February 2026 arXiv papers — page 2

Hui-Ju Hung, Guang-Siang Lee, Chia-Hsun Lu, Chih-Ya Shen

In hybrid workforce configurations, it is important to decide which employees should work onsite or remotely while ensuring the collaboration benefits against contact-based health risks and skill requirements. In this paper, we formulate the Risk-aware Skill-coverage Hybrid Workforce Configuration (RSHWC) problem on a two-layer social network that balances p

Hongyang Liu, Bo Chen, Rong Chen, Dongdong Kang

Refractory multi-principal element alloys (MPEAs) are key research focus for excellent high-temp properties and engineering potential. Deformation mechanisms/mechanical behaviors of quaternary NbTaTiZr MPEA under high strain rates/extreme temps remain unclear. We built a variable-composition ML potential for NbTaTiZr, combined with MD simulations to study ef

Kota Dohi, Harsh Purohit, Tomoya Nishida, Takashi Endo

Effectively searching time-series data is essential for system analysis, but existing methods often require expert-designed similarity criteria or rely on global, series-level descriptions. We study language-driven segment retrieval: given a natural language query, the goal is to retrieve relevant local segments from large time-series repositories. We build

Andrew Zhuoer Feng, Cunxiang Wang, Bosi Wen, Yidong Wang

Large language model alignment via reinforcement learning depends critically on reward function quality. However, static, domain-specific reward models are often costly to train and exhibit poor generalization in out-of-distribution scenarios encountered during RL iterations. We present RLAR (Reinforcement Learning from Agent Rewards), an agent-driven framew

Ramesh Sreekantan

A theorem of Manin and Drinfeld states that any divisor of degree $0$ on the cusps of a modular curve is torsion in the Jacobian. An elegant proof of this result was provided by Elkik using mixed Hodge theory. Rohrlich proved a generalization of this to Fermat curves. In this note we reprove his results along the lines of the work of Elkik. We then use the s

Evgeny Kagan, Christian Jost, Tobias Lieberum, Sebastian Schiffels

Motivated by the widespread adoption of iterative project management techniques, we study the effects of workflow -- iterative or sequential -- on innovative behavior and performance. We conduct a series of laboratory experiments. Our first experiment shows that, in an open-ended creative challenge, iterative task completion leads to better outcomes than seq

Hio-Ieng Un, Jordi Ferrer Orri, Ian E. Jacobs, Naoya Fukui

Understanding growth evolution and thereby implementing precise microstructural tuning of two-dimensional (2D) conjugated coordination polymers (cCPs) is crucial to achieve efficient electronic conduction towards their full potential and to observe materials' intrinsic properties. However, fundamental understanding of how 2D cCPs films grow remains very limi

Minkyoung Cho, Insu Jang, Shuowei Jin, Zesen Zhao

Fine-tuning Multimodal Large Language Models (MLLMs) with parameter-efficient methods like Low-Rank Adaptation (LoRA) is crucial for task adaptation. However, imbalanced training dynamics across modalities often lead to suboptimal accuracy due to negative interference, a challenge typically addressed with inefficient heuristic methods such as manually tuning

Yibo Qiu, Shu'ang Sun, Haoliang Ye, Ronald X Xu

Long-horizon precision manipulation in laboratory automation, such as pipette tip attachment and liquid transfer, requires policies that respect strict procedural logic while operating in continuous, high-dimensional state spaces. However, existing approaches struggle with reward sparsity, multi-stage structural constraints, and noisy or imperfect demonstrat

Leo Thomas Ramos, Angel D. Sappa

We introduce FCBNet, an efficient model designed for weed segmentation. The architecture is based on a fully frozen ConvNeXt backbone, the proposed Feature Correction Block (FCB), which leverages efficient convolutions for feature refinement, and a lightweight decoder. FCBNet is evaluated on the WeedBananaCOD and WeedMap datasets under both RGB and multispec

Shiqi Chen, Jingze Gai, Ruochen Zhou, Jinghan Zhang

Real-world tool-using agents operate over long-horizon workflows with recurring structure and diverse demands, where effective behavior requires not only invoking atomic tools but also abstracting, and reusing higher-level tool compositions. However, existing benchmarks mainly measure instance-level success under static tool sets, offering limited insight in

Qinghui He, Haifeng Zhang, Xiuli Bi, Bo Liu

The accelerating advancement of generative models has introduced new challenges for detecting AI-generated images, especially in real-world scenarios where novel generation techniques emerge rapidly. Existing learning paradigms are likely to make classifiers data-dependent, resulting in narrow decision margins and, consequently, limited generalization abilit

Yijing Ke, Zihan Zhang, Ruosong Wang

We study computationally and statistically efficient reinforcement learning under the linear $Q^{\pi}$ realizability assumption, where any policy's $Q$-function is linear in a given state-action feature representation. Prior methods in this setting are either computationally intractable, or require (local) access to a simulator. In this paper, we propose a c

Rui Ma, Yifeng Wang, Ziteng Yang, Jing Guo

Visual analysis and reconstruction of pipeline inner walls remain challenging in industrial inspection scenarios. This paper presents a dedicated reconstruction system for pipeline inner walls via industrial endoscopes, which is built on panoramic image stitching technology. Equipped with a custom graphical user interface (GUI), the system extracts key frame

Margherita Fabini, Andrea Pascucci, Alessio Rondelli

We study a stochastic optimal control problem motivated by the operation of a large ensemble of residential storage devices coordinated by an energy aggregator. The aggregator remunerates prosumers in exchange for direct control of their batteries and seeks to jointly (i) reduce local supply-demand imbalances and (ii) exploit intraday price fluctuations thro

Hsin Lin, Yan-Lun Chen, Ren-Hung Hwang, Chia-Mu Yu

Backdoor attacks pose a critical threat to the security of deep neural networks, yet existing efforts on universal backdoors often rely on visually salient patterns, making them easier to detect and less practical at scale. In this work, we introduce a novel imperceptible universal backdoor attack that simultaneously controls all target classes with minimal

Debjyoti Chakraborty

This paper presents a controlled empirical study of biologically motivated local learning for handwritten digit recognition. We evaluate an STDP-inspired competitive proxy and a practical hybrid benchmark built on the same spiking population encoder. The proxy is motivated by leaky integrate-and-fire E/I circuit models with three-factor delayed reward modula

Laurent Pagnier, Melvyn Tyloo, Akshita Jindal, Pragati Thakur

Predicting the response of an observed system to a known input is a fruitful first step to accurately control the system's dynamics. Despite the recent advances in fully data-driven algorithms, the most interpretable way to reach this goal is through mechanistic mathematical modeling. Here, we leverage optimal control and propose a closed-loop iterative meth

Thomas Lloyd, Daire Ó Broin, Martin Harrigan

Voting is the primary mechanism through which Decentralised Autonomous Organisations (DAOs) reach decisions. Although transparent, the voting process can be opaque: it can involve many interacting smart contracts. The nexus of the decision-making process can be relocated and the true voter demographic obfuscated. DAOs can also govern other DAOs, a process kn

Marry Kong, Rina Buoy, Sovisal Chenda, Nguonly Taing

While document layout analysis for Latin scripts has advanced significantly, driven by the advent of large multimodal models (LMMs), progress for the Khmer language remains constrained because of the scarcity of annotated training data. This gap is particularly acute for scene documents, where perspective distortions and complex backgrounds challenge traditi

Evgeny Kagan, Kyle Hyndman, Anyan Qi

To grow their businesses, entrepreneurs often rely on equity funding. This paper focuses on two elements of entrepreneur-investor equity negotiations: the number of potential investors and the contractual complexity surrounding investor protection. Our approach involves a theoretical model and a series of laboratory experiments that analyze the effects of di

Pietro Caputo, Matteo Quattropani, Federico Sau

We study the mixing time of the averaging process on a large random $d$-regular graph, $d\ge 3$, and prove an $L^2$-cutoff with an explicit cutoff time. Somewhat surprisingly, we uncover a phase transition at the finite, fixed degree $d=10$: for small degrees, i.e., $d\le 10$, the averaging process mixes as fast as the corresponding random walk on the same g

Roger Koenker, Jiaying Gu

Two strategies are explored for robustifying classical denoising procedures for the Gaussian sequence model. First, the Hodges and Lehmann (1952) restricted Bayes approach is used to reduce sensitivity to the specification of the initial prior distribution. Second, alternatives to the Gaussian noise assumption are explored. In both cases proposals of Huber (

Guang Yang, Praveen Kumar

Next-generation neutrino oscillation experiments such as DUNE require percent-level knowledge of neutrino--nucleon interaction cross sections. The nucleon axial form factor $F_A(Q^2)$, parameterized by the axial mass $\MA$, is the dominant source of uncertainty in the quasi-elastic channel, and the parity-violating structure function $xF_3$ is poorly constra

Marry Kong, Rina Buoy, Sovisal Chenda, Nguonly Taing

Khmer is a low-resource language characterized by a complex script, presenting significant challenges for optical character recognition (OCR). While document printed text recognition has advanced because of available datasets, performance on other modalities, such as handwritten and scene text, remains limited by data scarcity. Training modality-specific mod

Joonhyung Bae

The automated piano enables note densities, polyphony, and register changes far beyond human physical limits, yet the three dominant traditions for composing such textures--Nancarrow's tempo canons, Xenakis's stochastic distributions, and L-system grammars--have developed in isolation. This paper presents Amanous, a hardware-aware composition system for Yama

Qiben Yan, John P. T. Stenger, Daniel Gunlycke

Data flow scheduling for high-throughput multibeam satellites is a challenging NP-hard combinatorial optimization problem. As the problem scales, traditional methods, such as Mixed-Integer Linear Programming and heuristic schedulers, often face a trade-off between solution quality and real-time feasibility. In this paper, we present a hybrid quantum-classica

Ziqi Xue, Dingxian Wang, Yimeng Bai, Shuai Zhu

Generative recommendation has emerged as a scalable alternative to traditional retrieve-and-rank pipelines by operating in a compact token space. However, existing methods mainly rely on discrete code-level supervision, which leads to information loss and limits the joint optimization between the tokenizer and the generative recommender. In this work, we pro

Wujun Shao, Dongwei Fan, Chenzhou Cui, Yunfei Xu

With the rapid advancements in observational technologies and the widespread implementation of large-scale sky surveys, diverse electromagnetic wave data (e.g., optical and infrared) and non-electromagnetic wave data (e.g., gravitational waves) have become increasingly accessible. Astronomy has thus entered an unprecedented era of data abundance and complexi

André Baião Raposo, John Bulava, Jeremy R. Green, Andrew D. Hanlon

We present preliminary results on the $I=0$, $S=-2$ $H$ dibaryon in $N_{\rm f}=2+1$ QCD. The calculation is performed with heavier-than-physical quarks ($m_\pi \approx 280$ MeV) on a single CLS ensemble. Correlation matrices are constructed using the distillation technique and the three relevant channels, $\Lambda\Lambda$, $N\Xi$, $\Sigma\Sigma$, are investi

Yihui Li, Chengxin Lv, Zichen Tang, Hongyu Yang

We present TokenSplat, a feed-forward framework for joint 3D Gaussian reconstruction and camera pose estimation from unposed multi-view images. At its core, TokenSplat introduces a Token-aligned Gaussian Prediction module that aligns semantically corresponding information across views directly in the feature space. Guided by coarse token positions and fusion

Amaia Cardiel, Eloi Zablocki, Elias Ramzi, Eric Gaussier

Large language models (LLMs) are increasingly used as reasoning engines in autonomous driving, yet their decision-making remains opaque. We propose to study their decision process through counterfactual explanations, which identify the minimal semantic changes to a scene description required to alter a driving plan. We introduce DRIV-EX, a method that levera

Xingguo Xu, Zhanyu Liu, Weixiang Zhou, Yuansheng Gao

Multi-modal object Re-Identification (ReID) aims to exploit complementary information from different modalities to retrieve specific objects. However, existing methods often rely on hard token filtering or simple fusion strategies, which can lead to the loss of discriminative cues and increased background interference. To address these challenges, we propose

Zihang Wang, Xu Li, Benwu Wang, Wenkai Zhu

Explainability and transparent decision-making are essential for the safe deployment of autonomous driving systems. Scene captioning summarizes environmental conditions and risk factors in natural language, improving transparency, safety, and human--robot interaction. However, most existing approaches target structured urban scenarios; in off-road environmen

Bjorn Andreas Ager-Hart, Melissa Beerbower, Pamela E. Harris, Joakim Jakovleski

Fubini rankings with $n$ competitors are $n$-tuples with entries in $[n]=\{1,2,3,\ldots, n\}$ that encode the conclusion of a race that allows ties. Since Fubini rankings are parking functions, we can study their parking outcomes, which are permutations encoding the final parking order of the cars using the Fubini ranking as a preference list. We establish t

Sandip Das, Sweta Das, Sk Samim Islam

The \textsc{Dominating Set} problem is a classical and extensively studied topic in graph theory and theoretical computer science. In this paper, we examine the algorithmic complexity of several well-known exact-distance variants of domination, namely \textsc{$r$-Step Domination}, \textsc{$r$-Hop Domination}, and \textsc{$r$-Hop Roman Domination}. Let $G$ be

Yimeng Liu, Fangwei Zhang, Maolin Gan, Jialuo Du

The world is undergoing a major demographic shift as older adults become a rapidly growing share of the population, creating new challenges for driving safety. In car-dependent regions such as the United States, driving remains essential for independence, access to services, and social participation. At the same time, aging can introduce gradual changes in v

Melih Şahin, Ozgur B. Akan

Molecular communication (MC) enables information exchange in nanoscale sensor networks operating in biological environments, yet privacy remains largely unaddressed. We integrate local differential privacy (LDP) into diffusion-based MC by privatizing each user's measurement at the transmitter and conveying the resulting randomized report over the MC channel.

Lizhao You, Nanqing Zhou, Guanglong Pang, Jiajie Huang

Link Adaptation (LA) that dynamically adjusts the Modulation and Coding Schemes (MCS) to accommodate time-varying channels is crucial and challenging in cellular networks. Deep reinforcement learning (DRL)-based LA that learns to make decision through the interaction with the environment is a promising approach to improve throughput. However, existing DRL-ba

Rina Buoy, Dylan berkamp Fouepe Dongmo, Vesal Khean, Simone Marinai

Reading has always been an integral part of both professional and personal life. Character and layout recognition and understanding by computers are well-explored areas. Nevertheless, how characters and layout are read and perceived by humans remains relatively underexplored. This work contributes to the field of human-document interaction (HDI) by investiga

Guoquan Wei, Liu Shi, Shaoyu Wang, Mohan Li

Noise and artifacts during computed tomography (CT) scans are a fundamental challenge affecting disease diagnosis. However, current methods either involve excessively long reconstruction times or rely on data-driven models for optimization, failing to adequately consider the valuable information inherent in the data itself, especially medical 3D data. This w

Andrew Zhuoer Feng, Cunxiang Wang, Yu Luo, Bosi Wen

Large Language Models have evolved from single-round generators into long-horizon agents, capable of complex text synthesis scenarios. However, current evaluation frameworks lack the ability to assess the actual synthesis operations, such as outlining, drafting, and editing. Consequently, they fail to evaluate the actual and detailed capabilities of LLMs. To

Xizhi Hu, Xiaodong Chen, Jianqiao Xu, Ignazio Ciufolini

Earth tidal perturbations affecting laser-ranged satellites are critical for refining satellite orbital dynamics modeling, and their accurate computation represents a prerequisite for high-precision fundamental physical effects and geodetic investigations based on satellite orbit analysis. This study focuses on the tidal perturbations induced by the asymmetr

Hiromichi Ono

We investigate Tree Iterated Function Systems (TIFSs), which we introduce in this paper. TIFSs are the generalizations of Iterated Function Systems in which we take the maps independently at each step and each block. In this paper, we give the definition of TIFSs and the limit sets of them. We show a formula for the Hausdorff dimension of the limit sets of T

Eva Feillet, Ryan Whetten, David Picard, Alexandre Allauzen

State-of-the-art speech-to-text models typically employ Transformer-based encoders that model token dependencies via self-attention mechanisms. However, the quadratic complexity of self-attention in both memory and computation imposes significant constraints on scalability. In this work, we propose a novel token-mixing mechanism, the Polynomial Mixer (PoM),

Yushan Han, Hui Zhang, Qiming Xia, Yi Jin

Collaborative perception empowers autonomous agents to share complementary information and overcome perception limitations. While early fusion offers more perceptual complementarity and is inherently robust to model heterogeneity, its high communication cost has limited its practical deployment, prompting most existing works to favor intermediate or late fus

Jinge Yang, Jianfu Yang

In this paper, we consider the asymptotic behavior of the ground state solution $u_s$ of the nonlinear fractional Laplacian equation \begin{equation}\label{eq:0.1a} (-\Delta)^su+Vu=|u|^{p-2}u\quad x\in \mathbb{R}^n \end{equation} by taking $s$ as a parameter, where $n\geq 4$, $2<p<\frac{2n}{n-2}$, $V$ is a potential function. We show that for a fixed $p$, th

Prashant C. Raju

The capacity to precisely edit genomes has outpaced our ability to predict the consequences. A cell can be genetically perfect and therapeutically useless: edited exactly as intended, yet unstable, drifting toward unintended fates, or selected for properties that compromise safety. This paradox reflects a deeper gap in how we evaluate biological intervention

Donald Woukeng

We introduce a persistence-type invariant for finite weighted graphs based on combinatorial multivector dynamics. For each threshold parameter, a relation matrix determines a graph multivector field, whose induced directed dynamics admits a Morse decomposition given by its strongly connected components. As the threshold varies, these multivector fields form

Zhe Wu, Donglin Mo, Hongjin Lu, Junliang Xing

Existing mobile device control agents often perform poorly when solving complex tasks requiring long-horizon planning and precise operations, typically due to a lack of relevant task experience or unfamiliarity with skill execution. We propose K2-Agent, a hierarchical framework that models human-like cognition by separating and co-evolving declarative (knowi

Youngjin Yoo, Han Liu, Bogdan Georgescu, Yanbo Zhang

Foundation models pre-trained on large-scale datasets demonstrate strong transfer learning capabilities; however, their adaptation to complex multi-label diagnostic tasks-such as comprehensive head CT finding detection-remains understudied. Standard parameter-efficient fine-tuning methods such as LoRA apply uniform adaptations across pathology types, which m

Malgorzata Kowalczuk

For each countable ordinal $\alpha \ge 2$, the ideals $\mathsf{conv}_\alpha$ were introduced in ``Critical ideals for countable compact spaces'' (to appear in Fund. Math., see also arXiv:2503.12571) to characterize compact countable spaces homeomorphic to $\omega^\alpha \cdot n+1$ with the order topology. We study the structure of these ideals in the Kat\v{e

Yunfei Feng, Xi Zhao, Cheng Zhang, Dahu Feng

Mobile agents can autonomously complete user-assigned tasks through GUI interactions. However, existing mainstream evaluation benchmarks, such as AndroidWorld, operate by connecting to a system-level Android emulator and provide evaluation signals based on the state of system resources. In real-world mobile-agent scenarios, however, many third-party applicat

Jinmin Yu, Shaofang Hong

Let $l\ge 1$ be an integer. For any multiple index $\mathbf{s}=(s_1,s_2,\cdots,s_l)\in\mathbb{Z}_{\geq 1}^l$ with $s_l>1$, the multiple zeta value (MZV for short) is defined by \begin{align*} \zeta(s_1,s_2,\cdots,s_l):=\sum_{1\leq k_1<k_2<\cdots<k_l} \frac{1}{k_1^{s_1}k_2^{s_2}\cdots k_l^{s_l}} \end{align*} and the multiple $t$-value is defined by \begin{ali

Dounia Darkaoui, Martin Weimann

Let C be a projective curve defined over a field k and let D be a divisor of C. The Riemann-Roch space L(D) is the set of rational functions on C for which certain zeros are imposed and certain poles are allowed, with some multiplicities determined by D. Riemann-Roch spaces play a fundamental role in algebraic geometry due to the central place of the Riemann

Li Fang, Yu Wang, Aibin Zang

In this paper, the Cauchy problem for a one-dimensional heat conducting compressible non-Newtonian fluid is considered. The constitute equation of the non-Newtonian fluid is determined by two nonlinear terms $(|u_x|^{q-2}u_x)_x$ and $(|\theta_x|^{p-2}\theta_x)_x$ with $1<p,q<2.$ When the vacuum occurs at the far field, the local and global existence of stron

Haoran Ji, Zheyuan Xie, Xiaofang Fu, Zihan Cui

Dimensionality of a physical system, conventionally an invariant geometric characteristic, fundamentally governs the universality class of phase transitions and the landscape of emergent collective phenomena. In low-dimensional or layered high-temperature superconductors, the macroscopic phase coherence of superconducting orders is typically confined in two

Chaoyue He, Xin Zhou, Xinjia Yu, Lei Zhang

Sustainability disclosure standards (e.g., GRI, SASB, TCFD, IFRS S2) are comprehensive yet lengthy, terminology-dense, and highly cross-referential, hindering structured analysis and downstream use. We present SSKG Hub (Sustainability Standards Knowledge Graph Hub), a research prototype and interactive web platform that transforms standards into auditable kn

Daniel Tweneboah Anyimadu, Mohammed M. Abdelsamea, Ahmed Karam Eldaly

Low-field magnetic resonance imaging (MRI) provides affordable access to diagnostic imaging but suffers from prolonged acquisition and limited image quality. Accelerated imaging can be achieved with k-space undersampling, while super-resolution (SR) and image quality transfer (IQT) methods typically rely on spatial-domain post-processing. In this work, we pr

Peishen Yan, Shuang Liang, Yang Hua, Linshan Jiang

Federated learning (FL) enables collaborative model training over distributed private data. However, sustaining open participation requires incentive mechanisms that compensate contributors for their resources and risks. Enabled by Web3 primitives, especially blockchains, recent FL proposals incorporate incentive mechanisms for open participation, yet most f

I. A. Nechaev, V. M. Silkin, E. E. Krasovskii

A higher-order relativistic k.p model is developed to describe plasmon excitations in two-dimensional (2D) electronic systems with spin-orbit coupling (SOC) and magnetic-exchange interactions. Derived entirely from ab initio band structure, the model allows for a non-Rashba spin-momentum locking and enables a direct coupling of the exchange field to the real

Vrushali Shinde, Lata Kadam

A transversal coalition in a hypergraph $H$ is a partition of the vertex set $U$ into two subsets $U_1$ and $U_2$ such that neither $U_1$ nor $U_2$ alone intersects every hyperedge of $H$, but their union, $U_1 \cup U_2$, intersects every hyperedge in $H$. In this work, we investigate transversal coalition partitions in \( r \)-uniform hypergraphs. Specifica

Anoop Bhat, Geordan Gutow, Zhongqiang Ren, Sivakumar Rathinam

The Moving Target Vehicle Routing Problem (MT-VRP) seeks trajectories for several agents that intercept a set of moving targets, subject to speed, time window, and capacity constraints. We introduce an exact algorithm, Branch-and-Price with Relaxed Continuity (BPRC), for the MT-VRP. The main challenge in a branch-and-price approach for the MT-VRP is the pric

X. L. Pan, H. X. Song, Y. Sun, F. C. Wu

DFT+X methods, such as DFT+U and DFT+DMFT, are important supplements to standard density functional theory when strong on-site Coulomb interactions are present. However, the involvement of external parameters in the underlying model Hamiltonian introduces intrinsic ambiguity when comparing the total energies obtained with different model parameters. This ren

Meng Gao, Jinjiang Li, Linji Long, Min Zhang

In this paper, it is proved that, for any $\gamma_1,\gamma_2,\gamma_3,\gamma_4,\gamma_5\in(\frac{28}{29},1)$, every sufficiently large integer $n$ subject to $n\equiv5\pmod{24}$ can be represented as the sum of five squares of primes, i.e., \begin{equation*} n=p_1^2+p_2^2+p_3^2+p_4^2+p_5^2, \end{equation*} such that $p_i=\lfloor m_i^{1/\gamma_i}\rfloor$ for

Jin Gao, Yijun Song

In this paper, we prove a generalized reverse H\"older inequality of harmonic functions on cable systems induced by post-critically finite (p.c.f.) self-similar sets. Furthermore, we also establish the H\"older regularity of harmonic functions on both bounded and unbounded p.c.f. self-similar sets, which does not involve heat kernel estimates and resistance

Maria Thaleia Passia, Yilin Zhao, Haozhe Wang, Steven A. Cummer

We present the synthesis of broadband multilayer metamaterial absorbers (MMA) based on MXenes, which are novel two-dimensional conductive materials with higher ohmic losses than copper. MXene resonator of different conductivity can be implemented at each layer or across the same layer, offering increased design flexibility. We examine the possibility of util

Yunlong Gao, Xinyue Liu, Yingbo Wang, Linlin Zong

Few-shot text classification aims to recognize unseen classes with limited labeled text samples. Existing approaches focus on boosting meta-learners by developing complex algorithms in the training stage. However, the labeled samples are randomly selected during the testing stage, so they may not provide effective supervision signals, leading to misclassific

Ying Liu, Yudong Han, Kean Shi, Liyuan Pan

Multimodal Large Language Models (MLLMs) have achieved remarkable performance by aligning pretrained visual representations with the linguistic knowledge embedded in Large Language Models (LLMs). However, existing approaches typically rely on final-layer visual features or learnable multi-layer fusion, which often fail to sufficiently exploit hierarchical vi

Haryanto M. Siahaan

We establish the black-hole Meissner effect for extremal Kerr--Bertotti--Robinson (Kerr--BR) black holes, which are exact solutions of the Einstein--Maxwell equations describing a rotating black hole immersed in a uniform Bertotti--Robinson electromagnetic universe. Using the near-horizon framework of Bi\v{c}\'ak and Hejda, we prove that for a purely magneti

Xiaohan Zhao, Xinyi Shang, Jiacheng Liu, Zhiqiang Shen

Dataset pruning has been widely studied for 2D images to remove redundancy and accelerate training, while particular pruning methods for 3D data remain largely unexplored. In this work, we study dataset pruning for 3D data, where its observed common long-tail class distribution nature make optimization under conventional evaluation metrics Overall Accuracy (

Reggie C. Pantig, Ali Övgün

We extend the finite-distance Jacobi-metric Gauss-Bonnet framework of Li \textit{et al}. [10.1103/PhysRevD.101.124058] to massive test particles carrying intrinsic spin. At pole-dipole order, the Mathisson-Papapetrou-Dixon dynamics generically drives the spatial ray away from Jacobi geodesics, so the standard Gauss-Bonnet construction must be reformulated to

Sahand Moslemi, Mayasah Lami, Anil Koyuncu

Assessing the correctness of patches generated by Automated Program Repair (APR) is a major bottleneck. Manual validation is labor-intensive and limited: exact matching overlooks valid variants, while semantic inspection is subjective and hard to reproduce. Existing Automated Patch Correctness Assessment (APCA) often relies on opaque predictive models that t

C. B. Z. Luo, C. Guo, L. P. Xiang, Y. H. Niu

In low-background particle physics experiments, surface deposition of radon progeny presents a significant background challenge. To characterize this contamination, a high-sensitivity surface $\alpha$-activity measurement system was developed, which employs a 3$\times$3 Si-PIN array operating in vacuum to perform $\alpha$-spectroscopy on samples. The system

Kunal Mukherjee, Cuneyt Gurcan Akcora, Murat Kantarcioglu

Agent-native social platforms such as Moltbook are rapidly emerging, yet they inherit and amplify classical influence and abuse attacks, where coordinated agents strategically comment and upvote to manipulate visibility and propagate narratives across communities. However, rigorous measurement and learning-based monitoring remain constrained by the absence o

Denis Borisov, Andrey Piatnitski

In the paper we introduce Orlicz type functional spaces defined in terms of nonlocal convolution type integral functionals and study the main properties of these spaces. We show in particular that, under natural convexity and growth conditions on the integrand, the corresponding spaces are Banach and separable. We also characterize the dual spaces and provid

Shengjie Xu, Bingsheng He

The primal dual hybrid gradient algorithm (PDHG), which is also known as the Arrow-Hurwicz method, is a fundamental algorithm for saddle point problems especially in imaging. It also inspires a great number of influential algorithms such as the stochastic PDHG and the Chambolle-Pock's primal dual algorithm. In the literature, convergence theory of the PDHG i

Jinfan Hu, Fanghua Yu, Zhiyuan You, Xiang Yin

This position paper argues that the evaluation of modern visual processing systems should no longer be driven primarily by single-metric image quality assessment benchmarks, particularly in the era of generative and perception-oriented methods. Image restoration exemplifies this divergence: while objective IQA metrics enable reproducible, scalable evaluation

Fedor Pakhomov, Julien Daoud

In the present paper, we consider Presburger arithmetic PrA and the theory of real closed fields RCF. Due to quantifier elimination in these theories, there are two kinds of natural ways to axiomatize them. Namely, on one hand, PrA can be axiomatized with the full schema of first-order induction, and RCF with the full schema of the first-order least upper bo

Xiangning Quan, Xiaoqiu Yuan, Junwei Zhang, Xuebing Peng

Substitutional doping effectively modulates carrier polarity of semiconducting two-dimensional (2D) transition metal dichalcogenides (TMDs) like MoS2. Although Fe doping typically induces n-type conductivity in monolayer MoS2, anomalous p-type behavior has also been experimentally reported, the origin of which remains unresolved. Here, we prove that this ano

Maria-Thaleia Passia, Steven A. Cummer

We present a systematic, computationally efficient approach for synthesizing 3D-printable all-dielectric devices. Inverse-design optimization methods lead to devices of a continuous dielectric constant profile with complex and conformal shapes. However, stereolithography 3D printers have a limited range of materials; usually, only resin and air are available

Bharat Pratap Chauhan, Dipti Dubey

In this paper, we introduce the notion of (strictly) semimonotone matrices of exact order $k$, where $0\leq k\leq n$, and explore their properties. We fully characterize the $3 \times 3$ (strictly) semimonotone matrices of exact order $2$, and show that the class of $3 \times 3$ semimonotone matrices of exact order $2$ forms a subclass of inverse $\mathbf{Z}

Jin Zeng, Yupeng Qi, Hui Li, Chengming Li

Large language models (LLMs) are increasingly adopted as the backbone of recommender systems. However, user-item interactions in real-world scenarios are non-stationary, making preference drift over time inevitable. Existing model update strategies mainly rely on global fine-tuning or pointwise editing, but they face two fundamental challenges: (i) imbalance

Cedric Damour

We propose a retrodictive forecasting paradigm for time series: instead of predicting the future from the past, we identify the future that best explains the observed present via inverse MAP optimization over a Conditional Variational Autoencoder (CVAE). This conditioning is a statistical modeling choice for Bayesian inversion; it does not assert that future

Krzysztof Jodłowski, Chih-Ting Lu

The singlet majoron $J$ is the pseudo-Nambu-Goldstone boson of a global, anomaly-free $U(1)_{B-L}$ symmetry whose spontaneous breaking generates Majorana masses for right-handed neutrinos. At tree level, the only direct coupling of $J$ to Standard Model fields is $J\nu\nu\propto m_\nu/f$ (where $m_\nu$ denotes the light neutrino mass and $f$ the $B-L$ breaki

Jason Lucas, Matt Murtagh-White, Adaku Uchendu, Ali Al-Lawati

Multilingual falsehoods threaten information integrity worldwide, yet detection benchmarks remain confined to English or a few high-resource languages, leaving low-resource linguistic communities without robust defense tools. We introduce BLUFF, a comprehensive benchmark for detecting false and synthetic content, spanning 79 languages with over 202K samples,

Fabian Scheidt, Jasin Machkour, Michael Muma

False discovery rate (FDR) control is a popular approach for maintaining the integrity of statistical analyses, especially in high-dimensional data settings, where multiple comparisons increase the risk of false positives. FDR control has been extensively researched for real-valued data. However, the complex data case, which is relevant for many signal proce

Zheng Hu, Yuxin Chen, Yongsen Pan, Xu Yuan

Semantic IDs (SIDs) are compact discrete representations derived from multimodal item features, serving as a unified abstraction for ID-based and generative recommendation. However, learning high-quality SIDs remains challenging due to two issues. (1) Collision problem: the quantized token space is prone to collisions, in which semantically distinct items ar

Ruixiang Mao, Xiangnan Ma, Dan Chen, Ziming Zhu

Test-Time Scaling has shown notable efficacy in addressing complex problems through scaling inference compute. However, within Large Audio-Language Models (LALMs), an unintuitive phenomenon exists: post-training models for structured reasoning trajectories results in marginal or even negative gains compared to post-training for direct answering. To investiga

Daniel Autenrieth

This paper presents the first systematic measurement of educational alignment in Large Language Models. Using a Delphi-validated instrument comprising 48 items across eight educational-theoretical dimensions, the study reveals that GPT-5.1 exhibits highly coherent preference patterns (99.78% transitivity; 92.79% model accuracy) that largely align with humani

Zhehang Ye, Ximing Xie, Hao Qin, Xingqi Zhang

The pinching-antenna system (PASS) has been proposed as a promising solution for mitigating line-of-sight (LoS) blockages by dynamically repositioning pinching antennas (PAs) along a dielectric waveguide. This paper develops a fairness-oriented downlink design for a non-orthogonal multiple access (NOMA)-enabled PASS, where the longitudinal placement of PAs a

Yunzhong Qiu, Zhiyao Cen, Zhongyi Pei, Chen Wang

Large time series models (LTMs) have emerged as powerful tools for universal forecasting, yet they often struggle with the inherent diversity and nonstationarity of real-world time series data, leading to an unsatisfactory trade-off between forecasting accuracy and generalization. Rather than continually finetuning new LTM instances for each domain, we propo

Joris Verhagen, Elias Krantz, Chelsea Sidrane, David Dörner

We present an experimental validation framework for space robotics that leverages underwater environments to approximate microgravity dynamics. While neutral buoyancy conditions make underwater robotics an excellent platform for space robotics validation, there are still dynamical and environmental differences that need to be overcome. Given a high-level spa

Deijany Rodriguez Linares, Oksana Moryakova, Håkan Johansson

This paper presents joint sampling frequency offset (SFO) estimation and compensation algorithms based on the Farrow structure. Unlike conventional approaches that treat estimation and compensation separately, the proposed framework exploits the interpolator structure to enable a low-complexity, fully time-domain solution applicable to arbitrary bandlimited

A. K. de Almeida, A. F. S. Ferreira, L. B. T. Santos, F. Monteiro

Context. Various simplified models have been investigated to understand the complex dynamical environment near irregular asteroids. We propose a generalized dipole-segment model (GDSM) to describe the gravitational fields of elongated bodies. The proposed model extends the dipole-segment model (DSM) by including variable pole masses and a connecting rod whil

Shibing Mo, Jiarui Zhang, Jiayu Xie, Xiangyi Teng

In order to evaluate the invulnerability of networks against various types of attacks and provide guidance for potential performance enhancement as well as controllability maintenance, network controllability robustness (NCR) has attracted increasing attention in recent years. Traditionally, controllability robustness is determined by attack simulations, whi

Muhammad Kashif, Alberto Marchisio, Muhammad Shafique

Hybrid quantum-classical neural networks (HQNNs) integrate quantum circuits with classical layers, each operating under fundamentally different computational paradigms, which makes hardware resource estimation challenging. The training of quantum circuits on real devices requires thousands of circuit executions, which is impractical on current NISQ devices.

Zhanwang Liu, Yuting Li, Haoyuan Gao, Yexin Li

Catastrophic forgetting, the tendency of neural networks to forget previously learned knowledge when learning new tasks, has been a major challenge in continual learning (CL). To tackle this challenge, CL methods have been proposed and shown to reduce forgetting. Furthermore, CL models deployed in mission-critical settings can benefit from uncertainty awaren

Shu-Xun Yang, Cunxiang Wang, Haoke Zhang, Wenbo Yu

Agentic systems augment large language models with external tools and iterative decision making, enabling complex tasks such as deep research, function calling, and coding. However, their long and intricate execution traces make failure diagnosis and root cause analysis extremely challenging. Manual inspection does not scale, while directly applying LLMs to

Pengqiang Dong, Hanbo Sun, Chao Wu, Ping Li

Magnetoelectric coupling is vital for exploring fundamental science and driving the development of high-density memory and energy-efficient spintronic devices. Altermagnets, which merge the benefits of ferromagnets and antiferromagnets, pave the way for unprecedented magnetoelectric coupling effects. However, the spin splitting in altermagnets is robustly pr