arXiv preprints from January 1, 2026 through September 23, 2026 — 02:43:13 EST
Contextualization is essential for production automatic speech recognition (ASR) systems, where user-provided phrases must be recognized accurately under strict latency constraints. Although many context-biasing methods improve recognition accuracy, they often do not address the practical requirements of modern production ASR systems:...
Virtual distillation is a powerful near-term error-mitigation primitive, but it is also a spectral filter: it amplifies the dominant eigenvector component already present in the noisy density matrix. We show that, for a finite-band variational state, this filtering cannot create new concentration inside a set of accepted measurement...
Natural-language workflows offer a software-like interface for agents: domain experts can write reusable procedures, and agents can execute them as instructions. This promise is not yet reliable. Workflow descriptions often leave data dependencies implicit, so the executor must infer which prior results a step should use; agents can...
We prove that stable nonlocal minimal cones in three dimensions are flat, for $s$ sufficiently close to zero. Our approach develops new structural properties of stable $s$-minimal surfaces in every dimension as $s \downarrow 0$, notably a compactness theory and pinching theorems near a half-space.
We parametrize the isometry and equivalence classes of skew constacyclic codes over a finite field by classifying the corresponding classes of their ambient rings, and present algorithms for the parametrizations. We achieve a tight classification by taking all possible Hamming-weight preserving isomorphisms between their ambient Petit...
Producing very large unweighted event samples for high-multiplicity processes is limited by expensive matrix-element evaluations and low unweighting efficiencies. We present the first end-to-end GPU-resident event-generation workflow that integrates normalizing-flow proposals with the parton-level event generator Pepper....
Markov chain Monte Carlo algorithms can be viewed as feedback devices that compare a proposed move with the target distribution and then accept or reject it. In this paper the Maxwell demon is identified with the acceptance module: it measures a proposed edge, stores the outcome in the accept/reject bit, and uses that bit to shape the...
Heisenberg uncertainties lead to inevitable fluctuations in the measurement outcomes for quantum-mechanical observables. For quantum fields, these uncertainties result in random spatial structures in snapshots of a field, even when the field is in its ground (vacuum) state. Such `vacuum fluctuations' are at the heart of a wide range...
Exciton condensates provide a platform to study quasiparticle pairing, Bose-Einstein condensation-Bardeen-Cooper-Schrieffer (BEC-BCS) crossover, and excitonic topological phenomena. Achieving a nonequilibrium exciton condensate allows the ultimate tunability of these emergent phenomena. Yet, evidence of a light-induced, nonequilibrium...
We reconstruct the recent star formation and core-collapse supernova (ccSN) histories of the Solar Neighborhood from the past trajectories of young star clusters. Using a \textit{Gaia}-based cluster sample with newly derived ages, masses, and bulk 3D velocities, we integrate orbits backward in an assumed axisymmetric Galactic...
We present a theoretical framework for self consistent treatment of nonlinear light-matter interactions in the ultra-fast strong-field regime based on numerical solution of Maxwell's equations and semiconductor Bloch equations. This framework is shown to describe high-order harmonic generation and propagation in bulk semiconductors,...
In the article a new approach to construction of generalized coherent states in quantum billiards is proposed. The coherent states are defined as the projections of a Gaussian wave function on the basis of the eigenstates of the quantum billiard, continued outside. The continuation is built as the solution of the equivalent...
We report experimental evidence for the lower-hybrid drift instability in the current sheet normal direction of electron-only magnetic reconnection. In our laser-driven capacitor-coil experiment, the system size ($\sim$3 ion skin depths) places it in the electron-only regime. Yet, Thomson scattering reveals out-of-plane electron drift...
Aaronson and Ambainis (Theory of Computing, 2014) conjectured that quantum query algorithms admit efficient almost-everywhere classical simulation: for any $T$-query quantum algorithm, its acceptance probability can be approximated on a $(1-δ)$ fraction of inputs, up to $ε$ additive error, using $\mathrm{poly}(T, 1/ε, 1/δ)$ classical...
Transverse baryon acoustic oscillation (BAO) measurements are increasingly used for cosmological inference, and carry a calibration that no such inference can constrain. A constant error in the transverse BAO scale is exactly degenerate with the combination $r_{\rm d} h$: it leaves the goodness of fit unchanged and the recovered...
Semi-inclusive deep-inelastic scattering (SIDIS) of leptons is a vital tool for probing the three-dimensional momentum space partonic structure of the nucleon. Reliable extraction of the intrinsic collinear or transverse momentum dependent parton distributions from SIDIS data requires careful treatment of QED radiative effects beyond...
Warm inflation in $F(Φ)R$ scalar-tensor theories of gravity is investigated in the `defining' frame, where the theory and its parameter values are specified. Translating the resulting dynamics to the Einstein frame, we find that the dissipation ratio is suppressed by the modified-gravity effects. Thus, although the effective...
Strong atom-photon interactions in optical cavities are a key resource for quantum information processing, quantum networking, and the exploration of quantum optical effects. Optical tweezer arrays offer scalable, site-resolved control of neutral atoms, but their integration with high-cooperativity cavity QED systems remains...
The discovery of massive galaxies at redshifts $z\gtrsim10$ by the James Webb Space Telescope (JWST) has renewed interest in cosmological mechanisms capable of enhancing early structure formation while preserving the successful large-scale predictions of the standard $Λ$CDM model. We investigate a phenomenological scenario in which an...
It is known that the attractive singular inverse-square potential gives rise to the critical quantum collapse in the framework of the three-dimensional (3D) linear Schroedinger equation. This article summarizes theoretical results which demonstrate suppression of the collapse, caused by this singular potential, and the creation of the...
Spatial statistics has grown from kriging for spatial prediction into a broad framework for learning from complex dependent data. This article traces that development from random fields and spectral methods to Bayesian hierarchical models and scalable computation. It then connects these foundations to Spatial AI, where graph learning...
Decision tree-based models are widely used in machine learning due to their interpretability and strong empirical performance. However, training decision trees can be computationally expensive, particularly for large and high-dimensional datasets, largely due to the exhaustive search over candidate splits at each node. To improve...
This paper develops a general transfer learning framework for nonparametric regression with data consisting of multiple groups. Under the assumption that groups share a common structure along with group-specific deviations in additive form, the proposed method employs a two-stage offset learning procedure: the first stage pools data...
Autism spectrum disorder (ASD) is associated with heterogeneous alterations across distributed brain systems, creating challenges for whole-connectome inference. The difficulty arises not only from the large number of connections, but also from dependence among effects indexed by anatomically and functionally related region pairs. We...
Practitioners inferring causality from observational data usually rely on a single method and treat its output as causal truth. Recent tools select an optimal method for a dataset, and recent ensembles aggregate multiple causal-discovery algorithms into one graph, but little work pools evidence across different mathematical...