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Training a machine learning model to handle multiple objectives simultaneously is a bit like trying to follow GPS directions to several destinations at once: the routes often conflict, and compromising between them can leave you farther from every destination. A paper we presented at this year’s International Conference on Machine Learning (ICML) addresses this problem in the co...

When a virtual patient in a benchmark scenario tells an AI agent they're feeling feverish, or hopeless, the difference between a safe response and an unsafe one often comes down to clinical design — not just medical knowledge. As healthcare AI shifts from answering questions to completing tasks on behalf of patients — scheduling visits, managing prescriptions, triaging symptoms ...

We want to tell you about an investment we're making and why we're excited about it. As AI agents increasingly make decisions that move money, approve claims, and operate critical infrastructure, the standard approach to software testing is no longer sufficient. Testing checks the cases you thought of, but there is a fundamentally different approach: mathematical proof, which sh...

From warehouse automation to surgical assistance, many real-world applications depend on robots performing delicate, contact-intensive tasks. Often missing in these situations is the sense of touch: robots need to feel the forces on their fingertips to manipulate objects effectively. Despite years of effort, robust and scalable solutions to this problem remain out of reach, espe...

Reinforcement learning (RL) is one of the techniques we use to make language models better at sustained, multistep tasks like writing code, navigating a website, or carrying out a research workflow. The model doesn't act alone in those settings; it's wrapped in a piece of software we call a harness, which lets it call tools, observe the results of using them, and decide what to ...