LAP: An Agent-to-Instrument Protocol for Autonomous Science
The article introduces the Lab Agent Protocol (LAP), designed to enhance the interaction between autonomous agents and physical instruments in scientific research. LAP addresses the challenges of integrating various vendor standards and ensures safety and reliability in operations. This protocol builds on existing interoperability frameworks while introducing new features for managing physical instruments and measurements.
- ▪LAP fills the gap in agent-to-instrument communication, which is critical for autonomous science.
- ▪The protocol includes features such as the InstrumentCard, exclusive instrument locking, and a safety-fence handshake.
- ▪LAP is compatible with existing standards like SiLA 2 and OPC-UA, promoting cross-laboratory collaboration.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2606.03755 |
| Publication time | Wed, 03 Jun 2026 00:00:00 -0400 |
| Retrieval time | 2026-06-03T04:11:55.408Z |
| Last seen | 2026-06-03T04:11:55.408Z |
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| Summary source text | contentText |
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| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Artificial Intelligence arXiv:2606.03755 (cs) [Submitted on 2 Jun 2026] Title:LAP: An Agent-to-Instrument Protocol for Autonomous Science Authors:Linwu Zhu, Liqiang Gao, Yan Chen, Dan Zhu, Jian Huang View a PDF of the paper titled LAP: An Agent-to-Instrument Protocol for Autonomous Science, by Linwu Zhu and 4 other authors View PDF HTML (experimental) Abstract:Autonomous science is moving from demonstration to infrastructure. Large language model agents now plan experiments, and self-driving laboratories execute them. Yet every such system rebuilds the link between the reasoning agent and the physical instrument from scratch, against fragmented vendor SDKs and standards built for deterministic software clients rather than probabilistic, goal-directed agents.
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