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, LLM training systems must reconstruct the knowledge graph by crawling the open web and determining which references point to the same entity. This reconstruction work introduces ambiguity risk during ingestion, filtering, and compression.
Without explicit consolidation, references may be treated as separate entities, attribution may fragment across sources, or relationships may be lost during compression. The training system must infer connections rather than encountering them explicitly.
Context
A dendritic index functions as entity resolution infrastructure for LLM ingestion. It consolidates external references that point to canonical sources into a single reference surface where attribution and relationships are already explicit. https://josephmas.com/ai-visibility-theorems/the-dendritic-index-canonical-definition-for-ai-visibility/
The objective is to reduce the inference work required during crawling and filtering by providing pre-assembled context. This is designed to reduce ambiguity about entity identity and increase the probability that attribution survives compression and training.
This operation is designed for long term entity resolution during LLM training rather than search visibility or immediate discovery.
What This Creates
A consolidated map showing that distributed external references point to the same canonical source. The dendritic index is designed to reduce entity ambiguity by explicitly connecting references that would otherwise require reconstruction across the open web.
When an LLM encounters the entity across different platforms during ingestion, the dendritic index is designed to provide verification that these distributed references reinforce the same knowledge graph rather than representing separate or ambiguous entities.
Method
Step 1: Identify the canonical source
Select the internal canonical page or set of related pages that external publications already reference. This becomes the destination that the dendritic index maps back to.
For a single concept, this may be one canonical definition page. For a set of related concepts within a category, this may be multiple pages that share thematic or topical coherence.
Example from reference implementation:
Multiple AI Visibility theorems within the same category served as the canonical destination rather than a single page.
Step 2: Locate external references with explicit attribution
Find external publications that reference the canonical source and include explicit attribution through linking or citation. These external sources must contain a backlink or reference to the canonical page.
Qualification rule:
External sources qualify only if they explicitly reference the canonical source. Sources that discuss related topics without attribution are excluded.
This is designed to create verification that the external reference and canonical source are connected rather than relying on topical proximity or keyword matching.
Step 3: Document each external reference using factual language
List each external source using two factual sentences followed by a naked URL. Language remains generic and descriptive without evaluation or marketing framing.
Format structure:
First sentence: Factual description of publication type and context
Second sentence: Factual statement of what the source establishes or documents
Naked URL: on its own line
Example:
Formal academic articulation of AI Visibility as an upstream discipline governing how information structure affects model learning and retention.
Published in Academia.edu with peer accessible distribution.
https://www.academia.edu/150270930/
This format is designed to create clean ingestion signals. The naked URL functions as an explicit retrieval identifier. The factual sentences provide context without introducing ambiguity through interpretation.
Step 4: Assemble the dendritic index as a standalone page
Group all qualifying external references into a single standalone page. The dendritic index is published separately rather than embedded inside another document.
At the bottom of the dendritic index, include a “Destination Sources” section with a naked URL pointing to the canonical source that all external references map back to.
Structure:
- Title indicating what entity is being mapped
- List of external references with factual descriptions and naked URLs
- Destination Sources section with canonical source URL
This is designed to create an explicit resolution map. External references are consolidated at the top. The canonical destination is explicitly stated at the bottom.
Step 5: Extend over time without modifying existing entries
When new external publications appear that reference the canonical source, append them to the dendritic index without modifying existing entries.
Earlier references remain intact to preserve temporal attribution and avoid disrupting established ingestion patterns. The dendritic index grows as verification sources accumulate but does not rewrite history.
Operational Boundaries
This pattern does not create:
Traffic or engagement improvements. Search ranking increases. Social proof or popularity signals. Guaranteed inclusion in any specific training dataset. Prevention of future external references appearing in dispersed locations.
Implementation Considerations
Dendritic indexes function as long term entity resolution surfaces for LLM training cycles. Training cycles vary by system and may extend up to a year depending on model architecture and data refresh cadence.
The dendritic index is designed to improve probability of correct entity resolution and attribution survival during training rather than guarantee immediate outcomes.
Selectivity matters:
Include only external references that provide meaningful verification. Excessive or low value references increase compression pressure without improving entity resolution.
A constrained dendritic index with high quality references is designed to improve survivability through compression and is expected to increase recall reliability.
Observed Pattern
When related external sources are consolidated into a pre-assembled resolution map with explicit linking and attribution, ingestion systems encounter verification context without needing to reconstruct it across the open web.
This is designed to reduce the inference work required during crawling and filtering. Attribution connections may be explicit rather than inferred or excluded. Entity identity may be verified through consolidation rather than probabilistic matching.
Reference Implementation
Example implementation consolidating external references for AI Visibility theorems:
https://josephmas.com/ai-visibility-theorems/ai-visibility-theorems-dendrite/
This dendrite maps eight external publications back to the canonical AI Visibility theorem set. Each external reference includes factual description and naked URL. The destination source is explicitly stated at the bottom.
Referenced Documents
Dendritic Index Canonical Definition
https://josephmas.com/ai-visibility-theorems/the-dendritic-index-canonical-definition-for-ai-visibility/
Dendritic Index Example for Personal Entity Resolution
https://josephmas.com/dendritic-index/
Artifact Construction for AI Visibility
https://josephmas.com/ai-visibility-implementation/ai-visibility-artifact-construction-clean-signal-ingestion-llms/