arXiv preprints from January 1, 2026 through September 23, 2026 — 12:47:01 EST
We study in the bottom-up framework the possibility to generate the quark mass hierarchies without fine-tuning, the quark mixing and CP-violation (CPV) in a flavour model with $S^\prime_4$ modular symmetry having minimal number of parameters. The model is considered in the vicinity of the fixed point $τ_\text{T}= i\infty$,...
The Clifford algebra language allows us to rewrite the Lamé-Navier system in terms of the Euclidean Dirac operator. In this paper, the main question we shall be concerned with is whether or not a higher order Lipschitz function on the boundary $Γ$ of a Jordan domain $Ω\subset\mathbb{R}^m$ can be decomposed into a sum of the two...
It is known that for a two-layer fluid system, the kinetic equation governing the evolution of the spectrum of the wave field given by the standard wave turbulence theory (WTT) breaks down due to the existence of a "double resonance", and the spectrum can evolve on a time scale much faster than that predicted by the standard WTT. In...
Recent advances in language models (LMs) have driven significant progress in various software engineering tasks. However, existing LMs still struggle with complex programming scenarios due to limitations in data quality, model architecture, and reasoning capability. This research systematically addresses these challenges through three...
Retrieval-Augmented Generation (RAG) integrates external knowledge to enhance Large Language Models (LLMs), yet systems remain susceptible to two critical flaws: providing correct answers without explicit grounded evidence and producing fabricated responses when the retrieved context is insufficient. While prior research has addressed...
Agentic AI marks an important transition from single-step generative models to systems capable of reasoning, planning, acting, and adapting over long-lasting tasks. By integrating memory, tool use, and iterative decision cycles, these systems enable continuous, autonomous workflows in real-world environments. This survey examines the...
Understanding biomedical experiments provides a foundation for downstream tasks, e.g., laboratory automation, and facilitates effective cross-disciplinary communication. Two challenges, High Information Density (HID) and Multi-Step Reasoning (MSR), pose unique difficulties for precise experimental understanding. Extracting structured...
We present a new blocking linearizable stack implementation which utilizes sharding and fetch&increment to achieve significantly better performance than all existing concurrent stacks. The proposed implementation is based on a novel elimination mechanism and a new combining approach that are efficiently blended to gain high...
The proliferation of Large Language Models (LLMs) presents transformative potential for healthcare, yet practical deployment is hindered by the absence of frameworks that assess real-world clinical utility. Existing benchmarks test static knowledge, failing to capture the dynamic, application-oriented capabilities required in clinical...
Shared accountability records are often used by parties who may never agree about causation, responsibility, or normative interpretation. For such records, neutrality cannot be achieved by omitting contested information, because accountability requires preserving the claims parties made, with their sources and provenance. Nor can...
We present the UV/X-ray joint spectral analyses of four Seyfert~1 galaxies (PG 0804+761, NGC 7469, SWIFT J1921.1-5842, and SWIFT J1835.0+3240) using the data acquired with the Ultraviolet Imaging Telescope and Soft X-ray Telescope onboard AstroSat. We model the intrinsic UV/X-ray continuum with the accretion disk, warm and hot...
The design of reliable, valid, and diverse molecules is fundamental to modern drug discovery, as improved molecular generation supports efficient exploration of the chemical space for potential drug candidates and reduces the cost of early design efforts. Despite these needs, current chemical language models that generate molecules as...
Recent facial texture generation methods prefer to use deep networks to synthesize image content and then fill in the UV map, thus generating a compelling full texture from a single image. Nevertheless, the synthesized texture UV map usually comes from a space constructed by the training data or the 2D face generator, which limits the...
Three-dimensional medical image segmentation is a fundamental yet computationally demanding task due to the cubic growth of voxel processing and the redundant computation on homogeneous regions. To address these limitations, we propose \textbf{TokenSeg}, a boundary-aware sparse token representation framework for efficient 3D medical...
The advancement of deep learning has greatly improved supervised image classification. However, labeling data is costly, prompting research into unsupervised learning methods such as contrastive learning. In real-world scenarios, fully unlabeled datasets are rare, making semi-supervised learning (SSL) highly relevant in scenarios...
Molecular graph learning benefits from positional signals that capture both local neighborhoods and global topology. Two widely used families are spectral encodings derived from Laplacian or diffusion operators and anchor-based distance encodings built from shortest-path information, yet their precise relationship is poorly...
We provide two representations of the Segal category $\mathcal{X}$ modeling natural phenomena, the first one being based on the concept of micro-reversibility, producing a long sequence $Σ$ of categories as a resolution of $\mathcal{X}$, the second one providing graded categories cofibered in groupoids over the categories of $Σ$,...
Quartz tuning fork-based atomic force microscopy (QTF-AFM) has become a powerful tool for high-resolution imaging of both conductive and insulating samples, including semiconductor structures and metal-coated surfaces as well as soft matter under ambient conditions, while also enabling measurements in more demanding environments...
Large Language Models (LLMs) have enabled Multi-Agent Systems (MASs) where agents interact through natural language to solve complex tasks or simulate multi-party dialogues. Recent work on LLM-based MASs has mainly focused on architecture design, such as role assignment and workflow orchestration. In contrast, this paper targets the...
The aim of this text is to provide a linguistically accessible, but comprehensive introduction into a variety of topics in dynamical systems and its applications. Whilst preliminary knowledge of dynamical systems is useful, it is not essential and readers are only assumed to have familiarity with foundational undergraduate mathematics...
Let $\mathcal{A}$ be a $k$-th order $n$-dimensional tensor, and we denote by ${\rm am}(λ, \mathcal{A})$ the algebraic multiplicity of the eigenvalue $λ$ of $\mathcal{A}$. The projective eigenvariety $\mathbb{V}_λ(\mathcal{A})$ is defined as the set of eigenvectors of $\mathcal{A}$ associated with $λ$, considered in the complex...
We present a Neumann series of spherical Bessel functions representation for solutions of the Sturm--Liouville equation in impedance form \[ (κ(x)u')' + λκ(x)u = 0,\quad 0 < x < L, \] in the case where $κ\in W^{1,2}(0,L)$ and has no zeros on the interval of interest. The $x$-dependent coefficients of this representation can be...
Distributed energy trading and carbon asset management involve high-frequency, small-value settlements with strong audit requirements. Fully on-chain designs incur excessive cost, while purely off-chain approaches lack verifiable consistency. This paper presents a hybrid on-chain and off-chain settlement framework that anchors...
Multiagent reinforcement learning, as a prominent intelligent paradigm, enables collaborative decision-making within complex systems. However, existing approaches often rely on explicit action exchange between agents to evaluate action value functions, which is frequently impractical in real-world engineering environments due to...
Phase-field simulations of liquid metal dealloying (LMD) can capture complex microstructural evolutions but can be prohibitively expensive for large domains and long time horizons. In this paper, we introduce a fully convolutional, conditionally parameterized U-Net surrogate designed to extrapolate far beyond its training data in both...