The Is Sequence and TLMM: A Proposed Mapping from Meta-Ontology to Topological Latent Manifold Model Domain Mapping Series II: Cognitive and Latent-Space Dynamics (v5.0)

This release presents The Is Sequence and TLMM (v5.0), a proposed interpretive mapping between the Is Sequence (I → D → S → A → F) and the Topological Latent Manifold Model (TLMM). The work constitutes Domain Mapping Series II of the Is framework and extends the progression established in The Is (v3.0) and Structural Differentiation Cosmology (v4.0). The five-stage ontological sequence is interpreted in latent-space terms as: I — Latent GroundD — Topological DistinctionS — Manifold OrganizationA — Observable ProjectionF — Reconstruction Loop Three theoretical supplements are introduced: • Persistent Homology as a scale-aware topological tool• The Latent Void Principle, treating absence as a potentially structural component• Cross-Domain Mapping, proposing candidate shared latent structures across domains In addition, a TLMM-specific falsifiability framework (C16–C20) is proposed, covering persistence, void structure, cross-domain alignment, reconstruction stability, and scale generalization. All mappings are interpretive and domain-level. No ontological reduction, algorithmic derivation, or empirical validation claim is asserted. Reviewer-safe language conventions are applied throughout. The release includes: • Full paper (13 sections)• 11 figures (Fig.1–Fig.11)• Reproducible Python figure-generation script• README documentation This work is intended as a conceptual and methodological contribution exploring possible relationships between meta-ontology, topology, latent-space reconstruction, and cognitive dynamics.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-06-13
DOI
https://doi.org/10.5281/zenodo.20678555
Primary Topic
Topological and Geometric Data Analysis
Type
preprint
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preprint

The Is Sequence and TLMM: A Proposed Mapping from Meta-Ontology to Topological Latent Manifold Model Domain Mapping Series II: Cognitive and Latent-Space Dynamics (v5.0)

Koji Okino
Zenodo (CERN European Organization for Nuclear Research)
Topological and Geometric Data Analysis
preprint

The Is Sequence and TLMM: A Proposed Mapping from Meta-Ontology to Topological Latent Manifold Model Domain Mapping Series II: Cognitive and Latent-Space Dynamics (v5.0)

Koji Okino
preprint en

Abstract

This release presents The Is Sequence and TLMM (v5.0), a proposed interpretive mapping between the Is Sequence (I → D → S → A → F) and the Topological Latent Manifold Model (TLMM). The work constitutes Domain Mapping Series II of the Is framework and extends the progression established in The Is (v3.0) and Structural Differentiation Cosmology (v4.0). The five-stage ontological sequence is interpreted in latent-space terms as: I — Latent GroundD — Topological DistinctionS — Manifold OrganizationA — Observable ProjectionF — Reconstruction Loop Three theoretical supplements are introduced: • Persistent Homology as a scale-aware topological tool• The Latent Void Principle, treating absence as a potentially structural component• Cross-Domain Mapping, proposing candidate shared latent structures across domains In addition, a TLMM-specific falsifiability framework (C16–C20) is proposed, covering persistence, void structure, cross-domain alignment, reconstruction stability, and scale generalization. All mappings are interpretive and domain-level. No ontological reduction, algorithmic derivation, or empirical validation claim is asserted. Reviewer-safe language conventions are applied throughout. The release includes: • Full paper (13 sections)• 11 figures (Fig.1–Fig.11)• Reproducible Python figure-generation script• README documentation This work is intended as a conceptual and methodological contribution exploring possible relationships between meta-ontology, topology, latent-space reconstruction, and cognitive dynamics.

Zenodo (CERN European Organization for Nuclear Research)
United States Department of Labor (US)
Topological and Geometric Data Analysis
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