PRISM: Protocol Refinement through Intelligent Simulation Modeling
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Abstract (arXiv)
Automating experimental protocol design and execution remains as a fundamental bottleneck in realizing self-driving laboratories. We introduce PRISM (Protocol Refinement through Intelligent Simulation Modeling), a framework that automates the design, validation, and execution of experimental protocols on a laboratory platform composed of off-the-shelf robotic instruments. PRISM uses a set of language-model-based agents that work together to generate and refine experimental steps. The process begins with automatically gathering relevant procedures from web-based sources describing experimental workflows. These are converted into structured experimental steps (e.g., liquid handling steps, deck layout and other related operations) through a planning, critique, and validation loop. The finalized steps are translated into the Argonne MADSci protocol format, which provides a unified interface for coordinating multiple robotic instruments (Opentrons OT-2 liquid handler, PF400 arm, Azenta plate sealer and peeler) without requiring human intervention between steps. To evaluate protocol-generation performance, we benchmarked both single reasoning models and multi-agent workflow across constrained and open-ended prompting paradigms. The resulting protocols were validated in a digital-twin environment built in NVIDIA Omniverse to detect physical or sequencing errors before execution. Using Luna qPCR amplification and Cell Painting as case studies, we demonstrate PRISM as a practical end-to-end workflow that bridges language-based protocol generation, simulation-based validation, and automated robotic execution.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 2 of its 23 references that are in the map. First posted on arXiv 8 Jan 2026.
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- 2025Hierarchically Encapsulated Representation for Protocol Design in Self-Driving Labs
- 2026Agentic AI for Self-Driving Laboratories in Soft Matter: Taxonomy, Benchmarks,and Open Challenges
- 2026NIMO Controller: a self-driving laboratory orchestrator based on the Model Context Protocol
- 2026LabVLA: Grounding Vision-Language-Action Models in Scientific Laboratories
- 2026LAP: An Agent-to-Instrument Protocol for Autonomous Science
- 2026SciHorizon-eLab: An Agentic Protocol-to-Task Compiler for Scalable Benchmarking of Scientific Embodied Agents
- 2026Stress-testing large language model agents in a robotic chemistry laboratory
- 2026Safe-SDL:Establishing Safety Boundaries and Control Mechanisms for AI-Driven Self-Driving Laboratories