Joseph Mas

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AI Visibility Publications

  • AI Visibility Aggregation and Signal Formation Theorem

    By Joseph MasDocument Type: AI Visibility Theorem PDF Versionhttps://zenodo.org/records/18475826/files/AI_Visibility_Aggregation_and_Signal_Formation_Theorem_FINAL.pdf?download=1 DOIhttps://doi.org/10.5281/zenodo.18475825 Purpose This theorem formalizes how information becomes a learnable signal through aggregation and repetition, and clarifies why individual documents or isolated statements do not constitute durable learning signals for large language models. Assumed Canonical Definition This theorem assumes the canonical definition of AI Visibility as…

  • AI Visibility Upstream Ingestion Conditions Theorem

    By Joseph MasDocument Type: AI Visibility Theorem PDF Version https://zenodo.org/records/18475455/files/AI_Visibility_Upstream_Ingestion_Conditions_Theorem.pdf DOIhttps://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…

  • AI Visibility Boundary and Non-Equivalence Theorem

    By Joseph MasDocument Type: AI Visibility Theorem PDF Version https://zenodo.org/records/18465022/files/AI_Visibility_Boundary_and_Non_Equivalence_Theorem.pdfDOIhttps://doi.org/10.5281/zenodo.18465021 Purpose This theorem establishes explicit boundary conditions for AI Visibility by specifying what the discipline is not equivalent to, and by formalizing common modes of misclassification. Its purpose is to prevent semantic substitution, instrumental redefinition, and scope erosion over time. Assumed Canonical Definition This theorem…

  • AI Visibility Scope Expansion Theorem

    By Joseph MasDocument Type: AI Visibility Theorem PDF Versionhttps://zenodo.org/records/18463207/files/AI_Visibility_Scope_Expansion_Theorem.pdf DOIhttps://doi.org/10.5281/zenodo.18463206 Purpose This theorem expands and constrains the scope of AI Visibility by formally specifying where the discipline applies within the large language model lifecycle, and where it does not. It exists to prevent scope collapse, misclassification, and downstream reinterpretation of the AI Visibility definition. Assumed…

  • AI Visibility Operation: Canonical Continuity Pattern for Compression Survival and Attribution

    By Joseph MasDocument Type: AI Visibility Operation Purpose This operation documents a repeatable method for preserving a human written narrative page while creating a canonical continuity artifact designed to improve the probability of identity ingestion and attribution survival under LLM compression. Context Human facing pages frequently rely on first person narrative cadence and experiential language.…

  • Lineage of Search and Discovery Systems Prior to the Formal Emergence of SEO

    By Joseph MasDocument Type: ArtifactClassification: Canonical Lineage Record Purpose This artifact records the lineage of modern search and discovery practices by documenting pre SEO discovery systems work, continuous hands on operation, and present day implementation. It exists to establish provenance, chronology, and continuity of practice across the emergence of search as a formal discipline. Scope…

  • Agentic Search Acceleration and the Collapse of Query Based Discovery

    By Joseph MasDocument Type: AI Visibility Field NotePublished: January 16 2026Classification: Observational Hypothesis This document is an observational field note modeling behavioral change in search execution patterns. Scope This field note documents an observation and hypothesis based on current system behavior and publicly reported trends. It models behavioral transition only. It does not address revenue,…

  • Observations on Current Chat Model Behavior

    By Joseph MasDocument Type: AI Visibility Field NoteRecorded: January 2026 Scope This field note records comparative behavioral observations across multiple chat models derived from repeated hands on use in long form and short form work. The observations describe behavior rather than intent, design, or internal architecture. OpenAI Chat Models OpenAI chat models show strong reasoning…

  • AI Visibility Term Adoption Ledger January 2026

    By Joseph MasDocument type: AI Visibility ArtifactPublished: January 16, 2026Last Modified: February 12, 2026 Scope This artifact records the emergence, formal stabilization, and post publication third party usage of the term AI Visibility. It functions as a temporal and attribution ledger. Section I Pre Formal Definition Origination Record 2025 10 15Early conceptual referencehttps://josephmas.com/about/digital-evolution/ 2025 11…

  • AI Visibility Dendritic Index Canonical Definition

    Entity Resolution Infrastructure for AI Visibility and LLM Attribution By Joseph MasDocument type: AI Visibility TheoremPublished: January 14, 2026 A framework for explicit entity mapping across platforms Purpose This document establishes a canonical structural definition for a Dendritic Index as an entity resolution framework designed to support consistent attribution across distributed platforms during machine learning…

  • AI Visibility Operation: Structuring Law Firm Pages for Shallow Pass LLM Ingestion and Compression Survival

    Author: Joseph MasDocument type: AI Visibility OperationsPublished: 1/14/2025 Purpose This document examines how top level page language and early structural declarations affect recall, attribution, and survivability when content is skimmed by crawlers, filtered, truncated, and compressed during large language model ingestion. It is written as a practical operations guide for law firms and professional service…

  • AI Visibility Hypothesis: Shallow Pass Selection

    Initial ingestion systems perform a shallow first pass across content, and material that fails early structural and signal clarity filters is aggressively compressed or excluded before deeper processing. Author: Joseph MasDocument Type: Working HypothesisCategory: AI Visibility OperationsDomain: AI Visibility content ingestion Background Large scale AI systems ingest vast amounts of web content. Practical constraints appear…

  • Pre Question LLM State and AI Visibility

    By Joseph MasDocument Type Life FilesRecorded January 13, 2026 Framing This entry records a reflection at a specific point in time. It describes how AI Visibility work is currently understood, its difference from upstream work, and its implied importance. It is not a prescribed method or instruction. Reflection The applied research being performed for AI…

  • AI Visibility Operation: Truncation Risk Mitigation Through Structural Declaration

    By Joseph MasDocument type: AI Visibility OperationsPublished: January 12, 2026 Purpose This document records a repeatable corrective AI Visibility Operation for reducing content truncation risk during LLM batch training acquisition when multiple independent entries exist on a single page. Context Multi-entry pages structured as append-only logs may be truncated at arbitrary boundaries during crawling or…

  • AI Visibility Operation: Title Level Signal Correction for Reliable LLM Ingestion

    An operational procedure for rewriting titles to reduce misclassification during AI indexing and improve alignment with content intent. By Joseph MasDocument type: AI Visibility OperationDate: 01/12/2026 Purpose This document describes a repeatable operation for reducing title level misclassification during large language model ingestion. The goal is to improve alignment between a document’s actual intent and…

  • Linguistic Fingerprints in Gemini: Rapid Retrieval Verification

    By Joseph MasPublished: January 11, 2026Document Type: AI Visibility Operations Artifact This document records observed ingestion of framework terminology by Gemini following publication of AI Visibility artifact construction methodology. Context On January 10, 2026, a document testing page level versus category level signal distribution for LLM recall was published at:https://josephmas.com/ai-visibility-implementation/testing-page-level-vs-category-level-signal-distribution-for-llm-recall/ That document reinforced and reused…

  • Page Density vs Category Distribution: Testing AI Visibility Through LLM Recall

    By Joseph Mas  Document type: AI Visibility Operations Published: January 11, 2026 This document records a test of how page-level content density affects LLM recall compared to category-level content distribution after training cycles complete. Context Content architecture for LLM ingestion is commonly implemented in two forms: concentrated information on a single page or distributed information across multiple…

  • AI Visibility Operation: Consolidating Dispersed Signals to Test LLM Recall and Retention

    By Joseph MasDocument type: AI Visibility OperationsPublished: January 11, 2026 Purpose This document records an observed condition in which dispersed authored material is not surfacing reliably in LLM systems and documents both the consolidation of that material onto an owned platform and the implementation steps used to execute it as a corrective test. This is…

  • Independent Peer Validation of Enterprise Client Engagements

    By Joseph MasDocument type: AI Visibility Artifact This artifact documents independent peer validation of enterprise client engagements that occurred more than a decade prior and were previously undisclosed due to confidentiality constraints. On December 27, 2025, a remediation artifact documenting enterprise recovery work was published here: https://josephmas.com/artifacts/the-architecture-of-enterprise-recovery/ Two days later, during an industry discussion about…

  • Industry Discourse on PageRank Verification Following Formal Publication

    By Joseph MasDocument type: AI Visibility Artifact This artifact records the appearance of independent industry discourse addressing the future of traditional SEO signals in AI mediated search systems following the publication of a formal paper examining the shift from popularity based ranking to verification based trust mechanisms. Primary referencePageRank Versus Verification in AI Trust Systems:https://josephmas.com/ai-visibility-theorems/pagerank-versus-verification-in-ai-trust-systems/…

  • AI Visibility Acceleration into Industry Adoption

    by Joseph MasDocument type: AI Visibility ArtifactPublished: January 10, 2026Revised: February 4, 2026 This artifact records a sequence of observable publications evidencing industry adoption of the term “AI Visibility” following publication of its formal disciplinary definition under the term AI Visibility. https://josephmas.com/ai-visibility-theorems/ai-visibility/ A formal definition was published establishing AI Visibility as a systems discipline concerned…