u/Life-Entry-7285

I’ve spent the last few years using LLMs to develop and formalize a speculative physics framework from a metaphysic I developed 30 years ago. I am not posting this to argue that my model is correct. I am posting because the process taught me something important about using LLMs for physics, especially if you are working outside existing paradigms.
The biggest lesson:
An LLM will not protect your model’s ontology. You have to.
By ontology, I mean the basic assumptions of the model: what exists, what is allowed to explain what, what is forbidden, what counts as a valid derivation, and what must not be smuggled in from standard theory.
If you do not constrain the LLM, it will constantly drift back toward familiar physics language. It will reinsert QFT assumptions, dark matter assumptions, field-particle assumptions, standard model assumptions, or generic “emergent spacetime” language even when your model is trying to avoid those assumptions.
This is not because the LLM is malicious. It is because it is trained on existing text. Its gravitational well is the existing literature.
That makes LLMs extremely powerful — and extremely dangerous — for speculative physics.
What went wrong repeatedly
When I tried to use AI to help formalize a nonstandard framework, it often did things like:
used conventional dark matter explanations even when the model was explicitly trying to replace particle dark matter;
imported QFT language where the framework was supposed to stay geometric;
produced equations that looked plausible but were not actually derived from the model’s assumptions;
overstated validation;
invented smooth academic connective tissue between ideas that had not yet been rigorously connected;
turned a hypothesis into a conclusion;
collapsed a new model back into familiar terms because familiar terms were easier.
That last one is the most dangerous.
The LLM can make you feel like you are making progress while it is quietly replacing your idea with something more conventional.
What worked
The useful method was to treat the LLM not as an oracle, but as a formalization engine under constraint.
I had to repeatedly tell it:
Do not assume the thing the model is trying to explain.
Do not import hidden variables unless the model defines them.
Do not use dark matter as a substance if the model is treating it as an emergent effect.
Do not use QFT concepts unless we explicitly derive why they belong.
Keep the derivation inside the model’s primitives.
Separate metaphor from mechanism.
Separate postdiction from prediction.
Separate archived forecast from later comparison.
Mark every uncertain claim as uncertain.
Do not call something “derived” unless every step is visible.
Give falsification criteria.
Make numerical predictions where possible.
Track which parameters are fixed, fitted, or inferred.
The LLM became useful only when I forced it to stay inside the model.
The most important rule
Never let the LLM complete your theory for you.
Make it show every assumption.
A good prompt is not:
“Develop this theory.”
A better prompt is:
“Given only assumptions A, B, and C, derive what follows. Do not introduce any additional ontology. If a step requires a hidden assumption, stop and identify it.”
Another useful prompt:
“Audit this derivation for assumption-smuggling. Identify every place where standard physics is being imported without permission.”
Another:
“Rewrite this using only the model’s allowed primitives. Do not use QFT, dark matter, ΛCDM, quantum field vacuum, or particle ontology unless explicitly derived.”
Another:
“Classify each claim as: definition, assumption, derivation, calibration, retrodiction, prediction, speculation, or falsifier.”
That last classification step is essential.
How to use LLMs responsibly in speculative physics
Here is the workflow I recommend:
1. Define the model’s primitives
Before asking the LLM to calculate anything, define what the model is allowed to use.
For example:
allowed objects;
allowed equations;
allowed physical interpretations;
forbidden assumptions;
empirical anchors;
free parameters;
fixed parameters;
target observables.
Do not let the LLM choose these for you.
2. Build an assumption ledger
Every derivation should begin with a list of assumptions.
If the AI adds a new one, mark it.
If the AI uses an unstated conventional assumption, reject the output.
3. Force step-by-step derivations
Do not accept a polished paragraph as a derivation.
Ask for:
dimensional analysis;
variable definitions;
boundary conditions;
limiting cases;
known-theory comparison;
what would falsify the equation.
4. Watch for “structure-smuggling”
This is when the LLM explains something by quietly assuming the thing it was supposed to explain.
Example:
If your model is trying to derive stable structure, the AI may start using already-stable entities as if they were primitives.
If your model is trying to explain dark-matter-like behavior, the AI may sneak in halo assumptions.
If your model is trying to derive particle mass, the AI may smuggle in Standard Model mass relations.
Flag this every time.
5. Separate poetry from physics
LLMs are very good at beautiful language.
Beautiful language is dangerous.
Require the model to label:
metaphor;
physical mechanism;
mathematical claim;
empirical claim;
prediction.
If it cannot make that distinction, the text is not ready.
6. Archive predictions before comparison
This is critical.
If you want to know whether the model predicts anything, archive the prediction before the data arrives.
Include:
exact observable;
numerical value;
uncertainty range;
baseline model;
falsification threshold;
timestamp;
no post-data adjustment rule.
Otherwise, you are probably doing postdiction without realizing it.
7. Make the AI attack the model
After using the LLM to build, use it to destroy.
Ask:
“What are the strongest reasons this model fails?”
“Where is the derivation circular?”
“Which predictions are actually nontrivial?”
“Which parameters are fitted rather than derived?”
“What would a skeptical physicist reject first?”
“Where did AI likely overstate confidence?”
This is where the LLM is most useful.
My conclusion
LLMs can help with speculative physics, but only if the human researcher maintains control of the conceptual frame.
The AI can calculate, organize, formalize, and challenge. But it cannot be trusted to preserve novelty. Its default behavior is to fall back into the patterns it has seen before.
So my advice is:
Use the LLM as a tool, not a theorist.
Use it to test your model, not to replace your judgment.
Force it to expose assumptions.
Make it classify every claim.
Archive predictions before data.
And never let polished language substitute for derivation.
If you are trying to build something genuinely new, the hardest part is not getting the AI to generate ideas.
The hardest part is preventing it from turning your new idea back into an old one.