Compatibility of Classical Physics, Relativity, and Quantum Mechanics with the NKTg Law

Abstract
This publication presents a consistent systematization of the compatibility between the pillars of modern physics and the NKTg Law on varying inertia. The NKTg Law is shown to be non-contradictory to classical theories, while allowing their dynamical structures to be abstracted into a unified state relationship among position, velocity, and mass.


Scope Statement
This publication focuses on establishing the conceptual consistency and compatibility of the NKTg Law with existing physical theories. The content is not intended to replace, refute, or claim full proof of any established theories.


Theoretical Basis
The NKTg Law describes the tendency of motion of a physical object through the relationship among:
• Position (x)
• Velocity (v)
• Mass (m)

General formulation:
NKTg = f(x, v, m)

The system is characterized by two core product quantities (unit: NKTm):
• NKTg₁ = x • p : positional inertia
• NKTg₂ = (dm/dt) • p : varying inertia


1. With Isaac Newton – Classical Mechanics
When the mass of the system does not vary with time, the NKTg Law reduces to a static inertial state.
• Condition: dm/dt = 0
• Consequence: NKTg₂ = 0
• Remaining expression: NKTg = NKTg₁ = x • p

In the context of classical mechanics and Newtonian gravitation, the quantity x • p preserves the structure of the momentum state, reflecting orbital stability and periodic motion.
In the limit of systems with large mass and velocities much smaller than the speed of light, the NKTg Law is structurally compatible, both in form and physical consequences, with all fundamental laws of classical mechanics.


2. With Albert Einstein – Relativity
In high-energy systems where mass and energy can be converted into one another, the NKTg Law extends into a regime of varying inertia.
• As velocity approaches the speed of light, energy and mass are related by E = mc²
• In this case: dm/dt ≠ 0 ⇒ NKTg₂ emerges and plays a dominant role

The NKTg Law describes the dynamical tendency of the system through changes in mass–energy states, without contradicting the fundamental relations of relativity.
In extreme configurations such as strong gravitational fields, the relative imbalance between NKTg₁ (associated with spatial structure) and NKTg₂ (associated with energy state) consistently reflects the phenomenology of relativistic objects, including systems with event horizons.


3. With Werner Heisenberg – Quantum Mechanics
At the microscopic scale, when considering particles with very small mass or mass approaching zero, the varying inertia component vanishes.
• Condition: m → 0 ⇒ dm/dt = 0 ⇒ NKTg₂ = 0
• Then: NKTg = NKTg₁ = x • p

In the quantum context, x and p are no longer interpreted as definite values, but correspond to uncertainties in position and momentum.
The expression x • p is structurally compatible with the Heisenberg uncertainty inequality (Δx • Δp) and does not violate the foundational mathematical form of quantum mechanics.


Conclusion: Synthesis of Physical Regimes

Reference Frame Representative Principle Characteristic Condition Dominant NKTg Component
Newton Classical mechanics dm/dt = 0 NKTg₁ = x • p
Einstein Relativity Energy-dominated regime NKTg₂ ≠ 0
Heisenberg Uncertainty principle m → 0 NKTg₁ (x, p)

General Significance
The NKTg Law enables the stages of physical development to be described as different state limits of a single general dynamical relationship among position, velocity, and mass. This approach opens a unified and continuous perspective from the microscopic to the macroscopic scale, without breaking the foundational structure of existing physical theories.

The NKTg Law ecosystem has now matured; the focus is no longer on debating its validity, but on how to apply NKTg Law effectively. NKTg AI is a pioneering application built upon the theoretical foundation of NKTg Law.

NKTg AI is not a summarization tool. It is a specialized Language Decoding System that uses physical algorithms to measure semantic energy and extract the Core Content — the core logical genetic code of a text.

1. The Nature of Core Content

Core Content is the sentence with the highest density of executable actions and the most concrete outcome in a text. If removed, the text loses key information that cannot be inferred from the rest.

It is an original sentence from the source text — not interpreted or altered by subjective reasoning.

2. Philosophy

Generative AI

Reads and reinterprets a text using the AI’s own language.

NKTg AI

Measures the energy of each word and sentence to identify core content that already exists. Returns the author’s original linguistic genetic code without generating new content.

3. Text Structural Genetic Code

AMP (Amplifying) + DAMP (Damping) + STABLE = 100%

AMP Amplifying > 55%

Increasing energy — Actions, Assertions, Execution, Results. Text has a clear focal point; Core Content has high reliability.

DAMP Damping > 55%

Decreasing energy — Conditions, Context, Risks, Exceptions, Counterarguments. Text tends toward condition analysis or risk assessment.

STABLE AMP ≈ DAMP

Balanced state — Technical info, Pure data, Data tables, Descriptive content. A prominent Core Content may not exist.

4. Output Modes

:brain: Left Brain (Extraction) — Distillation

Standard

Retains the most important sentences by the Golden Ratio. Best for quick reading.

Condensed

Removes repetitive or semantically similar sentences. Best for long texts.

Essence

Converges to a single Core Content sentence. Recommended when AMP > 55%.

:brain: Right Brain (Addition) — Expansion

Refined

Preserves content nucleus with minimum necessary context.

Expanded

Core Content combined with surrounding relevant context.

Comprehensive (100%)

Full text with DAMPING components marked for easy distinction.

5. Value for Experts & Managers

  • Quantifiability: AMP%, DAMP%, Compression%, retained sentences
  • Causality: Core Content often in Action → Result structures
  • Decision-Making: Quickly evaluate whether to read, examine, or act
  • Objectivity: Algorithm-based, not subjective emphasis

6. Value for General Users

  • Read Quickly: Identify the highest-action-density sentence instantly
  • Evaluate Before Reading: Observe AMP/DAMP in round 1
  • Objective Comparison: Compare Core Content across multiple texts
  • Identify Key Points: Spot conditions, warnings, or risks

7. Limitations

NKTg AI does not perform well on handwritten documents — OCR accuracy drops significantly compared to printed text, which may affect extraction quality.

8. NKTg Law — Variable Inertia Algorithm

NKTg = f(x, v, m)
p = m × v
NKTg₁ = x × p (Semantic Potential Energy)
NKTg₂ = (dm/dt) × p (Semantic Kinetic Energy)
NKTg(total) = f(NKTg₁, NKTg₂)

All processing runs directly in the browser via WebAssembly.

  • Text never leaves the user’s device
  • No server, no cloud, no remote database
  • History is stored in localStorage — residing entirely on the user’s device