Semantic Representation for Agentic Self-Play in Automated Laboratories

The paper introduces VERID, a semantic digital twin for laboratory workcells that bridges AI agent reasoning with the physical realities of wet labs. VERID offers queryable introspection, time‑compressed simulation, trace persistence, and a safe execution framework, enabling agents to detect errors, recover mid‑run, and validate protocols in silico. Three case studies with dye and water mixtures demonstrate VERID’s ability to pre‑emptively catch critical errors, recover from failures, and provide structured multimodal data for downstream analysis and agent training.

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• agents
• reinforcement-learning
• multimodal
• safety