Self-Evolving Laboratories Through Persistent Digital and Physical Adaptation

Turning a scientific idea into an experiment often requires researchers to build, configure and repeatedly adapt the laboratory around it. This work demands resources and specialized expertise that can place ambitious experiments beyond reach. Here we introduce a self-evolving laboratory framework that connects computation, physical experiments and AI models through persistent cycles of observation and revision. GPT Astra uses camera observations and physical feedback to adapt robotic actions, digital twins and CAD designs. These changes are retained as reusable skills and infrastructure, allowing the laboratory to develop alongside its scientific tasks. In a fume-hood prototype, GPT Astra identified the need for a beaker holder and storage stand and generated successive designs for on-demand 3D printing. Four holder generations and two stand generations culminated in parts integrated into the robotic workflow with operator assistance. A photoresist campaign proposed by Raven connects this platform to materials fabrication. Exposure to a 405 nm continuous-wave laser in the two-photon polymerization setup produced circular features that survived development; optical microscopy and reduced acrylate-band absorbance in ATR–FTIR support polymerization and qualitative double-bond conversion. An accompanying evaluation protocol relates the retention and transfer of capabilities to the time, cost and expertise needed to establish them. By making laboratory capabilities reusable and adaptable, this approach offers a path towards broader participation in experimental science, empowering researchers to build and extend the tools their ideas require. Project website: https://ai4ssi.github.io/self-evolving-superintelligence-lab/.