December 2025 arXiv papers — page 26

Qingyang Li, Yibiao Yang, Maolin Sun, Jiangchang Wu

Compilers are fundamental to modern software development, making the effective identification and resolution of compiler faults essential. However, localizing these faults to specific source files remains highly challenging due to the complexity and scale of modern compiler infrastructures. In this study, we propose MultiConf, a novel approach that automatic

Mohammad Amin Amini, Gretar Tryggvason, Ehsan Amani

We investigate the Ferrofluid Drop Targeting (FDT) for the treatment of the Retinal Detachment (RD), considering, for the first time, the real 3D geometry of an eye and magnets configurations as well as the viscoelastic rheology of the medium, i.e., the Vitreous Humor (VH). A Front-Tracking Method (FTM) is extended to handle a general 3D unstructured Euleria

Qinglin Zeng, Kaitong Cai, Ruiqi Chen, Qinhan Lv

Maintaining narrative coherence and visual consistency remains a central challenge in open-domain video generation. Existing text-to-video models often treat each shot independently, resulting in identity drift, scene inconsistency, and unstable temporal structure. We propose CoAgent, a collaborative and closed-loop framework for coherent video generation th

Liu Cao, Zhaoyu Liu, Dongyu Wei, Yuan Yang

Transmitter vehicles that broadcast 6G Cellular Vehicle-to-Everything (C-V2X)-based messages, e.g., Basic Safety Messages (BSMs), are prone to be impacted by PHY issues due to the lack of dynamic high-fidelity Radio Environment Map (REM) with dynamic location variation. This paper explores a lightweight diffusion-based generative approach, the Coordinate-Con

Pawissanutt Lertpongrujikorn

Cloud computing has fundamentally transformed application development, yet a gap remains between the serverless promise of simplified deployment and its practical realization due to fragmentation across function runtimes, state management, and orchestration. This dissertation addresses this gap through empirical validation and technical innovation, establish

Aparna V C, Shashank Shekhar, Sheetal Kalyani

Multiplicative fading is a major limitation of reconfigurable intelligent surfaces (RIS), restricting their effective coverage in both existing sub-6GHz systems and future mmWave networks. Although active RIS architectures mitigate this issue, they require high power consumption and introduce practical challenges due to the need for integrated amplifiers. Re

Shalender Singh, Santosh Kumar

Quantum entanglement is commonly assumed to be fragile at ambient temperature and over macroscopic distances, where thermal noise and dissipation are expected to rapidly suppress nonclassical correlations. Here we show that this intuition fails for collective quantum modes whose dynamics is governed by reduced open-system channels rather than by microscopic

Hayami Iizuka, Tomohiro Harada

We develop a formulation of nonlinear cosmological perturbations on superhorizon scales in multi-fluid systems. It is based on the Arnowitt-Deser-Misner formalism combined with a spatial gradient expansion characterized by a small expansion parameter defined as the ratio of the comoving wavenumber to the Hubble scale. The background spacetime is assumed to b

Avinash Kumar Jha

We present a systematic study of temperature-driven nonaffine rearrangements in a model amorphous solid across the full thermodynamic range, from a high-temperature liquid, through supercooled and sub-glass regimes, into deep glassy states. The central result is a quantitative characterisation of the componentwise nonaffine residual displacements, obtained b

Fabian Gröger, Simone Lionetti, Philippe Gottfrois, Alvaro Gonzalez-Jimenez

The adoption of artificial intelligence in dermatology promises democratized access to healthcare, but model reliability depends on the quality and comprehensiveness of the data fueling these models. Despite rapid growth in publicly available dermatology images, the field lacks quantitative key performance indicators to measure whether new datasets expand cl

Yiming Lu, Tingyu Lu, Di Zhang, Lili Ye

Aluminum nanoparticles (ANPs) are among the most energy-dense solid fuels, yet the atomic mechanisms governing their transition from passivated particles to explosive reactants remain elusive. This stems from a fundamental computational bottleneck: ab initio methods offer quantum accuracy but are restricted to small spatiotemporal scales (< 500 atoms, picose

Bin Shen, Feng Du, Rui Li, Hang Su

Frustrated Kondo lattices are ideal platforms for studying how both the Kondo effect and quantum fluctuations compete with the magnetic exchange interactions that drive magnetic ordering. Here, we investigate the effect of tuning the heavy-fermion compound CePdIn, which crystallizes in the geometrically frustrated ZrNiAl-type structure, using applied magneti

Hongwei Xu, Weichao Zheng, Zai Yang

This paper addresses the problem of estimating the Hermitian Toeplitz covariance matrix under practical hardware constraints of sparse observations and coarse quantization. Within the triangular-dithered quantization framework, we propose an estimator called Toeplitz-projected sample covariance matrix (Q-TSCM) to compensate for the quantization-induced bias,

Kichang Lee, Sungmin Lee, Jaeho Jin, JeongGil Ko

Modern cloud-based AI training relies on extensive telemetry and logs to ensure accountability. While these audit trails enable retrospective inspection, they struggle to address the inherent non-determinism of deep learning. Stochastic operations, such as dropout, create an ambiguity surface where attackers can mask malicious manipulations as natural random

Bin Xia, Bohao Peng, Jiyang Liu, Sitong Wu

Recently unified generation and editing models have achieved remarkable success with their impressive performance. These models rely mainly on text prompts for instruction-based editing and generation, but language often fails to capture users intended edit locations and fine-grained visual details. To this end, we propose two tasks: scribble-based editing a

Zhuoqi Lyu, Qing Ke

Subject classification schemes are foundational to the organization, evaluation, and navigation of scientific knowledge. While expert-curated systems like Scopus provide widely used taxonomies, they often suffer from coarse granularity, subjectivity, and limited adaptability to emerging interdisciplinary fields. Data-driven alternatives based on citation net

Jihang Wang, Dongcheng Zhao, Ruolin Chen, Qian Zhang

Spiking Neural Networks (SNNs) utilize spike-based activations to mimic the brain's energy-efficient information processing. However, the binary and discontinuous nature of spike activations causes vanishing gradients, making adversarial robustness evaluation via gradient descent unreliable. While improved surrogate gradient methods have been proposed, their

Jinpeng Liu, Yuanhong Teng, Yu Chen, Yixuan Wang

The nanoscale charge environment critically influences semiconductor physics and device performance. While conventional bulk characterization techniques provide volume-averaged defect properties, they lack the spatial resolution to resolve nanoscale charge heterogeneity and identify microscopic noise sources. Here, we utilize single PL5 centers in 4H-SiC as

Madoka Horie, Takuya Yamauchi

In this note, we compute the $L$-function of the projective smooth surface $S$ over $\mathbb{Q}$ that parametrizes cuboids whose geometric properties are studied in detail by Stoll and Testa. As a byproduct, we completely determine the structure of ${\rm Pic}(S_{\overline{\mathbb{Q}}})$ as a ${\rm Gal}(\overline{\mathbb{Q}}/\mathbb{Q})$-module.

Khoa Vo, Taisei Hanyu, Yuki Ikebe, Trong Thang Pham

Recent Vision-Language-Action (VLA) models have made impressive progress toward general-purpose robotic manipulation by post-training large Vision-Language Models (VLMs) for action prediction. Yet most VLAs entangle perception and control in a monolithic pipeline optimized purely for action, which can erode language-conditioned grounding. In our real-world t

Andrew Salch, Gunjeet Singh

Given subsets $\mathcal{C},\mathcal{F}$ of a preorder $\mathcal{A}$, we give necessary and sufficient conditions for $\mathcal{A}$ to admit the structure of a model category whose cofibrant objects are $\mathcal{C}$ and whose fibrant objects are $\mathcal{F}$. We give various classification results for model structures on preorders by describing model struct

Xiangyu Wang

We establish an almost sharp L^r to L^p estimate for oscillatory integral operators satisfying the cinematic curvature condition. The proof combines Wolff's two-ends reduction with refined decoupling inequalities.

Ayad Habib Shemail, Ahmed Razzaq Al-Lami, Amal Hadi Rashid

This article addresses the fuzzy logistic regression model under conditions of multicollinearity, which causes instability and inflated variance in parameter estimation. In this model, both the response variable and parameters are represented as fuzzy triangular numbers. To overcome the multicollinearity problem, various Liu-type estimators were employed: Fu

Jipeng Gan, Le Liang, Hua Zhang, Chongtao Guo

Semantic communication has been introduced into collaborative perception systems for autonomous driving, offering a promising approach to enhancing data transmission efficiency and robustness. Despite its potential, existing semantic communication approaches predominantly rely on analog transmission models, rendering these systems fundamentally incompatible

Xingwu Zeng, Can Zhang

In this paper, we consider the bilinear approximate controllability for the complex Ginzburg-Landau (CGL) equation with a power-type nonlinearity of any integer degree on a torus of arbitrary space dimension. Under a saturation hypothesis on the control operator, we show the small-time global controllability of the CGL equation. The proof is obtained by deve

Hamed Damirchi, Ehsan Abbasnejad, Zhen Zhang, Javen Shi

Large pre-trained models have transformed machine learning, yet adapting these models effectively to exhibit precise, concept-specific behaviors remains a significant challenge. Task vectors, defined as the difference between fine-tuned and pre-trained model parameters, provide a mechanism for steering neural networks toward desired behaviors. This has given

Aritra Ghosh, Bijan Bagchi, A. Ghose-Choudhury, Partha Guha

We revisit the canonical quantization to assess the spectrum of the modified Emden equation $\ddot{x} + kx\dot{x} + \omega^2 x + \frac{k^2}{9}x^3 = 0$, which is an isochronous case of the Li\'enard-Kukles equation. While its classical isochronicity and canonical quantization, leading to polynomial solutions with an exactly-equispaced spectrum have been discu

Lucky Susanto, Anasta Pranawijayana, Cortino Sukotjo, Soni Prasad

Large language models (LLMs) are increasingly adopted in high-stakes domains such as healthcare and medical education, where the risk of generating factually incorrect (i.e., hallucinated) information is a major concern. While significant efforts have been made to detect and mitigate such hallucinations, predicting whether an LLM's response is correct remain

Harish Kumarasubramanian, Jayakanth Ravichandran

Understanding and controlling the chemical states both in the bulk and at the interfaces of complex oxide thin films is essential for engineering a wide range of electronic, optical, and magnetic functionalities, which arise through emergent phenomena such as two-dimensional electron gases, interfacial magnetism, and associated phase transitions. Here, we de

Róbert Juhász, Igor D. Kovács, Beáta Oborny

The survival of populations hinges on their ability to offset local extinctions through new colonizations. The dispersal area ($A$) plays a crucial role in this process, as it determines the probability of finding colonizable vacant sites. We investigated the spatial colonization-extinction dynamics in a lattice model (a contact process), exploring various f

Taeyoung Song, Sanghyun Kang, Yu Hsin Kuo, Jiayi Chen

Advanced logic transistors require gate dielectrics that achieve sub-nanometer equivalent oxide thickness (EOT), suppress leakage, and satisfy three key requirements: (i) compatibility with RMG-like high-temperature processing, (ii) sufficient Vth tunability for multi-Vth design, and (iii) high device reliability. However, meeting all of these requirements s

Joyee Ghosh

Bayesian variable selection (BVS) depends critically on the specification of a prior distribution over the model space, particularly for controlling sparsity and multiplicity. This paper examines the practical consequences of different model space priors for BVS in logistic regression, with an emphasis on streaming data settings. We review some popular and w

Xin Chen, Kang Luo, Yangyi Xiao, Hesheng Wang

Reliable and precise detection of small and irregular objects, such as meteor fragments and rocks, is critical for autonomous navigation and operation in lunar surface exploration. Existing multimodal 3D perception methods designed for terrestrial autonomous driving often underperform in off world environments due to poor feature alignment, limited multimoda

Shen Changqing, Xu Bingzhou, Qi Bosong, Zhang Xiaojian

Multiply connected freeform surface features are widely encountered in industrial components, where toolpath generation often suffers from discontinuities, sharp turns, non-uniform scallop heights, and incomplete boundary coverage. This paper proposes a scalar-field variational optimization method for milling that produces continuous, boundary-conforming, an

Edwin Vargas, Jhon Lopez, Henry Arguello, Ashok Veeraraghavan

Ensuring the authenticity and ownership of digital images is increasingly challenging as modern editing tools enable highly realistic forgeries. Existing image protection systems mainly rely on digital watermarking, which is susceptible to sophisticated digital attacks. To address this limitation, we propose a hybrid optical-digital framework that incorporat

Akshara Satheesh, Rajib Chattopadhyay

Traditionally, during the monsoon season, more rainfall is received along the Western Ghats, the Northern Gangetic plains, the central belt, and northeast India. However, recently, there has been a shift in this canonical monsoon rainfall pattern on the monthly to seasonal scale. In this study, we quantify an east-west asymmetric trend in monthly to seasonal

Francesca Anceschi, Cristina Marcelli, Francesca Papalini

We prove existence results for Dirichlet boundary value problems for equations of the type \begin{align*} \left( \Phi(k(t) x'(t) ) \right)' = f(t, x(t) , x'(t) ) \qquad \text{for a.e. } t \in I:=[0,T] , \end{align*} where $\Phi : J \to \mathbb{R} $ is a generic possibly non-monotone differential operator defined in a open interval $J\subseteq \mathbb{R}$, $k

Monika, Priyadarshi Dey, Zachary Easley

This paper investigates the projection operators that lie in the algebra generated by powers of an $n$-potent operator $T$ on a complex Banach space, where $T^n = T$. We give a complete description of all projections in the algebra $\operatorname{comb}(T) = \text{span}\{T, T^2, \dots, T^{n-1}\}$, and prove that each such projection is uniquely determined by,

Saisab Sadhu, Ashim Dhor

Large Language Models (LLMs) are increasingly deployed as automated tutors to address educator shortages; however, they often fail at pedagogical reasoning, frequently validating incorrect student solutions (sycophancy) or providing overly direct answers that hinder learning. We introduce Hierarchical Pedagogical Oversight (HPO), a framework that adapts stru

Hamed Damirchi, Cristian Rodriguez-Opazo, Ehsan Abbasnejad, Zhen Zhang

The Lottery Ticket Hypothesis (LTH) suggests that over-parameterized neural networks contain sparse subnetworks ("winning tickets") capable of matching full model performance when trained from scratch. With the growing reliance on fine-tuning large pretrained models, we investigate whether LTH extends to parameter-efficient fine-tuning (PEFT), specifically f

Thang Pang Ern, Malcolm Tan Jun Xi, Loh Wei Xuan Ryan

The function \[f(a,b)=\frac{\gcd(a+b,ab)}{\gcd(a,b)}\] is of interest in this paper. We then ask a natural question regarding how often $f(a,b)=1$ is. We yield the limiting density $\rho=\prod_{p}\left(1-\frac{1}{p^2(p+1)}\right)\approx 0.88151$ which is an Euler product that unexpectedly matches the quadratic class number constant from the theory of real qu

Cristina Marcelli

The paper concerns front propagation for the following mono-stable reaction-diffusion-advection equation \[f(u)u_x + g(u)u_\tau = [d(u)|u_x|^{p-2} u_x]_x+ \rho(u), \quad (x,\tau)\in \R\times [0,+\infty).\] Besides existence and non-existence results for traveling wave solutions, the main focus is their classification: we provide criteria to establish if they

Zhenqian Chen, Baoquan Zhong, Xiang Li, Qing Dai

RL post-training for LLMs has been widely scaled to enhance reasoning and tool-using capabilities. However, RL post-training interleaves training and inference workloads, exposing the system to faults from both sides. Existing fault tolerance frameworks for LLMs target either training or inference, leaving the optimization potential in the asynchronous execu

Suhua Wang, Zifan Wang, Xiaoxin Sun, D. J. Wang

As an endangered language, Manchu presents unique challenges for speech synthesis, including severe data scarcity and strong phonological agglutination. This paper proposes ManchuTTS(Manchu Text to Speech), a novel approach tailored to Manchu's linguistic characteristics. To handle agglutination, this method designs a three-tier text representation (phoneme,

Yao-Bei Liu, Stefano Moretti

This work explores the discovery potential of the first-generation weak isosinglet Vector-Like Leptons (VLLs), denoted by $E^\pm$, via pair production at future electron-positron colliders. Our analysis adopts a comprehensive framework that incorporates beam polarization configurations and leverages detailed detector simulations. We focus on two distinct mul

Tanish Baranwal, Himanshu Gaurav Singh, Jathushan Rajasegaran, Jitendra Malik

We propose Video Gaussian Masked Autoencoders (Video-GMAE), a self-supervised approach for representation learning that encodes a sequence of images into a set of Gaussian splats moving over time. Representing a video as a set of Gaussians enforces a reasonable inductive bias: that 2-D videos are often consistent projections of a dynamic 3-D scene. We find t

Hassan Wasswa, Timothy Lynar

Although AI-based models have achieved high accuracy in IoT threat detection, their deployment in enterprise environments is constrained by reliance on stationary datasets that fail to reflect the dynamic nature of real-world IoT NetFlow traffic, which is frequently affected by concept drift. Existing solutions typically rely on periodic classifier retrainin

Zahid Ullah, Muhammad Hilal, Eunsoo Lee, Dragan Pamucar

Several review papers summarize cardiac imaging and DL advances, few works connect this overview to a unified and reproducible experimental benchmark. In this study, we combine a focused review of cardiac ultrasound segmentation literature with a controlled comparison of three influential architectures, U-Net, Attention U-Net, and TransUNet, on the Cardiac A

ChulMin Oh, Jimin Cho, Juyeon Park, Hoyeon Lee

Organoids are three-dimensional (3D) in vitro models for studying tissue development, disease progression, and physiological responses. Holotomography (HT) enables long-term, label-free imaging of live organoids by reconstructing volumetric refractive-index (RI) maps, but quantitative analysis is limited by the missing-cone artifact, which introduces anisotr

Jian Li, Karthik Gopinath, Brian L. Edlow, Adrian V. Dalca

Cortical surface parcellation is a fundamental task in both basic neuroscience research and clinical applications, enabling more accurate mapping of brain regions. Model-based and learning-based approaches for automated parcellation alleviate the need for manual labeling. Despite the advancement in parcellation performance, learning-based methods shift away

Zihan Liu, Xiangning Ren, Dezhang Kong, Yipeng Zhang

Infrared small object detection urgently requires semi-supervised paradigms due to the high cost of annotation. However, existing methods like SAM face significant challenges of domain gaps, inability of encoding physical priors, and inherent architectural complexity. To address this, we designed a Hierarchical MoE Adapter consisting of four white-box neural

Longfei Fang, Yongtao Li, Huiqiu Lin, Jie Ma

A graph is color-critical if it contains an edge whose deletion reduces its chromatic number. This class of graphs, including cliques and odd cycles, plays a central role in extremal graph theory. In this paper, following an influential line of research initiated by Bollob\'as-Nikiforov, we study the spectral supersaturation problem for color-critical graphs

Zihan Weng, Chanlin Yi, Pouya Bashivan, Jing Lu

Decoding fine-grained movement from non-invasive surface Electromyography (sEMG) is a challenge for prosthetic control due to signal non-stationarity and low signal-to-noise ratios. Generic self-supervised learning (SSL) frameworks often yield suboptimal results on sEMG as they attempt to reconstruct noisy raw signals and lack the inductive bias to model the

Hong-Bae Jeon

In this paper, we introduce a new wireless paradigm termed fluid-active reconfigurable intelligent surface (FARIS) that combines fluid-based port repositioning with per-element active amplification to enhance the performance of 6G networks. To realistically characterize the hardware operation, we first develop a circuit-level abstraction of the FARIS archite

Chuantao Li, Zhi Li, Jiahao Xu, Jie Li

Numerous studies attempt to mitigate classification bias caused by class imbalance. However, existing studies have yet to explore the collaborative optimization of imbalanced learning and model training. This constraint hinders further performance improvements. To bridge this gap, this study proposes a collaborative optimization Boosting model of multiclass

Yudai Kubono, Satoshi Tojo

The logic of awareness, first proposed by Fagin and Halpern, addressed the problem of logical omniscience by introducing the notion of awareness and distinguishing explicit knowledge from implicit knowledge. In their framework, explicit knowledge was defined as the conjunction of implicit knowledge and awareness, each of which was represented by modal operat

Ajaykrishnan E S, Daniel Lokshtanov

We study the problem of Envy-Free Incomplete Connected Fair Division, where exactly p vertices of an undirected graph must be allocated to agents such that each agent receives a connected share and does not envy another agent's share. Focusing on agents with additive valuations, we show that the problem remains computationally hard when parameterized by p an

Taihang Lei, Banglei Guan, Minzu Liang, Pengju Sun

Accurate measurement of shock wave motion parameters with high spatiotemporal resolution is essential for applications such as power field testing and damage assessment. However, significant challenges are posed by the fast, uneven propagation of shock waves and unstable testing conditions. To address these challenges, a novel framework is proposed that util

Zirong Liu, Heping Wang, Kai Wang

This paper is devoted to discussing the weighted linear tensor product problems in the worst case setting. We consider algorithms that use finitely many evaluations of arbitrary continuous linear functionals. We investigate exponential $(s, t)$-weak tractability (EXP-$(s, t)$-WT) with $\max(s,t)<1$ and exponential uniform weak tractability (EXP-UWT) under th

Sadia Asif, Israel Antonio Rosales Laguan, Haris Khan, Shumaila Asif

The proliferation of Large Language Models (LLMs) has intensified concerns about manipulative or deceptive behaviors that can undermine user autonomy, trust, and well-being. Existing safety benchmarks predominantly rely on coarse binary labels and fail to capture the nuanced psychological and social mechanisms constituting manipulation. We introduce \textbf{

Wei Liu, Chao Peng, Pengfei Gao, Aofan Liu

The issue localization task aims to identify the locations in a software repository that requires modification given a natural language issue description. This task is fundamental yet challenging in automated software engineering due to the semantic gap between issue description and source code implementation. This gap manifests as two mismatches:(1) symptom

Jong-Ik Park, Shreyas Chaudhari, Srinivasa Pranav, Carlee Joe-Wong

In many deployed systems (multilingual ASR, cross-hospital imaging, region-specific perception), multiple pretrained specialist models coexist. Yet, new target domains often require domain expansion: a generalized model that performs well beyond any single specialist's domain. Given a new target domain, existing methods obtain a single strong initialization

Qizhi Wang

Origin-destination (OD) flow prediction remains a core task in GIS and urban analytics, yet practical deployments face two conflicting needs: high accuracy and clear interpretability. This paper develops AMBIT, a gray-box framework that augments physical mobility baselines with interpretable tree models. We begin with a comprehensive audit of classical spati

R. J. Fries, P. Virupapuram, J. Purcell, H. Anconetani

We discuss the quantum mechanics of coalescence of quark-antiquark pairs into mesons using a non-relativistic quark model. We derive the coalescence probabilities assuming a harmonic oscillator potential and generic Gaussian wave packet shapes for the initial quarks and antiquarks. Our particular emphasis is on modeling excited states of the meson spectrum c

Sukhyun Jeong, Yong-Hoon Choi

Text-based 3D motion generation aims to automatically synthesize diverse motions from natural-language descriptions to extend user creativity, whereas motion editing modifies an existing motion sequence in response to text while preserving its overall structure. Pose-code-based frameworks such as CoMo map quantifiable pose attributes into discrete pose codes

Kai-Hsiang Hsieh, Monyneath Yim, Wen-Hsiao Peng, Jui-Chiu Chiang

Joint compression of point cloud geometry and attributes is essential for efficient 3D data representation. Existing methods often rely on post-hoc recoloring procedures and manually tuned bitrate allocation between geometry and attribute bitstreams in inference, which hinders end-to-end optimization and increases system complexity. To overcome these limitat

Jiatao Quan, Ziyue Li, Tian Qi Zhu, Yuxuan Li

As large language models (LLMs) are embedded into mental health technologies, they are often framed either as tools assisting therapists or autonomous therapeutic systems. Such perspectives overlook their potential to mediate relational complexities in therapy, particularly for systemically marginalized clients. Drawing on in-depth interviews with 12 therapi

Huye Chen, Shaofei Du

Let $G$ be a transitive permutation group on $\Omega$ containing two points $\alpha, \beta$ such that $G_{\alpha}\cap G_{\beta}=1$. The Saxl graph $\Sigma(G)$ of $(G, \Omega)$ is defined as the graph with vertex set $\Omega$, where two vertices $\alpha', \beta'$ are adjacent if and only if $G_{\alpha'}\cap G_{\beta'}=1$. Burness and Giudici conjectured that

Guo-Jie Jason Gao

We propose a dynamical trapping system composed of multiple chasers subject to target-tracking forces utilizing the velocity and position information of a single escaping target. To successfully capture the target, dividing chasers into multiple groups while each group approaching its assigned destination in the proper vicinity of the target is essential. Mo

Qizhi Wang

GraphRAG systems improve multi-hop retrieval by modeling structure, but many approaches rely on expensive LLM-based graph construction and GPU-heavy inference. We present SPRIG (Seeded Propagation for Retrieval In Graphs), a CPU-only, linear-time, token-free GraphRAG pipeline that replaces LLM graph building with lightweight NER-driven co-occurrence graphs a

Huye Chen, Shaofei Du, Weicong Li

Let $G$ be a transitive permutation group on a set $\Omega$, and suppose $G_{\alpha}\cap G_{\beta}=1$ for some distinct $\alpha, \beta\in\Omega$. The Saxl graph $\Sigma(G)$ of $(G, \Omega)$ is defined as the graph with vertex set $\Omega$, where two vertices $\alpha', \beta'$ are adjacent if and only if $G_{\alpha'}\cap G_{\beta'}=1$. Burness and Giudici con

Congwen Liu

In this paper, we classify positive solutions to the CR Yamabe equation on the Heisenberg group $\mathbb{H}^n$. We show that all such solutions are Jerison-Lee bubbles, without imposing any finite-energy or a priori symmetry assumptions. This result can be regarded as an analogue for $\mathbb{H}^n$ of the celebrated Caffarelli-Gidas-Spruck classification the

Chansong Lim, Haz Sameen Shahgir, Yue Dong, Jia Chen

Local railway committees need timely situational awareness after highway-rail grade crossing incidents, yet official Federal Railroad Administration (FRA) investigations can take days to weeks. We present a demo system that populates Highway-Rail Grade Crossing Incident Data (Form 57) from news in real time. Our approach addresses two core challenges: the fo

Huye Chen, Shaofei Du, Weicong Li

Let $G$ be a transitive permutation group on $\Omega$ with two points $\alpha, \beta\in\Omega$ such that $G_{\alpha}\cap G_{\beta}=1$. The Saxl graph $\Sigma(G)$ of the pair $(G,\Omega)$ is the graph with vertex set $\Omega$, while two vertices $\alpha', \beta'$ are adjacent if and only if $G_{\alpha'}\cap G_{\beta'}=1$. It was conjectured by Burness and Giu

Jiacheng Li, Jianchao Tan, Zhidong Yang, Feiye Huo

Low-Rank Adaptation (LoRA) is a widely adopted parameter-efficient fine-tuning (PEFT) method. However, its linear adaptation process limits its expressive power. This means there is a gap between the expressive power of linear training and non-linear training. To bridge this gap, we propose AFA-LoRA, a novel training strategy that brings non-linear expressiv

Haley Mody, Namish Bansal, Dennies Kiprono Bor, Edward J. Oughton

Electrical substations are a significant component of an electrical grid. Indeed, the assets at these substations (e.g., transformers) are prone to disruption from many hazards, including hurricanes, flooding, earthquakes, and geomagnetically induced currents (GICs). As electrical grids are considered critical national infrastructure, any failure can have si

Yaozhenghang Ma, Ke Yang, Yuxuan Zhou, Hua Wu

Kitaev materials are of great interest due to their potential in realizing quantum spin liquid (QSL) states and applications in topological quantum computing. In the pursuit of realizing Kitaev QSL, a Mott insulator with strong bond-dependent frustration and weak geometric frustration is highly desirable. Here we explore Kitaev physics in the van der Waals t

Junsheng Yao, Lichao Mou, Qingyu Li

Monocular 3D building reconstruction from remote sensing imagery is essential for scalable urban modeling, yet existing methods often require task-specific architectures and intensive supervision. This paper presents the first systematic evaluation of SAM 3D, a general-purpose image-to-3D foundation model, for monocular remote sensing building reconstruction

Misao Sasaki, Jianing Wang

Black hole relics are of significant interest in cosmology and theoretical physics. In this work, we consider tiny primordial black holes (PBHs) ( $M_{\text {PBH }} \lesssim 10^7 \mathrm{~g}$ ) which are generated soon after the end of inflation and evaporate and reheat the Universe before big bang nucleosynthesis (BBN), but leave their remnants due to incom

Md Abu Obaida Zishan, Annajiat Alim Rasel

Locating objects for the visually impaired is a significant challenge and is something no one can get used to over time. However, this hinders their independence and could push them towards risky and dangerous scenarios. Hence, in the spirit of making the visually challenged more self-sufficient, we present SonoVision, a smart-phone application that helps th

Peleg Shefi, Amir Ayali, Gal A. Kaminka

In collective motion, perceptually-limited individuals move in an ordered manner, without centralized control. The perception of each individual is highly localized, as is its ability to interact with others. While natural collective motion is robust, most artificial swarms are brittle. This particularly occurs when vision is used as the sensing modality, du

Zhicheng Zhao, Yuancheng Xu, Andong Lu, Chenglong Li

Optical and Synthetic Aperture Radar (SAR) fusion-based object detection has attracted significant research interest in remote sensing, as these modalities provide complementary information for all-weather monitoring. However, practical deployment is severely limited by inherent challenges. Due to distinct imaging mechanisms, temporal asynchrony, and registr

Arleen Natalie, Budiarso, Ridho Irwansyah

Magnetohydrodynamics (MHD) generators present a promising pathway for clean energy conversion by directly transforming conductive fluids' kinetic energy into electricity. This study investigates the impact of electrode geometry modifications on the performance of a vortex-type seawater MHD generator. Three electrode designs, partial, whole-area, and spiral,

Vinesh Vijayan

Classical chaos theory rests on the notion of universality, whereby disparate dynamical systems share identical scaling laws. Existing universality classes, however, implicitly assume Markovian dynamics. Here, a logistic map endowed with power law memory is used to show that Feigenbaum universality breaks down when temporal correlations decay sufficiently sl

Jie Zhou, Xin Chen, Jie Zhang, Zhe Li

Large language models (LLMs) are increasingly reshaping learning paradigms, cognitive processes, and research methodologies across diverse domains. As their adoption expands, effectively integrating LLMs into professional fields and clarifying their role in domain-specific applications has become a key challenge for enterprise digital transformation and broa

Cattalyya Nuengsigkapian

Retrieval-Augmented Generation (RAG) in open-domain settings faces significant challenges regarding irrelevant information in retrieved documents and the alignment of generated answers with user intent. We present HiFi-RAG (Hierarchical Filtering RAG), the winning closed-source system in the Text-to-Text static evaluation of the MMU-RAGent NeurIPS 2025 Compe

Zibin Liu, Banglei Guan, Yang Shang, Zhenbao Yu

Camera calibration is an essential prerequisite for event-based vision applications. Current event camera calibration methods typically involve using flashing patterns, reconstructing intensity images, and utilizing the features extracted from events. Existing methods are generally time-consuming and require manually placed calibration objects, which cannot

Yuan Xue, Eric Y. Yang, Zipei Zhang

Based on the work by C{\'o}rdova-Costa-Hsin (arXiv:2412.16681), we propose an EFT-style, Lagrangian procedure to gauge finite 0-form symmetries in untwisted Dijkgraaf-Witten gauge theories on closed oriented manifolds using higher gauging condensation defects and point out its limitations. Using this proposal, we construct effective actions of untwisted Dijk

Khalfalla Awedat, Mohamed Abidalrekab, Gurcan Comert, Mustafa Ayad

LiDAR-based perception in autonomous systems is constrained by fixed vertical beam resolution and further compromised by beam dropout resulting from environmental occlusions. This paper introduces SuperiorGAT, a graph attention-based framework designed to reconstruct missing elevation information in sparse LiDAR point clouds. By modeling LiDAR scans as beam-

Junki Tanaka, Riku Matsumura, Taichi Miyagawa

Reliable extraction of Ca$(p,p\alpha)$ cross sections requires accurate correction for oxygen and carbon impurities in calcium targets. In this work, the relative amounts of these light elements in $^{40,42,44,48}$Ca targets are determined using 65-MeV proton elastic scattering, where the Ca/Mylar yield ratios provide a direct measure of the corresponding O

Lorenzo Pirovano

We study the thermodynamic cost of maintaining a continuous binary record of a vacuum or no-vacuum measurement. Modeling the monitoring as a time-binned click or no-click process with finite bandwidth, we treat the outcomes as a classical register that is reset after each bin. Landauer's principle then yields an operational lower bound on the dissipated heat

Jingyuan Yang, Weibin Luo, Hui Huang

An image conveys meaning through both its visual content and emotional tone, jointly shaping human perception. We introduce Controllable Emotional Image Content Generation (C-EICG), which aims to generate images that remain faithful to a given content description while expressing a target emotion. Existing text-to-image models ensure content consistency but

Nella Rotundo, Gantumur Tsogtgerel

We establish global well-posedness and regularity for the Navier-Stokes-{\alpha}{\beta} system endowed with the wall-eddy boundary conditions proposed by Fried and Gurtin (2008). These conditions introduce a tangential vorticity traction proportional to wall vorticity and provide a continuum-mechanical model for near-wall turbulence. Our analysis begins with

Zining Wang, Jian Gao, Weimin Fu, Xiaolong Guo

Analog circuit design remains a knowledge- and experience-intensive process that relies heavily on human intuition for topology generation and device parameter tuning. Existing LLM-based approaches typically depend on prompt-driven netlist generation or predefined topology templates, limiting their ability to satisfy complex specification requirements. We pr

Taihei Kuroiwa, Daiki Yamazaki, Keita Takahashi, Kodai Shiba

Two-stage stochastic programming often discretizes uncertainty into scenarios, but scenario enumeration makes expected recourse evaluation scale at least linearly in the scenario count. We propose qGAN-QAOA, a unified quantum-circuit workflow in which a pre-trained quantum generative adversarial network encodes the scenario distribution and QAOA optimizes fi

Hong Guo, Hang Liu, Yun Soo Myung

In asymptotically anti-de Sitter (AdS) spacetime, we consider a real massiver scalar field in the Einstein-Maxwell-scalar (EMS) model and examine both scalar-hairy black hole solutions induced by the nonminimal coupling to the Maxwell field and tachyonic-hairy solutions driven by the scalar potential. When the scalar potential vanishes, scalar-hairy black ho

Gary Martinez-Nunez

We provide an algebro-geometric combinatorial description of normal varieties endowed with an effective action of an algebraic torus over arbitrary fields. This description is achieved in terms of divisorial fans endowed with a Galois semilinear action. This work concludes the description of normal $T$-varieties over fields.

Dan Li, Zhengxuan Wang, Chuanguang Zhang, Chunlan Ma

Materials featuring kagome lattices have attracted significant research interest due to their unique geometric frustration, which gives rise to rich physical phenomena such as non-trivial topology, spin fluctuations, and superconductivity. In this work, using CaPd5 as the prototype structure, we discover and systematically investigate a new class of kagome s

Mark Ballard, Guanqun Song, Ting Zhu

The exponential increase in artificial satellites, growing from 852 in 2004 to over 9,000 in 2023, has intensified the risk of the Kessler Syndrome: a cascading chain reaction of orbital collisions. This paper analyzes the dynamics of space debris accumulation to identify the primary orbital features contributing to this systemic risk. We compiled and analyz

Davy Romine, Andrew Kingery, Guanqun Song, Ting Zhu

Recent advances in satellite technology have introduced a new frontier of wireless networking by establishing Low Earth Orbit (LEO) Satellite networks that work to connect difficult to reach areas and improve global connectivity. These novel advancements lack robust open-source simulation models that can highlight potential bottlenecks or potential wasted re