arXiv preprints from January 1, 2026 through September 24, 2026 — 13:52:16 EST
Within the Segal-Bargmann representation, a generalized Rabi model is considered that includes both two-photon and asymmetric terms. It is shown that, through a suitable transformation, nearly exact solutions can be obtained using the Bethe ansatz approach. Applying this approach to the meromorphic structure of the resulting...
Large language models (LLMs) are increasingly used as automatic evaluators of generative AI outputs, a paradigm often referred to as "LLM-as-a-judge." In practice, LLM judges are imperfect predictions for the underlying truth and can exhibit systematic, non-random errors. Two main approaches have recently been proposed to address this...
Dynamical reweighting of path measures is a powerful approach for accurately evaluating slow molecular processes using modified potential energy surfaces used in enhanced sampling methods. Integrating this reweighting framework into the CP2K electronic-structure and molecular-dynamics (MD) software package delivers a robust and widely...
Purpose: To accelerate MRI further, rapid B0 field modulations can be applied during oversampled readout to capture additional physical information, e.g., Wave-CAIPI, FRONSAC, local B0 coils modulations. These methods, however, introduce additional non-Fourier-encoded dimension that cannot be resolved by FFT, posing significant...
We present a novel framework for analyzing blockchain consensus mechanisms by modeling blockchain growth as a Partially Observable Stochastic Game (POSG) which we reduce to a set of Partially Observable Markov Decision Processes (POMDPs) through the use of the mean field approximation. This approach formalizes the decision-making...
Ultra-high-resolution 360-degree video streaming is severely constrained by the massive bandwidth required to deliver immersive experiences. Current viewport prediction techniques predominately rely on kinematics or low-level visual saliency, treating users as passive physical objects governed by inertia. This theoretical limitation...
Multi-group classification arises in many prediction and decision-making problems, including applications in epidemiology, genomics, finance, and image recognition. Although classification methods have advanced considerably, much of the literature focuses on binary problems, and available extensions often provide limited flexibility...
As large language models (LLMs) transition from chat interfaces to integral components of stochastic pipelines and systems approaching general intelligence, the ability to faithfully sample from specified probability distributions has become a functional requirement rather than a theoretical curiosity. We present the first...
Exciton dynamics in layered magnetic semiconductors provide a sensitive probe of the interplay between spin order and light-matter interaction. Here, we study thin CrSBr layers using time-resolved photoluminescence spectroscopy in an external magnetic field, revealing a step-like reduction in the exciton lifetime from 11 to 7 ps,...
We report the successful detection of the "Arid" Meteor Shower (IAU\#1130 ARD), predicted to emerge for the first time in 2021, using a publicly accessible YouTube live camera developed by us. This live camera, installed on the Subaru Telescope dome in the summit region of Maunakea, Hawai'i, features a wide field of view (70 deg by 40...
This work investigates how measuring information entropy of text can be used to estimate its readability. We propose a visualization framework that can be used to approximate information entropy of text using multiple language models and visualize the result. The end goal is to use this method to estimate and improve readability and...
Finite element (FE) simulations emulating transcatheter pulmonary valve (TPV) system deployment in patient-specific right ventricular outflow tracts (RVOT) assume material properties for the RVOT and adjacent tissues. Sensitivity of the deployment to variation in RVOT material properties is unknown. Moreover, the effect of a...
We give a Hamiltonian formulation of %the first order Weyl--Einstein--Cartan gravity which is covariant from the viewpoint of the geometry of the principal fiber bundle. The connection is represented by a $1$-form with values in the Poincaré Lie algebra, which is defined on the total space of the orthonormal frame bundle fibered over...
Electromagnetic formation flying (EMFF) is challenging due to the complex coupling between the electromagnetic fields generated by each satellite in the formation. To address this challenge, this article uses alternating magnetic field forces (AMFF) to decouple the electromagnetic forces between each pair of satellites. The key idea...
Knowledge distillation (KD) has the potential to accelerate MARL by employing a centralized teacher for decentralized students but faces key bottlenecks. Specifically, there are (1) challenges in synthesizing high-performing teaching policies in complex domains, (2) difficulties when teachers must reason in out-of-distribution (OOD)...
Inspired by the pullback method in the recent work of Sangiovanni-Vincentelli--Skinner, we reconstruct the diagonal class of Darmon--Rotger. Moreover, we reinterpret the computation of the $p$-adic regulator formula.
Amorphous elastomers exhibit significant rate-stiffening and unique viscous flow characteristics across a wide range of strain rates, often undergoing glass transition above a strain rate threshold. We have developed a thermodynamically-consistent and micromechanically-inspired constitutive model for soft elastomers to capture the...
Optimal damping aims at determining a vector of damping coefficients $ν$ that maximizes the decay rate of a mechanical system's response. This problem can be formulated as the minimization of the trace of the solution of a Lyapunov equation whose coefficient matrix depends on $ν$. For physical relevance, the damping coefficients must...
Large language models (LLMs) have been widely applied across various domains of finance. Since their training data are largely derived from human-authored corpora, LLMs may inherit a range of human biases. Behavioral biases can lead to instability and uncertainty in decision-making, particularly when processing financial information....
This paper discusses the modeling of acoustic wave fields in microstructured elastic solids within the framework of Riemann-Cartan geometry. We consider a scenario in which microstructural deformations occur significantly faster than those of the bulk material. This time-scale separation creates apparent geometric incompatibilities at...
Wind turbines play a critical role in the shift toward sustainable energy generation. Their operation relies on multiple interconnected components, and a failure in any of these can compromise the entire system's functionality. Detecting faults accurately is challenging due to the intricate, non-linear, and non-stationary nature of...
As an emergent process, creativity relies on explorations via sampling and prototyping for problem construction. These activities compile knowledge, provide a context enveloping the solution, and answer questions. With Generative AI, practitioners can go beyond sampling existing media towards instantly generating and remixing new...
This work approaches the multidimensional scaling problem from a novel angle. We introduce a scalable method based on the h-plot, which inherently accommodates asymmetric proximity data. Instead of embedding the objects themselves, the method embeds the variables that define the proximity to or from each object. It is straightforward...
CLIP and BiomedCLIP are examples of vision-language foundation models and offer strong cross-modal embeddings; however, they are not optimized for fine-grained medical retrieval tasks, such as retrieving clinically relevant radiology reports using chest X-ray (CXR) image queries. To address this shortcoming, we propose a multi-task...
This paper deals with distributed optimization problems that use compressed communication to achieve efficient performance and mitigate communication bottleneck. We propose a family of compression schemes in which operators transform vectors fed to their input according to a Markov chain, i.e. the stochasticity of the compressors...