GXL: Generative Expert Labs

GXL is an applied research lab building AI systems for scientific innovation. Scientific progress is accelerating, but the way we do science has not kept pace. Knowledge is expanding faster than any individual, or any single lab, can read, verify, and build on. The bottleneck is no longer ideas, compute, or data. It is attention, iteration speed, and the ability to turn messy research into reliable action. That's why we're building AI systems that work at the depth science requires, capture and extend human expertise, and preserve trust through transparent, verifiable processes.

GXL spun out of the Stanford ecosystem, with roots in the James Zou group. Projects like Virtual Lab and Paper2Agent explored how to turn scientific artifacts into systems that can be used, not just read. We build AI that captures and amplifies the expertise and practices of great researchers, how they read, reason, test, debug, and communicate. Our goal is to help more people do high-quality scientific work with less friction.

What guides our work

Curation over Summarization

There are more papers, repos, benchmarks, and must-read threads than any human team can track. Even within narrow subfields, it is easy to miss something critical. We are building systems that turn overload into leverage, by helping researchers find what matters and ignore what does not.

Traceability and Transparency

Traceability is essential for trust in scientific work. We are building systems where every claim can be traced back to its source, and where performance is measured with clear, repeatable evaluations. Our goal is to make verification fast and routine, so researchers can catch errors early and avoid chasing false leads.

Deep work requires deeper tools

Many research tools today are strong at breadth. They gather sources, paraphrase abstracts, and produce confident syntheses. But, that’s not where the real work happens. To be effective, AI needs to understand methods, assumptions, edge cases, dataset quirks, statistical choices, and reproducibility gaps. We are focused on systems that help teams interrogate those details and reduce avoidable mistakes.

Process as the substrate

Most tools focus on final deliverables: the published result. But scientific progress depends on understanding the workflow that led there. We think the next substrate for science will preserve more of the research process, not just the results.

Expertise should scale

Frontier models are powerful, but much of the world's scientific expertise lives outside model weights. It lives in tacit practices, decision heuristics, domain instincts, and the hard-won taste that comes from years of work. Our ambition is to help capture that expertise and make it widely usable, not by replacing experts, but by encoding what scientists know into systems that can extend their reach.

Our long-term vision

We are building a system where multiple specialized agents collaborate to generate and solve core scientific questions to create an abundant therapeutic future.

To join us on this journey, follow us on X and LinkedIn for updates or reach out at learn@gxl.ai.

More soon.