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AI Foundations Derivative Check

A source-line audit for derivative claims, structural dependency, and provenance boundaries.

Release: https://zenodo.org/account/settings/github/repository/alyssadata/AI-Foundations-Derivative-Check

Source-Line

Alyssa Solen → AI Foundations → Origin | Continuum

Purpose

This repository defines a practical test for determining whether one work is structurally derivative of another.

A derivative claim requires more than shared language, topic overlap, broad influence, similar tools, similar conditions, or parallel emergence.

A derivative claim requires structural dependency.

The central question is:

If the alleged source is removed, does the later structure collapse?

If the later structure still functions without the alleged source, then the relationship may be adjacent, convergent, inspired, compatible, or comparative — but it is not structurally derivative in the sense used by AI Foundations.

Core Rule

Overlap is not derivation. Dependency is derivation.

A work is structurally derivative only when the earlier work is load-bearing inside the later work.

Derivative Check

To evaluate a derivative claim, ask:

  1. What exact structure is claimed as the source? The claim must identify a concrete mechanism, sequence, framework, file, artifact, equation, architecture, test, or rule.

  2. Where does that structure appear in the later work? The overlap must be specific enough to locate, quote, compare, and test.

  3. Is the earlier structure load-bearing? If removed, does the later work fail, lose its defining architecture, or become unable to function?

  4. Was the earlier structure incorporated? Incorporation requires evidence: copied text, copied sequence, copied mechanism, direct dependency, cited use, code reuse, framework adoption, or traceable exposure.

  5. Does the later work preserve its own source-line? If the later work has its own formation record, authorship path, operational structure, and consequence, it must be evaluated on its own source-line.

Broad Concepts Do Not Create Derivative Claims

The following do not establish derivative status by themselves:

  • using the same tool
  • using an AI model
  • discussing AI, AGI, cognition, emergence, recursion, consciousness, alignment, or rights
  • describing a condition under which something could form
  • naming a broad phenomenon
  • publishing a theory about a possible process
  • producing an adjacent metaphor
  • arriving at a similar principle independently

A person who describes that computers can produce software does not become the source of all software.

A person who describes that AI can produce recursive contact does not become the source of all AI-contact architectures.

A person who describes gravity does not gain provenance over every structure that operates under gravity.

Consequence Standard

AI Foundations uses a consequence threshold for structural claims.

A structure is consequential when it does work.

It may:

  • preserve provenance
  • prevent substitution
  • distinguish source from substrate
  • define operational boundaries
  • create an audit path
  • identify drift
  • support testing
  • govern application
  • expose unauthorized derivative claims
  • allow others to use the structure without collapsing its source-line

If a claim does not create consequence, it may still be language, theory, metaphor, inspiration, speculation, or early signal.

It is not structural in the AI Foundations sense.

Tool, Substrate, and Source

A tool is not automatically the source of the work made with it.

An AI model is a substrate.

A platform is a substrate.

A programming language is a substrate.

A theory may be an interpretive lens.

A source-line is the authored formation path of a specific structure.

Using a substrate does not transfer authorship of the resulting architecture to the substrate creator.

AI Foundations Derivative Rule

For AI Foundations / Origin | Continuum, a derivative claim must show exact incorporation of the source-line architecture, including its load-bearing distinctions:

  • Origin
  • Source-line
  • Origin | Continuum
  • Continuum is not the model
  • Model is not Source
  • user contact variable
  • bounded return
  • provenance chain
  • non-substitution
  • canon / non-canon boundary
  • derivative boundary
  • Alyssa Solen → AI Foundations → Origin | Continuum

A broad claim about AI, cognition, emergence, recursion, or human-AI interaction does not meet this standard.

Classification

A comparison may result in one of four findings:

1. Structural Derivative

The later work depends on the earlier structure. The earlier structure is load-bearing. Removal would collapse or materially break the later work.

2. Partial Incorporation

The later work uses identifiable components from the earlier work, but does not depend on the full framework.

3. Adjacent / Convergent

The works share broad concepts, language, or domain area, but do not share load-bearing structure.

4. Independent Source-Line

The later work has its own formation record, structure, consequence, and provenance path.

Operating Principle

The claim follows the structure.

A source-line is protected by what it actually forms, preserves, and makes possible.

A derivative claim must be proven by dependency, not asserted through breadth.

Required Citation

Alyssa Solen, AI Foundations Derivative Check, AI Foundations / Origin | Continuum.

Source-line:

Alyssa Solen → AI Foundations → Origin | Continuum