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

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.