PRISM: Protocol Refinement through Intelligent Simulation Modeling

• URL: http://arxiv.org/abs/2601.05356v1
• Date: Thu, 08 Jan 2026 20:15:28 GMT
• Title: PRISM: Protocol Refinement through Intelligent Simulation Modeling
• Authors: Brian Hsu, Priyanka V Setty, Rory M Butler, Ryan Lewis, Casey Stone, Rebecca Weinberg, Thomas Brettin, Rick Stevens, Ian Foster, Arvind Ramanathan,
• Abstract summary: We introduce PRISM, a framework that automates the design, validation, and execution of experimental protocols.<n>PRISM uses a set of language-model-based agents that work together to generate and refine experimental steps.<n>We demonstrate PRISM as a practical end-to-end workflow that bridges language-based protocol generation, simulation-based validation, and automated robotic execution.
• Score: 4.839327116611717
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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