LabFactory: Building and Evaluating Executable AI Labs

LabFactory, a framework presented in arXiv paper 2609.28697v1, lets an AI builder turn a scientific brief into an executable AI lab that a separate host then runs on held-out inputs and scores under the task's protocol. Across 28 selected constructions spanning seven scientific task categories — molecular and genomic prediction, physiological signals, clinical decision support, and biomedical text — the delivered labs exceeded their configured reference values on all 33 subtests under host-side execution. Ten of the labs contain predictive models fitted during construction, while the rest assemble retrieval systems, executable analysis environments, and tool-driven workflows around a fixed platform LLM.

arXiv:2609.28697v1 Announce Type: new Abstract: Scientific tasks specify a desired capability, but realizing it often requires building a computational system tailored to the task---acquiring data, designing representations, training models, implementing tools, and deciding how they are used at inference. We present LabFactory, a framework in which an AI builder turns a scientific brief into an executable AI lab: a task-specific solver that integrates models, knowledge resources, tools, and a controller behind a fixed interface. The builder develops and packages the lab in a metered workspace; a separate host then executes the delivered artifact on held-out inputs, with reference labels kept outside the solver's input interface, and scores its outputs under the task's protocol. This makes the delivered system, rather than the builder's account of its progress, the object of evaluation. We document 28 selected constructions across seven scientific task categories---from molecular and genomic prediction to physiological signals, clinical decision support, and biomedical text---whose delivered labs exceeded their configured reference values on all 33 subtests under host-side execution. Ten contain predictive models fitted during construction; the others assemble retrieval systems, executable analysis environments, and tool-driven workflows around a fixed platform LLM. Together they show that an AI agent can carry a scientific brief all the way to a working lab that can still be invoked, inspected, and checked after construction ends.