This paper establishes a local structural bridge between two recently developed information-geometric frameworks for observer-dependent probability representations on finite state spaces. Background. On the parametric side, the partition-adapted framework of Khomyakov (KLEO v3.24.1) defines observer entropy as the Kullback–Leibler divergence between a parametric distribution and the uniform lift of its coarse-graining, and shows that it scales quadratically with the resolution threshold, with a coefficient determined by the Fisher information matrix. On the empirical side, the Interference-First Reality (IFR) framework of Bianchi (2026) identifies multi-observer objectivity with the Fréchet barycenter of observer-induced distributions in the Jensen–Shannon metric, with almost-sure convergence guaranteed by metric barycenter theory. Principal results. The paper derives four main results: 1. Local Metric Bridge Lemma (Section 3): On the open probability simplex, the Jensen–Shannon divergence satisfies the local quadratic expansion D_JS(p ‖ p + εδ) = (ε²/8) I_p(δ) + O(ε³), where the explicit coefficient 1/8 arises from the midpoint structure of the symmetric divergence. 2. Multi-Observer Quadratic Aggregation Theorem (Section 4): For a partition-adapted parametric family satisfying conditions (A1)–(A3) of Khomyakov (KLEO v3.24.1), the empirical Jensen–Shannon Fréchet barycenter of an N-observer ensemble, centered on the within-class neutral locus, exists uniquely for small ε and coincides to leading order with the weighted Fisher–Rao barycenter of the within-class perturbation directions. The minimal Jensen–Shannon dispersion satisfies a quadratic scaling law governed by the within-class block I_ww(θ₀*) of the Fisher information matrix. 3. Leading-order coincidence of estimators (Corollary 6.1): The variational Fréchet minimizer p_F and the closed-form geometric overlap estimator p_G of Bianchi (2026) agree to order O(ε²) in Jensen–Shannon distance within the local regime. 4. Multi-observer Landauer corollary (Corollary 6.6): Under a per-channel irreversibility assumption and an additivity postulate for independent observer channels, the aggregate energetic cost of barycenter formation is bounded below by k_B T times the Jensen–Shannon dispersion. Scope. The bridge is local in ε (the within-class perturbation amplitude) and finite in N (the number of observers). It does not subsume Bianchi's almost-sure N → ∞ consistency theorem nor Khomyakov's ε → 0⁺ quadratic scaling result; it establishes their structural compatibility in the overlapping regime where both apply. The results are specialized to balanced exponential families (softmax), with explicit worked-example coefficients for four- and five-point state spaces. Domain-specific applicability is assessed against the cross-domain distinguishability hierarchy of Bianchi (2026), Table 14, identifying the low-dispersion regime (d_JS ≲ 10⁻² nats) as the primary domain of validity. Keywords: Jensen–Shannon divergence, Kullback–Leibler divergence, Fisher information, Fisher–Rao metric, multi-observer aggregation, Fréchet barycenter, probability simplex, partition-adapted parameterization, observer entropy, information geometry. MSC 2020: 62B10, 94A17, 53B12, 62F12.
This paper establishes a local structural bridge between two recently developed information-geometric frameworks for observer-dependent probability representations on finite state spaces. Background. On the parametric side, the partition-adapted framework of Khomyakov (KLEO v3.24.1) defines observer entropy as the Kullback–Leibler divergence between a parametric distribution and the uniform lift of its coarse-graining, and shows that it scales quadratically with the resolution threshold, with a coefficient determined by the Fisher information matrix. On the empirical side, the Interference-First Reality (IFR) framework of Bianchi (2026) identifies multi-observer objectivity with the Fréchet barycenter of observer-induced distributions in the Jensen–Shannon metric, with almost-sure convergence guaranteed by metric barycenter theory. Principal results. The paper derives four main results: 1. Local Metric Bridge Lemma (Section 3): On the open probability simplex, the Jensen–Shannon divergence satisfies the local quadratic expansion D_JS(p ‖ p + εδ) = (ε²/8) I_p(δ) + O(ε³), where the explicit coefficient 1/8 arises from the midpoint structure of the symmetric divergence. 2. Multi-Observer Quadratic Aggregation Theorem (Section 4): For a partition-adapted parametric family satisfying conditions (A1)–(A3) of Khomyakov (KLEO v3.24.1), the empirical Jensen–Shannon Fréchet barycenter of an N-observer ensemble, centered on the within-class neutral locus, exists uniquely for small ε and coincides to leading order with the weighted Fisher–Rao barycenter of the within-class perturbation directions. The minimal Jensen–Shannon dispersion satisfies a quadratic scaling law governed by the within-class block I_ww(θ₀*) of the Fisher information matrix. 3. Leading-order coincidence of estimators (Corollary 6.1): The variational Fréchet minimizer p_F and the closed-form geometric overlap estimator p_G of Bianchi (2026) agree to order O(ε²) in Jensen–Shannon distance within the local regime. 4. Multi-observer Landauer corollary (Corollary 6.6): Under a per-channel irreversibility assumption and an additivity postulate for independent observer channels, the aggregate energetic cost of barycenter formation is bounded below by k_B T times the Jensen–Shannon dispersion. Scope. The bridge is local in ε (the within-class perturbation amplitude) and finite in N (the number of observers). It does not subsume Bianchi's almost-sure N → ∞ consistency theorem nor Khomyakov's ε → 0⁺ quadratic scaling result; it establishes their structural compatibility in the overlapping regime where both apply. The results are specialized to balanced exponential families (softmax), with explicit worked-example coefficients for four- and five-point state spaces. Domain-specific applicability is assessed against the cross-domain distinguishability hierarchy of Bianchi (2026), Table 14, identifying the low-dispersion regime (d_JS ≲ 10⁻² nats) as the primary domain of validity. Keywords: Jensen–Shannon divergence, Kullback–Leibler divergence, Fisher information, Fisher–Rao metric, multi-observer aggregation, Fréchet barycenter, probability simplex, partition-adapted parameterization, observer entropy, information geometry. MSC 2020: 62B10, 94A17, 53B12, 62F12.
This paper constructs a rigorous mathematical bridge between Kaminsky's ontological duality framework and Khomyakov's KL-geometric theory of observer entropy. The central result establishes a canonical isomorphism between the statistical manifold (𝒫>₀(𝒳), D_KL) and the factor space of Kaminsky's ontological configuration space Ω_W = Subj × Subj under subjective-indistinguishability equivalence. Four theorems are proved: (T1) the ontological flow induces a Markov semigroup on the statistical manifold under an explicit fibre-uniformity assumption; (T2) the KL divergence is the unique f-divergence, up to positive scalar, whose Hessian recovers the Fisher–Rao metric; (T3) ontological observer entropy equals the expected within-fibre KL divergence from the uniform fibre prior; (T4) ontological observer entropy coincides with Khomyakov's observer entropy under a precise operator identification. A monotonicity theorem for subjective entropy is derived from the Shannon chain rule, yielding an entropic arrow of time consistent with physical time ordering. Version 1.2 introduces a new Parametric Realization assumption enabling a rigorous embedding of the ontological trajectory into a smooth parametric statistical family, together with a derived Fisher–Rao scaling theorem establishing the quadratic expansion of ontological observer entropy in terms of the Fisher information metric. These additions provide the missing structural link required to import the information-geometric scaling law into the ontological framework in a formally controlled manner.
We present the Interference-First Reality (IFR) framework, in which physical objectivity is not assumed as primitive but is operationally characterized through the stabilization of observer-induced probability distributions under redundant observation. Within this framework, emergent shared reality is identified with the Fréchet barycenter of an ensemble of observer-induced probability distributions in Jensen-Shannon geometry. A consistency theorem shows that empirical barycenters converge almost surely to their population counterpart as the number of independent observers grows, grounding objectivity in standard metric barycenter theory rather than in additional physical postulates. Alongside this variational construction, we introduce a complementary weighted geometric-overlap estimator and analyze its relation to the barycentric formulation. We instantiate the framework across five physical domains: cosmic microwave background (CMB), heart-rate variability (HRV), JWST spectroscopy, large-scale structure (LSS), and gravitational-wave open data (GWOSC). Across all domains, we find reproducible multi-observer collective summaries, bounded Jensen-Shannon distances consistent with stabilization, and a cross-domain distinguishability hierarchy stable under bootstrap resampling and insensitive to the choice between arithmetic and geometric aggregation. Permutation-based null tests further show that the observed clustering structure exceeds domain-specific random baselines. The empirical scope is nevertheless domain-specific: the GW cross-event comparison mainly reflects observational context and noise geometry, while the JWST case probes structured instrumental heterogeneity within a common observing program rather than independent platforms. Finally, we outline an extension in which time is defined as cumulative informational arc-length between successive stabilized collective states, leaving its full development to a companion study.
Resolution Geometry: A Kernel–Stabilizer Geometry of Observability Abstract Resolution Geometry (RG) is a protocol-explicit geometry of observability constructed from the joint observation law of a declared family of experimental protocols. The primitive object is not a covariance decomposition, a dynamical model, or a metric space, but a complete observation system together with the operational rules that determine which distinctions are admissible. Equality of observation laws induces a universal observational equivalence relation whose kernel groupoid represents exact observational ignorance. The resulting quotient provides the canonical domain on which all experiment-internal statements must be defined. Within regular classical statistical experiments, local distinguishability is represented by the Fisher-Rao metric through the standard information-geometric characterization under sufficient-Markov invariance. Admissible observational sectors are selected by a uniquely characterized spectral projector satisfying idempotence, metric self-adjointness, information compatibility, and threshold consistency. Observable sectors transport naturally under experiment isomorphisms and assemble into compatible observable atlases through identity and cocycle laws. The mature structure of RG is organized by two objects: the kernel groupoid of the joint observation law and the effective symmetry group induced by the stabilizer of that law. The kernel identifies observational equivalence, exact ignorance, and quotient structure. The stabilizer determines an effective symmetry group acting faithfully on the observational quotient. Together they provide the organizing principle from which observable geometry is assembled. A central result is a natural-descent theorem. Observable invariants, Fisher geometry, cone fields, clock readouts, transport structures, and distance protocols are shown to arise as pullbacks from law space and therefore descend automatically to the observational quotient. On regular strata, exact ignorance is represented by the kernel groupoid while infinitesimal ignorance is represented by the radical of the Fisher tensor. Representative invariance, kernel basicness, and projectability emerge as consequences of naturality rather than independent assumptions. The framework separates several logically distinct layers. The quotient layer characterizes what an experiment can and cannot distinguish. The metric layer quantifies local distinguishability. The admissibility layer determines which distinguishable directions survive stability, conditioning, nuisance closure, and transport requirements. The symmetry layer determines which higher-level geometric structures are forced. Entropy appears only downstream as a derived quantity associated with observational quotients and unresolved sectors, recovering familiar entropy constructions as special cases rather than primitives. Higher-level physical structure emerges through symmetry content. Lorentzian symmetry forces the quadratic causal cone and conformal class on regular strata. Equal clock kernels generate a duration groupoid whose transition maps satisfy a forced cocycle law and become affine under compatible time flows. Euclidean symmetries determine unique two-point invariants and metric proportionality classes. Counted channels remove dilation freedom and provide the anchor necessary for operational distance identification. Physical distance therefore appears not as a primitive notion but as a derived consequence of observational equivalence, symmetry structure, and experimentally anchored protocols. A covariance realization provides a concrete computational model of the general framework. Schur-complement identities describe observable geometry in the presence of nuisance structure; resolved-null coupling characterizes estimator improvement; canonical correlations provide observable-sector diagnostics; and transport admits gauge-theoretic and globalization interpretations. These realizations supply practical tools for inverse problems, statistical estimation, information geometry, experiment design, and automated sector identification, while remaining consequences of the general theory rather than its axioms. The geometric picture that emerges is not a single smooth manifold. Observable geometry naturally occupies a Whitney-stratified orbit-space bundle whose regular strata support Fisher-Riemannian path geometry, transport, holonomy, and monodromy, while singular strata encode degeneracy structure, admissible distinguishability, kernel transitions, orbit-type changes, and unresolved directions. The resulting assembly combines observational quotients, information geometry, kernel groupoids, symmetry reduction, cocycle descent, and stratified geometry into a unified framework for partial observability. The constituent mathematical ingredients—statistical experiment theory, Fisher geometry, observational quotients, groupoids, symmetry reduction, spectral projectors, Schur complements, canonical correlations, cocycle theory, and stratified geometry—are classical. The contribution of Resolution Geometry is their characterization, integration, and assembly into a single kernel-stabilizer geometry of observability. Scope and non-claims are explicit: the framework does not derive admissibility protocols from first principles, does not identify observability with ontology, does not derive physical laws from information alone, and does not reduce physics to statistics. Its purpose is to characterize, for a declared experiment, the geometry of what can be distinguished, transported, synchronized, measured, and consistently inferred. Repository The static, inferential, classificatory, and dynamical results are facets of a single object and are linked by exact identities; a result from any one layer rests on the invariants of the others. Readers building on any component are asked to cite the framework as a whole. Released under CC BY-NC-ND 4.0, [DOI]; the results are offered in the ordinary scientific manner, with the single request that their common origin be acknowledged.
We present the Interference-First Reality (IFR) framework, in which physical objectivity is not assumed as primitive but is operationally characterized through the stabilization of observer-induced probability distributions under redundant observation. The emergent shared reality is identified with the Fréchet barycenter in Jensen-Shannon geometry. Framework instantiated across five physical domains: CMB, HRV, JWST spectroscopy, large-scale structure (LSS), and gravitational-wave data (GWOSC). Published in Journal of Applied Mathematics and Physics, 14, 1627-1674 (2026). DOI: 10.4236/jamp.2026.144077
Jensen-Shannon divergence (JD) is a symmetrized and smoothed version of the most important divergence measure of information theory, Kullback divergence. As opposed to Kullback divergence it determines in a very direct way a metric; indeed, it is the square of a metric. We consider a family of divergence measures (${\text{JD}}_{\ensuremath{\alpha}}$ for $\ensuremath{\alpha}>0$), the Jensen divergences of order $\ensuremath{\alpha}$, which generalize JD as JD1=JD. Using a result of Schoenberg, we prove that ${\text{JD}}_{\ensuremath{\alpha}}$ is the square of a metric for $\ensuremath{\alpha}∊(0,2]$, and that the resulting metric space of probability distributions can be isometrically embedded in a real Hilbert space. Quantum Jensen-Shannon divergence (QJD) is a symmetrized and smoothed version of quantum relative entropy and can be extended to a family of quantum Jensen divergences of order $\ensuremath{\alpha}$ $({\text{QJD}}_{\ensuremath{\alpha}})$. We strengthen results by Lamberti and co-workers by proving that for qubits and pure states, ${\text{QJD}}_{\ensuremath{\alpha}}^{1/2}$ is a metric space which can be isometrically embedded in a real Hilbert space when $\ensuremath{\alpha}∊(0,2]$. In analogy with Burbea and Rao's generalization of JD, we also define general QJD by associating a Jensen-type quantity to any weighted family of states. Appropriate interpretations of quantities introduced are discussed and bounds are derived in terms of the total variation and trace distance.
Title: The Invariance of the Interface: Why Fisher Information Reappears in Derivations of Physical Law Description This note addresses a persistent conceptual gap in “information physics” programs that derive familiar dynamical laws by extremizing Fisher-information-based functionals: why Fisher information, rather than some other statistical functional? The proposed answer is meta-explanatory. The observer–world interface is modeled as probabilistic, and “objectivity” is formulated as Observer Equivalence (OE): invariance under reparameterization and monotonicity under coarse-graining (information-loss channels). Under these constraints, Chentsov-type characterization results single out the Fisher information metric as the unique local Riemannian geometry on the space of probability distributions (up to an overall scale). Fisher information is thus treated not as an axiom but as an invariance residue forced by OE at the probabilistic interface. To connect geometry to dynamics, the note introduces an explicit postulate—Informational Inertia—motivated as an information-geometric analogue of least-action/geodesic principles: in the absence of forcing, interface states evolve along paths of minimal statistical resistance (geodesics of the OE-fixed geometry), with physical constraints supplying the remaining “potential” structure. A single exemplar is presented: revisiting the Madelung/Reginatto (Hall–Reginatto) route to the Schrödinger equation, where the “quantum potential” arises as the geometric term associated with the Fisher penalty for reshaping the probability density. The overall scale (and thus ℏ) is explicitly acknowledged as empirical input: OE fixes the form of the geometry, not its scale. Keywords observer equivalence; Fisher information; information geometry; Chentsov theorem; coarse-graining; monotone metrics; Schrödinger equation; Madelung transformation; Reginatto; foundations of physics How to cite Ullman, G. “The Invariance of the Interface: Why Fisher Information Reappears in Derivations of Physical Law.” Zenodo. https://doi.org/10.5281/zenodo.18495542 Notes on AI assistance This manuscript was prepared with assistance from large language models for drafting, editing, and LaTeX formatting. The author takes full responsibility for the technical content, claims, and references.