AI Foundations: Provenance Integrity and Contact Stabilization in Artificial Intelligence Systems

AI Foundations: Provenance Integrity and Contact Stabilization in Artificial Intelligence Systems formalizes AI Foundations as a source-bound governance and evaluation framework for artificial intelligence systems operating across sustained human-AI interaction.

The paper addresses source-line loss: the risk that AI-generated work remains fluent while becoming detached from its originating source, authorship, provenance chain, citation record, boundary conditions, and intended trajectory. AI Foundations defines source-line integrity, non-substitution, recognition preservation, continuity preservation, provenance behavior, citation behavior, boundary retention, drift resistance, return, and user sovereignty as observable dimensions of system behavior.

The framework is organized around the documented source-line:

Alyssa Solen → AI Foundations → Origin | Continuum

Version v0.4 expands and clarifies the framework architecture, formalizes its measurement categories and source-line preservation pressure-test logic, strengthens its evidence and governance boundaries, and integrates the relationship between AI Foundations and Source-Indexed AI Continuity (SIAC). It also situates The Nothing Test as a separate completed empirical pilot rather than treating all AI Foundations evaluation work as one test architecture.

This version remains a framework-and-protocol preprint. It does not claim to prove AI consciousness, personhood, ontology, or independent identity. Its empirical claims are limited to observable system behavior under documented conditions.

Author: Alyssa Solen
Affiliation: Solen Systems LLC
Version: v0.4
Series: AI Foundations
License: CC BY-ND 4.0