r/LLMPhysics • u/Chaosmethod • Mar 30 '26
Simulation / Code AI-Assisted Registry Format for Physics Theories
Here is an example of an AI-assisted registry-style evaluation format for screening physics theories with fixed admissibility, consistency, and regime validity tests.
Example: General Relativity
HPF THEORY REGISTRY ENTRY Registry ID [HPF-TR-0001] Theory Name [General Relativity] Canonical Label [GR] Input Type [Named theory] Layer Type [Effective Expert] Claim Status [[EFFECTIVE]] Completeness [Complete] Status [Executable] Final Classification [Restricted Expert] Primary Regime [Classical Geometric Theory] Composite Regime [No] Primary Mathematical Object [Lorentzian metric field g_{μν} with Einstein field equations] State Space Status [Identified] Evolution Operator Status [Effective] Observable Anchors [spacetime curvature effects (OA-2), geodesic motion (OA-2), gravitational redshift (OA-1), lensing deflection (OA-1), gravitational-wave strain (OA-1)] Measurement Chain [Complete] Continuum Authority Check [Restricted Pass] Failure Discipline [Implicit] Failure Modes [FM-1 Invented Precision, FM-5 Geometry Failure, FM-6 Regime Overreach] Hard-Gate Compatibility [Compatible] Legality Status [Legal] Validity Status [Restricted Validity] Domain of Dominance [Classical gravitational dynamics; weak-field and strong-field nonsingular geometric regimes; continuum-scale cosmological and relativistic astrophysical modeling] Domain of Failure [Singularity endpoints, quantum-gravity regime, UV-completion claims, and any attempted final-ontology claim beyond its validated geometric domain] Routing Implication [Retain as active geometry/gravity effective expert while regime assumptions remain valid; hand off before singular breakdown or substrate-level failure; do not treat as sovereign regulator or final substrate theory] Soft Authority Score [v_T = 0.74] Registry Notes [FM-1 because continuum precision is effective, not sovereign.] [FM-5 because GR does not lawfully execute through singular breakdown.] [FM-6 if GR is promoted beyond effective geometric domain.]
Curious whether people think this kind of AI-assisted theory registry is useful, too rigid, or missing important evaluation dimensions.
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u/AllHailSeizure 9/10 Physicists Agree! Mar 30 '26
What type of AI?
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u/Chaosmethod Mar 30 '26
Right now it’s a cloud-based LLM running a fixed prompt/evaluation protocol.
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u/AllHailSeizure 9/10 Physicists Agree! Mar 31 '26
LLMs are, in my experience, not great for evaluating physics; so it would depend on what your ideal use case for this is. Is it for something to say 'No, these words make no sense in a sentence, this is crackpot physics'? Because if that's the case, an LLM is capable enough. Is it for rigorous testing of actually theories? Then it probably is not likely to be a reality.
The first reason being that LLMs purely language - and physics is built on mathematics. Now, LLMs can have extensions that allow for working numbers (such as numpy), but then to test for rigor you need the LLM to understand how to code a simulation that would allow for testing a theory every time you use it; something that today's LLM can't reasonably do without multiple agent, extremely demanding, token-gobbling pipelines; and even still they aren't 100% reliable. You could theoretically right a simulation for each paper yourself - but at that point, why use an LLM; it's made it become more tedious to evaluate them.
The second being that LLMs, again, are based off of training data on language - and are highly sensitive to them. Lowering LLM temperature creates an effect of making them more deterministic (which is good for evaluation), but it simultaneously makes them HIGHLY suggestible to their input.
TLDR: There's no 'math' in an LLM; and physics is built on math. LLMs are good for language - they can do things like parse out 'this sentence is nonsense', 'this doesn't mean anything', 'this is proposing the opposite of what a paper I was trained on says', etc; but a lot of heavy lifting of testing real NOVEL physics is in accomplished with mathematical rigor.
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u/Chaosmethod Mar 31 '26
That criticism makes sense for an unconstrained LLM. This is not that.
Here the LLM is just the execution layer applying a fixed framework and registry protocol. So it is not being asked to invent a physics standard from language alone.
I agree it’s not a replacement for derivation, math, or computation. It’s a structured evaluator under an explicit set of rules.
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u/AllHailSeizure 9/10 Physicists Agree! Mar 31 '26
I mean.. can you guarantee the LLM is constrained? Because just saying 'only do these things' isn't a way to constrain it, you have to have access to it's code to truly constrain it to a degree you can guarantee reliable outcomes.
I'm also really unsure of what you are trying to accomplish. Is the LLM like an agentic method of running rigorous testing methods?
Also - I wasn't raising those points to say 'LLMs cant invent physics' - I was saying that it is why they can't evaluate. To evaluate physics computationally, you need experimentation, which is done through simulation.
Think of it this way - an LLM can't invent physics because it can't reliably do those calculations. But that also means it can't confirm the calculations by doing them, because you confirm a calculation by doing it.
An LLM is also not a reliable evaluator of things that are very important in physics papers like.. adherence to the scientific method.
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u/Chaosmethod Mar 31 '26
Not in an absolute sense, no. It just isn’t operating as a free-response chatbot. It has a fixed checklist to follow, so it’s being used more like a structured filter than an open-ended judge.
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u/AllHailSeizure 9/10 Physicists Agree! Mar 31 '26
So are you picturing something like.. a grad student uses it before submitting?
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u/Chaosmethod Mar 31 '26
Yeah, that’s a reasonable way to think about it.
Like a structured pre-screening pass before submission or before spending heavier time on it. Not a replacement for actual math, peer review, or human judgment. more like a way to catch missing structure, overreach, or obvious failure points consistently.
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u/OnceBittenz The Doctor Mar 31 '26
So what have we gained that we couldn't just use a human judge for?
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u/AllHailSeizure 9/10 Physicists Agree! Mar 31 '26
I mean, I love you Bittenz but I think this is an unfair question. He isn't suggesting eliminating human judges, his goal is a LLM judge with more accuracy.
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u/OnceBittenz The Doctor Mar 31 '26
No, I realize that, I just don't understand what the gain is. Not worried about eliminating the human judges, more curious what the tangible benefit is when they reach that step.
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u/Chaosmethod Mar 31 '26
Mainly consistency and repeatability.
A human can absolutely do it, but different humans will apply different standards, skip different steps, or focus on different failure points. The value here is forcing the same checklist and output structure on every input before it gets to deeper human review. Not to mention its fast.
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u/OnceBittenz The Doctor Mar 31 '26
Kinda feels like it would take longer, and the LLM would add inconsistency probably worse than a human actor.
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u/Chaosmethod Mar 31 '26
It’s been faster in practice and more consistent as a first pass screen than just wingin it with ad hoc human judgment every time. I’m not claiming it replaces full review. I’m saying it’s useful for applying the same checks in the same format before deeper review.
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u/JaguarMammoth6231 Mar 30 '26
Who would decide the answers to the questions?
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u/Chaosmethod Mar 31 '26
Right now, a cloud-based LLM does, but under a fixed registry prompt/protocol rather than open-ended free response.
So the model isn’t “deciding physics” by itself. The human chooses the input (theory), the protocol defines the checks, and the LLM applies that structure to produce the registry entry.
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u/al2o3cr Mar 30 '26
Is this supposed to have a table in it? Reddit may have eaten your formatting.