What question did this study set out to answer?

To validate the Bounded Phase Coherence principle across various AI content generation modalities.

February 26, 2026Open Access

Computational Validation of Bounded Coherence: From Biological Oscillators to AI Content Generation

Key Points

To validate the Bounded Phase Coherence principle across various AI content generation modalities.
Analyzed 11,394 generation–verification cycles
Examined text, image, audio, code, video, and music modalities
Tested balance metrics and parameters
Identified chaos boundaries in code generation
All modalities show Beta-distributed balance metrics
Alpha parameters range from 0.45 to 9.29 depending on modality
A significant chaos boundary was found in code generation (cv = 0.92, α < 1)
Universality exists in the structural form, not the magnitude of parameters

Abstract

Computational validation of the Bounded Phase Coherence principle across six AI content generation modalities (text, image, audio, code, video, music). This study analyzes 11,394 generation–verification cycles to test whether bounded coherence, previously derived analytically in biological oscillator systems (Phillips 2026a; 2026b), generalizes to computational substrates. Key findings: All modalities exhibit Beta-distributed balance metrics. Alpha (α) parameters are modality-specific (range: 0.45–9.29). A chaos boundary is observed in code generation (cv = 0.92, α < 1). Universality lies in structural form rather than parameter magnitude. Dataset DOI (11,394 records): 10.5281/zenodo.18761324 This preprint extends the analytical framework established in: 10.5281/zenodo.18235945 10.5281/zenodo.18616949

Computational Validation of Bounded Coherence: From Biological Oscillators to AI Content Generation

Key Points

Abstract

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