arXiv preprints from January 1, 2026 through September 23, 2026 — 07:48:32 EST
By exploiting the Aharonov-Casher effect we demonstrate a suppression of magnetic flux tunneling in a Hybrid Charge Quantum Interference Device. The main part of this device is two Josephson junctions with a small superconducting island between them. To minimize phase fluctuations across Josephson junctions, this structure is embedded...
This paper investigates the dynamics of vegetation patterns in water-limited ecosystems using a generalized Klausmeier model that incorporates non-local plant dispersal within a finite habitat. We establish the well-posedness of the system and provide a rigorous analysis of the conditions required for vegetation survival. Our results...
We study Johnson amenability for unconditional direct sums of Banach algebras. Given a family $(A_i)_{i\in I}$ of Banach algebras and a Banach sequence lattice $E$ on~$I$, the $E$-sum $\bigl(\bigoplus_{i\in I} A_i\bigr)_{\!E}$ carries a natural Banach algebra structure via coordinatewise multiplication. Under the hypothesis that $C_E...
Given a coaction $δ$ of a locally compact group $G$ on a $\mathrm{C}^*$-algebra $A$, we study the relationship between two different forms of coaction invariance of ideals of $A$ and the ideals of the corresponding crossed product $\mathrm{C}^*$-algebra $A \rtimes_δ G$. In particular, we characterize when these two notions of...
Robust text-to-SQL over complex, real-world databases remains brittle even with modern LLMs: iterative refinement often introduces syntactic and semantic drift, corrections tend to be non-transferable across queries, and naive use of large context windows scales poorly. We propose a controlled text-to-SQL framework built around...
Recent advances in mathematical reasoning typically rely on massive scale, yet the question remains: can strong reasoning capabilities be induced in small language models ($\leq1.5\text{B}$) under extreme constraints? We investigate this by training models on a single A40 GPU (48GB) for under 24 hours using Reinforcement Learning with...
Deep research agents powered by Large Language Models (LLMs) can perform multi-step reasoning, web exploration, and long-form report generation. However, existing systems remain largely autonomous, assuming fully specified user intent and evaluating only final outputs. In practice, research goals are often underspecified and evolve...
We present the first comprehensive evaluation of cross-lingual unlearning in multilingual LLMs. Using translated TOFU benchmarks in seven language/script variants, we test major unlearning algorithms and show that most fail to remove facts outside the training language, even when utility remains high. However, subspace-projection...
We study the class structure of finite-alphabet Markov chains with arbitrary memory length. To capture the structural constraints induced by prohibited transitions, we introduce the skeleton of a higher-order transition kernel, defined as a reduced set of contexts encoding all essential zero-probability patterns. To each skeleton we...
Accurate characterization of carbon nanotube morphologies in electron microscopy images is vital for exposure assessment and toxicological studies, yet current workflows rely on slow, subjective manual segmentation. This work presents a unified framework leveraging vision foundation models to automate the quantification and...
Model merging has emerged as a promising technique for combining multiple fine-tuned models into a single multitask model without retraining. However, the factors that determine whether merging will succeed or fail remain poorly understood. In this work, we investigate why specific models are merged better than others. To do so, we...
Gaussian graphical models provide a powerful framework for studying conditional dependencies in multivariate data, with widespread applications spanning biomedical, environmental sciences, and other data-rich scientific domains. While the Graphical Horseshoe (GHS) method has emerged as a state-of-the-art Bayesian method for sparse...
This paper addresses the challenge of classroom allocation in higher education institutions, with an explicit emphasis on accessibility for Persons with Disabilities (PwDs). Employing a case study of a university's computer science department, the paper proposes an Integer Linear Programming (ILP)-based optimization model, which is...
The internal energy associated with the defect microstructure of strongly deformed crystals provides an important driving force for grain boundary motion during recrystallization. Typical dislocation microstructures are strongly heterogeneous and this heterogeneity affects the motion of recrystallization boundaries. In this study, a...
Losses from two-level systems (TLS) associated with amorphous oxides remain one of the primary limitations to the performance of superconducting qubits and microwave cavities. Niobium resonators are widely used in quantum science experiments, yet niobium's natural oxide layer contains various types of oxides whose relative...
Ransomware is still one of the most serious cybersecurity threats. Victims often pay but fail to regain access to their data, while also facing the danger of losing data privacy. These uncertainties heavily shape the attacker-victim dynamics in decision-making. In this paper, we introduce and analyze zkRansomware. This new ransomware...
Large language models (LLMs) often hallucinate, yet most existing fact-checking methods treat factuality evaluation as a binary classification problem, offering limited interpretability and failing to capture fine-grained error types. In this paper, we introduce InFi-Check, a framework for interpretable and fine-grained fact-checking...
Quantum computation (QC) and digital quantum simulation (DQS) essentially require two- or multi-qubit controlled-NOT or -phase gates. We propose an alternative pathway for QC and DQS using a three-qubit parity gate in a Rydberg atom array. The basic principle of the Rydberg atom parity gate (RPG) is that the operation on the target...
Dominant sequence models like the Transformer represent structure implicitly through dense attention weights, incurring quadratic complexity. We propose RewriteNets, a novel neural architecture built on an alternative paradigm: explicit, parallel string rewriting. Each layer in a RewriteNet contains a set of learnable rules. For each...
Real-time traffic prediction is critical for managing transportation systems during hurricane evacuations. Although data-driven graph-learning models have demonstrated strong capabilities in capturing the complex spatiotemporal dynamics of evacuation traffic at a network level, they mostly consider a single dimension (e.g.,...
Large language model-based agents are rapidly evolving from simple conversational assistants into autonomous systems capable of performing complex, professional-level tasks in various domains. While these advancements promise significant productivity gains, they also introduce critical safety risks that remain under-explored. Existing...
This work introduces a robust single-channel inverse filter for dereverberation of non-ideal recordings, validated on real audio. The developed method focuses on the calculation and modification of a discrete impulse response in order to filter the characteristics from a known digital single channel recording setup and room...
The theory of $f(R)$ gravity with constant curvature (i.e. constant scalar curvature) admits rotating and charged black hole solutions obtained from the Kerr-Newman-(A)dS metrics of general relativity through appropriate rescalings of the metric parameters. In this paper, we focus on the Kerr-Newman-de Sitter case and present a...
A constructive modification of the moving frame method is developed in this paper for the construction of relative invariants of regular Lie group actions. Let a relative invariant $I$ of weight $ω$ transform according to the rule $$ I(g \cdot \boldsymbol x) = μ(g, \boldsymbol x)^ω I(\boldsymbol x), $$ where $μ: G \times \mathcal{M}...
Spinodal decomposition can degrade corrosion resistance and embrittle materials. The ability to quickly, conclusively, and non-destructively detect the onset of spinodal decomposition before catastrophic materials degradation would represent a significant advance in materials testing. We demonstrate that spinodal decomposition can be...