October 2025 arXiv papers — page 136

Kianoosh Shokri, Lucia Moura, Brett Stevens

Two projective (affine) planes with the same point sets are orthogoval if the common intersection of any two lines, one from each, has size at most two. The existence of a pair of orthogoval projective planes has been proven and published independently many times. A strength-$t$ covering array, denoted by CA$(N; t, k, v)$, is an $N \times k$ array over a $v$

Junhyuk Son, Young-Wook Lee, Chul Chung, Seunghyun Park

Supernova (SN) cosmology is based on the key assumption that the luminosity standardization process of Type Ia SNe remains invariant with progenitor age. However, direct and extensive age measurements of SN host galaxies reveal a significant (5.5{\sigma}) correlation between standardized SN magnitude and progenitor age, which is expected to introduce a serio

Yizhi Li, Yanyan Lu, Jianxin Zhong, Lijun Meng

This paper demonstrates a pathway to topological superconductivity in monolayer triangular lattices through long-range pairing without requiring spin-orbit coupling and magnetic field, contrasting conventional frameworks reliant on superconductivity and spin-orbit coupling and time-reversal symmetry (TRS) breaking. Berry curvature analysis reveals spontaneou

Doyoon Park, Xin Deng, Jie Deng

Silica (SiO2) is fundamental to both industrial technology and planetary science, yet the phase relations of its high-pressure polymorphs remain poorly constrained. Here, we develop two machine learning potentials (MLPs) for SiO2 that faithfully represent the SCAN and PBEsol exchange-correlation functionals over a wide temperature (1000-10000 K) and pressure

Shizhuo Li, Unay Dorken Gallastegi, Shohei Fujita, Yuting Chen

Purpose: To improve the accuracy of multiparametric estimation, including myelin water fraction (MWF) quantification, and reduce scan time in 3D-QALAS by optimizing sequence parameters, using a self-supervised multilayer perceptron network. Methods: We jointly optimize flip angles, T2 preparation durations, and sequence gaps for T1 recovery using a self-supe

Anej Svete, Ashish Sabharwal

Masked diffusion models (MDMs) for text offer a compelling alternative to traditional autoregressive language models. Parallel generation makes them efficient, but their computational capabilities and the limitations inherent in their parallelism remain largely unexplored. To this end, we characterize what types of reasoning problems MDMs can provably solve

Renlei Jiang, Chuanhou Gao, Denis Dochain

In the field of molecular computation based on chemical reaction networks (CRNs), leveraging parallelism to enable coupled mass-action systems (MASs) to retain predefined computational functionality has been a research focus. MASs exhibiting this property are termed composable. This paper investigates the structural conditions under which two MASs are compos

Yuxuan Huang, Kangzhong Wang, Eugene Yujun Fu, Grace Ngai

Backchannels are subtle listener responses, such as nods, smiles, or short verbal cues like "yes" or "uh-huh," which convey understanding and agreement in conversations. These signals provide feedback to speakers, improve the smoothness of interaction, and play a crucial role in developing human-like, responsive AI systems. However, the expression of backcha

Surya Tejaswi Yerramsetty, Almas Fathimah

Clinical trials are central to medical progress because they help improve understanding of human health and the healthcare system. They play a key role in discovering new ways to detect, prevent, or treat diseases, and it is essential that clinical trials include participants with appropriate and diverse medical backgrounds. In this paper, we propose a syste

Zhuoyuan Wang, Tongyao Jia, Pharuj Rajborirug, Neeraj Ramesh

Ensuring safe autonomous driving in the presence of occlusions poses a significant challenge in its policy design. While existing model-driven control techniques based on set invariance can handle visible risks, occlusions create latent risks in which safety-critical states are not observable. Data-driven techniques also struggle to handle latent risks becau

Ganlin Chen, Deepak V Pillai, Yufeng Zheng, Liang Qi

Metastable alloys, such as $\beta$-phase titanium (Ti) alloys with a body-centered cubic (BCC) lattice, can exhibit exceptional mechanical properties through the interplay of multiple deformation mechanisms -- diffusionless phase transformations, deformation twinning, and conventional dislocation slip. However, understanding how these mechanisms compete or c

Andrey Bryutkin, Youssef Marzouk

Lattice field theories are fundamental testbeds for computational physics; yet, sampling their Boltzmann distributions remains challenging due to multimodality and long-range correlations. While normalizing flows offer a promising alternative, their application to large lattices is often constrained by prohibitive memory requirements and the challenge of mai

Nuntipat Narkthong, Yukui Luo, Xiaolin Xu

The run-time electromagnetic (EM) emanation of microprocessors presents a side-channel that leaks the confidentiality of the applications running on them. Many recent works have demonstrated successful attacks leveraging such side-channels to extract the confidentiality of diverse applications, such as the key of cryptographic algorithms and the hyperparamet

Bo-Han Wu, Mahmoud Jalali Mehrabad, Mengjie Yu, Dirk Englund

Accurate evaluation of nonlinear photonic integrated circuits requires separating input and output coupling efficiencies (i.e., $\eta_1$ and $\eta_2$), yet the conventional linear-transmission calibration method recovers only their product (i.e., $\eta_1\,\eta_2$) and therefore introduces systematic bias when inferring on-chip performance from off-chip data.

Dezhi Luo, Qingying Gao, Hokin Deng

Spatial world models, representations that support flexible reasoning about spatial relations, are central to developing computational models that could operate in the physical world, but their precise mechanistic underpinnings are nuanced by the borrowing of underspecified or misguided accounts of human cognition. This paper revisits the simulation versus r

Zicong Zhou, Baihan Zhao, Andreas Mang, Guojun Liao

This paper introduces VPreg, a novel diffeomorphic image registration method. This work provides several improvements to our past work on mesh generation and diffeomorphic image registration. VPreg aims to achieve excellent registration accuracy while controlling the quality of the registration transformations. It ensures a positive Jacobian determinant of t

Jingyu Song, Zhenxin Li, Shiyi Lan, Xinglong Sun

Benchmarking autonomous driving planners to align with human judgment remains a critical challenge, as state-of-the-art metrics like the Extended Predictive Driver Model Score (EPDMS) lack context awareness in nuanced scenarios. To address this, we introduce DriveCritic, a novel framework featuring two key contributions: the DriveCritic dataset, a curated co

Zheng Wang, Beichen Ruan, Zhuoheng Li, Shu-Shen Lyu

The quest for robust, intrinsically magnetic topological materials exhibiting the quantum anomalous Hall (QAH) effect is a central challenge in condensed matter physics and the application of revolutionary electronics. However, progress has been hampered by the limited number of candidate materials, which often suffer from poor stability and complex synthesi

Ruoyu Sun, Da Song, Jiayang Song, Yuheng Huang

As Large Language Models (LLMs) continue to revolutionize Natural Language Processing (NLP) applications, critical concerns about their trustworthiness persist, particularly in safety and robustness. To address these challenges, we introduce TRUSTVIS, an automated evaluation framework that provides a comprehensive assessment of LLM trustworthiness. A key fea

Xijun Wang, Tanay Sharma, Achin Kulshrestha, Abhimitra Meka

As AR/VR technologies become integral to daily life, there's a growing need for AI that understands human social dynamics from an egocentric perspective. However, current LLMs often lack the social awareness to discern when to intervene as AI assistant. This leads to constant, socially unaware responses that may disrupt natural conversation and negatively im

Shrunal Pothagoni, Dylan Miley, Tyrus Berry, Jeremy K. Mason

Convolutional neural networks are increasingly being used to analyze and classify material microstructures, motivated by the possibility that they will be able to identify relevant microstructural features more efficiently and impartially than human experts. While up to now convolutional neural networks have mostly been applied to light optimal microscopy an

Mingda Li, Xinyu Li, Weinan Zhang, Longxuan Ma

Uncertainty Quantification (UQ) is a promising approach to improve model reliability, yet quantifying the uncertainty of Large Language Models (LLMs) is non-trivial. In this work, we establish a connection between the uncertainty of LLMs and their invariance under semantic-preserving intervention from a causal perspective. Building on this foundation, we pro

Victor Olaiya, Adwait Nadkarni

Tools focused on cryptographic API misuse often detect the most basic expressions of the vulnerable use, and are unable to detect non-trivial variants. The question of whether tools should be designed to detect such variants can only be answered if we know how developers use and misuse cryptographic APIs in the wild, and in particular, what the unnatural usa

Kawon Han, Kaitao Meng, Alexandra Chatzicharistou, Christos Masouros

Orthogonal frequency division multiplexing (OFDM) is one of the most widely adopted waveforms for integrated sensing and communication (ISAC) systems, owing to its high spectral efficiency and compatibility with modern communication standards. This paper investigates the sensing performance of OFDM-based ISAC for multi-target delay (range) estimation under s

Yifu Ding, Ruicheng Ao, Pablo Duenas-Martinez, Thomas Magnanti

Many industrial sites and digital logistics platforms rely on diesel-powered light-duty trucks to transport workers and small-scale facilities, which results in a significant amount of greenhouse gas emissions (GHGs). To address this, we develop a robust model for planning charging infrastructure to electrify light-duty trucks at industrial sites. The model

Jingche Chen, Han Hong

We establish curvature obstruction theorems for manifolds with boundary. Our main theorems show that, for dimensions up to 7, a topologically nontrivial compact manifold with boundary cannot have a metric of positive $m$-intermediate curvature if the boundary is $m$-convex, and some rigidity result holds if $m$-intermediate curvature is nonnegative. This non

Te Li, Le Zhang

In this paper, we investigate the long-time behavior of a passive scalar advected by a parallel shear flow in an infinite cylinder with unbounded cross section, in the regime where the viscosity coefficient satisfies $\nu \ll 1$, and in arbitrary spatial dimension. Under the assumption of an infinite cylinder, that is, $x \in \mathbb{R}$, the corresponding F

Shihan Guo, Ping Lin, Yifan Wang, Xiaohe Yue

We propose a parallelizable partitioned (loosely coupled) scheme for fluid structure interaction (FSI) problems, specifically designed for efficient computation in modern engineering simulations. The FSI problem under consideration involves an incompressible viscous fluid, governed by the Navier--Stokes equations, with a thick linear elastic structure. The s

Yingchen Zhang, Ruqing Zhang, Jiafeng Guo, Wenjun Peng

Generative retrieval (GR) is an emerging paradigm that leverages large language models (LLMs) to autoregressively generate document identifiers (docids) relevant to a given query. Prior works have focused on leveraging the generative capabilities of LLMs to improve GR, while overlooking that their reasoning capabilities could likewise help. This raises a key

Aaradhya Pandey, Arnab Auddy, Haolin Zou, Arian Maleki

Machine unlearning seeks to efficiently remove the influence of selected data while preserving generalization. Significant progress has been made in low dimensions $(p \ll n)$, but high dimensions pose serious theoretical challenges as standard optimization assumptions of $\Omega(1)$ strong convexity and $O(1)$ smoothness of the per-example loss $f$ rarely h

Ningkang Peng, Yuzhe Mao, Yuhao Zhang, Linjin Qian

Out-of-Distribution (OOD) detection is a cornerstone for the safe deployment of AI systems in the open world. However, existing methods treat OOD detection as a binary classification problem, a cognitive flattening that fails to distinguish between semantically close (Near-OOD) and distant (Far-OOD) unknown risks. This limitation poses a significant safety b

Peter J. Forrester, Anas A. Rahman, Bo-Jian Shen

We present some review material relating to the topic of optimal asymptotic expansions of correlation functions and associated observables for $\beta$ ensembles in random matrix theory. We also give an introduction to a related line of study that we are presently undertaking.

Wugeng Zheng, Guohou Shan

Online freelance marketplaces, a rapidly growing part of the global labor market, are creating a fair environment where professional skills are the main factor for hiring. While these platforms can reduce bias from traditional hiring, the personal information in user profiles raises concerns about ongoing discrimination. Past studies on this topic have mostl

Ruoxia Chen, Kai Yang, Morten M. Smedskjaer, N. M. Anoop Krishnan

The accuracy of molecular simulations is fundamentally limited by the interatomic potentials that govern atomic interactions. Traditional potential development, which relies heavily on ab initio calculations, frequently struggles to reproduce the experimentally observed properties that govern real material behavior. To address this challenge, we present a ma

Xingrui Zhuo, Jiapu Wang, Gongqing Wu, Zhongyuan Wang

Inductive Knowledge Graph Reasoning (KGR) aims to discover facts in open-domain KGs containing unknown entities and relations, which poses a challenge for KGR models in comprehending uncertain KG components. Existing studies have proposed Knowledge Graph Foundation Models (KGFMs) that learn structural invariances across KGs to handle this uncertainty. Recent

Hongyuan Lin, Yi Liu, Shizhao Wang, Chun-Hian Lee

The random Fourier method (RFM) is widely employed for synthetic turbulence due to its mathematical clarity and simplicity. However, deviations remain between prescribed inputs and synthetic results, and the origin of these errors has not been fully evaluated. This study aims to systematically evaluate the effects of spectral coefficient calibration, grid co

Rishi Patel, Emmanouil Pountourakis, Samuel Taggart

This paper considers behavior-based price discrimination in the repeated sale of a non-durable good to a single long-lived buyer, by a seller without commitment power. We assume that there is a mixed population of forward-looking ``sophisticated'' buyers and myopic ``naive'' buyers. We investigate the impact of these dynamics on the seller's ability to learn

Aditya Puttaparthi Tirumala

Marketing Mix Modeling (MMM) estimates the impact of marketing activities on business outcomes such as sales or revenue. Traditional MMM approaches rely on linear regression or Bayesian hierarchical models that assume channel independence and struggle to capture temporal dynamics and non-linear saturation. DeepCausalMMM addresses these limitations by combini

Qiaomei Zhao, Xingdong Zhao, Jieli Qin

Due to its peculiar superfluid-crystal duality feature, supersolid has received great research interest. Recently, researchers have paid much attention to its elastic response properties; however, the inelastic deformation has barely been explored. In this work, we study the transition from elastic to inelastic deformation of a dipolar supersolid Bose-Einste

Margarida Pereira, Guillermo Currás-Lorenzo, Mateus Araújo

Numerical security proofs based on conic optimization are known to deliver optimal secret-key rates, but so far they have mostly assumed that the emitted states are fully characterized. In practice, this assumption is unrealistic, since real devices inevitably suffer from imperfections and side channels that are extremely difficult to model in detail. Here,

Yi Zuo, Zitao Wang, Lingling Li, Xu Liu

Text-to-image (T2I) diffusion models have recently demonstrated significant progress in video editing. However, existing video editing methods are severely limited by their high computational overhead and memory consumption. Furthermore, these approaches often sacrifice visual fidelity, leading to undesirable temporal inconsistencies and artifacts such as bl

Michael P. Friedlander, Sharvaj Kubal, Yaniv Plan, Matthew S. Scott

Small regularizers can preserve linear programming solutions exactly. This paper provides the first average-case analysis of exact regularization: with a standard Gaussian cost vector and fixed constraint set, bounds are established for the probability that exact regularization succeeds as a function of regularization strength. Failure is characterized via t

Mark Koch, Agustín Borgna, Craig Roy, Alan Lawrence

Linear types enforce no-cloning and no-deleting theorems in functional quantum programming. However, in imperative quantum programming, they have not gained widespread adoption. This work aims to develop a quantum type system that combines ergonomic linear typing with imperative semantics and maintains safety guarantees. All ideas presented here have been im

J. Gregory Pauloski, Kyle Chard, Ian T. Foster

As data-driven methods, artificial intelligence (AI), and automated workflows accelerate scientific tasks, we see the rate of discovery increasingly limited by human decision-making tasks such as setting objectives, generating hypotheses, and designing experiments. We postulate that cooperative agents are needed to augment the role of humans and enable auton

Shuai Fu, Jian Zhou, Qi Chen, Huang Jing

Diffusion probabilistic models (DPMs) have demonstrated remarkable progress in generative tasks, such as image and video synthesis. However, they still often produce hallucinated samples (hallucinations) that conflict with real-world knowledge, such as generating an implausible duplicate cup floating beside another cup. Despite their prevalence, the lack of

Chen Zheng, Yuhang Cai, Deyi Liu, Jin Ma

Modern large language models leverage Mixture-of-Experts (MoE) architectures for efficient scaling, but face a critical challenge: functionally similar experts are often selected simultaneously, creating redundant computation and limiting effective model capacity. Existing auxiliary balance loss methods improve token distribution but fail to address the unde

Tri Minh-Triet Pham, Diego Elias Costa, Weiyi Shang, Jinqiu Yang

Obstacle detection is crucial to the operation of autonomous driving systems, which rely on multiple sensors, such as cameras and LiDARs, combined with code logic and deep learning models to detect obstacles for time-sensitive decisions. Consequently, obstacle detection latency is critical to the safety and effectiveness of autonomous driving systems. Howeve

Yubo Zhang, Xiao-Yang Liu, Xiaodong Wang

We develop an unsupervised deep learning framework for real-time scalable and generalizable downlink beamforming in multi-user multiple-input single-output (MU-MISO) systems. The proposed semi-amortized lifted learning-to-optimize (SALLO) framework employs a multi-layer Transformer to iteratively refine an auxiliary variable and the beamformer solution, with

Hoda Kalabizadeh, Ludovica Griffanti, Pak-Hei Yeung, Ana I. L. Namburete

Deep learning models for medical image segmentation often struggle when deployed across different datasets due to domain shifts - variations in both image appearance, known as style, and population-dependent anatomical characteristics, referred to as content. This paper presents a novel unsupervised domain adaptation framework that directly addresses domain

Evgeni Grishin, Isobel M. Romero-Shaw, Alessandro A. Trani

A significant fraction of gravitational-wave mergers are expected to be eccentric in the Laser-Interferometer-Space-Antenna (LISA) frequency band, $10^{-4} - 10^{-1}$ Hz. Several LIGO-Virgo-KAGRA events show potential hints of residual eccentricity at 10 Hz, pointing to dynamical or triple origins for part of the population, where von-Zeipel-Lidov-Kozai osci

Guofang Wei, Ling Xiao

In this paper, we prove that the first eigenfunction of the Laplacian for a horo-convex domain $\Omega\subset\mathbb H^n$ is super log-concave when $\text{diam}(\Omega)$ is not large. Our result is optimal in the sense that there are counterexamples %are constructed for the cases when $\Omega$ is not horo-convex or when $\text{diam}(\Omega)$ is large respect

Albert M. Fisher, Marina Talet

We classify the invariant Borel measures for adic transformations, where the alphabets have bounded size and the measure is finite on the path space of some sub-Bratteli diagram. We develop a nonstationary version of the Frobenius normal form for a reducible matrix, present an appropriate nonstationary notion of distinguished eigenvector, and prove a nonstat

Benjamin Wehmeyer, Andrés Yagüe López, Benoit Côté, Maria K. Pető

We run a three-dimensional Galactic chemical evolution (GCE) model to follow the propagation of Mn-53 from supernovae of type Ia (SNIa), Fe-60 from core-collapse supernovae (CCSNe), Hf-182 from intermediate mass stars (IMSs), and Pu-244 from neutron star mergers (NSMs) in the Galaxy. We compare the GCE of these short-lived radioactive isotopes (SLRs) to rece

Mauricio Che, Raquel Perales, Christina Sormani

The intrinsic timed-Hausdorff distance between timed-metric spaces, first introduced by Sakovich--Sormani, yields a weak notion of convergence for space-times. In this paper we prove a compactness theorem for the intrinsic timed-Hausdorff convergence of timed-metric spaces using timed-Fr\'echet maps. Our proof introduces the notion of "addresses" and provide

Kaixuan Yang, Wei Xiang, Zhenshuai Chen, Tong Jin

Infrared and visible image fusion aims to integrate complementary information from co-registered source images to produce a single, informative result. Most learning-based approaches train with a combination of structural similarity loss, intensity reconstruction loss, and a gradient-magnitude term. However, collapsing gradients to their magnitude removes di

Evgeni Grishin, Isobel M. Romero-Shaw, Alessandro A. Trani

A significant fraction of compact-object mergers in galactic nuclei are expected to be eccentric in the Laser Interferometer Space Antenna (LISA) frequency sensitivity range, $10^{-4} - 10^{-1}\ \rm Hz$. Several compact binaries detected by the LIGO-Virgo-KAGRA Collaboration may retain hints of residual eccentricity at $\sim 10$ Hz, suggesting dynamical or t

Adil M. Bagirov, Ramiz M. Aliguliyev, Nargiz Sultanova, Sona Taheri

Finding "true" clusters in a data set is a challenging problem. Clustering solutions obtained using different models and algorithms do not necessarily provide compact and well-separated clusters or the optimal number of clusters. Cluster validity indices are commonly applied to identify such clusters. Nevertheless, these indices are typically relativ

Chelsea Adelman, Steph Sallum, Matthew De Furio, Josh Eisner

Kernel phase interferometry (KPI) is a post-processing technique that treats a conventional telescope as an interferometer by accurately modeling a telescope pupil as an array of virtual subapertures. KPI provides angular resolution within the diffraction limit by eliminating instrumental phase errors to first order. It has been successfully demonstrated to

Thomas W. Mitchel, Hyunwoo Ryu, Vincent Sitzmann

In this paper, we identify that the key criterion for determining whether a model is truly capable of novel view synthesis (NVS) is transferability: Whether any pose representation extracted from one video sequence can be used to re-render the same camera trajectory in another. We analyze prior work on self-supervised NVS and find that their predicted poses

Adnan Jafar, Xun Jia

Current radiation therapy treatment planning is limited by suboptimal plan quality, inefficiency, and high costs. This perspective paper explores the complexity of treatment planning and introduces Human-Centric Intelligent Treatment Planning (HCITP), an AI-driven framework under human oversight, which integrates clinical guidelines, automates plan generatio

Maria Girardi, Ralph Howard

Let $U$ be an open set in $\mathbb{R}^d$. A continuous function $f\colon U \to \mathbb{R}$ is strongly nowhere differentiable if and only if for each $\gamma\in(0,1]$ and for each unit speed $C^{1,\gamma}$ curve $c\colon [a,b] \to U$, the composition $f\circ c \colon [a,b] \to \mathbb{R}$ is nowhere differentiable on $(a,b)$. For bounded $U$, let $\overline

Anupam Nayak, Tong Yang, Osman Yagan, Gauri Joshi

Reverse Kullback-Leibler (KL) divergence-based regularization with respect to a fixed reference policy is widely used in modern reinforcement learning to preserve the desired traits of the reference policy and sometimes to promote exploration (using uniform reference policy, known as entropy regularization). Beyond serving as a mere anchor, the reference pol

Wei-qi Xia, Xiao-ting Zheng, Xiao-wei Chen, Gui-hua Chen

Quantum droplets, stabilized by beyond-mean-field effects, represent a novel state of matter in quantum many-body systems. While previous studies have focused primarily on dipolar and contact-interacting systems, quadrupolar condensates remain relatively unexplored. In this work, we explore the formation, structural properties, and dynamical behaviors of qua

Jitendra Sharma, Arthur Carvalho, Suman Bhunia

Rapid advancement in generative AI and large language models (LLMs) has enabled the generation of highly realistic and contextually relevant digital content. LLMs such as ChatGPT with DALL-E integration and Stable Diffusion techniques can produce images that are often indistinguishable from those created by humans, which poses challenges for digital content

Tooba Aamir, Marthie Grobler, Giovanni Russello

Within the cyber-physical-social-climate nexus, all systems are deeply interdependent: cyber infrastructure facilitates communication, data processing, and automation across physical systems (such as power grids and networks), while social infrastructure provides the human capital and societal norms necessary for the system's functionality. Any disruption wi

Valter Borges, Matheus Andrade Ribeiro de Moura Horácio, João Paulo dos Santos

In this article, we investigate a gradient almost Ricci soliton with harmonic Weyl tensor. We first prove that its Ricci tensor has at most three distinct eigenvalues of constant multiplicities in a neighborhood of a regular point of the potential function. Then, we classify those with exactly two distinct eigenvalues. It is worth mentioning that the case wi

Colby Schimelfenig, Federico Serrano, Corey Halverson, Annesh Mukhopadhyay

Self-trapping is a hallmark phenomenon of nonlinear dynamics. It has significant applications in modern physics, including band structure engineering, phase transition dynamics, quantum metrology, and more. Dilute-gas Bose-Einstein condensates (BECs), in which self-trapping can arise from interatomic interactions, are a prime testbed for probing nonlinear dy

Hariharan Ramasubramanian, Alvaro Vazquez-Mayagoitia, Ganesh Sivaraman, Atul C. Thakur

Machine learning interatomic potentials (MLIPs) have revolutionized the modeling of materials and molecules by directly fitting to ab initio data. However, while these models excel at capturing local and semi-local interactions, they often prove insufficient when an explicit and efficient treatment of long-range interactions is required. To address this limi

Yee Man Choi, Xuehang Guo, Yi R. Fung, Qingyun Wang

Large Language Models (LLMs) have emerged as powerful assistants for scientific writing. However, concerns remain about the quality and reliability of the generated text, including citation accuracy and faithfulness. While most recent work relies on methods such as LLM-as-a-Judge, the reliability of LLM-as-a-Judge alone is also in doubt. In this work, we ref

Ankit Goyal, Hugo Hadfield, Xuning Yang, Valts Blukis

Vision-Language-Action models (VLAs) hold immense promise for enabling generalist robot manipulation. However, the best way to build them remains an open question. Current approaches often add complexity, such as modifying the existing vocabulary of a Vision-Language Model (VLM) with action tokens or introducing special action heads. Curiously, the simplest

Tao Wang, Yu Shi

We investigate the adiabatic elimination of fast variables in relativistic stochastic mechanics, which is analyzed by using the equation of motion and the distribution function, with relativistic corrections explicitly derived. A new dimensionless parameter is introduced to characterize the timescale. The adiabatic elimination is compared with the path integ

Muhammad Faraz Ul Abrar, Nicolò Michelusi, Erik G. Larsson

Classical optimization theory deals with fixed, time-invariant objective functions. However, time-varying optimization has emerged as an important subject for decision-making in dynamic environments. In this work, we study the problem of learning from streaming data through a time-varying optimization lens. Unlike prior works that focus on generic formulatio

Shreya Agrawal, Mohammed Alewi Hassen, Emmanuel Asiedu Brempong, Boris Babenko

Precipitation nowcasting, which predicts rainfall up to a few hours ahead, is a critical tool for vulnerable communities in the Global South frequently exposed to intense, rapidly developing storms. Timely forecasts provide a crucial window to protect lives and livelihoods. Traditional numerical weather prediction (NWP) methods suffer from high latency, low

Pranjal Dutta, Vladimir Lysikov

Border complexity captures functions that can be approximated by low-complexity ones. Debordering is the task of proving an upper bound on some non-border complexity measure in terms of a border complexity measure, thus getting rid of limits. Debordering lies at the heart of foundational complexity theory questions relating Valiant's determinant versus perma

Jesse Griff-McMahon, Christopher A. Walsh, Vicente Valenzuela-Villaseca, Sophia Malko

Strong magnetic fields are naturally self-generated in high-power, laser-solid interactions through the Biermann-battery mechanism. This work experimentally characterizes the 3D location and strength of these fields, rather than path-integrated quantities, through multi-view proton radiography and tomographic inversion on the OMEGA laser. We infer magnetic f

Marek Maciaszek, Bartłomiej Baur

Optically active defects in hexagonal boron nitride (hBN) are promising candidates for active components in emerging quantum technologies, such as single-photon emitters and spin centers. However, further progress in hBN-based quantum technologies requires a deeper understanding of the physics and chemistry of hBN defects. In this work, we employ ab initio c

Patrick Müller, Kristian König, Emily Burbach, Gordon W. F. Drake

We report measurements and theoretical calculations of the fine-structure splittings in all three $1s2s\,^3\!S_1\rightarrow\,1s2p\,^3\!P_{0,1,2}$ transitions in the heliumlike systems of the isotopes $^{12,13,14}$C. The metastable triplet state was efficiently populated in an electron beam ion source and the C$^{4+}$ ions were electrostatically accelerated t

Yu-Mi Wu, Sihun Lee, Yufeng Xi, Stephen D. Funni

Moiré superlattice in two-dimensional (2D) materials provides a powerful platform to engineer emergent electronic states, yet the construction of moiré superlattices remains lab-scale, involving much trial and error and with little control. Here, we demonstrate the construction of a heterostrain-induced moiré superlattice in transition metal dichalcogenides

Liyang Hu, Chong Chen

In positron emission tomography (PET), it is indispensable to perform attenuation correction in order to obtain the quantitatively accurate activity map (tracer distribution) in the body. Generally, this is carried out based on the estimated attenuation map obtained from computed tomography or magnetic resonance imaging. However, except for errors in the att

Jannes van Poppelen, Annica M. Black-Schaffer

Magic-angle twisted bilayer graphene (TBG) with its flat bands provides a rich platform for exploring emergent electronic orders. Similarly, periodically buckled monolayer graphene has been proposed as a tunable alternative for realizing flat bands. Here, we investigate the combined effect of buckling and twisting in bilayer graphene. We find that periodic b

Lee Grimberg, Svyatoslav Kostyukovets, Moshe G. Harats

Monolayers of transition-metal dichalcogenides have shown that uniaxial strain changes both the photoluminescence emission energy and intensity. The changes are attributed to the band-structure evolution under tensile strain where both the bandgap decreases and a direct-to-indirect transition occurs. This was shown for relatively high strains, whereas this i

Siddharth Tourani, Jayaram Reddy, Akash Kumbar, Satyajit Tourani

Dynamic scene rendering and reconstruction play a crucial role in computer vision and augmented reality. Recent methods based on 3D Gaussian Splatting (3DGS), have enabled accurate modeling of dynamic urban scenes, but for urban scenes they require both camera and LiDAR data, ground-truth 3D segmentations and motion data in the form of tracklets or pre-defin

Dishant Sisodia, Sarika Jalan

Reservoir computing has emerged as a powerful framework for time series modelling and forecasting including the prediction of discontinuous transitions. However, the mechanism behind its success is not yet fully understood. This letter elucidates the functioning of reservoir computing by examining its successful prediction of boundary and attractor merging c

Hideo Aoki

There is a recent upsurge of interests in flat bands in condensed-matter systems and the consequences for magnetism and superconductivity. This article highlights the physics, where peculiar quantum-mechanical mechanisms for the physical properties such as flatband ferromagnetism and flatband superconductivity that arise when the band is not trivially flat b

Claire Voisin

We continue our investigation of the geometry of the Albanese morphism on 0-cycles. We provide an example of a smooth projective variety with representable CH_0-group but with no universal 0-cycle, which answers a question asked by Colliot-Thélène. Our construction relies on a counterexample to the integral Hodge conjecture provided by Benoist and Ottem.

Joao Vitor C. Lovato, Edgar Huayra, Emmanuel G. de Oliveira

The effective cross section of double parton scattering in high-energy hadron collisions has been measured in proton--proton collisions, with significant variation among final-state observables, contrary to the idea of a universal value. Building upon our previous work, we incorporate the dependence on both the parton longitudinal momentum fraction $x$ and t

Lawrence Frolov

Consider a non-relativistic quantum particle with wave function $ψ$ in a bounded $C^2$ region $Ω\subset \mathbb{R}^n$, and suppose detectors are placed along the boundary $\partial Ω$. Assume the detection process is irreversible, its mechanism is time independent and also hard, i.e., detections occur only along the boundary $\partial Ω$. Under these conditi

Maryam Khanahmadi, Klaus Mølmer

Generating non-Gaussian states and converting them into traveling wavepackets is crucial yet challenging for scalable, fault-tolerant quantum computing. We present a hardware-efficient approach that simultaneously achieves both tasks by combining an engineered nonlinear dissipation with a linear transmission loss from a superconducting circuit to a waveguide

Hai-Liang Wu, Li-Yuan Wang, He-Xia Ni

In this paper, by some arithmetic properties of the Pell sequence and some $p$-adic tools, we study certain cyclotomic matrices involving squares over finite fields. For example, let $1=s_1,s_2,\cdots,s_{(q-1)/2}$ be all the nonzero squares over $\mathbb{F}_{q}$, where $q=p^f$ is an odd prime power with $q\ge7$. We prove that the matrix $$B_q((q-3)/2)=\left[

Mohamadreza Delbari, George C. Alexandropoulos, Robert Schober, Vahid Jamali

In this chapter, we investigate the mathematical foundation of the modeling and design of reconfigurable intelligent surfaces (RIS) in both the far- and near-field regimes. More specifically, we first present RIS-assisted wireless channel models for the far- and near-field regimes, discussing relevant phenomena, such as line-of-sight (LOS) and non-LOS links,

Minghao Guo, Victor Zordan, Sheldon Andrews, Wojciech Matusik

We introduce Kinematic Kitbashing, an optimization framework that synthesizes articulated 3D objects by assembling reusable parts conditioned on an abstract kinematic graph. Given the graph and a library of articulated parts, our method optimizes per-part similarity transformations that place, orient, and scale each component into a coherent articulated obje

Boyun Hu, Kunlun Qi

The numerical approximation of the Boltzmann collision operator presents significant challenges arising from its high dimensionality, nonlinear structure, and nonlocal integral form. In this work, we propose a Fourier Neural Operator (FNO) based framework to learn the Boltzmann collision operator and its simplified BGK model across different dimensions. The

Dharunish Yugeswardeenoo, Harshil Nukala, Ved Shah, Cole Blondin

Large Language Models (LLMs) have demonstrated impressive reasoning capabilities but continue to struggle with arithmetic tasks. Prior works largely focus on outputs or prompting strategies, leaving the open question of the internal structure through which models do arithmetic computation. In this work, we investigate whether LLMs encode operator precedence

Huawei Jiang, Husna Mutahira, Gan Huang, Mannan Saeed Muhammad

Accurate detection of cardiac abnormalities from electrocardiogram recordings is regarded as essential for clinical diagnostics and decision support. Traditional deep learning models such as residual networks and transformer architectures have been applied successfully to this task, but their performance has been limited when long sequential signals are proc

Jungbin Cho, Minsu Kim, Jisoo Kim, Ce Zheng

Human motion is inherently diverse and semantically rich, while also shaped by the surrounding scene. However, existing motion generation approaches fail to generate semantically diverse motion while simultaneously respecting geometric scene constraints, since constructing large-scale datasets with both rich text-motion coverage and precise scene interaction

Peter Bradshaw, Ilkyoo Choi, Alexandr Kostochka

A \emph{request} on a graph assigns a preferred color to a subset of the vertices. A graph $G$ is \emph{$\epsilon$-flexibly $k$-choosable} if for every $k$-list assignment $L$ and every request $r$ on $G$, there is an $L$-coloring such that an $\epsilon$-fraction of the requests are satisfied. This notion was introduced in 2019 by Dvo\v{r}\'ak, Norin, and Po

Zhengxu Tang, Zizheng Wang, Luning Wang, Zitao Shuai

Text-to-video (T2V) generation models have made significant progress in creating visually appealing videos. However, they struggle with generating coherent sequential narratives that require logical progression through multiple events. Existing T2V benchmarks primarily focus on visual quality metrics but fail to evaluate narrative coherence over extended seq

Sofia C. Brown, Ravid Shaniv, Ruomu Zhang, Chris Reetz

Sensing via a mechanical frequency shift is a powerful measurement tool, and, therefore, understanding and mitigating frequency noise affecting mechanical resonators is imperative. Thermomechanical noise fundamentally limits mechanical frequency stability, and its impact can be reduced with increased coherent amplitude of mechanical motion. However, large en

Jiawen Li, Pascal Lefevre, Anwar Pp Abdul Majeed

Based on Stochastic Gradient Descent (SGD), the paper introduces two optimizers, named Interpolational Accelerating Gradient Descent (IAGD) as well as Noise-Regularized Stochastic Gradient Descent (NRSGD). IAGD leverages second-order Newton Interpolation to expedite the convergence process during training, assuming relevancy in gradients between iterations.

Alexei Oblomkov, Lev Rozansky

We categorify the action of $U_q(\mathfrak{gl}_{1|1})$ on the tensor product of its vector representations $(\mathbb{C}^{1|1})^{\otimes N}$. The generators $E$ and $F$ are represented by Fourier-Mukai functors between the derived categories of coherent sheaves on the total spaces of "semi-parabolic" vector bundles over the Grassmannians $Gr(k,N)$.