Research
When you see robots participating in running races or performing folk dances on stage, you might envision a future where a simple natural language command is all it takes for a robot to tidy up a desk, clean a room, or even serve tea. For…
By: Ahmed Awadallah, Sahil Gupta, Yash Lara, Yadong Lu, Hussein Mozannar, Akshay Nambi, Zach Nussbaum, Yash Pandya, Aravind Rajeswaran, Corby Rosset, Alexey Taymanov, Luiz do Valle, Vibhav Vineet, Spencer Whitehead, Andrew Zhao We are excited to introduce the Fara1.5 family of computer use agent (CUA)…
By Zachary Huang, Tyler Payne, Gagan Bansal, Will Epperson, Wenyue Hua, Adam Fourney, Amanda Swearngin, Maya Murad, Ece Kamar, Saleema Amershi As AI agents are increasingly deployed to handle real transactions and negotiations, they can exhibit vulnerabilities that traditional safety testing struggles to fully capture. Our prior work on Magentic Marketplace found significant vulnerability for smaller…
By Yadong Lu1, Lingrui Xu2, Chao Huang2, Ahmed Awadallah11Microsoft Research, 2The University of Hong Kong Instead of solving web tasks by predicting where to click one at a time, we only give the model a terminal where it has the full freedom to spawn browser…
By Ziyi Liu (opens in new tab), Bahar Sarrafzadeh, Pei Zhou, Longqi Yang (opens in new tab), Ashish Sharma Imagine you are in a high-stakes group discussion, stuck in a circular argument with no consensus in sight. Now, imagine an AI agent sitting at that table. Unlike traditional tools that…
By Corby Rosset, Pratyusha Sharma, Andrew Zhao, Miguel Gonzalez-Fernandez, Ahmed Awadallah We share lessons learned from building a best-in-class verifier for computer use agent trajectories on the web, called the Universal Verifier. False positive rates drop to near zero (vs. ≥45% for WebVoyager, ≥22% for…
Vasilis Kontonis, Yuchen Zeng, Shivam Garg, Lingjiao Chen, Hao Tang, Ziyan Wang, Ahmed Awadallah, Eric Horvitz, John Langford, Dimitris Papailiopoulos We taught models to compress their own chain-of-thought mid-generation. Peak KV cache drops 2–3x, throughput nearly doubles, and the erased reasoning blocks leave traces…
By Ben Finkelshtein (opens in new tab) (University of Oxford), Silviu Cucerzan, Sujay Kumar Jauhar, and Ryen W. White (Microsoft) Think about the last time you opened a shared document at work. Behind that simple action lies a complex network of relationships: the colleagues who edited the file before you, the team site on…
By Taiwei Shi, Sihao Chen, Longqi Yang, Jaime Teevan Reinforcement Learning is at the core of building and improving frontier AI models and products. Yet most state-of-the-art RL methods learn primarily from outcomes: a scalar reward signal that says whether an attempt worked, not why…
By Jaime Teevan, Chief Scientist & Technical Fellow In recent years we’ve all lived through the transition to cloud computing, a sudden shift to remote work, and now the rapid rise of AI. Each individually has felt like a seismic event, but in reality they…
Imagine an AI assistant that can navigate a computer the same way humans do—clicking buttons, filling out forms, and moving between applications—all by simply interpreting what's on the screen. This vision is becoming a reality through computer use agents—AI systems designed to operate software interfaces…
Extracting useful information from long videos, whether meeting recordings, experimental data, or lecture content, requires painstaking manual review. AI tools offer some help: language-vision models can summarize short clips or answer questions when videos are divided into clear scenes or chapters. But for hours‑long recordings…
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