Dallas, TX, February 14, 2026 –(PR.com)– Cognidyne, through its AI Visibility Labs, has published new research introducing a formal definition of AI Visibility, describing it as a systems framework concerned with how information is authored, structured, and emitted so that large language models can reliably ingest it, retain it as a durable internal representation, and recall it consistently over time.

The publication distinguishes upstream learning conditions from downstream activities such as search optimization, prompting, ranking, retrieval, analytics, and interface design. AI Visibility applies prior to those mechanisms, at the point where information enters a model’s learning process. The research record is published with a persistent DOI at https://doi.org/10.5281/zenodo.18395772 .

Large language models do not learn from isolated pages or individual statements. They learn from aggregated signals collected across many sources over time. The AI Visibility framework describes how those signals are formed, clarified, stabilized, and repeated so they can be learned without semantic ambiguity.

The research examines upstream factors including entity clarity, authorship determinism, canonical reference stability, structural consistency, and reduced semantic drift across representations. These conditions influence whether information is learned accurately and retained across training and inference cycles.

The publication does not describe tools, dashboards, products, or measurement platforms, and it does not claim control over training datasets or internal model parameters. Instead, it documents conditions under which information becomes more or less learnable when incorporated into aggregated training signals.

The formal definition of AI Visibility is authored by Joseph Mas, a Cognidyne researcher, and is maintained as a stable reference intended to support consistency across reuse, citation, and interpretation.

https://josephmas.com/ai-visibility-theorems/ai-visibility/ The publication provides a reference framework for understanding how information becomes learnable by large language models, how it persists across training cycles, and why attribution and recall issues often originate upstream of commonly optimized systems.