October 2025 arXiv papers — page 101

Anton Romen, Johannes Knolle, Michael Knap

Prethermalization phenomena in driven systems are generally understood via a local Floquet Hamiltonian obtained from a high-frequency expansion. Remarkably, recently it has been shown that a driven Kitaev spin liquid with fractionalized excitations can realize a quasi-stationary state that is not captured by this paradigm. Instead distinct types of fractiona

Matúš Dopiriak, Eugen Šlapak, Juraj Gazda, Devendra Singh Gurjar

Connected and autonomous vehicles (CAVs) offload computationally intensive tasks to multi-access edge computing (MEC) servers via vehicle-to-infrastructure (V2I) communication, enabling applications within the vehicular metaverse, which transforms physical environment into the digital space enabling advanced analysis or predictive modeling. A core challenge

Sujit Sakharam Damase, Apoorva Khare

We show that a normed linear space is isometrically isomorphic to an inner product space if and only if it is a strongly $n$-point homogeneous metric space for any (or every) $n \geqslant 3$. The counterpart for $n=2$ is the Banach-Mazur problem.

Hassan Gharoun, Mohammad Sadegh Khorshidi, Kasra Ranjbarigderi, Fang Chen

Despite extensive research on neural network calibration, existing methods typically apply global transformations that treat all predictions uniformly, overlooking the heterogeneous reliability of individual predictions. Furthermore, the relationship between improved calibration and effective uncertainty-aware decision-making remains largely unexplored. This

V. Hnizdo

An outline is given of how Jackson may have obtained the inhomogeneous wave equations for the auxiliary functions $\Psi$ and $\bf V$ in his influential 2002 AJP paper on the transformation from the Lorenz gauge to other electromagnetic gauges. It clarifies the roles of these functions in the calculation of the Coulomb-gauge vector potential ${\bf A}_C$ by sh

Dongjin Lee, Gregory S. H. Paek, Seo-Won Chang, Changwan Kim

Recently, machine learning-based real/bogus (RB) classifiers have demonstrated effectiveness in filtering out artifacts and identifying genuine transients in real-time astronomical surveys. However, the rarity of transient events and the extensive human labeling required for a large number of samples pose significant challenges in constructing training datas

Rikard Vinge, Isabelle Wittmann, Jannik Schneider, Michael Marszalek

We introduce NeuCo-Bench, a novel benchmark framework for evaluating (lossy) neural compression and representation learning in the context of Earth Observation (EO). Our approach builds on fixed-size embeddings that act as compact, task-agnostic representations applicable to a broad range of downstream tasks. NeuCo-Bench comprises three components: (i) an ev

Hassan Hamad, Yingru Xu, Liang Zhao, Wenbo Yan

Tool-augmented large language models (LLMs) are increasingly employed in real-world applications, but tool usage errors still hinder their reliability. We introduce ToolCritic, a diagnostic framework that evaluates and improves LLM behavior in multi-turn, tool-augmented dialogues. ToolCritic detects eight distinct error types specific to tool-calling (e.g.,

Masoud Khairi Atani, Alon Harell, Hyomin Choi, Runyu Yang

The trade-off between general-purpose foundation vision models and their specialized counterparts is critical for efficient feature coding design and is not yet fully understood. We investigate this trade-off by comparing the feature versatility of the general-purpose Hiera encoder against the segmentation-specialized Segment Anything Model 2 (SAM2). Using a

Hibiki Kunisawa, Ryuya Watanabe, Jun-ichi Yamaura, Yoshimitsu Kohama

We report torque magnetometry results on single crystals of karpenkoite Co3(V2O7)(OH)2 2H2O, a model candidate for the kagome antiferromagnet. No field-induced phase transition is detected up to 45 T for B||c and B||a. Instead, the torque reveals a continuous spin reorientation toward saturation, most likely governed by a dominant Dzyaloshinskii-Moriya inter

Manav Batavia, Kesavan Mohana Sundaram, Vaibhav Pandey, Taylor Murray

The arithmetic rank of an ideal in a polynomial ring over an algebraically closed field is the smallest number of equations needed to define its vanishing locus set-theoretically. We determine the arithmetic rank of the generic $m$-residual intersection of an ideal generated by $n$ indeterminates for all $m\geq n$ and in every characteristic. We further give

Mahshid Khazaei Shadfar, Farzam Nosrati, Ali Mortezapour, Vincenzo Macri

Quantum coherence is a key resource underpinning quantum technologies, yet it is highly susceptible to environmental decoherence, especially in thermal settings. While frequency modulation (FM) has shown promise in preserving coherence at zero temperature, its effectiveness in realistic, noisy thermal environments remains unclear. In this work, we investigat

Mihail Arabadji, Porter Morgan

We construct smooth manifolds with order two $\pi_1$ and even intersection forms which are irreducible, meaning they do not decompose into non-trivial connected sums. Their intersection forms being even implies that their universal covers admit spin structures. Such manifolds are determined up to homeomorphism by their Euler characteristic $e$, signature $\s

Ahmal Jawad Zafar, Sunil William Dutta, Matthew Joseph Case, Zachary Diamond

We investigated the effects of scanning speed, beam configuration, and dose-rate modeling on the FLASH effect in post-mastectomy proton transmission-beam (TB) planning and evaluated whether optimizing the spot-scanning path can enhance FLASH. Five left-sided post-mastectomy patients (32 Gy in 5 fractions) were replanned with single-energy (249 MeV) tangentia

Md Ahmed Al Muzaddid, William J. Beksi

Advanced feature extraction methods have significantly contributed to enhancing the task of person re-identification. In addition, modifications to objective functions have been developed to further improve performance. Nonetheless, selecting better class representatives is an underexplored area of research that can also lead to advancements in re-identifica

Juan Pablo De Rasis

We give first-order definitions of Campana and Darmon points in algebraic function fields in one variable over number fields. These sets are geometric generalizations of $n$-full integers (integers whose nonzero valuations are at least $n$) and perfect $n$th powers, respectively, to more general algebraic varieties. For this we exploit the theory of quadrati

Cayo Dória, Plinio G. P. Murillo

In this article, we construct an arithmetic hyperbolic $6-$orbifold $\mathcal{O}$ such that, any square-rootable Salem number of degree at most $4$ over $\mathbb{Q}$ is realized as the exponential of the length of a closed geodesic in $\mathcal{O}$. We also prove that $n=6$ is the minimal dimension among arithmetic hyperbolic orbifolds of the first type wher

Hoang-Son Nguyen, Xiao Fu

Latent component identification from unknown nonlinear mixtures is a foundational challenge in machine learning, with applications in tasks such as disentangled representation learning and causal inference. Prior work in nonlinear independent component analysis (nICA) has shown that auxiliary signals -- such as weak supervision -- can support identifiability

Mohammad R. Salmanpour, Sonya Falahati, Amir Hossein Pouria, Amin Mousavi

Lung cancer remains the leading cause of cancer mortality, with CT imaging central to screening, prognosis, and treatment. Manual segmentation is variable and time-intensive, while deep learning (DL) offers automation but faces barriers to clinical adoption. Guided by the Knowledge-to-Action framework, this study develops a clinician-in-the-loop DL pipeline

Pedram Fekri, Majid Roshanfar, Samuel Barbeau, Seyedfarzad Famouri

Cardiac catheterization remains a cornerstone of minimally invasive interventions, yet it continues to rely heavily on manual operation. Despite advances in robotic platforms, existing systems are predominantly follow-leader in nature, requiring continuous physician input and lacking intelligent autonomy. This dependency contributes to operator fatigue, more

Chongyuan Bi, Jie Liang

Depth-image-based rendering is a key view synthesis algorithm in 3D video systems, which enables the synthesis of virtual views from texture images and depth maps. An efficient view synthesis distortion estimation model is critical for optimizing resource allocation in real-time applications such as interactive free-viewpoint video and 3D video streaming ser

Nguyen Do, Bach Ngo, Youval Kashuv, Canh V. Pham

We study the Quality of Service Degradation (QoSD) problem, in which an adversary perturbs edge weights to degrade network performance. This setting arises in both network infrastructures and distributed ML systems, where communication quality, not just connectivity, determines functionality. While classical methods rely on combinatorial optimization, and re

Syed Konain Abbas, Sandip Purnapatra, M. G. Sarwar Murshed, Conor Miller-Lynch

Large fingerprint datasets, while important for training and evaluation, are time-consuming and expensive to collect and require strict privacy measures. Researchers are exploring the use of synthetic fingerprint data to address these issues. This paper presents a novel approach for generating synthetic fingerprint images (both spoof and live), addressing co

Yutong Zhong

Multimodal 3D grounding has garnered considerable interest in Vision-Language Models (VLMs) \cite{yin2025spatial} for advancing spatial reasoning in complex environments. However, these models suffer from a severe "2D semantic bias" that arises from over-reliance on 2D image features for coarse localization, largely disregarding 3D geometric inputs and resul

Leixu Huang, Zedian Shao, Teodora Baluta

Federated learning (FL) enables fine-tuning large language models (LLMs) across distributed data sources. As these sources increasingly include LLM-generated text, provenance tracking becomes essential for accountability and transparency. We adapt LLM watermarking for data provenance in FL where a subset of clients compute local updates on watermarked data,

Zara Osborn, Amanda Karakas, Devika Kamath, Robert Izzard

Asymptotic giant branch (AGB) stars are important to chemical evolution at metallicity $Z \sim 0.0001$ ($\text{[Fe/H]} \approx -2.2$) as they contribute significantly to the production of nitrogen, lead, and dust in the early Universe. The contribution of AGB stars to the chemical evolution of the Universe is often quantified using the chemical yields from s

H. Blas, A. C. R. do Bonfim, L. T. Teixeira, G. K. R. de Souza

We present a study of a quasi-integrable deformation of the three-particle open Toda chain, constructed by introducing a translation-invariant three-body interaction terms. Although this modification explicitly breaks the exact integrability of the standard Toda model, it retains fundamental structural properties, including energy and momentum conservation.

Mubarek Mohammed

The ever increasing complexity of the hardware design process demands improved hardware design and verification methodologies. With the advent of generative AI various attempts have been made to automate parts of the design and verification process. Large language models (LLMs) as well as specialized models generate hdl and testbenches for small components,

Jake Kettinger, Shahriyar Roshan-Zamir

The B\"or\"oczky configuration of lines and (multiple) points exhibits extremal behavior in commutative algebra and combinatorics. Examples of this appear in the context of the containment problem for ordinary and symbolic powers and the proof of the Dirac-Motzkin conjecture by Green and Tao. This paper studies the algebraic properties of B\"or\"oczky config

Kyle Cox, Jiawei Xu, Yikun Han, Rong Xu

An interesting behavior in large language models (LLMs) is prompt sensitivity. When provided with different but semantically equivalent versions of the same prompt, models may produce very different distributions of answers. This suggests that the uncertainty reflected in a model's output distribution for one prompt may not reflect the model's uncertainty ab

Daniele Perri, Glennys Farrar

There is a claim in the literature that charged dark matter particles in the mass range $100 (q_{\rm X}/e)^2~\mathrm{TeV} \leq m_{\rm X} \leq 10^8 (q_{\rm X}/e)~\mathrm{TeV}$ are allowed, based on arguing that heavy charged particles cannot reach the Earth from outside the magnetized region of the Milky Way (Chuzhoy-Kolb, 2009). We point out that this claim

Augustine O. Munagi

In his classic text, \emph{Combinatory Analysis}, MacMahon defined a perfect partition of a positive integer $n$ as a partition whose parts contain exactly one partition of every positive integer not exceeding $n$. In this paper we apply the same definition to overpartitions which are integer partitions with the additional property that the final occurrence

Zeinab Alizadeh, Azadeh Farsi, Afrooz Jalilzadeh

Nash Equilibrium and its robust counterpart, Distributionally Robust Nash Equilibrium (DRNE), are fundamental problems in game theory with applications in economics, engineering, and machine learning. This paper addresses the problem of DRNE, where multiple players engage in a noncooperative game under uncertainty. Each player aims to minimize their objectiv

Shraman Pramanick, Effrosyni Mavroudi, Yale Song, Rama Chellappa

We introduce ED-VTG, a method for fine-grained video temporal grounding utilizing multi-modal large language models. Our approach harnesses the capabilities of multimodal LLMs to jointly process text and video, in order to effectively localize natural language queries in videos through a two-stage process. Rather than being directly grounded, language querie

Kevin P. O'Keeffe

We explore if RL can be useful for symbolic mathematics. Previous work showed contrastive learning can solve linear equations in one variable. We show model-free PPO \cite{schulman2017proximal} augmented with curiosity-based exploration and graph-based actions can solve nonlinear equations such as those involving radicals, exponentials, and trig functions. O

Bingqi Shang, Yiwei Chen, Yihua Zhang, Bingquan Shen

Large language model (LLM) unlearning is a key approach for removing undesired data, knowledge, or behaviors from pretrained models while retaining their general utility. Yet, with the rise of open-weight LLMs, we ask: can the unlearning process itself be backdoored, appearing successful under normal conditions yet reverting to pre-unlearned behavior when a

Reuben R. W. Wang, John L. Bohn

We discuss the influence of collisions on the dynamics of an ultracold gas whose constituents interact via dipolar forces. This dynamics is governed by the elastic scattering cross section of the molecules, which is to some extent under the experimentalist's control. We compare side-by-side several different situations, highlighting their similarities and di

Giovanni Miano, Loris Maria Cangemi, Carlo Forestiere

The control of interactions among quantum emitters through nanophotonic structures offers significant opportunities for quantum technologies. However, a rigorous theoretical description of the interaction of multiple quantum emitters with complex, dispersive dielectric objects remains challenging. Here, we introduce an approach based on the modified Langevin

Noor Islam S. Mohammad

Toxic text classification for online moderation remains challenging under extreme class imbalance, where rare but high-risk labels such as threat and severe_toxic are consistently underdetected by conventional models. We propose CoGate-LSTM, a parameter-efficient recurrent architecture built around a novel cosine-similarity feature gating mechanism that adap

Adam Mielke, Mads Peter Sørensen, John Wyller

We design a linear chain trick algorithm for dynamical systems for which we have oscillatory time histories in the distributed time delay. We make use of this algorithmic framework to analyse memory effects in disease evolution in a population. The modelling is based on a susceptible-infected-recovered SIR - model and on a susceptible-exposed-infected-recove

Qiusi Zhan, Angeline Budiman-Chan, Abdelrahman Zayed, Xingzhi Guo

Large language model (LLM) based search agents iteratively generate queries, retrieve external information, and reason to answer open-domain questions. While researchers have primarily focused on improving their utility, their safety behaviors remain underexplored. In this paper, we first evaluate search agents using red-teaming datasets and find that they a

J. Menezes, R. Barbalho

We investigate the adaptive Ambush strategy in cyclic models following the rules of the spatial rock-paper-scissors game. In our model, individuals of one species possess cognitive abilities to perceive environmental cues and assess the local density of the species they dominate in the spatial competition for natural resources. Based on this assessment, they

Monika Zamojska, Jarosław A. Chudziak

Simulating nuanced human social dynamics with Large Language Models (LLMs) remains a significant challenge, particularly in achieving psychological depth and consistent persona behavior crucial for high-fidelity training tools. This paper introduces TACLA (Transactional Analysis Contextual LLM-based Agents), a novel Multi-Agent architecture designed to overc

Mingyan Yang, Guanjie Wang, Manqi Luo, Yifei Liu

LLM agents, which often comprise parallel inference tasks, are commonly adopted to solve real-world problems. When serving such task-parallel LLM agents in shared GPU servers, the scheduler is expected to attain fast agent completion with guaranteed worst-case performance. For that objective, our insight is to selectively pampering agents based on their comp

Ani Vanyan, Alvard Barseghyan, Hakob Tamazyan, Tigran Galstyan

Foundation models have advanced machine learning across various modalities, including images. Recently multiple teams trained foundation models specialized for remote sensing applications. This line of research is motivated by the distinct characteristics of remote sensing imagery, specific applications and types of robustness useful for satellite image anal

Kunal. Ghosh, Gargi Sengupta, Rukhsar Parveen, Y. S. Mayya

In this study, we quantify how charged particle concentration affects the neutralization rate of aerosol particles, focusing on the role of ion dynamics shaped by internal electric fields arising from net space charge. Conventional neutralizer performance is typically evaluated using the $N_it$ product, which assumes quasi-neutral conditions and neglects ele

Lanni Bu, Lauren Levine, Amir Zeldes

Recent LLM benchmarks have tested models on a range of phenomena, but are still focused primarily on natural language understanding for extraction of explicit information, such as QA or summarization, with responses often targeting information from individual sentences. We are still lacking more challenging, and importantly also multilingual, benchmarks focu

Firas Ben Ameur, Rayan Dhib, Yahia Battach, Andrea Lani

The loss of STEREO-B in 2014 created a persistent blind spot in Extreme Ultraviolet (EUV) imaging of the solar farside. We present HelioFill, to the authors' knowledge, the first denoising-diffusion inpainting model that restores full-Sun EUV coverage by synthesizing the STEREO-B sector from Earth-side (SDO) and STEREO-A views. Trained on full-Sun maps from

Ya-Jie Wu, Tong Li, Junpeng Hou

The celebrated family of the Hall effect plays a fundamental role in modern physics. Starting from the anomalous Hall effect (AHE) and the quantum AHE (QAHE) with broken time-reversal symmetry (TRS) to their spinful generalizations, including spin Hall effect (SHE) and quantum SHE (QSHE) protected by TRS, they reveal rich transport and topological phenomena.

Alexander I. Efimov

In this paper we study the category of localizing motives $\operatorname{Mot}^{\operatorname{loc}}$ -- the target of the universal finitary localizing invariant of idempotent-complete stable categories as defined by Blumberg-Gepner-Tabuada. We prove that this (presentable stable) category is rigid symmetric monoidal in the sense of Gaitsgory and Rozenblyum.

Anwar Ahmed Khan, Shama Siddiqui, Indrakshi Dey

Industrial Internet of Things (IIoT) promises to revolutionize industrial operations and productions through utilizing Machine-to-Machine (M2M) communications. Since each node in such environments generates various types of data with diverse service requirements, MAC protocol holds crucial importance to ensure efficient delivery. In this context, simple to c

A. I. Lvov, V. A. Petrunkin, S. G. Popov, B. B. Wojtsekhowski

A theoretical possibility to determine the proton Compton scattering cross section from the reaction $ep\rightarrow ep\gamma$ is studied in the kinematics with a small transversal momentum transfer in the electron leg. With the exception of the region of forward photon directions and extreme photon energies close to the maximum ones or zero, registration of

Ignacio M. De la Jara, Cristian Rodriguez-Opazo, Edison Marrese-Taylor, Felipe Bravo-Marquez

Temporal video grounding is a fundamental task in computer vision, aiming to localize a natural language query in a long, untrimmed video. It has a key role in the scientific community, in part due to the large amount of video generated every day. Although we find extensive work in this task, we note that research remains focused on a small selection of vide

Masahiro Kaneko, Zeerak Talat, Timothy Baldwin

Iterative jailbreak methods that repeatedly rewrite and input prompts into large language models (LLMs) to induce harmful outputs -- using the model's previous responses to guide each new iteration -- have been found to be a highly effective attack strategy. Despite being an effective attack strategy against LLMs and their safety mechanisms, existing defense

Yuanhe Zhang, Ilja Kuzborskij, Jason D. Lee, Chenlei Leng

Large Language Models (LLMs) demonstrate strong performance on mathematical problems when prompted with Chain-of-Thought (CoT), yet it remains unclear whether this success stems from search, rote procedures, or rule-consistent reasoning. To address this, we propose modeling CoT as a certain rule-based stochastic process over directed acyclic graphs (DAGs), w

Mehdi Moradi

It is shown that infinite, discrete, Kazhdan property (T) groups never have the {\it finite-dimensional density} (FDD) property. This answers a conjecture of Lubotzky and Shalom affirmatively.

Chang Liu, Danial Chitnis

Circuit schematics play a crucial role in analog integrated circuit design, serving as the primary medium for human understanding and verification of circuit functionality. While recent large language model (LLM)-based approaches have shown promise in circuit topology generation and device sizing, most rely solely on textual representations such as SPICE net

Yuval Reif, Guy Kaplan, Roy Schwartz

Large language models (LLMs) often encode word-form variation (e.g., walk vs. walked) as linear directions in the embedding space. However, standard tokenization algorithms treat such variants as distinct words with different vocabulary entries, quickly filling the size-capped token vocabulary with surface-form variation (e.g., walk, walking, Walk) at the ex

Masahiro Kaneko, Timothy Baldwin

Adversarial attacks by malicious users that threaten the safety of large language models (LLMs) can be viewed as attempts to infer a target property $T$ that is unknown when an instruction is issued, and becomes knowable only after the model's reply is observed. Examples of target properties $T$ include the binary flag that triggers an LLM's harmful response

Alkesh Punjabi, Allen H. Boozer

An analytic model of the magnetic field line behavior in a stellarator is used to study the subtlety of the concept of an outermost magnetic surface. The analytic model that we use has a central region of nested magnetic surfaces. The outermost perfectly confining surface has a toroidal flux of 0.86 of the toroidal flux of the outermost confining surface. Th

Lucas J. Hyland, Simon P. Ellingsen, Mark J. Reid

The 6.7~GHz methanol maser transition is exclusively associated with young, high-mass stars and represents a potential target for astrometric studies, including accurate determination of their distance through trigonometric parallax measurements. There are more than 1000 known 6.7~GHz methanol maser sources in the Milky Way; however, not all are suitable tar

Tsun-An Hsieh, Sebastian Braun

Generative models have shown robust performance on speech enhancement and restoration tasks, but most prior approaches operate offline with high latency, making them unsuitable for streaming applications. In this work, we investigate the feasibility of a low-latency, real-time generative speech restoration system based on flow-matching (FM). Our method tackl

Juncheng Dong, Yang Yang, Tao Liu, Yang Wang

The efficiency of GPU kernels is central to the progress of modern AI, yet optimizing them remains a difficult and labor-intensive task due to complex interactions between memory hierarchies, thread scheduling, and hardware-specific characteristics. While recent advances in large language models (LLMs) provide new opportunities for automated code generation,

Tsun-An Hsieh, Minje Kim

Generative target speaker extraction (TSE) methods often produce more natural outputs than predictive models. Recent work based on diffusion or flow matching (FM) typically relies on a small, fixed number of reverse steps with a fixed step size. We introduce Adaptive Discriminative Flow Matching TSE (AD-FlowTSE), which extracts the target speech using an ada

Francis Bloch, Bhaskar Dutta, Marcin Dziubiński

We propose and study a model of strategic network design and exploration where the hider, subject to a budget constraint restricting the number of links, chooses a connected network and the location of an object. Meanwhile, the seeker, not observing the network and the location of the object, chooses a network exploration strategy starting at a fixed node in

Haiyan Liu, Jeanine Houwing-Duistermaat

When measurements fall below or above a detection threshold, the resulting data are missing not at random (MNAR), posing challenges for statistical analysis. For example, in longitudinal biomarker studies, observations may be subject to detection limits. Functional principal component analysis (FPCA) is commonly used method for dimension reduction of dense a

Yahli Hecht, Muli Safra

We establish deterministic hardness of approximation results for the Shortest Vector Problem in $\ell_p$ norm ($\mathsf{SVP}_p$) and for Unique-SVP ($\mathsf{uSVP}_p$) for all $p > 2$. Previously, no deterministic hardness results were known, except for $\ell_\infty$. For every $p > 2$, we prove constant-ratio hardness: no polynomial-time algorithm approxima

Xuying Ning, Dongqi Fu, Tianxin Wei, Wujiang Xu

Real-world multimodal data usually exhibit complex structural relationships beyond traditional one-to-one mappings like image-caption pairs. Entities across modalities interact in intricate ways, with images and text forming diverse interconnections through contextual dependencies and co-references. Graphs provide powerful structural information for modeling

Luca Zanella, Massimiliano Mancini, Yiming Wang, Alessio Tonioni

Given a task and a set of steps composing it, Video Step Grounding (VSG) aims to detect which steps are performed in a video. Standard approaches for this task require a labeled training set (e.g., with step-level annotations or narrations), which may be costly to collect. Moreover, they process the full video offline, limiting their applications for scenari

Junhao Zhao, Zishuai Liu, Ruili Fang, Jin Lu

The recognition of Activities of Daily Living (ADLs) from event-triggered ambient sensors is an essential task in Ambient Assisted Living, yet existing methods remain constrained by representation-level limitations. Sequence-based approaches preserve temporal order of sensor activations but are sensitive to noise and lack spatial awareness, while image-based

Amit Moryossef, Clara Meister, Pavel Stepachev, Desmond Elliott

We present UTF8Tokenizer, a minimalist byte-level tokenizer that maps text exactly to IDs corresponding to the bytes underlying the text's UTF-8 encoding (e.g., byte x09 is token ID 9). Unlike prior byte-level approaches (Xue et al., 2021; Pagnoni et al., 2025), our implementation never introduces out-of-range IDs (i.e. there is no token ID 256) or auxiliary

Akif Islam, Mohd Ruhul Ameen

Bengali social media platforms have witnessed a sharp increase in hate speech, disproportionately affecting women and adolescents. While datasets such as BD-SHS provide a basis for structured evaluation, most prior approaches rely on either computationally costly full-model fine-tuning or proprietary APIs. This paper presents the first application of Paramet

Seckin Damar, Gulsah Hancerliogullari Koksalmis

This study investigates behavioral intention to use healthcare metaverse platforms among medical students and physicians in Turkey, where such technologies are in early stages of adoption. A multi-theoretical research model was developed by integrating constructs from the Innovation Diffusion Theory, Embodied Social Presence Theory, Interaction Equivalency T

Yuanzhi Zhu, Eleftherios Tsonis, Lucas Degeorge, Vicky Kalogeiton

Diffusion and flow models achieve high generative quality but remain computationally expensive due to slow multi-step sampling. Distillation methods accelerate them by training fast student generators, yet most existing objectives lack a unified theoretical foundation. In this work, we propose Di-Bregman, a compact framework that formulates diffusion distill

Maniesha Singh, Anter El-Azab

A comprehensive analysis is presented for the diffusivity of oxygen defects and oxygen self-diffusion in ThO$-2$. The migration energy and diffusivity of oxygen defects with nominal charges have been investigated using density functional theory and phonon simulations. The pathway for the lowest migration energy barrier of oxygen vacancies was found to be alo

Ahmed Khaled, Kaan Ozkara, Tao Yu, Mingyi Hong

Gradient orthogonalization is a simple strategy that shows great utility in speeding up gradient descent. The Muon optimizer (Jordan, Jin, et al., 2024) combines gradient orthogonalization with first-order momentum and achieves significant improvement in data efficiency over Adam/AdamW (Loshchilov and Hutter, 2019) for language model training. However, when

Kanghui Ning, Zijie Pan, Yushan Jiang, Anderson Schneider

Time series reasoning is emerging as the next frontier in temporal analysis, aiming to move beyond pattern recognition towards explicit, interpretable, and trustworthy inference. This paper presents a BlueSky vision built on two complementary directions. One builds robust foundations for time series reasoning, centered on comprehensive temporal understanding

Nhat A. Nghiem, Trung V. Phan

We explore the physical quantum properties of atoms in fractal spaces, both as a theoretical generalization of normal integer-dimensional Euclidean spaces and as an experimentally realizable setting. We identify the threshold of fractality at which Ehrenfest atomic instability emerges, where the Schr\"{o}dinger equation describing the wave-function of a sing

Rikhil Tanugula, Dheeraj Chintapalli, Sunkalp Chandra

We present Lark, a biologically inspired decision-making framework that couples LLM-driven reasoning with an evolutionary, stakeholder-aware Multi-Agent System (MAS). To address verbosity and stakeholder trade-offs, we integrate four mechanisms: (i) plasticity, which applies concise adjustments to candidate solutions; (ii) duplication and maturation, which c

Varoujan Gorjian, Michael W. Werner

Variability studies are a powerful tool for studying the structures of unresolved sources. One such type of variability study, called reverberation mapping (RM), established that the dominant source of infrared radiation from an active galactic nucleus (AGN) was from dust absorption and re-emission, which demonstrated that the optical brightening and fading

Lin Yu, Zhihui Liu, Kathy Han, Olli Saarela

Recent causal inference literature has introduced causal effect decompositions to quantify sources of observed inequalities or disparities in outcomes, but these approaches are typically limited to pairwise comparisons. In healthcare delivery settings, both the exposure of interest-hospital or healthcare unit-and sociodemographic group membership may be poly

Shurong Lin, Aleksandra Slavković, Deekshith Reddy Bhoomireddy

In the social sciences, small- to medium-scale datasets are common, and linear regression is canonical. In privacy-aware settings, much work has focused on differentially private (DP) linear regression, but mostly on point estimation with limited attention to uncertainty quantification. Meanwhile, synthetic data generation (SDG) is increasingly important for

Praveenbalaji Rajendran, Mojtaba Safari, Wenfeng He, Mingzhe Hu

Recent advancements in artificial intelligence (AI), particularly foundation models (FMs), have revolutionized medical image analysis, demonstrating strong zero- and few-shot performance across diverse medical imaging tasks, from segmentation to report generation. Unlike traditional task-specific AI models, FMs leverage large corpora of labeled and unlabeled

Andreas Haupt

Algorithmic recommendation based on noisy preference measurement is prevalent in recommendation systems. This paper discusses the consequences of such recommendation on market concentration and inequality. Binary types denoting a statistical majority and minority are noisily revealed through a statistical experiment. The achievable utilities and recommendati

Kimiya Jozani, Nihal A. Sageer, Hode Eldardiry, Sait Tunc

Timely and effective decision-making is critical during epidemics to reduce preventable infections and deaths. This demands integrated models that jointly capture disease dynamics, vaccine distribution, regional disparities, and behavioral responses. However, most existing approaches decouple epidemic forecasting from logistics planning, hindering adaptive a

Pingzhi Li, Morris Yu-Chao Huang, Zhen Tan, Qingquan Song

Knowledge Distillation (KD) accelerates training of large language models (LLMs) but poses intellectual property protection and LLM diversity risks. Existing KD detection methods based on self-identity or output similarity can be easily evaded through prompt engineering. We present a KD detection framework effective in both white-box and black-box settings b

Paascal Grosset, James Ahrens

As simulations produce more data than available disk space on supercomputers, many simulations are employing in situ analysis and visualization to reduce the amount of data that needs to be stored. While in situ visualization offers potential for substantial data reduction, its efficacy is hindered by the need for a priori knowledge. First, we need to know w

Junren Chen, Lijun Ding, Dong Xia, Ming Yuan

We consider the estimation of a $d_1\times d_2\times d_3$ tensor $X^\star$ of Tucker rank $(r_1,r_2,r_3)$ from the nonlinear observations $\{y_i=f_i(\langle A_i,X^\star\rangle)\}_{i=1}^n$. We develop a unified approach that first constructs a gradient map from the data and then establishes the {\it tensor restricted approximate invertibility condition} (T-RA

Anish Chedalavada

We explain how the geometric framework introduced in arXiv:2508.11621 [math.AG] provides a universal property for the 2-rings of perfect complexes on qcqs spectral or Dirac spectral schemes. As an application, given a qcqs spectral or Dirac spectral scheme $X$ this produces a comparison morphism from $\operatorname{Spec} \mathrm{Perf}_{X}$ to $X$ itself, whi

Donggu Lee, Sung Joon Maeng, Ismail Guvenc

Uncrewed aerial vehicles (UAVs) have emerged as a flexible platform for providing coverage over challenging environments, particularly for public safety and surveillance missions in urban areas. However, deploying the UAVs in dense urban areas introduces unique challenges, most notably asymmetric uplink (UL, remote controller to UAV) interference due to a hi

Ama Bandara, Viviana Centritto Arrojo, Heqi Deng, Masoud Babaie

The scalability of quantum computing systems is constrained by the wiring complexity and thermal load introduced by dense wiring for control, readout and synchronization at cryogenic temperatures. To address this challenge, we explore the feasibility of wireless communication within a cryostat for a multi-core quantum computer, focusing on wireless channel c

Saša Ilijić, Ana Babić, Dora Ivrlač, Andrew DeBenedictis

A general expression for the elastic potential energy of a helical spring is derived using the basic concepts of elasticity theory and geometry. Both the translational and the rotational displacement of the spring's moving ends are considered. The resulting expression is employed to derive the general relation between force and spring height, and corrections

Surendra Ghentiyala

Differential privacy for the 2020 census required an estimated 90 terabytes of randomness [GL20], an amount which may be prohibitively expensive or entirely infeasible to generate. Motivated by these practical concerns, [CSV25] initiated the study of the randomness complexity of differential privacy, and in particular, the randomness complexity of $d$ counti

Ganglin Tian, Anastase Alexandre Charantonis, Camille Le Coz, Alexis Tantet

This study aims to improve the spatial representation of uncertainties when regressing surface wind speeds from large-scale atmospheric predictors for sub-seasonal forecasting. Sub-seasonal forecasting often relies on large-scale atmospheric predictors such as 500 hPa geopotential height (Z500), which exhibit higher predictability than surface variables and

Shah Hussain, Sikander Azam, Umme Habiba, Qaiser Rafiq

Rare earth doping is an effective way to convert chemically stable oxides into multifunctional materials with coupled electronic, optical, and magnetic properties. We present first principles calculations of pristine and Tm3+ doped Ca2SnO4 to understand how localized 4f states change the structural, electronic, magnetic, and optical behavior of the host. Pri

Mark Towers, Yali Du, Christopher Freeman, Timothy J. Norman

Debugging is a core application of explainable reinforcement learning (XRL) algorithms; however, limited comparative evaluations have been conducted to understand their relative performance. We propose a novel evaluation methodology to test whether users can identify an agent's goal from an explanation of its decision-making. Utilising the Atari's Ms. Pacman

G. G. Ryzhkova, I. Yu. Kurochkin, T. N. Rudneva, A. V. Zotov

We report a three-stage synthesis of a hybrid metal-carbon 2D material, in which cobalt atoms are covalently embedded in the graphene-like carbon (GLC) matrix. The resulting material (CoGLC) exhibits a distinctive XRD pattern indicative of the ordered arrangement of cobalt atoms in the layers. Furthermore, we demonstrate the fabrication of surfactant-free co

Ersin Das, William A. Welch, Patrick Spieler, Keenan Albee

Ensuring safe real-time control of ship-mounted cranes in unstructured transportation environments requires handling multiple safety constraints while maintaining effective payload transfer performance. Unlike traditional crane systems, ship-mounted cranes are consistently subjected to significant external disturbances affecting underactuated crane dynamics

Austin Drake, Hang Dong

We present a novel architecture for safely integrating Large Language Models (LLMs) into interactive game engines, allowing players to "program" new behaviors using natural language. Our framework mitigates risks by using an LLM to translate commands into a constrained Domain-Specific Language (DSL), which configures a custom Entity-Component-System (ECS) at

Christine Sowa Lepird, Kathleen M. Carley

In the rise of the digital era, it's easier than ever to create nefarious websites to spread misinformation. A more recent phenomenon in the United States has been the creation of inauthentic local news websites to further an information operation campaign. This paper is a review of the 7 instances in which local news websites were created to influence resid