October 2025 arXiv papers — page 221

Yuyan Bu, Qiang Sheng, Juan Cao, Shaofei Wang

The emergence of fake news on short video platforms has become a new significant societal concern, necessitating automatic video-news-specific detection. Current detectors primarily rely on pattern-based features to separate fake news videos from real ones. However, limited and less diversified training data lead to biased patterns and hinder their performan

Mizanur Rahman, Amran Bhuiyan, Mohammed Saidul Islam, Md Tahmid Rahman Laskar

Recent advances in large language models (LLMs) have enabled a new class of AI agents that automate multiple stages of the data science workflow by integrating planning, tool use, and multimodal reasoning across text, code, tables, and visuals. This survey presents the first comprehensive, lifecycle-aligned taxonomy of data science agents, systematically ana

Chendong Wang, Donglin Bai, Yifan Yang, Xiao Jin

We present \emph{Video-in-the-Loop} (ViTL), a two-stage long-video QA framework that preserves a fixed token budget by first \emph{localizing} question-relevant interval(s) with a low-fps skim and then \emph{answering} via span-aware reallocation of visual tokens at higher effective frame rate, emitting an interleaved output with both spans and the final opt

Kushal Vyas, Ashok Veeraraghavan, Guha Balakrishnan

Implicit neural representations (INRs) have achieved remarkable successes in learning expressive yet compact signal representations. However, they are not naturally amenable to predictive tasks such as segmentation, where they must learn semantic structures over a distribution of signals. In this study, we introduce MetaSeg, a meta-learning framework to trai

Hao Wu, Yuan Gao, Xingjian Shi, Shuaipeng Li

To address the dual challenges of inherent stochasticity and non-differentiable metrics in physical spatiotemporal forecasting, we propose Spatiotemporal Forecasting as Planning (SFP), a new paradigm grounded in Model-Based Reinforcement Learning. SFP constructs a novel Generative World Model to simulate diverse, high-fidelity future states, enabling an "ima

Guangyu Shen, Siyuan Cheng, Xiangzhe Xu, Yuan Zhou

Large Language Models (LLMs) can acquire deceptive behaviors through backdoor attacks, where the model executes prohibited actions whenever secret triggers appear in the input. Existing safety training methods largely fail to address this vulnerability, due to the inherent difficulty of uncovering hidden triggers implanted in the model. Motivated by recent f

Deng Jinghua, Hou Jianfeng, Hu caiyun, Liu xizhi

We study an anti-Ramsey extension of the classical Corr\'{a}di--Hajnal Theorem: how many colors are needed to color the complete graph on $n$ vertices in order to guarantee a rainbow copy of $t K_{3}$, that is, $t$ vertex-disjoint triangles. We provide a conjecture for large $n$, consisting of five classes of different extremal constructions, corresponding t

Sumanth Varambally, Marshall Fisher, Jas Thakker, Yiwei Chen

Foundation models for weather science are pre-trained on vast amounts of structured numerical data and outperform traditional weather forecasting systems. However, these models lack language-based reasoning capabilities, limiting their utility in interactive scientific workflows. Large language models (LLMs) excel at understanding and generating text but can

Thanapol Popit, Natthapath Rungseesiripak, Monthol Charattrakool, Saksorn Ruangtanusak

Fluid voice-to-voice interaction requires reliable and low-latency detection of when a user has finished speaking. Traditional audio-silence end-pointers add hundreds of milliseconds of delay and fail under hesitations or language-specific phenomena. We present, to our knowledge, the first systematic study of Thai text-only end-of-turn (EOT) detection for re

Kai Cao, Yucong Duan, Wensheng Gan

In a quantitative sequential database, numerous efficient algorithms have been developed for high-utility sequential pattern mining (HUSPM). HUSPM establishes a relationship between frequency and significance in the real world and reflects more crucial information than frequent pattern mining. However, high average-utility sequential pattern mining (HAUSPM)

Jiarui Liu, Jivitesh Jain, Mona Diab, Nishant Subramani

Although large language models (LLMs) have tremendous utility, trustworthiness is still a chief concern: models often generate incorrect information with high confidence. While contextual information can help guide generation, identifying when a query would benefit from retrieved context and assessing the effectiveness of that context remains challenging. In

David Rogers, Valerio Mariani, Cong Wang, Ryan Coffee

We describe a new end-to-end experimental data streaming framework designed from the ground up to support new types of applications -- AI training, extremely high-rate X-ray time-of-flight analysis, crystal structure determination with distributed processing, and custom data science applications and visualizers yet to be created. Throughout, we use design ch

Jahidul Arafat, Fariha Tasmin, Sanjaya Poudel, Iftekhar Haider

Medical AI faces challenges in privacy-preserving collaborative learning while ensuring fairness across heterogeneous healthcare institutions. Current federated learning approaches suffer from static architectures, slow convergence (45-73 rounds), fairness gaps marginalizing smaller institutions, and scalability constraints (15-client limit). We propose Adap

Shi-Cheng Liu, Lei-Hua Liu, Bichu Li, Hai-Qing Zhang

Many quantum gravitational frameworks, such as DBI inflation, k-essence, and effective field theories obtained by integrating out heavy modes, can lead to a non-trivial sound speed. Meanwhile, our universe can be described as an open system. Under the non-trivial sound speed, we employ the method of open quantum systems combined with Arnoldi iterations to st

Yu-Fei Shih, An-Zi Yen, Hen-Hsen Huang, Hsin-Hsi Chen

People often struggle to remember specific details of past experiences, which can lead to the need to revisit these memories. Consequently, lifelog retrieval has emerged as a crucial application. Various studies have explored methods to facilitate rapid access to personal lifelogs for memory recall assistance. In this paper, we propose a Captioning-Integrate

Zicong He, Boxuan Zhang, Weihao Liu, Ruixiang Tang

The meteoric rise of foundation models (FMs) has expanded their capabilities far beyond conventional tasks. Creativity, long regarded as a hallmark of human intelligence and a driver of innovation, is now increasingly recognized as a critical dimension of machine intelligence in the era of generative FMs, complementing traditional measures of accuracy. Howev

Sahil Joshi, Agniva Chowdhury, Amar Kanakamedala, Ekam Singh

Softmax Attention has a quadratic time complexity in sequence length, which becomes prohibitive to run at long contexts, even with highly optimized GPU kernels. For example, FlashAttention-2/3 (exact, GPU-optimized implementations of Softmax Attention) cannot complete a single forward-backward pass of a single attention layer once the context exceeds ~4 mill

Narasimha Kumar, Dwipanjana Shit

Let $q = p^e \geq 7$ be an odd prime power, and set $A := \mathbb{F}_q[T]$. In this article, we construct an infinite two-parameter family of Drinfeld $A$-modules of rank $3$ such that, for every non-zero prime ideal $\mathfrak{l}$ of $A$, the associated mod-$\mathfrak{l}$, $\mathfrak{l}$-adic, and adelic Galois representations are surjective. These results

Zhilong Liu, Wentao Liu, Xiaofang Liu, Jieci Wang

Motivated by the profound connection between quantum mechanics and spacetime geometry, particularly the conjectured correspondence between wormholes and quantum entanglement as proposed in the ER=EPR framework, this study investigate the influence of wormhole geometries on quantum information extraction. We examine the correlation-specifically mutual informa

Alexandria Wheeler, Wenliang Zhang

Let $R$ be a noetherian commutative ring. Of great interest is the question whether one can find an explicit integer $k$ such that $\overline{I^{k+n}}\subseteq I^n$ for each ideal $I$ and each integer $n\geq 1$ (the notation $\overline{I^{k+n}}$ denotes the integral closure of $I^{k+n}$). In this article, we investigate this question and obtain optimal value

Minh Hoang Nguyen, Su Nguyen Thiet

Recognizing and processing Classical Chinese (Han-Nom) texts play a vital role in digitizing Vietnamese historical documents and enabling cross-lingual semantic research. However, existing OCR systems struggle with degraded scans, non-standard glyphs, and handwriting variations common in ancient sources. In this work, we propose a fine-tuning approach for Pa

Bo Yang, Yunkui Chen, Lanfei Feng, Yu Zhang

Despite rapid advances in multimodal large language models, agricultural applications remain constrained by the scarcity of domain-tailored models, curated vision-language corpora, and rigorous evaluation. To address these challenges, we present the AgriGPT-VL Suite, a unified multimodal framework for agriculture. Our contributions are threefold. First, we i

Eric Jahns, Davi Moreno, Milan Stojkov, Michel A. Kinsy

Spiking Neural Networks (SNNs) have emerged as energy-efficient alternatives to traditional artificial neural networks, leveraging asynchronous and biologically inspired neuron dynamics. Among existing neuron models, the Leaky Integrate-and-Fire (LIF) neuron has become widely adopted in deep SNNs due to its simplicity and computational efficiency. However, t

Xuankang Zhang, Jiangming Liu

The COVID-19 pandemic causes severe social and economic disruption around the world, raising various subjects that are discussed over social media. Identifying pandemic-related named entities as expressed on social media is fundamental and important to understand the discussions about the pandemic. However, there is limited work on named entity recognition o

Yujie Zhou, Cheng Peng, Rulong Wang, Yong Xiao

Semantic communication shifts the focus from bit-level accuracy to task-relevant semantic delivery, enabling efficient and intelligent communication for next-generation networks. However, existing multi-modal solutions often process all available data modalities indiscriminately, ignoring that their contributions to downstream tasks are often unequal. This n

Yang Xu, Xuanming Zhang, Samuel Yeh, Jwala Dhamala

Deception is a pervasive feature of human communication and an emerging concern in large language models (LLMs). While recent studies document instances of LLM deception, most evaluations remain confined to single-turn prompts and fail to capture the long-horizon interactions in which deceptive strategies typically unfold. We introduce a new simulation frame

Songmei Yu, Andrew Zagula

Collaborative group projects are integral to computer science education, fostering teamwork, problem-solving, and industry-relevant skills. However, assessing individual contributions within group settings remains challenging. Traditional approaches, including equal grade distribution and subjective peer evaluations, often lack fairness, objectivity, and sca

Nges Brian Njungle, Eric Jahns, Michel A. Kinsy

The widespread adoption of Machine Learning as a Service raises critical privacy and security concerns, particularly about data confidentiality and trust in both cloud providers and the machine learning models. Homomorphic Encryption (HE) has emerged as a promising solution to this problems, allowing computations on encrypted data without decryption. Despite

Nges Brian Njungle, Eric Jahns, Milan Stojkov, Michel A. Kinsy

Deep learning has become a cornerstone of modern machine learning. It relies heavily on vast datasets and significant computational resources for high performance. This data often contains sensitive information, making privacy a major concern in deep learning. Spiking Neural Networks (SNNs) have emerged as an energy-efficient alternative to conventional deep

Jianqing Fan, Yihong Gu, Ximing Li

This paper investigates the score-based diffusion models for density estimation when the target density admits a factorizable low-dimensional nonparametric structure. To be specific, we show that when the log density admits a $d^*$-way interaction model with $\beta$-smooth components, the vanilla diffusion model, which uses a fully connected ReLU neural netw

Yaxin Hou, Bo Han, Yuheng Jia, Hui Liu

Current long-tailed semi-supervised learning methods assume that labeled data exhibit a long-tailed distribution, and unlabeled data adhere to a typical predefined distribution (i.e., long-tailed, uniform, or inverse long-tailed). However, the distribution of the unlabeled data is generally unknown and may follow an arbitrary distribution. To tackle this cha

Ping Zhang, Yibin Zhang

This paper studies the long-time evolution of two point vortices under the 2D Navier-Stokes tokes equations. Starting from initial data given by a pair of Dirac measures, we derive an asymptotic expansion for the vorticity over time scales significantly longer than the advection time, yet shorter than the diffusion time. Building on previous works \cite{GS24

Ming Gao, Bryon Aragam

We study the optimal sample complexity of variable selection in linear regression under general design covariance, and show that subset selection is optimal while under standard complexity assumptions, efficient algorithms for this problem do not exist. Specifically, we analyze the variable selection problem and provide the optimal sample complexity with exa

Xue-Cheng Tai, Hao Liu, Lingfeng Li, Raymond H. Chan

The Transformer architecture has revolutionized the field of sequence modeling and underpins the recent breakthroughs in large language models (LLMs). However, a comprehensive mathematical theory that explains its structure and operations remains elusive. In this work, we propose a novel continuous framework that rigorously interprets the Transformer as a di

Hoang Anh Just, Myeongseob Ko, Ruoxi Jia

Distilling long-form reasoning from teacher models into smaller students requires selecting which candidate solutions to train on. Recent work argues that one should select responses the student model assigns highest probability, i.e., favoring solutions ``natural'' to the student. However, we find that this approach works within a single teacher but fails w

Michael Yang

Hierarchical Pooling Models have demonstrated strong performance in classifying graph-structured data. While numerous innovative methods have been proposed to design cluster assignments and coarsening strategies, the relationships between clusters are often overlooked. In this paper, we introduce Inter-cluster Connectivity Enhancement Pooling (ICEPool), a no

Addina Rahaman

Changing climate conditions threaten the natural permafrost thaw-freeze cycle, leading to year-round soil temperatures above 0{\deg}C. In Alaska, the warming of the topmost permafrost layer, known as the active layer, signals elevated greenhouse gas release due to high carbon storage. Accurate soil temperature prediction is therefore essential for risk mitig

Xiangjun Mi, Frank Mueller

Quantum error correction (QEC) is essential for scalable quantum computing, yet decoding errors via conventional algorithms result in limited accuracy (i.e., suppression of logical errors) and high overheads, both of which can be alleviated by inference-based decoders. To date, such machine-learning (ML) decoders lack two key properties crucial for practical

Ananya Raghu, Anisha Raghu, Nithika Vivek, Sofie Budman

Dysarthria is a motor speech disorder that results in slow and often incomprehensible speech. Speech intelligibility significantly impacts communication, leading to barriers in social interactions. Dysarthria is often a characteristic of neurological diseases including Parkinson's and ALS, yet current tools lack generalizability across languages and levels o

Gu Zhang, Igor Gornyi, Yuval Gefen

Determination of the anyonic statistical braiding phase has relied on either Aharonov-Bohm inter- ference experiments or cross-correlations measured in collider platforms. Here we consider collider setups, supporting stationary far-from-equilibrium anyonic beams, characterized by an effective chemical potential and an effective temperature, which carry unive

Kirandeep Kaur, Preetam Prabhu Srikar Dammu, Hideo Joho, Chirag Shah

Personalized AI agents are becoming central to modern information retrieval, yet most evaluation methodologies remain static, relying on fixed benchmarks and one-off metrics that fail to reflect how users' needs evolve over time. These limitations hinder our ability to assess whether agents can meaningfully adapt to individuals across dynamic, longitudinal i

Siddharth Kumar Singh, Chengyu Wang, Adbhut Gupta, Kirk W. Baldwin

The fractional quantum Hall state (FQHS) observed in the lowest Landau level at filling factor $\nu=1/2$ in wide quantum wells has been enigmatic for decades because the two-dimensional electron system (2DES) has a bilayer charge distribution but with significant interlayer tunneling. Of particular interest is whether the 1/2 FQHS in this system has a one-co

Roy Siegelmann, Enrique Mallada

We propose a method for data-driven practical stabilization of nonlinear systems with provable guarantees, based on the concept of Nonparametric Chain Policies (NCPs). The approach employs a normalized nearest-neighbor rule to assign, at each state, a finite-duration control signal derived from stored data, after which the process repeats. Unlike recent work

Mladen Bestvina, Kenneth Bromberg, Alexander J. Rasmussen

We study a finite sequence of graphs, beginning with the curve graph and ending with a graph quasi-isometric to a tree. There is a Lipschitz map from one graph in the sequence to the next. This sequence was first introduced by Hamenst\"adt. We prove (as conjectured by Hamenst\"adt) that the graphs in this sequence are hyperbolic and that the coarse fibers of

Dmitry Ustalov, Egor Bogomolov, Alexander Bezzubov, Yaroslav Golubev

The rapid advancement of workflows and methods for software engineering using AI emphasizes the need for a systematic evaluation and analysis of their ability to leverage information from entire projects, particularly in large code bases. In this challenge on optimization of context collection for code completion, organized by JetBrains in collaboration with

Robert Klas, Martin Gebhardt, Jan Rothhardt, Jens Limpert

High harmonic generation (HHG) is a crucial technology for compact, high-brightness extreme ultraviolet (XUV) and soft X-ray sources, which are key to advancing both fundamental and applied sciences. The availability of advanced driving lasers, with tunable wavelength, power, and pulse duration, opens new opportunities for optimizing HHG-based sources. While

Paul Steinacker, Gauri Goenka, Rocky Yue Su, Tuomo Tanttu

Single nuclear spins in silicon are a promising resource for quantum technologies due to their long coherence times and excellent control fidelities. Qubits and qudits have been encoded on donor nuclei, with successful demonstrations of Bell states and quantum memories on the spin-1/2 $^{31}$P and cat-qubits on the spin-7/2 $^{123}$Sb nuclei. Isoelectronic n

Calin Galeriu

We investigate a theory of time-symmetric action-at-a-distance electrodynamic or gravitational interactions where the four-forces depend only on the electric charges, the rest masses, the position four-vectors, and the four-velocities of the two interacting particles, but not on their four-accelerations or higher derivatives. The goal is to prove that the pr

Pierpaolo Massoli

This study proposes a novel approach based on the Ising model for analyzing socio-economic emerging patterns between municipalities by investigating the observed configuration of a network of selected territorial units which are classified as being central hubs or peripheral areas. This is interpreted as being a reference of a system of interacting territori

Nizar El Idrissi, Hicham Zoubeir

Invertibility is important in ring theory because it enables division and facilitates solving equations. Moreover, (nonassociative) rings can be endowed with an extra ''structure'' such as order and topology allowing more richness in the theory. The two main theorems of this article are contributions to invertibility in the context of partial

Shuhei Mano, Nobuki Takayama

We show that contiguity relations of hypergeometric functions of several variables give a direct sampling algorithm from the conditional distribution of toric models in statistics. The algorithm is based on a Markov chain on a lattice generated by a matrix $A$. A correspondence between decomposable graphical models and $A$-hypergeometric systems is discussed

Emerson Melo, David Müller

We establish a link between a class of discrete choice models and the theory of online learning and multi-armed bandits. Our contributions are: (i) sublinear regret bounds for a broad algorithmic family, encompassing Exp3 as a special case; (ii) a new class of adversarial bandit algorithms derived from generalized nested logit models \citep{wen:2001}; and (i

Min Woo Sun, Alejandro Lozano, Javier Gamazo Tejero, Vishwesh Nath

Embedding vision-language models (VLMs) are typically pretrained with short text windows (<77 tokens), which forces the truncation of long-format captions. Yet, the distribution of biomedical captions from large-scale open source literature reveals that a huge portion of captions far exceed 77 tokens. To this end, we investigate the impact of pretraining on

Fabia F. Athena, Cooper A. Voigt, Mengkun Tian, Anjan Goswami

Since the initial synthesis of van der Waals two-dimensional indium selenide was first documented in 1957, five distinct polymorphs and their corresponding polytypes have been identified. In this study, we report a unique phase of indium selenide via Scanning Transmission Electron Microscopy (STEM) analysis in the synthesized large-area films -- which we hav

Alexandr Fonari, Simon D. Elliott, Casey N. Brock, Yan Li

We investigate the use of first principles thermodynamics based on periodic density functional theory (DFT) to examine the gas-surface chemistry of an oxidized ruthenium surface reacting with hydrogen gas. This reaction system features in the growth of ultrathin Ru films by atomic layer deposition (ALD). We reproduce and rationalize the experimental observat

Sadie Cutler, Ben DeFay, Scott McArt, Kirstin Petersen

Pollinators are critical to the world's ecosystems and food supply, yet recent studies have found pollination shortfalls in several crops, including strawberry. This is troubling because wild and managed pollinators are currently experiencing declines. One possibility is to try and provide supplemental pollination solutions. These solutions should be afforda

David R. Gruber

Strategic Communication Protocols provide a structured approach for first contact with interstellar objects that demonstrate technological characteristics and high levels of threat. The protocols find their starting point in an ISO Information-Communication Paradox, namely, as our knowledge of an ISO's threatening capabilities increases, the probability of s

Tsiry Avisoa Randrianasolo

The Gray--Scott model governs the interaction of two chemical species via a system of reaction-diffusion equations. Despite its simple form, it produces extremely rich patterns such as spots, stripes, waves, and labyrinths. That makes it ideal for studying emergent behavior, self-organization, and instability-driven pattern formation. It is also known for it

Jatin Prakash, Anirudh Buvanesh

Reinforcement learning (RL) with outcome-based rewards has proven effective for improving large language models (LLMs) on complex reasoning tasks. However, its success often depends on the base model occasionally sampling correct solutions. When no correct solutions are sampled, training encounters a zero-reward barrier where learning stalls due to zero grad

Zainab Saad, Jialin Yang, Henry Leung, Steve Drew

The growing reliance on large-scale data centers to run resource-intensive workloads has significantly increased the global carbon footprint, underscoring the need for sustainable computing solutions. While container orchestration platforms like Kubernetes help optimize workload scheduling to reduce carbon emissions, existing methods often depend on centrali

Chengxiao Wang, Isha Chaudhary, Qian Hu, Weitong Ruan

Large Language Models (LLMs) can produce catastrophic responses in conversational settings that pose serious risks to public safety and security. Existing evaluations often fail to fully reveal these vulnerabilities because they rely on fixed attack prompt sequences, lack statistical guarantees, and do not scale to the vast space of multi-turn conversations.

Md Zahin Hossain George, Md Khorshed Alam, Md Tarek Hasan

This systematic literature review examines the role of machine learning in fraud detection within digital banking, synthesizing evidence from 118 peer-reviewed studies and institutional reports. Following the PRISMA guidelines, the review applied a structured identification, screening, eligibility, and inclusion process to ensure methodological rigor and tra

Xander M. de Wit, Hessel J. Adelerhof, André Freitas, Rudie P. J. Kunnen

Turbulent flows laden with small bubbles are ubiquitous in many natural and industrial environments. From the point of view of numerical modeling, to be able to handle a very large number of small bubbles in direct numerical simulations, one traditionally relies on the one-way coupling paradigm. There, bubbles are passively advected and are non-interacting,

Amanda Gatto Lamas, Jacopo Gliozzi, Taylor L. Hughes

We extend a recently defined measure of symmetry breaking, the entanglement asymmetry, to higher-form symmetries. In particular, we focus on Abelian topological order in two dimensions, which spontaneously breaks a 1-form symmetry. Using the toric code as a primary example, we compute the entanglement asymmetry and compare it to the topological entanglement

Ilyoung Jung, Frank G. Schroer, Philip Richerme

Laser-driven operations are a common approach for engineering one- and two-qubit gates in trapped-ion arrays. Measuring key parameters of these lasers, such as beam sizes, intensities, and polarizations, is central to predicting and optimizing gate speeds and stability. Unfortunately, it is challenging to accurately measure these properties at the ion locati

Dong Shu, Yanguang Liu, Huopu Zhang, Mengnan Du

Predicting corporate earnings surprises is a profitable yet challenging task, as accurate forecasts can inform significant investment decisions. However, progress in this domain has been constrained by a reliance on expensive, proprietary, and text-only data, limiting the development of advanced models. To address this gap, we introduce \textbf{FinCall-Surpr

Ville Cantory, Darya Biparva, Haoyu Tan, Tongyu Nie

Spatiotemporal sensitivity to high frequency information declines with increased peripheral eccentricity. Foveated rendering exploits this by decreasing the spatial resolution of rendered images in peripheral vision, reducing the rendering cost by omitting high frequency details. As foveation levels increase, the rendering quality is reduced, and traditional

Riku Kurama, Ruoxi Li, Henry Talbott, Rachel Webb

Let $V$ be a linear representation of a connected complex reductive group $G$. Given a choice of character $\theta$ of $G$, Geometric Invariant Theory defines a locus $V^{ss}_\theta(G) \subseteq V$ of semistable points. We give necessary, sufficient, and in some cases equivalent conditions for the existence of $\theta$ such that a maximal torus $T$ of $G$ ac

Hanzhe Wei, Jiajun Wu, Jialin Yang, Henry Leung

Time series anomaly detection plays a crucial role in a wide range of fields, such as healthcare and internet traffic monitoring. The emergence of large language models (LLMs) offers new opportunities for detecting anomalies in the ubiquitous time series data. Traditional approaches struggle with variable-length time series sequences and context-based anomal

Soobin Cho, Renming Song

Assume $\alpha\in (0, 2)$ and $d\ge 2$. Let $\mathcal L^\alpha$ be the generator of a symmetric, but not necessarily isotropic, $\alpha$-stable process $X$ in $\mathbb R^d$ whose L\'evy density is comparable with that of an isotropic $\alpha$-stable process. In this paper, we show that the $C^{1, \rm Dini}$ regularity assumption on an open set $D\subset \mat

Soroush Zare, Tao Zhu, Luis M. Nieto, Shuo Lu

Extreme mass-ratio inspirals (EMRIs) are among the key targets for future space-based gravitational wave detectors. The gravitational waveforms emitted by EMRIs are highly sensitive to the orbital dynamics of the small compact object, which in turn are determined by the geometry of the underlying spacetime. In this paper, we explore the de- tectability of re

Jiyoung Yoo, Jingwei Hu, Lee F. Ricketson

We propose an explicit particle method for the Vlasov-Fokker-Planck equation that conserves energy at the fully discrete level. The method features two key components: a deterministic and conservative particle discretization for the nonlinear Fokker-Planck operator (also known as the Lenard-Bernstein or Dougherty operator), and a second-order explicit time i

Iryna Stanishevska, Seth Guikema

Thunderstorm-driven power outages are difficult to predict because most storms do not cause damage, convective processes occur rapidly and chaotically, and the available public data are noisy and incomplete. Severe convective storms now account for a large and rising share of U.S. weather losses, yet thunderstorm-induced outages remain understudied. We devel

Théo Michelot, Emma Storey

Hidden Markov models (HMMs) have been used increasingly to understand how movement patterns of animals arise from behavioural states. An animal is assumed to transition between behavioural states through time, as described by transition probabilities. Within each state, the movement typically follows a discrete-time random walk, where steps between successiv

Andrew Linshaw, Arim Song, Uhi Rinn Suh

Let $\mathfrak{g}$ be a basic Lie superalgebra and $f$ be an odd nilpotent element in an $\mathfrak{osp}(1|2)$ subalgebra of $\mathfrak{g}$. We provide a mathematical proof of the statement that the W-algebra $W^k(\mathfrak{g},F)$ for $F=-\frac{1}{2}[f,f]$ is a vertex subalgebra of the SUSY W-algebra $W_{N=1}^k(\mathfrak{g},f)$, and that it commutes with all

Robert S. Aviles, Peter A. Beerel

Adiabatic Quantum-Flux-Parametron (AQFP) logic is a promising emerging superconducting technology for ultra-low power digital circuits, offering orders of magnitude lower power consumption than CMOS. However, AQFP scalability is challenged by excessive buffer overhead due to path balancing technology constraints. Addressing this, recent AQFP works have propo

Sameep Vani, Shreyas Jena, Maitreya Patel, Chitta Baral

While Video Large Language Models (Video-LLMs) have demonstrated remarkable performance across general video understanding benchmarks-particularly in video captioning and descriptive tasks-they consistently underperform on tasks that require fine-grained temporal understanding. This limitation arises due to the lack of visual complexity and temporal nuance i

Jean-Baptiste Vienney

A differential category is an additive symmetric monoidal category, that is, a symmetric monoidal category enriched over commutative monoids, with an algebra modality, axiomatizing smooth functions, and a deriving transformation on this algebra modality, axiomatizing differentiation. Lemay proved that a comonoidal algebra modality has at most one deriving tr

Raven Beutner, Bernd Finkbeiner

Strategy logic (SL) is a powerful temporal logic that enables first-class reasoning over strategic behavior in multi-agent systems (MAS). In many MASs, the agents (and their strategies) cannot observe the global state of the system, leading to many extensions of SL centered around imperfect information, such as strategy logic with imperfect information (SL$_

Tom Hutchcroft

Statistical mechanical systems at and near their points of phase transition are expected to exhibit rich, fractal-like behaviour that is independent of the small-scale details of the system but depends strongly on the dimension in which the model is defined. Moreover, many models are conjectured to have an upper critical dimension with important quantitative

Otobong Jerome, Geesara Prathap Kulathunga, Devitt Dmitry, Eugene Murawjow

Off-road environments present unique challenges for autonomous navigation due to their complex and unstructured nature. Traditional global path-planning methods, which typically aim to minimize path length and travel time, perform poorly on large-scale maps and fail to account for critical factors such as real-time performance, kinematic feasibility, and mem

Mostafa Bendahmane, Mohamed Mehdaoui, Mouhcine Tilioua

The purpose of this paper is to establish the well-posedness of martingale (probabilistic weak) solutions to stochastic degenerate aggregation--diffusion equations arising in biological and public health contexts. The studied equation is of a stochastic degenerate parabolic type, featuring a nonlinear two-sidedly degenerate diffusion term accounting for repu

Behrooz Mirzaii, Abraham Rojas Vega

In this article, we investigate the Schur multiplier of the special linear group $\mathrm{SL}_2(A)$ over finite commutative local rings $A$. We prove that the Schur multiplier of these groups is isomorphic to the $K$-group $K_2(A)$ whenever the residue field $A/\mathfrak{m}_A$ has odd characteristic and satisfies $|A/\mathfrak{m}_A| \neq 3,5,9$. As an applic

Fahim Sayed

Vanishing-off subgroups, generalized Camina pair and other related subgroups have played a significant role in the study of group structure. The primary goal of this paper is to study their analogs in the setting of supercharacter theory. We establish several properties of these subgroups which includes connections with supercharacter theory products.

Weiqing He, Xiang Li, Tianqi Shang, Li Shen

Large language models (LLMs) raise concerns about content authenticity and integrity because they can generate human-like text at scale. Text watermarks, which embed detectable statistical signals into generated text, offer a provable way to verify content origin. Many detection methods rely on pivotal statistics that are i.i.d. under human-written text, mak

Anirban Samanta, Shun-Hung Lee, Chun-Yi Cheng, Samuel Palermo

3D interconnects have emerged as a solution to address the scaling issues of interconnect bandwidth and the memory wall problem in high-performance computing (HPC), such as High-Bandwidth Memory (HBM). However, the copper-based electrical interconnect retains fundamental limitations. Dense I/O for high-speed signals lead to degraded signal quality for end-to

Raven Beutner, Bernd Finkbeiner

Hyperproperties generalize traditional trace properties by relating multiple execution traces rather than reasoning about individual runs in isolation. They provide a unified way to express important requirements such as information flow and robustness properties. Temporal logics like HyperLTL capture these properties by explicitly quantifying over execution

Amrita Suresh, Nobuko Yoshida

Systems of communicating automata are prominent models for peer-to-peer message-passing over unbounded channels, but in the general scenario, most verification properties are undecidable. To address this issue, two decidable subclasses, Realisable with Synchronous Communication (RSC) and k-Multiparty Compatibility} (k-MC), were proposed in the literature, wi

Doron Zeilberger

Fifteen years ago, then-Carleton-undergrad Isaac Hodes, proved that the Golden Ratio is evil. In this modest contribution to human knowledge, we show that in fact, every fifth real number is evil, and we present lots of other interesting numbers that are evil. We also show (in addition to many other fascinating facts), that the expected evil-location of a ra

Kilian Bönisch, Vasily Golyshev, Albrecht Klemm

We prove the modularity of mixed periods associated with singular fibers of specific families of Calabi-Yau threefolds. This is done by "fibering out", i.e. by expressing these periods as integrals of periods of families of K3 surfaces and by using modularity properties of the latter. Besides classical periods of holomorphic modular forms and meromorphic mod

Dan Andrei Tudor

In this paper, we give an overview of mean drift conditions for the state-space classification of discrete-time Markov Chains and we present a new transience criterion for uniformly bounded Markov Chains with asymptotically zero drift. The criterion does not need a condition on the second-moment drifts and can be applied to certain chains for which other cri

Pierre Marie Ngougoue Ngougoue

We study a three-dimensional barotropic compressible Navier-Stokes flow interacting with a viscoelastic shell that occupies a portion of the fluid boundary. The analysis is entirely Eulerian and the moving interface is parametrised by a localised Hanzawa transform supported near the shell patch, which preserves the transport structure of the continuity equat

Irfan Javed, Viqar Husain

We use a Hamiltonian version of the semiclassical Einstein equation to study classical gravity coupled to a quantum scalar field with potential in spherical symmetry. The system is defined by effective constraints where the matter terms are replaced by their expectation values in a quantum state. For the static case, we find numerically that the resulting eq

Johannes Bäumler, Benedikt Jahnel, Jonas Köppl, Bas Lodewijks

We introduce a site-wise domination criterion for local percolation models, which enables the comparison of one-arm probabilities even in the absence of stochastic domination. The method relies on a local-to-global principle: if, at each site, one model is more likely than the other to connect to a subset of its neighbors, for all nontrivial such subsets, th

Kai Wu, Philip Cho, Rainer Spurzem, Long Wang

As a continuation of DRAGON-II, we present the DRAGON-III project, which focuses on the simulations of million-body globular clusters and nuclear clusters over 10 Gyr. We report on its preliminary results on globular clusters. The first 100 Myr of the simulations have produced 41 pulsars, 191 X-ray binaries, 17 gravitational wave sources, and one black hole-

Alexis Montoison, Jean-Baptiste Caillau

We present a fully Julia-based, GPU-accelerated workflow for solving large-scale sparse nonlinear optimal control problems. Continuous-time dynamics are modeled and then discretized via direct transcription with \texttt{OptimalControl.jl} into structured sparse nonlinear programs. These programs are compiled into GPU kernels using \texttt{ExaModels.jl}, leve

Mohamed ElKabbash

Entanglement witnessing is essential for quantum technologies such as computing, key distribution, and networking. Conventional bulk-optics methods require sequential reconfiguration across multiple polarization bases, limiting efficiency and scalability. We propose a metasurface-based analyzer that performs dual-basis (\sigma_z and \sigma_y) projections sim

Huascar Sanchez, Briland Hitaj

Multi-LLM collaboration promises accurate, robust, and context-aware solutions, yet existing approaches rely on implicit selection and output assessment without analyzing whether collaborating models truly complement or conflict. We introduce LLM Chemistry -- a framework that measures when LLM combinations exhibit synergistic or antagonistic behaviors that s

Andrew Campbell, Valentin De Bortoli, Jiaxin Shi, Arnaud Doucet

We present self-speculative masked diffusions, a new class of masked diffusion generative models for discrete data that require significantly fewer function evaluations to generate samples. Standard masked diffusion models predict factorized logits over currently masked positions. A number of masked positions are then sampled, however, the factorization appr

Maria Gorelik, Vladimir Hinich, Vera Serganova

The center of a semisimple Lie algebra can be described as the algebra of W-invariant functions on the dual of the Cartan subalgebra. The centers of many Lie superalgebras have a similar description, but the defining equivalence relation on the dual of the Cartan subalgebra is not given by a finite group action. Lagrangian equivalence relations that we intro