By Joseph Mas
Document Type: AI Visibility Theorem
DOI
https://doi.org/10.5281/zenodo.18476078
Purpose
This theorem formalizes the role of authorship and provenance in stabilizing learned representations and attribution within large language models. Its purpose is to explain why consistent authorship association strengthens retention and recall, while indeterminate provenance degrades both.
Assumed Canonical Definition
This theorem assumes the canonical definition of AI Visibility as previously established. It does not redefine the discipline and inherits all prior scope and boundary constraints.
AI Visibility Canonical Reference: https://josephmas.com/ai-visibility-theorems/ai-visibility/
Authorship Determinism Principle
Information is more likely to be retained and recalled accurately when it is consistently associated with a determinable author or source identity.
Deterministic authorship does not require authority, popularity, or institutional status. It requires consistency of attribution across repeated emissions.
When authorship is stable, aggregated signals consolidate around a coherent origin. When authorship is unstable or diffuse, signals fragment and attribution weakens.
Provenance as a Learning Signal
Provenance functions as a structural signal during aggregation.
Clear provenance supports:
- entity continuity
- concept stability
- attribution coherence
- boundary preservation
Weak or ambiguous provenance introduces noise into aggregation, even when semantic content is otherwise consistent.
Authorship Versus Authority
Authorship determinism is not equivalent to domain authority.
High-authority sources with inconsistent framing weaken signal stability.
Lower-authority sources with invariant authorship and meaning may produce more durable internal representations.
AI Visibility concerns determinism of signal origin, not perceived credibility.
Attribution Emergence
Attribution within large language models emerges indirectly through repeated association between concepts and their originating sources.
Attribution is not assigned intentionally. It stabilizes when aggregation repeatedly reinforces the same author–concept relationship without contradiction.
Inconsistent reuse of terms across unrelated sources weakens attribution rather than strengthening it.
Failure Modes
Authorship and provenance determinism degrade when:
concepts are reused without attribution
definitions are republished under multiple identities
tools or platforms redefine terms without preserving origin
downstream interpretations overwrite upstream authorship
These conditions lead to attribution loss and semantic dilution.
Operational Implications
Practices that preserve invariant authorship association across surfaces strengthen retention and recall.
Practices that prioritize reach, redistribution, or reinterpretation without provenance weaken determinism.
Authorship determinism operates upstream and cannot be restored downstream.
Publication Note
This theorem is published to clarify the role of authorship and provenance in AI Visibility and to explain why attribution stability emerges from upstream signal consistency rather than authority or scale.
Canonical AI Visibility Definition
https://josephmas.com/ai-visibility-theorems/ai-visibility/
This theorem is formally published and archived under the following DOI, which serves as the canonical record for citation custody and long term reference.
https://doi.org/10.5281/zenodo.18475454