Signals are classified by deterministic rules and carry provenance-based confidence. Each item links to its original source.
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, te…
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 bel…
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 soft…
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…
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 skil…
How Rust, Lean, Aeneas, and AI agents are helping scale formal verification for production cryptographic algorithms At a glance SymCrypt develops new verified cryptography using Rust, Aeneas, and Lean to provide higher security assurance. We prove that their…
At a glance Aurora 1.5 is a major extension of Microsoft’s Aurora Earth System foundation model that adds 22 more weather variables relevant to energy, agriculture, transport, and climate risk, along with hourly temporal resolution and probabilistic ensemble…