PRISM: Protocol Refinement through Intelligent Simulation Modeling
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866909985471987712 |
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| author | Hsu, Brian Setty, Priyanka V Butler, Rory M Lewis, Ryan Stone, Casey Weinberg, Rebecca Brettin, Thomas Stevens, Rick Foster, Ian Ramanathan, Arvind |
| author_facet | Hsu, Brian Setty, Priyanka V Butler, Rory M Lewis, Ryan Stone, Casey Weinberg, Rebecca Brettin, Thomas Stevens, Rick Foster, Ian Ramanathan, Arvind |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_05356 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | PRISM: Protocol Refinement through Intelligent Simulation Modeling Hsu, Brian Setty, Priyanka V Butler, Rory M Lewis, Ryan Stone, Casey Weinberg, Rebecca Brettin, Thomas Stevens, Rick Foster, Ian Ramanathan, Arvind Robotics Artificial Intelligence Multiagent Systems Quantitative Methods 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. |
| title | PRISM: Protocol Refinement through Intelligent Simulation Modeling |
| topic | Robotics Artificial Intelligence Multiagent Systems Quantitative Methods |
| url | https://arxiv.org/abs/2601.05356 |