Physical AI Reference System (PAI-RS) — Specification

Physical AI Reference System (PAI-RS) V1 — Specification defines engineering contracts for describing, realizing, testing and assessing systems within the PAI-RS V1 reference structure. Building on the Canonical Reference Core defined separately in RS01—Task & Physical Context, Intelligent Capability, Capability Formation, Physical Grounding & Engagement, and Realization, with cross-cutting Evidence, Verification & Assurance—the Specification sets out applicable requirements for engineering responsibilities, system representations, interface contracts, claims, evidence and conformance. The document specifies minimum descriptions of tasks, operating conditions, system boundaries, capabilities and Formation provenance; establishes representation and task-relevant projection responsibilities; and describes interface requirements according to declared applicability. For action-capable closed-loop Profiles, it distinguishes execution and safety responsibilities and requires appropriate causal traceability without imposing those action-specific obligations universally on every Physical AI system. Its assurance provisions establish claim-specific evidence packages, a Claim Registry, and verification feedback. Verification levels V1–V6 cover structure, behavior, capability, physical causality, robustness and reproducibility; ten reference test families provide a menu selected according to the Claim and Profile. Conformance statements remain bounded by declared scope and available evidence. This Technical note is the independently versioned Specification for PAI-RS V1. It includes four SVG figures and relies on the separately citable Definition (RS01) for authoritative Core semantics.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-10-15
DOI
https://doi.org/10.5281/zenodo.23278967
Primary Topic
Systems Engineering Methodologies and Applications
Type
article
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article

Physical AI Reference System (PAI-RS) — Specification

Ltd. Zyllion Data Technology (Shanghai) Co., Chen Gang
Zenodo (CERN European Organization for Nuclear Research)
Systems Engineering Methodologies and Applications
article

Physical AI Reference System (PAI-RS) — Specification

Ltd. Zyllion Data Technology (Shanghai) Co., Chen Gang
article en

Abstract

Physical AI Reference System (PAI-RS) V1 — Specification defines engineering contracts for describing, realizing, testing and assessing systems within the PAI-RS V1 reference structure. Building on the Canonical Reference Core defined separately in RS01—Task & Physical Context, Intelligent Capability, Capability Formation, Physical Grounding & Engagement, and Realization, with cross-cutting Evidence, Verification & Assurance—the Specification sets out applicable requirements for engineering responsibilities, system representations, interface contracts, claims, evidence and conformance. The document specifies minimum descriptions of tasks, operating conditions, system boundaries, capabilities and Formation provenance; establishes representation and task-relevant projection responsibilities; and describes interface requirements according to declared applicability. For action-capable closed-loop Profiles, it distinguishes execution and safety responsibilities and requires appropriate causal traceability without imposing those action-specific obligations universally on every Physical AI system. Its assurance provisions establish claim-specific evidence packages, a Claim Registry, and verification feedback. Verification levels V1–V6 cover structure, behavior, capability, physical causality, robustness and reproducibility; ten reference test families provide a menu selected according to the Claim and Profile. Conformance statements remain bounded by declared scope and available evidence. This Technical note is the independently versioned Specification for PAI-RS V1. It includes four SVG figures and relies on the separately citable Definition (RS01) for authoritative Core semantics.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 17%
Systems Engineering Methodologies and Applications
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