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Discovery Foundation Models are proposed as general-purpose systems for enabling open-ended scientific discovery through iterative problem formulation, hypothesis testing, and evidence-based revision across dry and wet lab settings. The paper introduces a framework with capabilities like problem discovery and continual improvement, instantiated with systems like Zetema and GALILEO.
Anthropic's MHS preview standardizes device interfaces for lab and manufacturing equipment, reducing integration work but highlighting challenges with AI model control, such as failover issues and the need for deterministic code in fast control steps.
AI accelerates drug discovery by predicting candidates and reducing costs, but success depends on high-quality data and integration with lab systems to close the data loop and validate predictions.
This paper presents an AI agent that integrates large language models with laboratory orchestration software, allowing scientists to create, monitor, and manage automated lab protocols using natural language. Evaluated on three simulated labs, the agent achieves a 97% first-attempt protocol generation success rate and requires far fewer interface actions.
The Institute of Science Tokyo has launched a fully automated medical research laboratory operated entirely by robots, including the Maholo LabDroid humanoid, with plans to scale to 2,000 units by 2040. This initiative aims to automate the full scientific discovery pipeline to address researcher shortages and minimize human error in experimental workflows.
Anthropic and HHMI Janelia Research Campus are previewing the Model Hardware Standard (MHS), a shared specification that enables AI agents to safely operate physical devices in labs and manufacturing, drastically reducing integration time.