Universal Latent Representation in FRC

We propose a unified mathematical framework showing that the representational universality of modern foundational models arises from a shared finite latent domain. Building on the Finite Ring Continuum (FRC) framework, we model all modalities as epistemic projections of a common latent set \(\mathcal{Z} \subset \mathcal{U}_{t}\), where \(\mathcal{U}_{t}\) is a symmetry-complete finite-field shell. Using the uniqueness of minimal sufficient representations, we prove the Universal Subspace Theorem, establishing that independently trained embeddings coincide, up to bijection, as coordinate charts on the same latent structure.
The rows state the paper’s claims in their current form. Kinds follow convention 6 of the corpus: a row is a relation unless marked [value] or [chart]. The source column names the body statement that carries each row and links its witnesses as tokens — py, lean and leanML, each followed by the row’s label: the python check that decides the row, its declaration in the core (no axioms) and its declaration on Mathlib.
#DescriptionStatus Sources
A. Inputs
A1p04001Finite sets and maps: a map between finite sets of equal cardinality is injective exactly when it is bijective; a set of cardinality at most $|U|$ injects into $U$; unique factorisation in $\Z$ (the Gödel code).I
standard; [G"odel 1931; Smullyan 1991]
A2p04002Sufficiency: minimal sufficient statistics are unique up to bijection; invoked as motivation only — no probability enters, the operative notion is set-theoretic (B3).I
[Lehmann & Scheff'e 1950; Keener 2010]
A3p04003The corpus: the shell $\Fp$, $\p=4\kap+1$, with its frame and quarter-turn (1:B2, B3); its frames $x\mapsto a+bx$ (1:C2; 00:C1); the Carrier and its readings as incomplete projections (00:A6, A6); finiteness and counting by return (00:A2, A7); $4\mid\p-1$ from the two involutions (00:B3); consolidation as frame maps, innovation as the extension $\Fpq$ where $\sqrt\nu$ is written (3:B5).I
A4p04004The empirical record: cross-modal alignment and unsupervised translation between independently trained embeddings; grokking; emergence, transfer under shared structure, latent knowledge, interpolation of arbitrary labels; hyperspherical embeddings; floating point as a finite subset of $\R$; sphere packing.I
[Radford et al. 2021; Jha et al. 2025; Power et al. 2022; Wei et al. 2022; Hernandez et al. 2022; Burns et al. 2022; Zhang et al. 2017; Chen et al. 2020; Gao et al. 2021; Ethayarajh 2019; Wang & Isola 2020; Goldberg 1991; Conway & Sloane 1999]
B. The latent domain and adequacy
B1p04005The latent domain: a finite set $\mathcal Z\subset\mathcal U_t=\Fp$, the complete relational domain a model reconstructs from its data; the shell is the ambient set and a reading of it is a projection (00:A4).D
Def. 1; Section 3
master: 00:A4
B2p04006Observation maps: $g_m:\mathcal Z\to\mathcal X_m$ deterministic, one per modality, carrying its resolution; no stochastic model and no conditional independence is assumed; every cross-modal relation factors through $\mathcal Z$.D
Eq. (2); Eq. (1)
B3p04007Adequacy: $E_m:\mathcal X_m\to W_m$ is adequate when $E_m\circ g_m=\varphi_m$ is a bijection $\mathcal Z\to W_m$; minimality, $W_m=\varphi_m(\mathcal Z)$, is part of the bijection, so the minimal adequate representation of Section 2 and Definition 2 are one notion.D
Def. 2
B4p04008Adequacy makes the observation map injective on the latent domain: $g_m(z)=g_m(z')$ forces $z=z'$; $\mathcal Z$ is the domain modality $m$ resolves completely.T
python sourceLean, the coreLean, Mathlib
Section 3
B5p04009Uniqueness up to bijection: for adequate $E_1$, $E_2$ of one $g$, $\psi=\varphi_2\circ\varphi_1^{-1}:W_1\to W_2$ is a bijection with $E_2\circ g=\psi\circ E_1\circ g$ — $E_2=\psi\circ E_1$ on $g(\mathcal Z)$, on all of $\mathcal X$ when $\mathcal X=g(\mathcal Z)$; $\psi$ is the unique map with this property.T
python sourceLean, the coreLean, Mathlib
Lemma 2; Lemma 1
B6p04010The embedding into the shell: $|W_m|=|\mathcal Z|\le\p$, so an injection $\iota_m:W_m\hookrightarrow\mathcal U_t$ exists — the one place finiteness enters; canonically $\iota_m=\varphi_m^{-1}$ followed by the inclusion; $\psi_m:=\iota_m\circ\varphi_m:\mathcal Z\hookrightarrow\mathcal U_t$ is modality $m$'s chart of $\mathcal Z$.T
python sourceLean, the coreLean, Mathlib
Section 3
C. The Universal Subspace Theorem
C1p04011Theorem 1 (i), (ii): every chart $\psi_m$ is injective, and $\Psi_{m\to n}:=\psi_n\circ\psi_m^{-1}:\psi_m(\mathcal Z)\to\psi_n(\mathcal Z)$ is a well-defined bijection, with $\Psi_{n\to k}\circ\Psi_{m\to n}=\Psi_{m\to k}$ and $\Psi_{m\to m}$ the identity.T
python sourceLean, the coreLean, Mathlib
Thm. 1
C2p04012Theorem 1 (iii): with $\iota_m=\varphi_m^{-1}$ every chart is the inclusion $\mathcal Z\hookrightarrow\mathcal U_t$ and $\psi(\mathcal Z)=\mathcal Z$; canonical structurally (unique once $\mathcal Z$ is fixed), not algorithmically; “subspace” means subset.T
python sourceLean, the coreLean, Mathlib
Thm. 1
C3p04013The canonical lift $L_m:=\varphi_m^{-1}\circ E_m:\mathcal X_m\to\mathcal Z$ satisfies $L_m\circ g_m=\mathrm{id}_{\mathcal Z}$, and $L_m(x)$ is the unique $z$ with $E_m(x)=\varphi_m(z)$.T
python sourceLean, the coreLean, Mathlib
Cor. 1
C4p04014Cross-modal consistency: $\psi\circ L_m\circ g_m=\psi\circ L_n\circ g_n=\psi$ — observations of one latent state lift to one point — and $\iota_m(W_m)=\iota_n(W_n)=\psi(\mathcal Z)=\mathcal Z$.T
python sourceLean, the coreLean, Mathlib
Cor. 2
C5p04015Charts of the shell itself: for $\mathcal Z=\Fp$ the charts respecting the arithmetic are the frames $x\mapsto a+bx$ (1:C2), $\p(\p-1)$ of them, and $\Psi_{m\to n}$ between two frames is the one affine map carrying the first to the second (simply transitive) — the corpus’s coordinate systems are the paper’s charts.T
python sourceLean, the core
Section 3
D. The reading
D1p04016Foundational embeddings are adequate representations of one finite latent domain: cross-modal alignment, transferability and semantic coherence are the bijections $\Psi_{m\to n}$ (C1), consequences of the shared domain, not of architecture; the empirical record (A4) is their registered consequence. Falsifier: two adequate embeddings of one domain with no bijective translation on the resolved states.R
Section 4; Section 5.1; 00:A8
D2p04017Scope and limits: the theorem uses the finiteness of $\mathcal Z$ alone, no algebraic or geometric property of $\mathcal U_t$; the shell as host is the ontology’s claim (A3); optimisation decides when a representation is recovered, not which; non-stationary or weakly factorisable data may admit none; coordinate alignment may be intractable; nonlinearity as expansion and depth as shell complexity are analogies (Y1).D
Section 1; Section 5; Interp. 2, 3
E. Finite ring geometry of embeddings
E1p04018The hypersphere [chart]: a Euclidean chart $\mathrm{Emb}:\mathcal Z\to\R^{d}$ equivariant for a symmetry acting transitively on $\mathcal Z$ and orthogonally on $\R^{d}$ has constant norm — $\mathrm{Emb}(\mathcal Z)\subset S^{d-1}$. The shell’s character chart $z\mapsto(\cos2\pi kz/\p,\sin2\pi kz/\p)_{k}$ is such a chart under translation, $\|\mathrm{Emb}(z)\|^{2}=\p-1$; in the shell’s arithmetic $\sum_{k}\g^{kz}\g^{k(n-z)}=n$ for every $z$.T
python sourceLean, the coreLean, Mathlib
Section 6
E2p04019Quantisation [chart]: a digital embedding has coordinates in a finite $\mathcal Q\subset\R$; at step $\Delta$ the distinguishable points on a sphere of radius $R$ number $N(R)\sim C_d(R/\Delta)^{d-1}$ (grid counts fit exponent $d-1$ at $d=2,3$); at fixed $d$, $\Delta$ the capacity grows with $R$.I
python source
Section 6.2; [Goldberg 1991; IEEE754Standard 2008; Conway & Sloane 1999]
E3p04020Gödel collapse: for distinct primes $p_1,\dots,p_d$ the code $G(x)=\prod p_i^{x_i}$ is injective on $\N^{d}$ (unique factorisation), and $G\bmod q$ stays injective on a finite set once $q$ exceeds its maximum; instance $d=3$, $x_i\le4$, primes $2,3,5$: $125$ codes, maximum $810\,000$, $q=810\,013$; signed coordinates are shifted first.T
python sourceLean, the coreLean, Mathlib
Section 6.3
E4p04021The reading of Section 6: the sphere is the continuous shadow of the shell — uniform radius the absence of intrinsic attributes, direction the relational content; the coordinate count is a representational artefact, every quantised vector one element of one field (E3); the radius reads the resolved domain’s size; Gödel codes establish representability only, not geometry or training.D
Section 6; 00:A8
Y. Hypotheses
Y1p04024Depth and the shell hierarchy: whether nonlinear layers correspond to innovation steps $\mathcal U_t\hookrightarrow\mathcal U_{t+1}$ and depth to arithmetic complexity (Interpretations 2, 3) as a theorem; named as future work (iii), not claimed.O
Section 7
T. Tasks
T1p04025Learning objectives as approximations of adequacy (contrastive, masked prediction), algorithms for the projection maps $g_m$, and a general algebraic account of multimodal learning; named as future work (i), (ii), (iv), not claimed.O
Section 7

Status

I
Import. A standard result, or a corpus result, used without reproof.
D
Definition. A naming or declared convention, carrying no empirical content.
T
Theorem. Derived, and machine-verified in exact arithmetic by the named witness.
R
Realisation. States what a physical term denotes in the substrate: constitutive, not interpretive, and carrying its falsifier.
O
Open. Genuinely unresolved.

Sources

python source
Gold: the python check that decides the predicate, at its line in the script (the source, rendered on this site); on hover the deciding family and the families that corroborate it, and the verdict of the last run (red when it fails). The script page's bar runs the predicate's cell of the package's notebook on Google Colab (one cell per predicate; the whole notebook from the header badge).
Lean, the coreLean, Mathlib
The predicate as one Lean declaration, named by its accession key (p04008) — the conjunction of the theorems that prove it — at its line in the rendered module, one mark per library that proves it, the core's first. Green: the core (lean/FrcCore) — the statement on the shell ring, by computation and induction, no Mathlib, no axioms: the finitist witness. Violet: Mathlib (lean/FrcLedger) — the classical statement for every shell on Mathlib's hierarchy (its axioms, propext, Classical.choice, Quot.sound, on hover). Red: a proof the gate rejects (an unfinished proof, native_decide) — not a witness. A predicate proved in both libraries shows both marks; one mark, one proof. The module page's bar opens the module as one executable file in Lean Live (the core on plain Lean, no Mathlib; the file elaborates in the browser and prints every predicate's axioms at its end); the header's two Lean badges count the marks of each colour and open the two executables.
section
The statement in the paper's body that carries the predicate: a proposition, a remark or a section.
rests on
The predicates it rests on, by label: realisations, definitions and imports of this ledger, predicates of other ledgers (32:B3) and of the master (00:D15).
What the reader must grant. Four imports: the finite-set facts of A1, the sufficiency principle of A2 as motivation, the corpus of A3, the empirical record of A4. Five declarations (B1–B3, D2, E4): the latent domain, the observation maps, adequacy, the paper’s scope, and the reading of the sphere. Ten theorem rows (B4–B6, C1–C5, E1, E3): B4–B6 and C1–C4 are set theory, proved twice for arbitrary types — in the core with no axioms and on Mathlib’s hierarchy — and decided on every finite model of the sizes named; C5 is the frame group of 1-algebra; E1 is proved for any transitive isometric action and holds exactly for the shell’s character chart; E3 is unique factorisation with its reduction. One import with a chart, E2. One realisation, D1: learned embeddings as adequate representations of one finite domain, with its falsifier. Not rows: the analogies of Interpretations 2 and 3, the biological and AGI readings of Sections 5.4–5.5, which the paper offers as interpretation. Two open rows, Y1 and T1. The title claim — a universal latent representation — is C1–C2 read through D1: independently trained embeddings are charts of one finite latent set, coinciding in the canonical parametrisation, and the shell is the set that hosts them (B6), with the frames of 1-algebra as its own charts (C5).