The Integrated BiologyEnvironment:A New Way Biologists Work
Scientific Trace: Making Scientific Judgments Traceable in a Self-Evolving Laboratory
Research agents can already search the literature, call tools, and generate research recommendations. Yet when presented with a specific conclusion, biologists still need to ask: What inputs and conditions underpin it? Which calculations or experiments have validated it? The answers determine whether a recommendation is ready for the next experiment and whether unexpected results can be traced back to the steps that need to be reexamined. This article introduces SE-Fab's Scientific Trace, a feature that helps biologists and agents trace execution evidence and review key judgments, making scientific judgments traceable in a self-evolving laboratory.
Hardware MCP: A Hardware Standard for Self-Evolving Laboratories
Large language models can already read papers, write code, and design experiments. Yet in most laboratories they remain advisers that can talk but cannot act, because instruments do not speak their language. This article introduces Hardware MCP, a hardware standard that enables agents to discover devices, understand their operational boundaries, and continuously accumulate device knowledge through real experiments.
SE-Fab: Self-Evolving Foundry for the Embodied AI Scientist
SE-Fab is an AI4S infrastructure concept for an autonomous enzyme-evolution laboratory: it turns research intent into schedulable, executable, traceable work, then uses real experimental feedback to generate a better next round.
Plant Editing Self-Evolving Laboratory: Rewriting the Timeline of Breeding
Plant breeding is entering a new phase: an AI-agent plant genome-editing laboratory combines automated experimentation with a continuously evolving dry–wet loop, challenging the traditional ten-year wait for a breeding result.
From Protocol to Code: An Experimental Coding Workstation Building Automation Assets 24/7
An AI-agent experimental coding workstation moves protocol-to-code work upstream: it retrieves public protocols, organizes SOPs, adds local device constraints, writes device code, and keeps validating and revising—so every run leaves behind reusable laboratory automation assets.