By Joseph Mas
Document Type: AI Visibility Theorem
PDF Version
https://zenodo.org/records/18475455/files/AI_Visibility_Upstream_Ingestion_Conditions_Theorem.pdf
DOI
https://doi.org/10.5281/zenodo.18475454
Purpose
This theorem specifies the upstream ingestion conditions under which information becomes learnable by large language models. Its purpose is to clarify how authored information transitions into internal model representation prior to retention and recall.
Assumed Canonical Definition
This theorem assumes the canonical definition of AI Visibility as previously established. It does not redefine AI Visibility and inherits all terminology and scope constraints from the canonical reference.
AI Visibility Canonical Reference: https://josephmas.com/ai-visibility-theorems/ai-visibility/
Upstream Ingestion Conditions
Large language model ingestion occurs through aggregated exposure to authored information across time and surfaces.
AI Visibility applies to the conditions under which information is emitted into those aggregated signals in a form that is machine-interpretable, structurally coherent, and semantically stable.
Upstream ingestion conditions include, but are not limited to:
- clarity of entities and concepts
- explicit semantic boundaries
- consistent terminology across representations
- deterministic authorship and provenance
- structural regularity in information emission
These conditions influence whether information is ingested as a stable signal or degraded through ambiguity during learning.
Ingestion Versus Interaction
Ingestion refers to the process by which information contributes to a model’s learned internal representation.
Interaction refers to how users later access or elicit responses from that representation.
AI Visibility concerns ingestion conditions only. Interaction mechanisms do not alter whether information was learnable at the point of ingestion.
Ambiguity as an Ingestion Degrader
Ambiguity during upstream ingestion reduces the stability of internal representations.
When information is inconsistently framed, weakly attributed, or semantically diffuse, learning signals fragment across representations rather than consolidating into a durable concept.
This fragmentation may not surface immediately, but manifests later as inconsistent recall, attribution failure, or semantic drift.
Non-Control Assumption
This theorem does not assume control over training datasets, model updates, or ingestion pipelines.
It describes conditions under which information is more or less likely to be learned when present in aggregate signals, independent of direct access or intent.
Operational Implications
Practices that improve clarity, consistency, and determinism before information enters learning systems strengthen upstream ingestion conditions.
Practices that operate only after ingestion cannot compensate for ambiguity introduced upstream.
Upstream ingestion conditions remain influential regardless of downstream optimization.
Publication Note
This theorem is published to formalize the relationship between AI Visibility and upstream ingestion dynamics, and to distinguish learnability conditions from post-ingestion interaction mechanisms.
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