October 2025 arXiv papers — page 13

Kentaro Ozeki, Risako Ando, Takanobu Morishita, Hirohiko Abe

Normative reasoning is a type of reasoning that involves normative or deontic modality, such as obligation and permission. While large language models (LLMs) have demonstrated remarkable performance across various reasoning tasks, their ability to handle normative reasoning remains underexplored. In this paper, we systematically evaluate LLMs' reasoning capa

Qingtao Hao, Ze-Xu He, Na Zuo, Yang Chen

Quantum sensing utilizing nitrogen-vacancy (NV) centers in diamond has emerged as a transformative technology for probing magnetic phase transition1-4, evidencing Meissner effect of superconductors1,5-9, and visualizing stress distribution3,9 under extreme conditions. Recent development in NV configurations and hydrostatic environments have raised the operat

Shahab Moradi Torkashvand, Arina Kharazi, Emad Sadeghi, Seyed Hossein Hesamedin Sadeghi

The proliferation of inverter-based resources challenges traditional microgrid protection by introducing variable fault currents and complex transients. This paper presents a statistically adaptive differential protection scheme based on Kullback-Leibler divergence, implemented via a Bartlett-corrected G-statistic computed on logarithm-transformed current ma

Reda El Makroum, Sebastian Zwickl-Bernhard, Lukas Kranzl

The electricity sector transition requires substantial increases in residential demand response capacity, yet Home Energy Management Systems (HEMS) adoption remains limited by user interaction barriers requiring translation of everyday preferences into technical parameters. While large language models have been applied to energy systems as code generators an

Tanmay Mishra, Dakota Hamilton, Mads R. Almassalkhi

The increasing integration of renewable energy sources and distributed energy resources (DER) into modern power systems introduces significant uncertainty, posing challenges for maintaining grid flexibility and reliability. Hybrid energy systems (HES), composed of controllable generators, flexible loads, and battery storage, offer a decentralized solution to

Anirban Ray, Vera Galinova, Florian Jug

Computational Super-Resolution (CSR) in fluorescence microscopy has, despite being an ill-posed problem, a long history. At its very core, CSR is about finding a prior that can be used to extrapolate frequencies in a micrograph that have never been imaged by the image-generating microscope. It stands to reason that, with the advent of better data-driven mach

Alexander Soloviev

We report on the existence of a hydrodynamic attractor in the Mueller-Israel-Stewart framework of a fluid living in the novel geometry discovered recently by Grozdanov. This geometry, corresponding to a hyperbolic slicing of dS$_3\times\mathbb{R}$, complements previous analyses of attractors in Bjorken (flat slicing) and Gubser (spherical slicing) flows. The

Jackson Saunders, Emil Prodan, Camelia Prodan

Here we show the first experimental implementation of the fully general Kitaev chain with complex-valued order parameter $\Delta$ and site-varying synthetic chemical potential $\mu$, using a passive multilayer acoustic resonator design and fabrication. Our laboratory model faithfully reproduces the key symmetries and the topological phase diagram of the mode

Xin-Biao Xu, Yu Zeng, Jia-Qi Wang, Zheng-Hui Tian

Electronic and photonic chips revolutionized information technology through massive integration of functional elements, yet phonons as fundamental information carriers in solids remain underestimated. Here, we demonstrate large-scale programmable phononic integrated circuits (PnICs) for complex signal processing. We developed a comprehensive library of gigah

Lingyue Fu, Xin Ding, Linyue Pan, Yaoming Zhu

Current evaluation for Large Language Model (LLM) code agents predominantly focus on generating functional code in single-turn scenarios, which fails to evaluate the agent's capability for continuous code optimization and multi-turn iterative development. To bridge this gap, we introduce CATArena, a framework designed to evaluate the evolutionary capabilitie

Shaswata Chowdhury, M. A. Krishnakumar, Manjari Bagchi, Bhal Chandra Joshi

The Indian Pulsar Timing Array (InPTA) has recently published its second data release (DR2), comprising the timing analysis of seven years of data on 27 millisecond pulsars (MSPs), observed simultaneously in the 300-500 MHz (band 3) and 1260-1460 MHz (band 5), using the upgraded Giant Metrewave Radio Telescope (uGMRT). The low-frequency data, particularly in

Arne Thomsen, Tilman Tröster, François Lanusse

Cosmological field-level inference requires differentiable forward models that solve the challenging dynamics of gas and dark matter under hydrodynamics and gravity. We propose a hybrid approach where gravitational forces are computed using a differentiable particle-mesh solver, while the hydrodynamics are parametrized by a neural network that maps local qua

Marit R. Fiechter, Gabriel Laude, Jeremy O. Richardson

Instanton theory has arisen as a practical tool for calculating tunneling splittings in molecular systems. Unfortunately, the original formulation of instanton theory fundamentally breaks down when trying to calculate the level splitting in asymmetric double wells, as there is no imaginary-time periodic orbit connecting the two non-degenerate minima. We have

A. Chaika, A. O. Oliinyk, I. V. Yatsuta, M. Edwards

Atomtronic quantum sensors based on trapped superfluids offer a promising platform for high-precision inertial measurements where the dynamics of quantized vortices can serve as sensitive probes of external forces. We analytically investigate persistent current oscillations between two density-coupled Bose-Einstein condensate rings and show that the vortex d

Lev Buhovsky, Maksim Stokić

On an open, connected symplectic manifold $(M,\omega)$, the group of Hamiltonian diffeomorphisms forms an infinite-dimensional Fr\'echet Lie group with Lie algebra $C^{\infty}_c(M)$ and adjoint action given by pullbacks. We prove that this action is flexible: for any non-constant $u \in C^{\infty}(M)$, every $f \in C^{\infty}_c(M)$ can be expressed as a weig

Laurent Freidel, Josh Kirklin

In this work, we study the classical phase space for the gravitational degrees of freedom along a null ray. We construct gauge-invariant observables localized on a null ray segment that commute with those localized on the complement; thus, the phase space describes a genuine gravitational subsystem compatible with both locality and diffeomorphism invariance.

Gang Li, Chunlei Zhai, Teng Wang, Shaun Li

Visual navigation algorithms for quadrotors often exhibit a large variation in performance when transferred across different vehicle platforms and scene geometries, which increases the cost and risk of field deployment. To support systematic early-stage evaluation, we introduce FLYINGTRUST, a high-fidelity, configurable benchmarking framework that measures h

Pascal Koiran, Rafael Oliveira

We prove a generalization to Jennrich's uniqueness theorem for tensor decompositions in the undercomplete setting. Our uniqueness theorem is based on an alternative definition of the standard tensor decomposition, which we call matrix-vector decomposition. Moreover, in the same settings in which our uniqueness theorem applies, we also design and analyze an e

Sebastian Basterrech, Shuo Shan, Debabrata Adhikari, Sankhya Mohanty

In this study, we leverage a mixture model learning approach to identify defects in laser-based Additive Manufacturing (AM) processes. By incorporating physics based principles, we also ensure that the model is sensitive to meaningful physical parameter variations. The empirical evaluation was conducted by analyzing real-world data from two AM processes: Dir

Fulin Lin, Shaowen Chen, Ruishan Fang, Hongwei Wang

While Multi-Agent Systems (MAS) excel at complex tasks, their growing autonomy with operational complexity often leads to critical inefficiencies, such as excessive token consumption and failures arising from misinformation. Existing methods primarily focus on post-hoc failure attribution, lacking proactive, real-time interventions to enhance robustness and

Niklas Notter, Markus Aichhorn, Anna Galler

Monolayer 1T-TaS$_2$ hosts a star-of-David charge-density wave (CDW) that stabilizes a low-temperature Mott-insulating state. Recent time-resolved spectroscopies indicate a coupling between the CDW amplitude mode and the electronic correlation strength, yet the role of the screened Coulomb interaction remains unclear. Using the constrained random-phase appro

Yufeng Cui, Honghao Chen, Haoge Deng, Xu Huang

We introduce Emu3.5, a large-scale multimodal world model that natively predicts the next state across vision and language. Emu3.5 is pre-trained end-to-end with a unified next-token prediction objective on a corpus of vision-language interleaved data containing over 10 trillion tokens, primarily derived from sequential frames and transcripts of internet vid

Xinjin Li, Yulie Lu, Jinghan Cao, Yu Ma

Recent advances in Visual Question Answering (VQA) have demonstrated impressive performance in natural image domains, with models like LLaVA leveraging large language models (LLMs) for open-ended reasoning. However, their generalization degrades significantly when transferred to out-of-domain scenarios such as remote sensing, medical imaging, or math diagram

Johnathas D. S. Forte, Seung Gyo Jeong, Anand Santhosh, Seungjun Lee

The magnetic ground state of RuO$_2$ has been under intense debate. Using first-principles calculations, we show that compressive strain along [001] direction stabilizes an altermagnetic phase in RuO$_2$ thin films grown on (100) and (110) TiO$_2$ substrates. We further identify that compressive strain enhances the density of states near the Fermi level, res

Manjunath Prasad Holenarasipura Rajiv, B. M. Vidyavathi

In real-world environments, AI systems often face unfamiliar scenarios without labeled data, creating a major challenge for conventional scene understanding models. The inability to generalize across unseen contexts limits the deployment of vision-based applications in dynamic, unstructured settings. This work introduces a Dynamic Context-Aware Scene Reasoni

Nils Porsche, Flurin Müller-Diesing, Sweta Banerjee, Miguel Goncalves

Confocal laser endomicroscopy (CLE) is a non-invasive, real-time imaging modality that can be used for in-situ, in-vivo imaging and the microstructural analysis of mucous structures. The diagnosis using CLE is, however, complicated by images being hard to interpret for non-experienced physicians. Utilizing machine learning as an augmentative tool would hence

Nathanael Nussbaumer, Markus Böck, Jürgen Cito

Probabilistic programming is a rapidly developing programming paradigm which enables the formulation of Bayesian models as programs and the automation of posterior inference. It facilitates the development of models and conducting Bayesian inference, which makes these techniques available to practitioners from multiple fields. Nevertheless, probabilistic pro

Jikang Deng, Hui Zhou, Mohamed-Slim Alouini

In post-disaster scenarios, the rapid deployment of adequate communication infrastructure is essential to support disaster search, rescue, and recovery operations. To achieve this, uncrewed aerial vehicle (UAV) has emerged as a promising solution for emergency communication due to its low cost and deployment flexibility. However, conventional untethered UAV

Yinrong Hong, Zhiquan Tan, Kai Hu

Large Language Models (LLMs) face significant inference latency challenges stemming from their autoregressive design and large size. To address this, speculative decoding emerges as a solution, enabling the simultaneous generation and validation of multiple tokens. While recent approaches like EAGLE-2 and EAGLE-3 improve speculative decoding using dynamic tr

Filippo Scaramuzza, Renato Cordeiro Ferreira, Giovanni Quattrocchi, Damian Andrew Tamburri

Classical software verification and validation techniques, such as procedural audits, formal methods, or model documentation, are the traditional mechanisms used to achieve the verifiable accountability now required by regulations like the EU AI Act. These methods are either expensive or heavily manual, and ill-suited for the opaque, "black box" nature of mo

Kun Luo, Hongjin Qian, Zheng Liu, Ziyi Xia

Reinforcement Learning with Verifiable Rewards (RLVR) is a promising approach for enhancing agentic deep search. However, its application is often hindered by low \textbf{Reward Density} in deep search scenarios, where agents expend significant exploratory costs for infrequent and often null final rewards. In this paper, we formalize this challenge as the \t

Wondimagegn Abebe Demissie, Stefano Roccella, Rudy Rossetto, Antonio Minnocci

Olive tree biovolume estimation is a key task in precision agriculture, supporting yield prediction and resource management, especially in Mediterranean regions severely impacted by climate-induced stress. This study presents a comparative analysis of three deep learning models U-Net, YOLOv11m-seg, and Mask RCNN for segmenting olive tree crowns and their sha

Sejal Babel, Martha Łącka, Marcel Mroczek

We consider an action of a countable amenable group on a compact metric space, focusing on the set of generic points with respect to a fixed F{\o}lner sequence. For a given characteristic class, we prove that the set of points that are generic (along the F{\o}lner sequence) for some measure in this class is closed with respect to the Besicovitch pseudometric

Saleh Albeaik, Faisal Alsallum, Mohamad Alrished

People tend to walk in groups, and interactions with those groups have a significant impact on crowd behavior and pedestrian traffic dynamics. Social norms can be seen as unwritten rules regulating people interactions in social settings. This article studies people interactions with groups and the emergence of group proxemics. Group zones, zone occupancy cou

Enrique Lopez-Rodriguez, Josep M. Girart, Miguel Pérez-Torres, Mar Mezcua

Galaxy mergers trigger starburst activity and galactic outflows that enrich the circumgalactic medium, profoundly impacting galaxy evolution. These phenomena are intrinsically linked to the physical conditions of the medium, which is permeated by magnetic (B) fields affecting its transport and dynamics. Here, we spatially resolve, $0.24$" (96 pc), the B-fiel

Wen Xie, Yanjun Zhu, Gijs Overgoor, Yakov Bart

Advertisers commonly need multiple versions of the same advertisement (ad) at varying durations for a single campaign. The traditional approach involves manually selecting and re-editing shots from longer video ads to create shorter versions, which is labor-intensive and time-consuming. In this paper, we introduce a framework for automated video ad clipping

Hao Xie, Zixun Huang, Yushen Zuo, Yakun Ju

Spine segmentation, based on ultrasound volume projection imaging (VPI), plays a vital role for intelligent scoliosis diagnosis in clinical applications. However, this task faces several significant challenges. Firstly, the global contextual knowledge of spines may not be well-learned if we neglect the high spatial correlation of different bone features. Sec

Shuyue Fu, Di Wu, Shengping Gong

As interest in the Earth-Moon transfers renewed around the world, understanding the solution space of transfer trajectories facilitates the construction of transfers. This paper is devoted to reporting a novel or less-reported phenomenon about the solution space of bi-impulsive Earth-Moon transfers in the Earth-Moon planar circular restricted three-body prob

Cesare Barbera, Lorenzo Perini, Giovanni De Toni, Andrea Passerini

Developing trustworthy Machine Learning (ML) models requires their predicted probabilities to be well-calibrated, meaning they should reflect true-class frequencies. Among calibration notions in multiclass classification, strong calibration is the most stringent, as it requires all predicted probabilities to be simultaneously calibrated across all classes. H

Jorge Echavarria, Muhammad Nufail Farooqi, Amit Devra, Santana Lujan

We study the problem of adding native pulse-level control to heterogeneous High Performance Computing-Quantum Computing (HPCQC) software stacks, using the Munich Quantum Software Stack (MQSS) as a case study. The goal is to expand the capabilities of HPCQC environments by offering the ability for low-level access and control, currently typically not foreseen

Sejal Babel, Martha Łącka

We endow the set of all invariant measures of a topological dynamical system with a metric $\bar{\rho}$, which induces a topology stronger than the the weak$^*$-topology. Then, we study the closedness of ergodic measures within a characteristic class under this metric. Specifically, we show that if a sequence of generic points associated with ergodic measure

Nathan Torelli, Madalyne Day, Jan Unkelbach

Background and Purpose: Increasing the number of arcs in volumetric modulated arc therapy (VMAT) allows for better intensity modulation and may improve plan quality. However, this leads to longer delivery times, which may cause patient discomfort and increase intra-fractional motion. In this study, it was investigated whether the delivery of different VMAT p

Sumit Mukherjee, Jonte R. Hance

Wigner's Friend-type paradoxes challenge the assumption that events are absolute -- that when we measure a system, we obtain a single result, which is not relative to anything or anyone else. These paradoxes highlight the tension between quantum theory and our intuitions about reality being observer-independent. Building on a recent result that developed the

Yibo Wang, Tingui Wang, Shifeng Huang, Jiazheng Zhu

Stars on bound orbits around a supermassive black hole may undergo repeated partial tidal disruption events (rpTDEs), producing periodic flares. While several candidates have been suggested, definitive confirmation of these events remains elusive. We report the discovery of AT2023uqm, a nuclear transient that has exhibited at least five periodic optical flar

Nikita Tsoy, Nikola Konstantinov

Shortcuts, spurious rules that perform well during training but fail to generalize, present a major challenge to the reliability of deep networks (Geirhos et al., 2020). However, the impact of shortcuts on feature representations remains understudied, obstructing the design of principled shortcut-mitigation methods. To overcome this limitation, we investigat

Francesco De Roma, Luca Maffioli, Edward R. Smith, Antonio Buffo

The use of dissipative particle dynamics (DPD) simulation to study the rheology of fluids under shear has always been of great interest to the research community. Despite being a powerful tool, a limitation of DPD is the need to use high shear rates to obtain viscosity results with a sufficiently high signal-to-noise ratio (SNR). This often leads to simulati

Fagui He, Yan Yan Grisan Qiu, Simone Mearini, Vitaliy Feyer

Two-dimensional bismuth (Bi) is a promising platform for quantum and energy technologies due to strong spin-orbit coupling, high thermoelectric efficiency, and magnetoresistance. However, scalable and flexible synthesis of high-quality Bi with fast research turnaround remains challenging. We report a controlled colloidal synthesis of Bi nanosheets with tunab

Nina Herrmann, Jan Stenkamp, Benjamin Karic, Stefan Oehmcke

Deploying machine learning models on compute-constrained devices has become a key building block of modern IoT applications. In this work, we present a compression scheme for boosted decision trees, addressing the growing need for lightweight machine learning models. Specifically, we provide techniques for training compact boosted decision tree ensembles tha

Claus Kadelka

Canalization is a key organizing principle in complex systems, particularly in gene regulatory networks. It describes how certain input variables exert dominant control over a function's output, thereby imposing hierarchical structure and conferring robustness to perturbations. Degeneracy, in contrast, captures redundancy among input variables and reflects t

Samantha Creech, Francesca Civano, Daniel R. Wik, Ross Silver

X-ray surveys are one of the most unbiased methods for detecting Compton Thick (CT; $N_{{\mathrm{H}}} \geq 10^{24}$ cm$^{-2}$) AGN, which are thought to comprise up to $60\%$ of AGN within $z \lesssim 1.0$. These CT AGN are often difficult to detect with current instruments, but the X-ray data within the JWST-North Ecliptic Pole (NEP) Time Domain Field (TDF)

Catarina C. Ribeiro, Nele Link, Jan-Lukas Schäfer, Carina Breuer

As interest in sustainable materials grows, paper is being reimagined as a multifunctional substrate with significant potential for future technologies for innovative, environmentally friendly solutions. This study investigates the swelling behavior and environmental responsiveness of a copolymer, poly(N,N-dimethylacrylamide-co-4-methacryloyloxybenzophenone)

Shaocheng Liu, Qi Chen, Minquan Cheng

Characterization of entropy functions is of fundamental importance in information theory. By imposing constraints on their Shannon outer bound, i.e., the polymatroidal region, one obtains the faces of the region and entropy functions on them with special structures. In this series of two papers, we characterize entropy functions on the $2$-dimensional faces

Prathamesh Kothavale, Sravani Boddepalli

Conventional robots possess a limited understanding of their kinematics and are confined to preprogrammed tasks, hindering their ability to leverage tools efficiently. Driven by the essential components of tool usage - grasping the desired outcome, selecting the most suitable tool, determining optimal tool orientation, and executing precise manipulations - w

Jack FitzGerald, Aristotelis Lazaridis, Dylan Bates, Aman Sharma

We present EdgeRunner 20B, a fine-tuned version of gpt-oss-20b optimized for military tasks. EdgeRunner 20B was trained on 1.6M high-quality records curated from military documentation and websites. We also present four new tests sets: (a) combat arms, (b) combat medic, (c) cyber operations, and (d) mil-bench-5k (general military knowledge). On these militar

Adam Parhi, Dina Prialnik

We present a fully integrated model of comet evolution that couples thermal and compositional processes with dynamical processes continuously, from formation to present-day activity. The combined code takes into account changes in orbital parameters that define the heliocentric distance as a function of time, which is fed into the thermal/compositional evolu

Peter Bastian, Nils Friess

Coarse spaces are essential to ensure robustness w.r.t. the number of subdomains in two-level overlapping Schwarz methods. Robustness with respect to the coefficients of the underlying partial differential equation (PDE) can be achieved by adaptive (or spectral) coarse spaces involving the solution of local eigenproblems. The solution of these eigenproblems,

Katerina Gratsea, Matthew Otten

Quantum computing promises revolutionary advances in modeling materials and molecules. However, the up-to-date runtime estimates for utility-scale applications on certain quantum hardware systems are in the order of years rendering quantum computations impractical. Our work incorporates state-of-the-art innovations in all key aspects of the fault-tolerant qu

Min Hou, Xin Liu, Le Wu, Chenyi He

Cross-Domain Sequential Recommendation (CDSR) seeks to improve user preference modeling by transferring knowledge from multiple domains. Despite the progress made in CDSR, most existing methods rely on overlapping users or items to establish cross-domain correlations-a requirement that rarely holds in real-world settings. The advent of large language models

Sujit K. Dalui, Shantanu Desai

Numerous observations have shown that the dark matter halo surface density, defined as the product of core radius and halo central density of cored dark matter haloes is nearly constant and independent of galaxy mass over a whole slew of galaxy types. Here we calculate the surface density in cold dark matter(CDM) and self-interacting dark matter (SIDM) model

Daiki Miura, Masaru Hongo, Hidetoshi Taya, Tetsuo Hatsuda

We derive the one-pion exchange potential (OPEP) in the presence of a homogeneous magnetic field using chiral perturbation theory with nonrelativistic nucleons. Our approach is applicable not only to weak magnetic fields but also to strong ones up to around the pion-mass scale. The Green's function of charged pions is modified by the magnetic field, leading

Miranda Anna Christ, Adrián Csiszárik, Gergely Becsó, Dániel Varga

This paper investigates the structure of linear operators introduced in Hernandez et al. [2023] that decode specific relational facts in transformer language models. We extend their single-relation findings to a collection of relations and systematically chart their organization. We show that such collections of relation decoders can be highly compressed by

Alessio Colombo, Alessandra Vizzaccaro, Cyril Touzé, André de F. Stabile

This work introduces a parametric simulation-free reduced order model for incompressible flows undergoing a Hopf bifurcation, leveraging the parametrisation method for invariant manifolds. Unlike data-driven approaches, this method operates directly on the governing equations, eliminating the need for full-order simulations. The proposed model is computed at

Aidan Furlong, Robert Salko, Xingang Zhao, Xu Wu

The use of ML in engineering has grown steadily to support a wide array of applications. Among these methods, deep neural networks have been widely adopted due to their performance and accessibility, but they require large, high-quality datasets. Experimental data are often sparse, noisy, or insufficient to build resilient data-driven models. Transfer learni

Jiaying Wang, Jonas Benjamin Ohland, Yen-Yu Chang, Vedhas Pandit

High-energy laser facilities such as PHELIX at GSI require excellent beam pointing stability for reproducibility and relative independence for future experiments. Beam pointing stability has been traditionally achieved using simple proportional-integral-derivative (PID) control which removes the problem of slow drift, but is limited because of the time delay

David Williams, Max Hort, Maria Kechagia, Aldeida Aleti

Software Engineering (SE) research involving the use of Large Language Models (LLMs) has introduced several new challenges related to rigour in benchmarking, contamination, replicability, and sustainability. In this paper, we invite the research community to reflect on how these challenges are addressed in SE. Our results provide a structured overview of cur

Shane Nicholas, Mohammad Omidyeganeh, Alfredo Pinelli, Alessandro Monti

Inspired by the spontaneous behaviour observed in filamentous layers -- where the balance between flow-induced drag and structural elasticity dictates the filaments' equilibrium streamlined posture -- we perform a series of large-eddy simulations to investigate how filament inclination affects turbulent shear flows developing both above and within a canopy o

Huajie Tan, Cheng Chi, Xiansheng Chen, Yuheng Ji

The proliferation of collaborative robots across diverse tasks and embodiments presents a central challenge: achieving lifelong adaptability, scalable coordination, and robust scheduling in multi-agent systems. Existing approaches, from vision-language-action (VLA) models to hierarchical frameworks, fall short due to their reliance on limited or dividual-age

Mustafa Mullahasanoglu

This brief review surveys recent progress driven by the gauge/Yang-Baxter equation (YBE) correspondence. This connection has proven to be a powerful tool for discovering novel integrable lattice spin models in statistical mechanics by exploiting dualities in supersymmetric gauge theories. In recent years, research has demonstrated the use of dual gauge theor

Saleh Albeaik, Mohamad Alrished, Faisal Alsallum

This article investigates the pedestrian group as an emergent agent. The article explores empirical data to derive emergent agency and formation state spaces and outline recurring patterns of walking behavior. In this analysis, pedestrian trajectories extracted from surveillance videos are used along with manually annotated pedestrian group memberships. We c

Adrien Weihs, Andrea L. Bertozzi, Matthew Thorpe

Higher-Order Hypergraph Learning (HOHL) was recently introduced as a principled alternative to classical hypergraph regularization, enforcing higher-order smoothness via powers of multiscale Laplacians induced by the hypergraph structure. Prior work established the well- and ill-posedness of HOHL through an asymptotic consistency analysis in geometric settin

Margarita Cabrera-Bean, Josep Vidal, Sergio Fernandez-Bertolin, Albert Roso-Llorach

In the classical setting, the training of a Hidden Markov Model (HMM) typically relies on a single, sufficiently long observation sequence that can be regarded as representative of the underlying stochastic process. In this context, the Expectation Maximization (EM) algorithm is applied in its specialized form for HMMs, namely the Baum Welch algorithm, which

Shovan Dutta

This is a concise, pedagogical introduction to the dynamic field of open quantum systems governed by Markovian master equations. We focus on the mathematical and physical origins of the widely used Lindblad equation, its unraveling in terms of stochastic pure-state trajectories and the corresponding continuous measurement protocols, the structure of steady s

Daniel J. Netherwood, Ben S. Humphries, Connor Robbins, Doireann O'Kiely

We model the formation and evolution of wrinkles in a floating elastic sheet under uniaxial compression. This is a canonical setup in the study of wrinkling, and whilst its static equilibrium configuration is well characterised, its dynamics are not. In this work, we focus on modelling the transition from early, inertia-dominated wrinkle growth to late-time

Mykhailo Pavliuk, Tomáš Bzdušek, Askar Iliasov

We investigate $s$-wave superconductivity in negatively curved geometries, focusing on Cayley trees and the hyperbolic plane. Using a self-consistent Bogoliubov-de Gennes approach for trees and a BCS treatment of the hyperbolic continuum, we establish a unified mean-field framework that captures the role of boundaries in hyperbolic spaces. For finite Cayley

Ruilin Wang, Huixia Li, Yuexiao Ma, Xiawu Zheng

Inference latency stands as a critical bottleneck in the large-scale deployment of Large Language Models (LLMs). Speculative decoding methods have recently shown promise in accelerating inference without compromising the output distribution. However, existing work typically relies on a dualistic draft-verify framework and lacks rigorous theoretical grounding

Florin Avram, Rim Adenane, Lasko Basnarkov, Andras Horvath

We aim to study boundary stability and persistence of positive odes in mathematical epidemiology models by importing structural tools from chemical reaction networks. This is largely a review work, which attempts to bring closer together the fields of mathematical epidemiology (ME), and chemical reaction networks (CRNs), based on several observations. We sta

Ulrike A. Nuber, Viktor Stein

Biological engineering, the convergence between engineering and biology, is at the forefront of significant advances in healthcare, agriculture, and environmental sustainability, making it highly relevant to current scientific and societal challenges. We take a comprehensive look at this broad and interdisciplinary domain, structure it into three main areas

Abdelhakim Ziani, András Horváth, Paolo Ballarini

Heavy-tailed distributions, prevalent in a lot of real-world applications such as finance, telecommunications, queuing theory, and natural language processing, are challenging to model accurately owing to their slow tail decay. Bernstein phase-type (BPH) distributions, through their analytical tractability and good approximations in the non-tail region, can

Shuaishuai Liu, Gergely Acs, Gergely Biczók

Smart home devices such as video doorbells and security cameras are becoming increasingly common in everyday life. While these devices offer convenience and safety, they also raise new privacy concerns: how these devices affect others, like neighbors, visitors, or people passing by. This issue is generally known as interdependent privacy, where one person's

Xiaoquan Xu

We prove that every quasicontinuous domain that fails to be quasialgebraic admits the unit interval [0, 1] as its monotone Lawson-continuous image. As a result, every countable quasicontinuous domain is quasialgebraic.

Yair Elboher, Yuval Pinter

Diacritics restoration in Hebrew is a fundamental task for ensuring accurate word pronunciation and disambiguating textual meaning. Despite the language's high degree of ambiguity when unvocalized, recent machine learning approaches have significantly advanced performance on this task. In this work, we present DiVRit, a novel system for Hebrew diacritization

Daniele Pranzetti, Domenico Giuseppe Salluce

The dual formulations of an infinite tower of tree-level soft theorems in asymptotically flat spacetimes for scattering amplitudes in the standard energy-momentum basis and for correlators of a 2D celestial conformal field theory imply a correspondence between the celestial operator product expansion (OPE) and the higher spin charge bracket. We apply such co

Hsiu-Yuan Huang, Chenming Tang, Weijie Liu, Clive Bai

Existing Reinforcement Learning from Verifiable Rewards (RLVR) methods, such as Group Relative Policy Optimization (GRPO), have achieved remarkable progress in improving the reasoning capabilities of Large Reasoning Models (LRMs). However, they exhibit limited exploration due to reliance on on-policy rollouts which are confined to the current policy's distri

Bao-Xi Sun, Ying-Ying Fan, Qin-Qin Cao

The attractive interaction of the $\phi$ meson and the proton is reported by the ALICE Collaboration, and the corresponding scattering length $f_0$ is given as $Re(f_0)=0.85\pm0.34(stat)\pm0.14(syst)$ fm and $Im(f_0)=0.16\pm0.10(stat)\pm0.09(syst)$ fm. The fact that the real part is significant in contrast to the imaginary part indicates a dominating role of

Truong Hai Dang, Jingyu Xiao, Yintong Huo

The evolution of web applications relies on iterative code modifications, a process that is traditionally manual and time-consuming. While Large Language Models (LLMs) can generate UI code, their ability to edit existing code from new design requirements (e.g., "center the logo") remains a challenge. This is largely due to the absence of large-scale, high-qu

Araceli Bonifant, Brady Young

We present a proof of the conjecture by Bonifant and Milnor (see arXiv:2503.08868) regarding the similarity between the connectedness locus of the curve $\mathcal{S}_p$ at Misiurewicz parameters and their corresponding filled Julia sets in a neighborhood of the corresponding free co-critical point. The proof is in parallel with the generalization of Tan Lei'

Katsuhiko Matsuzaki, Fei Tao

We construct an explicit example of an asymptotically conformal chord-arc curve that fails to be asymptotically smooth. This implies that a function belonging to both the little Bloch space and BMOA does not necessarily lie in VMOA, and that a strongly quasisymmetric homeomorphism which is symmetric is not necessarily strongly symmetric. We also provide a co

Dipak Meher, Carlotta Domeniconi

Human smuggling networks are increasingly adaptive and difficult to analyze. Legal case documents offer critical insights but are often unstructured, lexically dense, and filled with ambiguous or shifting references, which pose significant challenges for automated knowledge graph (KG) construction. While recent LLM-based approaches improve over static templa

Ziqian Liu, Xin Wang, Junyu Lu, Longzhao Liu

Political polarization, fueled by public discourse and echo chambers, threatens the foundation of democratic elections. However, traditional one-dimensional opinion models -- assuming ``support for one party equals opposition to another'' -- fail to capture the nuanced dynamics of swing voters (including neutrals, left leaners and right leaners), who are cri

Youssef Attia El Hili, Albert Thomas, Malik Tiomoko, Abdelhakim Benechehab

Model and hyperparameter selection are critical but challenging in machine learning, typically requiring expert intuition or expensive automated search. We investigate whether large language models (LLMs) can act as in-context meta-learners for this task. By converting each dataset into interpretable metadata, we prompt an LLM to recommend both model familie

Vinícius Ferraria, Eurico Ruivo

The edge detection task is essential in image processing aiming to extract relevant information from an image. One recurring problem in this task is the weaknesses found in some detectors, such as the difficulty in detecting loose edges and the lack of context to extract relevant information from specific problems. To address these weaknesses and adapt the d

Clara Colombatto, Sean Rintel, Lev Tankelevitch

Generative Artificial Intelligence (GenAI) can aid humans in a wide range of tasks, but its effectiveness critically depends on users being able to evaluate the accuracy of GenAI outputs and their own expertise. Here we asked how confidence in self and GenAI contributes to decisions to seek and rely on advice from GenAI ('prospective confidence'), and how ad

Anwesh Kumar Mishra, Deekshya Roy Sarkar, Prachi Prajapati, Alka Singh

Pulsating variables play a significant role in shaping modern astronomy. Presently it is an exciting era in observational study of variable stars owing to surveys like OGLE and TESS. The vast number of sources being discovered by these surveys is also creating opportunities for 1-2m class telescopes to provide follow-up observations to characterize these. We

John Kerfoot, Emanuele Mendicelli, David Schaich

We employ quantum variational methods to investigate a single-site interacting fermion-boson system -- an example of a minimal supersymmetric model that can exhibit spontaneous supersymmetry breaking. Our study addresses the challenges inherent in calculating mixed fermion-boson systems and explores the potential of quantum computing to advance their analysi

Roberto Vacca

We prove existence of aCM and Ulrich sheaves respect to ample and globally generated polarisations on a class of special finite coverings $f:X\to\mathbb{P}^n$, which in particular contains cyclic ones. In the case of rank $2$ on double coverings, we have a precise description of the zero loci of such sheaves which allows us to study their geometry and classi

Francesco D'Emilio, Yongxi Lin, Nathan A. Wagner, Brett D. Wick

We examine dyadic paraproducts and commutators in the non-homogeneous setting, where the underlying Borel measure $\mu$ is not assumed to be doubling. We first establish a pointwise sparse domination for dyadic paraproducts and related operators with symbols $b \in \textrm{BMO}(\mu)$, improving upon an earlier result of Lacey, where the symbol $b$ was assume

Victor Carmona, Anja Švraka

We prove the statement in the title, solving in this way a conjecture stated by Ginot for manifolds with corners. Along the way, we establish a derived Swiss-cheese additivity theorem and an alternative proof for the hyperdescent of factorization algebras over those manifolds.

Pau Juan-Bartroli, Esteban Muñoz-Sobrado

The sustainability of cooperation is crucial for understanding the progress of societies. We study a repeated game in which individuals decide the share of their income to transfer to other group members. A central feature of our model is that individuals may, with some probability, switch incomes across periods, our measure of income mobility, while the ove

Dinesh Kumar Keshari, Avijit Pal, Bhaskar Paul

A $7$-tuple of commuting bounded operators $\mathbf{T} = (T_1, \dots, T_7)$ defined on a Hilbert space $\mathcal{H}$ is said to be a \textit{$\Gamma_{E(3; 3; 1, 1, 1)}$-contraction} if $\Gamma_{E(3; 3; 1, 1, 1)}$ is a spectral set for $\mathbf{T}$. Let $(S_1, S_2, S_3)$ and $(\tilde{S}_1, \tilde{S}_2)$ be tuples of commuting bounded operators on $\mathcal{H}

Mustafa Fuad Rifet Ibrahim, Maurice Meijer, Alexander Schlaefer, Peer Stelldinger

Continuous electrocardiogram (ECG) monitoring via wearable devices is vital for early cardiovascular disease detection. However, deploying deep learning models on resource-constrained microcontrollers faces reliability challenges, particularly from Out-of-Distribution (OOD) pathologies and noise. Standard classifiers often yield high-confidence errors on suc