March 2026 arXiv papers — page 104

Lukas Bayer, Sheethal Bhat, Andreas Maier

Accurate multi-organ segmentation in abdominal CT scans is essential for computer-aided diagnosis and treatment. While convolutional neural networks (CNNs) have long been the standard approach in medical image segmentation, transformer-based architectures have recently gained attention due to their ability to model long-range dependencies. In this study, we

Yang Zeng, Zhen Chen, Lei Xu

Drop impacts are ubiquitous in natural and industrial processes, yet the influence of drop shape on impact force remains a fundamental open question. Combining experiments with theoretical analysis, we show that drop shape plays a critical role, with impact force varying by more than an order of magnitude solely due to changes in shape. By uncovering self-si

Wanjia Zhao, Ludwig Schmidt, James Zou, Vidhisha Balachandran

Tool-augmented large language models (LLMs) must tightly couple multi-step reasoning with external actions, yet existing benchmarks often confound this interplay with complex environment dynamics, memorized knowledge or dataset contamination. In this paper, we introduce ZebraArena, a procedurally generated diagnostic environment for studying reasoning-action

Mohammadhossein Homaei, Iman Khazrak, Rubén Molano, Andrés Caro

Industrial Cyber-Physical Systems (ICPS) face growing threats from cyber-attacks that exploit sensor and control vulnerabilities. Digital Twin (DT) technology can detect anomalies via predictive modelling, but current methods cannot distinguish attack types and often rely on costly full-system shutdowns. This paper presents i-SDT (intelligent Self-Defending

Maxime Poli, Manel Khentout, Angelo Ortiz Tandazo, Ewan Dunbar

We introduce DiscoPhon, a multilingual benchmark for evaluating unsupervised phoneme discovery from discrete speech units. DiscoPhon covers 6 dev and 6 test languages, chosen to span a wide range of phonemic contrasts. Given only 10 hours of speech in a previously unseen language, systems must produce discrete units that are mapped to a predefined phoneme in

Thi Huyen Nguyen, Koustav Rudra, Wolfgang Nejdl

Advances in social media data dissemination enable the provision of real-time information during a crisis. The information comes from different classes, such as infrastructure damages, persons missing or stranded in the affected zone, etc. Existing methods attempted to classify text and images into various humanitarian categories, but their decision-making p

Leonardo Becchetti, Franceso Salustri, Nazaria Solferino

Historical episodes such as the World War I "live-and-let-live" system and the Christmas Truce of 1914 demonstrate that opposing military units can establish spontaneous, local cooperation even in extreme conflict environments. Such cooperative behavior is typically fragile and temporary, while large-scale wars persist. We develop a hierarchical decision pro

Jason P. Beech, Jonas O. Tegenfeldt

We present a lithographic method for fabricating free-standing microparticles directly from SUEX dry-film epoxy resist. Unlike conventional SU-8 particle fabrication, which requires patterning on solid substrates followed by sacrificial-layer release, our approach eliminates substrate use entirely and produces particles with near 100% yields. The process sup

Lei Yu, Peng Wang, Jingyuan Zhang, Xin Wang

SQL query comprehension is a significant challenge due to complex syntax, diverse join types, and deep nesting. Many queries lack adequate comments, severely hindering code readability, maintainability, and knowledge transfer. Automated SQL comment generation faces two main challenges: limited datasets that inadequately represent complex real-world queries,

Piotr Bizoń, Tomasz Romańczukiewicz

We study long-time dynamics of small even perturbations of the soliton in 1D quadratic Klein-Gordon equation. The soliton possesses both an internal mode and the unstable mode. On a codimension-one manifold of fine-tuned initial data the instability is suppressed and the internal mode decays slowly by transferring energy into the continuum. We show that this

Xin Li, Shiming Yu, Leming Shen, Jianing Zhang

Traditional RAN systems are closed and monolithic, stifling innovation. The openness and programmability enabled by Open Radio Access Network (O-RAN) are envisioned to revolutionize cellular networks with control-plane applications--xApps. The development of xApps (typically by third-party developers), however, remains time-consuming and cumbersome, often re

Cheng-Cheng Xie, Hai-Jun Tian, Jian-Rong Shi, Ze-Ming Zhou

Using a sample of 116 wide binary systems as coeval and chemically homogeneous stellar pairs, we investigate the factors governing lithium depletion in main-sequence stars. We recover the well-established morphology of the lithium--effective temperature ($T_{\mathrm{eff}}$) relation, including the Li dip (6200--6600\,K), the Li plateau (6000--6200\,K), and a

Zhang Liu, Xianbin Wang, Shumin Lian, Lianfen Huang

To support future diverse applications, multi-link operation (MLO) has been introduced in the Wi-Fi 7 standard (IEEE 802.11be) to enable concurrent communication over multiple frequency bands. This new capability relies on a two-tier medium access control (MAC) architecture, where the upper MAC (U-MAC) allocates traffic across links and the lower MAC (L-MAC)

Shimaa Naser, Maryam Tariq, Raneem Abdel-Rahim, De Mi

Direct handset-to-satellite (DHTS) communication is emerging as a core capability of 6G non-terrestrial networks, enabling standard devices to directly access low Earth orbit (LEO) satellites. While LEO provides the physical access layer for DHTS, large-scale device connectivity introduces challenges in mobility management, interference control, spectrum eff

Bingqi Ma, Linlong Lang, Ming Zhang, Dailan He

The dual-stream transformer architecture-based joint audio-video generation method has become the dominant paradigm in current research. By incorporating pre-trained video diffusion models and audio diffusion models, along with a cross-modal interaction attention module, high-quality, temporally synchronized audio-video content can be generated with minimal

Yingchao Cheng, Meijia Wang, Zhifeng Hao, Rajkumar Buyya

While Spiking Neural Networks (SNNs) promise to circumvent the severe Size, Weight, and Power (SWaP) constraints of edge intelligence, the field currently faces a "Deployment Paradox" where theoretical energy gains are frequently negated by the inefficiencies of mapping asynchronous, event-driven dynamics onto traditional von Neumann substrates. Transcending

Vasileios Komianos, Emmanuel Rovithis, Athanasios Tsipis

This paper presents an analysis of five years (2021 - 2025) of conference discourse across six digital art conferences, aiming to trace thematic shifts associated with the rapid development of emerging technologies, namely artificial intelligence (AI), immersive technologies (including XR and the metaverse), and blockchain technologies and non-fungible token

Lu Yu, Haiyang Zhang, Changsheng Xu

Due to the impressive zero-shot capabilities, pre-trained vision-language models (e.g., CLIP), have attracted widespread attention and adoption across various domains. Nonetheless, CLIP has been observed to be susceptible to adversarial examples. Through experimental analysis, we have observed a phenomenon wherein adversarial perturbations induce shifts in t

Ye Kyaw Thu, Thazin Myint Oo, Thepchai Supnithi

We present the first systematic benchmark on a standardized iteration of the publicly available Burmese Handwritten Digit Dataset (BHDD), which we have designated as myMNIST Benchmarking. While BHDD serves as a foundational resource for Myanmar NLP/AI, it lacks a comprehensive, reproducible performance baseline across modern architectures. We evaluate eleven

Xuan Liu, Xiaobin Chang

Weight regularization methods in continual learning (CL) alleviate catastrophic forgetting by assessing and penalizing changes to important model weights. Elastic Weight Consolidation (EWC) is a foundational and widely used approach within this framework that estimates weight importance based on gradients. However, it has consistently shown suboptimal perfor

Zhenghua Hu, Tairan Dan, Zeyu Tao, Jiacheng Qian

Just as Caesar declared "alea iacta est" (the die is cast) upon crossing the Rubicone river, lane change decisions in autonomous vehicles also represent critical points of no return. RUBICONe addresses this challenge by recognizing that lane change decision-making relying solely on a single vehicle's perception would be as precarious as crossing an unknown r

Nejc Geržinič, Mark van Hagen, Hussein Al-Tamimi, Niels van Oort

Access and egress trips constitute a substantial part of a train trip in minds of travellers, often being the deciding factor whether to travel by train at all. Despite a host of studies analysing individual legs within a multimodal trip chain, the full chain within a multimodal trip - including access, main and egress - has seen very limited attention. To u

Momose Oyama, Yusuke Takase, Hidetoshi Shimodaira

We propose a method that represents language models by log-likelihood vectors over prompt-response pairs and constructs model maps for comparing their conditional distributions. In this space, squared Euclidean distances between models are approximately proportional to the KL divergence between the corresponding conditional distributions. Experiments on a la

Siyan Lin, Zhen-Yu Zheng, Shu Chen

We investigate the distribution of fidelity zeros in two-band topological models by extending the phase transition driving parameter into the complex plane. Within the biorthogonal formulation, we unveil that fidelity zeros are related to momentum modes for which the real part of the energy gap vanishes. Guided by this relation, we analyze the Kitaev chain,

Peng Zhu, Kangjia Xue, Lin Wang, Yuliang Zhang

This paper presents the design and implementation of a high-density, deterministic trigger distribution system tailored for the C-band photocathode electron gun test platform at the Southern Advanced Photon Source (SAPS). Implemented within a scalable 6U VME modular architecture, the system achieves high-density integration by consolidating a master controll

Morten Byholt, Nils Lid Hjort

A basic issue in both teaching of and practice of statistics is the interplay between modelling assumptions and inference performance. The general message conveyed is that stronger assumptions lead to better statistical performance of the relevant estimators, tests and confidence intervals, provided that these assumptions hold. On the other hand, fewer assum

Eunseon Choi, Junwoo Hong, Daehan Lee, Sanghyun Park

Accurate localization in autonomous driving is critical for successful missions including environmental mapping and survivor searches. In visually challenging environments, including low-light conditions, overexposure, illumination changes, and high parallax, the performance of conventional visual odometry methods significantly degrade undermining robust rob

Shaolin Wang, Yi Mei, Haoyang Che, He Jiang

Software systems have traditionally been designed for human interaction, emphasizing graphical user interfaces, usability, and cognitive alignment with end users. However, recent advances in large language model (LLM)-based agents are changing the primary consumers of software systems. Increasingly, software is no longer only used by humans, but also invoked

Jiahe Wang, Cong Liang, Xuandong Huang, Yuxin Wang

Facial behavior constitutes the primary medium of human nonverbal communication. Existing synthesis methods predominantly follow two paradigms: coarse emotion category labels or one-hot Action Unit (AU) vectors from the Facial Action Coding System (FACS). Neither paradigm reliably renders fine-grained facial behaviors nor resolves anatomically implausible ar

Stavros Christodoulou, Nicolaos Toumbas

We study soft emission in QED during scattering of Faddeev-Kulish dressed states. The incoming and outgoing charged particles are accompanied by coherent clouds of soft photons with energies below a characteristic infrared scale $E_d$. We focus on explicit processes that allow the dependence of the soft factors on the hard particles' momenta and total an

Wei Wang, Yakun Li

In this paper, we propose and develop a novel nonlocal variational technique based on saturation-value similarity for color image restoration. In traditional nonlocal methods, image patches are extracted from red, green and blue channels of a color image directly, and the color information can not be described finely because the patch similarity is mainly ba

Swarnendu Banik, Manish Das, Shiv Ram Dubey, Satish Kumar Singh

Vision Transformers have excelled in computer vision but their attention mechanisms operate independently across layers, limiting information flow and feature learning. We propose an effective cross-layer attention propagation method that preserves and integrates historical attention matrices across encoder layers, offering a principled refinement of inter-l

Nikolaos D. Tantaroudas, Guanqun Gai, Ilias Karachalios

Model Reference Adaptive Control based on Lyapunov stability theory is developed for gust load alleviation of nonlinear aeroelastic systems. The controller operates on a nonlinear reduced-order model derived from Taylor series expansion and eigenvector projection of the coupled fluid-structure-flight dynamic equations. The complete MRAC formulation is presen

Arka Bhattacharyya, Sanjoy Biswas, Saurabh Niyogi

We investigate the possibility of realizing strong first-order electroweak phase transition (SFOEWPT) in an effective field theory framework where the Standard Model is extended with a complex scalar singlet ($\phi$) charged under a local $U(1)_D$ gauge group. The tree-level scalar potential contains a dimension-six term of the form $|H|^2|\phi|^4$. We show

Yusen Wu, Xiaotie Deng

Existing Retrieval-Augmented Generation (RAG) methods for code struggle to capture the high-level architectural patterns and cross-file dependencies inherent in complex, theory-driven codebases, such as those in algorithmic game theory (AGT), leading to a persistent semantic and structural gap between abstract concepts and executable implementations. To addr

Eduar Castrillo Velilla

DRESS is a deterministic, parameter-free framework for structural graph refinement that iteratively refines the structural similarity of edges in a graph to produce a canonical fingerprint: a real-valued edge vector, obtained by converging a nonlinear dynamical system to its unique fixed point. $\Delta$-DRESS is a member of the DRESS family of graph fingerpr

Akhilesh Badra, Hemant Kumar Singh

In this paper, we introduce a novel distance-like notion of furtherness for finite topological spaces, demonstrating that every finite space can be viewed as an asymmetric pseudometric space. In particular, we show that every finite T0 space is asymmetric metric space. The topology induced by the forward balls coincides with the original topology of the spac

Abhinaba Basu, Pavan Chakraborty

Evaluating whether explanations faithfully reflect a model's reasoning remains an open problem. Existing benchmarks use single interventions without statistical testing, making it impossible to distinguish genuine faithfulness from chance-level performance. We show that faithfulness is not a fixed property but an operator-dependent quantity that changes

Anqi Wang, Lei Han, Jiahua Dong, Muzhi Zhou

Digital platforms frequently reproduce heteronormative norms and structural biases, limiting inclusive communication between LGBTQ+ and cisgender individuals. The Metaverse, with its affordances for identity fluidity, presence, and community governance, offers a promising site for reimagining such interactions. To investigate this potential, we conducted par

Duc V. Nguyen, Huyen T. T. Tran

Short video streaming platforms have gained immense popularity in recent years, transforming the way users consume video content. A critical aspect of user interaction with these platforms is the swipe gesture, which allows users to navigate through videos seamlessly. However, the delay between a user's swipe action and the subsequent video playback can sign

Sebastian Bartling, Kazuhiro Ito

We prove a geometric local constancy theorem for affine Springer fibers in families of close local fields. Consequently, stable orbital integrals are locally constant in these families, and both the base change fundamental lemma and the standard endoscopic fundamental lemma transfer from characteristic zero to arbitrary positive characteristic.

Jerome Ramos, Feng Xia, Xi Wang, Shubham Chatterjee

Training conversational recommender systems (CRS) requires extensive dialogue data, which is challenging to collect at scale. To address this, researchers have used simulated user-recommender conversations. Traditional simulation approaches often utilize a single large language model (LLM) that generates entire conversations with prior knowledge of the targe

Xiang Zhou, Hong Shang, Zijian Zhan, Tianyu He

Deep unrolled models (DUMs) have become the state of the art for accelerated MRI reconstruction, yet their robustness under domain shift remains a critical barrier to clinical adoption. In this work, we identify coil sensitivity map (CSM) estimation as the primary bottleneck limiting generalization. To address this, we propose UEPS, a novel DUM architecture

Yicheng Hu, Xinyu Lin, Shulin Li, Wenjie Wang

Subcellular localization is a crucial biological task for drug target identification and function annotation. Although it has been biologically realized that subcellular localization is closely associated with protein structure, no existing dataset offers comprehensive 3D structural information with detailed subcellular localization annotations, thus severel

Jiahao Zhang, Yilong Wang, Suhang Wang

Graph neural networks (GNNs) are widely used for learning from graph-structured data in domains such as social networks, recommender systems, and financial platforms. To comply with privacy regulations like the GDPR, CCPA, and PIPEDA, approximate graph unlearning, which aims to remove the influence of specific data points from trained models without full ret

Akira Saito, Ryo Sugai, Zhongxu Wang, Hidetaka Saomoto

This paper proposes a robust damage identification method using noisy frequency response functions (FRFs) and topology optimization. We formulate the damage identification problem as an inverse problem of generating the damage topology of the structure from measured dynamic responses of the structure to given external dynamic loading. The method is based on

Maryam Bajalan, Peter Boyvalenkov, Ferruh Özbudak

Mixed (asymmetric) orthogonal arrays (MOAs) generalize classical orthogonal arrays by allowing columns over different alphabets. However, their study requires very different structural tools than those used for symmetric orthogonal arrays (OAs), since several key features of the symmetric setting are no longer available in the mixed case, including Euclidean

Shenggui Li, Chao Wang, Yikai Zhu, Yubo Wang

Large language models incur high inference latency due to sequential autoregressive decoding. Speculative decoding alleviates this bottleneck by using a lightweight draft model to propose multiple tokens for batched verification. However, its adoption has been limited by the lack of high-quality draft models and scalable training infrastructure. We introduce

Yang Liu

We present a study of dark matter and the strong CP problem within a globally consistent framework of Type IIA string theory, compactified on a $T^6/(\mathbb{Z}_2 \times \mathbb{Z}_2)$ orientifold with intersecting D6-branes (Model A), for which we provide a complete moduli stabilization and supersymmetry breaking scenario based on the STU model and the KL m

Jianxi Liu

We study the typical structure of oriented graphs and digraphs that do not contain a blow-up T_{r+1}^t of a transitive tournament. For any integers r >= 2, t >= 1 and any real a in (3/2,2], we prove that almost all T_{r+1}^t-free oriented graphs and almost all T_{r+1}^t-free digraphs are r-partite. This extends the results of Kuhn, Osthus, Townsend and Zhao

Hoang T. H. Cao, Hai D. V. Trinh, Tho Quan, Lan V. Truong

Recent work has shown that Transformers can perform in-context learning for linear regression under restrictive assumptions, including i.i.d. data, Gaussian noise, and Gaussian regression coefficients. However, real-world data often violate these assumptions: the distributions of inputs, noise, and coefficients are typically unknown, non-Gaussian, and may ex

Enoch Hyunwook Kang

As autonomous AI agents increasingly mediate online platform markets, a fundamental question emerges: do these markets generate stable strategic outcomes? In repeated strategic environments, the Nash equilibrium provides a natural benchmark for this stability. However, empirical evidence on off-the-shelf LLM agents is mixed, leaving it unclear whether indepe

Lei Zhang, Hai-Shan Liu

We investigate the spontaneous scalarization of charged Taub-NUT black holes within the framework of Einstein-Maxwell-scalar-Gauss-Bonnet gravity. By selecting a suitable coupling function, the theory admits the analytic charged Taub-NUT geometry as a solution. We demonstrate that this scalar-free background becomes unstable within specific parameter regimes

Jiacheng Tang, Zhiyuan Zhou, Zhuolin He, Jia Zhang

Planning-oriented end-to-end driving models show great promise, yet they fundamentally learn statistical correlations instead of true causal relationships. This vulnerability leads to causal confusion, where models exploit dataset biases as shortcuts, critically harming their reliability and safety in complex scenarios. To address this, we introduce CausalVA

Michael Zurel, Santanil Jana, Nadish de Silva

We present applications of quantum quadratic residue codes in magic state distillation. This includes showing that existing codes which are known to distill magic states, like the $5$-qubit perfect code, the $7$-qubit Steane code, and the $11$-qutrit and $23$-qubit Golay codes, are equivalent to certain quantum quadratic residue codes. We also present new ex

Ivaxi Sheth, Zeno Jonke, Amin Mantrach, Saab Mansour

As large language models are increasingly deployed across diverse real-world applications, extending automated evaluation beyond English has become a critical challenge. Existing evaluation approaches are predominantly English-focused, and adapting them to other languages is hindered by the scarcity and cost of human-annotated judgments in most languages. We

Mingshi Yan, Zhiyong Cheng, Yahong Han, Meng Wang

Traditional recommendation methods, which typically focus on modeling a single user behavior (e.g., purchase), often face severe data sparsity issues. Multi-behavior recommendation methods offer a promising solution by leveraging user data from diverse behaviors. However, most existing approaches entangle multiple behavioral factors, learning holistic but im

Yunsong Zhang, Tianlin Li, Mingyang Yang, Feitian Zhang

Fiber-reinforced pneumatic twisted-and-coiled actuators (FR-PTCAs) offer high power density and compliance but their strong hysteresis and lack of intrinsic proprioception limit effective closed-loop control. This paper presents a self-sensing FR-PTCA integrated with a conductive nickel wire that enables intrinsic force estimation and indirect displacement i

Xue Yang, Rigui Zhou, Shizheng Jia, Dax Enshan Koh

Quantum Generative Adversarial Networks (QGANs) offer a promising path for learning data distributions on near-term quantum devices. However, existing QGANs for image synthesis avoid direct full-image generation, relying on classical post-processing or patch-based methods. These approaches dilute the quantum generator's role and struggle to capture global im

Jianxi Liu, Meili Liang

We prove a conjecture of K\"uhn, Osthus, Townsend and Zhao \cite{kuhn2017structure} stating that almost every $C_k$-free oriented graph on $n$ vertices has $\Theta(n)$ backwards edges in a transitive-optimal ordering. The same holds for $C_k$-free digraphs when $k$ is even. Our proof combines the hypergraph container method with a stability analysis and an i

Xinlei Pan, Jingyu Li, Jianfeng Hu

Non-Arrhenius grain growth has been observed in a range of polycrystalline materials; however, its fundamental mechanisms, particularly whether the process is thermally activated or exhibits anti-thermally activation, remain controversial. In this study, SrTiO3 was employed as a model system to systematically investigate non-Arrhenius grain growth behavior t

Zilong Hu, Hongming Fei, Prosanta Gope, Jack Miskelly

Dynamic Random Access Memory (DRAM) is pervasive in computer systems. Cell vulnerabilities caused by unintended phenomena (forced retention failure, latency alteration, rowhammer and rowpress) lead to unintended bit flips in memory. These phenomena have been explored as attacks to violate data integrity and confidentiality during normal operation, but also e

Amanda A. Howard, Nicholas Zolman, Bruno Jacob, Steven L. Brunton

Kolmogorov-Arnold networks (KANs) have arisen as a potential way to enhance the interpretability of machine learning. However, solutions learned by KANs are not necessarily interpretable, in the sense of being sparse or parsimonious. Sparse identification of nonlinear dynamics (SINDy) is a complementary approach that allows for learning sparse equations for

Jiaqi Zhou, Sen Zhao, Xiyuan Zhang, Suyu Xiao

This study evaluates the radiation tolerance of a 4H-SiC PIN detector under X-ray irradiation up to \SI{2}{MGy} (Si) at \SI{160}{keV}. The detector features a fully epitaxial vertical PIN structure with mesa terminations and field plates. Comprehensive pre- and post-irradiation characterization includes I-V/C-V measurements, charge collection efficiency (CCE

Khushiyant

This paper transfers three statistical methods from particle physics to multirotor propeller fault detection: the likelihood ratio test (LRT) for binary detection, the CLs modified frequentist method for false alarm rate control, and sequential neural posterior estimation (SNPE) for quantitative fault characterization. Operating on spectral features tied to

Xiang Chen, Fangfang Yang, Chunlei Meng, Yuxian Dong

Medical vision--language models (MVLMs) are increasingly used as perceptual backbones in radiology pipelines and as the visual front end of multimodal assistants, yet their reliability under real clinical workflows remains underexplored. Prior robustness evaluations often assume clean, curated inputs or study isolated corruptions, overlooking routine acquisi

Haonan Ping, Jian Jiang, Cheng Yuan, Qizhen Sun

Accurate segmentation of tissues and instruments in surgical scenes is annotation-intensive due to irregular shapes, thin structures, specularities, and frequent occlusions. While SAM models support point, box, and mask prompts, points are often too sparse and boxes too coarse to localize such challenging targets. We present SCISSR, a scribble-promptable fra

Rudy Arthur, Guillherme Machado

Environmental, Social and Governance (ESG) rating is a way for investors to prioritise investments in companies with good corporate behaviour. However, ESG ratings are vulnerable to greenwashing in a number of ways. In this paper we study the effect that trade with badly rated companies has on a target company's own rating. To do this we introduce a measurem

Nathan Thomas Carruth

A code is called solid if, roughly speaking, any correctly-transmitted codeword in an arbitrarily corrupted string of codewords can still be decoded correctly and unambiguously. So-called variable-length solid codes, in which codewords may differ in length, have been studied by various authors. In this short note, we observe that a recent construction of var

Yongwei Jiang, Yixiong Zou, Yuhua Li, Ruixuan Li

Cross-domain few-shot object detection (CD-FSOD) aims to adapt pretrained detectors from a source domain to target domains with limited annotations, suffering from severe domain shifts and data scarcity problems. In this work, we find a previously overlooked phenomenon: models exhibit dispersed and unfocused attention in target domains, leading to imprecise

Jiangtao Luo, Bingbing Xu, Shaohua Xia, Yongyi Ran

Sending massive Earth observation data produced by low Earth orbit (LEO) satellites back to the ground for processing consumes a large amount of on-orbit bandwidth and exacerbates the space-to-ground link bottleneck. Most prior work has concentrated on optimizing the routing of raw data within the constellation, yet cannot cope with the surge in data volume.

Sheng Pan, Niansheng Tang

Decentralized Federated Learning (DFL) remains highly vulnerable to adaptive backdoor attacks designed to bypass traditional passive defense metrics. To address this limitation, we shift the defensive paradigm toward a novel active, interventional auditing framework. First, we establish a dynamical model to characterize the spatiotemporal diffusion of advers

Jiangdong Ai, Bin Chen, Ming Chen, Tianxiao Zhao

In 1959, Erd\H{o}s and Gallai showed that every $2$-connected graph $G$ contains a cycle of length at least $\frac{2|E(G)|}{|V(G)|-1}$. This result was subsequently extended to weighted graphs by Bondy and Fan in 1991. A natural local variant of this problem arises by considering, for each edge $e\in E(G)$, the quantity $c(e)$, defined as the length of the l

Min-yung Kim, Jinwook Kim, Ken Pfeuffer, Sang Ho Yoon

As extended reality (XR) technologies rapidly become as ubiquitous as today's mobile devices, supporting one-handed interaction becomes essential for XR. However, the prevalent Gaze + Pinch interaction model partially supports unimanual interaction, where users select, move, and rotate objects with one hand, but scaling typically requires both hands. In this

Adithya Parthasarathy, Aswathnarayan Muthukrishnan Kirubakaran, Akshay Deshpande, Ram Sekhar Bodala

Accurate calibration is essential for instruments whose measurements must remain traceable, reliable, and compliant over long operating periods. Fixed-interval programs are easy to administer, but they ignore that instruments drift at different rates under different conditions. This paper studies calibration scheduling as a predictive maintenance problem: gi

Konwoo Kim, Suhas Kotha, Yejin Choi, Tatsunori Hashimoto

Synthetic data augmentation has emerged as a promising solution when pre-training is constrained by data rather than compute. We study how to design synthetic data algorithms that achieve better loss scaling: not only lowering loss at finite compute but especially as compute approaches infinity. We first show that pre-training on web data mixed with syntheti

Yinan Xia, Haotian Zhang, Huiming Wang

Large Reasoning Models (LRMs) have shown exceptional reasoning capabilities, but they also suffer from the issue of overthinking, often generating excessively long and redundant answers. For problems that exceed the model's capabilities, LRMs tend to exhibit the overconfidence phenomenon, generating overly short but incorrect answers, which may contribute to

Andrew Choi, Xinjie Wang, Zhizhong Su, Wei Xu

The strong performance of large vision-language models (VLMs) trained with reinforcement learning (RL) has motivated similar approaches for fine-tuning vision-language-action (VLA) models in robotics. Many recent works fine-tune VLAs directly in the real world to avoid addressing the sim-to-real gap. While real-world RL circumvents sim-to-real issues, it inh

Rajib Mandal, Sudip Kumar Guin

In this paper, we first establish Landau-Bloch-type theorems for poly $(K,K')$-elliptic harmonic mappings, which are sharp in some given cases. Thereafter, we provide several coefficient bounds for $(K,K')$-elliptic and $K$-quasiregular polyharmonic mappings with bounded minimum distortion. Furthermore, using these coefficient bounds, we establish Landau-Blo

Abhinaba Basu, Pavan Chakraborty

Large language models (LLMs) are increasingly used for high-stakes decisions, yet their susceptibility to spurious features remains poorly characterized. We introduce ICE-Guard, a framework applying intervention consistency testing to detect three types of spurious feature reliance: demographic (name/race swaps), authority (credential/prestige swaps), and fr

Manjie Hu, Chao Ding

This paper explores generalized slice monogenic functions by introducing their operator symbols, representation formula, and integral formula. The study extends the Teodorescu transform to a broader class of theorems and inferences, providing new analytical tools for function theory in this setting. Additionally, the Hodge decomposition is established, provi

Long Chen, Hao Fang, Yi Ching Chou, Haoyuan Zhao

Low Earth Orbit (LEO) satellite networks such as Starlink and Project Kuiper are increasingly integrated with cloud infrastructures, forming an important internet backbone for global web services. By extending connectivity to remote regions, oceans, and disaster zones, these networks enable reliable access to applications ranging from real-time WebRTC commun

P. Padilla-López, R. F. Maldonado, J. A. Toalá, E. Tejeda

As stars evolve, they undergo significant changes in their physical properties, which can have a profound impact on the planets orbiting them. In particular, the mass lost through stellar wind may be partially accreted by orbiting planets. We present the results of 18 simulations of one-planet systems with planetary masses of 0.5, 1, 2.5, 5, 10, and 13~$\mat

Hyun-kyu Ko, Jihyeon Park, Younghyun Kim, Dongheok Park

Creating dynamic, view-consistent videos of customized subjects is highly sought after for a wide range of emerging applications, including immersive VR/AR, virtual production, and next-generation e-commerce. However, despite rapid progress in subject-driven video generation, existing methods predominantly treat subjects as 2D entities, focusing on transferr

Liwei Che, Zhiyu Xue, Yihao Quan, Benlin Liu

Counting serves as a simple but powerful test of a Large Vision-Language Model's (LVLM's) reasoning; it forces the model to identify each individual object and then add them all up. In this study, we investigate how LVLMs implement counting using controlled synthetic and real-world benchmarks, combined with mechanistic analyses. Our results show that LVLMs d

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

We report the first observation of the decay $D_s^+ \to \pi^+\pi^0\pi^0\eta$ in a data set corresponding to an integrated luminosity of 7.33 fb$^{-1}$, collected in $e^+e^-$ collisions by the BESIII detector at center-of-mass energies between 4.128 and 4.226 GeV. An unexpectedly large branching fraction $\mathcal{B}( D_s^+ \to a_0(980)^+ f_0(500), a_0(980)^+

Zachary Allen, Max Conway, Lyle Antieau, Allen Ponraj

Electric vehicles (EV) create an urgent need for scalable battery recycling, yet disassembly of EV battery packs remains largely manual due to high design variability. We present our Robotic Agentic Platform for Intelligent Disassembly (RAPID), designed to investigate perception-driven manipulation, flexible automation, and AI-assisted robot programming in r

Chenxu Liu, Fanchuan Kong, Erin Mentuch Cooper, Dustin Davis

We present the first statistical census of emission-line variable active galactic nuclei (EVA) at cosmic noon by combining untargeted and deep HETDEX spectroscopy with multi-epoch spectra from SDSS, DESI, and LAMOST. Anchoring all candidates to a HETDEX spectroscopic epoch and requiring AGN classification in either the HETDEX or the external epoch(s), we ide

Haining Xu, Yang Zhang, Shenqi Yang, Zhiwei Yuan

Randomness in optical systems emerges as a powerful resource for generating complex, non-deterministic light-matter interactions. In particular, random plasmonic metasurfaces harness nanoscale disorder to produce unique and irreproducible optical responses, positioning them as an ideal platform for physical unclonable function in secure optical authenticatio

Meng Chen, Ruifang Liu

Let $\mathcal{G}=\{G_1, G_2, \ldots , G_{kn}\}$ be a family of balanced bipartite graphs on the same vertex set $[2n]$. A rainbow $k$-factor of $\mathcal{G}$ is defined as a $k$-factor such that any two distinct edges come from different graphs in $\mathcal{G}.$ In this paper, we provide a tight sufficient condition in terms of the spectral radius for a fami

Mahta Rafiee, Heydar Soudani, Zahra Abbasiantaeb, Mohammad Aliannejadi

Deep research agents have emerged as LLM-based systems designed to perform multi-step information seeking and reasoning over large, open-domain sources to answer complex questions by synthesizing information from multiple information sources. Given the complexity of the task and despite various recent efforts, evaluation of deep research agents remains funda

Rahul Chhimpa, Avinash Chand Yadav

In discrete models describing growing rough interfaces of the Kardar-Parisi-Zhang universality class, we examine height fluctuations at a fixed site as a function of time in the monolayer unit. For small systems, we show that it is possible to reach the stationary state. We compute the two-time autocorrelation and power spectra independently. The correlation

Yixuan Zhang, Ruihao Zhu, Qiaomin Xie

Motivated by the principle of satisficing in decision-making, we study satisficing regret guarantees for nonstationary $K$-armed bandits. We show that in the general realizable, piecewise-stationary setting with $L$ stationary segments, the optimal regret is $\Theta(L\log T)$ as long as $L\geq 2$. This stands in sharp contrast to the case of $L=1$ (i.e., the

Elad Yoshai, Ariel D. Yoshai, Natan T. Shaked

In digital pathology, whole-slide images routinely exceed gigapixel resolution, making computationally intensive generative super-resolution (SR) impractical for routine deployment. We introduce CAFlow, an adaptive-depth single-step flow-matching framework that routes each image tile to the shallowest network exit that preserves reconstruction quality. CAFlo

Maximilian Bachmaier, Gia Dvali, Juan Sebastián Valbuena-Bermúdez, Michael Zantedeschi

In this paper, we explore the implications of the so-called slingshot effect. It represents a rather general phenomenon occurring when a localized source, such as a monopole, quark, or a $D$-brane, crosses a domain wall separating the confined (Higgsed) and unconfined (Coulomb) phases of the crossing source. The crossover is accompanied by a stretched ``stri

Daqing Wan

An asymptotic formula with a square root error term is obtained for the number of elements with given trace and norm in a finite semisimple algebra over a finite field. This extends previous results from finite etale algebras (commutative case) to finite semi-simple algebras (non-commutative case). The main idea is to apply the Eichler formula for Gauss sums

Hongjia Zhai, Qi Zhang, Xiaokun Pan, Xiyu Zhang

Open-vocabulary scene understanding with online panoptic mapping is essential for embodied applications to perceive and interact with environments. However, existing methods are predominantly offline or lack instance-level understanding, limiting their applicability to real-world robotic tasks. In this paper, we propose OnlinePG, a novel and effective system

Sudhanva Joshi, Sunil Kumar Mishra

Traversable wormhole teleportation in the Sachdev-Ye-Kitaev (SYK) model links quantum channel integrity to black hole interior dynamics, using teleportation fidelity to probe holographic scrambling. We subject the SYK boundary to a gravitational-wave (GW)-inspired periodic Floquet deformation, mimicking a leading-order metric-strain perturbation from the JT-

Teerapong Panboonyuen

This handbook presents a systematic treatment of the foundations and architectures of artificial intelligence for motor insurance, grounded in large-scale real-world deployment. It formalizes a vertically integrated AI paradigm that unifies perception, multimodal reasoning, and production infrastructure into a cohesive intelligence stack for automotive risk

Zizhao Hu, Mohammad Rostami, Jesse Thomason

Persona prompting can steer LLM generation towards a domain-specific tone and pattern. This behavior enables use cases in multi-agent systems where diverse interactions are crucial and human-centered tasks require high-level human alignment. Prior works provide mixed opinions on their utility: some report performance gains when using expert personas for cert