October 2025 arXiv papers — page 184

Yidi Liu, Xueyang Fu, Jie Huang, Jie Xiao

Ultra-High Definition (UHD) image restoration faces a trade-off between computational efficiency and high-frequency detail retention. While Variational Autoencoders (VAEs) improve efficiency via latent-space processing, their Gaussian constraint often discards degradation-specific high-frequency information, hurting reconstruction fidelity. To overcome this,

Emilio Estevan, María Sierra-Torralba, Eduardo López-Larraz, Luis Montesano

Wearable EEG devices have emerged as a promising alternative to polysomnography (PSG). As affordable and scalable solutions, their widespread adoption results in the collection of massive volumes of unlabeled data that cannot be analyzed by clinicians at scale. Meanwhile, the recent success of deep learning for sleep scoring has relied on large annotated dat

Alexander Rubinstein, Benjamin Raible, Martin Gubri, Seong Joon Oh

Evaluating modern machine learning models has become prohibitively expensive. Benchmarks such as LMMs-Eval and HELM demand thousands of GPU hours per model. Costly evaluation reduces inclusivity, slows the cycle of innovation, and worsens environmental impact. The typical approach follows two steps. First, select an anchor subset of data. Second, train a map

Fengji Zhang, Xinyao Niu, Chengyang Ying, Guancheng Lin

Recent advances in Large Language Models (LLMs) and Reinforcement Learning (RL) have led to strong performance in open-domain question answering (QA). However, existing models still struggle with questions that admit multiple valid answers. Standard QA benchmarks, which typically assume a single gold answer, overlook this reality and thus produce inappropria

Shihe Zhou, Ruikun Li, Huandong Wang, Yong Li

Forecasting state evolution of network systems, such as the spread of information on social networks, is significant for effective policy interventions and resource management. However, the underlying propagation dynamics constantly shift with new topics or events, which are modeled as changing coefficients of the underlying dynamics. Deep learning models st

Petar Hristov, Ingrid Hotz, Talha Bin Masood

We present theory and practice for robust implementations of bivariate Jacobi set and Reeb space algorithms. Robustness is a fundamental topic in computational geometry that deals with the issues of numerical errors and degenerate cases in algorithm implementations. Computational topology already uses some robustness techniques for the development of scalar

Lina Zhang, De-Cheng Zou, Yun Soo Myung

Th spontaneous scalarization of the Einstein-Euler-Heisenberg (EEH) black hole is performed in the EEH-scalar theory by introducing an exponential scalar coupling (with $\alpha$ coupling constant) to the Maxwell term.Here, the EEH black hole as a blad black hole is described by mass $M$ and magnetic charge $q$ with an action parameter $\mu$. A choice of $\mu

Yifang Yin, Shengkai Chen, Yiyao Li, Lu Wang

Precipitation nowcasting predicts future radar sequences based on current observations, which is a highly challenging task driven by the inherent complexity of the Earth system. Accurate nowcasting is of utmost importance for addressing various societal needs, including disaster management, agriculture, transportation, and energy optimization. As a complemen

Gaia Fabj, Christopher Tiede, Connar Rowan, Martin Pessah

The disks of active galactic nuclei (AGNs) provide a natural environment where stellar-mass black holes (BHs) can dynamically pair, undergo repeated interactions, and eventually merge. It is commonly assumed that gas accretion will both efficiently spin up disk-embedded black holes and align the orbits of embedded binaries with the disk plane, leading to mer

Ziyi Dong, Yurui Zhang, Changmao Li, Naomi Rue Golding

Current text detection datasets primarily target natural or document scenes, where text typically appear in regular font and shapes, monotonous colors, and orderly layouts. The text usually arranged along straight or curved lines. However, these characteristics differ significantly from anime scenes, where text is often diverse in style, irregularly arranged

Takahiro Sakaue, Enrico Carlon

Active polymers are ubiquitous in nature, and often kicked by persistent noises that break detailed balance. In order to capture the out-of-equilibrium dynamics of such active polymers, we propose a simple yet reliable analytical framework based on a compounding formula. Connecting polymeric dynamics to the isolated monomeric behavior via the notion of tensi

Mats Viberg, Daniele Gerosa, Tomas McKelvey, Thomas Eriksson

A passive radar system uses one or more so-called Illuminators of Opportunity (IO) to detect and localize targets. In such systems, a reference channel is often used at each receiving node to capture the transmitted IO signal, while targets are detected using the main surveillance channel. The purpose of the present contribution is to analyze a method for es

Yuki Sato, Soma Nagahama, Shunsuke Kitou, Hajime Sagayama

Molecular beam epitaxy enables the growth of thin film materials with novel properties and functionalities. Typically, the lattice constants of films and substrates are designed to match to minimise disorders and strains. However, significant lattice mismatches can result in higher-order epitaxy, where commensurate growth occurs with a period defined by inte

Yi-Duo Chen, Zhi-Xi Wu, Jian-Yue Guan

Environmental feedback mechanisms are ubiquitous in real-world complex systems. In this study, we incorporate a homogeneous environment into the evolutionary dynamics of a three-state system comprising cooperators, defectors, and empty nodes. Both coherence resonance and equilibrium states, resulting from the tightly clustering of cooperator agglomerates, en

Junpeng Hu, Shi Jin, Nana Liu, Lei Zhang

Quantum computing holds significant promise for scientific computing due to its potential for polynomial to even exponential speedups over classical methods, which are often hindered by the curse of dimensionality. While neural networks present a mesh-free alternative to solve partial differential equations (PDEs), their accuracy is difficult to achieve sinc

Tianrui Zhang, Yichen Liu, Zilin Guo, Yuxin Guo

Generative models have been widely applied to world modeling for environment simulation and future state prediction. With advancements in autonomous driving, there is a growing demand not only for high-fidelity video generation under various controls, but also for producing diverse and meaningful information such as depth estimation. To address this, we prop

Qiushi Tian, Churong Liang, Kairan Hong, Runnan Li

Cryptocurrency markets present formidable challenges for trading strategy optimization due to extreme volatility, non-stationary dynamics, and complex microstructure patterns that render conventional parameter optimization methods fundamentally inadequate. We introduce Cypto Genetic Algorithm Agent (CGA-Agent), a pioneering hybrid framework that synergistica

Yutao Ma, Xujia Meng

Let $Z_1, \cdots, Z_n$ denote the eigenvalues of the product $\prod_{j=1}^{k_n} \boldsymbol{A}_j$, where $\{\boldsymbol{A}_j\}_{1 \le j \le k_n}$ are independent $n\times n$ complex Ginibre matrices. Define $\alpha = \lim\limits_{n \to \infty} \frac{n}{k_n}$. We prove that $X_n,$ a suitably rescaled version of $\max_{1 \le j \le n} |Z_j|^2,$ converges weakly

Srijita Basu, Haraldsson Bengt, Miroslaw Staron, Christian Berger

Cooperative, Connected and Automated Mobility (CCAM) are complex cyber-physical systems (CPS) that integrate computation, communication, and control in safety-critical environments. At their core, System-on-Chip (SoC) platforms consolidate processing units, communication interfaces, AI accelerators, and security modules into a single chip. AUTOSAR (AUTomotiv

Leigang Qu, Ziyang Wang, Na Zheng, Wenjie Wang

Video Foundation Models (VFMs) exhibit remarkable visual generation performance, but struggle in compositional scenarios (e.g., motion, numeracy, and spatial relation). In this work, we introduce Test-Time Optimization and Memorization (TTOM), a training-free framework that aligns VFM outputs with spatiotemporal layouts during inference for better text-image

Jonathan Elmer, Kazal Kadr

Let $p>0$ be a prime, $k$ a field of characteristic $p$ and $G$ and elementary abelian $p$-group of order $q = p^n$. Let $W$ be an indecomposable $kG$-module of dimension 2 and define $V_i=S^{i-1}(W^*)$ for each $i=1 \ldots q$. We show that $V_2 \otimes V_i \cong V_{i+1} \oplus V_{i-1}$ provided $i$ is not divisible by $p$, and that $V_2 \otimes V_p$ is inde

Virgile Troude, Didier Sornette

We identify a new universality class of phase transitions that arises in non-normal systems, challenging the classical view that transitions require eigenvalue instabilities. In traditional bifurcation theory, critical phenomena emerge when spectral stability is lost; here, we show that transitions can occur even when all equilibria are spectrally stable. Th

Charles Elbar, Filippo Santambrogio

We study a one-dimensional cross-diffusion system for two interacting populations on the torus, with a fast-diffusion law with exponent $0< \alpha\le 1$ and different external potentials. For arbitrary non-negative $L^{1}$ initial data with bounded entropy and a mixing condition we prove the existence of global weak solutions. This extends the recent result

Kasper E. Heintz, Darach Watson, Francesco Valentino, Rashmi Gottumukkala

Recent observations have revealed a remarkably rapid buildup of cosmic dust in the interstellar medium (ISM) of high redshift galaxies, with complex dust compositions and large abundances already appearing at redshifts $z>6$. Here we present a comprehensive, joint analysis of observations taken with the {\em James Webb Space Telescope} (JWST) and the Atacama

Diego García-Pérez, Emilio Parrado-Hernández, John Shawe-Taylor

This paper presents four theoretical contributions that improve the usability of risk certificates for neural networks based on PAC-Bayes bounds. First, two bounds on the KL divergence between Bernoulli distributions enable the derivation of the tightest explicit bounds on the true risk of classifiers across different ranges of empirical risk. The paper next

Gabriela Wojak, Ernest Górka, Michał Ćwiąkała, Dariusz Baran

This paper examines how Polish consumers are adapting to online insurance purchasing channels and what factors influence their preferences. Drawing on a structured survey of 100 respondents with varied demographic profiles, the study explores purchasing frequency, channel usage, price sensitivity, trust, and decision-making behaviors. Results indicate a clea

Carles Martorell, Rubén Calvo, Adrián Roig, Alessia Annibale

The neural model introduced by Sompolinsky, Crisanti, and Sommers (SCS) nearly four decades ago has become a paradigmatic framework for studying complex dynamics in random recurrent networks. In its original formulation, with balanced positive and negative couplings, the model exhibits two phases: a quiescent regime, where all activity ceases, and a regime o

Madis Jürviste, Joonatan Jakobson

This article presents research conducted at the Institute of the Estonian Language between 2022 and 2025 on the application of large language models (LLMs) to the study of 17th and 18th century Estonian dictionaries. The authors address three main areas: enriching historical dictionaries with modern word forms and meanings; using vision-enabled LLMs to perfo

Fugui Ma

The generalized Jeffreys-type law is formulated as a multi-term time-fractional Jeffreys-type equation, whose dynamics exhibit rich scaling crossover phenomena entailing different diffusion mechanisms. In this work, we provide a novel physical explanation for the equation from first principles, beginning with a microscopic description based on the continuous

Shengxiang Deng, Xudong Li, Yangjing Zhang

Kernel quantile regression (KQR) extends classical quantile regression to nonlinear settings using kernel methods, offering a powerful tool for modeling conditional distributions. However, its application to large-scale datasets remains challenging due to two intrinsic difficulties: the nonsmoothness of the quantile check loss and the computational burden im

L. Balkenhol, A. Coerver, C. L. Reichardt, J. A. Zebrowski

In this work, we construct foreground-marginalised versions of the SPT-3G D1 and SPTpol cosmic microwave background (CMB) B-mode polarisation likelihoods. The compression is performed using the CMB-lite framework and we use the resulting data sets to constrain anisotropic cosmic birefringence, parametrised by the amplitude of a scale-invariant anisotropic bi

Qunyi Zhang, Songan Zhang, Jiaqi Liu, Jinbao Wang

Anomaly detection plays a pivotal role in manufacturing quality control, yet its application is constrained by limited abnormal samples and high manual annotation costs. While anomaly synthesis offers a promising solution, existing studies predominantly treat anomaly synthesis as an auxiliary component within anomaly detection frameworks, lacking systematic

Adam Dejl, James Barry, Alessandra Pascale, Javier Carnerero Cano

Despite demonstrating remarkable performance across a wide range of tasks, large language models (LLMs) have also been found to frequently produce outputs that are incomplete or selectively omit key information. In sensitive domains, such omissions can result in significant harm comparable to that posed by factual inaccuracies, including hallucinations. In t

Rebecca Westhäußer, Wolfgang Minker, Sebatian Zepf

Large language models (LLMs) increasingly serve as the central control unit of AI agents, yet current approaches remain limited in their ability to deliver personalized interactions. While Retrieval Augmented Generation enhances LLM capabilities by improving context-awareness, it lacks mechanisms to combine contextual information with user-specific data. Alt

Yongqi Ding, Lin Zuo, Mengmeng Jing, Kunshan Yang

Brain-inspired spiking neural networks (SNNs) promise to be a low-power alternative to computationally intensive artificial neural networks (ANNs), although performance gaps persist. Recent studies have improved the performance of SNNs through knowledge distillation, but rely on large teacher models or introduce additional training overhead. In this paper, w

Kyumin Lee, Minjin Jeon, Sanghwan Jang, Hwanjo Yu

Answering complex real-world questions requires step-by-step retrieval and integration of relevant information to generate well-grounded responses. However, existing knowledge distillation methods overlook the need for different reasoning abilities at different steps, hindering transfer in multi-step retrieval-augmented frameworks. To address this, we propos

Murtaza Rangwala, Farag Azzedin, Richard O. Sinnott, Rajkumar Buyya

Byzantine-robust decentralized federated learning (DFL) protects peer-to-peer training from malicious clients. The dominant defenses rely on similarity-based filtering, in which each client exchanges full model vectors with every neighbor before any filtering decision; this communication grows with the model dimension and scales poorly as models grow. We pro

Frederik M. Andersen, Marc A. Suchard, Carsten Wiuf, Samir Bhatt

We define symmetric and asymmetric branching trees, a class of processes particularly suited for modeling genealogies of inhomogeneous populations where individuals may reproduce throughout life. In this framework, a broad class of Crump-Mode-Jagers processes can be constructed as (a)symmetric Sevast'yanov processes, which count the branches of the tree. Ana

Zhe Dong, Yuzhe Sun, Haochen Jiang, Tianzhu Liu

Remote sensing images inevitably suffer from various degradation factors during acquisition, including atmospheric interference, sensor limitations, and imaging conditions. These complex and heterogeneous degradations pose severe challenges to image quality and downstream interpretation tasks. Addressing limitations of existing all-in-one restoration methods

Xiangyu Li, Yawen Zeng, Xiaofen Xing, Jin Xu

LLM-based financial agents have attracted widespread excitement for their ability to trade like human experts. However, most systems exhibit a "profit mirage": dazzling back-tested returns evaporate once the model's knowledge window ends, because of the inherent information leakage in LLMs. In this paper, we systematically quantify this leakage issue across

Tingfeng Hong, Pingye Ren, Xinlong Xiao, Chao Wang

Balancing multiple objectives is critical for user satisfaction in modern recommender and search systems, yet current Multi-Task Fusion (MTF) methods rely on static, manually-tuned weights that fail to capture individual user intent. While Reinforcement Learning (RL) offers a path to personalization, traditional approaches often falter due to training instab

Yanzheng Wang, Jianlei Zhao, Qian Wu, Xiaoming Zhou

A novel anti-P-pseudo-Hermitian mechanical system that integrates piezoelectric actuators and sensors with non-reciprocal coupling into mechanical beams is proposed. This configuration enables the system to exhibit programmable exceptional points (EPs), which are critical for enhancing sensitivity in sensing applications. Our theoretical analysis, supported

Corey Bacal Switzer

We consider several variants of Baumgartner's axiom for $\aleph_1$-dense sets defined on the Baire and Cantor spaces in terms of Lipschitz functions with respect to the usual metric. A variation of Baumgartner's original argument shows that these variants are consistent. However, unlike in the case of the classical $\mathsf{BA}$, we are able to give many app

Tomoki Hirosawa, Pieter M. Gunnink, Alexander Mook

We study magnon-plasmon coupling in effectively two-dimensional stacks of van der Waals layers in the context of the band structure topology. Invoking the quasiparticle approximation, we show that the magnetic dipole coupling between the plasmons in a metallic layer and the magnons in a neighboring magnetic layer gives rise to a Berry curvature. As a result,

Peiran Wu, Zhuorui Yu, Yunze Liu, Chi-Hao Wu

The rapid progress of large language models (LLMs) has laid the foundation for multimodal models. However, visual language models (VLMs) still face heavy computational costs when extended from images to videos due to high frame rates and long durations. Token compression is a promising solution, yet most existing training-free methods cause information loss

Yu-Kun Zhang, Shao-Wen Wei

In this paper, we study the kinematic effects of spinning test particles orbiting the Kerr-Bertotti-Robinson black hole. Employing with the Mathisson-Papapetrou-Dixon equations, we explore the dynamics of precessing orbits and distinct orbital types, including circular orbits and innermost stable circular orbits. Our results reveal the substantial impact of

Alexander Schmidhuber, Jonathan Z. Lu, Noah Shutty, Stephen Jordan

We introduce Hamiltonian Decoded Quantum Interferometry (HDQI), a quantum algorithm that utilizes coherent Bell measurements and the symplectic representation of the Pauli group to reduce Gibbs sampling and Hamiltonian optimization to classical decoding. For a signed Pauli Hamiltonian $H$ and any degree-$\ell$ polynomial ${P}$, HDQI prepares a purification o

Fanwei Zhua, Jiaxuan He, Xiaoxiao Chen, Zulong Chen

Automatic grading of subjective questions remains a significant challenge in examination assessment due to the diversity in question formats and the open-ended nature of student responses. Existing works primarily focus on a specific type of subjective question and lack the generality to support comprehensive exams that contain diverse question types. In thi

Djamel Abid

This paper is dedicated to studying the existence of nontrivial positive solutions for a Kirchhoff-type problem with sign change nonlinearities and a singular term, Using the Nehari manifold and EkelandS variational principle we prove that for the appropriate choice of {\lambda} our problem has at least two positive solutions for both subcritical and critica

Mario M. Attard

This paper investigates Srinivasa Ramanujan's initial intuitive methodology for assigning the finite value -1/12 to the sum of the divergent infinite series of all positive integers. We systematically examine Ramanujan's initial method, originally sketched in his notebooks, and set the methodology into an algebraic framework. The methodology has limited appl

Chongmyung Kwon, Yujin Kim, Seoeun Park, Yunji Lee

Drug recommendation is an essential task in machine learning-based clinical decision support systems. However, the risk of drug-drug interactions (DDI) between co-prescribed medications remains a significant challenge. Previous studies have used graph neural networks (GNNs) to represent drug structures. Regardless, their simplified discrete forms cannot full

Kuan-Yu Chen, Yi-Cheng Lin, Jeng-Lin Li, Jian-Jiun Ding

Backdoor data poisoning is a crucial technique for ownership protection and defending against malicious attacks. Embedding hidden triggers in training data can manipulate model outputs, enabling provenance verification, and deterring unauthorized use. However, current audio backdoor methods are suboptimal, as poisoned audio often exhibits degraded perceptual

Kuan-Yu Chen, Kuan-Lin Chen, Yu-Chieh Yu, Jian-Jiun Ding

In Music Information Retrieval (MIR), modeling and transforming the tone of musical instruments, particularly electric guitars, has gained increasing attention due to the richness of the instrument tone and the flexibility of expression. Tone morphing enables smooth transitions between different guitar sounds, giving musicians greater freedom to explore new

A. Pratelli, V. Scattaglia

This article is devoted to extend the "$\eps-\eps^\beta$ property" to the case of clusters in an Euclidean space with a double density.

Wanying Huang, J. Jude Kline, Priscilla Man

We propose a refinement of correlated equilibrium based on mediator errors, called correlated perfect equilibrium (CPE). In finite games, the set of CPE is nonempty and forms a finite union of convex sets. Like perfect equilibrium, a CPE never assigns positive probability to any weakly dominated strategy. We provide a dual representation of CPE and demonstra

Yufei Tong, Guanjie Cheng, Peihan Wu, Feiyi Chen

High-quality remote sensing (RS) image acquisition is fundamentally constrained by physical limitations. While Multi-Frame Super-Resolution (MFSR) and Pansharpening address this by exploiting complementary information, they are typically studied in isolation: MFSR lacks high-resolution (HR) structural priors for fine-grained texture recovery, whereas Panshar

Enrico Ampellio, Blazhe Gjorgiev, Giovanni Sansavini

Engineering design involves demanding models encompassing many decision variables and uncontrollable parameters. In addition, unavoidable aleatoric and epistemic uncertainties can be very impactful and add further complexity. The state-of-the-art adopts two steps, uncertainty quantification and design optimization, to optimize systems under uncertainty by me

R. W. S. Pessoa, M. H. Næss, J. C. Bijos, C. M. Rebello

This paper presents a hybrid approach to predict the evolution of technological maturity in R and D projects, using the oil and gas sector as an example. Integrating System Dynamics (SD) and Agent Based Modelling (ABM) allows the proposed multi level framework to capture uncertainties in work effort, team size, and project duration, which influence technolog

Paweł Raźny

In this article we provide a version of the Leray-Serre spectral sequence for equidimensional (i.e. smooth with all orbits of the same dimension) actions of compact connected Lie groups on compact manifolds. The main part of this article consists of the proof of the description of the second page of said spectral sequence. This description provides a link be

Ruochen Li, Zhichao Chen, Zhaoting Zhang, Renjie Guo

Battery swapping stations (BSS) offer a fast and scalable alternative to conventional electric vehicle (EV) charging, gaining growing policy support worldwide. However, existing BSS control strategies typically rely on heuristics or low-fidelity degradation models, limiting profitability and service level. This paper proposes BSS-MPC: a real-time, degradatio

Xingyu Ren, Michael C. Fu, Pierre L'Ecuyer

We develop a novel stochastic derivative estimation framework for sample performance functions that are discontinuous in the parameter of interest, based on the multidimensional Leibniz integral rule. When discontinuities arise from indicator functions, we embed the indicator functions into the sample space, yielding a continuous performance function over a

Georgios Diamantopoulos, Nikos Tziritas, Rami Bahsoon, Georgios Theodoropoulos

The necessity of blockchain systems to remain decentralised limits current solutions to blockchain governance and dynamic management, forcing a trade-off between control and decentralisation. In light of the above, this work proposes a dynamic and decentralised blockchain management mechanism based on digital twins. To ensure decentralisation, the proposed m

Aistis Atminas, Valentas Kurauskas

Which permutations of a probability distribution on integers minimize variance? Let $X$ be a random variable on a set of integers $\{x_1, \dots, x_N\}$ such that $\mathbb{P}(X_i = x_i) = p_i$, $i \in \{1,\dots,N\}$. Let $(p^{(1)}, \dots, p^{(N)})$ be the sequence $(p_1, \dots, p_N)$ ordered non-increasingly. Let $X^+$ be the random variable defined by $\math

Zhenning Liu, William DeRocco, Shiming Gu, Emil T. Khabiboulline

The gravitational fields of astrophysical bodies bend the light around them, creating multiple paths along which light from a distant source can arrive at Earth. Measuring the difference in photon arrival time along these different paths provides a means of determining the mass of the lensing system, which is otherwise difficult to constrain. This is particu

Tomas Polednicek, Radka Vozabova, Boris Tomasik

Elliptic flow of deuterons is measured on simulated collisions events of Pb+Pb at CMS energy of 2.76 TeV per colliding nucleon pair. We use hybrid model that includes hydrodynamics for the deconfined phase and hadron transport as an afterburner. For deuterons, two production mechanisms are examined: coalescence, and direct thermal production during hadronisa

Jiayu Yang, Yuxuan Fan, Songning Lai, Shengen Wu

Large Language Models (LLMs) require efficient knowledge editing (KE) to update factual information, yet existing methods exhibit significant performance decay in multi-hop factual recall. This failure is particularly acute when edits involve intermediate implicit subjects within reasoning chains. Through causal analysis, we reveal that this limitation stems

D. Chee Yong Ong, I. Bukhori, K. S. Sim, K. Beng Gan

Scanning Electron Microscopy (SEM) images often suffer from noise contamination, which degrades image quality and affects further analysis. This research presents a complete approach to estimate their Signal-to-Noise Ratio (SNR) and noise variance (NV), and enhance image quality using NV-guided Wiener filter. The main idea of this study is to use a good SNR

Yukihiro Kanda, Naoya Kitajima

Semilocal cosmic string is a line-like non-topological soliton associated with the breakdown of the $SU(2)_{\rm global} \times U(1)_{\rm gauge}$ symmetry to the $U(1)_{\rm global}$ symmetry. The broken phase has two massless Nambu-Goldstone (NG) modes as dynamical fields, and they can be emitted by semilocal strings. In this paper, we numerically show that s

Sauri Bhattacharyya, Marco Grilli, Bernard van Heck

We study the decoherence of a collection of Majorana zero modes weakly coupled to a gapless reservoir of non-interacting fermions. Using the Born-Markov approximation, we derive a Lindblad master equation for the dissipative dynamics of the Majorana zero modes. Due to the long-range coupling between Majorana zero modes mediated by the gapless reservoir, the

Hyeonseok Moon, Seongtae Hong, Jaehyung Seo, Heuiseok Lim

Recent frontier-level LLMs have saturated many previously difficult benchmarks, leaving little room for further differentiation. This progress highlights the need for challenging benchmarks that provide objective verification. In this paper, we introduce MCBench, a benchmark designed to evaluate whether LLMs can execute string-matching NLP metrics by strictl

Miguel Bermudez

A simple covariant model is presented where the signature of the metric is a dynamical field. Degenerate minima of a curvature-minimizing potential correspond to Euclidean, Lorentzian or mixed phases of geometry. The Lorentzian phase emerges as the only stable configuration supporting causal propagation.

Verena Blaschke, Miriam Winkler, Barbara Plank

Research on cross-dialectal transfer from a standard to a non-standard dialect variety has typically focused on text data. However, dialects are primarily spoken, and non-standard spellings cause issues in text processing. We compare standard-to-dialect transfer in three settings: text models, speech models, and cascaded systems where speech first gets autom

Dave Murray-Rust, Kars Alfrink, Cristina Zaga

Artificial intelligence has become a part of the provision of governmental services, from making decisions about benefits to issuing fines for parking violations. However, AI systems rarely live up to the promise of neutral optimisation, creating biased or incorrect outputs and reducing the agency of both citizens and civic workers to shape the way decisions

Xinren Zhang, Sixi Cheng, Zixin Zhong, Jiadong Yu

Multi-agent systems (MAS) solve complex problems through coordinated autonomous entities with individual decision-making capabilities. While Multi-Agent Reinforcement Learning (MARL) enables these agents to learn intelligent strategies, it faces challenges of non-stationarity and partial observability. Communications among agents offer a solution, but questi

Alexander Borichev, Gérard Fantolini, El-Hassan Youssfi

We consider the commutativity problem for the Berezin transform on weighted Fock spaces. Given a real number $m>0$, for every $\alpha >0$ we denote by $B_{\alpha}$ the Berezin transform associated to the measure $\mu_{m}^{\alpha}$ with density proportional to $e^{-\alpha |z|^m}$ with respect to Lebesgue measure on the complex plane and normalized so that $\m

K. S. Sim, I. Bukhori, D. C. Y. Ong, K. B. Gan

Scanning Electron Microscopy (SEM) is critical in nanotechnology, materials science, and biological imaging due to its high spatial resolution and depth of focus. Signal-to-noise ratio (SNR) is an essential parameter in SEM because it directly impacts the quality and interpretability of the images. SEM is widely used in various scientific disciplines, but it

Madoka Hagiri, Kazushi Okamoto, Koki Karube, Kei Harada

Complementary recommendations suggest combinations of useful items that play important roles in e-commerce. However, complementary relationships are often subjective and vary among individuals, making them difficult to infer from historical data. Unlike conventional history-based methods that rely on statistical co-occurrence, we focus on the underlying usag

Boyu Li, Siyuan He, Hang Xu, Haoqi Yuan

In recent years, Multimodal Large Language Models (MLLMs) have demonstrated the ability to serve as high-level planners, enabling robots to follow complex human instructions. However, their effectiveness, especially in long-horizon tasks involving dual-arm humanoid robots, remains limited. This limitation arises from two main challenges: (i) the absence of s

Heyang Liu, Yuhao Wang, Ziyang Cheng, Ronghua Wu

The advancement of multimodal large language models has accelerated the development of speech-to-speech interaction systems. While natural monolingual interaction has been achieved, we find existing models exhibit deficiencies in language alignment. In our proposed Code-Switching Speech-to-Speech Benchmark (CS3-Bench), experiments on 7 mainstream models demo

Stanisław Pawlak

This study delves into the plasticity of neural networks, offering empirical support for the notion that critical learning periods and warm-starting performance loss can be avoided through simple adjustments to learning hyperparameters. The critical learning phenomenon emerges when training is initiated with deficit data. Subsequently, after numerous deficit

Xinliang Frederick Zhang, Anhad Mohananey, Alexandra Chronopoulou, Pinelopi Papalampidi

Models employing long chain-of-thought (CoT) reasoning have shown superior performance on complex reasoning tasks. Yet, this capability introduces a critical and often overlooked inefficiency -- overthinking -- models often engage in unnecessarily extensive reasoning even for simple queries, incurring significant computations without accuracy improvements. W

Sumit Sharma

Advances in portability and low cost of plenoptic cameras have revived interest in light field imaging. Light-field imaging has evolved into a technology that enables us to capture richer visual information. This high-dimensional representation of visual data provides a powerful way to understand the scene, with remarkable improvement in traditional computer

Hamees Sayed, Pranath Reddy, Michael W. Toomey, Sergei Gleyzer

Gravitational lensing is one of the most powerful probes of dark matter, yet creating high-fidelity lensed images at scale remains a bottleneck. Existing tools rely on ray-tracing or forward-modeling pipelines that, while precise, are prohibitively slow. We introduce FlowLensing, a Diffusion Transformer-based compact and efficient flow-matching model for str

Md. Faiyaz Abdullah Sayeedi, Md. Mahbub Alam, Subhey Sadi Rahman, Md. Adnanul Islam

The rise of Large Language Models (LLMs) has redefined Machine Translation (MT), enabling context-aware and fluent translations across hundreds of languages and textual domains. Despite their remarkable capabilities, LLMs often exhibit uneven performance across language families and specialized domains. Moreover, recent evidence reveals that these models can

Maximilian C. Hübl, Carl P. Goodrich

Rational design strategies for self-assembly require a detailed understanding of both the equilibrium state and the assembly kinetics. While the former is starting to be well understood, the latter remains a major theoretical challenge, especially in programmable systems and the so-called semiaddressable regime, where binding is often nondeterministic and th

Jingyu Peng, Jiansen He

Finite-amplitude low-frequency Alfv\'en waves are commonly found in plasma environments, such as space plasmas, and play a crucial role in ion heating. The nonlinear interaction between oblique Alfv\'en wave spectra and ions has been studied. As the number of wave modes increases, ions are more likely to exhibit chaotic motion and experience stochastic heati

Chong-Qiang Ye, Heng-Ji Li, Jian Li, Xiao-Yu Chen

Quantum blockchains provide inherent resilience against quantum adversaries and represent a promising alternative to classical blockchain systems in the quantum era. However, existing quantum blockchain architectures largely depend on entanglement to maintain inter-block connections, facing challenges in stability, consensus efficiency, and system verificati

Kurt Girstmair

Let $p$ be a prime $\equiv 3$ mod 4, $p>3$, and suppose that 10 has the order $(p-1)/2$ mod p. Then $1/p$ has a decimal period of length $(p-1)/2$. We express the frequency of each digit $0,\ldots,9$ in this period in terms of the class numbers of two imaginary quadratic number fields. We also exhibit certain analogues of this result, so for the case that 10

Gideon Lee, Jonathan A. Gross, Masaya Fukami, Zhang Jiang

The state hidden subgroup problem (StateHSP) is a recent generalization of the hidden subgroup problem. We present an algorithm that solves the non-abelian StateHSP over $N$ copies of the dihedral group of order $8$ (the symmetries of a square). This algorithm is of interest for learning non-Pauli stabilizers, as well as related symmetries relevant for the p

Erjia Xiao, Lingfeng Zhang, Yingbo Tang, Hao Cheng

In this report, we describe the technical details of our submission to the IROS 2025 RoboSense Challenge Social Navigation Track. This track focuses on developing RGBD-based perception and navigation systems that enable autonomous agents to navigate safely, efficiently, and socially compliantly in dynamic human-populated indoor environments. The challenge re

Arnab Chatterjee

Inspired by the "power-of-two-choices" model from random graphs, we investigate the possibility of limited choices of online clause choices that could shift the satisfiability threshold in random $k$-SAT.Here, we introduce an assignment symmetric, non-adaptive, topology-oblivious online rule called \emph{MIDDLE-HEAVY}, that prioritizes balanced sign profile

Haojie Jin, Jierui Ren, Yisong Chen, Guoping Wang

We propose a novel framework for Russian Roulette and Splitting (RRS) tailored to wavefront path tracing, a highly parallel rendering architecture that processes path states in batched, stage-wise execution for efficient GPU utilization. Traditional RRS methods, with unpredictable path counts, are fundamentally incompatible with wavefront's preallocated memo

Xabier de Juan, Santiago Mazuelas

The Median-of-Means (MoM) is a robust estimator widely used in machine learning that is known to be (minimax) optimal in scenarios where samples are i.i.d. In more grave scenarios, samples are contaminated by an adversary that can inspect and modify the data. Previous work has theoretically shown the suitability of the MoM estimator in certain contaminated s

Sachindranath Jayaraman, Dhashna T. Pillai

A quaternionic matrix-valued regular function is a map $F: \Omega \rightarrow M_n(\mathbb{H})$ whose entries are (left) regular functions of a quaternion variable, where $\Omega$ is a domain in $\mathbb{H}$. Our aim is to bring out some maximum norm principles for such functions. We derive an SVD type decomposition theorem for such functions, using the notio

Guowei Zou, Haitao Wang, Hejun Wu, Yukun Qian

The ability to learn multi-modal action distributions is indispensable for robotic manipulation policies to perform precise and robust control. Flow-based generative models have recently emerged as a promising solution to learning distributions of actions, offering one-step action generation and thus achieving much higher sampling efficiency compared to diff

Kyle Gulshen, Tali Kaufman

We construct a family of constant-rate highly-symmetric self-dual qLDPC codes on high dimensional expanders. This is the first self-dual code constructed on high dimensional expanders and also the first such code with a rich (e.g. transitive) symmetry group, whose order exceeds the number of qubits. From this symmetry, we identify an extensive set of logical

Yao Yao

We construct a second-quantized representation with a structure of balanced ternary formalism, which involves three substances in organic molecular materials, namely electron, hole and charge-transfer exciton, into a uniform framework. The quantum thermodynamic of excitons is investigated in a closed and compact manner, benefitting from the interplay of the

Sanghwa Kim, Dohyun Ahn, Seungki Min

We study the problem of estimating a continuous ability parameter from sequential binary responses by actively asking questions with varying difficulties, a setting that arises naturally in adaptive testing and online preference learning. Our goal is to certify that the estimate lies within a desired margin of error, using as few queries as possible. We prop

Somasekhara Goud Sontti, Pankaj G. Pallewar, Amritendu Bhuson Ghosh, Arnab Atta

In this study, two phase gas-shear-thinning liquid flow in a square microchannel is numerically investigated using the coupled level set and volume of fluid (CLSVOF) methods. A systematic investigation is carried out to explore the influence of polyacrylamide (PAM) concentration, surface tension, velocity ratios, and contact angle on the gas slug length, vol

Yuqin Zhao, Linghui Ye, Haihang Xia, Luke Seed

Software-defined radio (SDR) plays an important role in the communication field by providing a flexible and customized communication system for different purposes according to the needs. To enhance the performance of SDR applications, hardware accelerators have been widely deployed in recent years. In facing this obstacle, a necessity arises for a high-level