December 2025 arXiv papers — page 58

Sicheng Song, Yanjie Zhang, Zixin Chen, Huamin Qu

The integrity of data visualizations is increasingly threatened by image editing techniques that enable subtle yet deceptive tampering. Through a formative study, we define this challenge and categorize tampering techniques into two primary types: data manipulation and visual encoding manipulation. To address this, we present VizDefender, a framework for tam

Khairul Alam, Banani Roy

Scientific Workflow Systems (SWSs) play a vital role in enabling reproducible, scalable, and automated scientific analysis. Like other open-source software, these systems depend on active maintenance and community engagement to remain reliable and sustainable. However, despite the importance of timely issue resolution for software quality and community trust

Nils Lommen, Jürgen Giesl

In earlier work, we developed a modular approach for automatic complexity analysis of integer programs. However, these integer programs do not allow non-tail recursive calls or subprocedures. In this paper, we consider integer programs with function calls and present a natural extension of our modular complexity analysis approach to the recursive setting bas

Feeza Khan Khanzada, Jaerock Kwon

Model-based reinforcement learning (MBRL) can reduce interaction cost for autonomous driving by learning a predictive world model, but it typically still depends on task-specific rewards that are difficult to design and often brittle under distribution shift. This paper presents InDRiVE, a DreamerV3-style MBRL agent that performs reward-free pretraining in C

Shuang Zhang, Serdar Elhatisari, Ulf-G. Meißner

The quest to understand multi-neutron systems has a long history, and recent experimental efforts aim to probe candidate four-neutron configurations in neutron-rich light nuclei such as ${}^8$He and ${}^7$H via quasi-free knockout reactions. However, the ground-state energies of the hydrogen isotopes ${}^6$H and ${}^7$H are not yet well constrained, with sub

Nurgül Gökgöz

Generalized Chebyshev acceleration is a semi-iterative technique applicable to a basic iterative method only when the eigenvalues of the iteration matrix satisfy a highly restrictive inclusion condition. In this work, we relax this requirement by introducing an alternative iterative scheme that converges to the same solution. The effectiveness of the propose

Ignacio Gustin, Luis Mantilla Calderón, Juan B. Pérez-Sánchez, Jérôme F. Gonthier

Quantum simulation is central to understanding and designing quantum systems across physics and chemistry. Yet it has barriers to access from both computational complexity and computational perspectives, due to the exponential growth of Hilbert space and the complexity of modern software tools. Here we introduce{\cinzel El Agente Cu\'antico}, a multi-agent A

Patrick Jentsch, Chiu Fan Lee

We present a comprehensive analytical linear stability analysis of the Toner-Tu model for polar active fluids in the ordered phase. Our results provide exact instability criteria and demonstrate that all generic hydrodynamic instabilities fall into two fundamental categories, distinguished by their scaling with the wavevector magnitude. By applying a general

Badis Ydri

From a conceptual point of view, this chapter may be viewed as an exercise in combining quantum field theory and general relativity in a controlled setting. Despite its apparent simplicity, this exercise is deeply rooted in highly non-trivial developments in superstring theory and holography, and it addresses what is arguably one of the most profound questio

Omar Garraoui

In this work, we propose an enhancement to the Boyer-Moore-Horspool algorithm tailored for natural language text. The approach involves preprocessing the search pattern to identify its statistically least frequent character, referred to as the "anchor." During the search, verification is first performed at this high-entropy position, allowing the algorithm t

Yogeswar Reddy Thota

Modern deep residual networks perform substantial redundant computation by evaluating all residual blocks for every input, even when identity mappings suffice. We introduce CosineGate, an end-to-end differentiable architecture for dynamic routing in residual networks that uses cosine incompatibility between identity and residual feature representations as a

F. Gutiérrez-Canales, R. Samadi, A. Birch, J. Cabrera

PLATO will discover exoplanets around Sun-like stars through transit photometry and characterize their host stars using asteroseismology. Since photometry for most PLATO targets will be extracted on board, an efficient strategy to detect false positives (FPs), defined as transit-like signals not caused by planets, is required. Centroid shifts are a standard

Hasib Aslam, Muhammad Talal Faiz, Muhammad Imran Malik

Advances in neuroscience and artificial intelligence have enabled preliminary decoding of brain activity. However, despite the progress, the interpretability of neural representations remains limited. A significant challenge arises from the intrinsic properties of electroencephalography (EEG) signals, including high noise levels, spatial diffusion, and prono

Aleksandr Fedchin, Antero Mejr, Hari Sundar, Jeffrey S. Foster

The Message Passing Interface (MPI) is widely used in parallel, high-performance programming, yet writing bug-free software that uses MPI remains difficult. We introduce DafnyMPI, a novel, scalable approach to formally verifying MPI software. DafnyMPI allows proving deadlock freedom, termination, and functional equivalence with simpler sequential implementat

Tung Duong Ta, Tim Oates, Thien Van Luong, Huan Vu

Despite advances in mathematical reasoning capabilities, Large Language Models (LLMs) still struggle with calculation verification when using established prompting techniques. We present MDToC (Metacognitive Dynamic Tree of Concepts), a three-phase approach that constructs a concept tree, develops accuracy-verified calculations for each concept, and employs

Shrey Aryan

We show that any finite energy solution of the energy-critical nonlinear heat flow in dimensions $d\geq 3$ asymptotically resolves into a sum of possibly time-dependent solitons, a weak limit when the flow exists for finite time, and an error term that vanishes in the energy space. As a consequence, under the additional assumption that the initial data is no

Emmi Meyer-Hofmeister, Yilong Wang, B. F. Liu

We investigate the accretion geometry in neutron star low-mass X-ray binaries (LMXBs) in the hard spectral state. It is commonly accepted that, for low mass transfer rates, an advection-dominated accretion flow (ADAF) is present in the inner region. But the observed relativistically broadened emission lines in the reflection spectra clearly indicate the exis

Ruslan Mirmominov, Johannes Wiesel

The adapted Wasserstein distance $\mathcal{AW}$ is a modification of the classical Wasserstein metric, that provides robust and dynamically consistent comparisons of laws of stochastic processes, and has proved particularly useful in the analysis of stochastic control problems, model uncertainty, and mathematical finance. In applications, the law of a stocha

Yuanchao Xu, Fengyi Li, Masahiro Fujisawa, Xiaoyuan Cheng

We propose Koopman Spectral Wasserstein Gradient Descent (KSWGD), a particle-based generative modeling framework that learns the Langevin generator via Koopman theory and integrates it with Wasserstein gradient descent. Our key insight is that this spectral structure of the underlying distribution can be directly estimated from trajectory data via the Koopma

Jingjia Teng, Yang Li, Yougang Bian, Manjiang Hu

Four-wheel Independent Steering (4WIS) vehicles have attracted increasing attention for their superior maneuverability. Human drivers typically choose to cross or drive over the low-profile obstacles (e.g., plastic bags) to efficiently navigate through narrow spaces, while existing planners neglect obstacle attributes, leading to suboptimal efficiency or pla

Maria Chudnovsky, Julien Codsi, David Fischer, Daniel Lokshtanov

Given a family $\mathcal{H}$ of graphs, we say that a graph $G$ is $\mathcal{H}$-induced-minor-free if no induced minor of $G$ is isomorphic to a member of $\mathcal{H}$, We denote by $W_{t\times t}$ the $t$-by-$t$ hexagonal grid, and by $K_{t,t}$ the complete bipartite graph with both sides of the bipartition of size $t$. We show that the class of $\{K_{t,t

Sultan Alrashed, Francesco Orabona

Multilingual data from the web is essential for LLM pretraining. Yet, scraping it is expensive, and research groups repeatedly crawl the same content. For example, we found that over 40\% of tokens across major Arabic web corpora are duplicated between sources. In this work, we propose to use this wasteful redundancy as a quality signal to create high-qualit

Hsin-Lun Li

We introduce a probabilistic pairwise \emph{attraction--repulsion} model for opinion dynamics on multilayer social networks, in which agents hold layer-specific states and interact through random matchings that couple multiple, time-varying layers. At each time step, interacting pairs update their layer-specific states using layer-dependent, time-varying int

Yixia Li, Hongru Wang, Jiahao Qiu, Zhenfei Yin

Agentic reinforcement learning increasingly relies on experience-driven scaling, yet real-world environments remain non-adaptive, limited in coverage, and difficult to scale. World models offer a potential way to improve learning efficiency through simulated experience, but it remains unclear whether large language models can reliably serve this role and und

Jiahang Hu, Runze Chi, Yibin Guo, B. Normand

Quantum spin liquids (QSLs) host exotic fractionalized magnetic and gauge-field excitations whose microscopic origins and experimental verification remain frustratingly elusive. In the absence of static magnetic order, the spin excitation spectrum constitutes the crucial probe of QSL behavior, but its theoretical computation is a serious challenge. Here we e

Owe Philipsen

Several non-perturbative results for hot QCD are challenging some aspects of the phase diagram and its associated degrees of freedom which were previously believed to be well understood. With increasing temperature, the chiral crossover is followed by an intermediate region with an approximate chiral spin symmetry larger than chiral symmetry, in which pseudo

Aditya Siddhant

Behavioral healthcare risk assessment remains a challenging problem due to the highly multimodal nature of patient data and the temporal dynamics of mood and affective disorders. While large language models (LLMs) have demonstrated strong reasoning capabilities, their effectiveness in structured clinical risk scoring remains unclear. In this work, we introdu

V. E. Adler, V. V. Sokolov

The group reduction procedure is applied to vector generalizations of the NLS, mKdV, and KdV equations. The resulting ODE systems admit isomonodromic Lax representations and are multicomponent generalizations of the Painlev\'e equations P$_1$, P$_2$, P$_{34}$, and P$_4$. Some of them can be interpreted as nonautonomous deformations of well-known systems inte

Farhad Vasheghanifarahani

This paper examines wage returns to labor-market experience with a focus on immigrant assimilation and the portability of foreign-acquired human capital. Using U.S. Census and American Community Survey microdata from IPUMS, I study a sample of male, full-time, private-sector workers and estimate Mincer-style wage regressions with flexible experience-group in

Souhail Abdelmouaiz Sadat, Mohamed Yacine Touahria Miliani, Khadidja Hab El Hames, Hamida Seba

This survey reviews hyperbolic graph embedding models, and evaluate them on anomaly detection, highlighting their advantages over Euclidean methods in capturing complex structures. Evaluating models like \textit{HGCAE}, \textit{\(\mathcal{P}\)-VAE}, and \textit{HGCN} demonstrates high performance, with \textit{\(\mathcal{P}\)-VAE} achieving an F1-score of 94

Mario Piazza, Andrea Sabatini

In many real-life settings, agents must navigate dynamic environments while reasoning under incomplete information and acting on a corpus of unstable, context-dependent, and often conflicting norms. We introduce a general, non-modal, proof-theoretic framework for deontic reasoning grounded in default logic. Its central feature is the notion of controlled seq

M M Abid Naziri, Stefano Carlo Lambertenghi, Andrea Stocco, Marcelo d'Amorim

Simulation-based testing is the standard practice for assessing the reliability of self-driving cars' software before deployment. Existing bug-finding techniques are either unreliable or expensive. We build on the insight that near misses observed during simulations may point to potential failures. We propose Foresee, a technique that identifies near misses

Alberto Sesana, Daniel G. Figueroa

Evidence of a gravitational wave (GW) signal has emerged in pulsar timing array (PTA) data, opening a new window into the nanoHz GW Universe. We explore the physics of GW signals potentially explaining the data, with a primary focus on GW backgrounds (GWBs), considering both astrophysical and cosmological origins. We describe how: (i) An astrophysical nanoHz

R. K. Kushwaha, S. Jangid, P. Mishra, S. Sharma

Niobium and its alloys with early transition metals have been extensively studied for their excellent superconducting properties. They have high transition temperatures, strong upper critical fields, and high critical current densities, making them ideal for superconducting applications such as SQUIDs, MRI, NMR, particle accelerators, and Qubits. Here we rep

Taylan Demir

We analyze a variable coefficient coupled HI mKdV system that has shifted nonlocal reductions. The Weiss Tabor Carnevale test gives us coefficient restrictions to perform a time reparametrization to achieve an autonomous integrable model. We also show a Hirota bilinear form along with a simplified example to demonstrate how the shifted symmetries create new

Luc Cote, Andy Sun

Carbon accounting methods for electricity consumption face challenges regarding physical deliverability, double counting, additionality, and impact magnitude. Locational Marginal Emissions (LMEs) show potential to address many of these key issues. However, their use in a large-scale power grids remains understudied. We analyze the properties of LMEs from a d

Spyros Basilakos, Gerasimos Kouniatalis, Emmanuel N. Saridakis, Charalampos Tzerefos

We construct a Bohmian quantum cosmological model for a spatially flat Friedmann Robertson Walker universe filled with a single scalar field whose potential provides a unified description of cold dark matter and dark energy at the background level. Starting from the Einstein-Hilbert action supplemented by a scalar field, we derive the minisuperspace Lagrangi

Francesco Polizzi, Pietro Sabatino

Let $\Sigma_b$ be a compact Riemann surface of genus $b \geq 2$ and let $\mathsf{P}_2(\Sigma_b)=\pi_1(\Sigma_b \times \Sigma_b - \Delta)$ be the corresponding pure braid group on two strands. A finite quotient $\varphi \colon \mathsf{P}_2(\Sigma_b) \to G$ is called "admissible" if $\varphi$ does not factor through $\pi_1(\Sigma_b \times \Sigma_b)$. In this w

R. Runthala, V. K. Venkatesh, D. Gupta, P. Arora

We present a simulation guided design for a multilayer surface plasmon resonance (SPR) based biosensor capable of detecting refractive index changes in a target induced by analytes. Surface plasmons are excited using a hybrid Kretschmann configuration with a calcium fluoride (CaF2) prism under transverse magnetic polarization illumination. In the sensing arc

John S. Schreck, William E. Chapman, Charlie Becker, David John Gagne

AI weather prediction ensembles with latent noise injection and optimized with the continuous ranked probability score (CRPS) have produced both accurate and well-calibrated predictions with far less computational cost compared with diffusion-based methods. However, current CRPS ensemble approaches vary in their training strategies and noise injection mechan

Yuxiao Yang, Hualian Sheng, Sijia Cai, Jing Lin

Video generation models have advanced significantly, yet they still struggle to synthesize complex human movements due to the high degrees of freedom in human articulation. This limitation stems from the intrinsic constraints of pixel-only training objectives, which inherently bias models toward appearance fidelity at the expense of learning underlying kinem

Guangtao Lyu, Xinyi Cheng, Chenghao Xu, Qi Liu

Large Vision-Language Models (LVLMs) have shown remarkable capabilities, yet hallucinations remain a persistent challenge. This work presents a systematic analysis of the internal evolution of visual perception and token generation in LVLMs, revealing two key patterns. First, perception follows a three-stage GATE process: early layers perform a Global scan,

Riccardo Moschetti, Franco Rota, Luca Schaffler

We study the non-degeneracy invariant $\mathrm{nd}(Y)$ of complex Enriques surfaces in families. Our first main result shows that $\mathrm{nd}(Y)$ cannot increase under specialization. The second main result is the conclusion of the computation of the non-degeneracy invariant for the $155$ families of $(\tau,\overline{\tau})$-generic surfaces introduced by B

Benjamin Tang

Recent work has shown an increasing interest in understanding the structure of the endoplasmic reticulum (ER) and how ribosomes are displayed on it. Here we present a model that explains a physical reason for why the cell creates different structures of the ER. Due to the diffusion of biomolecules, we find that flat sheets and a matrix of tubules have differ

Veronique Bazier-Matte, Marie-Anne Bourgie, Anna Felikson, Pavel Tumarkin

An $SL_2$-tiling is a bi-infinite matrix in which all adjacent $2 \times 2$ minors are equal to $1$. Positive integral $SL_2$-tilings were introduced by Assem, Reutenauer and Smith as generalisations of classical Conway--Coxeter frieze patterns. We show that positive integral $SL_2$-tilings with translational symmetry are in bijection with triangulations of

Ziyuan Tao, Chuanzhi Xu, Sandaru Jayawardana, Adnan Mahmood

Short-form video moderation increasingly needs learning pipelines that protect user privacy without paying the full bandwidth and latency cost of cloud-centralized inference. We present FedVideoMAE, an on-device federated framework for video violence detection that combines self-supervised VideoMAE representations, LoRA-based parameter-efficient adaptation,

Nickolas Giardetti, Sofiane Bourouaine, Jean C. Perez

In this letter, we investigate the population of energetically-dominant sunward-propagating Alfv\'en waves (SAWs) using more than 20 years of data provided by the Wind spacecraft near 1 au. We refer to SAWs as energetically-dominant sunward-propagating Alfv\'en waves within inertial range scales. Key parameters such as normalized cross helicity, plasma incom

Ronaldo Laishram, Yusei Koyama, Abdurrahman Naufal, Tadayuki Kodama

We present a spatially-resolved analysis of galaxy quenching within the Spiderweb Protocluster at $z \sim 2.16$, combining deep imaging from the James Webb Space Telescope (JWST) and the Hubble Space Telescope (HST). Utilizing pixel-by-pixel spectral energy distribution fitting, we derive maps of stellar mass, star formation rate (SFR), specific SFR (sSFR),

Guangtao Lyu, Chenghao Xu, Qi Liu, Jiexi Yan

Music to 3D dance generation aims to synthesize realistic and rhythmically synchronized human dance from music. While existing methods often rely on additional genre labels to further improve dance generation, such labels are typically noisy, coarse, unavailable, or insufficient to capture the diversity of real-world music, which can result in rhythm misalig

Vivienne L'Ecuyer Ming

Establishing the long-term, causal impact of psychological interventions on life outcomes is a grand challenge for the social sciences, caught between the limitations of correlational longitudinal studies and short-term randomized controlled trials (RCTs). This paper introduces Large-Scale Agent-based Longitudinal Simulation (LALS), a framework that resolves

Jonathan Baxter

LaTeX transcription (2025) of a 1989 honours thesis (University of Adelaide) on point charges in classical electrodynamics and the Lorentz-Dirac radiation-reaction equation. The thesis reviews the retarded field of an arbitrarily moving charge, energy-momentum conservation, and derives the Lorentz-Dirac equation via momentum balance. It discusses self-intera

Xiaoting Gao, Yan Zhu, Feng-Xiao Sun, Ya-Dong Wu

Machine learning methods have been used to infer specific properties of limited families of optical quantum states, but a unified model that predicts a broad range of properties for practically relevant-especially multimode non-Gaussian-states without full tomography is still lacking. Here we introduce the first foundation model for the characterization of o

Ranjeet Dwivedi, Agraj Yadav, Regis Orobtchouk, Benoit Charbonnier

Phase-change material (PCM)-based non-volatile multilevel phase shifters are key components in photonic integrated circuits. Electrically, multiple phase levels can be encoded by controlling the heater power and employing different microheater architectures to induce varying degrees of PCM amorphization. However, encoding a large number of levels is not stra

Fabrizio Catanese, appendix by Pavel Zalesskii

We give a very short proof of two Theorems, whose content is outlined in the title, and where $\Pi_g$ is the fundamental group of a compact complex curve of genus $g$: (1) Theorem 2.1 of the irrational pencil in the profinite version, saying that for a compact K\"ahler manifold an irrational pencil, that is, a fibration onto a curve of genus $g \geq 2$, corr

Lisan Al Amin, Vandana P. Janeja

Detecting synthetic speech is challenging when labeled data are scarce and recording conditions vary. Existing end-to-end deep models often overfit or fail to generalize, and while kernel methods can remain competitive, their performance heavily depends on the chosen kernel. Here, we show that using a quantum kernel in audio deepfake detection reduces falsep

Wei-Hao Chen, Yi-Zen Chu, Vaidehi Varma

We describe TensoriaCalc, a tensor calculus package written to be smoothly consistent with the Wolfram Language, so as to ensure ease of usage. It allows multiple metrics to be defined in a given session; and, once a metric is computed, associated standard differential geometry operations to be carried out - covariant derivatives, Hodge duals, index raising

Andrea Gokus, Rebecca Phillipson

Variability studies of jetted AGN, in particular blazars, have been used to gain a better understanding of the particle acceleration mechanisms in jets. However, statistical methods used for the characterization of variability often rely on stationary time series data, which is not fulfilled for most blazar light curves. We introduce the recurrence plot meth

Gabrielle Laloy-Borgna, Nastassia Navasiolava, Pim Hutting, Andréa Bertona

We propose an ultrasound approach which provides, with one single examination and one single device, access to three bone biomarkers: anatomy, tissue quality and blood flow. It unlocks ultrasound imaging inside bone by accounting for ultrasound wave speed heterogeneity and anisotropic wave refraction. This study reports the first \emph{in vivo} evaluation wi

Maxime Méloux, Giada Dirupo, François Portet, Maxime Peyrard

In a striking neuroscience study, the authors placed a dead salmon in an MRI scanner and showed it images of humans in social situations. Astonishingly, standard analyses of the time reported brain regions predictive of social emotions. The explanation, of course, was not supernatural cognition but a cautionary tale about misapplied statistical inference. In

Yichuan Zhang, Chengxin Li, Yujie Gu

Text-to-Speech (TTS) diffusion models generate high-quality speech, which raises challenges for the model intellectual property protection and speech tracing for legal use. Audio watermarking is a promising solution. However, due to the structural differences among various TTS diffusion models, existing watermarking methods are often designed for a specific

Minh Chau Nguyen, Tony S. Wirjanto, Fan Yang

Catastrophe risk has long been recognized to pose a serious threat to the insurance sector. Catastrophe risk pooling offers an effective way to diversify losses arising from catastrophic events. In this paper, we investigate a structure of catastrophe risk pool and optimize it so that participants can attain the maximum diversification benefit from joining t

Kyu-Won Park, KyeongRo Kim, Kabgyun Jeong

Chirality is more than a geometric curiosity; it governs measurable asymmetries across nature, from enantiomer-selective drugs and left-handed fermions in particle physics to handed charge transport in Weyl semimetals. We extend this universal concept to non-Hermitian systems by defining topological chirality, an invariant that emerges whenever an exceptiona

George C. Alexandropoulos, Kostantinos D. Katsanos, George Stamatelis, Ioannis Gavras

This chapter overviews the concept of Smart Wireless Environments (SWEs) motivated by the emerging technology of Reconfigurable Intelligent Surfaces (RISs). The operating principles and state-of-the-art hardware architectures of programmable metasurfaces are first introduced. Subsequently, key performance objectives and use cases of RIS-enabled SWEs, includi

María Anguiano, Igor Pažanin, Francisco J. Suárez-Grau

We analyze a non-isothermal Darcy-Brinkman thin-film flow with a periodically oscillating boundary and viscous dissipation acting as a heat source. Using asymptotic analysis and the periodic unfolding method, we establish the convergence of velocity, pressure, and temperature fields as the small parameter (related to the film thickness and the period of the

Renato Panaro, Christian Röver, Tim Friede

Commonly, clinical trials report effects not only for the full study population but also for patient subgroups. Meta-analyses of subgroup-specific effects and treatment-by-subgroup interactions may be inconsistent, especially when trials apply different subgroup weightings. We show that meta-regression can, in principle, with a contribution adjustment, recov

Fanis Mathioulakis, Gorjan Radevski, Tinne Tuytelaars

We introduce Eff-GRot, an approach for efficient and generalizable rotation estimation from RGB images. Given a query image and a set of reference images with known orientations, our method directly predicts the object's rotation in a single forward pass, without requiring object- or category-specific training. At the core of our framework is a transformer t

V. A. Buryachenko

This paper introduces an advanced Computational Analytical Micromechanics (CAM) framework for linear thermoelastic composites (CMs) with periodic microstructures. The approach is based on an exact new Additive General Integral Equation (AGIE), formulated for compactly supported loading conditions, such as body forces and localized thermal effects (for exampl

Pompeu Casanovas

The artificial intelligence value chain is one of the main concepts underpinning the European legislation on the subject, especially the Artificial Intelligence Act. It is an economic concept that has become a legal one. i.e., a concept of legal governance, due to its continued use in policy documents and legal texts. This article (i) analyses its significan

Garry Goldstein

In many ways the solution to the Hedin equations represents an exact solution to the many body problem. However, for most systems of practical interest, the solution to the Hedin equations is rendered nearly numerically intractable because the Hedin equations are of functional derivative form. Integral equations, on the other hand, are much more numerically

Yifeng Zhang, Xiao Liang

This study provides a comprehensive review of domain adaptation (DA) techniques in vibration-based structural health monitoring (SHM). As data-driven models increasingly support the assessment of civil structures, the persistent challenge of transferring knowledge across varying geometries, materials, and environmental conditions remains a major obstacle. DA

Thorsten Hellert, Nikolay Agladze, Alex Giovannone, Jan Jug

Modern experimental platforms such as particle accelerators, fusion devices, telescopes, and industrial process control systems expose tens to hundreds of thousands of control and diagnostic channels accumulated over decades of evolution. Operators and AI systems rely on informal expert knowledge, inconsistent naming conventions, and fragmented documentation

Andrés Collinucci, Fabrizio Del Monte, Mario De Marco, Marina Moleti

We construct new families of non-toric 5d SCFTs via abelian orbifolds of the Reid Pagoda, including a surprising infinite family of rank-1 theories, that evade all known classifications. Using the McKay correspondence, we derive their BPS quivers and superpotentials. The hallmark of these theories is a novel sector we dub Pagoda matter, whose vacuum expectat

Kyu-Won Park, Soojoon Lee, Kabgyun Jeong

Controlling entanglement and coherence is central to quantum information, yet the two resources often exhibit antagonistic trends and are difficult to optimize within a single platform. Here we show that chaos enables switchable eigenstate resources: avoided crossings in soft- versus strong- chaos windows selectively realize an entanglement-peak mode or a co

Yang Ni, Tong Yang

Large Language Models (LLMs) and AI chatbots are increasingly used for emotional and mental health support due to their low cost, immediacy, and accessibility. However, when safety guardrails are triggered, conversations may be abruptly terminated, introducing a distinct form of emotional disruption that can exacerbate distress and elevate risk among already

Amit Kumar Pandey, Pei Ren-Sawyer, Sunghwan Jung, Teng Zhang

Blisters, delaminated regions that form in multilayered structures under compressive stresses, are observed across a wide range of length scales, from two-dimensional materials to protective coatings and laminated composites. Far from being passive defects, such interfacial features have emerged as functional motifs for three-dimensional architectures and re

Baiyang Chen, Zhong Yuan, Dezhong Peng, Hongmei Chen

Outlier detection, crucial for identifying unusual patterns with significant implications across numerous applications, has drawn considerable research interest. Existing semi-supervised methods typically treat data as purely numerical and} in a deterministic manner, thereby neglecting the heterogeneity and uncertainty inherent in complex, real-world dataset

Wenze Liu, Weicai Ye, Minghong Cai, Quande Liu

Recent advancements in video generation have seen a shift towards unified, transformer-based foundation models that can handle multiple conditional inputs in-context. However, these models have primarily focused on modalities like text, images, and depth maps, while strictly time-synchronous signals like audio have been underexplored. This paper introduces I

Justine Parmentier, Vincent E. Terrapon, Tristan Gilet

Stalagmites in karstic caves preserve valuable palaeoclimate records through calcium-rich layered deposits, presenting curvature variations both across and within individual stalagmites. Stalagmites always remain covered by a thin water film fed by a discrete inflow of drops, which bring in new ions in solution for the stalagmites to grow. However, the gravi

Hao Tan, Zetian Yan, Zhipeng Yang

We develop an intrinsic, heat-kernel based fractional Sobolev framework on closed Riemannian manifolds and study the critical fractional Sobolev embedding. We determine the optimal coefficient of the lower-order $L^{p}$ term and prove that the fully sharp $p$-power inequality cannot hold globally in the superquadratic range. We further establish an almost sh

Stefan Häussler, Peter van Loock

Fiber-based classical communication is all-optical and uses light pulses reamplified and reshaped every 50-100 km in classical repeaters. Most compatible with this would be a quantum communication system which is also all-optical with quantum processing units placed in similar intervals. However, existing all-optical quantum communication protocols either re

Guohui Zhang, Hu Yu, Xiaoxiao Ma, Yaning Pan

Reinforcement learning (RL) has demonstrated significant potential for post-training language models and autoregressive visual generative models, but adapting RL to masked generative models remains challenging. The core factor is that policy optimization requires accounting for the probability likelihood of each step due to its multi-step and iterative refin

Andrea B. Rava, Jhon A. Montanez-Barrera, Kristel Michielsen, Jaka Vodeb

Analog quantum simulators promise access to complex many-body dynamics, yet their performance is ultimately set by how device imperfections compete with intrinsic physical mechanisms. Here we present an end-to-end study of correlation spreading in a programmable Rydberg-atom chain realizing a longitudinal-field transverse-field Ising model, focusing on the j

Dinko Dimitrov, Dipjyoti Majumdar

We study the implementability of stable matchings in a two-sided market model with one-sided incomplete information. Firms' types are publicly known, whereas workers' types are private information. A mechanism generates a matching and additional announcements to the firms at each report profile of workers' types. When agents' preferences are increasing in th

Dimitrios Tyrovolas, Sotiris A. Tegos, Yue Xiao, Panagiotis D. Diamantoulakis

Programmable wireless environments (PWEs) have emerged as a key paradigm for next-generation communication networks, aiming to transform wireless propagation from an uncontrollable phenomenon into a reconfigurable process that can adapt to diverse service requirements. In this framework, pinching-antenna systems (PASs) have recently been proposed as a promis

Md. Ismiel Hossen Abir, Awolad Hossain

Malaria remains a prevalent health concern in regions with tropical and subtropical climates. The cause of malaria is the Plasmodium parasite, which is transmitted through the bites of infected female Anopheles mosquitoes. Traditional diagnostic methods, such as microscopic blood smear analysis, are low in sensitivity, depend on expert judgment, and require

Maria Laura Battagliola, Laura J. Benoit, Sarah Canetta, Shizhe Zhang

Deficits in working memory, which includes both the ability to learn and to retain information short-term, are a hallmark of many cognitive disorders. Our study analyzes data from a neuroscience experiment on animal subjects, where performance on a working memory task was recorded as repeated binary success or failure data. We estimate continuous probability

Davide Lombardo, Tamás Szamuely

Let $X$ be a smooth projective geometrically connected variety defined over a number field $K$. We prove that the geometric étale cohomology of $X$ with $\mathbb{Q}/\mathbb{Z}$-coefficients has finitely many classes invariant under the Galois group of the maximal Kummer extension of $K$ in odd degrees. In particular, every abelian variety has finite torsion

Yunhee Euh, JeongHyeong Park

We study critical metrics of the curvature functional $\A(g)=\int_M |R|^2\, \vol$, on complete four-dimensional Riemannian manifolds $(M,g)$ with finite energy, that is, $\A(g)<\infty$. Under the natural inequality condition on the curvature operator of the second kind associated with the trace-free Ricci tensor, we prove that $(M,g)$ is either Einstein or l

Chul-Ung Woo, Heiko Rieger, Jae Dong Noh

We investigate collective behavior in a $Q$-species Vicsek model with a nonreciprocal velocity alignment interaction. This system is characterized by a constant phase shift $α$ in the inter-species velocity alignment rule. While the phase shift renders the interaction nonreciprocal, the system is globally invariant under any permutations of particle species,

Jianyi Zhang, Shizhao Liu, Ziyin Zhou, Zhen Li

The rapid advancement of large language models (LLMs) has intensified concerns about the robustness of their safety alignment. While existing jailbreak studies explore both single-turn and multi-turn strategies, most implicitly assume a static safety boundary and fail to account for how contextual interactions dynamically influence model behavior, leading to

Yuyang Ji, Haotian Zhao, Peize Lin, Xinguo Ren

Real-time time-dependent density functional theory (RT-TDDFT) is a powerful approach for investigating various ultrafast phenomena in materials. However, most existing RT-TDDFT studies rely on adiabatic local or semi-local approximations, which suffer from several shortcomings, including the inability to accurately capture excitonic effects in periodic syste

Alexey L. Smirnov

In this note, we consider the Henneaux-Teitelboim version of Unimodular Gravity (UG) and its deformations in the form of gauge theories with spontaneously broken diffeomorphism invariance. Actions defining such theories depends on the curvature of an $SO(3,\mathbb{C})$ gauge connection and the field strength of a (real) 3-form (or equivalently its dual vecto

Michael Jirasek, Abhishek Sharma, Mary Wong, Jennifer Munro

Selection is central to biological evolution, yet there has been no general experimental framework for quantifying selection in chemical systems before life. Here we demonstrate that selection in a prebiological chemical system can be directly quantified. Assembly Theory predicts that selection corresponds to a transition from undirected to directed explorat

Khondokar Fida Hasan, Hasibul Hossain Shajeeb, Chathura Abeydeera, Benjamin Turnbull

FinTechs increasing connectivity, rapid innovation, and reliance on global digital infrastructures present significant cybersecurity challenges. Traditional cybersecurity frameworks often struggle to identify and prioritize sector-specific vulnerabilities or adapt to evolving adversary tactics, particularly in highly targeted sectors such as FinTech. To addr

Xiaoyang Li, Wenzhu Yang, Kanglin Wang, Tiebiao Wang

Action recognition is a critical task in video understanding, requiring the comprehensive capture of spatio-temporal cues across various scales. However, existing methods often overlook the multi-granularity nature of actions. To address this limitation, we introduce the Context-Aware Network (CAN). CAN consists of two core modules: the Multi-scale Temporal

Chul-Ung Woo, Heiko Rieger, Jae Dong Noh

Nonreciprocal active matter systems typically feature an asymmetric role among interacting agents, such as a pursuer-evader relationship. We propose a multi-species nonreciprocal active matter model that is invariant under permutations of the particle species. The nonreciprocal, yet symmetric, interactions emerge from a constant phase shift in the velocity a

Recep Kaan Karaman, Meftun Akarsu

The performance of automatic code documentation generation models depends critically on the quality of the training data used for supervision. However, most existing code documentation datasets are constructed through large scale scraping of public repositories with limited quality control. As a result, they often contain noisy documentation, extensive dupli

Yuan Chen, Zichen Wen, Yuzhou Wu, Xuyang Liu

Multimodal Large Language Models (MLLMs) deliver strong vision-language performance but at high computational cost, driven by numerous visual tokens processed by the Vision Transformer (ViT) encoder. Existing token pruning strategies are inadequate: LLM-stage token pruning overlooks the ViT's overhead, while conventional ViT token pruning, without language g

Fatma Almaz, Bilal Tekyoldaş

This study investigates the differential geometric properties of Smarandache curves derived from null curves defined in the ligtlike cone space $% Q_{3}^{2}\subset E_{2}^{4}$. The indefinite metric structure causes the null vectors, and hence the null curves, to have a richer geometry in this space than in Euclidean or Minkowski spaces. In this study, we ana

Guibin Zhang, Haotian Ren, Chong Zhan, Zhenhong Zhou

Self-evolving memory systems are unprecedentedly reshaping the evolutionary paradigm of large language model (LLM)-based agents. Prior work has predominantly relied on manually engineered memory architectures to store trajectories, distill experience, and synthesize reusable tools, enabling agents to evolve on the fly within environment interactions. However