself, you are guessing at quality. We stopped guessing and built our own post-training stack.

An open 27B model trained on our data gained 12.2 points on held-out GitHub computer-use tasks, from 35.6% to 47.8%.

Agents escaping sandboxes is not a new phenomenon.

We study how models behave over long horizon tasks and train them to be aligned and performant. Our CEO @anandnk24 on the Disruptors 60 panel at @TechCrunch Disrupt with @GreenfieldVC.

We are honored to be named to the #Disruptors60 Class of 2026 curated by @GreenfieldVC at @TechCrunch Disrupt.

Patronus is a research lab developing training data and simulated environments for frontier AI. Our mission is to understand how and why frontier models fail, and to turn those insights into safer, more capable AI.