FAQ

It is a conceptual framework proposing that physical systems can be modeled as informational structures governed by coherence, recursion, and harmonic organization. It does not replace physics but offers a unified lens for cross-scale patterns.

THD is a mathematical model describing how systems evolve through three repeating phases—emergence, differentiation, and integration—represented by the 3-6-9 harmonic pattern.

CEP states that stable informational systems tend to increase structural coherence over time. This is a descriptive rule, not a new physical force.

No. It is framed as an informational interpretation that complements existing physics and offers new ways of modeling stability, organization, and cross-domain patterns.

Yes. The papers outline several proposed observational and computational tests. These models remain open to refinement, critique, and falsification.

The informational substrate is a conceptual layer used to model how systems store, transform, and stabilize information. It is not presented as a new physical medium but as a mathematical abstraction for describing coherence.

Informational fields represent gradients, relationships, or flows between components of a system. They help map stability, boundaries, and systemic organization.

Order states are a conceptual ladder describing increasing informational integration—from basic stability to complex reflective systems. They do not imply consciousness or metaphysics.

An attractor pattern in which systems tend toward stable structures aligned with the 3-6-9 harmonic recursion described in the THD model.

Because coherence provides a measurable, generalizable way to compare systems across domains—physical, biological, cognitive, or computational.

It represents a three-phase transformation pattern: emergence (3), contrast/expansion (6), and integration/stability (9). This is used as a modeling tool, not a physical law.

THD does not replace existing theories. Instead, it models them as manifestations of deeper informational structures, offering a unifying interpretation.

THD uses a family of equations modeling recursion, coherence, and harmonic scaling. They are published with the associated papers and preprints.

Several papers are released with DOIs and open access. Peer review is ongoing, and critique is welcomed.

The papers outline predictions related to early cosmological structure, coherence transitions, system stability curves, and recursive informational behavior.

All manuscripts, metrics, and proposed tests are fully published. Anyone can run simulations, compare predictions, or critique assumptions.

Yes. The work includes specific conditions under which the framework would be proven invalid or incomplete.

It offers conceptual tools for analyzing coherence, stability, recursion, and pattern formation. These may inspire new approaches in computational, biological, and physical contexts.

The harmonic and coherence-based modeling techniques may help with optimization, stability, and interpretability.

It discusses reflective informational systems but does not claim to define consciousness itself.

No. It is an informational interpretation and modeling toolkit that coexists with established physics.

No. The framework does not claim to predict personal life events, metaphysical realities, or supernatural phenomena.

No. It is purely a modeling framework and does not make metaphysical or existential claims.

A computational framework that uses informational-coherence models to evaluate patterns, consistency, and stability in data.

It focuses on coherence-based evaluation rather than purely statistical prediction.

No. It only analyzes the data it is explicitly given and has no access to personal accounts or external systems.

It refers to theoretical communication models based on informational coherence, not electromagnetic signals. This is exploratory research and not an established technology.

No. It is a conceptual modeling area, not a deployed communication system.

A structured workflow for research: coherence modeling, validation pathways, and iterative refinement.

Some aspects may be useful for conceptual modeling, early-stage design, and pattern identification.

Upcoming work includes expanded mathematical derivations, computational tools, educational materials, and collaborative research proposals.

Yes—several are planned for open release, allowing researchers and students to run experiments and explore the models.

By reading the published work, offering critique, participating in open discussions, or collaborating on proposed tests.