March 2026 arXiv papers — page 96

Jing-Qi Chen, Hai-Liang Chen, Zheng-Wei Liu, Xuefei Chen

ATLAS J1138-5139 is a newly detected ultra-compact double white dwarf (DWD) system which is composed of a $1.02\,M_{\odot}$ carbon-oxygen white dwarf (CO WD) and a $0.24\,M_{\odot}$ helium (He) WD with an orbital period of about 27.68 min, making it one of the shortest-period DWD systems known. The future evolution and final fate of this system remain unexpl

Dip Roy, Rajiv Misra, Sanjay Kumar Singh, Anisha Roy

Activation-based linear probing is widely proposed as a method for both detecting and correcting hallucinations in autoregressive language models. We present an empirical study across seven models spanning 117M to 7B parameters and three architecture families (GPT-2, Pythia, Qwen-2.5) that documents a robust asymmetry: linear probes can detect hallucination

Chao Wang, Xudong Tan, Jianjian Cao, Kangcong Li

Multimodal Large Language Models have achieved significant success in offline video understanding, yet their application to streaming videos is severely limited by the linear explosion of visual tokens, which often leads to Out-of-Memory (OOM) errors or catastrophic forgetting. Existing visual retention and memory management methods typically rely on uniform

Chuhan Wang, Hao Chen

Image tokenization plays a central role in modern generative modeling by mapping visual inputs into compact representations that serve as an intermediate signal between pixels and generative models. Diffusion-based decoders have recently been adopted in image tokenization to reconstruct images from latent representations with high perceptual fidelity. In con

Ziren Jiang, Lingfeng Huo, Jue Hou, Mary Vaughan-Sarrazin

Accurately predicting hospital readmission risks using electronic health records (EHRs) is critical for effective patient management and healthcare resource allocation. Patient populations in health systems are highly heterogeneous across different primary diagnoses, necessitating tailored yet interpretable prediction models. We propose a hierarchical modeli

Yao Xiao, Rafael A. Calvo

Against rising global loneliness, AI companions promise connection, yet accumulating evidence suggests that, for some users and contexts, intensive companion-style use can correlate with increased loneliness and reduced offline socialisation. This position paper challenges the dominant "AI as companion" paradigm by proposing a shift: from AI that simulates r

Zhenyu Yang, Gensheng Pei, Tao Chen, Yichao Zhou

Modern vision models must capture image-level context without sacrificing local detail while remaining computationally affordable. We revisit this tradeoff and advance a simple principle: decouple the roles of global reasoning and local representation. To operationalize this principle, we introduce ConvNeur, a two-branch architecture in which a lightweight n

Minghe Xu, Rouying Wu, ChiaWei Chu, Xiao Wang

Event-based pedestrian attribute recognition (PAR) leverages motion cues to enhance RGB cameras in low-light and motion-blur scenarios, enabling more accurate inference of attributes like age and emotion. However, existing two-stream multimodal fusion methods introduce significant computational overhead and neglect the valuable guidance from contextual sampl

Yu Yvonne Wu, Yuwei Zhang, Hyungjun Yoon, Ting Dang

Wearable foundation models (WFMs), trained on large volumes of data collected by affordable, always-on devices, have demonstrated strong performance on short-term, well-defined health monitoring tasks, including activity recognition, fitness tracking, and cardiovascular signal assessment. However, most existing WFMs primarily map short temporal windows to pr

Haoyu Zhang, Zhihao Yu, Rui Wang, Yaochu Jin

Modern computer vision requires balancing predictive accuracy with real-time efficiency, yet the high inference cost of large vision models (LVMs) limits deployment on resource-constrained edge devices. Although Evolutionary Neural Architecture Search (ENAS) is well suited for multi-objective optimization, its practical use is hindered by two issues: expensi

Dong-Xiao Zhang, Hu Lou, Jun-Jie Zhang, Jun Zhu

Adversarial vulnerability in vision and hallucination in large language models are conventionally viewed as separate problems, each addressed with modality-specific patches. This study first reveals that they share a common geometric origin: the input and its loss gradient are conjugate observables subject to an irreducible uncertainty bound. Formalizing a N

V. S. Maduri, K. B. Nakshatrala

Porous materials -- natural or engineered -- often exhibit dual pore-network structures that govern processes such as mineral exploration and hydrocarbon recovery from tight shales. Double porosity/permeability (DPP) mathematical models describe incompressible fluid flow through two interacting pore networks with inter-network mass exchange. Despite signific

Marisa Kirisame, Thomas J. Porter, Ruqing Yang, Jianqiu Zhao

Live programming systems aim to quickly show programmers the dynamic impacts of program edits. To do so, they re-execute the program whenever it is edited, which poses a computational challenge when programs become large or complex. This has led to the need for incrementality in the implementation of live program interpreters. This paper introduces Chordata,

Calvin Ang, Sungyoon Kim, Mert Pilanci

We study entrywise scalar quantization of two matrices prior to multiplication. Given $A\in R^{m\times k}$ and $B\in R^{k\times n}$, we quantize entries of $A$ and $B$ independently using scalar quantizers with $K_X$ and $K_Y$ levels per entry, and form $\widehat C=\widehat A\,\widehat B$. The objective is to minimize the matrix multiplication mean-squared e

Xinyu Guo, Yazhou Zhang, Jing Qin

Eliciting explicit, step-by-step reasoning traces from large language models (LLMs) has emerged as a dominant paradigm for enhancing model capabilities. Although such reasoning strategies were originally designed for problems requiring explicit multi-step reasoning, they have increasingly been applied to a broad range of NLP tasks. This expansion implicitly

Jong-Wan Lee, Ed Bennett, Yannick Dengler, Deog Ki Hong

We report new results obtained in our lattice studies of the $Sp(4)$ gauge theory coupled to two fundamental Dirac fermions. This theory provides a candidate for the dynamical origin of dark matter models within the strongly interacting massive particle paradigm. We employ L\"uscher's formalism to analyse finite-volume energy levels and study the scattering

Mayar Nour, Atrisha Sarkar, Mohamed H. Zaki

Work zone navigation remains one of the most challenging manoeuvres for autonomous vehicles (AVs), where constrained geometries and unpredictable traffic patterns create a high-risk environment. Despite extensive research on AV trajectory planning, few studies address the decision-making required to navigate work zones safely. This paper proposes a novel gam

Yuan-Hao Pu, Guo-Hong Lei, Yang Xu, Xun-Zhou Chen

Astronomical spectra, which encode rich astrophysical and chemical information, are fundamental to understanding celestial objects and universal laws. The advent of large-scale spectroscopic surveys, generating tens of millions of spectra, presents significant challenges for efficient data processing and analysis. To address these challenges, we develop an A

Kevin G Hare, Hachem Hichri

We introduce the \emph{Parry order} $\mathrm{Ord}_P(\beta)$, defined as the largest integer $n$ for which $\beta^n$ is a Parry number. This leads to a natural partition of the set of Perron numbers as follows: \[ \mathcal{P} = \left( \bigcup_{n \geq 0} H_n \right) \cup H_\infty, \] where $H_n$ is the class of Perron numbers with Parry order $n$, and $H_\inft

Li Guo, Xiaoyan Wang, Huhu Zhang

We provide a general notion of induced structures of operated algebras in the context of unary-binary operads. This notion fully captures the binary quadratic relations encoded by a unary-binary operad, thereby unifying and formalizing the various constructions that have appeared in the literature under the informal term of ``induced structures''. As an appl

Zhongrui Yu, Zhao Wang, Yijia Xie, Yida Wang

Feedforward reconstruction is crucial for autonomous driving applications, where rapid scene reconstruction enables efficient utilization of large-scale driving datasets in closed-loop simulation and other downstream tasks, eliminating the need for time-consuming per-scene optimization. We present StreetForward, a pose-free and tracker-free feedforward frame

Liu hung ming

Video world models trained with Joint Embedding Predictive Architectures (JEPA) acquire rich spatiotemporal representations by predicting masked regions in latent space rather than reconstructing pixels. This removes the visual verification pathway of generative models, creating a structural interpretability gap: the encoder has learned physical structure in

Muhammad Hassan Maqsood, Yanming Zhu, Alfred Lam, Getamesay Dagnaw

Histopathology nuclei segmentation is crucial for quantitative tissue analysis and cancer diagnosis. Although existing segmentation methods have achieved strong performance, they are often computationally heavy and show limited generalization across datasets, which constrains their practical deployment. Recent SAM-based approaches have shown great potential

John Paul P. Miranda, Rhiziel P. Manalese, Mark Anthony A. Castro, Renen Paul M. Viado

This study examined how moral disengagement influences Filipino college students' intention to use ChatGPT in academic writing. The model tested five mechanisms: moral justification, euphemistic labeling, displacement of responsibility, minimizing consequences, and attribution of blame. These mechanisms were analyzed as predictors of attitudes, subjective no

John Paul P. Miranda, Rhiziel P. Manalese, Sheila M. Geronimo, Vernon Grace M. Maniago

This study examined how student interns in Philippine higher education use intelligent tools during their OJT. Data were collected from 384 respondents using a structured questionnaire that asked about AI tool usage, task-specific applications, and perceptions of confidence, ethics, and support. Analysis of task-based usage identified four main purposes: pro

Danyang Jiang, Linhua Jiang, Shuqi Fu, Zijian Zhang

Near-IR Paschen lines are potentially an excellent tracer of Type 1 AGNs that is hardly affected by dust extinction. JWST allows us, for the first time, to explore Paschen-line objects at redshift z>1. Here we present a study of 62 AGNs with broad Pa$\alpha$ and Pa$\beta$ lines at 1<z<3 using data from the JWST COSMOS-3D program. These AGNs are efficiently s

Getamesay Dagnaw, Xuefei Yin, Muhammad Hassan Maqsood, Yanming Zhu

Deep learning models have achieved strong performance in medical image analysis, but their internal decision processes remain difficult to interpret. Concept Bottleneck Models (CBMs) partially address this limitation by structuring predictions through human-interpretable clinical concepts. However, existing CBMs typically overlook the contextual dependencies

Xiaoying Wang, Yumeng He, Jingkai Shi, Jiayin Lu

Monocular depth estimation remains challenging for transparent objects, where refraction and transmission are difficult to model and break the appearance assumptions used by depth networks. As a result, state-of-the-art estimators often produce unstable or incorrect depth predictions for transparent materials. We propose SeeClear, a novel framework that conv

Lei Wang, Xi Ding, Yongsheng Gao, Piotr Koniusz

Learning from structured multi-way data, represented as higher-order tensors, requires capturing complex interactions across tensor modes while remaining computationally efficient. We introduce Uncertainty-driven Kernel Tensor Learning (UKTL), a novel kernel framework for $M$-mode tensors that compares mode-wise subspaces derived from tensor unfoldings, enab

Yijiang Li, Zilinghan Li, Kyle Chard, Ian Foster

Artificial Intelligence for scientific applications increasingly requires training large models on data that cannot be centralized due to privacy constraints, data sovereignty, or the sheer volume of data generated. Federated learning (FL) addresses this by enabling collaborative training without centralizing raw data, but scientific applications demand mode

Linrui Shou, Zilang Chen, Wenjia Xu, Yiyue Luo

We present a zero-shot deformation reconstruction framework for soft robots that operates without any visual supervision at inference time. In this work, zero-shot deformation reconstruction is defined as the ability to infer object-wide deformations on previously unseen soft robots without collecting object-specific deformation data or performing any retrai

Ken X. Zhao, Tomas Chor, Eric Skyllingstad, Jonathan Nash

Turbulent heat and freshwater transport at ice-ocean interfaces controls glacier and iceberg melt rates, yet the underlying physics remains poorly constrained. Parameterizations that assume shear boundary layer scaling are commonly used, which neglects meltwater buoyancy-driven convective processes. Using Direct Numerical Simulations with realistic salt diff

Quang Dang Huynh, Xuefei Yin, Andrew Busch, Hugo G. Espinosa

Video-based human pose estimation remains challenged by motion blur, occlusion, and complex spatiotemporal dynamics. Existing methods often rely on heatmaps or implicit spatio-temporal feature aggregation, which limits joint topology expressiveness and weakens cross-frame consistency. To address these problems, we propose a novel node-centric framework that

Krzysztof Findeisen, Kian-Tat Lim, Dan Speck, Hsin-Fang Chiang

Vera C. Rubin Observatory's Prompt Processing system will automatically process 10 TB of raw images to produce up to 10 million transient alerts per night. We summarize how Prompt Processing meets its throughput, latency, and reliability requirements and present results from Rubin Observatory Commissioning.

Marius Lemm, Israel Michael Sigal, Jingxuan Zhang

We prove the Davies-Gaffney (i.e., integrated Nash-Aronson) type diffusive upper bounds on the propagators of parabolic equations in $L^p$-sense for all $1\le p\le\infty$. Our approach is based on a simple exponential deformation argument that does not require hypoellipticity. It provides a unified approach to diffusive upper bounds that covers a wide class

Betty Xiong, Jillian Fisher, Benjamin Newman, Meng Hu

We introduce an expert curated, real-world benchmark for evaluating document-grounded question-answering (QA) motivated by generic drug assessment, using the U.S. Food and Drug Administration (FDA) drug label documents. Drug labels contain rich but heterogeneous clinical and regulatory information, making accurate question answering difficult for current lan

Zhongfu Ma, Di Zhu

Governing equations are fundamental for describing and predicting dynamic urban geographic systems. Unlike physical systems guided by first principles, urban spatiotemporal phenomena emerge from coupled geographic processes that lack deterministic theoretical foundations, making the discovery of governing equations elusive and largely heuristic. Spatiotempor

Yiheng Chen, Alina Hagen, Fan Yang, Ratna B. Dougherty

Wildfires require governments to communicate under conditions of urgency, uncertainty, and intense public scrutiny, yet such communication now unfolds within a digitally mediated environment shaped by polarization and engagement-based amplification. We analyze over 1.3 million wildfire-related social media posts from California (2016-2025) to examine how ins

Davide Traini, José Manuel Alcalde-Llergo, Mariana Buenestado-Fernández, Domenico Ursino

This study analyzes behavioral engagement in SONAR, a virtual reality application designed for sign language training and validation. We focus on three automatically derived engagement indicators (Visual Attention (VA), Video Replay Frequency (VRF), and Post-Playback Viewing Time (PPVT)) and examine their relationship with learning performance. Participants

Xuefeng Li, Rob Armitage, Daniel Feezell

We study carrier transport, distribution, and recombination in dual-color c-plane InGaN/GaN LEDs using spectral analysis and small-signal electroluminescence (SSEL). The emissions from green and blue quantum wells (QWs) were experimentally separated and analyzed. Spectral analysis and SSEL independently demonstrate that emission from the green QW is dominant

Sima Ashayer, Hoang H. Nguyen, Yu Liang, Mina Sartipi

Pedestrian intention prediction needs to be accurate for autonomous vehicles to navigate safely in urban environments. We present a lightweight, socially informed architecture for pedestrian intention prediction. It fuses four behavioral streams (attention, position, situation, and interaction) using highway encoders, a compact 4-token Transformer, and globa

J. Ben Tamo, Yuxing Lu, Benoit L. Marteau, Micky C. Nnamdi

Large Language Models (LLMs) are fluent but prone to hallucinations, producing answers that appear plausible yet are unsupported by available evidence. This failure is especially problematic in high-stakes domains where decisions must be justified by verifiable information. We introduce \textbf{EvidenceRL}, a reinforcement learning framework that enforces ev

José Manuel Alcalde-Llergo, Mariana Buenestado Fernández, Carlos Enrique George-Reyes, Andrea Zingoni

This study develops machine learning models to assess Media and Information Literacy (MIL) skills specifically in the context of disinformation among students, particularly future educators and communicators. While the digital revolution has expanded access to information, it has also amplified the spread of false and misleading content, making MIL essential

Dimitrios Chatziparaschis, Elia Scudiero, Brent Sams, Konstantinos Karydis

The dynamic and heterogeneous nature of agricultural fields presents significant challenges for object detection and localization, particularly for autonomous mobile robots that are tasked with surveying previously unseen unstructured environments. Concurrently, there is a growing need for real-time detection systems that do not depend on large-scale manuall

Jilong Xu, Xiaojun Cui

While discrete-time Stochastic Portfolio Theory (SPT) provides a robust framework for market analysis, existing work on functional generation has predominantly focused on long-only portfolios defined on the entire unit simplex. This paper extends the geometric framework of functional generation to the broader class of bankruptcy-proof long-short portfolios d

Siqi Miao, Ziyang Chen, Yuhong Luo, Hans Hao-Hsun Hsu

While Large Language Model (LLM) multi-agent systems (MAS) offer a transformative approach to simulating human behavior in complex systems, it remains largely unexplored whether these simulations can replicate realistic structural and temporal dynamics from a dynamic network perspective. Our evaluation indicates that existing frameworks excel at generating p

Jonathan P. Bowen, Henri Habrias

Jean-Raymond Abrial is one of the central figures in the development of formal methods for software and systems engineering. Over a career spanning more than five decades, he has played a decisive role in the creation of the Z specification notation, the B-Method, and Event-B, and in demonstrating their applicability to large-scale industrial systems. This p

Ghislain Lefebvre

This work presents the implementation of single-qubit gates, including $R_x$ and $R_z$ gates realized using transparent adhesive tape, and $R_y$ gates obtained with optically active maple and agave solutions. These gates form the native gate set of a simple photonic system and are subsequently used to construct a Hadamard gate. Two forms of two-qubit gates a

Gregory D. Scholes

The main purpose of thispaper is to show that composite quantum-like (QL) systems can closely mimic the separable states of quantum systems, and that suitable physical systems exhibiting these states exist. It is shown that QL graphs can closely emulate states of composite quantum systems, such as coupled two-level systems that display separable linear combi

Chung-Hsuan Tung, Zhenzhou Qi, Tingjun Chen

Energy detection is widely used for spectrum sensing, but accurately localizing the time and frequency occupation of signals in real-time for efficient spectrum sharing remains challenging. To address this challenge, we present RISE, a software-based spectrum sensing system designed for real-time signal detection and localization. RISE treats time-frequency

Abdullahi Isa Ahmed, Ana Maria Drăgulinescu, El Mehdi Amhoud

The evolution of Internet of Things (IoT) into multi-layered environments has positioned Low-Power Wide Area Networks (LPWANs), particularly Long Range (LoRa), as the backbone for connectivity across both surface and subterranean landscapes. However, existing LoRa-based network designs often treat ground-based wireless sensor networks (WSNs) and wireless und

Nina Effenberger, Reto Knutti

Ensembles of regional-global climate model combinations show substantial spread in projected wind and solar resources. Using 31 RCM-GCM pairs, we quantify the sources of this spread with a spatially and seasonally resolved variance decomposition, separating contributions from RCMs and GCMs. For both wind speed and solar radiation, RCMs dominate the variabili

G. M. Wysin

I analyze the nonlinear Hamiltonian equations of motion for a one-dimensional chain of transverse magnetic nano-islands, seeking solutions for different types of static domain-walls (DWs) connecting uniform static states. The system of elongated magnetic islands oriented transverse ($y$-direction) to the chain direction ($x$-direction) experiences an applied

Michal Horák, Michael Foltýn, Viktor Bajo, Petr Dub

Localized surface plasmon resonances are self-sustained, collective oscillations of free electrons in metallic nanostructures. They have a wide range of applications. The most common plasmonic metals are noble metals, such as gold and silver. However, there are applications, such as surface-enhanced Raman spectroscopy, in which using non-noble metals is adva

Yi-Heng Chi, Ping Zhou, Yang Chen, Lei Sun

It has been an unsolved question what leads a supernova remnant (SNR) to a thermal composite rather than a typical shell-like morphology, and what causes recombining plasma inside it. With the 13-ks observation of the Following-up X-ray Telescope onboard the Einstein Probe, we give an overall X-ray picture of W28, one of the prototypical thermal composite SN

Tamitake Itoh, Yuko S. Yamamoto

Electromagnetic (EM) coupling between molecular exciton and plasmon has been studied using in Rayleigh scattering or extinction spectroscopy. However, evaluating EM coupling involving subradiant plasmon is challenging because this resonance does not manifest clearly in far-field spectra. In this study, we developed a method to evaluate such coupling using EM

Anthony Veit Berg, Ablai Forster, Tim Hansson, Alexandra J. Jernstedt

Hydroxyapatite (HA) on a magnesium (Mg) surface is studied using density functional theory, to help understand the effect of HA coating and alloying in the surfaces of Mg-based biodegradable implants. We determine the adsorption energies and structural changes of a single layer of HA on pure Mg(0001) and on sparsely calcium (Ca) or zinc (Zn) doped Mg(0001) a

Kang Lan, Xiangji Cai, Zhongxiao Man, Shijie Xie

The generation of exciton valley coherence typically requires linearly polarized (LP) light as an external coherent drive, whereas circularly polarized (CP) light fails to induce coherence. Here, we develop a unified, microscopically-grounded open-quantum-system framework within a five-level model incorporating bright-dark exciton interactions in monolayer W

Robin Spanier, Thorsten Hoeser, John Truckenbrodt, Felix Bachofer

The increasing use of marine spaces by offshore infrastructure, including oil and gas platforms, underscores the need for consistent, scalable monitoring. Offshore development has economic, environmental, and regulatory implications, yet maritime areas remain difficult to monitor systematically due to their inaccessibility and spatial extent. This study pres

Tianyu Yang, Gianluca Gubbiotti, Marco Madami, Haiming Yu

The concept of moiré superlattices has recently been introduced into the field of magnonics, enabling unprecedented control over spin-wave propagation and confinement in nanoscale magnonic devices. In this work, we report a numerical investigation on the nanocavity in a trilayer magnetic moiré superlattice structure consisting of antidot lattices. By tuning

Zhouwei Zhai, Min Yang, Jin Li

Faceted search acts as a critical bridge for navigating massive ecommerce catalogs, yet traditional systems rely on static rule-based extraction or statistical ranking, struggling with emerging vocabulary, semantic gaps, and a disconnect between facet selection and underlying retrieval. In this paper, we introduce GenFacet, an industrial-grade, end-to-end ge

Makoto R. Kikuchi, Yuria Kobayashi, Satoshi Takada

We investigate the rheology of a sheared granular gas composed of hard spheres with a velocity-dependent restitution coefficient. Using kinetic theory, we derive the shear viscosity and show that it exhibits an S-shaped dependence on the shear rate when the restitution coefficient switches between two values depending on the collision velocity. As a result,

Nitin Gupta, Vishal Pallagani, John A. Aydin, Biplav Srivastava

Generalized planning studies the construction of solution strategies that generalize across families of planning problems sharing a common domain model, formally defined by a transition function $γ: S \times A \rightarrow S$. Classical approaches achieve such generalization through symbolic abstractions and explicit reasoning over $γ$. In contrast, recent Tr

Alexandre Le Mercier, Chris Develder, Thomas Demeester

State space models (SSMs) like Mamba offer efficient alternatives to Transformer-based language models, with linear time complexity. Yet, their adversarial robustness remains critically unexplored. This paper studies the phenomenon whereby specific short input phrases induce a partial amnesia effect in such models, by irreversibly overwriting information in

David Pascual Solis, Alex Windey, Soumik Bandyopadhyay, Andrea Legramandi

Understanding how quantum systems transition from integrable to fully chaotic behavior remains a central open problem in physics. The Sachdev--Ye--Kitaev (SYK) model provides a paradigmatic framework for studying many-body chaos and holography, yet it captures only the strongly correlated limit, leaving intermediate regimes unexplored. Here, we investigate t

Nathaniel J. Wei, Adina Y. Fleisher, John W. Kurelek, Marcus N. Hultmark

Wind turbines operating in the atmospheric boundary layer are constantly exposed to time-varying flow conditions. These disturbances often occur on similar time scales to wind-turbine controllers, which may interfere with wind-farm control strategies that operate under steady-flow assumptions. This study aims to investigate the significance of such time vari

Daniel W. Boutros, Xin Liu, Marita Thomas, Edriss S. Titi

In this paper, we initiate the rigorous mathematical analysis of the elastic-viscous-plastic (EVP) sea-ice model, which was introduced in [E. C. Hunke and J. K. Dukowicz, J. Phys. Oceanogr., 27, 9 (1997), 1849-1867]. The EVP model is one of the standard and most commonly used dynamical sea-ice models. We study a regularized version of this model. In particul

Wenyu Xin, Chun-Ming Yip, Ken&#39;ichi Nomoto, Xianfei Zhang

Massive stars are significant sites for the weak s-process (ws-process). $^{22}$Ne and $^{16}$O are, respectively, the main neutron source and poison for the ws-process. In the metal-poor stars, the abundance of $^{22}$Ne is limited by the metallicity, so that the contribution of $^{22}$Ne($α$, n)$^{25}$Mg reaction on the s-process is weaker. Conversely, the

Sylvain Ribault

We review 2d CFT in the bootstrap approach, and sketch the known exactly solvable CFTs with no extended chiral symmetry: Liouville theory, (generalized) minimal models, limits thereof, and loop CFTs, including the $O(n)$, Potts and $PSU(n)$ CFTs. Exact solvability relies on local conformal symmetry, and on the existence of degenerate fields. We show how thes

Jeffrey S. Geronimo, Plamen Iliev

We define a class of Bernstein-Szegő measures on $\mathbb{R}^2$ and we establish their spectral properties, providing a natural extension of the one-dimensional theory. We also derive conditions involving finitely many moments, which are new in the two-dimensional setting, and which completely characterize these measures. A key ingredient in the theory on th

Saikat Dutta, Biplab Banerjee, Hamid Rezatofighi

Open-Vocabulary Semantic Segmentation (OVSS) assigns pixel-level labels from an open set of text-defined categories, demanding reliable generalization to unseen classes at inference. Although modern vision-language models (VLMs) support strong open-vocabulary recognition, their representations learned through global contrastive objectives remain suboptimal f

Luc Cote, Andy Sun

We propose a market design for real-time electricity markets that utilizes a two-layered dispatch mechanism to systematically incorporate carbon accounting into grid operations. In this mechanism, ``dispatch'', the centralized allocation of generation resources to meet system load, is executed via a hierarchical structure where the first layer minimizes fina

Di Zhang

This paper computationally investigates whether thought requires a language-like format, as posited by the Language of Thought (LoT) hypothesis. We introduce the ``AI Private Language'' thought experiment: if two artificial agents develop an efficient, inscrutable communication protocol via multi-agent reinforcement learning (MARL), and their performance dec

Vasco Xu, Brian Chen, Eric J. Gonzalez, Andrea Colaço

Mid-air gestures in Extended Reality (XR) often cause fatigue and imprecision. Surface-based interactions offer improved accuracy and comfort, but current egocentric vision methods struggle due to hand tracking challenges and unreliable surface plane estimation. We introduce SurfaceXR, a sensor fusion approach combining headset-based hand tracking with smart

Akihiro Miyagawa

We show that any $L^2$-bounded rational function in free semicircular random variables is a bounded operator, which implies the coincidence of the usual spectrum and $L^2$-spectrum for rational functions. Based on this observation, we also compute the spectra of several polynomials in free circular random variables.

Matthew Flathers, Griffin Smith, Julian Herpertz, Zhitong Zhou

Generative video models are increasingly capable of producing complex depictions of mental health experiences, yet little is known about how these systems represent conditions like depression. This study characterizes how OpenAI's Sora 2 generative video model depicts depression and examines whether depictions differ between the consumer App and developer AP

Juan Orendain, Ivan Sanchez, José A. Zapata

We introduce homotopy lattice gauge fields (HLGFs), a version of gauge fields over a discretized base, based on a notion of higher parallel transport that enriches the usual parallel transport along paths on a lattice to also consider higher dimensional paths. Higher dimensional data keeps information about the parallel transport along homotopies of curves.

Fujian Jia, Jiwen Gu, Cheng Lu, Dezhi Zhao

Understanding mechanistic relationships among genes and their impacts on biological pathways is essential for elucidating disease mechanisms and advancing precision medicine. Despite the availability of extensive molecular interaction and pathway data in public databases, integrating heterogeneous knowledge sources and enabling interpretable multi-step reaso

Arthur C. B. de Oliveira, Ruigang Wang, Ian R. Manchester, Eduardo D. Sontag

This note establishes a theoretical framework for finding (potentially overparameterized) approximations of a function on a compact set with a-priori bounds for the generalization error. The approximation method considered is to choose, among all functions that (approximately) interpolate a given data set, one with a minimal Lipschitz constant. The paper est

Alyssa Chan, Taein Kwon, Andrew Zisserman

Fingerspelling is a critical component of British Sign Language (BSL), used to spell proper names, technical terms, and words that lack established lexical signs. Fingerspelling recognition is challenging due to the rapid pace of signing and common letter omissions by native signers, while existing BSL fingerspelling datasets are either small in scale or tem

Dong Yin, Takeshi Miki, Vladislav Lesnichenko, Vasyl Gural

Portfolio backtesting is the primary tool for evaluating investment strategies before deployment, yet practitioners implicitly assume that different engines produce identical results for the same strategy. we formalise implementation risk, the systematic divergence in backtested portfolio metrics arising solely from differences in how engines implement the s

Maximilian Schweikart, Linnea Grans-Samuelsson, Aleks Kissinger, Benjamin Rodatz

Decoding a quantum error correction code is generally NP-hard, but corrections must be applied at a high frequency to suppress noise successfully. Matchable codes, like the surface code, exhibit a special structure that makes it possible to efficiently, approximately solve the decoding problem through minimum-weight perfect matching (MWPM). However, this eff

Omar Eidous, Noura Almasri

This papers presents a generalization of the Weitzman overlapping coefficient, originally defined for two probability density functions, to a setting involving k independent distributions, denoted by Delta. To estimate this generalized coefficient, we develop nonparametric methods based on kernel density estimation using k independent random samples (k>=2).

Amarda Shehu

Most AI literacy courses for non-technical undergraduates emphasize conceptual breadth over technical depth. This paper describes UNIV 182, a prerequisite-free course at George Mason University that teaches undergraduates across majors to understand, use, evaluate, and build AI systems. The course is organized around five mechanisms: (1) a unifying conceptua

Marcelo C. Vicentin, Michael A. Strauss, Laerte Sodré, Robert M. Yates

We use the L-GALAXIES semi-analytic model to investigate the evolution of Brightest Cluster Galaxies (BCGs) found in clusters at $\rm z \sim 0$. BCGs are typically located in the central region of galaxy clusters, near the bottom of the potential well, exposing them to different environmental conditions compared to galaxies in the cluster outskirts or in the

Yirang Park, David E. Bernal Neira

Conceptual process design is a crucial aspect of chemical engineering that involves process synthesis. Mixed-integer nonlinear programming is a powerful framework for modeling such design problems by combining discrete and continuous variables; however, the combinatorial complexity of discrete choices, coupled with nonlinearities, presents challenging monoli

Queenie Luo, Gary King, Michael Puett, Michael D. Smith

We address a not-widely-recognized subset of exploratory search, where a user sets out on a typically long "search quest" for the perfect wedding dress, overlooked research topic, killer company idea, etc. The first few outputs of current large language models (LLMs) may be helpful but only as a start, since the quest requires learning the search space and e

India Bhalla-Ladd, Eleanor March, James Owen Weatherall

We discuss and then resolve a tension between how physicists treat gauge bosons and the celebrated "Wu-Yang dictionary", which identifies particle physics terminology with that of principal bundles and principal connections. We show how this tension leads to an interpretative choice that is not widely discussed in the physics literature. We then show how the

Oishi Banerjee, Sung Eun Kim, Alexandra N. Willauer, Julius M. Kernbach

Everyday photographs taken with ordinary cameras are already widely used in telemedicine and other online health conversations, yet no comprehensive benchmark evaluates whether vision-language models can interpret their medical content. Analyzing these images requires both fine-grained natural image understanding and domain-specific medical reasoning, a comb

Sheng Lu, Hao Chen, Rui Yin, Juyan Ba

Recent vision-language models (VLMs) have shown strong generalization and multimodal reasoning abilities in natural domains. However, their application to medical diagnosis remains limited by the lack of comprehensive and structured datasets that capture real clinical workflows. To advance the development of VLMs for clinical applications, particularly in ga

Tianlong Wang, Pinqiao Wang, Weili Shi, Sheng li

Large language models (LLMs) with advanced cognitive capabilities are emerging as agents for various reasoning and planning tasks. Traditional evaluations often focus on specific reasoning or planning questions within controlled environments. Recent studies have explored travel planning as a medium to integrate various verbal reasoning tasks into real-world

Zenan Li, Zhaoyu Li, Kaiyu Yang, Xiaoxing Ma

Mathematical reasoning demands two critical, complementary skills: constructing rigorous proofs for true statements and discovering counterexamples that disprove false ones. However, current AI efforts in mathematics focus almost exclusively on proof construction, often neglecting the equally important task of finding counterexamples. In this paper, we addre

G. F. Moreira, A. Lykholat, R. G. Dias, A. M. Marques

This paper focuses on the quantum state transfer in a one-dimensional (1D) high-root topological insulator (HRTI) with an arbitrary number of domains. We present the possibility of having multiple transfer processes in the same model due to the existence of various edge states in distinct energy gaps, which may benefit recent (de)multiplexing technologies. W

Ivan Reyes-Amezcua, Francisco Lopez-Tiro, Clément Larose, Christian Daul

The reliability of artificial intelligence (AI) in medical imaging critically depends on its robustness to heterogeneous and corrupted images acquired with diverse devices across different hospitals which is highly challenging. Therefore, this paper introduces FedAgain, a trust-based Federated Learning (Federated Learning) strategy designed to enhance robust

Nikola Machacova, Biplab Sanyal

In this paper, we examine the properties of spin-polarized interfaces consisting of single-molecule magnet bis(cyclopentadienyl)cobalt(II) (cobaltocene) and two-dimensional magnetic materials, semiconducting CrI$_3$ and metallic Fe$_3$GeTe$_2$, using first-principles density functional theory based calculations. Our calculated adsorption energies indicate th

Kristen M. Edwards, Maxwell Bauer, Claire Jacquillat, A. John Hart

This work examines how AI, especially agentic systems, is being adopted in engineering and manufacturing workflows, what value it provides today, and what is needed for broader deployment. This is an exploratory and qualitative state-of-practice study grounded in over 30 interviews across four stakeholder groups (large enterprises, small/medium firms, AI dev

Luise Ge, Daniel Halpern, Gregory Kehne, Yevgeniy Vorobeychik

Most social choice rules assume access to full rankings, while current alignment practice -- despite aiming for diversity -- typically treats voters as anonymous and comparisons as independent, effectively extracting only about one bit per voter. Motivated by this gap, we study social choice under an extreme communication budget in the linear social choice m

Issam Mahraj, Mossab Oublal, Andrzej Ptok

The orthorhombic crystal structure of the MgSnN$_2$ compound with Pna2$_1$ symmetry has been investigated as a low-cost, non-toxic material for photovoltaic (PV) applications using density functional theory (DFT) and spectroscopic limited maximum efficiency (SLME) calculations. A detailed analysis of the electronic and optical properties was performed using

Tia Miceli, Erik Gottschalk, Donovan Tooke, Evan Milton

Reliable, high-intensity operation of the Fermilab Accelerator Complex is critical to the success of the Long-Baseline Neutrino Facility and Deep Underground Neutrino Experiment. We describe the requirements and infrastructure necessary to support routine use of artificial intelligence and machine learning (AI/ML) in the accelerator control system. Three cap

Anik Burman, Sayantan Choudhury, Debangan Dey

Shuffled regression concerns settings in which covariates and responses are observed without their correct pairing. In dependent-data problems, a second form of missing correspondence can arise when responses are also detached from the latent temporal, spatial, or geometric domain that induces their dependence structure. We study regression under this joint