March 2026 arXiv papers — page 56

N. V. Krasnikov

In this paper we study the ultraviolet behavior of the invariant charge in QED. We show that for complex momenta the invariant charge does not have Landau pole singularity. We can define new invariant charge as real part of standard invariant charge. New invariant charge is limited from above and does not have Landau pole singularity. Also we use the $1/N$ p

Vedansh Arya, Seongmin Jeon, Vesa Julin

The recent work of Morini-Oronzio-Spadaro and the third author shows that, in three dimensions, a flat-flow solution of the volume-preserving mean curvature flow that converges to a single ball, which is the case for instance when the initial perimeter is less than that of two disjoint balls, converges exponentially fast in Hausdorff distance. In this paper

Pungponhavoan Tep, Marc Bernacki

Predicting microstructure evolution during thermomechanical treatment is essential for determining the final mechanical properties of a material, yet conventional simulations based on Partial Differential Equations (PDEs) remain computationally expensive. Our prior Deep Learning (DL) framework using Convolutional Long Short-Term Memory (ConvLSTM) has proven

Matteo Salvatori, Filippo Vannella, Sebastian Macaluso, Stylianos E. Trevlakis

HandOver (HO) control in cellular networks is governed by a set of HO control parameters that are traditionally configured through rule-based heuristics. A key parameter for HO optimization is the Cell Individual Offset (CIO), defined for each pair of neighboring cells and used to bias HO triggering decisions. At network scale, tuning CIOs becomes a tightly

Jinkai Gao

In this paper, we are interested in the existence and asymptotic behavior of least energy solutions to the upper critical Choquard equation \begin{equation*} \begin{cases} -\Delta u+au=\displaystyle\left(\int_{\Omega}\frac{u^{6-\alpha}(y)}{|x-y|^\alpha}dy\right)u^{5-\alpha}&\mbox{in}\ \Omega, u>0 \ \ &\mbox{in}\ \Omega, u=0 \ \ &\mbox{on}\ \partial \Omega, \

Jun-Qi Tao, Yang Liu, Yu Sha, Xiang Fan

The deformation of heavy nuclei leaves characteristic imprints on the initial conditions of relativistic heavy-ion collisions. However, event-by-event fluctuations make the quantitative extraction of this information challenging. This study examines the identifiability of the quadrupole ($\beta_2$) and hexadecapole ($\beta_4$) deformation parameters from nuc

Simone Mauro

We study the quasilinear elliptic system \[ -\textbf{div}(A(x,\boldsymbol u)|D\boldsymbol u|^{p-2}D\boldsymbol u) +\frac{1}{p}\nabla_{\boldsymbol s}A(x,\boldsymbol u)|D\boldsymbol u|^p = \boldsymbol g(x,\boldsymbol u) \quad \text{in } \Omega, \qquad \boldsymbol u = 0 \text{ on } \partial\Omega, \] where $p>1$, $\Omega\subset\mathbb R^N$ is a bounded domain w

Adel Dabah, Andreas Herten

Graph representation is a powerful abstraction of real-world objects and relations. Computing the Graph Edit Distance (GED) between graphs is critical in domains such as bioinformatics, machine learning, and pattern recognition. GED measures the minimum number of edit operations required to transform one graph into another. However, the high computational co

Ryugo Morita, Stanislav Frolov, Brian Bernhard Moser, Ko Watanabe

Diffusion models have demonstrated high-quality performance in conditional text-to-image generation, particularly with structural cues such as edges, layouts, and depth. However, lighting conditions have received limited attention and remain difficult to control within the generative process. Existing methods handle lighting through a two-stage pipeline that

Ravshan Ashurov, Yusuf Fayziyev, Nuriddin Khushvaktov

Despite the growing interest in fractional generalizations of classical fluid dynamics equations, the fractional Rayleigh--Stokes problem has previously been studied almost exclusively using the Riemann--Liouville fractional derivative. To the authors' knowledge, an explicit analytical form of the solution for the Caputo derivative case has not been establis

Leonard Nürnberg, Simon Bernatz, Borek Foldyna, Michael T. Lu

Coronary artery calcification (CAC) is a strong predictor of cardiovascular risk but remains underutilized in clinical routine thoracic imaging due to the need for dedicated imaging protocols and manual annotation. We present DeepCAC2, a publicly available dataset containing automated CAC segmentations, coronary artery calcium scores, and derived risk catego

Hadar Peer, Carlos Hernandez, Sven Koenig, Ariel Felner

Empirical evaluation in multi-objective search (MOS) has historically suffered from fragmentation, relying on heterogeneous problem instances with incompatible objective definitions that make cross-study comparisons difficult. This standardization gap is further exacerbated by the realization that DIMACS road networks, a historical default benchmark for the

Aditya Narendra, Mukhammadrizo Maribjonov, Dmitry Makarov, Dmitry Yudin

This paper introduces Knowledge Graph based Massively Multi-task Model-based Policy Optimization (KG-M3PO), a framework for multi-task robotic manipulation in partially observable settings that unifies Perception, Knowledge, and Policy. The method augments egocentric vision with an online 3D scene graph that grounds open-vocabulary detections into a metric,

Xintao Zong, Xian Zhong, Wenxuan Liu, Jianhao Ding

Spiking neural networks (SNNs) have recently shown strong potential in unimodal visual and textual tasks, yet building a directly trained, low-energy, and high-performance SNN for multimodal applications such as image-text retrieval (ITR) remains highly challenging. Existing artificial neural network (ANN)-based methods often pursue richer unimodal semantics

Runxin Zhang, Yulin Shao, Hongyu An, Zhijin Qin

Semantic communication, enabled by deep joint source-channel coding (DeepJSCC), is widely expected to inherit the vulnerability of deep learning to adversarial perturbations. This paper challenges this prevailing belief and reveals a counterintuitive finding: semantic communication systems exhibit unanticipated adversarial robustness that can exceed that of

Qinghua Lei, Didier Sornette

Geomaterials often exhibit progressive creep characterized by an initial decelerating phase, frequently followed by an extended period of approximately constant deformation rate, and ultimately an accelerating regime leading to catastrophic failure. Despite extensive research, the timing of rupture and its relationship to the different creep phases, particul

Ye Leng, Junjie Chu, Mingjie Li, Chenhao Lin

Recently, multimodal large language models (MLLMs) have emerged as a unified paradigm for language and image generation. Compared with diffusion models, MLLMs possess a much stronger capability for semantic understanding, enabling them to process more complex textual inputs and comprehend richer contextual meanings. However, this enhanced semantic ability ma

Yuheng Feng, Wen Zhang, Haodong Duan, Xingxing Zou

We present PosterIQ, a design-driven benchmark for poster understanding and generation, annotated across composition structure, typographic hierarchy, and semantic intent. It includes 7,765 image-annotation instances and 822 generation prompts spanning real, professional, and synthetic cases. To bridge visual design cognition and generative modeling, we defi

Shicong Liu, Xianghao Yu, Robert Schober

Near-field beamfocusing with extremely large aperture arrays can effectively enhance physical layer security. Nevertheless, even small estimation errors of the eavesdropper's location may cause a pronounced focal shift, resulting in a severe degradation of the secrecy rate. In this letter, we propose a physics-informed robust beamforming strategy that levera

J. J. H. van Gemert, V. Breschi, D. R. Yntema, K. J. Keesman

Sensor placement for leakage detection in water distribution networks is an important and practical challenge for water utilities. Recent work has shown that graph neural networks can estimate and predict pressures and detect leaks, but their performance strongly depends on the available sensor measurements and configurations. In this paper, we investigate h

I. Kharuk

We present the Ising noise filter, a highly portable, graph-based pre-filtering algorithm for early-stage background suppression in particle accelerators and astrophysical detectors. Standard noise rejection methods relying on track fitting suffer from severe combinatorial explosion. Our method bypasses this by mapping individual detector hits to a network o

Valentin Leplat, Salman Ahmadi-Asl, Junjun Pan, Henni Ouerdane

Quaternion-valued representations provide a convenient way to model coupled multi-channel signals (e.g., RGB imagery, polarization data, vector fields, and multi-detector time series). Yet practical and numerically reliable software support remains far less mature than those based on the real/complex setting. Here, we present QuatIca, an open-source Python l

Yu-Chen Kang, Yu-Chien Tang, An-Zi Yen

Knowledge Tracing (KT) is a critical technique for modeling student knowledge to support personalized learning. However, most KT systems focus on binary correctness prediction and cannot diagnose the underlying conceptual misunderstandings that lead to errors. Such fine-grained diagnostic feedback is essential for designing targeted instruction and effective

N. T. Duy, Duy H. Nguyen, Do Thi Ha, Duong Van Loi

We revisit the dark matter phenomenology of a flavor-dependent $U(1)_X$ gauge extension of the Standard Model, where anomaly cancellation predicts the existence of exactly three fermion generations and requires the presence of three right-handed neutrinos. In Ref.~\cite{VanLoi:2023utt}, a strong hierarchy between the vacuum expectation values of two singlet

Nils Lid Hjort

Suppose data are fitted to some parametric model but that the true model happens to be one with an additional parameter. When a parameter is to be estimated one can use likelihood estimation in the wider model or in the narrow model. Including the extra parameter in the model means less bias but larger sampling variability. Two basic questions are addressed

Mayank Joshi, Tanumoy Pramanik, Mengting Jiang, Yu Xing

Miniaturized quantum light sources that operate directly in optical fibers are an attractive platform for optical quantum technologies. However, most miniaturized spontaneous parametric down- conversion (SPDC) sources still rely on objective-lens-based free-space pumping and collection, which limits compactness, robustness, and direct compatibility with fibe

Ximing Wang, Chengran Yang, Chidambaram Aditya Somasundaram, Jayne Thompson

Quantum circuits that generate coherent superpositions of stochastic processes are key to many downstream quantum-accelerated tasks, such as risk analysis, importance sampling, and DNA sequencing. However, traditional methods for designing such circuits from data face immense challenges, given the exponential growth in the size of the associated probability

Rohit Radhakrishnan, Prasana Kumar, Induja Pavithran, R. I. Sujith

In several natural and engineering systems, changes in control parameters can trigger bifurcations that lead to sustained or growing periodic oscillations, indicating the onset of oscillatory instabilities. Such emergent behaviour often results from positive feedback between interacting subsystems, resulting in large-amplitude oscillations that can be detrim

Ranjit Singh, Leonid A. Barinov, Grigori G. Amosov, Anatoly V. Masalov

We investigate the quantum evolution of the pump field in second-harmonic generation under strong pump depletion. Starting from a coherent state, the pump develops a nonclassical phase-space structure resembling a Schr\"odinger cat state. This behavior originates from phase instability induced by vacuum fluctuations of the harmonic mode. A rigorous quantum a

Yiming Chen, Guozheng Dai

This paper establishes quantitative correlation inequalities between monotone events and structured threshold objects in both the discrete cube and Gaussian space. We prove that for any increasing balanced family, there exists a linear threshold function yielding a covariance lower bound of $c \frac{\log n}{\sqrt{n}}$, and extend this principle to halfspaces

Florian Odi Stummer

Current prompting paradigms for large language models (LLMs), including Chain-of-Thought (CoT) and Tree-of-Thoughts (ToT), follow linear or tree-structured reasoning paths that lack persistent memory, strategic dormancy, and cross-domain synthesis. We present the Enhanced Mycelium of Thought (EMoT) framework, a bio-inspired reasoning architecture that organi

Christopher D. Long

For simultaneous independent events with finitely many outcomes, consider the expected-utility problem with nonnegative wagers and an endogenous cash position. We prove a short support theorem for a broad class of strictly increasing strictly concave utilities. On any fixed support family and at any optimal portfolio with positive cash, summing the active fi

Haichao Xu

The fact that luminous X-ray coronae and Ultra-Fast Outflows (UFOs) are both inferred to originate from the innermost regions of active galactic nuclei (AGNs) suggests a deep physical connection between them. However, standard magnetic buoyancy models struggle to transport sufficient energy through the radiation-pressure-dominated inner disc to sustain both

Baochen Xiong, Yifan Xu, Xiaoshan Yang, Yaguang Song

The rapid evolution of Multimodal Large Language Models (MLLMs) is bottlenecked by the saturation of high-quality public data, while vast amounts of diverse multimodal data remain inaccessible in privacy-sensitive silos. Federated Learning (FL) offers a promising solution to unlock these distributed resources, but existing research focuses predominantly on f

Kohei Shinohara

We derive the conserved energy-like quantity and ensemble measure for Martyna--Tobias--Klein (MTK) barostats in which only a restricted subset of the cell degrees of freedom are active. In the standard fully anisotropic MTK formulation, the number of barostat degrees of freedom is $d^{2}$, where $d$ is the spatial dimension. When only $n_c$ axes of the cell

Zhuoran Li, Zhiyang Li, Kaijun Zhou, Jinyu Gu

Despite the promise of Vision-Language-Action (VLA) models as generalist robotic controllers, their robustness against perceptual noise and environmental variations in out-of-distribution (OOD) tasks remains fundamentally limited by the absence of long-term memory, causal failure attribution, and dynamic intervention capability. To address this, we propose R

Qiuhui Chen, Yushan Deng, Xuancheng Yao, Yi Hong

Alzheimer's disease (AD) diagnosis requires integrating neuroimaging with heterogeneous clinical evidence and reasoning under established criteria, yet most multimodal models remain opaque and weakly guideline-aligned. We present AD-Reasoning, a multimodal framework that couples structural MRI with six clinical modalities and a rule-based verifier to generat

Jipeng Liu, Haichao Shi, Siyu Xing, Rong Yin

While Vision-Language Models (VLMs) like CLIP have emerged as a dominant paradigm for generalizable deepfake detection, a representational disconnect remains: their semantic-centric pre-training is ill-suited for capturing non-semantic artifacts inherent to hyper-realistic synthesis. In this work, we identify a failure mode termed Optimization Collapse, wher

Dinh Dũng, Helmut Harbrecht, Van Kien Nguyen, Christoph Schwab

In this article, we consider the solution to elliptic diffusion problems on a class of random domains obtained by log-Gaussian random homothety of the unit disk respectively an annulus. We model the problem under consideration and verify the existence and uniqueness of the random solution by path-wise pullback to the nominal unit disk respectively annulus. W

Thow Min Jerald Cham, Mowen Zhao, Wenyi Zhou, Andrew Koerner

Epitaxial heterostructures of two-dimensional van der Waals magnets and topological insulators offer a powerful platform for probing interfacial spin interactions that govern magnetic textures in low-dimensional quantum systems, while simultaneously enabling highly efficient, atomically thin spin-orbit-torque memory and computing architectures. Despite this

Anjun Gao, Zhenglin Wan, Pingfu Chao, Shunyu Yao

The integration of GNSS data into portable devices has led to the generation of vast amounts of trajectory data, which is crucial for applications such as map-matching. To tackle the limitations of rule-based methods, recent works in deep learning for trajectory-related tasks occur. However, existing models remain challenging due to issues such as the diffic

Anuradha Rajput, Arnab Bhattacharjee, Annwesha Dutta

Active filaments driven by tangential forces can become trapped in a spinning state when attached to a heavy head, where activity and inertia drive persistent rotation rather than directed transport. Using three-dimensional Langevin dynamics of tangentially driven bead-spring chains anchored to a common heavy head, we demonstrate that increasing the filament

Giuseppe Bevilacqua, Valerio Biancalana, Roberto Cecchi

We present a system for generating arbitrary, triaxial magnetic waveforms with a spectral content spanning from DC to tens of kHz, a critical capability for quantum control and spin manipulation. To compensate for amplifier-coil dynamics, we implement a data-driven approach to identify a numerical compensation model. The method parametrizes the system respon

Caishuang Huang, Yang Qiao, Rongyu Zhang, Junjie Ye

Tool-use capabilities are vital for Large Language Models (LLMs) in finance, a domain characterized by massive investment targets and data-intensive inquiries. However, existing data synthesis methods typically rely on a reverse synthesis paradigm, generating user queries from pre-sampled tools. This approach inevitably introduces artificial explicitness, yi

Yingji Shen, Jie Tang, Li Wang, Yongcheng Wu

Reactor CEvNS experiments provide a powerful probe of the physics beyond the Standard Model (BSM) with the intense flux of neutrinos, photons, and other particles produced in nuclear reactors. In this work, we investigate the sensitivity of reactor CEvNS experiments to the dark axion portal, which connects the axion or axion-like particle to the dark photon.

Sunwhi Kim, Sunyul Kim

Generative AI now produces photorealistic portraits that circulate widely in social and newslike contexts. Human ability to distinguish real from synthetic faces is time-sensitive because image generators continue to improve while public familiarity with synthetic media also changes. Here, we provide a time-stamped snapshot of human ability to distinguish re

Huanyu Li, Dewei Wang, Xinmiao Wang, Xinzhe Liu

Humanoid robots often need to balance competing objectives, such as maximizing speed while minimizing energy consumption. While current reinforcement learning (RL) methods can master complex skills like fall recovery and perceptive locomotion, they are constrained by fixed weighting strategies that produce a single suboptimal policy, rather than providing a

Jie Tian, Xin Xu, Qijun Zhi, Jiguang Lu

A more realistic description of the magnetosphere is crucial for understanding the radiation emitted by pulsars. In this paper, we revisit the annular gap model by employing a rotating dipole field, which is more realistic than the static dipole field, as an approximation of the magnetic structure of the pulsar magnetosphere. Compared with the static dipole

Jiawen Wen, Suixuan Qiu, Zihang Luo, Xiaofei Yang

Deep learning methods, including Convolutional Neural Networks, Transformers and Mamba, have achieved remarkable success in hyperspectral image (HSI) classification. Nevertheless, existing methods exhibit inflexible integration of local-global representations, inadequate handling of spectral-spatial scale disparities across heterogeneous bands, and susceptib

Andrea Manzoni

Standard LoRA fine-tuning of Mixture-of-Experts (MoE) models applies adapters to every expert, yet our profiling shows that per-layer expert routing is highly skewed: a small subset of experts handles most tokens in each layer, while many others are rarely activated ("cold"). We propose MoE-Sieve, a simple routing-guided framework for LoRA fine-tuning, and p

Yeqi He, Liang Li, Zhiwen Yang, Xichun Sheng

Diffusion models have demonstrated remarkable performance in image generation, particularly within the domain of style transfer. Prevailing style transfer approaches typically leverage pre-trained diffusion models' robust feature extraction capabilities alongside external modular control pathways to explicitly impose style guidance signals. However, these me

Michael Frank, Cristian Ivanescu

We investigate the structural reasons behind the existence and non-existence of bounded module functionals and bounded module operators on Hilbert C*-modules whose kernels are not orthogonally complemented. The key motivating example was introduced by J. Kaad and M. Skeide in their 2023 paper. Building on their ideas and related developments, we obtain a str

Zhiyao Tan, Liu Li, Huazhen Lin

Deep neural networks (DNNs) achieve remarkable predictive performance but remain difficult to interpret, largely due to overparameterization that obscures the minimal structure required for interpretation. Here we introduce DeepIn, a self-interpretable neural network framework that adaptively identifies and learns the minimal representation necessary for pre

Xianqi Liu, Xiangru Li, Ziyu Fang

Metal-poor (MP) stars are important targets for investigating the chemical evolution of the early universe. Among them, Carbon-Enhanced Metal-Poor (CEMP) stars have attracted extensive attention due to their rarity and astrophysical significance. Owing to their low occurrence rate, the identification of MP stars and CEMP stars remains a task of considerable

Haiyang Xu, Ronghuan Wu, Li-Yi Wei, Nanxuan Zhao

Graphic icons are a cornerstone of modern design workflows, yet they are often distributed as flattened single-path or compound-path graphics, where the original semantic layering is lost. This absence of semantic decomposition hinders downstream tasks such as editing, restyling, and animation. We formalize this problem as semantic layer construction for fla

Yadong Niu, Tianzi Wang, Heinrich Dinkel, Xingwei Sun

General audio understanding is a fundamental goal for large audio-language models, with audio captioning serving as a cornerstone task for their development. However, progress in this domain is hindered by existing datasets, which lack the scale and descriptive granularity required to train truly versatile models. To address this gap, we introduce ACAVCaps,

Kaiyuan Ji, Yixuan Gao, Lu Sun, Yushuo Zheng

Advertising images significantly impact commercial conversion rates and brand equity, yet current evaluation methods rely on subjective judgments, lacking scalability, standardized criteria, and interpretability. To address these challenges, we present A^3 (Advertising Aesthetic Assessment), a comprehensive framework encompassing four components: a paradigm

Sooryansh Asthana, Conan Alexander, Anubhav Kumar Srivastava, T. S. Mahesh

Quantum probes that enable enhanced exploration and characterization of complex systems are central to modern science, spanning applications from biology to astrophysics and chemical design. In large many-body quantum systems, interactions delocalize phase information across many degrees of freedom, dispersing it away from accessible measurements and limitin

Xiaoyong Guo, Nanjie Li, Zijie Zeng, Kai Wang

Contextual automatic speech recognition (ASR) with Speech-LLMs is typically trained with oracle conversation history, but relies on error-prone history at inference, causing a train-test mismatch in the context channel that we term contextual exposure bias. We propose a unified training framework to improve robustness under realistic histories: (i) Teacher E

Ruobing Wang, Xin Li, Yujie Fang, Mingzhong Wang

We propose Score-based Relaxation-guided Generation (SRG), a generative framework based on an approximate formulation of relaxation-guided stochastic differential equations (SDEs) for mixed-integer linear programming. SRG employs a Transformer-based score network that incorporates feasibility and optimality signals into score modeling, encouraging the learne

Linhao Li, Yuan Yao

We investigate the extension of pure-state symmetry protected topological phases to mixed-state regime with a strong U(1) and a weak $\mathbb{Z}_2$ symmetries in one-dimensional spin systems by the concept of quantum channels. We propose a corresponding topological phase order parameter for short-range entangled mixed states by showing that it is quantized a

Sa Zhu, Wanqian Zhang, Lin Wang, Xiaohua Chen

Open-Vocabulary Temporal Action Detection (OV-TAD) aims to classify and localize action segments in untrimmed videos for unseen categories. Previous methods rely solely on global alignment between label-level semantics and visual features, which is insufficient to transfer temporal consistent visual knowledge from seen to unseen classes. To address this, we

Mingxing Wu, Kouta Kondou, Taishi Chen, Satoru Nakatsuji

The noncollinear antiferromagnets Mn$_3$Sn/Ge emerge as promising spin-current sources with both in-plane and out-of-plane spin polarizations, thereby enabling field-free magnetization switching. However, the microscopic origin of the out-of-plane spin polarization remains under debate, specifically whether it arises from the magnetic spin Hall effect (MSHE)

Masahiro Kaminaga

We study stationary scattering for Schr\"odinger operators in $\mathbb R^3$ with finitely many concentric $\delta$--shell interactions of constant real strengths. Starting from the self--adjoint realization and the boundary resolvent formula for this model, we show that, after partial--wave reduction, the same finite-dimensional boundary matrices that arise

Yannik Mahlau, Lukas Berg, Bodo Rosenhahn

Finite-difference time-domain (FDTD) simulations often involve physical quantities spanning multiple orders of magnitude, such as the speed of light or electromagnetic field amplitudes. The standard practice for maintaining numerical accuracy in many FDTD implementations is to use 32-bit or 64-bit floating-point values to represent the electric and magnetic

Tian Guo, Hui Yuan, Chang Sun, Wei Zhang

Point cloud compression often introduces noticeable reconstruction artifacts, which makes quality enhancement necessary. Existing approaches typically assume prior knowledge of the distortion level and train multiple models with identical architectures, each designed for a specific distortion setting. This significantly limits their practical applicability i

Abidemi Orimogunje, Vukan Ninkovic, Ognjen Kundacina, Hyunwoo Park

Highly directional mmWave/THz links require rapid beam alignment, yet exhaustive codebook sweeps incur prohibitive training overhead. This letter proposes a sensing-assisted adaptive probing policy that maps multimodal sensing (radar/LiDAR/camera) to a calibrated prior over beams, predicts per-beam reward with a deep Q-ensemble whose disagreement serves as a

Chinmay Soni, Shivam Chourasia, Gaurav Kumar, Hitesh Kapoor

Applying large, proprietary API-based language models to text-to-SQL tasks poses a significant industry challenge: reliance on massive, schema-heavy prompts results in prohibitive per-token API costs and high latency, hindering scalable production deployment. We present a specialized, self-hosted 8B-parameter model designed for a conversational bot in CriQ,

K. Wimmer, T. Hüyük, S. M. Lenzi, A. Poves

Isospin symmetry in the $A=62$ mass system was investigated through Coulomb excitation reactions at the RIKEN Radioactive Isotope Beam Factory. Beams of $^{62}$Zn, $^{62}$Ga, and $^{62}$Ge were studied using the BigRIPS-ZeroDegree-DALI2$^+$ setup under identical experimental conditions, allowing for cancellation of systematic uncertainties. Inelastic scatter

Ziyin Zhou, Jianyi Zhang, Xu ji, Yilong Li

Large Language Models (LLMs) are essential for analyzing and addressing vulnerabilities in cybersecurity. However, among over 200,000 vulnerabilities were discovered in the past decade, more than 30,000 have been changed or updated. This necessitates frequent updates to the training datasets and internal knowledge bases of LLMs to maintain knowledge consiste

Li Gao, Fuzhi Yang, Jianhui Chen, Liu Liu

Despite significant advances in quadrupedal robotics, a critical gap persists in foundational motion resources that holistically integrate diverse locomotion, emotionally expressive behaviors, and rich language semantics-essential for agile, intuitive human-robot interaction. Current quadruped motion datasets are limited to a few mocap primitives (e.g., walk

Yusuf Wicaksono, Toshikaze Kariyado

Nonlinear Hall responses are a direct electrical probe of quantum geometry, but they are symmetry-forbidden in many pristine two-dimensional metals. We show that layer-selective magnetic proximity unlocks intrinsic linear and nonlinear Hall effects in metallic $1H-NbX_2$ ($X=\mathrm{S,Se,Te}$), where native $D_{3h}$ symmetry forces both the anomalous Hall co

Bingqing Wei, Zhongyu Xia, Dingai Liu, Xiaoyu Zhou

Vision-language models (VLMs) have shown remarkable general capabilities, yet embodied agents built on them fail at complex tasks, often skipping critical steps, proposing invalid actions, and repeating mistakes. These failures arise from a fundamental gap between the static training data of VLMs and the physical interaction for embodied tasks. VLMs can lear

Kazuhiro Yamada, Keisuke Fujii

Shooting location is a core indicator of offensive style in invasion sports. Existing basketball shot-chart analyses often use spatial information for descriptive visualization, location-based efficiency modeling, or clustering players into shooting archetypes, yet few studies provide a unified framework for fair comparison of shot-type-specific tendencies.

Xusen Guo, Mingxing Peng, Hongliang Lu, Hai Yang

Participatory urban sensing leverages human mobility for large-scale urban data collection, yet existing methods typically rely on centralized optimization and assume homogeneous participants, resulting in rigid assignments that overlook personal preferences and heterogeneous urban contexts. We propose MAPUS, an LLM-based multi-agent framework for personaliz

Chang Wei, Yuchen Fan, Chin Chun Ooi, Jian Cheng Wong

Physics-informed neural networks (PINNs) have shown promise for solving partial differential equations (PDEs) by directly embedding them into the loss function. Despite their notable success, existing PINNs often exhibit training instability and slow convergence when applied to strongly nonlinear fluid dynamics problems. To address these challenges, this pap

Wassim Swaileh, Mohammed-En-Nadhir Zighem, Hichem Telli, Salah Eddine Bekhouche

Islamic inheritance (Ilm al-Mawarith) is a multi-stage legal reasoning task requiring the identification of eligible heirs, resolution of blocking rules (hajb), assignment of fixed and residual shares, handling of adjustments such as awl and radd, and generation of a consistent final distribution. The task is further complicated by variations across legal sc

Cheng Chen, Ju-Jun Xie

In this work, we systematically investigate the evolution of spin polarization and quantum entanglement in cascade decays of baryon-antibaryon pairs, which are produced in electron-positron annihilation. We derive a fully analytical spin density matrix explicitly expressed in terms of spin polarization observables, extend this formalism to multi-step cascade

Hongbin Kim, Soung Yong Yun, Jaeguk Lee, Dong-Yeop Na

Voltage breakdown in high-voltage pulsed vacuum systems arises from nonlinear multiscale interactions among circuit dynamics, kinetic plasma evolution, and ion-induced secondary electron emission (SEE) at electrode surfaces. Although circuit-plasma co-simulation frameworks couple lumped circuits with particle-in-cell (PIC) solvers, most neglect energy-resolv

Thibault Fronville, Maximilian Pichler, Johannes Signer, Marius Grabow

Understanding how animals move through heterogeneous landscapes is central to ecology and conservation. In this context, step selection functions (SSFs) have emerged as the main statistical framework to analyze how biotic and abiotic predictors influence movement paths observed by radio tracking, GPS tags, or similar sensors. A traditional SSF consists of a

Carlo Marzo

An attempt to evade the strict uniqueness of consistent interactions involving spin-2 particles is made by modifying the Noether procedure from the outset. A vector field is introduced, coupled to a graviton already at the level of quadratic mixing. The byproduct is a gauge-invariant mass for the vector and novel consistent interactions, here derived and tes

Yachao Sun, Xuesong Li, Yanting Wang, Jing Zhou

Dynamic hysteresis, the rate-dependent lagged response of materials to external fields, underpins applications from energy-efficient transformers to gas storage systems. A fundamental yet unresolved question is how the hysteresis loop area $A$ scales with the field sweep rate $R$. Here, we reveal that a competition between the field sweep and thermal fluctua

Hongshen Zhao, Jingkang Tai, Yuhang Wu, Wenkang Zhang

Underwater Video Object Segmentation (VOS) is essential for marine exploration, yet open-air methods suffer significant degradation due to color distortion, low contrast, and prevalent camouflage. A primary hurdle is the lack of high-quality training data. To bridge this gap, we introduce $\textbf{UW-VOS}$, the first large-scale underwater VOS benchmark comp

Zongyang He, Xiangli Yang, Xian Gao, Zhiguo Wang

With the continuous improvement in the spatial resolution of optical remote sensing imagery, accurate road extraction has become increasingly important for applications such as urban planning, traffic monitoring, and disaster management. However, road extraction in complex urban and rural environments remains challenging, as roads are often occluded by trees

Kun-Yang Yu, Zhi Zhou, Shi-Yu Tian, Xiao-Wen Yang

Multimodal Large Language Models (MLLMs) have demonstrated remarkable reasoning capabilities across modalities such as images and text. However, tabular data, despite being a critical real-world modality, remains relatively underexplored in multimodal learning. In this paper, we focus on the task of Tabular-Vision Multi-Modal Understanding (TVMU) and identif

Hao Zhou, Siqi Cai, Hua Dai, Geng Yang

Differential privacy (DP) is crucial for safeguarding sensitive client information in federated learning (FL), yet traditional DP-FL methods rely predominantly on fixed gradient clipping thresholds. Such static clipping neglects significant client heterogeneity and varying privacy sensitivities, which may lead to an unfavorable privacy-utility trade-off. In

Ananda Rimal, Adarsha Rimal

Romanized Nepali, the Nepali language written in the Latin alphabet, is the dominant medium for informal digital communication in Nepal, yet it remains critically underresourced in the landscape of Large Language Models (LLMs). This study presents a systematic benchmarking of linguistic adaptation across three comparable-sized open-weight models: Llama-3.1-8

Xin Xu, Qijun Zhi, Jie Tian, Jiguang Lu

Rotating Radio Transients (RRATs) are a class of pulsar-like objects characterized by intermittent radio emissions. Among them, RRATs that exhibit both RRAT and normal pulsar (NP) states may represent a key evolutionary stage from nulling pulsars to RRATs. We performed a detailed analysis of RRAT J1574+4703 using the Five-hundred-meter Aperture Spherical Rad

Ju Kang, Yiyuan Niu, Yuanzhi Li, Quan-Xing Liu

Spatial patterning and synchronization are pervasive features of plankton communities, yet the mechanisms that allow such patterns to persist coherently under environmental noise remain unresolved. In vertically structured aquatic ecosystems, plankton populations are often organized into distinct layers, raising the question of how interactions between layer

Qi Dong, Ziheng Lin, Ning Ding

Retrieval-Augmented Generation (RAG) grounds Large Language Models (LLMs) in external knowledge but often suffers from flat context representations and stateless retrieval, leading to unstable performance. We propose Stateful Evidence-Driven RAG with Iterative Reasoning, a framework that models question answering as a progressive evidence accumulation proces

Xi Wang, Hui Ren, L. -N. Sun, K. -F. Cui

Nonadiabatic holonomic quantum computation (NHQC) offers intrinsic resilience to certain control imperfections. However, conventional nonadiabatic holonomic protocols are constrained by the fixed-pulse-area condition, which limits flexibility and prolongs duration of small-angle gates. Here we experimentally demonstrate a universal brachistochrone nonadiabat

Yao Chen, Yilong Chen, Yinqi Yang, Junyuan Shang

Existing approaches to increasing the effective depth of Transformers predominantly rely on parameter reuse, extending computation through recursive execution. Under this paradigm, the network structure remains static along the training timeline, and additional computational depth is uniformly assigned to entire blocks at the parameter level. This rigidity a

Yumeng Liu, Xiao-Xiao Long, Marc Habermann, Xuanze Yang

Recovering high-fidelity 3D hand geometry from images is a critical task in computer vision, holding significant value for domains such as robotics, animation and VR/AR. Crucially, scalable applications demand both accuracy and deployment flexibility, requiring the ability to leverage massive amounts of unstructured image data from the internet or enable dep

Shariq Murtuza

The proliferation of local Large Language Model (LLM) runners, such as Ollama, LM Studio and llama.cpp, presents a new challenge for digital forensics investigators. These tools enable users to deploy powerful AI models in an offline manner, creating a potential evidentiary blind spot for investigators. This work presents a systematic, cross platform forensi

Junheng Li, Lizhi Yang, Aaron D. Ames

Real-time humanoid teleoperation requires inverse kinematics (IK) solvers that are both responsive and constraint-safe under kinematic redundancy and self-collision constraints. While differential IK enables efficient online retargeting, its locally linearized updates are inherently basin-dependent and often become trapped near joint limits, singularities, o

Victor H. Aguiar, Nail Kashaev

Modern pretrained time-series foundation models can forecast without task-specific training, but they do not fully incorporate economic behavior. We show that teaching them basic economic logic improves how they predict demand using an experimental panel. We fine-tune Amazon Chronos-2, a transformer-based probabilistic time-series model, on synthetic data ge

Bahruz Suleymanli, Kutsal Bozkurt, Elias Khan, Haşim Güven

The dynamical influence of $Λ$ hyperons on the excited-state properties of closed-shell multi-$Λ$ Ca, Ni, Sn and Pb hypernuclei is investigated using the self-consistent Hartree-Fock + Random Phase Approximation in coordinate space. The strength distributions for the isoscalar monopole, isovector dipole, and isoscalar quadrupole modes are calculated, reveali

Maoxin Ji, Qiong Wu, Pingyi Fan, Kezhi Wang

Deploying six-dimensional movable antenna (6DMA) systems in Internet-of-Vehicles (IoV) scenarios can greatly enhance spectral efficiency. However, the high mobility of vehicles causes rapid spatio-temporal channel variations, posing a significant challenge to real-time 6DMA optimization. In this work, we pioneer the application of 6DMA in IoV and propose a l

Kaize Shi, Xueyao Sun, Qika Lin, Firoj Alam

Retrieval-augmented generation (RAG) has shown promising results in enhancing Q&A by incorporating information from the web and other external sources. However, the supporting documents retrieved from the heterogeneous web often originate from multiple sources with diverse writing styles, varying formats, and inconsistent granularity. Fusing such multi-sourc

Hieu Hoang, Dung Trung Tran, Hong Nguyen, Nam-Phong Nguyen

Online Action Detection (OAD) systems face two primary challenges: high computational cost and insufficient modeling of discriminative temporal dynamics against background motion. Adding optical flow could provides strong motion cues but it incurs significant computational overhead. We propose CAKE, a OAD Flow-based distillation framework to transfer motion