Published January 5, 2026 | Version 1.0
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The Phase–Scalar Spiral: Why Systems Fail When They Scale — and What Restores Coherence

  • 1. Independent Researcher, danceScape, Burlington, Ontario, Canada

Description

This paper constitutes Stage X of an independent Phase–Scalar research program developed between 2025–2026. It introduces the Phase–Scalar Spiral as a universal diagnostic for identifying why complex systems fracture as they scale.

Across mathematics, physics, distributed computing, artificial intelligence, biological systems, and organizational systems, recurring failure modes appear when systems scale: paradoxes in infinite constructions, divergences and "ghosts" in certain quantization programs, coordination breakdowns in distributed systems, hallucinations in large language models, dysregulated growth in biological systems, and metric-induced breakdowns in human institutions. These phenomena are typically treated as domain-specific anomalies.

This paper proposes a unifying diagnostic explanation: many scale-failures are representational mismatches generated when global measurement and abstraction (Scalar) outrun local relational coordination (Phase). Building on Phase–Scalar Reconstruction (PSR) and the Spiral Coordinate System (SCS), we formalize a general principle: coherence is preserved when Phase precedes Scalar at each recursive level of growth.

We present the Phase–Scalar Spiral as an applied synthesis: a cross-domain lens that explains why locality-preserving methods (e.g., renormalization, neighborhood-based computation, context-grounded inference) stabilize systems, while scalar-first expansions often produce artifacts (divergence, paradox, hallucination, institutional dysfunction). The contribution is methodological and diagnostic: it proposes no new physical laws and does not modify established domain formalisms. Instead, it offers a disciplined way to classify when a problem is ill-posed due to category collapse, and how to restore coherence by re-establishing local coordination before re-scaling.

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Additional details

Additional titles

Subtitle (English)
Restoring Coherence in Systems of Infinite Scale (Applications of Phase–Scalar Reconstruction and Spiral Coordinates)

Related works

Is derived from
Preprint: 10.5281/zenodo.18088686 (DOI)
Preprint: 10.5281/zenodo.18099232 (DOI)
Preprint: 10.5281/zenodo.18041277 (DOI)
Preprint: 10.5281/zenodo.18051253 (DOI)

Dates

Issued
2026-01-05
This paper constitutes Stage X of an independent Phase–Scalar research program developed between 2025–2026. It introduces the Phase–Scalar Spiral as a universal diagnostic for identifying why complex systems fracture as they scale. Across mathematics, physics, distributed computing, artificial intelligence, biological systems, and organizational systems, recurring failure modes appear when systems scale: paradoxes in infinite constructions, divergences and "ghosts" in certain quantization programs, coordination breakdowns in distributed systems, hallucinations in large language models, dysregulated growth in biological systems, and metric-induced breakdowns in human institutions. These phenomena are typically treated as domain-specific anomalies. This paper proposes a unifying diagnostic explanation: many scale-failures are representational mismatches generated when global measurement and abstraction (Scalar) outrun local relational coordination (Phase). Building on Phase–Scalar Reconstruction (PSR) and the Spiral Coordinate System (SCS), we formalize a general principle: coherence is preserved when Phase precedes Scalar at each recursive level of growth. We present the Phase–Scalar Spiral as an applied synthesis: a cross-domain lens that explains why locality-preserving methods (e.g., renormalization, neighborhood-based computation, context-grounded inference) stabilize systems, while scalar-first expansions often produce artifacts (divergence, paradox, hallucination, institutional dysfunction). The contribution is methodological and diagnostic: it proposes no new physical laws and does not modify established domain formalisms. Instead, it offers a disciplined way to classify when a problem is ill-posed due to category collapse, and how to restore coherence by re-establishing local coordination before re-scaling.