“标准提案草案（Standard Proposal Draft）”使用的文本。

采用 ISO / IEEE / 政策技术标准的中性语言，而不是论文语言，目标是：可讨论、可修订、可立项。

Draft Standard Proposal

Semantic Emergence Verification for AI Systems

(Working Title)

1. Scope

This proposed standard specifies a model-agnostic, auditable procedure for verifying whether semantic structures produced by AI systems exhibit sufficient stability to be relied upon in institutional, regulatory, or high-stakes decision contexts.

The standard applies to:

• large language models,
• multimodal generative systems producing structured meaning,
• AI-assisted decision-support systems.

It does not address:

• model consciousness,
• internal representations,
• training methods or datasets.

2. Purpose

The purpose of this standard is to provide a public, executable criterion for distinguishing:

• stable semantic structures from
• interaction-dependent or model-specific artifacts.

This enables:

• third-party auditing,
• cross-vendor comparison,
• proportionate governance and risk control.

3. Normative Definitions

Semantic Structure

A finite set of concepts, relations, and constraints that jointly support inference.

Semantic Emergence

A property of a semantic structure that satisfies all three criteria defined in Section 4.

Interaction History

Any dialogue, context accumulation, or implicit priming not explicitly included in the evaluated semantic representation.

4. Normative Criteria (Required)

A semantic structure SHALL be considered emergent if and only if it satisfies all of the following:

4.1 Transferability

The structure remains invariant under admissible transformations of context, domain, or task.

4.2 Compressibility

The structure admits a minimal representation that preserves inferential validity while eliminating reliance on extended interaction history.

4.3 Cross-Model Reproducibility

The structure can be reconstructed from its minimal representation across independently trained AI systems with equivalent structural properties.

Failure to satisfy any single criterion SHALL result in rejection of emergence status.

5. Verification Procedure (Informative but Recommended)

Verification SHOULD be performed using a reproducible protocol that includes:

• a predefined set of models,
• controlled decoding parameters,
• stochastic replicates,
• formal structure extraction,
• equivalence scoring with a fixed acceptance threshold.

All decisions SHALL be logged with explicit diagnostic reason codes.

6. Conformance and Auditability

An AI system or deployment MAY claim conformance to this standard only with respect to specific semantic structures, not global system intelligence.

Conformance claims SHALL be:

• structure-specific,
• time-bounded,
• reproducible by independent auditors.

7. Governance and Risk Use

Regulators and institutions MAY use this standard to:

• gate AI use in high-risk contexts,
• require semantic verification prior to deployment,
• distinguish exploratory AI use from decision-critical reliance.

This standard supports risk-proportionate governance without requiring access to proprietary model internals.

8. Non-Claims (Explicit Exclusions)

This standard does NOT:

• assert machine understanding or agency,
• certify correctness or truth,
• replace domain-specific validation.

It addresses epistemic stability, not ontology.

9. Rationale (Informative)

As fluent language generation becomes ubiquitous, reliance on AI outputs increasingly depends on semantic robustness rather than performance demonstrations. This standard provides a minimal, auditable foundation for such reliance.

10. Status

This document is a working draft intended for:

• standards bodies (ISO / IEC / IEEE),
• AI governance forums,
• public consultation and refinement.

One-Sentence Standard Summary (for policy documents)

A semantic structure may be trusted only if it can survive transfer, compression, and independent reproduction.