Overlay Layers v1: Meaning-Free Cores with Separable Interpretation Layers

PSL's invariant that every meaning field is always null has so far been applied within a single module at a time. This report generalizes it into an explicit architectural pattern -- a meaning-free core that is never modified, paired with a separable overlay (for machine-facing derived signals) or skin (for human-facing interpretation) that observes or annotates the core from the outside. We implement and verify two independent instances of this pattern. First, structural_time/realtime_overlay.py wraps CategoryDrift (StructuralTime's core drift tracker) without editing it, recording the correspondence between structural time (which advances only on update() calls) and wall-clock time, and computing whether the anomaly rate immediately after a large real-time gap differs from the baseline anomaly rate -- a concrete instrument for the open question of whether AI continuity leaves a measurable trace across session boundaries. Second, Fieldmap/skin.py renders a FieldMap (nodes, edges, and coupling matrices, with no field for content) as a human-readable 2D diagram, injecting node labels only at render time; we verify this against a real eight-node park FieldMap already used elsewhere in this project, confirming the underlying FieldMap/CategoryDrift classes are untouched. Both instances are motivated by a concrete, non-hypothetical case: production navigation systems already separate a meaning-free routing graph from a heavy, human-facing rendering layer, and the resource cost commonly attributed to that separation is in fact attributable to unrelated factors (planetary-scale graphs, live traffic data, and bundled unrelated services) rather than to the separation itself.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-09-30
DOI
https://doi.org/10.5281/zenodo.23049013
Primary Topic
Data Visualization and Analytics
Type
preprint
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preprint

Overlay Layers v1: Meaning-Free Cores with Separable Interpretation Layers

TOYOHIRO ARIMOTO, 5 Sonnet
Zenodo (CERN European Organization for Nuclear Research)
Data Visualization and Analytics
preprint

Overlay Layers v1: Meaning-Free Cores with Separable Interpretation Layers

TOYOHIRO ARIMOTO, 5 Sonnet
preprint en

Abstract

PSL's invariant that every meaning field is always null has so far been applied within a single module at a time. This report generalizes it into an explicit architectural pattern -- a meaning-free core that is never modified, paired with a separable overlay (for machine-facing derived signals) or skin (for human-facing interpretation) that observes or annotates the core from the outside. We implement and verify two independent instances of this pattern. First, structural_time/realtime_overlay.py wraps CategoryDrift (StructuralTime's core drift tracker) without editing it, recording the correspondence between structural time (which advances only on update() calls) and wall-clock time, and computing whether the anomaly rate immediately after a large real-time gap differs from the baseline anomaly rate -- a concrete instrument for the open question of whether AI continuity leaves a measurable trace across session boundaries. Second, Fieldmap/skin.py renders a FieldMap (nodes, edges, and coupling matrices, with no field for content) as a human-readable 2D diagram, injecting node labels only at render time; we verify this against a real eight-node park FieldMap already used elsewhere in this project, confirming the underlying FieldMap/CategoryDrift classes are untouched. Both instances are motivated by a concrete, non-hypothetical case: production navigation systems already separate a meaning-free routing graph from a heavy, human-facing rendering layer, and the resource cost commonly attributed to that separation is in fact attributable to unrelated factors (planetary-scale graphs, live traffic data, and bundled unrelated services) rather than to the separation itself.

Zenodo (CERN European Organization for Nuclear Research)
Data Visualization and Analytics
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