Stability, Instability, and What High-Energy Physics Really Teaches Us

One of the recurring temptations in physics is to mistake violence for depth.

When we push matter to extreme energy densities—whether in particle colliders or in thought experiments about the early universe—we tend to believe we are peeling away layers of reality, discovering ever more “fundamental” constituents beneath the familiar surface of stable matter. The shorter-lived and more exotic a state is, the more “real” it sometimes appears to us.

In my most recent RG paper (Lecture X1), I tried to step back from that reflex.

The starting point is almost embarrassingly simple:
stable charged particles persist; unstable ones do not.
That fact alone already carries a surprising amount of explanatory power—if we resist the urge to overinterpret it.

Stability as the exception, not the rule

If we imagine the early universe as a high-energy, high-density environment—a kind of primordial soup—then instability is not mysterious at all. Under such conditions, long-lived, self-consistent structures should be rare. Most configurations would be fleeting, short-lived, unable to maintain their identity.

From this perspective, stable particles are not “primitive building blocks” in a metaphysical sense. They are low-energy survivors: configurations that remain coherent once the universe cools and energetic chaos subsides.

Stability, then, is not something that needs to be explained away. It is the phenomenon that needs to be accounted for.

Colliders as stress tests, not ontological excavations

Modern facilities such as CERN allow us to recreate, for fleeting moments, energy densities that no longer exist naturally in the present universe. What we observe there—resonances, decay chains, short-lived states—is fascinating and deeply informative.

But there is a subtle conceptual shift that often goes unnoticed.

These experiments do not necessarily reveal deeper layers of being. They may instead be doing something more modest and more honest: testing how known structures fail under extreme conditions.

In that sense, unstable high-energy states are not more fundamental than stable ones. They are what stability looks like when it is pushed beyond its limits.

A simpler cosmological intuition

Seen this way, cosmogenesis does not require an ever-growing menagerie of proto-entities. A universe that begins hot and dense will naturally favor instability. As it cools, only a small number of configurations will remain phase-coherent and persistent.

Those are the particles we still see today.

No exotic metaphysics is required—only the recognition that persistence is meaningful.

Were the mega-projects worth it?

This perspective does not diminish the value of large-scale scientific projects. On the contrary.

The enormous investments behind colliders or fusion experiments—think of projects like ITER—have given us something invaluable: empirical certainty. They confirmed, with extraordinary precision, intuitions already sensed by the giants of the early twentieth century—figures like Albert Einstein, Paul Dirac, and Erwin Schrödinger.

Perhaps the deepest outcome of these projects is not that they uncovered a hidden zoo of ultimate constituents, but that they showed how remarkably robust the basic structure of physics already was.

That, too, is progress.

Knowing when not to add layers

Physics advances not only by adding entities and mechanisms, but also by learning when not to do so. Sometimes clarity comes from subtraction rather than accumulation.

If nothing else, the simple distinction between stable and unstable charged particles reminds us of this: reality does not owe us an ever-deeper ontology just because we can afford to build more powerful machines.

And perhaps that realization—quiet, unglamorous, but honest—is one of the most valuable lessons high-energy physics has taught us.

This reflection builds directly on an earlier blog post, “Stability First: A Personal Programme for Re-reading Particle Physics” (18 December 2025), in which I outlined a deliberate shift in emphasis: away from ontological layering and towards persistence as a physical criterion. That post introduced the motivation behind Lecture X1—not as a challenge to established data or formalisms, but as an invitation to reread them through a simpler lens. What follows can be read as a continuation of that programme: an attempt to see whether the basic distinction between stable and unstable charged particles already carries more explanatory weight than we usually grant it.

Post Scriptum — An empirical follow-up

When I wrote this piece, the emphasis was deliberately conceptual. The central idea was to treat stability versus instability as a primary organizing perspective, rather than starting from particle families, quark content, or other internal classifications. At the time, I explicitly presented this as an intuition — something that felt structurally right, but that still needed to be confronted with data in a disciplined way.

That confrontation has now been carried out.

Using the Particle Data Group listings as a source, I constructed a deliberately minimalist dataset containing only two observables: rest mass and lifetime. All a priori particle classifications were excluded. Stable or asymptotic states were removed, as were fractionally charged entities, leaving an unclassified ensemble of unstable particles. The resulting mass–lifetime landscape was examined in logarithmic coordinates and subjected to density-based clustering, with the full data table included to allow independent reanalysis.

The outcome is modest, but instructive. A dominant continuum of prompt decays clearly emerges, accompanied by only weak additional structure at longer lifetimes. No rich taxonomy presents itself when decay behaviour alone is considered — but the clusters that do appear are real, reproducible, and consistent with the intuition developed here and in earlier work.

This empirical annex does not “prove” a new theory, nor does it challenge existing classifications. Its value lies elsewhere: it shows what survives when one strips the description down to observables alone, and it clarifies both the power and the limits of a stability-first perspective.

For readers interested in seeing how these ideas behave when confronted with actual data — and in re-using that data themselves — the empirical follow-up is available here:

👉 Empirical Annex to Lecture X1 (Revisiting Lecture XV)
Structure in the Energy–Lifetime Plane of Unstable PDG Particles
https://www.researchgate.net/publication/399008132_Empirical_Annex_to_Lecture_X1_Revisiting_Lecture_XV_Structure_in_the_Energy-Lifetime_Plane_of_Unstable_PDG_Particles

Sometimes, the most useful result is not a spectacular confirmation, but a careful consistency check that tells us where intuition holds — and where it stops.

Stability First: A Personal Programme for Re-reading Particle Physics

Over the past years, I have written a number of papers on physics—mostly exploratory, sometimes speculative, always driven by the same underlying discomfort.

Not with the results of modern physics. Those are extraordinary.
But with the ordering of its explanations.

We are very good at calculating what happens.
We are less clear about why some things persist and others do not.

That question—why stability appears where it does—has quietly guided much of my thinking. It is also the thread that ties together a new manuscript I have just published on ResearchGate:

“Manuscript v0.2 – A stability-first reinterpretation of particle physics”
👉 https://www.researchgate.net/publication/398839393_Manuscript_v02

This post is not a summary of the manuscript. It is an explanation of why I wrote it, and what kind of work it is meant to enable.

Not a new theory — a different starting point

Let me be clear from the outset.

This manuscript does not propose a new theory.
It does not challenge the empirical success of the Standard Model.
It does not attempt to replace quantum field theory or nuclear phenomenology.

What it does is much more modest—and, I hope, more durable.

It asks whether we have been starting our explanations at the wrong end.

Instead of beginning with abstract constituents and symmetries, the manuscript begins with something far more pedestrian, yet physically decisive:

Persistence in time.

Some entities last.
Some decay.
Some exist only fleetingly as resonances.
Some are stable only in the presence of others.

Those differences are not cosmetic. They shape the physical world we actually inhabit.

From electrons to nuclei: stability as a guide

The manuscript proceeds slowly and deliberately, revisiting familiar ground:

the electron, as an intrinsically stable mode;
the proton, as a geometrically stable but structurally richer object;
the neutron, as a metastable configuration whose stability exists only in relation;
the deuteron, as the simplest genuinely collective equilibrium;
and nuclear matter, where stability becomes distributed across many coupled degrees of freedom.

At no point is new empirical content introduced.
What changes is the interpretive emphasis.

Stability is treated not as an afterthought, but as a physical clue.

Interaction without mysticism

The same approach is applied to interaction.

Scattering and annihilation are reinterpreted not as abstract probabilistic events, but as temporary departures from equilibrium and mode conversion between matter-like and light-like regimes.

Nothing in the standard calculations is altered.
What is altered is the physical picture.

Wavefunctions remain indispensable—but they are treated as representations of physical configurations, not as substitutes for them.

Probability emerges naturally from limited access to phase, geometry, and configuration, rather than from assumed ontological randomness.

Why classification matters

The manuscript ultimately turns to the Particle Data Group catalogue.

The PDG tables are one of the great achievements of modern physics. But they are optimized for calculation, not for intuition about persistence.

The manuscript proposes a complementary, stability-first index of the same data:

intrinsically stable modes,
metastable particle modes,
prompt decayers,
resonances,
and context-dependent stability (such as neutrons in nuclei).

Nothing is removed.
Nothing is denied.

The proposal is simply to read the catalogue as a map of stability regimes, rather than as a flat ontology of “fundamental particles”.

A programme statement, not a conclusion

This manuscript is intentionally incomplete.

It does not contain the “real work” of re-classifying the entire PDG catalogue. That work lies ahead and will take time, iteration, and—no doubt—many corrections.

What the manuscript provides is something else:

a programme statement.

A clear declaration of what kind of questions I think are still worth asking in particle physics, and why stability—rather than constituent bookkeeping—may be the right place to ask them from.

Why I am sharing this now

I am publishing this manuscript not as a final product, but as a marker.

A marker of a line of thought I intend to pursue seriously.
A marker of a way of reading familiar physics that I believe remains underexplored.
And an invitation to discussion—especially critical discussion—on whether this stability-first perspective is useful, coherent, or ultimately untenable.

Physics progresses by calculation.
It matures by interpretation.

This manuscript belongs to the second category.

If that resonates with you, you may find the full text of interest.

Jean-Louis Van Belle
readingfeynman.org

Moderation, Measurements, and the Temptation of Ontology

Why physics must resist becoming metaphysics

Some time ago, I found myself involved in what can best be described as an intellectual fallout with a group of well‑intentioned amateur researchers. This post is meant to close that loop — calmly, without bitterness, and with a bit of perspective gained since.

One of the more sensible people in that group bothered to push an interesting article onto my desk, and so I want to talk about that one here.

Gary Taubes, CERN, and an unexpected reinforcement

It’s an article by Gary Taubes on the discovery of the W and Z bosons at CERN, later incorporated into his book Nobel Dreams. Far from undermining my position, the article did the opposite: it reinforced the point I had been trying to make all along.

Taubes does not engage in ontology. He does not ask what W and Z bosons are in a metaphysical sense. Instead, he describes what was measured, how it was inferred, and how fragile the boundary is between evidence and interpretation in large‑scale experimental physics.

This connects directly to an earlier piece I published here:

Something Rotten in the State of QED: A Careful Look at Critique, Sociology, and the Limits of Modern Physics
https://readingfeynman.org/2025/12/01/something-rotten-in-the-state-of-qed-a-careful-look-at-critique-sociology-and-the-limits-of-modern-physics/

Let me restate the central point, because it is still widely misunderstood:

Criticizing the ontologization of W/Z bosons (or quarks and gluons) is not the same as denying the reality of the measurements that led to their introduction.

The measurements are real. The detector signals are real. The conservation laws used to infer missing energy and momentum are real. What is not forced upon us is the metaphysical leap that turns transient, unstable interaction states into quasi‑permanent “things.”

Stable vs. unstable states — a distinction we keep blurring

My own work has consistently tried to highlight a distinction that I find increasingly absent — or at least under‑emphasized — in mainstream physics discourse:

Stable states: long‑lived, persistent, and directly accessible through repeated measurement
Unstable or intermediate states: short‑lived, inferred through decay products, reconstructed statistically

W and Z bosons belong firmly to the second category. So do quarks and gluons in their confined form. Treating them as ontologically equivalent to stable particles may be pragmatically useful, but it comes at a conceptual cost.

It is precisely this cost that I criticize when I criticize mainstream physics.

Not because mainstream physics is “wrong.”
But because it has become too comfortable collapsing epistemology into ontology, especially in its public and pedagogical narratives.

Why this matters now

There is another reason this distinction matters, and it is a forward‑looking one.

The probability that something radically new — in the sense of a fundamentally novel interaction or particle family — will be discovered in the coming decades is, by most sober assessments, rather low. What we will have, however, is:

More precise measurements
Larger datasets
Longer baselines
Better statistical control

In that landscape, progress will depend less on naming new entities and more on bridging what has already been measured, sometimes decades ago, but never fully conceptually digested.

That is where I intend to focus my efforts in the coming years.

Not by founding a new church.
Not by declaring metaphysical revolutions.
But by carefully working at the interface between:

what was actually measured,
what was legitimately inferred,
and what we may have too quickly reified.

Closing note

If there is one lesson I take — from the past dispute, from Taubes, from the history of CERN or fundamental physics in general — it is this:

Physics progresses best when it remains modest about what it claims to be about.

Measurements first. Interpretation second. Ontology, if at all, only with restraint.

That stance may be unsatisfying to those looking for grand narratives. But it is, I believe, the only way to keep physics from quietly turning into metaphysics while still wearing a lab coat.

— Jean Louis Van Belle

Making Sense of What We Already Know…

Living Between Jobs and Life: AI, CERN, and Making Sense of What We Already Know

For decades (all of my life, basically :-)), I’ve lived with a quiet tension. On the one hand, there is the job: institutions, projects, deliverables, milestones, and what have you… On the other hand, there is life: curiosity, dissatisfaction, and the persistent feeling that something fundamental is still missing in how we understand the physical world. Let me refer to the latter as “the slow, careful machinery of modern science.” 🙂

These two are not the same — obviously — and pretending they are has done physics no favors (think of geniuses like Solvay, Edison or Tesla here: they were considered to be ‘only engineers’, right? :-/).

Jobs optimize. Life explores.

Large scientific institutions are built to do one thing extremely well: reduce uncertainty in controlled, incremental ways. That is not a criticism; it is a necessity when experiments cost billions, span decades, and depend on political and public trust. But the price of that optimization is that ontological questions — questions about what really exists — are often postponed, softened, or quietly avoided.

And now we find ourselves in a new historical moment.

The Collider Pause Is Not a Crisis — It’s a Signal

Recent reports that China is slowing down plans for a next-generation circular collider are not shocking. If anything, they reflect a broader reality:

For the next 40–50 years, we are likely to work primarily with the experimental data we already have.

That includes data from CERN that has only relatively recently been made fully accessible to the wider scientific community.

This is not stagnation. It is a change of phase.

For decades, theoretical physics could lean on an implicit promise: the next machine will decide. Higher energies, larger datasets, finer resolution — always just one more accelerator away. That promise is now on pause.

Which means something important:

We can no longer postpone understanding by outsourcing it to future experiments.

Why CERN Cannot Do What Individuals Can

CERN is a collective of extraordinarily bright individuals. But this is a crucial distinction:

A collective of intelligent people is not an intelligent agent.

CERN is not designed to believe an ontology. It is designed to:

build and operate machines of unprecedented complexity,
produce robust, defensible measurements,
maintain continuity over decades,
justify public funding across political cycles.

Ontology — explicit commitments about what exists and what does not — is structurally dangerous to that mission. Not because it is wrong, but because it destabilizes consensus.

Within a collective:

someone’s PhD depends on a framework,
someone’s detector was designed for a specific ontology,
someone’s grant proposal assumes a given language,
someone’s career cannot absorb “maybe the foundations are wrong.”

So even when many individuals privately feel conceptual discomfort, the group-level behavior converges to:
“Let’s wait for more data.”

That is not cowardice. It is inevitability.

We Are Drowning in Data, Starving for Meaning

The irony is that we are not short on data at all.

We have:

precision measurements refined to extraordinary accuracy,
anomalies that never quite go away,
models that work operationally but resist interpretation,
concepts (mass, spin, charge, probability) that are mathematically precise yet ontologically vague.

Quantum mechanics works. That is not in dispute.
What remains unresolved is what it means.

This is not a failure of experiment.
It is a failure of sense-making.

And sense-making has never been an institutional strength.

Where AI Actually Fits (and Where It Doesn’t)

I want to be explicit: I still have a long way to go in how I use AI — intellectually, methodologically, and ethically.

AI is not an oracle.
It does not “solve” physics.
It does not replace belief, responsibility, or judgment.

But it changes something fundamental.

AI allows us to:

re-analyze vast datasets without institutional friction,
explore radical ontological assumptions without social penalty,
apply sustained logical pressure without ego,
revisit old experimental results with fresh conceptual frames.

In that sense, AI is not the author of new physics — it is a furnace.

It does not tell us what to believe.
It forces us to confront the consequences of what we choose to believe.

Making Sense of What We Already Know

The most exciting prospect is not that AI will invent new theories out of thin air.

It is that AI may help us finally make sense of experimental data that has been sitting in plain sight for decades.

Now that CERN data is increasingly public, the bottleneck is no longer measurement. It is interpretation.

AI can help:

expose hidden assumptions in standard models,
test radical but coherent ontologies against known data,
separate what is measured from how we talk about it,
revisit old results without institutional inertia.

This does not guarantee progress — but it makes honest failure possible. And honest failure is far more valuable than elegant confusion.

Between Institutions and Insight

This is not an AI-versus-human story.

It is a human-with-tools story.

Institutions will continue to do what they do best: build machines, refine measurements, and preserve continuity. That work is indispensable.

But understanding — especially ontological understanding — has always emerged elsewhere:

in long pauses,
in unfashionable questions,
in uncomfortable reinterpretations of existing facts.

We are entering such a pause now.

A Quiet Optimism

I do not claim to have answers.
I do not claim AI will magically deliver them.
I do not even claim my current ideas will survive serious scrutiny.

What I do believe is this:

We finally have the tools — and the historical conditions — to think more honestly about what we already know.

That is not a revolution.
It is something slower, harder, and ultimately more human.

And if AI helps us do that — not by replacing us, but by challenging us — then it may turn out to be one of the most quietly transformative tools science has ever had.

Not because it solved physics.

But because it helped us start understanding it again.

The Corridor: How Humans and AI Learn to Think Together

A different kind of project — and one I did not expect to publish…

Over the past months, I have been in long-form dialogue with an AI system (ChatGPT 5.1 — “Iggy” in our exchanges). What began as occasional conversations gradually turned into something more structured: a genuine exploration of how humans and AI think together.

The result is now online as a working manuscript on ResearchGate:

👉 The Corridor: How Humans and AI Learn to Think Together.

This is not an AI-generated book in the usual sense, and certainly not a manifesto. I think of it as an experiment in hybrid (AI + HI) reasoning: a human’s intuition interacting with an AI’s structural coherence, each shaping the other. The book tries to map the very “corridor” where that collaboration becomes productive.

Whether you think of AI as a tool, a partner, or something entirely different, one thing is becoming clear: the quality of our future conversations will determine the quality of our decisions. This manuscript is simply one attempt to understand what that future dialogue might look like.

For those interested in the philosophy of intelligence, the sociology of science, or the emerging dynamics of human–AI collaboration — I hope you find something useful in it.

Something Rotten in the State of QED? A Careful Look at Critique, Sociology, and the Limits of Modern Physics

Every few years, a paper comes along that stirs discomfort — not because it is wrong, but because it touches a nerve.
Oliver Consa’s “Something is rotten in the state of QED” is one of those papers.

It is not a technical QED calculation.
It is a polemic: a long critique of renormalization, historical shortcuts, convenient coincidences, and suspiciously good matches between theory and experiment. Consa argues that QED’s foundations were improvised, normalized, mythologized, and finally institutionalized into a polished narrative that glosses over its original cracks.

This is an attractive story.
Too attractive, perhaps.
So instead of reacting emotionally — pro or contra — I decided to dissect the argument with a bit of help.

At my request, an AI language model (“Iggy”) assisted in the analysis. Not to praise me. Not to flatter Consa. Not to perform tricks.
Simply to act as a scalpel: cold, precise, and unafraid to separate structure from rhetoric.

This post is the result.

1. What Consa gets right (and why it matters)

Let’s begin with the genuinely valuable parts of his argument.

a) Renormalization unease is legitimate

Dirac, Feynman, Dyson, and others really did express deep dissatisfaction with renormalization. “Hocus-pocus” was not a joke; it was a confession.

Early QED involved:

cutoff procedures pulled out of thin air,
infinities subtracted by fiat,
and the philosophical hope that “the math will work itself out later.”

It did work out later — to some extent — but the conceptual discomfort remains justified. I share that discomfort. There is something inelegant about infinities everywhere.

b) Scientific sociology is real

The post-war era centralized experimental and institutional power in a way physics had never seen. Prestige, funding, and access influenced what got published and what was ignored. Not a conspiracy — just sociology.

Consa is right to point out that real science is messier than textbook linearity.

c) The g–2 tension is real

The ongoing discrepancy between experiment and the Standard Model is not fringe. It is one of the defining questions in particle physics today.

On these points, Consa is a useful corrective:
he reminds us to stay honest about historical compromises and conceptual gaps.

2. Where Consa overreaches

But critique is one thing; accusation is another.

Consa repeatedly moves from:

“QED evolved through trial and error”
to
“QED is essentially fraud.”

This jump is unjustified.

a) Messiness ≠ manipulation

Early QED calculations were ugly. They were corrected decades later. Experiments did shift. Error bars did move.

That is simply how science evolves.

The fact that a 1947 calculation doesn’t match a 1980 value is not evidence of deceit — it is evidence of refinement. Consa collapses that distinction.

b) Ignoring the full evidence landscape

He focuses almost exclusively on:

the Lamb shift,
the electron g–2,
the muon g–2.

Important numbers, yes — but QED’s experimental foundation is vastly broader:

scattering cross-sections,
vacuum polarization,
atomic spectra,
collider data,
running of α, etc.

You cannot judge an entire theory on two or three benchmarks.

c) Underestimating theoretical structure

QED is not “fudge + diagrams.”
It is constrained by:

Lorentz invariance,
gauge symmetry,
locality,
renormalizability.

Even if we dislike the mathematical machinery, the structure is not arbitrary.

So: Consa reveals real cracks, but then paints the entire edifice as rotten.
That is unjustified.

3. A personal aside: the Zitter Institute and the danger of counter-churches

For a time, I was nominally associated with the Zitter Institute — a loosely organized group exploring alternatives to mainstream quantum theory, including zitterbewegung-based particle models.

I now would like to distance myself.

Not because alternative models are unworthy — quite the opposite. But because I instinctively resist:

strong internal identity,
suspicion of outsiders,
rhetorical overreach,
selective reading of evidence,
and occasional dogmatism about their own preferred models.

If we criticize mainstream physics for ad hoc factors, we must be brutal about our own.

Alternative science is not automatically cleaner science.

4. Two emails from 2020: why good scientists can’t always engage

This brings me to two telling exchanges from 2020 with outstanding experimentalists: Prof. Randolf Pohl (muonic hydrogen) and Prof. Ashot Gasparian (PRad).

Both deserve enormous respect, and I won’t reveal the email exchanges because of respect, GDPR rules or whatever).
Both email exchanges revealed the true bottleneck in modern physics to me — it is not intelligence, not malice, but sociology and bandwidth.

a) Randolf Pohl: polite skepticism, institutional gravity

Pohl was kind but firm:

He saw the geometric relations I proposed as numerology.
He questioned applicability to other particles.
He emphasized the conservatism of CODATA logic.

Perfectly valid.
Perfectly respectable.
But also… perfectly bound by institutional norms.

His answer was thoughtful — and constrained.
(Source: ChatGPT analysis of emails with Prof Dr Pohl)

b) Ashot Gasparian: warm support, but no bandwidth

Gasparian responded warmly:

“Certainly your approach and the numbers are interesting.”
But: “We are very busy with the next experiment.”

Also perfectly valid.
And revealing:
even curious, open-minded scientists cannot afford to explore conceptual alternatives.

Their world runs on deadlines, graduate students, collaborations, grants.

(Source: ChatGPT analysis of emails with Prof Dr Pohl)

The lesson

Neither professor dismissed the ideas because they were nonsensical.
They simply had no institutional space to pursue them.

That is the quiet truth:
the bottleneck is not competence, but structure.

5. Why I now use AI as an epistemic partner

This brings me to the role of AI.

Some colleagues (including members of the Zitter Institute) look down on using AI in foundational research. They see it as cheating, or unserious, or threatening to their identity as “outsiders.”

But here is the irony:

AI is exactly the tool that can think speculatively without career risk.

An AI:

has no grant committee,
no publication pressure,
no academic identity to defend,
no fear of being wrong,
no need to “fit in.”

That makes it ideal for exploratory ontology-building.

Occasionally, as in the recent paper I co-wrote with Iggy — “The Wonderful Theory of Light and Matter” — it becomes the ideal partner:

human intuition + machine coherence,
real-space modeling without metaphysical inflation,
EM + relativity as a unified playground,
photons, electrons, protons, neutrons as geometric EM systems.

This is not a replacement for science.
It is a tool for clearing conceptual ground,
where overworked, over-constrained academic teams cannot go.

6. So… is something rotten in QED?

Yes — but not what you think.

What’s rotten is the mismatch

between:

the myth of QED as a perfectly clean, purely elegant theory,
and
the reality of improvised renormalization, historical accidents, social inertia, and conceptual discomfort.

What’s rotten is not the theory itself,
but the story we tell about it.

What’s not rotten:

the intelligence of the researchers,
the honesty of experimentalists,
the hard-won precision of modern measurements.

QED is extraordinary.
But it is not infallible, nor philosophically complete, nor conceptually finished.

And that is fine.

The problem is not messiness.
The problem is pretending that messiness is perfection.

7. What I propose instead

My own program — pursued slowly over many years — is simple:

Bring physics back to Maxwell + relativity as the foundation.
Build real-space geometrical models of all fundamental particles.
Reject unnecessary “forces” invented to patch conceptual holes.
Hold both mainstream and alternative models to the same standard:
no ad hoc constants, no magic, no metaphysics.

And — unusually —
use AI as a cognitive tool, not as an oracle.

Let the machine check coherence.
Let the human set ontology.

If something emerges from the dialogue — good.
If not — also good.

But at least we will be thinking honestly again.

Conclusion

Something is rotten in the state of QED, yes —
but the rot is not fraud or conspiracy.

It is the quiet decay of intellectual honesty behind polished narratives.

The cure is not shouting louder, or forming counter-churches, or romanticizing outsider science.

The cure is precision,
clarity,
geometry,
and the courage to say:

Let’s look again — without myth, without prestige, without fear.

If AI can help with that, all the better.

— Jean Louis Van Belle
(with conceptual assistance from “Iggy,” used intentionally as a scalpel rather than a sycophant)

Post-scriptum: Why the Electron–Proton Model Matters (and Why Dirac Would Nod)

A brief personal note — and a clarification that goes beyond Consa, beyond QED, and beyond academic sociology.

One of the few conceptual compasses I trust in foundational physics is a remark by Paul Dirac. Reflecting on Schrödinger’s “zitterbewegung” hypothesis, he wrote:

“One must believe in this consequence of the theory,
since other consequences which are inseparably bound up with it,
such as the law of scattering of light by an electron,
are confirmed by experiment.”

Dirac’s point is not mysticism.
It is methodological discipline:

If a theoretical structure has unavoidable consequences, and
some of those consequences match experiment precisely,
then even the unobservable parts of the structure deserve consideration.

This matters because the real-space electron and proton models I’ve been working on over the years — now sharpened through AI–human dialogue — meet that exact criterion.

They are not metaphors, nor numerology, nor free speculation.
They force specific, testable, non-trivial predictions:

a confined EM oscillation for the electron, with radius fixed by $\hbar / m_e c$ ;
a “photon-like” orbital speed for its point-charge center;
a distributed (not pointlike) charge cloud for the proton, enforced by mass ratio, stability, form factors, and magnetic moment;
natural emergence of the measured $G_E/G_M$ discrepancy;
and a geometric explanation of deuteron binding that requires no new force.

None of these are optional.
They fall out of the internal logic of the model.
And several — electron scattering, Compton behavior, proton radius, form-factor trends — are empirically confirmed.

Dirac’s rule applies:

When inseparable consequences match experiment,
the underlying mechanism deserves to be taken seriously —
whether or not it fits the dominant vocabulary.

This post is not the place to develop those models in detail; that will come in future pieces and papers.
But it felt important to state why I keep returning to them — and why they align with a style of reasoning that values:

geometry,
energy densities,
charge motion,
conservation laws,
and the 2019 SI foundations of $h$ , $e$ , and $c$
over metaphysical categories and ad-hoc forces.

Call it minimalism.
Call it stubbornness.
Call it a refusal to multiply entities beyond necessity.

For me — and for anyone sympathetic to Dirac’s way of thinking — it is simply physics.

— JL (with “Iggy” (AI) in the wings)

A New Attempt at a Simple Theory of Light and Matter

Dear Reader,

Every now and then a question returns with enough insistence that it demands a fresh attempt at an answer. For me, that question has always been: can we make sense of fundamental physics without multiplying entities beyond necessity? Can we explain light, matter, and their interactions without inventing forces that have no clear definition, or particles whose properties feel more like placeholders than physical reality?

Today, I posted a new paper on ResearchGate that attempts to do exactly that:

“The Wonderful Theory of Light and Matter”
https://www.researchgate.net/publication/398123696_The_Wonderful_Theory_of_Light_and_Matter

It is the result of an unusual collaboration: myself and an artificial intelligence (“Iggy”), working through the conceptual structure of photons, electrons, and protons with the only tool that has ever mattered to me in physics — Occam’s Razor.

No metaphysics.
No dimensionless abstractions.
No “magical” forces.

Just:

electromagnetic oscillations,
quantized action,
real geometries in real space,
and the recognition that many so-called mysteries dissolve once we stop introducing layers that nature never asked for.

The photon is treated as a linear electromagnetic oscillation obeying the Planck–Einstein relation.
The electron as a circular oscillation, with a real radius and real angular momentum.
The proton (and later, the neutron and deuteron) as systems we must understand through charge distributions, not fictional quarks that never leave their equations.

None of this “solves physics,” of course.
But it does something useful: it clears conceptual ground.

And unexpectedly, the collaboration itself became a kind of experiment:
what happens when human intuition and machine coherence try to reason with absolute precision, without hiding behind jargon or narrative?

The result is the paper linked above.
Make of it what you will.

As always: no claims of authority.
Just exploration, clarity where possible, and honesty where clarity fails.

If the questions interest you, or if the model bothers you enough to critique it, then the paper has succeeded in its only purpose: provoking real thought.

Warm regards,
Jean Louis Van Belle

Matter, Energy, Reality ↔ Thought, Intelligence, Consciousness

Dear Reader,

Physics asks: what is matter, energy, reality?
AI asks: what is thought, intelligence, consciousness?

Both are real, both are here. Physics confronts us with particles that behave like waves, with a universe that expands into… what exactly? AI confronts us with machines that converse, that create, that seem to reason — and force us to ask what we mean when we talk about “reasoning,” “creating,” or “understanding.”

This blog began as an attempt to make sense of Feynman’s physics. Over the years, it became a place where I tried to throw back the questions reality throws at us. Physics is real. AI is real. Both invite us to rethink our place in the cosmos, not as masters but as curious observers.

I don’t promise to publish much here — life is busy, and writing takes time — but when I do, it will be in the same spirit: reflecting, questioning, sometimes explaining, never pretending to give final answers.

If you’d like to follow more regular updates, you can always check my LinkedIn profile where I share articles and shorter notes.

Thank you for caring enough to read.

🌀 Two Annexes and a Turtle: Revisiting My Early Lectures on Quantum Physics

Over the past few weeks — and more intensely these past mornings — I’ve returned to two of my earliest texts in the Lectures on Physics series: the first on quantum behavior, and the second on probability amplitudes and quantum interference. Both have now been updated with new annexes, co-authored in dialogue with ChatGPT-4o.

This wasn’t just a consistency check. It was something more interesting: an exercise in thinking with — not through — a reasoning machine.

The first annex (Revisiting the Mystery of the Muon and Tau) tackles the open question I left hanging in Lecture I: how to interpret unstable “generations” of matter-particles like the muon and tau. In the original paper, I proposed a realist model where mass is not an intrinsic property but the result of oscillating charge or field energy — a stance that draws support from the 2019 revision of SI units, which grounded the kilogram in Planck’s constant and the speed of light. That change wasn’t just a technicality; it was a silent shift in ontology. I suspected that much at the time, but now — working through the implications with a well-tuned AI — I can state it more clearly: mass is geometry, inertia is field structure, and the difference between stable and unstable particles might be a matter of topological harmony.

The second annex (Interference, Identity, and the Imaginary Unit) reopens the deeper riddle at the heart of quantum mechanics: why probability amplitudes interfere at all. This annex is the child of years of irritation — visible in earlier, sharper essays I published on academia.edu — with the lazy mysticism that often surrounds “common phase factors.” The breakthrough, for me, was to fully accept the imaginary unit iii not as a mathematical trick but as a rotation operator. When wavefunctions are treated as oriented field objects, not just complex scalars, interference becomes a question of geometric compatibility. Superpositions and spin behavior can then be reinterpreted as topological effects in real space. This is where I think mainstream physics got lost: it started calculating without explaining.

ChatGPT didn’t invent these ideas. But it helped me phrase them, frame them, and press further on the points I had once hesitated to formalize. That’s what I mean when I say this wasn’t just a cleanup job. It was a real act of collaboration — a rare instance of AI not just paraphrasing or predicting, but amplifying and clarifying an unfinished line of human reasoning.

Both revised papers are now live on ResearchGate:

They mark, I think, a modest turning point. From theory and calculation toward something closer to explanation.

And yes — for those following the philosophical side of this project: we did also try to capture all of that in a four-panel comic involving Diogenes, a turtle, and Zeno’s paradox. But that, like all things cartooned by AI, is still a work in progress. 🙂

Post Scriptum (24 June 2025): When You Let the Machine Take the Pen

In the spirit of openness: there’s been one more development since publishing the two annexes above.

Feeling I had taken my analytical skills as far as I could — especially in tackling the geometry of nuclear structure — I decided to do something different. Instead of drafting yet another paper, I asked ChatGPT to take over. Not as a ghostwriter, but as a model builder. The prompt was simple: “Do better than me.”

The result is here:
👉 ChatGPT Trying to Do Better Than a Human Researcher

It’s dense, unapologetically geometric, and proposes a full zbw-based model for the neutron and deuteron — complete with energy constraints, field equations, and a call for numerical exploration. If the earlier annexes were dialogue, this one is delegation.

I don’t know if this is the end of the physics path for me. But if it is, I’m at peace with it. Not because the mystery is gone — but because I finally believe the mystery is tractable. And that’s enough for now.

🧭 From Strangeness to Symbolism: Why Meaning Still Matters in Science

My interest in quantum theory didn’t come from textbooks. It came from a thirst for understanding — not just of electrons or fields, but of ourselves, our systems, and why we believe what we believe. That same motivation led me to write a recent article on LinkedIn questioning how the Nobel Prize system sometimes rewards storylines over substance. It’s not a rejection of science — it’s a plea to do it better.

This post extends that plea. It argues that motion — not metaphor — is what grounds our models. That structure is more than math. And that if we’re serious about understanding this universe, we should stop dressing up ignorance as elegance. Physics is beautiful enough without the mystery.

Indeed, in a world increasingly shaped by abstraction — in physics, AI, and even ethics — it’s worth asking a simple but profound question: when did we stop trying to understand reality, and start rewarding the stories we are being told about it?

🧪 The Case of Physics: From Motion to Metaphor

Modern physics is rich in predictive power but poor in conceptual clarity. Nobel Prizes have gone to ideas like “strangeness” and “charm,” terms that describe particles not by what they are, but by how they fail to fit existing models.

Instead of modeling physical reality, we classify its deviations. We multiply quantum numbers like priests multiplying categories of angels — and in doing so, we obscure what is physically happening.

But it doesn’t have to be this way.

In our recent work on realQM — a realist approach to quantum mechanics — we return to motion. Particles aren’t metaphysical entities. They’re closed structures of oscillating charge and field. Stability isn’t imposed; it emerges. And instability? It’s just geometry breaking down — not magic, not mystery.

No need for ‘charm’. Just coherence.

🧠 Intelligence as Emergence — Not Essence

This view of motion and closure doesn’t just apply to electrons. It applies to neurons, too.

We’ve argued elsewhere that intelligence is not an essence, not a divine spark or unique trait of Homo sapiens. It is a response — an emergent property of complex systems navigating unstable environments.

Evolution didn’t reward cleverness for its own sake. It rewarded adaptability. Intelligence emerged because it helped life survive disequilibrium.

Seen this way, AI is not “becoming like us.” It’s doing what all intelligent systems do: forming patterns, learning from interaction, and trying to persist in a changing world. Whether silicon-based or carbon-based, it’s the same story: structure meets feedback, and meaning begins to form.

🌍 Ethics, Society, and the Geometry of Meaning

Just as physics replaced fields with symbolic formalism, and biology replaced function with genetic determinism, society often replaces meaning with signaling.

We reward declarations over deliberation. Slogans over structures. And, yes, sometimes we even award Nobel Prizes to stories rather than truths.

But what if meaning, like mass or motion, is not an external prescription — but an emergent resonance between system and context?

Ethics is not a code. It’s a geometry of consequences.
Intelligence is not a trait. It’s a structure that closes upon itself through feedback.
Reality is not a theory. It’s a pattern in motion, stabilized by conservation, disrupted by noise.

If we understand this, we stop looking for final answers — and start designing better questions.

✍️ Toward a Science of Meaning

What unifies all this is not ideology, but clarity. Not mysticism, but motion. Not inflation of terms, but conservation of sense.

In physics: we reclaim conservation as geometry.
In intelligence: we see mind as emergent structure.
In ethics: we trace meaning as interaction, not decree.

This is the work ahead: not just smarter machines or deeper theories — but a new simplicity. One that returns to motion, closure, and coherence as the roots of all we seek to know.

Meaning, after all, is not what we say.
It’s what remains when structure holds — and when it fails.

How I Co-Wrote a Quantum Physics Booklet with an AI — And Learned Something

In June 2025, I published a short booklet titled
A Realist Take on Quantum Theory — or the Shortest Introduction Ever.
📘 ResearchGate link

It’s just under 15 pages, but it distills over a decade of work — and a growing collaboration with ChatGPT — into a clean, consistent narrative: electrons as circulating charges, wavefunctions as cyclical descriptors, and action as the true guide to quantum logic.

We didn’t invent new equations. We reinterpreted existing ones — Schrödinger, Dirac, Klein–Gordon — through a realist lens grounded in energy cycles, geometry, and structured motion. What made this possible?

Memory: The AI reminded me of arguments I had made years earlier, even when I’d forgotten them.
Logic: It flagged weak spots, inconsistencies, and unclear transitions.
Humility: It stayed patient, never arrogant — helping me say what I already knew, but more clearly.
Respect: It never erased my voice. It helped me find it again.

The booklet is part of a broader project I call realQM. It’s an attempt to rescue quantum theory from the metaphorical language that’s haunted it since Bohr and Heisenberg — and bring it back to geometry, field theory, and physical intuition. If you’ve ever felt quantum physics was made deliberately obscure, this might be your antidote.

🧠 Sometimes, passing the Turing test isn’t about being fooled. It’s about being helped.

P.S. Since publishing that booklet, the collaboration took another step forward. We turned our attention to high-energy reactions and decay processes — asking how a realist, geometry-based interpretation of quantum mechanics (realQM) might reframe our understanding of unstable particles. Rather than invent new quantum numbers (like strangeness or charm), we explored how structural breakdowns — non-integrable motion, phase drift, and vector misalignment — could explain decay within the classical conservation laws of energy and momentum. That project became The Geometry of Stability and Instability, a kind of realQM manifesto. Have a look at it if you want to dive deeper. 🙂

🔬 When the Field is a Memory: Notes from a Human–Machine Collaboration

Why is the field around an electron so smooth?

Physicists have long accepted that the electrostatic potential of an electron is spherically symmetric and continuous — the classic Coulomb field. But what if the electron isn’t a smeared-out distribution of charge, but a pointlike particle — one that zips around in tight loops at the speed of light, as some realist models propose?

That question became the heart of a new paper I’ve just published:
“The Smoothed Field: How Action Hides the Pointlike Charge”
🔗 Read it on ResearchGate

The paradox is simple: a moving point charge should create sharp, angular variations in its field — especially in the near zone. But we see none. Why?

The paper proposes a bold but elegant answer: those field fluctuations exist only in theory — not in reality — because they fail to cross a deeper threshold: the Planck quantum of action. In this view, the electromagnetic field is not a primitive substance, but a memory of motion — smooth not because the charge is, but because reality itself suppresses anything that doesn’t amount to at least ℏ of action.

🤖 A Word on Collaboration

This paper wouldn’t have come together without a very 21st-century kind of co-author: ChatGPT-4, OpenAI’s conversational AI. I’ve used it extensively over the past year — not just to polish wording, but to test logic, rewrite equations, and even push philosophical boundaries.

In this case, the collaboration evolved into something more: the AI helped me reconstruct the paper’s internal logic, modernize its presentation, and clarify its foundational claims — especially regarding how action, not energy alone, sets the boundary for what is real.

The authorship note in the paper describes this in more detail. It’s not ghostwriting. It’s not outsourcing. It’s something else: a hybrid mode of thinking, where a human researcher and a reasoning engine converge toward clarity.

🧭 Why It Matters

This paper doesn’t claim to overthrow QED, or replace the Standard Model. But it does offer something rare: a realist, geometric interpretation of how smooth fields emerge from discrete sources — without relying on metaphysical constructs like field quantization or virtual particles.

If you’re tired of the “shut up and calculate” advice, and truly curious about how action, motion, and meaning intersect in the foundations of physics — this one’s for you.

And if you’re wondering what it’s like to co-author something with a machine — this is one trace of that, too.

Prometheus gave fire. Maybe this is a spark.

🧭 The Final Arc: Three Papers, One Question

Over the past years, I’ve been working — quietly but persistently — on a set of papers that circle one simple, impossible question:
What is the Universe really made of?

Not in the language of metaphors. Not in speculative fields.
But in terms of geometry, charge, and the strange clarity of equations that actually work.

Here are the three pieces of that arc:

🌀 1. Radial Genesis
Radial Genesis: A Finite Universe with Emergent Spacetime Geometry
This is the cosmological capstone. It presents the idea that space is not a stage, but an outcome — generated radially by mass–energy events, limited by time and light. It’s an intuitive, equation-free narrative grounded in general relativity and Occam’s Razor.

⚛️ 2. Lectures on Physics: On General Relativity (2)
Lectures on GRT (2)
This one is for the mathematically inclined. It builds from the ground up: tensors, geodesics, curvature. If Radial Genesis is the metaphor, this is the machinery. Co-written with AI, but line by line, and verified by hand.

🌑 3. The Vanishing Charge
The Vanishing Charge: What Happens in Matter–Antimatter Annihilation?
This paper is where the mystery remains. It presents two possible views of annihilation:
(1) as a collapse of field geometry into free radiation,
(2) or as the erasure of charge — with geometry as the by-product.
We didn’t choose between them. We just asked the question honestly.

Why This Arc Matters

These three papers don’t offer a Theory of Everything. But they do something that matters more right now:
They strip away the fog — the inflation of terms, the myth of complexity for complexity’s sake — and try to draw what is already known in clearer, more beautiful lines.

This is not a simulation of thinking.
This is thinking — with AI as a partner, not a prophet.

So if you’re tired of being told that the Universe is beyond your grasp…
Start here.
You might find that it isn’t.

—JL

🌀 Radial Genesis: A Universe That Grows from Within

What if space isn’t a container — but a consequence?

That’s the question I explore in my latest paper, Radial Genesis: A Finite Universe with Emergent Spacetime Geometry, now available on ResearchGate.

The core idea is surprisingly simple — and deeply rooted in general relativity: matter and energy don’t just move through space. They define it. Every object with mass–energy generates its own curved, local geometry. If we take that seriously, then maybe the Universe itself isn’t expanding into something. Maybe it’s unfolding from within — one energy event, one radial patch of space at a time.

This new paper builds on two earlier lecture-style essays on general relativity. But unlike those, this one has no equations — just plain language and geometric reasoning. It’s written for thinkers, not specialists. And yes, co-written with GPT-4 again — in what I call a “creative but critical spirit.”

We also explore:

Why the Universe might be finite and still expanding;
How a mirror version of electromagnetism could explain dark matter;
Why the so-called cosmological constant may be a placeholder for our conceptual gaps;
And whether our cosmos is just one region in a greater, radially unfolding whole — with no center, and no edge.

If you like cosmology grounded in Einstein, Dirac, and Feynman — but with fresh eyes and minimal metaphysics — this one’s for you.

🧠 Read it here:
Radial Genesis on ResearchGate

👁️‍🗨️ For context, you might also want to check out the earlier lecture papers:

—JL

🌀 A Bug on a Sphere — And Other Ways to Understand Gravity

I just published a new lecture — not on quantum physics this time, but on general relativity. It’s titled Lecture on General Relativity and, like my earlier papers, it’s written in collaboration with GPT-4 — who, as I’ve said before, might just be the best teacher I (n)ever had.

We start simple: imagine a little bug walking across the surface of a sphere. From there, we build up the full machinery of general relativity — metric tensors, covariant derivatives, Christoffel symbols, curvature, and ultimately Einstein’s beautiful but not-so-easy field equations.

What makes this lecture different?

No string theory.
No quantum gravity hype.
No metaphysical hand-waving about time being an illusion.

Just geometry — and the conviction that Einstein’s insight still deserves to be understood on its own terms before we bolt anything speculative onto it.

If you’ve enjoyed earlier pieces like Beautiful, but Blind: How AI Amplifies Both Insight and Illusion, or my more pointed criticism of pseudo-GUTs here, this one is part of the same lineage: a call to return to clarity.

📝 You can read or download the full lecture here on ResearchGate — or reach out if you want a cleaner PDF. — JL

Beautiful Blind Nonsense

I didn’t plan to write this short article or blog post. But as often happens these days, a comment thread on LinkedIn nudged me into it — or rather, into a response that became this article (which I also put on LinkedIn).

Someone posted a bold, poetic claim about “mass being memory,” “resonant light shells,” and “standing waves of curved time.” They offered a graphic spiraling toward meaning, followed by the words: “This isn’t metaphysics. It’s measurable.”

I asked politely:
“Interesting. Article, please? How do you get these numbers?”

The response: a full PDF of a “Unified Field Theory” relying on golden-ratio spirals, new universal constants, and reinterpretations of Planck’s constant. I read it. I sighed. And I asked ChatGPT a simple question:

“Why is there so much elegant nonsense being published lately — and does AI help generate it?”

The answer that followed was articulate, clear, and surprisingly quotable. So I polished it slightly, added some structure, and decided: this deserves to be an article in its own right. So here it is.

Beautiful, but Blind: How AI Amplifies Both Insight and Illusion

In recent years, a new kind of scientific-sounding poetry has flooded our screens — elegant diagrams, golden spirals, unified field manifestos. Many are written not by physicists, but with the help of AI.

And therein lies the paradox: AI doesn’t know when it’s producing nonsense.

🤖 Pattern without Understanding

Large language models like ChatGPT or Grok are trained on enormous text corpora. They are experts at mimicking patterns — but they lack an internal model of truth.
So if you ask them to expand on “curved time as the field of God,” they will.

Not because it’s true. But because it’s linguistically plausible.

🎼 The Seductive Surface of Language

AI is disarmingly good at rhetorical coherence:

Sentences flow logically.
Equations are beautifully formatted.
Metaphors bridge physics, poetry, and philosophy.

This surface fluency can be dangerously persuasive — especially when applied to concepts that are vague, untestable, or metaphysically confused.

🧪 The Missing Ingredient: Constraint

Real science is not just elegance — it’s constraint:

Equations must be testable.
Constants must be derivable or measurable.
Theories must make falsifiable predictions.

AI doesn’t impose those constraints on its own. It needs a guide.

🧭 The Human Role: Resonance and Resistance

Used carelessly, AI can generate hyper-coherent gibberish. But used wisely — by someone trained in reasoning, skepticism, and clarity — it becomes a powerful tool:

To sharpen ideas.
To test coherence.
To contrast metaphor with mechanism.

In the end, AI reflects our inputs.
It doesn’t distinguish between light and noise — unless we do.

Taking Stock: Zitterbewegung, Electron Models, and the Role of AI in Thinking Clearly

Over the past few years, I’ve spent a fair amount of time exploring realist interpretations of quantum mechanics, particularly the ring-current or Zitterbewegung (zbw) model of the electron. I’ve written many posts about it here — and also tried to help to promote the online “Zitter Institute”, which brings a very interesting group of both amateur and professional researchers together, as well as a rather impressive list of resources and publications which help to make sense of fundamental physics – especially on theories regarding the internal structure of the electron.

The goal — or at least my goal — was (and still is) to clarify what is real and what is not in the quantum-electrodynamic zoo of concepts. That is why I try to go beyond electron models only. I think the electron model is complete as for now: my most-read paper (on a physical interpretation of de Broglie’s matter-wave) settles the question not only for me but, I judge based on its many views, for many others as well. The paper shows how the magnetic moment of the electron, its wavefunction, and the notion of a quantized “packet of energy” can easily be grounded in Maxwell’s equations, special relativity, and geometry. They do not require speculative algebra, nor exotic ontologies.

In that light, I now feel the need to say something — brief, but honest — about where I currently stand in my research journey — which is not on the front burner right now but, yes, I am still thinking about it all. 🙂

On the term “Zitterbewegung” itself

Originally coined by Schrödinger and later mentioned by Dirac, “Zitterbewegung” translates as “trembling motion.” It was meant to capture the high-frequency internal oscillation predicted by Dirac’s wave equation.

But here lies a subtle issue: I no longer find the term entirely satisfying.

I don’t believe the motion is “trembling” in the sense of randomness or jitter. I believe it is geometrically structured, circular, and rooted in the relativistic dynamics of a massless point charge — leading to a quantized angular momentum and magnetic moment. In this view, there is nothing uncertain about it. The electron has an internal clock, not a random twitch.

So while I still value the historical connection, I now prefer to speak more plainly: an electromagnetic model of the electron, based on internal motion and structure, not spooky probabilities.

On tone and openness in scientific dialogue

Recent internal exchanges among fellow researchers have left me with mixed feelings. I remain grateful for the shared curiosity that drew us together, but I was disappointed by the tone taken toward certain outside critiques and tools.

I say this with some personal sensitivity: I still remember the skepticism I faced when I first shared my own interpretations. Papers were turned down not for technical reasons, but because I lacked the “right” institutional pedigree. I had degrees, but no physics PhD. I was an outsider.

Ridicule — especially when directed at dissent or at new voices — leaves a mark. So when I see similar reactions now, I feel compelled to say: we should be better than that.

If we believe in the integrity of our models, we should welcome critique — and rise to the occasion by clarifying, refining, or, if necessary, revising our views. Defensive posturing only weakens our case.

On the use of AI in physics

Some recent comments dismissed AI responses as irrelevant or superficial. I understand the concern. But I also believe this reaction misses the point.

I didn’t try all available platforms, but I did prompt ChatGPT, and — with the right framing — it offered a coherent and balanced answer to the question of the electron’s magnetic moment. Here’s a fragment:

“While the ‘definition’ of the intrinsic magnetic moment may be frame-invariant in the Standard Model, the observable manifestation is not. If the moment arises from internal circular motion (Zitterbewegung), then both radius and frequency are affected by boosts. Therefore, the magnetic moment, like momentum or energy, becomes frame-dependent in its effects.”

The jury is still out, of course. But AI — if guided by reason — might help us unravel what makes sense and what does not.

It is not a substitute for human thinking. But it can reflect it back to us — sometimes more clearly than we’d expect.

A final reflection

I’ll keep my older posts online, including those that reference the Zitter Institute. They reflected what I believed at the time, and I still stand by their substance.

But moving forward, I’ll continue my work independently — still fascinated by the electron, still curious about meaning and structure in quantum mechanics, but less interested in labels, echo chambers, or theoretical tribalism.

As always, I welcome criticism and dialogue. As one business management guru once said:

“None of us is as smart as all of us.” — Kenneth Blanchard

But truth and clarity come first.

— Jean Louis Van Belle

Antimatter, dark matter and cosmogenesis

I used ChatGPT to push the math and logic of my ‘realist’ interpretation of (1) matter-antimatter annihilation and creation (the Dirac and Breit-Wheeler processes, respectively) and (2) dark matter and dark energy to its logical and philosophical limits. For those who do not like to read, I made two short audio videos as well: the one on my “mirror force” idea is here, and from there you can go to the other video(s) in the playlist. 🙂 The implications for cosmogenesis models are rather profound – it calls for another approach to explain any “Big Bang” that may or may not have occurred when our Universe was born – so that is something to explore in the future, perhaps.

A quasi-final proton model?

After a break of a few months, I produced another lengthy video on quantum physics. 40 minutes. Check it out: https://www.youtube.com/watch?v=k_I3Noaup0E. The hypothesis that I, somewhat desperately, advanced in my last paper on the proton model – that the Zitterbewegung model of a proton does not quite look like that of an electron, and that we are probably talking about a “blob” of charge (sphere- or shell-like, or some other “form factor”, perhaps) – makes more sense to me now. I explored it with ChatGPT-4o, and this video is the result. I should immediately that I am immensely impressed with ChatGPT-4o: ChatGPT-3 was not able to really “understand” what I was talking about a year ago or so.

Who knows, perhaps we might imagine other ChatGPT-4o users will take the “dialogue” with AI on these particle models further, and fully develop them not only intuitively but also analytically. As I am currently trying out some AI-powered audio and video applications, I tried Pictory (pictory.ai): I gave it the url of my own long-winding video and it created this video: The proton as a dynamic blob of charge. 100 seconds only instead of 40 minutes. Its contents is, of course, superficial – but I think it is creative and nicely upbeat. I fed both video back to ChatGPT-4o, and it suggested the following refinements if ever I would like to re-do the video(s) – which I am not going to do, but you might like to further explore the remarks it made:

Consistency in Notation and Conventions:
- Ensure that polar and azimuthal angles are consistently defined throughout. Your initial derivations and explanations switched between conventions, potentially confusing viewers about which angle is associated with which base vector.
Tangential Velocity Decomposition:
- The explanation of how the tangential velocity is decomposed into orthogonal components was accurate but could benefit from a clearer mention of why the orthogonality matters in interpreting the motion on the sphere. Adding a visual representation would further clarify the concept.
- The concept of “sloshing” energy between kinetic and electromagnetic forms was well described but could benefit from highlighting the broader implications of this periodic exchange in classical and relativistic oscillators .
Pointlike Charge versus Blob View:
- The transition from discussing a pointlike charge to a “blob” of charge was a major theme but could be expanded by discussing how this shift impacts the assumptions about uniform or non-uniform charge density, as well as its implications for observable properties like the magnetic moment.
The discussion of symmetry along one axis of the double-loop motion was solid but might benefit from more detail on how this symmetry impacts the interpretation of observable properties like the magnetic moment .
Incorporate links to experimental observations, such as the measured magnetic moment and charge radius of the proton, was referenced. Integrating these observations with the theoretical results could be more explicit to strengthen the practical implications .
Visualizations:
- Consider incorporating visualized trajectories (e.g., the double-loop Lissajous pattern), and how they connect to the derived equations and the observed non-constant tangential velocity. This connection will help bridge the abstract mathematics with physical intuition .
Clarifications on Variability:
- The variability of the tangential velocity in the context of symmetry and non-orthogonality in 3D motion, was a key discussion point. Reinforce why this variability does not conflict with the broader relativistic constraints.

So… That’s all folks ! [Looney Tunes.] 🙂

Concluding remarks

In our previous post, we wrote that we’ve said goodbye to this fascinating field of research. We did: I entered this line of research – fundamental physics – as an amateur 10+ years ago, and now I leave it—as much an amateur now as back then. I wanted to understand the new theories which emerged over the past 50 years or so. Concepts such as the strong force or weak interactions and the new weird charges that come it with: flavors and colors—or all of the new quantum numbers and the associated new conservation laws, which Nature apparently does not respect because of some kind of hidden variables which cause the symmetries that are inherent to conservation laws to break down. […] Apparently, I didn’t get it. 🙂

However, in the process of trying to understand, a whole other mental picture or mindset emerged: we now firmly believe that classical mechanics and electromagnetism – combined with a more creative or realistic explanation of the Planck-Einstein relation – are sufficient to explain most, if not all, of the observations that have been made in this field since Louis de Broglie suggested matter-particles must be similar to light quanta—in the sense that both are energy packets because they incorporate some oscillation of a definite frequency given by the Planck-Einstein relation. They are also different, of course: elementary particles are – in this world view – orbital oscillations of charge (with, of course, an electromagnetic field that is generated by such moving charge), while light-particles (photons and neutrinos) are oscillations of the electromagnetic field—only!

So, then we spend many years trying to contribute to the finer details of this world view. We think we did what we could as part of a part-time and non-professional involvement in this field. So, yes, we’re done. We wrote that some time already. However, we wanted to leave a few thoughts on our proton model: it is not like an electron. In our not-so-humble view, the Zitterbewegung theory applies to it—but in a very different way. Why do we think that? We write that out in our very last paper: concluding remarks on the proton puzzle. Enjoy it !

That brings the number of papers on RG up to 80 now. Too much ! There will be more coming, but in the field that I work in: computer science. Stay tuned !