Why "4" times the reduced Compton wavelength? The number 4 appears twice (in 4·ƛ and 4π), suggesting it was chosen to make things work out.
"Tetrahedral structural limit" is asserted without derivation. Why tetrahedra? A tetrahedron is 3D—why would the proton radius (a measured charge distribution extent) involve tetrahedral geometry?
"Spherical field projection loss" of α/(4π) has no physical mechanism. How does a "projection loss" yield this specific fraction?
The fit is suspiciously good (3 ppm) for a formula with at least two free choices (the coefficient 4, and the form of the correction).
4. Muon Anomaly
a_μ = (α/(2π)) + (α²/12) + (α³/5)
This mimics QED perturbation theory—but incorrectly:
The actual QED expansion is:
a_μ = (α/2π) + C₂(α/π)² + C₃(α/π)³ + ...
Where C₂ ≈ 0.765857... and C₃ involves thousands of Feynman diagrams calculated over decades.
The author's version:
First term: α/(2π) (this is the Schwinger term, known since 1948)
Second term: α²/12 — This should be ~0.765857(α/π)² ≈ 4.1×10⁻⁶, but α²/12 ≈ 4.44×10⁻⁶. Wrong coefficient.
Third term: α³/5 ≈ 4.25×10⁻⁸ — The actual third-order contribution is much more complex.
Hey - plugged this into chatGPT 5.2 and it seems to think this theory needs more work.
“As written, this looks closer to sophisticated curve-fitting (numerology with constraints) than a legitimate geometric unification, mainly because the claimed “ppm agreement” is often not assessed against experimental uncertainties and because several integer/constant choices function like hidden degrees of freedom.”
Thanks for running this on GPT 5.2. It is fascinating to see AI critiquing AI-assisted work.
The critique regarding hidden degrees of freedom is a fair point. However, in curve-fitting, parameters are continuous: one can choose 4.1 or 3.9 to make the data fit. In this model, parameters are topological invariants (integers like 4 faces, 12 vertices, 20 faces). They are discrete and cannot be tuned.
The fact that this unadjustable logic yields results agreeing with experimental data within ppm implies either a massive statistical coincidence or a structural aspect.
It would be very interesting to run independent tests on different AIs with the whole context of the model and a standardized, consensual prompt. Beyond formal verification, this methodology could open paths that are difficult to navigate without AI assistance, helping to determine if the model stands as a possible foundation for a 'broad explanation of the observable', since the term 'ToE' instantly raises red flags. Kind of a pioneer peer-centaur-review. Just an idea.
a much more revelatory exercise would be to compare these derived values with measured values, then construct testable hypotheses regarding disparities.
The model shows that the surface and volume of an object scale with mass such that electrostatic and gravitational acceleration can be explained through this scaling relationship. This is considered a geometric or structural cost:
C_s ~ m^(1/3) + m^(-2/3)
In terms of intrinsic acceleration, surface and volume scale with mass as:
a_i ~ m^(1/3) + m^(-5/3)
This relationship holds for any object with charge ≠ 0 across electrostatic and gravitational regimes, so the free fall principle is strictly recovered only for mathematically neutral objects.
This allows drawing an intrinsic acceleration curve for objects with homogeneous density, and the minimum point of this curve is identified at:
m_ϕ ≈ 4.157 × 10^−9 kg
If the surface and volume of a not strictly neutral object determine its dynamic behavior, this would theoretically allow measuring m_ϕ with precision and deriving G without the historical dependence on the Planck mass. In this sense, it is a falsifiable proposal.
The geometric logic of the model allows establishing a geometric or informational saturation limit that eliminates GR singularities. At the same time, fundamental particles are not treated as dimensionless points but as polyhedral objects, which also eliminates the quantum gravity problem. The concept of infinity is considered, within the model, physically implausible.
From here, the model allows making the derivations included in this post, which I have not presented categorically, but as a proposal that seems at least statistically very unlikely to be achieved by chance.
The model does not question the precision of the Standard Model but postulates that the particle zoo represents not a collection of fundamental building blocks, but the result of proton fragmentation into purely geometric entities. The fact that these entities are not observed spontaneously in nature, but only as a consequence of forced interactions, seems to support this idea.
If you have to ask people whether or not your preprint resembles curve-fitting, you have just self-reported that you are an AI user with no academic background.
Good luck with the peer review, you're gonna need it.
I have reported nothing but numerical results. Making assumptions about me instead of looking at the numbers says more about your background than it does about mine.
> The author declares the intensive and extensive use of Gemini 2.5 Flash and Gemini 3.0 Pro (Google) and sincerely thanks its unlimited interlocution capacity. The author declares as their own responsibility the abstract formulation of the research, the conceptual guidance, and the decision-making in case of intellectual dilemma. The AI performed the mathematical verification of the multiple hypotheses considered throughout the process, but the author is solely responsible for the final content of this article. The prompts are not declared because they number in the thousands, because they are not entirely preserved, and because they contain elements that are part of the author’s privacy.
i will take longer, because at each step the process of lateral association occurs, this will foster imaginative variation of schema, and result in inspiration, an internally generated drive to pursue a goal, and experience the results.
i will not only complete the task, but will understand the many outcomes of task corruption as they relate to the components of the task.
you will obtain a set of right answers, i will discover the rules that govern the process.
Fair enough. However, it is practically impossible to complete such a task in a human lifetime. But even if it were possible, the main point stands: using computers to perform calcualtions is standard scientific practice. Discrediting a proposal solely because it uses AI is retrograde per se. It contradicts the history of technological progress and excludes potentially valid results based on intellectual prejudice.
I am referring to other comments in this thread that dismissed the proposal purely based on the use of AI tools. My comment about prejudice was not directed at you.
consider the conceptual model of particle as a polyhedral structure.
consider further, the [pred] values are an average, or a centroid of sort, related to a dynamic process, as a result, the straight edges, and faces of the polyhedron dont exist, they are virtual. what is actual is the variation of "curvature" as the object oscillates, further consider that [diff] is a measure of deviation that is in line with [exp] values.
Because AI has been in the center of the debate so far, I ran your comment through my AI system, and it concluded that you captured the essence of the model perfectly: the polyhedra are topological standing waves, and the edges are nodal lines. So [Pred] is the geometric attractor, and [Diff] is the amplitude of the oscillation around that limit. As I understand it myself, the polyhedra don't exist as real solids, but as an optimized way to distribute the intensity of the oscillation. Does this perspective make the results physically plausible in your view?
attached is the question of what is "oscillating" ?
is matter, composed of "spacetime" possessed of disequilibrial state?
or is matter something different than the surrounding "substance"?
where does the phenomenal energy originate to drive a proton for the duration of its existance [decay rate]. is there some topologic ultrastructure that constrains geometry and drives the process of being a proton?
Same as previous -
r_p = 4·ƛ_p·(1 - α/(4π))
Red flags:
Why "4" times the reduced Compton wavelength? The number 4 appears twice (in 4·ƛ and 4π), suggesting it was chosen to make things work out.
"Tetrahedral structural limit" is asserted without derivation. Why tetrahedra? A tetrahedron is 3D—why would the proton radius (a measured charge distribution extent) involve tetrahedral geometry?
"Spherical field projection loss" of α/(4π) has no physical mechanism. How does a "projection loss" yield this specific fraction?
The fit is suspiciously good (3 ppm) for a formula with at least two free choices (the coefficient 4, and the form of the correction).
4. Muon Anomaly
a_μ = (α/(2π)) + (α²/12) + (α³/5)
This mimics QED perturbation theory—but incorrectly:
The actual QED expansion is:
a_μ = (α/2π) + C₂(α/π)² + C₃(α/π)³ + ...
Where C₂ ≈ 0.765857... and C₃ involves thousands of Feynman diagrams calculated over decades.
The author's version:
First term: α/(2π) (this is the Schwinger term, known since 1948)
Second term: α²/12 — This should be ~0.765857(α/π)² ≈ 4.1×10⁻⁶, but α²/12 ≈ 4.44×10⁻⁶. Wrong coefficient.
Third term: α³/5 ≈ 4.25×10⁻⁸ — The actual third-order contribution is much more complex.
and the Gemini LLM goes on and on and on...
Hey - plugged this into chatGPT 5.2 and it seems to think this theory needs more work.
“As written, this looks closer to sophisticated curve-fitting (numerology with constraints) than a legitimate geometric unification, mainly because the claimed “ppm agreement” is often not assessed against experimental uncertainties and because several integer/constant choices function like hidden degrees of freedom.”
Thank you for sharing and happy holidays!
Thanks for running this on GPT 5.2. It is fascinating to see AI critiquing AI-assisted work.
The critique regarding hidden degrees of freedom is a fair point. However, in curve-fitting, parameters are continuous: one can choose 4.1 or 3.9 to make the data fit. In this model, parameters are topological invariants (integers like 4 faces, 12 vertices, 20 faces). They are discrete and cannot be tuned.
The fact that this unadjustable logic yields results agreeing with experimental data within ppm implies either a massive statistical coincidence or a structural aspect.
It would be very interesting to run independent tests on different AIs with the whole context of the model and a standardized, consensual prompt. Beyond formal verification, this methodology could open paths that are difficult to navigate without AI assistance, helping to determine if the model stands as a possible foundation for a 'broad explanation of the observable', since the term 'ToE' instantly raises red flags. Kind of a pioneer peer-centaur-review. Just an idea.
Thanks for your comment and happy holidays!
> sophisticated curve-fitting (numerology with constraints)
lol ChatGPT feeling sassy today, though I think it was well deserved.
Undefined/non-consensual prompt.
Based on your pre-previous post, this is nothing.
Your contribution is the opposite of "something".
a much more revelatory exercise would be to compare these derived values with measured values, then construct testable hypotheses regarding disparities.
That's precisely what the numbers show. "Pred:", predicted value. "Exp:", experimental value. "Diff", difference.
the next step is, why?
what assumptions does your current model make. what could change that would eliminate disparity. What plausible mechanisms explain [Diff]?
The model shows that the surface and volume of an object scale with mass such that electrostatic and gravitational acceleration can be explained through this scaling relationship. This is considered a geometric or structural cost:
In terms of intrinsic acceleration, surface and volume scale with mass as: This relationship holds for any object with charge ≠ 0 across electrostatic and gravitational regimes, so the free fall principle is strictly recovered only for mathematically neutral objects.This allows drawing an intrinsic acceleration curve for objects with homogeneous density, and the minimum point of this curve is identified at:
If the surface and volume of a not strictly neutral object determine its dynamic behavior, this would theoretically allow measuring m_ϕ with precision and deriving G without the historical dependence on the Planck mass. In this sense, it is a falsifiable proposal.The geometric logic of the model allows establishing a geometric or informational saturation limit that eliminates GR singularities. At the same time, fundamental particles are not treated as dimensionless points but as polyhedral objects, which also eliminates the quantum gravity problem. The concept of infinity is considered, within the model, physically implausible.
From here, the model allows making the derivations included in this post, which I have not presented categorically, but as a proposal that seems at least statistically very unlikely to be achieved by chance.
The model does not question the precision of the Standard Model but postulates that the particle zoo represents not a collection of fundamental building blocks, but the result of proton fragmentation into purely geometric entities. The fact that these entities are not observed spontaneously in nature, but only as a consequence of forced interactions, seems to support this idea.
If you have to ask people whether or not your preprint resembles curve-fitting, you have just self-reported that you are an AI user with no academic background.
Good luck with the peer review, you're gonna need it.
I have reported nothing but numerical results. Making assumptions about me instead of looking at the numbers says more about your background than it does about mine.
From the manuscript linked in your profile:
> The author declares the intensive and extensive use of Gemini 2.5 Flash and Gemini 3.0 Pro (Google) and sincerely thanks its unlimited interlocution capacity. The author declares as their own responsibility the abstract formulation of the research, the conceptual guidance, and the decision-making in case of intellectual dilemma. The AI performed the mathematical verification of the multiple hypotheses considered throughout the process, but the author is solely responsible for the final content of this article. The prompts are not declared because they number in the thousands, because they are not entirely preserved, and because they contain elements that are part of the author’s privacy.
This seems properly copied and pasted. Good job. I guess we agree that AI is already playing a central role in science, and physics is no exception.
> AI performed the mathematical verification
That should be done by the human writing the manuscript, i.e., you.
Absolutely not. Results don't depend on who performed the calculation or how it was done. Can you solve 12,672 Feynman diagrams by hand?
i can. and i will take longer than you.
i will take longer, because at each step the process of lateral association occurs, this will foster imaginative variation of schema, and result in inspiration, an internally generated drive to pursue a goal, and experience the results.
i will not only complete the task, but will understand the many outcomes of task corruption as they relate to the components of the task.
you will obtain a set of right answers, i will discover the rules that govern the process.
Fair enough. However, it is practically impossible to complete such a task in a human lifetime. But even if it were possible, the main point stands: using computers to perform calcualtions is standard scientific practice. Discrediting a proposal solely because it uses AI is retrograde per se. It contradicts the history of technological progress and excludes potentially valid results based on intellectual prejudice.
who discredited your proposal?
I am referring to other comments in this thread that dismissed the proposal purely based on the use of AI tools. My comment about prejudice was not directed at you.
consider the conceptual model of particle as a polyhedral structure.
consider further, the [pred] values are an average, or a centroid of sort, related to a dynamic process, as a result, the straight edges, and faces of the polyhedron dont exist, they are virtual. what is actual is the variation of "curvature" as the object oscillates, further consider that [diff] is a measure of deviation that is in line with [exp] values.
Because AI has been in the center of the debate so far, I ran your comment through my AI system, and it concluded that you captured the essence of the model perfectly: the polyhedra are topological standing waves, and the edges are nodal lines. So [Pred] is the geometric attractor, and [Diff] is the amplitude of the oscillation around that limit. As I understand it myself, the polyhedra don't exist as real solids, but as an optimized way to distribute the intensity of the oscillation. Does this perspective make the results physically plausible in your view?
it is one plausible interpretation.
attached is the question of what is "oscillating" ?
is matter, composed of "spacetime" possessed of disequilibrial state?
or is matter something different than the surrounding "substance"?
where does the phenomenal energy originate to drive a proton for the duration of its existance [decay rate]. is there some topologic ultrastructure that constrains geometry and drives the process of being a proton?
I have done nothing but associate your "numerical results" with other numberslop I see from LLMs. Again, you're self-reporting.
Can you share the results of your analysis by association? Or was it an instant mental calculation?