Biology doesn't operate in silos, and neither should the AI representation of it. Quine is an early-stage research effort to create a multimodal world model of biology. By connecting insights across biological scales and modalities, Quine helps scientists computationally search a space far larger than intuition allows and prioritize hypotheses before they reach the lab. Experimental results provide important feedback, helping researchers sharpen future research directions. The post Introducing Quine: An AI research system designed for the complexity of biology appeared first on Microsoft Research.
Read the original at Microsoft Research: Introducing Quine: An AI research system designed for the complexity of biology
How OpenSmartRoute helps
A team routing biology research through OpenSmartRoute gains immediate access to a multimodal world model that connects insights across scales. The system allows scientists to computationally search spaces far larger than intuition allows without building the infrastructure from scratch.
The router scores every candidate model on quality, cost, speed, and safety using weights the team sets per request. Hard rules ensure text with personal data stays on an on-premises model, preventing data leaks before they happen.
A new Quine model becomes one catalogue entry and competes on the next request without changing the app. The savings ledger shows exactly what each routed request cost compared to the most expensive model. Input guards spot prompt injection and personal data before a request leaves the network.
The team uses 'osr eval' to measure routing accuracy on their own prompts and can fail a build if it drops. This setup lets researchers prioritize hypotheses before they reach the lab while keeping costs predictable.