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arxiv: 2606.18491 · v1 · pith:KDDZ2S6Xnew · submitted 2026-06-16 · 🌌 astro-ph.CO · astro-ph.IM· hep-ph· stat.ME

The Coherence Principle: A Falsifiable Prior for Model Selection from the Grammar of Theories

Pith reviewed 2026-06-26 22:46 UTC · model grok-4.3

classification 🌌 astro-ph.CO astro-ph.IMhep-phstat.ME
keywords Coherence PrincipleBayesian model selectiontheoretical grammarcosmologyparticle physicsmodel priorssymmetriesconservation laws
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The pith

The Coherence Principle assigns Bayesian model priors according to compatibility with a theory's validated grammar of symmetries and conservation laws.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper proposes the Coherence Principle as a method for setting prior probabilities on competing models in Bayesian inference. It does this by assigning lower weights to models that make unmotivated violations of the established structural rules of physics, such as symmetries, conservation laws, locality, and Lorentz invariance. This approach makes the influence of theoretical knowledge explicit and testable rather than hidden in arbitrary prior choices. A sympathetic reader would care because it addresses the common problem where model selection depends heavily on unacknowledged prior assumptions, allowing data to play a clearer role when they are strong enough.

Core claim

The Coherence Principle is a reproducible prescription for assigning model priors according to compatibility with the validated structure of an existing theory. This structure, or grammar, includes symmetries, conservation laws, locality, Lorentz invariance, and universality patterns. Unmotivated violations of these rules incur a coherence cost, converted into a prior weight through a maximum-entropy exponential form controlled by one calibratable parameter α. The resulting prior is distinct from both the Bayesian Occam factor and naturalness: it penalizes not parameter volume or fine tuning, but departures from validated theoretical grammar.

What carries the argument

The Coherence Principle, which converts unmotivated violations of a theory's grammar into a coherence cost that determines prior weights via a maximum-entropy exponential form controlled by one calibratable parameter α.

If this is right

  • It favors the historically successful choices in reconstructed cases such as general relativity, Pauli's neutrino, parity violation, and special relativity.
  • In cosmology and particle physics it can be applied to neutrino mass mechanisms, dark energy and modified gravity, inflation, and beyond-Standard-Model sectors.
  • The principle leaves empirical likelihoods free to dominate when data are sufficiently constraining.
  • It turns trust in validated structural rules into a transparent, testable, and overrulable component of Bayesian inference.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same approach could be tested by checking whether the assigned priors correctly anticipate which models survive when new data become available in ongoing cosmological surveys.
  • If the grammar can be specified consistently, the principle might be extended to rank models in other domains that possess clear structural rules, such as certain areas of chemistry or materials science.
  • One could examine whether different choices of the single parameter α lead to stable rankings across multiple independent model-selection problems.

Load-bearing premise

The grammar of a theory can be defined objectively in the correct domain and historical time, and unmotivated violations can be identified consistently enough to assign a reproducible coherence cost.

What would settle it

A case where, with the grammar defined in the correct domain and time, the principle assigns a low prior to the model that later evidence confirms as correct, or a high prior to one that is ruled out.

read the original abstract

Bayesian model selection in cosmology and particle physics is often performed where posterior odds inherit a strong, often unacknowledged dependence on the prior assigned to competing models. Standard responses -- reference priors, hierarchical priors, or appeals to naturalness -- ignore relevant theoretical knowledge or rely on criteria hard to define operationally. We propose the \emph{Coherence Principle}: a reproducible prescription for assigning model priors according to compatibility with the validated structure of an existing theory. This structure, or \emph{grammar}, includes symmetries, conservation laws, locality, Lorentz invariance, and universality patterns. Unmotivated violations of these rules incur a coherence cost, converted into a prior weight through a maximum-entropy exponential form controlled by one calibratable parameter $\alpha$. The resulting prior is distinct from both the Bayesian Occam factor and naturalness: it penalizes not parameter volume or fine tuning, but departures from validated theoretical grammar. We illustrate the principle with examples from cosmology and fundamental physics: neutrino mass mechanisms, dark energy and modified gravity, inflation, beyond-Standard-Model sectors, and hierarchical astrophysical inference. We test it also on four historical cases -- general relativity, Pauli's neutrino, parity violation, and special relativity -- where evidential and theoretical contexts can be reconstructed. These examples show that it favors the historically successful choice when the proper grammar is defined in the correct domain and time. The Coherence Principle makes explicit a common but usually tacit part of physical reasoning: trust in validated structural rules. It turns this judgment into a transparent, testable, and overrulable component of Bayesian inference, leaving empirical likelihoods free to dominate when data are sufficiently constraining.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The manuscript proposes the Coherence Principle as a method for assigning priors in Bayesian model selection. Models incur a coherence cost, implemented via a maximum-entropy exponential prior controlled by a single parameter α, for unmotivated violations of the grammar of an established theory (symmetries, conservation laws, locality, Lorentz invariance, universality patterns). The principle is illustrated with examples from neutrino mass mechanisms, dark energy/modified gravity, inflation, BSM sectors, and hierarchical astrophysical inference, and tested on four historical cases (general relativity, Pauli's neutrino, parity violation, special relativity) where it favors the successful theory when the proper grammar is defined in the correct domain and time.

Significance. If the grammar and its violations can be defined objectively and reproducibly, the principle would convert tacit theoretical judgment into an explicit, testable, and overrulable component of Bayesian inference, distinct from both the Occam factor and naturalness arguments. This could be valuable in cosmology and particle physics where posterior odds are prior-sensitive and data are not yet decisive. The historical tests provide a form of empirical check, but the overall utility depends on whether the approach avoids the subjectivity it aims to replace.

major comments (2)
  1. [Abstract] Abstract: the claim that the principle 'favors the historically successful choice when the proper grammar is defined in the correct domain and time' makes success conditional on a retrospective choice of grammar. Without independent, pre-specified operational criteria for identifying the 'proper' grammar and 'unmotivated' violations, the prior assignment risks the same hindsight bias the method seeks to avoid, rendering the reproducibility claim unsupported by the presented evidence.
  2. [Abstract] Abstract: the prior depends on a single calibratable parameter α whose value must be chosen or fitted. The manuscript provides no procedure for calibrating α that is independent of the same historical cases used to validate the principle, which introduces a potential circularity between the prior construction and the validation data.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments. We respond point-by-point to the two major comments and outline revisions that address the concerns while preserving the manuscript's core claims.

read point-by-point responses
  1. Referee: [Abstract] Abstract: the claim that the principle 'favors the historically successful choice when the proper grammar is defined in the correct domain and time' makes success conditional on a retrospective choice of grammar. Without independent, pre-specified operational criteria for identifying the 'proper' grammar and 'unmotivated' violations, the prior assignment risks the same hindsight bias the method seeks to avoid, rendering the reproducibility claim unsupported by the presented evidence.

    Authors: We agree that the abstract phrasing risks implying hindsight bias and that the reproducibility claim requires stronger support. The grammar is intended to be the structure of an established theory as accepted at the relevant time on independent grounds, and 'unmotivated' refers to violations introduced without separate theoretical justification. To address this directly, we will revise the abstract to qualify the historical-test statement and add an explicit subsection providing operational criteria for grammar specification and for distinguishing motivated from unmotivated violations (e.g., presence of independent symmetry or unification arguments versus purely data-driven extensions). The historical cases will be reframed strictly as consistency checks. These changes will be made in the revised manuscript. revision: partial

  2. Referee: [Abstract] Abstract: the prior depends on a single calibratable parameter α whose value must be chosen or fitted. The manuscript provides no procedure for calibrating α that is independent of the same historical cases used to validate the principle, which introduces a potential circularity between the prior construction and the validation data.

    Authors: The manuscript does not currently supply an independent calibration procedure for α, so the potential circularity is a valid observation. In revision we will add a concrete calibration protocol that uses a separate collection of contemporary, well-established model-comparison problems (distinct from the four historical cases) in which the grammar is fixed by current theory and data sensitivity can be assessed. We will also note the option of marginalizing over α as a hyperparameter. The historical examples will remain validation illustrations only and will not be used for α calibration. The abstract and relevant sections will be updated accordingly. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected in the Coherence Principle proposal

full rationale

The paper introduces the Coherence Principle as an explicit prior construction using a maximum-entropy exponential form on coherence costs derived from an independently stated grammar of validated theoretical structures. No equations or derivations reduce by construction to their own inputs, no parameter fitting is presented as a prediction, and no self-citation chain is invoked to justify uniqueness or load-bearing premises. Historical illustrations are conditional on grammar definition but do not constitute fitted inputs renamed as predictions or self-definitional loops. The method remains self-contained against external benchmarks with the central claim independent of the examples.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim rests on one free parameter alpha and on the domain assumption that a theory possesses an identifiable grammar of structural rules whose violations can be quantified independently of the data being modeled.

free parameters (1)
  • α
    Single calibratable parameter that sets the strength of the exponential penalty converting coherence cost into prior weight.
axioms (1)
  • domain assumption The validated structure of an existing theory includes symmetries, conservation laws, locality, Lorentz invariance, and universality patterns that can be used to define a grammar.
    This grammar is the reference against which new models are scored for coherence.

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