April 2026 arXiv papers — page 129

Bo Tan, Qing-Long Zhou

Let \(Q \subseteq \mathbb{N}\) be a subset, and let \(\psi\colon \mathbb{N} \to [0, \tfrac{1}{2})\), \(\theta\colon \mathbb{N} \to \mathbb{R}\) be functions. Let \(\{A_q\}\) and \(\{B_q\}\) be sequences of integers such that \(\gcd(A_q, B_q) = 1\) and \(B_q > 0\) for all \(q\). Define \(W_Q^{\ast}(\psi,\theta)\) to be the set of \(x \in [0,1]\) for which \[

Tian-Yang Sun, Bo Liang, Ji-Yu Song, Song-Tao Liu

Transient noise artifacts, commonly referred to as glitches, pose a major challenge to parameter inference for space-based gravitational-wave (GW) observations. We develop a glitch-robust amortized inference framework for massive black hole binaries in the Taiji detector configuration by combining conditional normalizing flows, a time-frequency multimodal fu

Leonardo Comini, Sunny Vagnozzi, Abraham Loeb

Early observations from the James Webb Space Telescope (JWST) have revealed an overabundance of massive high-redshift galaxies, raising the question of whether this points to new physics beyond $\Lambda$CDM, or an enhanced formation efficiency of massive stars. We revisit this issue going beyond earlier analyses based on direct comparisons to theoretical bou

A. Saldana-Lopez, A. Gkini, M. J. Hayes, R. Lunnan

Direct observational constraints on the earliest, stellar-wind-dominated phases of galactic outflows remain scarce. We present medium-resolution VLT/X-shooter spectroscopy of six Type I superluminous supernova (SLSN-I) host galaxies at z = 0.15-0.51, exploiting the bright SLSN continua as single, down-the-barrel probes of the host interstellar medium. From n

Sujata Singh, Leon Carstens, M. Duc Le, R. Klingeler

Structural disorder in the magnetically frustrated pyrochlore system leads to intriguing magnetic states. We present the thermodynamic behavior and short range magnetic correlations in Tb$_{2}$Zr$_{2}$O$_{7}$ and Tb$_{2}$Zr$_{1.5}$Ti$_{0.5}$O$_{7}$ compounds. The parent compound Tb$_{2}$Zr$_{2}$O$_{7}$ has defect fluorite structure, which evolves toward the

Zebei Tong, Hongchang Chen, Yujie Lei, Gang Chen

Image generation technology can synthesize condition-specific images to supplement real-world industrial anomaly data and enhance anomaly detection model performance. Existing generation techniques rarely account for the pose and orientation of industrial components in assembly, making the generated images difficult to utilize for downstream application. To

Peng Xie, Abdulla Fawzy, Zhen Zhang, Amr Alanwar

We propose a matrix zonotope perturbation framework that leverages matrix perturbation theory to characterize how noise-induced distortions alter the dynamics within sets of models. The framework derives interpretable Cai-Zhang bounds for matrix zonotopes (MZs) and extends them to constrained matrix zonotopes (CMZs). Motivated by this analysis and the comput

Tinniam V Ganesh

This paper develops a unified Markov Decision Process (MDP) framework for optimising two recurring in-match decisions in T20 cricket, namely batting order selection and bowling plan assignment, directly in terms of win and defend probability rather than expected runs. A three-phase player profile engine (Powerplay, Middle, Death) with James-Stein shrinkage (

K. N. Zlatanov, M. Mallweger, M. Hennrich, N. V. Vitanov

The strong dipole-dipole interaction of trapped Rydberg ions offers the possibility of sub-microsecond entanglement gates. For example a two-qubit Control-Phase gate in 88 Sr + ions can be realized, by simultaneous excitation to the Rydberg states via stimulated Raman adiabatic passage (STIRAP) with simultaneous microwave induced dipole-dipole interaction. W

David Redka, Julian Vollmann, Nicolas Thomae, Maximilian Spellauge

Ultrashort-pulse laser ablation of metals near damage threshold is governed by homogeneous spallation, in which tensile unloading releases a nanometre-thin liquid film whose optical signatures are temporally evolving concentric Newton rings in pump--probe experiments. This well-established picture rests almost exclusively on single-pulse results obtained on

Côme Tabary

We build solutions to Kac's particle system and show that their empirical measures converge to the solution of the space-homogeneous Boltzmann equation in the regime of very soft potentials. This proves propagation of chaos for the last class of kernels for which it was still open. The proof relies on new estimates on the dissipation of the Fisher informatio

Wancheng Li, Daniel S. Han

We derive the asymptotic first passage time (FPT) distribution for space-dependent variable-order time-fractional diffusion, where the fractional exponent $\alpha(x)$ varies with position. For any sufficiently smooth $\alpha(x)$ on a finite domain with absorbing and reflecting boundaries, we show that the survival probability decays as $\Psi(t)\sim C\,t^{-\a

Antonio Farina, Massimo Guidi, Alfonso Veropalumbo, Claudio Guida

Cosmological parameter inference from galaxy clustering relies critically on accurate estimates of the covariance and precision matrices. These are often obtained from a limited number of mock catalogs, introducing noise and bias in the precision matrix when the data-vector dimension becomes comparable to the number of available realizations. We present the

Yanbo Zhang, Yaojun Chen

Let $F_n$, $W_n$, and $\widehat{K}_n$ be the graphs obtained by joining a vertex to $n$ independent edges, a cycle and a path of order $n-1$, respectively. In this paper, we give new bounds for the Ramsey numbers $R(F_n,F_m)$ and $R(W_n,W_n)$, which improve those due to Chen, Yu, and Zhao [EJC, 2021] and Mao, Wang, Magnant, and Schiermeyer [G&C, 2022], respe

Yi Ting Shen, Kentaroh Toyoda, Alex Leung

The rapid proliferation of Model Context Protocol (MCP)-based agentic systems has introduced a new category of security threats that existing frameworks are inadequately equipped to address. We present MCPThreatHive, an open-source platform that automates the end-to-end lifecycle of MCP threat intelligence: from continuous, multi-source data collection throu

Junjie Wang, Shuning Pan, Haoting Zhang, Qiuhan Jia

Machine learning potentials (MLPs) achieve near first-principles accuracy but often fail for atomic environments outside the training distribution. Active learning can mitigate this limitation; however, its application to large-scale simulations is hindered by the prohibitive cost of labeling entire configurations. Here, we develop a D-optimality-driven acti

Hongtao Xu, Jianchao Tan, Yuxuan Hu, Pengju Lu

While sparse attention mitigates the computational bottleneck of long-context LLM training, its distributed training process exhibits extreme heterogeneity in both \textit{1)} sequence length and \textit{2)} sparsity sensitivity, leading to a severe imbalance problem and sub-optimal model accuracy. Existing algorithms and training frameworks typically focus

Zesheng Wei, Mengxiang Li, Zilei Wang, Yang Deng

Personalized Large Language Models (LLMs) facilitate more natural, human-like interactions in human-centric applications. However, existing personalization methods are constrained by limited controllability and high resource demands. Furthermore, their reliance on static personality modeling restricts adaptability across varying situations. To address these

Jason Miller, Yizheng Yuan

We show for $\kappa \in (4,8)$ that the canonical conformally covariant measure on the conformal loop ensemble (CLE$_\kappa$) gasket, previously constructed indirectly by the first co-author and Schoug, can be realized as the limit of several natural approximation schemes. These include the Euclidean Minkowski content and its box-count variants, the properly

AlexKen Lee, Keyun Wu

We investigate one-loop corrections from torsion-induced four-fermion interactions to inflaton three-body decay and their impact on the associated stochastic gravitational-wave signal. We find a pronounced asymmetry in the dependence on the renormalization scale $u$. While the enhancement of the gravitational-wave spectrum remains modest, not exceeding rough

Omar Abou El Kheir, Dario Baratella, Marco Bernasconi

We developed a machine learning interatomic potential (MLIP) for Ge-rich GeSbTe alloys of interest for applications in phase change memories embedded in microcontrollers. The MLIP was generated by fitting with a neural network method a large database of energies and forces computed within density functional theory of elemental, binary, stoichiometric and non

Alessio Moreschini, Matteo Scandella

The invariance principle, through which the steady-state behavior of nonlinear systems was introduced by Isidori and Byrnes, is leveraged in this article to bring forth a unifying characterization of the frequency response of nonlinear systems. We show that, for systems under nonlinear periodic excitations, the frequency response can still be defined as a co

Huanghao Yin, Shenkun Xu, Kanle Shi, Junhai Yong

Text-conditioned image editing has greatly benefitted from the advancements in Image Diffusion Models. However, extending these techniques to facial video editing introduces challenges in preserving facial identity throughout the source video and ensuring consistency of the edited subject across frames. In this paper, we introduce DiffMagicFace, a unique vid

Ivan I. Shevchenko

The Melnikov-Arnold integrals (MA-integrals) is a well-known instrument used to measure the splitting of separatrices in Hamiltonian systems. In this article, we explore how calculation of MA-integrals can be used as well to estimate sizes of secondary resonances. Within the standard map model, we show how the newly developed MA-based procedure allows one to

Iuliu Cuceu, Marie Anne Bizouard

We present Basilic, a dedicated pipeline for Bayesian model selection and parameter estimation of short-duration gravitational-wave burst signals observable with ground-based detectors. Built on top of the bilby framework, Basilic combines modularity, pre-implemented burst models, and HTCondor integration to enable rapid, user-friendly analyses with minimal

Hans Werner Schürmann, Valery Serov

In a previous article we have proved non-existence of certain "solutions" of the cubically nonlinear Schr\"odinger equation in the general case, and presented solutions in the non-generic case. -- In the present article we describe a further family of solutions enlarging the set of non-generic solutions.

Qingsong Zhao

This paper studies local existence and the singularity formation of the solutions of the one-dimensional hyperbolic Navier-Stokes equations, in particular proving the gradient blow-up of the derivatives of the solutions. The underlying model introduces a relaxation mechanism that leads to hyperbolization, achieved both through a nonlinear Cattaneo law for he

Eyal Neuman, Sturmius Tuschmann

We study potential games on unimodular random graphs of bounded degree, where players interact through the underlying network. Using the unimodular measure, we define a well-posed global potential that captures both finite- and infinite-player games. A key observation is that the mass-transport principle identifies the first variation of this potential with

Hye Jin Rhee, Joseph Damilola Akinyemi

Accurate and resource-efficient automated diagnosis is a cornerstone of modern agricultural expert systems. While Convolutional Neural Networks (CNNs) have established benchmarks in plant pathology, their ability to capture long-range spatial dependencies is often limited by standard pooling layers, and their high memory footprint hinders deployment on porta

Venkata Abhinandan Kancharla

Large language models (LLMs) achieve strong performance across many natural language processing tasks, yet their decision processes remain difficult to interpret. This lack of transparency creates challenges for trust, debugging, and deployment in real-world systems. This paper presents an applied comparative study of three explainability techniques: Integra

Si-Yi Chen, Angela Sara Cacciapuoti, Marcello Caleffi

Conventional quantum routing operates under the entrenched assumption that pathfinding is a prerequisite for routing. This classical-inspired routing model imposes a restricting design option, which prevents scaling the quantumness to the network functioning. In this paper, we proposed a novel entanglement-driven routing framework that exploits multipartite

Yunsheng Lu, Zijiang Yang, Licheng Pan, Zhixuan Chu

Reward models are central to aligning large language models, yet they often overfit to spurious cues such as response length and overly agreeable tone. Most prior work weakens these cues directly by penalizing or controlling specific artifacts, but it does not explicitly encourage the model to ground preferences in the prompt's intent. We learn a decoder tha

Hillel Bar-Gera, Stephen D. Boyles, Liron Ravner

Vickrey's classic single-bottleneck departure time choice equilibrium model exhibits instability under many plausible day-to-day learning dynamics. Such instability is not observed in reality -- does this difference stem from the day-to-day dynamics or from one of the simplifying assumptions of the basic model? This paper explores a variant of the basic mode

Haoning Dang, Fei Wang, Yifan Chen, Zhouyu Liu

Integro-differential equations arise in a wide range of applications, including transport, kinetic theory, radiative transfer, and multiphysics modeling, where nonlocal integral operators couple the solution across phase space. Such nonlocality often introduces dense coupling blocks in deterministic discretizations, leading to increased computational cost an

Ben Baker, Richard D. Lange, Andrew Richmond, Nikolaus Kriegeskorte

Representations play a central role in the study of both biological and artificial intelligence, as well as philosophy of mind. Across neuroscience, computer science, and philosophy, a recurring theme is that representations not only carry information but should be ``useful'' for or ``usable'' by an agent in some sense. Here, we review how the ``usefulness''

Zihao Liu, Hantao Zhou, Jiguo Li, Jun Xu

User simulators are essential for the scalable training and evaluation of interactive AI systems. However, existing approaches often rely on shallow user profiling, struggle to maintain persona consistency over long interactions, and are largely limited to English or single-domain settings. We present MUSE, a multi-domain Chinese user simulation framework de

Valerio Sorichetti, Juraj Májek, Ivan Palaia, Fernanda Pérez-Verdugo

Quantitative modeling has become an essential tool in modern biophysics, driven by advances in both experimental techniques and theoretical frameworks. Powerful high-resolution techniques now provide detailed datasets spanning molecular to tissue scales, allowing to visualize cellular structures with unprecedented detail. In parallel, developments in soft an

Reem Alfayez, Manal Binkhonain

Sentiment analysis in software engineering focuses on understanding emotions expressed in software artifacts. Previous research highlighted the limitations of applying general off-the-shelf sentiment analysis tools within the software engineering domain and indicated the need for specialized tools tailored to various software engineering contexts. The develo

Artur Nicolau

Analytic self-maps of the unit disc whose hyperbolic derivative is uniformly bounded by a constant smaller than one, are called contractive. We describe these maps in terms of their Aleksandrov-Clark measures and in terms of their inner-outer factorization. In addition, we show that contractive inner functions can be described in terms of a certain mixing pr

Youling Huang, Guanqiao Chen, Junchi Yao, Lu Wang

World models have been emerging as critical components for assessing the consequences of actions generated by interactive agents in online planning and offline evaluation. In text-based environments, world models are typically evaluated and trained with single-step metrics such as Exact Match, aiming to improve the similarity between predicted and real-world

Minsik Lee, Seoi Hong, Chongmin Lee, Sieun Choi

Despite recent progress in automatic speech recognition (ASR), elderly ASR (EASR) remains challenging due to limited training data and the distinct acoustic and linguistic characteristics of elderly speech. In this work, we address data scarcity in EASR through a data augmentation pipeline that combines large language model (LLM)-based transcript paraphrasin

Tatsuro Kawakami, Hiromu Tanaka

Let $X$ be a projective klt threefold in characteristic $p>5$ and let $L$ be a nef Cartier divisor on $X$. We show that $H^1(X, -L)=0$ for the following two cases: (1) $K_X$ is not big and $L$ is big; (2) $-K_X$ is nef and $L$ is of numerical dimension two.

Zhengxi Lu, Fei Tang, Guangyi Liu, Kaitao Song

MLLM-based GUI agents have demonstrated strong capabilities in complex user interface interaction tasks. However, long-horizon scenarios remain challenging, as these agents are burdened with tasks beyond their intrinsic capabilities, suffering from memory degradation, progress confusion, and math hallucination. To address these challenges, we present UI-Copi

Joel Laudo, Adrian Buganza Tepole

Reduced-order models (ROMs) are essential for rapid simulation of complex biomechanical systems and for bridging the gap between high fidelity models and clinical application. However, ROMs for tissue growth and remodeling (G&R) remain largely unexplored. Here, we present a Neural Ordinary Differential Equation (NODE) ROM framework that learns latent dynamic

Shuhei Tsujie, Yuki Ueda

We extend the notions of finite free convolution and finite free cumulants to the setting of formal power series by introducing their natural analogues, namely $t$-deformed convolution and $t$-deformed cumulants. In this framework, we establish $t$-deformed analogues of the law of large numbers and the central limit theorem, revealing structural parallels wi

Jie Tang, Pei-Hong Gu

Over the past few decades, the hypothetically dark photon has been extensively studied from both phenomenological and experimental perspectives. It should be noted that the local symmetry for dark photon does not gauge the standard model Higgs scalar and chiral fermions. In this paper, we show that an artificially introduced $U(1)_X$ gauge group for dark pho

Tao Wang, Jingyao Lu, Xibo Wang, Haonan Huang

Large language models (LLMs) have shown remarkable capabilities in dialogue generation and reasoning, yet their effectiveness in eliciting user-known but concealed information in open-ended conversations remains limited. In many interactive AI applications, such as personal assistants, tutoring systems, and legal or clinical support, users often withhold sen

Aggelos Semoglou, Aristidis Likas, John Pavlopoulos

Determining the number of clusters is a central challenge in unsupervised learning, where ground-truth labels are unavailable. The Silhouette coefficient is a widely used internal validation metric for this task, yet its standard micro-averaged form tends to favor larger clusters under size imbalance. Macro-averaging mitigates this bias by weighting clusters

Runwei Lin, Ying Wang

Heart rate variability (HRV) analysis is important for the assessment of autonomic cardiovascular regulation. The inverse Gaussian process (IGP) has been widely used for beat-to-beat HRV modeling, as it gives a physiological relevant interpretation of heart depolarization process. A key challenge in IGP-based heartbeat modeling is the accurate estimation of

Adriana Caraeni, Alexander Shick, Andrew Lan

Recent advances in artificial intelligence (AI) have shown promise in automating key aspects of Agile project management, yet their impact on team cognition remains underexplored. In this work, we investigate cognitive offloading in Agile sprint planning by conducting a controlled, three-condition experiment comparing AI-only, human-only, and hybrid planning

Qiqi Jason Gu, Mikoláš Janota

Merging is a core operation in version control systems such as Git, but traditional line-based algorithms often yield spurious conflicts, particularly in the presence of refactorings or parallel edits. While syntax- and semantics-aware merging approaches can reduce conflicts, they introduce drawbacks such as loss of formatting, dependence on language-specifi

Jacopo Cossio, Daniele Lizzio Bosco, Riccardo Romanello, Giuseppe Serra

Quantum circuit optimization is a central task in Quantum Computing, as current Noisy Intermediate Scale Quantum devices suffer from error propagation that often scales with the number of operations. Among quantum operations, the CNOT gate is of fundamental importance, being the only 2-qubit gate in the universal Clifford+T set. The problem of CNOT gates min

Ivan Oladyshkin

We show that electron drag by nonequilibrium phonons describes the actual waveform and spectrum of terahertz pulses generated during femtosecond laser irradiation of metals. In contrast to previous models, there is a picosecond delay in the drag force development due to the relatively slow lattice heating and finite phonon lifetime. We also predict that, at

Kang Zhou

We extend the hidden zeros and $2$-split of tree-level ${\rm Tr}(\phi^3)$ amplitudes to loop-level Feynman integrands, apart from some physically irrelevant scaleless integrals. Our method is based on a certain factorization mechanism that occurs in Feynman diagrams when summing over shuffle permutations. The loop-level hidden zeros and $2$-split identified

Torben Wiedemann

We present CubicJordanMatrixAlg, a GAP package for symbolic computation in cubic Jordan matrix algebras and in related Lie-theoretic structures. As an application, we use it to compute certain (commutator) relations in $F_4$-graded groups that were constructed by De Medts and the author from cubic Jordan matrix algebras.

Zhihao Wang, Jie Jiang, Yanji Zheng, Wen Zhao

When charged particles are subjected to strong magnetic fields, they form discrete energy levels known as Landau levels. The Landau levels consist of a series of degenerate states of Landau modes, making them a promising platform for large-capacity information processing. However, to date, exploiting the high-order Landau modes and control their spatial dist

Yu Ge, Xin Tong, Nenad Vukmirović, Musa Furkan Keskin

We study uplink radio simultaneous localization and mapping (SLAM) in a pre-calibrated phase-coherent distributed MIMO (D-MIMO) system at a minimal sensing-resource operating point: a single-antenna user equipment (UE) transmits one narrowband pilot, and each single-antenna access points (APs) provides one complex observation. We formulate coherent matched-f

Jaemin Kim, Sungkyun Kim, Junyeol Lee, Jiwon Seo

Large Language Models (LLMs) are widely used across many domains, but their scale makes deployment challenging. Post-Training Quantization (PTQ) reduces memory footprint without retraining by leveraging a small calibration set. Recent Hessian-based PTQ methods compensate quantization error via cross-channel dependencies, but such approaches degrade at low bi

Doğa Evgür, Ozan Alp Topal, Özlem Tuğfe Demir

This paper investigates the uplink capacity of single-input single-output (SISO) systems assisted by a swarm of network-controlled repeaters (NCRs). We develop a rigorous wideband formulation based on OFDM signaling. Starting from the continuous-time passband model, we derive the capacity expression for the repeater-assisted OFDM channel, accounting for ampl

Dongjie Fu, Fangming Feng, Xize Cheng, Linjun Li

The rapid evolution of multimodal large models has revolutionized the simulation of diverse characters in speech dialogue systems, enabling a novel interactive paradigm. Character attributes are manifested not only in textual responses but also through vocal features, as speech conveys rich paralinguistic information that is challenging to quantify. This pos

Arya Shah, Vaibhav Tripathi, Mayank Singh, Chaklam Silpasuwanchai

Vision-language models are increasingly deployed in high-stakes settings, yet their susceptibility to sycophantic manipulation remains poorly understood, particularly in relation to how these models represent visual information internally. Whether models whose visual representations more closely mirror human neural processing are also more resistant to adver

Fabien Hoareau, François Le Maître

We study the Rokhlin lemma in the context of infinite measure-preserving bijections, and completely classify such bijections up to $\lambda$-approximate conjugacy, where $\lambda$ is the infinite measure which is preserved. This sharpens the classical version of the Rokhlin lemma, which only provides such a classification up to $\mu$-approximate conjugacy wh

Yue Chen, Yifei Sun, Lu Wang, Fangkai Yang

Traditional recommendation systems represent users and items as dense vectors and learn to align them in a shared latent space for relevance estimation. Recent LLM-based recommenders instead leverage natural-language representations that are easier to interpret and integrate with downstream reasoning modules. This paper studies how to construct effective tex

Xueyang Zhou, Yihan Sun, Xijie Gong, Guiyao Tie

Embodied AI research is increasingly moving beyond single-task, single-environment policy learning toward multi-task, multi-scene, and multi-model settings. This shift substantially increases the engineering overhead and development time required for stages such as evaluation environment construction, trajectory collection, model training, and evaluation. To

Allen Hoffmeyer, Christian Houdré

Using only the characteristic function, we derive short-time at-the-money (ATM) call-price asymptotics for the exponential CGMY model with activity parameter $Y\in(1,2)$. The Lipton--Lewis formula expresses the normalized ATM call price, denoted $c(t,0)$, in terms of the characteristic exponent, which, upon rescaling at the rate $t^{-1/Y}$ from the $Y$-stabl

Rejoy Chakraborty, Prasun Roy, Saumik Bhattacharya, Umapada Pal

Few-shot Font Generation aims to generate stylistically consistent glyphs from a few reference glyphs. However, capturing complex font styles from a few exemplars remains challenging, and the existing methods often struggle to retain discernible local characteristics in generated samples. This paper introduces DRG-Font, a contrastive font generation strategy

Unmesh Padalkar

In daily fantasy sports (DFS), match participation is highly time-sensitive. Users must act within a narrow window before a game begins, making match recommendation a time-critical task to prevent missed engagement and revenue loss. Existing recommender systems, typically designed for static item catalogs, are ill-equipped to handle the hard temporal deadlin

Nghia, Nguyen, Amer Wahed, Andy Quesada

Vision transformers (ViT) have been shown to allow for more flexible feature detection and can outperform convolutional neural network (CNN) when pre-trained on sufficient data. Due to their promising feature detection capabilities, we deployed ViTs for morphological classification of anaplastic large cell lymphoma (ALCL) versus classic Hodgkin lymphoma (cHL

Frank Huettner

For networks with externalities, where each component's worth may depend on the full network structure, balanced contributions and fairness lead to distinct component-efficient allocation rules. We characterize the unique component-efficient allocation rule satisfying balanced contributions -- the BCE rule. Existence is the main challenge: balanced contribut

Peng Xie, Amr Alanwar

Data-driven reachability analysis using matrix zonotopes faces a fundamental challenge: the number of generators in the reachable set grows exponentially during propagation, while current order reduction yields overly conservative approximations in data-driven settings. This paper introduces an orthogonal matrix-based framework that appropriately transfers t

Chen Wang, Yixin Zhu, Yongbin Zhu, Fengyuan Shi

Accurate lesion segmentation in ultrasound images is essential for preventive screening and clinical diagnosis, yet remains challenging due to low contrast, blurry boundaries, and significant scale variations. Although existing deep learning-based methods have achieved remarkable performance, these methods still struggle with scale variations and indistinct

G. R. Krishna Chand Avatar, Vivekanand Dabade

Ferromagnetic elastic slender structures offer the potential for large actuation displacements under modest external magnetic fields, due to the magneto-mechanical coupling. This paper investigates the phase portraits of the Hamiltonian governing the three-dimensional deformation of inextensible ferromagnetic elastic rods subjected to combined terminal tensi

Jaejoon Yoo, SuBeen Lee, Yerim Jeon, Miso Lee

3D Single Object Tracking (3D-SOT) aims to localize a target object across a sequence of LiDAR point clouds, given its 3D bounding box in the first frame. Recent methods have adopted a memory-based approach to utilize previously observed features of the target object, but remain limited to only a few recent frames. This work reveals that their temporal capac

Quentin Rolland, Fabrice Mayran de Chamisso, Jean-Baptiste Mouret

Imitation learning (IL) policies in robotics deliver strong performance in controlled settings but remain brittle in real-world deployments: rare events such as hardware faults, defective parts, unexpected human actions, or any state that lies outside the training distribution can lead to failed executions. Vision-based Anomaly Detection (AD) methods emerged

Shouzheng Huang, Meishan Zhang, Baotian Hu, Min Zhang

Large Language Models (LLMs) enhance their problem-solving capability by utilizing external tools. However, in open-world scenarios with massive and evolving tool repositories, existing methods relying on static embedding retrieval or parameter memorization of tools struggle to align user intent with tool semantics or generalize to unseen tools, respectively

Junlin Zhu, Baizhou Huang, Xiaojun Wan

As large language models become standard backends for content generation, practical provenance increasingly requires multi-bit watermarking. In provider-internal deployments, a key requirement is message symmetry: the message itself should not systematically affect either text quality or verification outcomes. Vocabulary-partition watermarks can break messag

Guy Tsafack, Antoine Tambue

The paper deals with the numerical treatment of index-1 stochastic differential-algebraic equations (SDAEs) with nonlinear coefficients that satisfy the local Lipschitz and the Khasminskii conditions. The key challenge here is the presence of a singular and non-autonomous matrix in the equation, which makes the numerical method challenging to analyze. To tac

Cenk Erdogan, Bennett Daniel, Benedikt Wotka, Ashish Sai

We investigate platform-native citation farming on ResearchGate by analyzing almost 3000 papers uploaded by five suspected boosting-service provider accounts. From the uploaded papers and associated metadata, we construct both paper-level and author-level citation networks. We introduce an interpretable structural signal for coordinated boosting, equal refer

Thomas S. Heinze

Deep Learning (DL) is becoming more and more widespread in clone detection, motivated by achieving near-perfect performance for this task. In particular in case of semantic code clones, which share only limited syntax but implement the same or similar functionality, Deep Learning appears to outperform conventional tools. In this paper, we want to investigate

Matteo Gallone

We study prethermalization in time-independent quantum many-body systems on a $d$-dimensional lattice with an extensive local Hamiltonian $H=N+\varepsilon P$, in the regime where $\varepsilon \ll 1$. We prove that the prethermal timescale is exponential in $\varepsilon_0/\varepsilon$, where $\varepsilon_0$ is an explicit scale determined by the locality expo

Thomas Durt, Jean Schneider

It is commonly accepted that the results of measurements simultaneously realized over two entangled subsystems are statistically correlated instantaneously regardless of the distance between them. In accordance with Bell theorem, everything happens in such measurements as if there was a correlation propagating at infinite speed between the two subsystems.The

Wenxuan Li, Zhenfei Zhang, Mi Zhang, Geng Hong

Large language models (LLMs) may memorize sensitive or copyrighted content, raising significant privacy and legal concerns. While machine unlearning has emerged as a potential remedy, prevailing paradigms rely on user-provided forget sets, making unlearning requests difficult to audit and exposing systems to secondary leakage and malicious abuse. We propose

Michał Makowski, Dominik Kowal, Muhammad Danang Birowosuto

Nanoplasmonic modification of scintillation has so far been explored mainly in the weak-coupling regime, where changes in the local density of optical states enhance radiative recombination via Purcell-type rate engineering. By contrast, strong light-matter coupling generates hybrid states that modify emission dynamics beyond simple decay-rate acceleration,

Celia Blanco, Jacob Haqq-Misra, George Profitiliotis

How long a technological civilization remains active, and what determines whether it collapses or persists, is a central question for both projecting humanity's future and assessing the prevalence of detectable intelligence in the galaxy. We model collapse-recovery dynamics across ten plausible futures for Earth-originating civilization using a hybrid determ

L. Francis, Ł. Tychoniec, E. F. van Dishoeck, A. D. Sellek

Protostellar outflows display wide-angle winds and collimated jets, the magnetocentrifugal launching of which enables accretion onto the protostar. The majority of the outflow mass is likely ejected or entrained molecular H$_2$, which can now be studied in unprecedented detail with JWST. Using JWST MIRI/MRS observations towards 13 single and 20 multiple Clas

Long Feng, Huifang Ma, Zhaojun Wang

This paper studies alpha testing in a high-dimensional conditional time-varying factor model with temporally dependent observations. Both factor loadings and alpha processes are allowed to vary smoothly over time, and the cross-sectional dimension may be comparable to or larger than the sample size. Using a B-spline sieve method, we develop a sum-type test f

Yong Wang

In \cite{HLZ2} and \cite{HHLZ}, using $E_8$ bundles, some modular forms over $SL(2,{\bf Z})$ were constructed on $12$-dimensional manifolds and the Witten-Freed-Hopkins anomaly cancellation formula was derived by these $SL(2,Z)$ modular forms. In this paper, we construct several similar $SL(2,Z)$ modular forms on any dimensional manifolds and some new anomal

Riccardo Cristoferi, Devin van der Gulik

We determine the explicit value of the optimal constant in the trace inequality for functions of bounded variations in the case the domain has a particular class of singularities.

Francesco Giovanni Celiberto

We examine the leading-power fragmentation of fully heavy pentaquarks in high-energy hadronic collisions. To this end, we complete the release of the hadron-structure-oriented PQ5Q1.0 fragmentation functions, by discussing the $P_{5c}$ set and delivering the $P_{5b}$ one. These functions incorporate an improved computation of the initial-scale input for the

Erik Fransson, Michael Xu, Prakriti Kayastha, Kevin Ye

Chalcogenide perovskites have emerged as promising lead free materials for photovoltaic and thermoelectric applications. Among them, BaZrS3 has attracted particular attention due to its thermal and chemical stability, favorable optoelectronic properties, and low thermal conductivity. Here, we combine molecular dynamics and Monte Carlo simulations based on ma

Rodrigo Cilla Ugarte, Miguel Ángel Patricio Guisado, Antonio Berlanga de Jesús, José Manuel Molina López

AI Assurance -- producing the machine-readable evidence required to demonstrate compliance with AI governance frameworks -- has mature policy scaffolding but lacks the infrastructure to operationalize it. Organizations building high-risk AI systems under the EU AI Act face a gap: frameworks such as the EU AI Act, ISO/IEC 42001, and NIST AI RMF specify what t

M. Z. Serikow, D. Bazin, M. A. Caprio, Y. Ayyad

The spectroscopy of $^{11}$Be is explored using the $^{10}$Be$(d,p)$$^{11}$Be transfer reaction performed in inverse kinematics at $9.6\,\MeV/u$ using the Active Target Time Projection Chamber (AT-TPC) inside the SOLARIS solenoid. This experiment is the first attempt at coupling the AT-TPC with SOLARIS to perform a high luminosity transfer reaction measureme

Mykola Makhortykh, Victoria Vziatysheva, Maryna Sydorova

The rise of generative artificial intelligence (genAI) models poses new possibilities and risks for how the past is remembered by accelerating content production and altering the process of information discovery. The most critical risk is historical misrepresentation, which ranges from the distortion of facts and inaccurate depiction of specific groups to mo

John Pellew, Faizan Raza

How do security scanners perform on real-world code? We present RealVuln, the first open-source benchmark comparing Rule-Based SAST, General-Purpose LLMs, and Security-Specialized scanners on 26 intentionally vulnerable Python repositories (educational and Capture-The-Flag applications) with 796 hand-labeled entries (676 vulnerabilities, 120 false-positive t

Mohsen Jalaeian-Farimani, Mohammad-R Akbarzadeh-T, Alireza Akbarzadeh, Mostafa Ghaemi

To date, various paradigms of soft-Computing have been used to solve many modern problems. Among them, a self organizing combination of fuzzy systems and neural networks can make a powerful decision making system. Here, a Dynamic Growing Fuzzy Neural Controller (DGFNC) is combined with an adaptive strategy and applied to a 3PSP parallel robot position contro

Mu-Qing Liu, Xiao-Hong Yang, De-Fu Bu

It is difficult to implement numerical simulations on a region extending from the vicinity of a black hole to the Bondi radius. Most previous numerical simulations have primarily concentrated on the region close to the black hole. They found that strong winds can be generated in the hot accretion flows near the black hole, and that radiative cooling signific

Svetlana Pavlitska, Haixi Fan, Konstantin Ditschuneit, J. Marius Zöllner

Sparse mixture-of-experts (MoE) layers have been shown to substantially increase model capacity without a proportional increase in computational cost and are widely used in transformer architectures, where they typically replace feed-forward network blocks. In contrast, integrating sparse MoE layers into convolutional neural networks (CNNs) remains inconsist

Nicklas Meineke, Guido Burkard

Spin shuttling has crystalized as a powerful and promising tool for establishing intermediate-range connectivity in semiconductor spin-qubit devices. Although experimental demonstrations have performed exceptionally well on different materials platforms, the question of how to handle areas of low valley splitting in silicon during shuttling remains unresolve

Rafflesia Khan, Nafiul Islam Khan

Large language model (LLM) agents on multi-step tasks suffer reasoning degradation, looping, drift, stuck states, at rates up to 30% on hard tasks. Current solutions include hard step limits (abrupt) or LLM-as-judge monitoring (10-15% overhead per step). This paper introduces the Cognitive Companion, a parallel monitoring architecture with two implementation

Carlo Alberto Antonini, Giulio Ciraolo, Michele Gatti

We investigate both qualitative and quantitative issues related to the classification of non-negative energy solutions to the anisotropic critical $p$-Laplace equation in $\mathbb{R}^n$, for $1<p<n$. Specifically, we establish an anisotropic version of Struwe's decomposition, along with the interaction estimate for the family of bubbles in this decomposition