Please turn JavaScript on

Microsoften-usresearchfeed

We bring you the latest updates from Microsoften-usresearchfeed through a simple and fast subscription.

We can deliver your news in your inbox, on your phone or you can read them here on this website on your personal news page.

Unsubscribe at any time without hassle.

Microsoften-usresearchfeed's title: Your request has been blocked. This could be due to several reasons.

Is this your feed? Claim it!

Publisher:  Unclaimed!
Message frequency:  0.22 / day

Message History

At a glance MindTopo is a new benchmark for testing topological reasoning in AI, evaluating whether multimodal models can understand concepts such as connectivity, enclosure, order, separation, and knots. The benchmark measures both reasoning and planning, testing not only whether models can recognize topological relationships in static images but also wh...

Read full story

Research Note: CARE-X is a research model and not a Microsoft product offering or medical device. It has not been cleared or approved by any regulatory authority and is not intended for clinical diagnosis, screening, or patient care. The results described below are retrospective research findings and do not establish the safety, effectiveness, or suitability of CARE-X for...


Read full story
At a glance Orchard is an open-source framework for scalable and cost-effective agentic AI research, built around Orchard Env, a reusable environment service for training and evaluating agents across task domains. The same Orchard infrastructure supports software-engineering, web-navigation, and personal-assistant agents, and can train them directly insid...

Read full story
Scaling fidelity over sheer count, targeting the capabilities agents actually lack, and evolving with the models they train. At a glance

We built twelve training worlds for computer-use agents: ten deep domain worlds and two capability worlds, each drilling a single control rendered in many forms (date pickers and nested filters). Depth is what makes th...


Read full story
At a glance Self-supervised. EvoLib enables large language models to learn from their own experience during inference, without requiring ground-truth labels or external feedback. From experience to knowledge. EvoLib transforms past attempts into reusable skills and reflective insights that can be applied to future tasks. ...

Read full story