Papers

This study investigates semiparametric efficient estimation of causal and structural parameters in a semi-supervised setting. In our setting, unlabeled auxiliary regressors are available in addition to labeled observations consisting of outcomes and regressors. Our goal is to construct estimators of causal and structural parameters whose asymptotic variances are smaller than those of estimators constructed using only labeled data. We refer to this framework as prediction-powered causal inference (PPCI). We first derive the efficient influence function and the efficiency bound, which imply that the use of auxiliary regressors can attain a smaller asymptotic variance than the efficiency bound attainable from labeled observations alone. Then, by combining the efficient influence function with the debiased machine learning (DML) framework, we propose methods that we call DML-PPCI. If we construct an estimating-equation estimator, we refer to the method as EE-DML-PPCI; if we construct a targeted-learning estimator, we refer to the method as TMLE-DML-PPCI. The asymptotic variances of both estimators match our derived efficiency bound. In the construction of the estimators, estimation of the efficient influence function plays an important role. In our study, the efficient influence function is also a Neyman orthogonal score, which depends on the Riesz representer and the regression function. For Riesz representer estimation, we develop semi-supervised generalized Riesz regression with convergence rate guarantees.

In this paper we establish optimal Lorentz estimates for the Riesz potentials acting on closed or co-closed $k$-forms of finite mass on a smooth, compact Riemannian manifold of dimension $n$: For $\alpha \in (0,n)$ and $k=1,\ldots,n-1$, there exists a constant $C>0$ such that \begin{align*} \| \mathcal{I}_{\alpha,k} F \|_{L^{n/(n-\alpha),1}(\Lambda^k)} \leq C \| F\|_{L^1(\Lambda^k)} \end{align*} for all $k$-forms $F \in L^1(\Lambda^k)$ orthogonal to the space of harmonic $k$-forms and satisfying $\mathrm{d} F=0$ or $\mathrm{d}^* F=0$. We show how this inequality implies analogous Lorentz bounds for solutions of the $k$-form Poisson equation and for the Hodge system with data having finite mass. These results include as a special case the div–curl system on the $3$-dimensional torus, where we answer an open question originally posed by J. Bourgain and H. Brezis.

Pinching antenna (PA) systems have recently emerged as a promising flexible-antenna technology, which can reconstruct the wireless propagation environment by dynamically adjusting the positions of pinching elements along dielectric waveguides, thereby providing new spatial degrees of freedom (DoFs) for enhancing wireless system performance. This paper investigates a multi-waveguide PA-based multi-cell communication system, focusing on the joint optimization of precoding matrices, waveguide power allocation, and antenna placement to maximize the weighted sum rate (WSR). In multi-cell scenarios, inter-cell interference typically leads to a highly coupled and nonconvex WSR maximization problem. To address this challenge, an efficient alternating optimization framework is adopted to optimize each variable in an iterative way. Specifically, fractional programming is first employed to reformulate the original problem by introducing auxiliary variables that decouple the signal and interference terms. Based on this reformulation, block coordinate descent is then applied to optimize the precoding matrices and power allocation, leading to closed-form or semi-closed-form updates. For the high-dimensional and nonconvex PA placement problem, particle swarm optimization (PSO) is utilized to perform an efficient search and improve scalability. Numerical results demonstrate that, under various system configurations, the proposed scheme significantly outperforms baseline methods, including average power allocation, fixed antenna placement, conventional multiple-input multiple-output (MIMO), and massive MIMO. These results highlight the strong potential of PA systems for large-scale multi-cell wireless communications.

We study a variant of the $k$-in-a-row game in which players alternatively claim positions until a $k$-in-a-row is created among all claimed positions. This leads to the constraint near $k$-in-a-row avoiding on configurations and the associated problem of determining their extremal densities of such configurations. We investigate this problem on two types of boards: the grid $\mathbb{Z}^2$ and hypercubes $[k]^d$. For the grid $\mathbb{Z}^2$, we establish nearly tight bounds on the maximum density $D(k,\mathbb{Z}^2)$, showing that $D(k,\mathbb{Z}^2)=1-\frac{2}{k}$ whenever $3\nmid k$, and determine both $D(3,\mathbb{Z}^2)$ and $d(3,\mathbb{Z}^2)$ exactly. We also bound the minimum density $d(k,\mathbb{Z}^2)$ up to a gap of $(8+o(1))k^{-1}$. For hypercubes $[k]^d$, we derive asymptotic bounds on $D(k,[k]^d)$ up to order $k^{-2}$ and obtain the exact value of $d(k,[k]^d)$. Our results contrast with the classical no-$(k+1)$-in-line problem, a similar problem imposing different constraint, where the trivial upper bound is conjectured to be attainable.

This paper studies a variation of the classic network alignment problem, named diffusion-network alignment. The goal is to align the vertices of a rooted diffusion tree to the vertices of a network, where the diffusion tree could be from a communication trace or contact tracing, and the network could be an online or offline social network. Different from the classic network alignment where both networks are fully observed, this model captures the information asymmetry of two networks. To solve this problem, this paper presents an efficient algorithm based on tree correlation tests to extract alignment information from local neighborhoods. We analyze the performance of the algorithm in the sparse graph regime and show that with high probability, all matched pairs are correct. Furthermore, for each vertex on the diffusion tree, this paper establishes an explicit lower bound on the probability that the vertex is correctly matched. These lower bounds are depth-dependent and increase as vertices get closer to the root.

We use the Guillemin–Abreu formalism to give a unified treatment of Kähler metrics with positive holomorphic bisectional curvature on $\mathbb C^n$ and positive holomorphic sectional curvature on $\mathcal O(-\ell)$ and the Hirzebruch manifolds $M_{n,\ell}$.

As large language models (LLMs) are increasingly deployed across heterogeneous hardware with varying resource constraints, the ability to adaptively manage the performance-efficiency trade-off without retraining is critical. We propose Drop-by-Drop, a novel multi-bitwidth post-training quantization framework enabling inference-time precision control over LLM weights from a single quantized model. As theoretical motivation, we prove that Gaussian sources are successively refinable under a weighted mean squared error distortion motivated by LLM loss functions: successive refinement across distortion levels incurs no rate penalty relative to encoding at each level separately. Drop-by-Drop approximates this hierarchical structure in practice by incorporating Matryoshka-style supervision into additive codebook training, inducing an ordering in which codebook prefixes yield accurate partial reconstructions at each precision level. Furthermore, a block-Hadamard rotation brings the weight distribution closer to our Gaussian source assumption. The result is a single model that serves multiple bitwidths by dropping codebooks, reducing storage and quantization cost relative to the static per-bitwidth models. Across Qwen, LLaMA, Gemma, and Mistral, Drop-by-Drop achieves lower perplexity than state-of-the-art multi-bitwidth methods with competitive zero-shot accuracy, while our specialized kernels further reduce decoding latency.

We study continuous-time reinforcement learning for the optimal switching problem, in which a decision-maker controls a diffusion process by switching among finitely many regimes, incurring both running and transition costs. To enable exploration, we relax the classical deterministic switching control to a randomized framework, where the switching decisions are governed by a continuous-time Markov chain with state-dependent generator, and augment the cost functional with a KL-divergence regularization weighted by a temperature parameter $\lambda$. Under mild assumptions on the coefficients, we establish that the regularized value function is the unique smooth solution of an elliptic Hamilton–Jacobi–Bellman system, and derive an explicit optimal Gibbs policy given by an exponential transformation of the value function differences across modes. We further prove that the regularized value function approximates the classical optimal value function with error of order $O\left(\lambda \log \frac{1}{\lambda}\right)$, which is consistent with analogous bounds established in other entropy-regularized control problems and is believed to be sharp. To solve the regularized problem numerically, we introduce a mirror descent flow in the dual logarithmic policy space, prove its well-posedness and the monotonic decrease of the value function along the flow, and establish quantitative error bound to the classical optimal value function. For a constant temperature scheduler, the convergence rate is of order $O\left(\frac{1}{e^{\lambda s} - 1}+\lambda \log\frac1\lambda\right)$, while under the annealing scheduler $\lambda_s = \frac{1}{\sqrt{1+s}}$, we obtain the rate $O\left(\frac{\log s}{\sqrt{s}}\right)$, which decays to zero as the flow time $s \to \infty$.

In this paper, we propose a new approach, called maximum utility split (MUS) scheme, which is built on random utility split (RUS) scheme but with a notable difference: one lottery is designed with two fixed outcomes but with varying probability, and the other has a deterministic outcome specifically chosen at the point where the range between the largest and smallest possible utility values is maximized. Consequently, the probability of random lottery is set such that the range of the ambiguity set of utility functions is reduced by half at the point. Under moderate conditions, we show that MUS can successively generate a sequence of such questionnaires and effectively reduce the ambiguity set, eventually converging to the true utility function as the number of questionnaires increases. The main challenge is to effectively identify the point with the largest utility range for a given ambiguity set constructed from preference information. Based on the structure of the ambiguity set, we propose an interval-based algorithm which identifies each certain-outcome lottery by solving a sequence of linear programs. Moreover, to deal with the case where elicitation terminates before the ambiguity set reduces to a singleton, we demonstrate how to figure out a nominal utility function by solving optimization programs. These identify the smallest and largest utility functions under the Kantorovich metric within the ambiguity set, after which we identify a nominal utility function located in the middle of them. Finally, numerical results demonstrate the efficiency of the MUS method and the performance of a robo-advisor system based on MUS-type queries and the nominal utility elicitation. While the main discussions focus on concave utility functions, we also demonstrate how the MUS approach can be extended to accommodate general non-concave utility functions, particularly S-shaped ones.

We introduce certain families of AF algebras associated to Bratteli diagrams arising from numerical semigroup theory, a branch of combinatorics. Curry-Schoenberg B-splines, staples of computer-aided design, provide insight into the statistical properties of these algebras. This permits us to consider certain ensembles of "rapidly growing" AF algebras from a probabilistic viewpoint.

The aim of this paper is to study $m$-partial isometries on Hilbert spaces, a natural extension of partial isometries and $m$-isometries. We establish structural and spectral results, characterize the $m$-partial isometric weighted shifts, and investigate similarity to $m$-isometries and $m$-partial isometries.

We introduce a partition-selection method to generalize the flow, chromatic, and Tutte polynomials of a graph by restricting the standard edge subgraph expansions to subgraphs given by prescribed connected vertex partitions. We establish similar deletion-contraction formulas and specialization relations for these polynomials, recovering all classical polynomial invariants when the selection is the set of all partitions. Next we study a relation between Jaeger et al.'s nonhomogeneous flows and a special class of partition-selected flow polynomials (called affine flow polynomials). Specifically, we give a geometric realization of nowhere-zero nonhomogeneous flows by restricting the edge-coordinate arrangement to affine flow spaces. The resulting characteristic polynomials coincide with Kochol's admissible assigning polynomials and with affine flow polynomials, which enumerate nowhere-zero nonhomogeneous flows over finite fields. To see the key role of the partition-selection framework, we further introduce boundary arrangements determined by the bond structure of a graph. Using the intersection posets of boundary arrangements, we obtain the classification of all restricted arrangements mentioned above, the comparison of unsigned coefficients of affine flow polynomials, and the decomposition formulas for affine flow polynomials.

Conventional communication systems, including both separation-based coding and learning-based joint source-channel coding (JSCC), are typically designed under Shannon's rate-distortion theory. However, relying on generic distortion metrics fails to capture complex human visual perception, often resulting in blurred or unrealistic reconstructions. In this paper, we propose Joint Source-Channel-Generation Coding (JSCGC), a generative communication paradigm that replaces the conventional decoder with a generative model at the receiver. The received signal is treated as a condition that controls the sampling process into the learned conditional distribution, reformulating communication from deterministic reconstruction for distortion minimization to controlled generation for mutual information maximization under perceptual constraints. Based on this formulation, we develop a unified joint training and efficient stochastic sampling framework, and provide theoretical analysis of its effectiveness in both learning and inference stages. Extensive experiments on latent-space image transmission demonstrate that the JSCGC consistently improves feature-based, semantic-level, and distributional quality across diverse channel conditions, while exhibiting a distinct error behavior characterized by semantic inconsistency rather than distortion.

We compute the capacity region of a sum of broadcast channels whose components are degraded, less-noisy, more-capable, deterministic, or semi-deterministic. We achieve this by showing that an auxiliary-receiver outer bound, previously introduced by some of the authors, matches Marton's inner bound. This result generalizes a previously known result for the sum of two reversely degraded broadcast channels due to El Gamal (1980). Moreover, we define a class of primary broadcast channels and show an analogous result for the sum of primary broadcast channels.

Spherical Interpolation of orDER $n$ (SIDER-$n$) is a recursive high-order interpolation method for data on the unit sphere $\mathbb{S}^2$, built from repeated spherical linear interpolation (SLERP). This paper develops a quaternion–Lie algebra framework for proving the local consistency of SIDER for smooth spherical curves sampled at equally spaced parameter values. Points on $\mathbb{S}$ are represented as pure unit quaternions, and interpolation errors are measured in fixed-base quaternion logarithmic coordinates. In this setting, each SLERP operation admits an exact Baker–Campbell–Hausdorff (BCH) representation, which converts the geometric interpolation problem into an algebraic problem involving filtered Lie-polynomial expansions. The BCH expansion shows that SLERP is affine to leading order, has no quadratic correction, and has a first nonlinear correction that is cubic and commutator-valued. Using this structure, we prove that SIDER2 has a third-order divided-error form with the same leading nodal factor as ordinary quadratic interpolation. We then show that the recursive SIDER step raises the order by one: the affine part gives the Neville-type finite-difference cancellation, while the nonlinear BCH remainder preserves the sharp filtered degree structure after the nodal factor is removed. Consequently, for every fixed $n\geq2$, $d_{\mathbb{S}^2}\bigl(\gamma(\theta h),P_i^{[n]}(\theta;h)\bigr) = O(h^{n+1}) $under the stated smoothness and small-stencil assumptions. The proof also identifies the shift-invariance of the leading divided-error coefficient as the algebraic compatibility condition underlying the SIDER recurrence.

Let $T_{1}, T_{2}, \dots, T_{k}$ be $k$ spanning trees of a graph $G$. For any pair of vertices $u$ and $v$, if the $u$–$v$ paths in the $k$ spanning trees are pairwise openly disjoint, then the spanning trees are called completely independent spanning trees (CISTs) of $G$. In this paper, we first prove that every 3-connected 2-outerplanar triangulated disc has two completely independent spanning trees. Next, for a 3-connected 3-outerplanar triangulated disc $G$, we provide sufficient conditions for $G$ to have two completely independent spanning trees. We provide an example of a 3-connected 4-outerplanar triangulation that does not have two completely independent spanning trees.

We establish an optimal upper bound formula for ensemble averages of flow quantities associated with the three-dimensional incompressible Navier-Stokes equations. The formula takes the form of a minimax problem, extending the framework developed by Tobasco, Goluskin, and Doering (2018) for finite-dimensional systems and by Rosa and Temam (2022) for the two-dimensional Navier-Stokes equations. The lack of global well-posedness for the 3D case presents a significant challenge, which we overcome by working within the space of Foias-Prodi stationary statistical solutions. The minimax formula is derived by exploiting suitable compactness and continuity properties of specific subspaces of probability measures under the weak topology of the phase space. A distinguishing feature of our result is the characterization of the maximizing measures: unlike the previous cases, the optimal bounds in 3D are achieved on extreme points that are specific convex combinations of at most two Dirac delta measures, instead of exactly one, a structure that naturally appears from the constraints given by the mean energy dissipation inequalities in the characterization of the Foias-Prodi stationary statistical solutions.

This paper investigates a class of nonautonomous multiscale McKean-Vlasov stochastic systems. By leveraging the nonautonomous Poisson equation, we rigorously establish both strong and weak averaging principles, accompanied by explicit convergence rates. Notably, the coefficients of the averaging equations derived in the general case retain dependence on the scaling parameter $\varepsilon$. However, under the additional assumptions that the fast-scale coefficients are either asymptotically convergent or time-periodic, we demonstrate that the slow component converges, in the strong or weak sense, to averaging equations with coefficients independent of $\varepsilon$.

We associate to each positroid variety in the Grassmannian $\mathrm{Gr}(k,n)$ a polyhedral complex, which we call a fence complex. Fence complexes consist of unions of faces of the Gelfand-Tsetlin polytope $P_{k,n}$ associated to a fundamental weight $\omega_k$. We show that these fence complexes are homeomorphic to closed balls. Furthermore, they endow the Gelfand-Tsetlin polytope with the structure of a regular CW complex, giving a polyhedral complex presentation of the regular CW complex structure on $\mathrm{Gr}(k,n)_{\geq 0}$. We also show that the Ehrhart polynomial of a fence complex equals the Hilbert polynomial of the associated positroid variety. We prove that under the Sturmfels-Gonciulea-Lakshmibai degeneration of $\mathrm{Gr}(k,n)$ to the toric variety of the Gelfand-Tsetlin polytope, positroid varieties degenerate to the reduced union of toric varieties corresponding to their fence complexes. As an application, we classify when positroid varieties contained inside hook Schubert varieties are arithmetically Gorenstein. We also derive a recursive character formula for cyclic Demazure modules, which we show is equivalent to a formula of Almousa, Gao and Huang.

The Jaynes-Cummings (JC) Hamiltonian is a paradigmatic model of light-matter interaction and, more generally, qubit-boson interactions, widely used across atomic, optical, and superconducting qubit platforms. In the multi-qubit setting, where n qubits are identically coupled to a single boson mode, this interaction is known as the Tavis-Cummings (TC) Hamiltonian. The structure of the TC model is usually understood in terms of two standard symmetries: permutation invariance of the qubits and a U(1) symmetry associated with conservation of the total excitation number. Here we identify an additional, independent "accidental" symmetry of the TC Hamiltonian and construct the corresponding conserved observable. We show that, for n>2 qubits, this symmetry imposes strong constraints on the realizable unitary transformations. These constraints persist in the presence of the global $J_z$ Hamiltonian, but are removed by adding $J_z^2$, even though $J_z^2$ preserves both permutation invariance and the U(1) symmetry. Finally, we explain the origin of this previously unnoticed symmetry using Schwinger's boson representation of angular momentum. These restrictions have important implications for controllability of the TC system and for its applications to quantum computing, which are investigated further in a companion paper.

We study the splitting behavior of parametrized families of polynomials over finite fields through a geometric and Galois-theoretic approach. While the underlying techniques are widely considered folklore in arithmetic geometry, they have rarely been written down explicitly. To maximize accessibility, we develop a framework based on classical Galois theory and the Chebotarev Density Theorem over an affine normal variety, avoiding the heavy machinery of Grothendieck's étale topology. The primary goal is to extend and conceptually explain a recent result by Slavov, which established the condition for square values of several polynomials over a finite field to be independent. In the case where $q\equiv 1 \pmod n$, we generalize this phenomenon to $n$-th power residues, and reframe this independence condition as the natural condition on Kummer extensions to be mutually linearly disjoint. Finally, we briefly mention how these results can be translated into the modern language of étale fundamental groups, generalizing the base to geometrically integral, normal schemes of finite type over $\mathbb{F}_q$.

We study eigenvalues and eigenfunctions of the “div-grad type" sub-Laplacian with respect to Popp's volume on a compact equiregular sub-Riemannian manifold $M$. Since Popp's volume is canonically determined by the sub-Riemannian structure of $M$, the spetra of the sub-Laplacian carry geometric meanings. In this paper, we first embed $M$ into the Hilbert space of square-summable sequences using eigenfunctions and then define a spectral distance between two compact equiregular sub-Riemannian manifolds. Our result is a sub-Riemannian analogue of Berard-Besson-Gallot's classical work in the Riemannian case.

Spherical Interpolation of orDER $n$ (SIDER-$n$) is a recursive high-order interpolation construction for data on the unit sphere $\mathbb{S}^2$, built from repeated spherical linear interpolation (SLERP). This paper gives a local consistency analysis of SIDER for smooth spherical curves sampled at equally spaced parameter values. The analysis is carried out in geodesic normal coordinates, which allows the SIDER recursion to be compared with classical Neville interpolation while retaining the curvature-dependent corrections introduced by SLERP. We first derive local expansions of SLERP and show that SIDER2 has third-order accuracy; its leading error has the same shifted nodal structure as Euclidean quadratic interpolation. We then prove that the adjacent SIDER2 errors entering SIDER3 have a common leading coefficient, so that the SIDER3 recurrence cancels the cubic term and yields fourth-order accuracy. Carrying the expansion one order further gives the corresponding coefficient compatibility for SIDER3 and proves fifth-order accuracy of SIDER4. Finally, we introduce a degree-filtered formal expansion framework for the general SIDER recursion. This framework proves that, for each fixed $n$, SIDER-$n$ preserves the required polynomial degree structure in the normalized stencil variable. Together with the interpolation conditions at the $n+1$ nodes, this yields the local consistency estimate $d_{\mathbb{S}^2}\bigl(\gamma(\theta h),P_i^{[n]}(\theta;h)\bigr)=O(h^{n+1})$ under the stated smoothness and small-stencil assumptions.

Digital elevation models (DEMs) have reached resolutions and sizes that only parallel computaters can efficiently process. One important application of DEMs is predicting how much water flows where, the so-called “flow routing problem” (a variation of which is the problem of determining the drainage area upstream of a point in a DEM). The traditional algorithm for flow routing is sequential, and attempts to parallelize this method have so far only been moderately successful. Herein, we build on earlier work in Richardson et al. (2014) and propose an algorithm and several variations that can efficiently solve the flow routing problem on very large models with very large numbers of parallel processes. For the largest model we use, with 1.88 billion points, the best algorithm herein can route water in 4.0 seconds on 12,288 processes of a computer cluster.

In this paper, we present a second-order linear scheme based on the variable-step Alikhanov formula and central difference discretization for the time-fractional Allen-Cahn equation. The nonlinear potential is treated explicitly via a second-order extrapolation with preprocessing, which enables the discrete maximum-bound principle (MBP) to be preserved through an appropriate stabilization technique. Moreover, by developing a discrete fractional Grönwall inequality together with the uniform boundedness of numerical solutions guaranteed by the MBP, we establish an $\alpha$-robust and optimal second-order maximum-norm error estimate under initial weak singularity assumption. In addition, energy stability is proved in the sense that the discrete original energy is uniformly bounded by the initial energy plus a high-order spatiotemporal correction term. Finally, extensive numerical experiments are presented to demonstrate the effectiveness of the proposed scheme.

In this paper, we introduce and prove a set of rank recurrence relations for $q$-Whittaker and Macdonald operators. We also derive an explicit expression for the $k$-th power of the $q$-Whittaker operators in terms of the $q$-deformed binomial probability distribution, and we express the rank recurrence relations for the Macdonald operators in terms of the Cauchy determinant.

Thermoelastic suspension bridges operate under strong thermo-mechanical coupling, and the classical Fourier law fails to capture the finite speed of thermal propagation observed in low-conductivity materials such as concrete. This paper investigates a suspension bridge model in which the heat flux is governed by the Cattaneo law with relaxation time $\tau$, and in which the bridge deck may or may not possess viscoelastic memory. Using operator semigroup theory, we establish well-posedness and derive sharp decay rates for both regimes. For the memoryless system ($\delta=0$), we show that the semigroup is polynomially stable with rate $t^{-1/2}$ when the structural parameters satisfy $\frac{\rho_1}{\kappa} = \frac{\rho_2}{b}$, and that the rate deteriorates to $t^{-1/4}$ otherwise; moreover, exponential stability fails whenever $\chi_0 \neq 0$, or $\chi_0 = 0$ and $\chi_1 = 0$, where $\chi_0$ and $\chi_1$ are parameter-dependent coefficients. In sharp contrast, when viscoelastic memory is present ($\delta=1$), the semigroup becomes exponentially stable, regardless of the structural parameter ratio. This reveals a regularizing role of viscoelastic memory: it converts polynomial decay into exponential decay and removes the parameter-ratio obstruction. The results provide a theoretical basis for exploiting inherent viscoelasticity in long-span bridges and for optimizing deck parameters in memoryless structures.

We study abstract dynamic programs on partially ordered spaces, pairing the order-theoretic approach to dynamic programming with topological and metric foundations. We show that readily verifiable forms of topological stability, such as global stability and contractivity of the policy operators, deliver the fundamental optimality properties of dynamic programming together with convergence of value function iteration, Howard policy iteration, and optimistic policy iteration. We also prove that stationary policies dominate nonstationary policy plans under very weak assumptions. Applications include Markov decision processes, structural estimation problems in which maximization and integration are interchanged, optimal stopping without discounting, and Bayesian sequential analysis. For the last two, our results weaken existing assumptions and extend algorithmic guarantees for foundational problems.

This paper presents an AI proof that there is no algorithm deciding whether a polynomial equation over the Gaussian integers in $18$ unknowns has a solution. The proof improves the $20$-unknown theorem of Matiyasevich and Sun. It follows their rationality criterion and integer test, but saves two variables: the clearing-denominator variable is avoided by imposing two integer conditions, and the remaining nonzero condition is absorbed into the relation-combining lemma by the one-variable gadget $(2R+1)(3R+1)$.

Frobenius' theorem settles, definitively, the question of three-dimensional real division algebras: there are none. What it leaves open – and what this paper addresses – is the question of which three-dimensional real associative algebras actually exist. We call these near-trinions and classify them completely, up to $\mathbb{R}$-algebra isomorphism. The answer is exactly six isomorphism classes; we provide canonical representatives, explicit multiplication tables, and a collection of module-theoretic invariants sufficient to distinguish every pair. The classification proceeds by stratifying on the radical dimension $d$: Wedderburn–Artin resolves the semisimple stratum; in the $d=1$ stratum, the Peirce decomposition locates the radical generator in either a diagonal or off-diagonal corner, producing the commutative and non-commutative branches respectively and ruling out the residue field $\mathbb{C}$ by a characteristic sign obstruction; and the $d=2$ stratum splits on whether Jac$(A)^2$ vanishes. The argument not only recovers the six classes but explains why the radical dimension takes values in $\{0,1,2\}$, why the $d=1$ case splits on commutativity, and why no near-trinion with residue field $\mathbb{C}$ can exist. One direction for subsequent work is proposed.