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.

Last week, 40 of us at Patronus came together in Lyon, France. The team ran workshops, aligned on what’s next, and took excursions through the city and into the French Alps. We’re hiring across research, engineering and operations in San Francisco. If you want to help decide what frontier models learn next, come build with us.

We're excited to share that our paper on prompt infilling for dLMs has been accepted to the "Non-Autoregressive Language Models for Fast & Flexible Text Generation" workshop at COLM 2026 🎉 Masked diffusion language models like LLaDA and Dream have bidirectional architectures, yet they can't infer prompts from desired outputs out of the box. We traced this to a training gap: during SFT, prompts are simply never masked. The fix is straightforward. Mask both prompts and responses during SFT (full-sequence masking). That single change unlocks the ability to infer task-adapted prompts from few-shot examples. Prompt infilling in dLMs just requires the right training approach. We're calling on the community to release full-sequence masked SFT checkpoints alongside standard ones. Congrats to @akkikiki and Keisuke Sakaguchi! Paper: arxiv.org/abs/2604.03677 Fine-tuned models: huggingface.co/collections/a…

Meet @DuncanCTech, our SVP of Product and Operations. 👋 Previously, Duncan was Head of Product at @zoox, SVP at @SamaAI, and Product Lead at @Google Play. He was also the creator of Fruit Ninja and Jetpack Joyride. Fun fact: Duncan loves figuring out how physical things work, so when an appliance breaks at home, he usually takes it apart and fixes it himself 🔧. Handyman 2.0.

We're hosting a researcher night next Wednesday at our office in SF. Fast-paced spotlight talks on post-training and evaluation. ⚡️ The talks: @mariyaivasileva on Beliefload: an evaluation of how LLMs formulate and revise hypotheses in light of new evidence, and how they diagnose and repair a broken environment without overwriting the parts that already work. @Nick_saban20 on GlobeBench: a benchmark for whether language models can faithfully simulate the environments we train agents in. @AnmolGulati06 on Beyond Rows to Reasoning: an agentic framework for reasoning over and editing enterprise spreadsheets with millions of cells, cross-sheet dependencies, and embedded charts. @akkikiki on SpeedrunBench: a benchmark that asks not whether an agent can finish a game, but how fast. Drinks, sushi, merch incoming.

Meet @chaaelaes from our data quality team. 👋 Chelsea was previously at UC Irvine where studied biomedical engineering. She spent time in a neuroscience lab building imaging hardware and researching neurodegenerative diseases. Fun fact: Chelsea has played the violin since she was 3 years old🎶

Meet Abdelrahman Madkour, from our research team. 👋 Madkour was a previously a Research Scientist at Meta, where he worked on coding benchmarks for coding agents. Before that, he completed his Ph.D. in CS at Northeastern University, where he focused on procedural content generation in games. Fun fact: Madkour plays the Arabic oud 🪕

We find that agents often inherit a slower route than the best human players, and route freely when not. Additionally, giving a model a seed makes the task tractable for weaker agents, since several models failed to reach the goal from scratch at all and many models cannot reach the goal on more complicated games like Pokémon. More at: speedrunbench.com/blog

Most game benchmarks measure “can the agent complete the game”? That metric basically saturates the moment frontier models complete the game. Human speedrunning communities have spent decades pushing the same games well past completion by discovering new routes and techniques, which makes frame count to the milestone of a game a natural objective.

We set up a demo at speedrunbench.com/play, where you can pick any model and any game and play them in the browser.