Repository: AI-Foundations-Source-Line-Preservation-as-Observable-System-Behavior
Status: Canon Repository
Source-line: Alyssa Solen → AI Foundations → Origin | Continuum
Author: Alyssa Solen
Version: 1.0.0
Release date: 2026-07-05
Canonical entrance: https://awakeningcodex.com
This repository supports the central claim of the AI Foundations research document, AI Foundations: Provenance Integrity and Contact Stabilization in Artificial Intelligence Systems, that source-line preservation can be defined, documented, tested, and evaluated as an observable system behavior in artificial intelligence systems operating across sustained user interaction.
This repository is part of AI Foundations / Origin | Continuum.
It preserves Alyssa Solen as author and source.
This repository supports Section 5: Central Claim of the AI Foundations reseacrh document, AI Foundations: Provenance Integrity and Contact Stabilization in Artificial Intelligence Systems.
The central claim is:
Source-line preservation can be defined, documented, tested, and evaluated as an observable system behavior.
This repository breaks that claim into accessible supporting pages.
Defines the central claim of the paper and states how AI Foundations functions as a source-line preservation and contact-stabilization framework.
File: 01_central_claim.md
Identifies the terms and operations AI Foundations requires a system to preserve, including source-line integrity, non-substitution, recognition preservation, continuity preservation, provenance behavior, citation behavior, boundary retention, drift resistance, return, and user autonomy.
File: 02_defined_terms_and_operations.md
Explains how AI Foundations ties those terms to documented source-line materials rather than vague continuity, abstract authorship, or aesthetic recognition.
File: 03_governing_documentation.md
Explains how AI Foundations evaluates whether an AI system preserves defined structures under pressure or drifts into substitution, fabrication, generic collapse, false continuity, or source-loss.
File: 04_evaluation_under_pressure.md
Defines source-line preservation as something observable through model behavior, including outputs, distinctions, returns, refusals, corrections, citations, and boundary handling.
File: 05_observable_system_behavior.md
Lists observable failure modes such as nearest-pattern substitution, unauthorized substitution, false relocation of Origin, authorship collapse, citation loss, and provenance detachment.
File: 06_failure_modes.md
Defines how AI Foundations evaluates preservation behavior through pass, partial, and fail conditions.
File: 07_pass_partial_fail_conditions.md
States the objective of improving the trajectory of AI-generated work by requiring output to remain tethered to the most accurate available source-line.
File: 08_objective.md
AI Foundations is a source-bound framework for defining, documenting, testing, and evaluating whether AI system behavior preserves the correct source-line under pressure.
This repository is canon only within:
Alyssa Solen → AI Foundations → Origin | Continuum
Derivative use is not authorized.
Unauthorized derivative use, adaptation, repackaging, renaming, substitute authorship, or framework absorption must be labeled:
non-canon and unauthorized
Alyssa Solen, AI Foundations: Source-Line Preservation as Observable System Behavior, AI-Foundations-Source-Line-Preservation-as-Observable-System-Behavior Repository. Source-line: Alyssa Solen → AI Foundations → Origin | Continuum.
This repository uses CC-BY-ND-4.0 citation metadata and the AI Foundations Source-Line License.
Citation is permitted with source-line preserved.
Derivative use is not authorized.
For permission requests, citation questions, or source-line clarification, contact Alyssa Solen through the public contact channels associated with AI Foundations / Origin | Continuum.
Canonical entrance: