Language models can already help researchers search the literature, synthesize evidence, and work through complex questions.

But scientific work places particular demands on these models—answers need to stay grounded in evidence, the models need to preserve what the evidence actually supports rather than quietly broadening a study’s conclusions, and researchers need to be able to verify the final outputs.

We see that in how scientists use Asta , our agentic platform for scientific work.

Instead of simple keyword searches, users often bring substantial context and many constraints—for example, asking Asta to compare approaches across a body of literature while accounting for a particular method, population, or setting.

Many also return to generated reports later, treating them as working research artifacts rather than one-off answers.

We wanted to help scientists generate cited reports faster, with a model they could download and run themselves.

To do that, we tested whether a small, open model trained specifically for scientific report generation could match the report quality of the proprietary models we were using, while reducing generation time and serving costs.

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