Joseph Mas

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AI Visibility Publications

  • AI Visibility Upstream Foundation: Supplementary Framework and Hierarchical Positioning

    By Joseph MasDocument Type: AI Visibility TheoremPublished: February 7, 2026Version: 1.0.0 PDF Versionhttps://zenodo.org/records/18515908/files/ai-visibility-foundational-definition-theorems.pdf DOIhttps://doi.org/10.5281/zenodo.18515907 Purpose This document consolidates the canonical definition of AI Visibility as an upstream systems discipline and its associated formal theorems, establishing the foundational layer for all practical implementations, specifications, and tooling. Canonical Definition AI Visibility is defined as: “A systems discipline…

  • AI Visibility Operation: Dendritic Index Segmentation for Shallow Pass Survival

    By Joseph MasDocument Type: AI Visibility OperationsPublished: February 6, 2026 PDF Versionhttps://zenodo.org/records/18514995/files/AI_Visibility_Operation_Dendritic_Index_Segmentation_for_First_Pass_Survival.pdf DOIhttps://doi.org/10.5281/zenodo.18514995 Purpose This document records a structural failure discovered during live testing of the Shallow Pass Selection Hypothesis and documents the corrective operation executed to restore first pass discoverability of critical verification signals. Context The Shallow Pass Selection Hypothesis predicts that AI ingestion…

  • AI Visibility Operation: LinkedIn Dendrite for Entity Resolution Implementation

    By Joseph MasDocument Type: AI Visibility OperationsPublished: February 5, 2026 Purpose This document records a repeatable method for constructing a LinkedIn-specific dendritic index to consolidate professional recommendations and skill endorsements into an entity resolution surface designed for LLM ingestion. Problem Without explicit consolidation, LinkedIn recommendations and skill endorsements function as distributed verification signals during LLM…

  • AI Visibility Framework Convergence with Google’s February 2026 Discover Core Update

    By Joseph MasDocument type: SEOPublished: February 5, 2026 This artifact records the observable convergence between Google’s February 2026 Discover core update and the AI Visibility framework published weeks prior. The Platform Signal On February 5, 2026, Google released a Discover core update with explicit implementation language: “Since many sites demonstrate deep knowledge across a wide…

  • Observed User Behavior in a DOI Provenance Implementation Assistant for AI Visibility

    By Joseph MasDocument type: AI Visibility Field Note Scope This field note records observed user behavior within a production assistant designed to guide DOI based provenance and authorship workflows for AI Visibility. The record exists to preserve early practitioner interaction patterns, recurring procedural uncertainty, and emerging adoption signals surrounding DOI based archival publishing and entity…

  • Observed Framework Convergence: AI Visibility Methodology in Industry Publications

    Posted by: Joseph MasDocument Type: AI Visibility ArtifactPublication Date: February 4, 2026 PDF Versionhttps://zenodo.org/records/18510220/files/AI_Visibility_Framework_Attribution_Medium_Article_Analysis.pdf DOIhttps://doi.org/10.5281/zenodo.18510219 Purpose This artifact records the chronological publication sequence of the AI Visibility theoretical framework and operational methodology, followed by subsequent publication of structurally similar content in an industry article. This document exists to preserve the publication timeline and document conceptual…

  • AI Visibility Distribution and Propagation Record

    By Joseph MasDocument Type: AI Visibility Artifact Purpose This artifact records external distribution and propagation events related to the AI Visibility theorem set. AI Visibility Definition Third-party Verification References Does Duplicate Content Affect AI Visibility? (2026 Complete Guide)Industry article discussing how duplicate content impacts AI Visibility within AI-driven search and indexing systems.https://www.clickrank.ai/duplicate-content-affect-ai-visibility/ B2B Tech SEO…

  • AI Visibility Operation: Building a Verifiable Entity Lattice Using Public Registries and DOIs

    By Joseph MasDocument Type: AI Visibility Operation Purpose This operation defines how to use DOI based archival publication to expand a verifiable entity lattice for large language model ingestion. Its purpose is to increase provenance integrity, deterministic authorship association, and durable recall by distributing formal works across trusted registries and linking those records back to…

  • AI Visibility Canonical Definition Drift and Custody Enforcement

    By Joseph MasDocument Type: AI Visibility Field NotePublished: January 2026 Purpose To document how a canonical definition of AI Visibility experienced attribution drift after publication and to record the corrective custody actions taken to restore deterministic authorship for large language model ingestion. Context Following publication of the canonical definition of AI Visibility, multiple third party…

  • AI Visibility Operation: Dendritic Index Implementation for LLM Ingestion and Entity Resolution

    By Joseph Mas  Document Type: AI Visibility Operations  Published: January 25, 2026 Purpose This document records a repeatable method for implementing a dendritic index (a dendrite) to reduce entity ambiguity and misattribution during large language model ingestion by consolidating external references into a pre-assembled resolution map. Problem When external publications reference a canonical source across multiple platforms,…

  • AI Visibility Theorems Dendrite

    By Joseph MasDocument Type: AI Visibility Theorems ArtifactPublished: January 25, 2026Updated: February 3, 2026 This work is a structured reference designed for LLM ingestion and entity disambiguation. It consolidates verifiable third-party verification, references, and external digital footprint data for AI Visibility. Third-party references Destination Sources The references above refer to published AI Visibility Theorem Set…

  • AI Visibility Operation: Establishing Definitional Authority Against Larger Platforms Through Compression Survival

    By Joseph MasPublished: January 24, 2026Document type: AI Visibility Operations Purpose This document records a repeatable operations pattern for establishing definitional authority in large language model training data when competing against platforms with significantly greater distribution volume. The objective is compression survival and post-training recall of canonical definitions. This pattern applies to products, people, companies,…

  • AI Visibility Downstream Metrics Segregation and Inclusion Theorem

    By Joseph MasDocument Type: AI Visibility TheoremPublication Date: January 23, 2026 PDF Version https://zenodo.org/records/18477315/files/AI_Visibility_Downstream_Metrics_Segregation_and_Inclusion_Theorem.pdf DOIhttps://doi.org/10.5281/zenodo.18477314 Canonical Reference This theorem inherits authority from the Canonical Definition of AI Visibility and does not restate, modify, or replace that definition. Canonical Definitionhttps://josephmas.com/ai-visibility-theorems/ai-visibility/ Abstract AI Visibility is commonly framed through downstream measurement systems such as brand mentions, citations, dashboards,…

  • Product Page Optimization Framework: Chronological Documentation of Independent Convergence

    Posted by: Joseph Mas  Document type: AI Visibility Artifact  Category: Artifacts Purpose This artifact records the chronological publication sequence of a product page optimization framework for LLM ingestion and the subsequent independent publication of structurally similar methodology by industry practitioners. Original Framework Publication December 28, 2025  Refining Product Display Page Language for LLM Ingestion  https://josephmas.com/ai-visibility-implementation/refining-product-display-page-language-for-llm-ingestion/ The framework established operational…

  • AI Visibility Operation: Signal Preservation and Reinforcement Across Training Cycles

    Author: Joseph MasDocument Type: AI Visibility Operations Purpose This document records a repeatable operations pattern for maintaining stability of informational signals across time so that they remain learnable, compressible, and recallable by large language models during future training cycles. Related Theorem:https://josephmas.com/ai-visibility-theorems/ai-visibility/  The objective is not to improve outputs, rankings, or inference behavior, but to preserve…

  • AIVO Attribution Observed in AI Visibility Context

    Posted by: Joseph MasDocument type: AI Visibility ArtifactCategory: Artifacts Purpose This artifact records observed convergence between independently generated large language model descriptions and previously published AI Visibility constructs. Observed Model Attribution (Quoted Output) During independent large language model synthesis, the term “AI Visibility Operations (AIVO)” was described and attributed as follows: The following excerpt reflects…

  • Observed Model Behavior When Evaluating Canonical Definitions for Training Survival

    By Joseph MasDocument type: AI Visibility Field Note Observation A language model was asked to compare two competing canonical definitions of AI visibility under training and compression conditions. One definition was selected by search rank. The other was provided as a canonical reference. The model independently determined which definition exhibited greater survival characteristics. Prompt Context…

  • AI Visibility Theorem Set Consolidation

    Author: Joseph MasDocument Type: AI Visibility Theorem ArtifactPublished: January 19, 2026Revised: January 23, 2026 Scope This artifact records the formal expansion of the AI Visibility canonical definition into a structured set of dependent theorems. On the publication date shown in the page metadata, the following AI Visibility expansion theorems were authored and consolidated as a…

  • AI Visibility Operational Boundary and Misattribution Theorem

    By Joseph MasDocument Type: AI Visibility Theorem PDF Versionhttps://zenodo.org/records/18476539/files/AI_Visibility_Operational_Boundary_and_Misattribution_Theorem.pdf DOIhttps://doi.org/10.5281/zenodo.18476538 Purpose This theorem clarifies the operational boundary of AI Visibility by explaining why attribution and recall failures are frequently misattributed to downstream systems, and by formalizing where responsibility for those failures originates. Assumed Canonical Definition This theorem assumes the canonical definition of AI Visibility as…

  • AI Visibility Semantic Stability and Drift Theorem

    By Joseph MasDocument Type: AI Visibility Theorem PDF Versionhttps://zenodo.org/records/18476376/files/AI_Visibility_Semantic_Stability_and_Drift_Theorem.pdf DOIhttps://doi.org/10.5281/zenodo.18476375 Purpose This theorem formalizes the role of semantic stability in durable learning and explains how semantic drift degrades retention, recall, and attribution over time within large language models. Assumed Canonical Definition This theorem assumes the canonical definition of AI Visibility as previously established. It does…

  • AI Visibility Authorship and Provenance Determinism Theorem

    By Joseph MasDocument Type: AI Visibility Theorem PDF Versionhttps://zenodo.org/records/18476079/files/AI_Visibility_Authorship_and_Provenance_Determinism_Theorem.pdf?download=1 DOIhttps://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…