Science and research

AI labs are now releasing research results in bulk with machine-checkable proofs, which shifts the debate from whether AI can do new maths to how credit and checking should work.

It offers DNA-synthesis screeners a way to verify that unfamiliar sequences came from vetted models, a practical biosecurity layer for open protein design.

It shows a frontier lab running end-to-end discovery with its own wet lab, raising both the promise and the biosecurity questions of agentic biology.

It is the first hard test of whether AI-discovered drugs can show efficacy at the scale regulators require for approval.

Free, precomputed variant-effect predictions lower the barrier for clinical genetics and rare-disease research worldwide.

It is the biggest AI maths claim yet and a test of credit, data use and verification norms when frontier labs do research.

Large-scale autoformalisation makes machine-checked verification of major mathematics practical, which also matters for trusting AI-generated proofs.

A benchmark designed to resist AI collapsed within months, and the harness-dependent scores show how much results hinge on evaluation setup.

Olympiad maths is now saturated as an AI benchmark, and Chinese labs reached the top alongside US ones.

It turns the Genesis order into funded work and a shared platform that companies, labs and universities will build on and compete for.

OpenAI called it the first time a prominent open problem central to a field of mathematics was solved autonomously by AI, and Gowers called it a milestone, shifting AI from solving obscure problems to famous ones.

It is among the largest bets yet that AI can shorten drug discovery, though the company still has no drug in human trials.