Physical AI Reference Framework (PAI-RF) — Specification

Physical AI Reference Framework (PAI-RF) V1.0 — Specification defines normative requirements for representing, implementing, exchanging, inspecting, and assessing engineering artifacts that claim conformance with PAI-RF V1.0. It operationalizes the five Reference Domains—Scenario, Mechanism, Capability, Behavior, and Component—together with typed Core relations, Cross-Cutting Concerns, and a Claim/Evidence/Assessment Overlay, according to the Core established in the separately published Definition (RF01). The Specification addresses object identity, System-of-Interest boundaries, Conditions and Constraints, Component Interfaces, Definition/Instance relations, Capability and Behavior distinctions, system capability versus AI contribution, cross-domain traceability, and evidence-supported claims. It establishes representation and engineering contracts, rules for Profiles and Extensions, applicability and conformance statements, verification requirements, and a governance process for Core changes. Three cumulative base conformance levels are defined: A (Core structural conformance), B (engineering representation conformance), and C (Claim/Evidence conformance), with optional Profile Qualification handled separately. An exhaustive normative Requirement Registry and a conformance checklist support reviewable assessment. The requirements are technology- and representation-independent. This document specifies the conditions under which an artifact may claim PAI-RF conformance; it does not mandate a particular software, control, robotics, or data architecture. RF02 is released under its own DOI so that normative updates can be managed independently of RF01.

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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.23279101
Primary Topic
Systems Engineering Methodologies and Applications
Type
article
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article

Physical AI Reference Framework (PAI-RF) — 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 Framework (PAI-RF) — Specification

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

Abstract

Physical AI Reference Framework (PAI-RF) V1.0 — Specification defines normative requirements for representing, implementing, exchanging, inspecting, and assessing engineering artifacts that claim conformance with PAI-RF V1.0. It operationalizes the five Reference Domains—Scenario, Mechanism, Capability, Behavior, and Component—together with typed Core relations, Cross-Cutting Concerns, and a Claim/Evidence/Assessment Overlay, according to the Core established in the separately published Definition (RF01). The Specification addresses object identity, System-of-Interest boundaries, Conditions and Constraints, Component Interfaces, Definition/Instance relations, Capability and Behavior distinctions, system capability versus AI contribution, cross-domain traceability, and evidence-supported claims. It establishes representation and engineering contracts, rules for Profiles and Extensions, applicability and conformance statements, verification requirements, and a governance process for Core changes. Three cumulative base conformance levels are defined: A (Core structural conformance), B (engineering representation conformance), and C (Claim/Evidence conformance), with optional Profile Qualification handled separately. An exhaustive normative Requirement Registry and a conformance checklist support reviewable assessment. The requirements are technology- and representation-independent. This document specifies the conditions under which an artifact may claim PAI-RF conformance; it does not mandate a particular software, control, robotics, or data architecture. RF02 is released under its own DOI so that normative updates can be managed independently of RF01.

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