What question did this study set out to answer?

The research aims to establish a framework that integrates multiple scales of observation into a unified theory of physical reality.

February 2, 2026Open Access

Indiscrete Scalar Ontology: A Universal Framework for Scale-Relative Combination and Emergence

Key Points

The research aims to establish a framework that integrates multiple scales of observation into a unified theory of physical reality.
Proposed the Indiscrete Scalar Ontology (ISO) framework for analyzing physical phenomena.
Defined three fundamental quantities: Scale (θ), Coupling (κ), and Resonance (ℛ).
Derived the ISO Fundamental Equation to express relationships between these quantities.
Demonstrated that scales coexist as valid ontological domains with conservation laws governing their interactions.
Identified applications in physics-related concepts like dark matter and quantum gravity.
Suggested connections to neuroscience and artificial intelligence concepts.

Abstract

We present Indiscrete Scalar Ontology (ISO), a theoretical framework proposing that physical reality consists of simultaneously coexisting scales, each constituting a fully valid ontological domain at its level of observation. ISO introduces three fundamental quantities — Scale (θ), Coupling (κ), and Resonance (ℛ) — unified by a conserved Combination Potential (Ω). The framework is founded on the principle that scale is intrinsic to systems, emerging as the sum of constituent scales rather than being imposed by external measurement. We derive a wave-based formulation yielding the ISO Fundamental Equation: Ω² = θ² + κ² + ℛ² + 2θκ cos φ_θκ + 2θℛ cos φ_θℛ + 2κℛ cos φ_κℛ. Conservation of Ω constrains the system to a sphere in (θ, κ, ℛ) -space, with known physical laws emerging as special cases at appropriate scale values. Applications are examined across physics (dark matter, quantum gravity, the measurement problem, cosmological constant), neuroscience (binding problem, consciousness, concept formation), and artificial intelligence (Synthetic Cognition). Working paper; peer review invited.

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