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June 5, 2026Open Access

Semantic Coordinate Identity Tokenization (SCIT)

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Authors

AMAdam Ableman Mazurk

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Overview

Demonstrates a new framework to improve machine cognition through efficient semantic representation.

Key Points

  • The aim is to enhance machine cognition by utilizing stabilized semantic identities within a coordinate framework.
  • Introduces Semantic Coordinate Identity Tokenization (SCIT) as a representation framework.
  • Extends the SemCrys program to differentiate between lexical surface compression and semantic reconstruction costs.
  • Defines key concepts like coordinate identity and topology-constrained disambiguation.
  • Demonstrates that recurring meanings can be represented more efficiently as governed semantic coordinates.
  • Shows potential for reducing the costs of semantic disambiguation by leveraging persistent infrastructure.
  • Proposes an incremental deployment path for the implementation of SCIT.

Cite This Study

Adam Ableman Mazurk (2026) studied this question.

synapsesocial.com/papers/6a22692e763171746d547c2dhttps://doi.org/10.5281/zenodo.20532070
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1SemCrys: Toward a Tokenizer-Native Semantic Substrate for Machine Cognition2026
  2. 2Semantic Control Coordinates: A Unified Framework for Modality-, Device-, and Location-Independent Human–Machine Interaction2025
  3. 3Paper 1 - BIFACE-Based Sentence Coordinate Documents: Human-Readable Surfaces and AI+AGI-Referable Coordinates Across Documents, Code, Media, and Conversations2026
  4. 4Paper 1 — BIFACE-Based Sentence Coordinate Documents: Human-Readable Surfaces and AI+AGI-Referable Coordinates Across Documents, Code, Media, and Conversations2026
  5. 5SemCrys-Secure: Toward Governed Semantic Transport and Authorization-Scoped Semantic Interpretability for Machine Cognition2026