October 2025 arXiv papers — page 92

Tongtong Liang, Alexander Cloninger, Rahul Parhi, Yu-Xiang Wang

Understanding generalization in overparameterized neural networks hinges on the interplay between the data geometry, neural architecture, and training dynamics. In this paper, we theoretically explore how data geometry controls this implicit bias. This paper presents theoretical results for overparametrized two-layer ReLU networks trained below the edge of s

R. Abbasi, M. Ackermann, J. Adams, S. K. Agarwalla

The IceCube Neutrino Observatory has observed extragalactic astrophysical neutrinos with an apparently isotropic distribution. Only a small fraction of the observed astrophysical neutrinos can be explained by known sources. Neutrino production is thought to occur in energetic environments that are ultimately powered by the gravitational collapse of dense reg

Teodora Reu, Sixtine Dromigny, Michael Bronstein, Francisco Vargas

Rectified Flows learn ODE vector fields whose trajectories are straight between source and target distributions, enabling near one-step inference. We show that this straight-path objective conceals fundamental failure modes: under deterministic training, low gradient variance drives memorization of arbitrary training pairings, even when interpolant lines bet

Zhiqi Kang, Rahaf Aljundi, Vaggelis Dorovatas, Karteek Alahari

As the field continues its push for ever more resources, this work turns the spotlight on a critical question: how can vision-language models (VLMs) be adapted to thrive in low-resource, budget-constrained settings? While large VLMs offer strong performance, they are impractical to deploy in such settings. Small VLMs, on the other hand, are efficient but typ

Nahid Binandeh Dehaghani, Rafal Wisniewski, A. Pedro Aguiar

We propose a quantum-assisted framework for solving constrained finite-horizon nonlinear optimal control problems using a barrier Sequential Quadratic Programming (SQP) approach. Within this framework, a quantum subroutine is incorporated to efficiently solve the Schur complement step using block-encoding and Quantum Singular Value Transformation (QSVT) tech

Canyi Chen, Ritoban Kundu, Wei Hao, Peter X. -K. Song

Structural equation models (SEMs) are fundamental to causal mediation pathway discovery. However, traditional SEM approaches often rely on \emph{ad hoc} model specifications when handling complex data structures such as mixed data types or non-normal data in which Gaussian assumptions for errors are rather restrictive. The invocation of copula dependence mod

Devin Ersoy, Brandon Lee, Ananth Shreekumar, Arjun Arunasalam

As users increasingly turn to large language model (LLM) based web agents to automate online tasks, agents may encounter dark patterns: deceptive user interface designs that manipulate users into making unintended decisions. Although dark patterns primarily target human users, their potentially harmful impacts on LLM-based generalist web agents remain unexpl

Patricia Delafuente, Arya Honraopatil, Lara J. Martin

This paper explores the application of Large Language Models (LLMs) and reasoning to predict Dungeons & Dragons (DnD) player actions and format them as Avrae Discord bot commands. Using the FIREBALL dataset, we evaluated a reasoning model, DeepSeek-R1-Distill-LLaMA-8B, and an instruct model, LLaMA-3.1-8B-Instruct, for command generation. Our findings highlig

Raoni Arroyo, Renato Semaniuc Valvassori

This article proposes a reading of quantum metaphysical indeterminacy from the perspective of Parsons' Nuclear Meinongianism. In doing so, we identify a fundamental incompatibility between a key feature of Parsons' theory and standard quantum mechanics. Our approach interprets quantum indeterminacy as property incompleteness. However, this move, when combine

Minerva M. Sardaneta, Philippe Amram, Roberto Rampazzo, Margarita Rosado

Isolated galaxies are rare yet invaluable for studying secular evolution, as their physical properties can remain largely unaffected by external influences for several billion years, primarily shaped by internal evolutionary processes. This study focuses on a representative sample of nearly edge-on ($i\geq80^{\circ}$) late-type galaxies selected from the Cat

Wan Ki Wong, Sahel Torkamani, Michele Ciampi, Rik Sarkar

Evaluating the usefulness of data before purchase is essential when obtaining data for high-quality machine learning models, yet both model builders and data providers are often unwilling to reveal their proprietary assets. We present PrivaDE, a privacy-preserving protocol that allows a model owner and a data owner to jointly compute a utility score for a ca

Marina Soares Marinho, Daniela Vianna, Livy Real, Altigran da Silva

This study presents the Jusbrasil Study on the Use of General-Purpose AIs in Law, proposing an experimental evaluation protocol combining legal theory, such as material correctness, systematic coherence, and argumentative integrity, with empirical assessment by 48 legal professionals. Four systems (JusIA, ChatGPT Free, ChatGPT Pro, and Gemini) were tested in

Alena Jarolímová, Jaroslav Hron, Karel Tůma, Josef Málek

The assumption that blood adheres to vessel walls, the ``no-slip'' boundary condition, is an essential premise of cardiovascular fluid dynamics. Yet, whether it holds true \emph{in vivo} has not been established. Using 4D flow magnetic resonance imaging of the human thoracic aorta and modeling blood as a Navier--Stokes fluid, we quantify the velocity of bloo

Tomasz Kania

For stochastic partial differential equations driven by Lévy noise, understanding when changes in the drift operator preserve the law of the solution is fundamental to filtering, control, and simulation. We extend law-comparison results for Ornstein--Uhlenbeck processes from bounded drift operators to generators of $C_0$-semigroups on a separable Hilbert spa

Junpeng Hou, Mark M. Seidel, Chuanwei Zhang

Recent advances in quantum communication have enabled long-distance secure information transfer through quantum channels, giving rise to quantum networks with unique physical and statistical properties. However, as in classical networks, the propagation of viruses in these systems could have severe consequences. Here, we investigate the critical problem of v

Joeran Beel, Bela Gipp, Tobias Vente, Moritz Baumgart

Recommender-systems research has accelerated model and evaluation advances, yet largely neglects automating the research process itself. We argue for a shift from narrow AutoRecSys tools -- focused on algorithm selection and hyper-parameter tuning -- to an Autonomous Recommender-Systems Research Lab (AutoRecLab) that integrates end-to-end automation: problem

Nursultan Mamatov, Philipp Kellmeyer

Accurate early prediction of in-hospital mortality in intensive care units (ICUs) is essential for timely clinical intervention and efficient resource allocation. This study develops and evaluates machine learning models that integrate both structured clinical data and unstructured textual information, specifically discharge summaries and radiology reports,

V. A. Bobkov, G. A. Bobkov, I. V. Bobkova

Proximity effect in thin-film superconductor (S)/magnet heterostructures with different types of magnets including ferromagnets, antiferromagnets and altermagnets is widely considered in the framework of an effective model, where the heterostructure is replaced by a homogeneous superconductor in the presence of a homogeneous exchange field of a corresponding

Vitor Pereira Matias, Daniel Perazzo, Vinicius Silva, Alberto Raposo

The problem of 3D reconstruction from posed images is undergoing a fundamental transformation, driven by continuous advances in 3D Gaussian Splatting (3DGS). By modeling scenes explicitly as collections of 3D Gaussians, 3DGS enables efficient rasterization through volumetric splatting, offering thus a seamless integration with common graphics pipelines. Desp

Arindam Fadikar, Abby Stevens, Mickael Binois, Nicholson Collier

Bayesian optimization (BO) is a powerful framework for estimating parameters of expensive simulation models, particularly in settings where the likelihood is intractable and evaluations are costly. In stochastic models every simulation is run with a specific parameter set and an implicit or explicit random seed, where each parameter set and random seed combi

Gareb Fernández-Rodríguez, Giuseppe Morello, Jonathan C. Tan, Enric Pallé

Unlocking the atmospheres of sub-Neptunes is among JWST's major achievements, yet such observations demand complex analyses that strongly affect interpretations. We present an independent reanalysis of the original JWST transmission spectrum of K2-18 b, to assess the robustness of previously claimed detections, explore the parameter space, and implications f

Mingjia Yan, Mohamed Warda, Balázs Németh, Lukas Kikuchi

Slender structures are ubiquitous in biological and physical systems, from bacterial flagella to soft robotic arms. The Cosserat rod provides a mathematical framework for slender bodies that can stretch, shear, twist and bend. In viscous fluid environments at low Reynolds numbers - as encountered in soft matter physics, biophysics, and soft continuum robotic

Esrat Ebtida Sakib, MD Ahnaf Akib, Md Muktadir Mazumder, Maliha Noushin Raida

Non-Functional Requirements (NFRs) play a critical role in determining the overall quality and user satisfaction of software systems. Accurately identifying and classifying NFRs is essential to ensure that software meets performance, usability, and reliability expectations. However, manual identification of NFRs from documentation is time-consuming and prone

Nikhil Verma, Manasa Bharadwaj, Wonjun Jang, Harmanpreet Singh

Large Language Models (LLMs) have redefined complex task automation with exceptional generalization capabilities. Despite these advancements, state-of-the-art methods rely on single-strategy prompting, missing the synergy of diverse reasoning approaches. No single strategy excels universally, highlighting the need for frameworks that fuse strategies to maxim

Ioannis Avgerinos, Ioannis Mourtos, Dimitrios Papathanasiou, Georgios Zois

The effect of resource allocation on manufacturing motivates us to examine a scheduling variant that is of practical significance yet remains overlooked. We examine a Hybrid Flexible Flowshop (HFFS), i.e., an environment where a set of jobs is scheduled across multiple stages (each stage having multiple identical machines) yet some jobs may skip some stages.

Bom Soo Kim

We identify a Hall viscosity term directly from the Dzyaloshinskii-Moriya interaction (DMI), that breaks parity symmetry, in the skyrmion motion of insulating magnets by time-averaging the magnon contribution to all orders. The viscosity term is proportional to the skyrmion charge. Skyrmion Hall angle shows significant dependence on the skyrmion shape and si

Rohan Choudhury, JungEun Kim, Jinhyung Park, Eunho Yang

Vision Transformers (ViTs) partition input images into uniformly sized patches regardless of their content, resulting in long input sequence lengths for high-resolution images. We present Adaptive Patch Transformers (APT), which addresses this by using multiple different patch sizes within the same image. APT reduces the total number of input tokens by alloc

Lucas Teinturier, Benjamin Charnay, Aymeric Spiga, Bruno Bezard

Brown dwarfs are massive, giant exoplanet analogues subject to variability and colour changes, known as the L/T transition, fundamental for their thermal evolution. The drivers of the L/T transition remain elusive, with atmospheric circulations and/or clouds usually suggested as potential mechanisms. Using a three-dimensional Global Climate Model including c

Paul-Tiberiu Miclea, Martin Sboron, Hardik Vaghasiya, Hoang Thinh Nguyen

Microplastics (MPs) are ubiquitous pollutants with demonstrated potential to impact ecosystems and human health. Their microscopic size complicates detection, classification, and removal, especially in biological and environmental samples. While techniques like optical microscopy, Scanning Electron Microscopy (SEM), and Atomic Force Microscopy (AFM) provide

Jakub Dobrowolski

We prove an asymptotic formula with a power-saving error term for a specific weighted second moment of $\mathrm{GL}(2)\times \mathrm{GL}(2)$ Rankin-Selberg $L$-function, $L(1/2,\pi\otimes \pi_0)$ over any number field $F$ where $\pi$ runs over representations with the non-archimedean conductor dividing an ideal which tends to infinity and $\pi_0$ is a fixed

Daniel Israel, Tian Jin, Ellie Cheng, Guy Van den Broeck

Most large language models are autoregressive: they generate tokens one at a time. Discrete diffusion language models can generate multiple tokens in parallel, but sampling from them requires a denoising order: a strategy for deciding which tokens to decode at each step. Determining a good denoising order is difficult, and existing approaches use heuristics

Bastian Castorene, Francisco J. Peña, Eric Suárez Morell, Caio Lewenkopf

In this work, quantum Stirling engines based on monolayer, AB-stacked bilayer, and ABC-stacked trilayer graphene under perpendicular magnetic fields are analyzed. Performance maps of the useful work \((\eta W)\) reveal a robust optimum at low magnetic fields and moderately low temperatures, with all stackings capable of reaching Carnot efficiency under suita

Zaineh Abughazzah, Emna Baccour, Loay Ismail, Amr Mohamed

The integration of Unmanned Aerial Vehicles (UAVs) into Open Radio Access Networks (O-RAN) enhances communication in disaster management and Search and Rescue (SAR) operations by ensuring connectivity when infrastructure fails. However, SAR scenarios demand stringent security and low-latency communication, as delays or breaches can compromise mission success

Shivam Singh, Yiming Chen, Agneet Chatterjee, Amit Raj

Personalized image generative models are highly proficient at synthesizing images from text or a single image, yet they lack explicit control for composing objects from specific parts of multiple source images without user specified masks or annotations. To address this, we introduce Chimera, a personalized image generation model that generates novel objects

Donggeon David Oh, Duy P. Nguyen, Haimin Hu, Jaime F. Fisac

Recent advances in reinforcement learning (RL) enable its use on increasingly complex tasks, but the lack of formal safety guarantees still limits its application in safety-critical settings. A common practical approach is to augment the RL policy with a safety filter that overrides unsafe actions to prevent failures during both training and deployment. Howe

J. François, L. Ravera

We treat the Mechanics of point particles as a 1-dimensional general-relativistic gauge field theory, which may be referred to as Mechanical Field Theory (MFT), exploiting the bundle geometry of Mechanical Field Space (MFS). The diffeomorphism covariance of MFT encodes its relational character, arising - as in all general-relativistic physics - via the conju

Jiawei Zhang, Andrew Estornell, David D. Baek, Bo Li

Large Language Models (LLMs) exhibit strong but shallow alignment: they directly refuse harmful queries when a refusal is expected at the very start of an assistant turn, yet this protection collapses once a harmful continuation is underway (either through the adversarial attacks or via harmful assistant-prefill attacks). This raises a fundamental question:

Rukuang Huang, Sungjun Cho, Chetan Gohil, Oiwi Parker Jones

Modelling the complex spatiotemporal patterns of large-scale brain dynamics is crucial for neuroscience, but traditional methods fail to capture the rich structure in modalities such as magnetoencephalography (MEG). Recent advances in deep learning have enabled significant progress in other domains, such as language and vision, by using foundation models at

Xuan Yao

We formulate stable Bernstein type theorems in certain positively curved ambient manifolds. In all dimensions, we prove that for any complete Riemannian manifold $(X^{n+1},g)$, if the Ricci curvature is non-negative and it positive BiRic curvature with $\alpha$-decay, then any complete, two-sided, stable minimal immersion must be totally geodesic and $\text{

Md Hasan Shahriar Rifat, Tanvir Khan, K. M. Mehedi Hassan

Improving energy efficiency by recovering waste heat and providing thermal protection is of high technological importance. This work investigates the chalcogenide compounds CdGa2Te4 and ZnGa2Te4 using density functional theory and BoltzTraP2 calculations. Both materials are dynamically stable, brittle, and elastically anisotropic. Electronic structure calcul

Shabnam Ataee, Hugo Huart, Andrei Popescu-Belis

This paper assesses the ability of large language models (LLMs) to translate texts that include inter-sentential dependencies. We use the English-French DiscEvalMT benchmark (Bawden et al., 2018) with pairs of sentences containing translation challenges for pronominal anaphora and lexical cohesion. We evaluate 12 LLMs from the DeepSeek-R1, GPT, Llama, Mistra

Thibaut Arnoulx de Pirey, Frédéric van Wijland

When out-of-equilibrium particles interact by means of pairwise forces, their stationary distribution in general exhibits many-body interactions. In the particular case of active particles, it has been shown numerically that the Motility Induced Phase Separation cannot be explained by the effective attraction emerging from two isolated particles, thereby hig

Justus Arweiler, Indra Jungjohann, Aparna Muraleedharan, Heike Leitte

Machine learning (ML) holds great potential to advance anomaly detection (AD) in chemical processes. However, the development of ML-based methods is hindered by the lack of openly available experimental data. To address this gap, we have set up a laboratory-scale batch distillation plant and operated it to generate an extensive experimental database, coverin

Isaac Wu, Michael Maslowski

As large language models (LLMs) become integrated into various sensitive applications, prompt injection, the use of prompting to induce harmful behaviors from LLMs, poses an ever increasing risk. Prompt injection attacks can cause LLMs to leak sensitive data, spread misinformation, and exhibit harmful behaviors. To defend against these attacks, we propose Co

Nadir Farhi

In this work, we address the problem of determining reliable policies in reinforcement learning (RL), with a focus on optimization under uncertainty and the need for performance guarantees. While classical RL algorithms aim at maximizing the expected return, many real-world applications - such as routing, resource allocation, or sequential decision-making un

Daniela Bubboloni, Francesco Fumagalli, Cheryl E. Praeger

The enhanced power graph, $\mathcal{E}(G)$, of a group $G$ has vertex set $G$ and two elements are adjacent if they generate a cyclic subgroup. In the case of finite groups, we identify some striking and unexpected properties of these graphs, as well as links between properties of $\mathcal{E}(G)$ and properties of the group $G$. We prove that if $\mathcal{E

Jiajun Fan, Chaoran Cheng, Shuaike Shen, Xiangxin Zhou

Flow-based generative models have shown remarkable success in text-to-image generation, yet fine-tuning them with intermediate feedback remains challenging, especially for continuous-time flow matching models. Most existing approaches solely learn from outcome rewards, struggling with the credit assignment problem. Alternative methods that attempt to learn a

Yixin Fang, Weili He

Matching-adjusted indirect comparison (MAIC) has been increasingly employed in health technology assessments (HTA). By reweighting subjects from a trial with individual participant data (IPD) to match the covariate summary statistics of another trial with only aggregate data (AgD), MAIC facilitates the estimation of a treatment effect defined with respect to

Guanni Qu, T. Yue, X. Zhang, S. Wei

The present study stems from the realization that the general problem relating to the analysis of wind-induced vibrations in suspension bridges still requires significant attention. Sidewalk railings, overhaul tracks, and deflectors are known to largely affect such dynamics. Here, the influence of a row of water-filled traffic barriers on the response of a s

Roberto Serafinelli, Fabrizio Nicastro, Alfredo Luminari, Yair Krongold

We present a high-resolution X-ray spectroscopic study of the Narrow-Line Seyfert 1 galaxy NGC 4051 using two XMM-Newton high-resolution Reflection Grating Spectrometer (RGS) observations. The spectra reveal three distinct layers of photoionized gas flowing outward from the central black hole: a low-ionization phase (LIP), a higher-ionization phase (HIP), an

Precious Eze, Stephanie Lunn, Bruk Berhane

Employers increasingly expect graduates to utilize large language models (LLMs) in the workplace, yet the competencies needed for computing roles across Africa remain unclear given varying national contexts. This study examined how six LLMs, namely ChatGPT 4, DeepSeek, Gemini, Claude 3.5, Llama 3, and Mistral AI, describe entry-level computing career expecta

Rudolf Beran

Directional data consists of unit vectors in q-dimensions that can be described in polar or Cartesian coordinates. Axial data can be viewed as a pair of directions pointed in opposite directions or as a projection matrix of rank 1. Historically, their statistical analysis has largely been based on a few low-order exponential family models of distributions fo

Nian Liu, Jian Cao

Argo is an international program that collects temperature and salinity observations in the upper two kilometers of the global ocean. Most existing approaches for modeling Argo temperature rely on localized modeling within moving windows, first estimating a prescribed mean structure and then fitting Gaussian processes (GPs) to the mean-subtracted anomalies.

Alicia Muth, E. Dov Neimand

Expanding on the graph theoretic ideas of k-component order connectivity and distance-l domination, we present a quadratic-complexity algorithm that finds a tree's minimum failure-set cardinality, i.e., the minimum cardinality any subset of the tree's vertices must have so that all clusters of vertices further away than some l do not exceed a cardinality thr

Zachary Slepian, Matthew Reinhard, Michael Bartlett

Here we explore from a theoretical perspective the sensitivity of the primary CMB anisotropy trispectrum to parity violation (PV) in large-scale structure (LSS). We focus on the Sachs-Wolfe term, which dominates at $\ell < 40$, after which the Doppler term takes over. We consider a model where the PV is only present out to some maximal scale $R$ of order a f

Paul Hamrick, Gary Hu

A family of graphs $\mathcal{F}$ is $H$-intersecting if the edge intersection of any two graphs in $\mathcal{F}$ contains a copy of a fixed graph $H$. A fundamental problem is to determine the maximum size of such a family. The trivial lower bound of $2^{\binom{n}{2} - e(H)}$ is known to be not sharp for some graphs, such as the $P_4$ graph, as shown by Chri

Bin-Bin Hu, Weijia Yao, Ming Cao

This paper addresses the problem of multi-robot navigation where robots maneuver on a desired \(m\)-dimensional (i.e., \(m\)-D) manifold in the $n$-dimensional Euclidean space, and maintain a {\it flexible spatial ordering}. We consider $ m\geq 2$, and the multi-robot coordination is achieved via non-Euclidean metrics. However, since the $m$-D manifold can b

Reshef Meir, Ganesh Ghalme

The well-known Condorcet Jury Theorem states that, under majority rule, the better of two alternatives is chosen with probability approaching one as the population grows. We study an asymmetric setting where voters face varying participation costs and share a possibly heuristic belief about their pivotality (ability to influence the outcome). In a costly vot

Emre Gürsoy, Gregor B. Vonbun-Feldbauer, Robert H. Meißner

Magnetite is an important mineral with many interesting applications related to its magnetic, electrical and thermal properties. Typically studied by electronic structure calculations, these methods are unable to capture the complex ion dynamics at relevant temperatures, time and length scales. We present a hybrid Monte Carlo/Molecular Dynamics (MC/MD) metho

Wei-Jer Chang, Akshay Rangesh, Kevin Joseph, Matthew Strong

Developing autonomous vehicles (AVs) requires not only safety and efficiency, but also realistic, human-like behaviors that are socially aware and predictable. Achieving this requires sim agent policies that are human-like, fast, and scalable in multi-agent settings. Recent progress in imitation learning with large diffusion-based or tokenized models has sho

Talya Eden, Ludmila Glinskih, Sofya Raskhodnikova

We investigate the computational efficiency of agnostic learning for several fundamental geometric concept classes in the plane. While the sample complexity of agnostic learning is well understood, its time complexity has received much less attention. We study the class of triangles and, more generally, the class of convex polygons with $k$ vertices for smal

Daniel Lenz, Nicolae Strungaru

We consider measurable and topological dynamical systems over locally compact abelian groups. Our main observation relates convergence of Wiener-Wintner type averages to eigenvalues of the dynamical system in question. As a consequence we infer existence of Fourier--Bohr coefficients for all characters for a set of points satisfying a specific genericity con

Boumediene Derras

The prediction of Intensity Measures (IMs) using Ground-Motion Models (GMM) is a fundamental component of seismic hazard assessment. However, the best estimation of IMs traditionally requires specialised searchers, programming expertise, and the manual sourcing of regression coefficients. This complexity creates a significant barrier to rapid, scenario-based

Ata Çelen, Marc Pollefeys, Daniel Barath, Iro Armeni

We introduce HouseTour, a method for spatially-aware 3D camera trajectory and natural language summary generation from a collection of images depicting an existing 3D space. Unlike existing vision-language models (VLMs), which struggle with geometric reasoning, our approach generates smooth video trajectories via a diffusion process constrained by known came

Jiajun Fan, Tong Wei, Chaoran Cheng, Yuxin Chen

Balancing exploration and exploitation during reinforcement learning fine-tuning of generative models presents a critical challenge, as existing approaches rely on fixed divergence regularization that creates an inherent dilemma: strong regularization preserves model capabilities but limits reward optimization, while weak regularization enables greater align

Geoffrey Janssens

In his $1994$ survey, Kleinert defined formally and formulated the problem to obtain unit theorems for unit groups of orders in a semisimple algebra $A$. If $A$ is a group algebra $FG$, it boils down to classifying all finite groups $G$ such that the unit groups of most orders in $FG$ belong to a prescribed class $\mathcal{G}$ of infinite groups. We solve th

Euzeli dos Santos

This paper introduces Prompt-to-Primal (P2P) Teaching, an AI-integrated instructional approach that links prompt-driven exploration with first-principles reasoning, guided and moderated by the instructor within the classroom setting. In P2P teaching, student-generated AI prompts serve as entry points for inquiry and initial discussions in class, while the in

Allan Borodin, Christodoulos Karavasilis, David Zhang

Interest in the random-order model (ROM) leads us to initiate a study of utilizing random-order arrivals to extract random bits with the goal of derandomizing algorithms. Besides producing simple algorithms, simulating random bits through random arrivals enhances our understanding of the comparative strength of randomized online algorithms (with adversarial

Henry Jervis, Paul C. Kainen

A sunlet is a cycle with a pendant edge attached at each vertex of the cycle. For the bipartite toroidal grid graphs $C_{2n} \Box C_{2n}$, factorizations into sunlets are given by homomorphisms from disjoint unions of $s$ copies of a sunlet for $s \in \{1, n, n^2\}, n \geq 3$ such that edges are mapped bijectively.

Katsiaryna Tsishchankava, Florian Kirchschlager, Anton Krieger, Thomas A. Stuber

Context: Interferometric observations of various nearby main-sequence stars show an unexpected infrared excess, raising the question of its origin. The two dominant interpretations favor hot exozodiacal dust or a faint companion, as both can produce similar signatures. Method: We modeled a system consisting of a star and a faint companion within a field of v

Mahsa Valizadeh, Xiangjue Dong, Rui Tuo, James Caverlee

Large Language Models (LLMs) excel at capturing latent semantics and contextual relationships across diverse modalities. However, in modeling user behavior from sequential interaction data, performance often suffers when such semantic context is limited or absent. We introduce LaMAR, a LLM-driven semantic enrichment framework designed to enrich such sequence

Gerlind Plonka, Anahita Riahi

In this paper we study the performance of image reconstruction methods from incomplete samples of the 2D discrete Fourier transform. Inspired by requirements in parallel MRI, we focus on a special sampling pattern with a small number of acquired rows of the Fourier transformed image. We show the importance of the low-pass set of acquired rows around zero in

Kurtis Williams, Zorayda Martinez, Melissa Ornelas

Photometric variability in massive, magnetic white dwarfs (WDs) on the timescales of less than a few hours is oft interpreted as being due to magnetic spots on the surface of a rotating star. Increasingly, numbers of these short period variables are being discovered with the continued growth of time-domain astronomy, testing theories of magnetic white dwarf

Joong Ho Choi, Jiayang Zhao, Jeel Shah, Ritvika Sonawane

Large Language Models (LLMs) deliver powerful reasoning and generation capabilities but incur substantial run-time costs when operating in agentic workflows that chain together lengthy prompts and process rich data streams. We introduce CompactPrompt, an end-to-end pipeline that merges hard prompt compression with lightweight file-level data compression. Com

Irena Lasiecka, Vando Narciso

The wave equation with energy critical sources and nonlinear damping defined on a 3D bounded domain is considered. It is shown that the resulting dynamical system admits a global attractor. Under the additional assumption of strong monotonicity of the damping at the origin, it is shown that the originally unstable quintic wave is uniformly stabilised to a fi

Jay Phil Yoo, Kazuma Kobayashi, Souvik Chakraborty, Syed Bahauddin Alam

Classical sensing rests on one foundational assumption: the quantity of interest must be colocated with the measurement device. This is not an engineering convenience. It is the organizing principle of every instrumentation standard developed over the past century, and it fails completely at aviation altitude, where no physical sensor can survive long enough

Alexander Boldachev

A formalization of a subject-event ontology is proposed for modeling complex dynamic systems without reliance on global time. Key principles: (1) event as an act of fixation - a subject discerns and fixes changes according to models (conceptual templates) available to them; (2) causal order via happens-before - the order of events is defined by explicit depe

Allison Chen, Sunnie S. Y. Kim, Angel Franyutti, Amaya Dharmasiri

How might messages about large language models (LLMs) found in public discourse influence the way people think about and interact with these models? To explore this question, we randomly assigned participants (N = 470) to watch short informational videos presenting LLMs as either machines, tools, or companions -- or to watch no video. We then assessed how st

Harshini Suresha, Kavitha SH

The red palm mite infestation has become a serious concern, particularly in regions with extensive palm cultivation, leading to reduced productivity and economic losses. Accurate and early identification of mite-infested plants is critical for effective management. The current study focuses on evaluating and comparing the ML model for classifying the affecte

Ziyu Lu, Anna J. Li, Alexander E. Ladd, Pascha Matveev

Neural activity forecasting is central to understanding neural systems and enabling closed-loop control. While deep learning has recently advanced the state-of-the-art in the time series forecasting literature, its application to neural activity forecasting remains limited. To bridge this gap, we systematically evaluated eight probabilistic deep learning mod

Stavros Mitsis, Ermos Hadjikyriakos, Humaid Ibrahim, Savvas Neofytou

Deploying emotion recognition systems in real-world environments where devices must be small, low-power, and private remains a significant challenge. This is especially relevant for applications such as tension monitoring, conflict de-escalation, and responsive wearables, where cloud-based solutions are impractical. Multimodal emotion recognition has advance

Arindam Bhattacharjee, Muktajyoti Saha

We study the evolution of charged, asymptotically de Sitter black holes close to the cold extremal branch of the phase space. We consider black hole sizes that are parametrically smaller than both their inverse temperature and the cosmological horizon. Unlike flat space, charged de Sitter black holes do not evolve towards extremality, but rather towards a th

Matthew Thoms, Hao Sun, Laurent Karim Béland

We present an automated benchmarking suite for face-centered-cubic (FCC) nickel that evaluates 47 quantitative metrics spanning both standard tests (equation of state, elastic constants, surface energies and phonons) and application-specific scenarios such as defect formation and migration, grain boundaries, step edges, close-range interactions, and vacancy

Zhenyu Bi, Meng Lu, Yang Li, Swastik Roy

Large Language Models (LLMs) have shown remarkable reasoning capabilities in mathematical and scientific tasks. To enhance complex reasoning, multi-agent systems have been proposed to harness the collective intelligence of LLM agents. However, existing collaboration structures are either predefined or rely on majority voting or round-table debates, which can

Ziyan Wang, Enmao Diao, Qi Le, Pu Wang

Structured pruning is a practical approach to deploying large language models (LLMs) efficiently, as it yields compact, hardware-friendly architectures. However, the dominant local paradigm is task-agnostic: by optimizing layer-wise reconstruction rather than task objectives, it tends to preserve perplexity or generic zero-shot behavior but fails to capitali

Aymane Hassini

The rise of Large Language Models (LLMs) has accelerated the long-standing goal of enabling natural language querying over complex, hybrid databases. Yet, this ambition exposes a dual challenge: reasoning jointly over structured, multi-relational schemas and the semantic content of linked unstructured assets. To overcome this, we present DynaQuery - a unifie

Hirofumi Noda, Satoshi Yamada, Shoji Ogawa, Kouichi Hagino

We observed the X-ray-bright ultra-luminous infrared galaxy, IRAS 05189$-$2524, with XRISM during its performance verification phase. The unprecedented energy resolution of the onboard X-ray microcalorimeter revealed complex spectral features at $\sim$7$-$9 keV, which can be interpreted as blueshifted Fe XXV/XXVI absorption lines with various velocity disper

Xinran Zhu, Liam Magee, Peg Mischler

Education in the era of generative AI faces a pivotal transformation. As AI systems reshape professional practices-from software development to creative design-educators must reconsider how to prepare students for a future where humans and machines co-construct knowledge. While tools like ChatGPT and Claude automate tasks and personalize learning, their educ

S. Nagy, J. Polonyi

It is well known that a minimal distance emerges in quantum field theories owing to the need to regularize the UV divergences. The macroscopical limit at large minimal distance, weak spatial resolution, is investigated for a self interacting scalar quantum field theory by the help of the renormalization group. The lowering of the cutoff always opens the dyna

Sanaz Zarei

Tunable bandpass terahertz filters are demanded in various key applications such as hyperspectral imagers, miniaturized spectrometers, and high-speed wireless communication systems. Here, a mechanically reconfigurable double-layered subwavelength metallic structure is presented for frequency-agile terahertz transmission bandpass filtering. The theoretically

Micol Benetti, Rudnei O. Ramos, Renato Silva, Gustavo S. Vicente

This work investigates a singularity-free early Universe within the paradigm of quantum cosmology. We develop a bouncing model where the singularity is resolved via the de Broglie--Bohm interpretation of quantum mechanics, which provides a deterministic trajectory for the scale factor through a quantum bounce. The primordial power spectrum for scalar perturb

Jun Ikeda

We introduce a compactification construction for abstract quasi-local C*-algebras over countable metric spaces equipped with an isometric group action which is functorial with respect to bounded spread isomorphisms. In $1$D, the construction recovers Ocneanu's Tube algebra for fusion spin chains, and provides a canonical bridge between infinite-volume observ

Asim Mohamed, Martin Gubri

Multilingual watermarking aims to make large language model (LLM) outputs traceable across languages, yet current methods still fall short. Despite claims of cross-lingual robustness, they are evaluated only on high-resource languages. We show that existing multilingual watermarking methods are not truly multilingual: they fail to remain robust under transla

Peilin Kang, Jintu Zhang, Enrico Trizio, TingJun Hou

The study of rare events is one of the major challenges in atomistic simulations, and several enhanced sampling methods towards its solution have been proposed. Recently, it has been suggested that the use of the committor, which provides a precise formal description of rare events, could be of use in this context. We have recently followed up on this sugges

Mariana Crisostomo Martins, Lucas Elias Cardoso Rocha, Lucas Cordeiro Romao, Taciana Novo Kudo

Despite the increasing development of Artificial Intelligence (AI) systems, Requirements Engineering (RE) activities face challenges in this new data-intensive paradigm. We identified a lack of support for problem discovery within AI innovation projects. To address this, we propose and evaluate DIP-AI, a discovery framework tailored to guide early-stage expl

Prateek Gothwal, Deeptimaan Banerjee, Ashis Kumer Biswas

Engagement detection in online learning environments is vital for improving student outcomes and personalizing instruction. We present ViBED-Net (Video-Based Engagement Detection Network), a novel deep learning framework designed to assess student engagement from video data using a dual-stream architecture. ViBED-Net captures both facial expressions and full

Daniel Meyer, Julia Münch

We consider postcritically finite rational maps $f\colon \widehat{\mathbb{C}} \to \widehat{\mathbb{C}}$ whose Julia set is the whole Riemann sphere $\widehat{\mathbb{C}}$. We call such a map an expanding rational Thurston map. Identifying $\widehat{\mathbb{C}}$ with the unit sphere $\mathbb{S}^2$ in $\mathbb{R}^3$, we show that $f$ may be extended on a neigh

Kazuki Kawamura, Kengo Nakai, Jun Rekimoto

We present ManzaiSet, the first large scale multimodal dataset of viewer responses to Japanese manzai comedy, capturing facial videos and audio from 241 participants watching up to 10 professional performances in randomized order (94.6 percent watched >= 8; analyses focus on n=228). This addresses the Western centric bias in affective computing. Three key fi

Yiran Wang, José Antonio Hernández López, Ulf Nilsson, Dániel Varró

Jupyter notebooks are widely used for machine learning (ML) prototyping. Yet, few debugging tools are designed for ML code in notebooks, partly, due to the lack of benchmarks. We introduce JunoBench, the first benchmark dataset of real-world crashes in Python-based ML notebooks. JunoBench includes 111 curated and reproducible crashes with verified fixes from

Aida Abiad, Boris Aronov, Mark de Berg, Julian Golak

Let $\mathcal{D}=\{D_0,\ldots,D_{n-1}\}$ be a set of $n$ topological disks in the plane and let $\mathcal{A} := \mathcal{A}(\mathcal{D})$ be the arrangement induced by $\mathcal{D}$. For two disks $D_i,D_j\in\mathcal{D}$, let $\Delta_{ij}$ be the number of connected components of $D_i\cap D_j$, and let $\Delta := \max_{i,j} \Delta_{ij}$. We show that the dia