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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...

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...

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. ...

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 code safely and correctly implements standard algorithms, notably for post-quantum cryptography...

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 forecasting. Released as open source on GitHub with model checkpoints on Hugging Face, Aurora 1.5 ...