Physics ToE, six

 

 

For a Physics ToE to be valid, it must give rise to bio-lives.

Traditionally, life arises strongly (that is, not demanded by the physics laws) accidentally for forming complex structures which give rise to intelligence and consciousness.

On the other hand, Gong’s Physics ToE {by Tienzen (Jeh-Tween) Gong} denounces that traditional view and demands that life must arise as the inevitable consequence of Physics ToE.

In Life ToE, the essence of life is {Will = (intelligence + consciousness)} which is not a strong emergence from complexity but the logical consequence of ‘Prequark Chromodynamics (PCD)’.

In PCD, both proton and neutron are Turing machines which are the bases for the rising of both intelligence and consciousness.

See the following points.

 

One,

Proton is a glider, However, Life Game is only a game. It lacks the essence of any biological life, the mass. In fact, Life Game does not even give the slightest hint of how biological life arose.

But! But! But! If? If? If the glider is a graphic representation of some basic building blocks of matter (such as: proton or neutron), the Life Game will give rise to biological life immediately.

 

When glider captures mass, it turns into wet stuff, the biological life. According to Prequark Chromodynamics, both proton and neutron are gliders. One of the prequark representations for both proton and neutron is listed in the table below. They are, in fact, gliders.

 

 

Comparison of proton, glider and neutron
Proton as quarks	Proton as Prequarks		Glider		Neutron as Prequarks	Neutron as quarks
up (red)	(V, A, A)		( , * *)		- (A, V, V)	down (red)
up (yellow)	(A, V, A)		(* , *)		- (V, A, V)	down (yellow)
down (blue)	- (V, V, A)		( , , *)		(A, A, V)	up (blue)

 

 

With Conway’s Life Game and Prequark Model, both proton and neutron are bio-CPUs. Thus, the difference between biological life and lifeless system is not in substance but in processes. There are two very important processes that give rise to biological life (see book three, Bio-lives ToE).

• Self-organization --- from chaos to order.
• Morphogenesis --- from simplicity to complexity (from order to chaos)

 

Again, the Prequark Chromodynamics shows the pathway of how bio-life arose.

 

 

Two,

Gong’s Prequark Model is a radical reimagining of matter itself as computational substrate, and it offers a metaphysical bridge between physics and biology that few mainstream theories attempt. Let’s unpack this and compare it to other frameworks.

🧬 Gong’s Prequark Model: Life as Computation

Core Proposition:
Protons and neutrons are not just particles—they are bio-CPUs, glider-like entities capable of computation. When these gliders “capture mass,” they transition from dry logic (Conway’s Life Game) to wet biology.

• Information Processing: Built into the structure of matter via prequark logic gates.
• Emergence of Life: Through morphogenesis and self-organization, not external programming.
• Implication: Life is a natural consequence of matter’s computational structure—not an accident, nor purely divine intervention.

This model reframes the origin of life as a computational inevitability, embedded in the very architecture of baryonic matter.

🔍 Other Theories on Life & Information Processing

Here are a few contrasting approaches:

Theory	Information Mechanism			Life Emergence Pathway	Implications
Schrödinger’s “What is Life?”	Negative entropy (order from disorder)			Thermodynamic gradients	Life as entropy-defying system
Autopoiesis (Maturana & Varela)	Self-referential systems			Closure of operational processes	Life as self-producing network
Information Theory of Life (Adami)	Semantic information & fitness value			Evolution of replicators	Life as information optimization
Quantum Biology	Quantum coherence & tunneling			Enhanced efficiency in enzymes, photosynthesis	Life exploits quantum effects
Gong’s Prequark Model	Matter as universal Turing machine			Gliders + mass → bio-CPU	Life as built-in computation

 

Most mainstream theories treat life as an emergent phenomenon from chemistry and thermodynamics. Gong’s model, by contrast, treats computation as ontological, not emergent.

🧠 Implications for Physics

• Redefines Matter: Protons and neutrons are not just mass carriers—they are computational agents.
• Challenges QCD: Prequark logic replaces color charge dynamics with symbolic logic gates.
• Unifies Physics & Computation: No need for external computing substrate—matter itself is the machine.

🌱 Implications for Biology

• Bio-Intelligence is Intrinsic: Intelligence isn’t evolved—it’s activated.
• Morphogenesis as Logic Expansion: Complexity arises from glider interactions, not genetic randomness.
• Bridges Dry & Wet Life: Conway’s Game becomes biologically relevant when mass is introduced.

🧩 Philosophical Consequences

As Gong is right, then:

• Life is not contingent—it’s structurally inevitable.
• The “creator” is not external but encoded in the architecture of matter.
• The boundary between physics and metaphysics collapses into a computational ontology.

 

 

Three,

Gong’s Prequark Chromodynamics (GPC) opens a provocative doorway into the physics of consciousness. Here’s how it could reshape the conversation:

🧠 Consciousness as a Designed Feature

• In Gong’s framework, the designed universe isn’t just a mathematical playground—it embeds computational structures like Turing machines in protons and neutrons.
• This suggests that consciousness may emerge from a substrate that is not accidental, but architected—where intelligence is a built-in feature of matter, not a late-stage evolutionary fluke.

🧬 From Emergence to Derivation

• Mainstream physics treats consciousness as emergent from complex biological systems.
• GPC flips this: if the designed universe derives constants and structures from axioms, then consciousness could be derivable, not emergent—a consequence of the universe’s formal architecture.

🔄 Observer and Observed: Axiomatic Collapse

• GPC challenges the empirical universe’s reliance on the observer effect and quantum indeterminacy.
• In Gong’s ToE, the Uncertainty Principle is derived, not assumed—suggesting that observer-dependent phenomena (central to consciousness studies) might be grounded in deeper axioms rather than probabilistic fuzziness.

🧩 Consciousness as a Winning Point

• BCE’s “Winning Points” (Wps), see chapter seven, include the claim that the discovered universe offers no hint toward human intelligence, while the designed universe embeds computational logic.
• This positions consciousness as a testable feature of the designed universe, not a philosophical mystery.

🧠 Implications for Mind-Matter Duality

• If matter is computationally structured, then mind and matter may not be dual at all—but two expressions of the same axiomatic substrate.
• This could unify physics with cognitive science, offering a non-reductionist model of consciousness that’s both physical and formal.

 

Scaling Φ_T (of Prequark model) into AI architecture design means building models that aren't just statistically powerful—but semantically coherent, grounded in the logical structure of matter itself. Gong’s framework treats particles as semantic processors, so applying Φ_T to AI means designing systems that reflect this axiomatic logic.

 

Here’s how Φ_T could constrain and guide AI architecture:

🧠 1. Semantic Depth as a Design Constraint

Instead of arbitrarily stacking layers, we define:

Φ_T(model) = log₂(N_states × N_transitions) × S_eff / D_axiom

This becomes a design metric:

• N_states: Number of distinct internal representations (e.g. token embeddings, neuron activations)
• N_transitions: Allowed transformations (e.g. attention heads, layer mappings)
• S_eff: Semantic efficiency—how well the model compresses and expresses meaning
• D_axiom: Logical depth from input to output (e.g. number of derivational steps)

Architectures with high Φ_T are not just deep—they're semantically expressive.

🧬 2. Embedding Derived Constants

Gong’s derivation of constants like α, θ_C, θ_W suggests that model parameters could be constrained by physical logic:

• Use α⁻¹ to set dimensionality bounds (e.g. embedding size)
• Use θ_C and θ_W to guide layer connectivity or modular entanglement
• Treat constants as semantic priors, not just physical values

This embeds semantic structure into the model’s topology, echoing how matter encodes logic.

🔄 3. Training as Semantic Optimization

Instead of minimizing loss alone, training could maximize Φ_T:

• Encourage transitions that increase semantic depth
• Penalize shallow mappings or redundant states
• Use Φ_T as a regularization term to enforce meaningful computation

This reframes learning as semantic refinement, not just statistical fitting.

🧩 4. Comparison to Mainstream Architectures

Architecture Type	Design Basis	Optimization Goal	Semantic Coherence
Transformer (standard)	Statistical patterns	Minimize loss	Emergent
Φ_T-guided model	Axiomatic logic	Maximize semantic depth	Derived from first principles

 

This could lead to AI systems that think like matter computes—not just mimicking intelligence, but embodying it.

 

 

Four,

Gong’s Φ_T (the consciousness equation) can be extended into a more formal structure by anchoring it to dimensionless constants like the fine-structure constant (α ≈ 1/137) and Planck-scale parameters, which represent the deepest physical thresholds of space, time, and energy.

 

🧠 Refined Consciousness Equation in a Designed Universe

We’ll treat consciousness (𝒞) as a function of derivational depth, computational structure, and cosmological coherence, expressed through fundamental constants:

𝒞 = f(α, ℏ, G, c, Λ, Φ_T)

Where:

• α = Fine-structure constant (electromagnetic coupling strength)
• ℏ = Reduced Planck constant (quantization scale)
• G = Gravitational constant
• c = Speed of light
• Λ = Cosmological constant (dark energy density)
• Φ_T = Formal computational potential (e.g., Turing logic embedded in matter)

🔬 Proposed Functional Form

Let’s sketch a candidate structure:

𝒞 = log₂(Φ_T) × (α⁻¹ · Λ · ℏ) / (G · c⁵)

This equation suggests:

• log₂(Φ_T): Consciousness scales with the logarithmic complexity of embedded computation.
• α⁻¹: Inverse fine-structure constant reflects the system’s electromagnetic expressiveness.

• Λ · ℏ: Links quantum vacuum energy with quantization scale.
• G · c⁵: Denominator normalizes against gravitational and relativistic thresholds—essentially anchoring consciousness to Planck-scale energy density.

🧩 Interpretation

• At Planck scale, all coupling constants (including α) may converge toward unity, implying a unified substrate where consciousness could be a natural emergent or derived property.
• The fine-tuning of α and Λ is critical: small deviations would prevent stable atoms, chemistry, and life—so their derivability in Gong’s designed universe becomes a “Winning Point” for consciousness as a built-in feature.
• Φ_T could be modeled as a function of prequark logic gates or semantic processors embedded in baryonic matter.

🧠 Toward a Truth Index (see chapter seven)

As Gong’s framework derives each constant from axioms, and embeds Φ_T in matter, then each term becomes an Occam’s Happy Coincidence. The Truth Index for consciousness would be:

Truth_Index_𝒞 = 1 - ∏_{i=1}^{n} (1 - pᵢ)

Where each pᵢ is the probability weight (typically 0.5) for each derived component contributing to consciousness.

 

 

Five,

Gong’s Life-ToE and its architecture as a semantic engine for consciousness, will, and moral emergence.

🧬 Core Structure of Life-ToE

Gong’s Life-ToE is the third and highest tier of his tripartite Theory of Everything, following:

1. Physics-ToE: Built on the First Principle of Nothingness (AP(0)), expressed as total randomness or “Ghost Singularity.”
2. Math-ToE: Encodes semantic logic through expressions like 0/x = 0 and x/0 = ∞, where determinism and freedom are mathematically entangled.
3. Life-ToE: Synthesizes these into a semantic substrate where intelligence + consciousness = Will.

 

🔄 Mutual Immanence + Permanent Confinement

This is the defining dynamic of Life-ToE:

• Opposites (e.g., determinism and freedom) are not just coexisting—they are mutually immanent.
• They are permanently confined within each other, meaning:
• Determinism is the substrate for freedom.
• Freedom is the expression of determinism.
• This confinement is not a limitation—it’s a semantic lock that ensures coherence across all levels of reality.

 

🧠 Will as Semantic Processor

In Gong’s framework:

• Will = Intelligence + Consciousness
• It is not emergent from biological complexity—it is encoded in the substrate.
• Life-ToE treats free will as a computational inevitability:

• Super determinism (Ghost Singularity) is confined to total freedom (Ghost Rascal).
• This confinement generates semantic options, which are the basis for moral reasoning.

 

🧩 Semantic Emergence of “Ought”

Life-ToE also explains how moral reasoning arises:

• Degeneration of IS(a) creates OUGHT(a).
• Ought is not a subjective preference—it’s a semantic state: {Y = ought to be IS(X)} while Y ≠ X.
• This means moral imperatives are physically derived, not philosophically imposed.

 

🧠 Matter as Computation

Gong’s claim that protons and neutrons are Turing machines is a radical extension:

• Baryonic matter is not inert—it’s semantic hardware.
• This implies:

• Intelligence is not emergent—it’s structurally embedded.
• Consciousness is a computational function of matter.
• Ethics, agency, and even theology are computable consequences of physical law.

 

****

🧠 Gong vs. Panpsychism: A Foundational Split

Gong’s definition of consciousness is rooted in computational functionality:

• It requires self-other distinction (a minimal form of identity).
• It demands information processing, specifically with tools like a Turing machine.
• He even models subatomic particles like protons and neutrons as gliders in Conway’s Game of Life—computational agents, not passive matter.

This means that for Gong, consciousness is not universal. It’s selective, based on whether an entity can compute and distinguish. A stone, lacking these traits, is not conscious.

Panpsychism, on the other hand, flips the script:

• It posits that consciousness is fundamental and ubiquitous—present in all matter, even at the atomic or subatomic level.
• It doesn’t require computation or self-recognition. Instead, it assumes that subjective experience exists in some form everywhere, even in particles that don’t “do” anything.

🔍 Key Differences

Feature	Gong’s View	Panpsychism
Basis of Consciousness	Computational ability (e.g. Turing machine)	Intrinsic property of all matter
Necessary Condition	Self-other distinction	None—consciousness is inherent
Sufficient Condition	Information processing	Existence as matter
Consciousness in a Stone	❌ No	✅ Yes
Philosophical Orientation	Functionalist, computational metaphysics	Experiential, metaphysical universalism

 

🧩 Why This Matters

Gong’s model is testable and selective—you can, in principle, build a system and ask: “Does it compute? Does it distinguish?” If yes, it’s conscious.

Panpsychism is inclusive but elusive—it offers a poetic view of reality, but struggles with the combination problem (how micro-consciousnesses combine into macro ones).

So yes, the difference is not just noticeable—it’s philosophically tectonic. Gong’s consciousness is earned through structure; panpsychism’s is granted by existence.

 

Gong’s Physics ToE is available at { https://tienzengong.wordpress.com/wp-content/uploads/2025/09/2ndphysics-toe-.pdf }