April 2026 arXiv papers — page 121

Gökçe Uludoğan, Buse Giledereli, Elif Ozkirimli, Arzucan Özgür

Proteins carry out biological functions through the coordinated action of groups of residues organized into structural arrangements. These arrangements, which we refer to as protein units, exist at an intermediate scale, being larger than individual residues yet smaller than entire proteins. A deeper understanding of protein function can be achieved by ident

Hongao Yang, Yongtao Yang, Yuanbin Liu, Tao Ding

Thermal boundary conductance (TBC) at dissimilar interfaces imposes a fundamental limit on electronic device performance, yet predicting and understanding heat transport across realistic, disordered boundaries remains elusive. Here, we develop a computational framework that combines machine-learned interatomic potentials with lattice dynamics to address the

Tianjun Zhang, Fengyi Zhang, Tianchen Deng, Lin Zhang

Visual Geometry Foundation Models (VGFMs) demonstrate remarkable zero-shot capabilities in local reconstruction. However, deploying them for kilometer-level Simultaneous Localization and Mapping (SLAM) remains challenging. In such scenarios, current approaches mainly rely on linear transforms (e.g., Sim3 and SL4) for sub-map alignment, while we argue that a

Wei Wei, Jin Zheng, Zining Wang, Weibin Feng

The exponential growth of financial research has rendered traditional systematic literature reviews (SLRs) increasingly impractical, as manual screening and narrative synthesis struggle to keep pace with the scale and complexity of modern scholarship. While the existing artificial intelligence (AI) and natural language processing (NLP) approaches often often

Richard M. Höfer, Eleni Hübner-Rosenau

We study the behaviour of the solution $u_\varepsilon$ to the Navier-Stokes equations with vanishing viscosity and a non-slip condition in a randomly perforated domain. We consider the space $\mathbb{R}^3$ where we remove $N$ holes that are i.i.d. distributed. The behaviour depends on the particle size $\varepsilon^\alpha=N^{-\alpha/3}$ and the viscosity $\v

Jorge Acosta-Hernández, Alexander Lex, Tingying He

We present the first empirical evaluation of techniques for encoding distributions of quantitative edge values within adjacency matrices. In many real-world networks, edges represent not a single value but a set of measurements. While adjacency matrices preserve structural clarity, their compact cells limit the simultaneous display of multiple values. To add

Nekane Fernandez, Ivan Valdes, Steven Van Vaerenbergh, Idoia de la Iglesia

Deploying deep neural networks on edge devices requires balancing accuracy, latency, and resource constraints under realistic execution conditions. To fit models within these constraints, two broad strategies have emerged: static compression techniques such as pruning and quantization, which permanently reduce model size, and dynamic approaches such as early

Rokgi Hong, Hongjun An, Sooyeon Ji, Jongho Lee

Developing an MR sequence is challenging and remains largely constrained by human intuition. Recently, AI-driven approaches have been proposed; however, most require an initial sequence for parameter optimization or extensive training datasets, limiting their general applicability. In this study, we propose "Sequence Search," an automated sequence design fra

Julian Jimenez Agudelo, Paola Soto, Ayat Zaki-Hindi, Jean-Sébastien Sottet

Network Digital Twins (NDTs) enable safe what-if analysis for 6G cloud-edge infrastructures, but adoption is often limited by fragmented workflows from telemetry to validation. We present a data-driven NDT framework that extends 6G-TWIN with a scalable pipeline for cloud-edge telemetry aggregation and semantic alignment into unified data models. Our contribu

Wei Zhang, Yihang Cheng, Zhirong Ye, Kezhen Huang

Generative Agents, owing to their precise modeling and simulation capabilities of human behavior, have become a pivotal tool in the field of Artificial Intelligence in Education (AIEd) for uncovering complex cognitive processes of learners. However, existing educational agents predominantly rely on static personas to simulate student learning behaviors, negl

Shengyu Guo, Tongrui Ye, Jianbo Zhang, Zicheng Zhang

Recent progress in Multimodal Large Language Models (MLLMs) has demonstrated remarkable advances in perception and reasoning, suggesting their potential for embodied intelligence. While recent studies have evaluated embodied MLLMs in interactive settings, current benchmarks mainly target capabilities to perceive, understand, and interact with external object

Gabriele Amante, Fortunata Panzera, Gabriele Centi, Jing Xie

The origin of enhanced reactivity in aqueous microdroplets remains debated, with interfacial electric fields (IEFs) often invoked as catalytic drivers. Here, we provide a quantum-mechanical, spatially resolved characterization of the electric field at air-water interfaces by combining deep-learning molecular dynamics with \emph{ab initio} re-sampling. Across

Yuan Sun, Xuan Wang, WeiLi Zhang, Wenxuan Zhang

We propose a compositional method for constructing a complete 3D head avatar from a single image. Prior one-shot holistic approaches frequently fail to produce realistic hair dynamics during animation, largely due to inadequate decoupling of hair from the facial region, resulting in entangled geometry and unnatural deformations. Our method explicitly decoupl

Enrico Francesco Giannico, Federico Nesti, Gianluca D'Amico, Mauro Marinoni

Obstacle detection in railway environments is crucial for ensuring safety. However, very few studies address the problem using a complete, modular, and flexible system that can both detect objects in the scene and estimate their distance from the vehicle. Most works focus solely on detection, others attempt to identify the track, and only a few estimate obst

Fan Yang, Xudong Wang, Lijun Yan, Yue Zhao

While significant advancements have been made in weak magnetic field detection, conventional high-sensitivity techniques are often limited by requirements for cryogenic operation or bulky setups. In this work, we develop a sensitive alternating magnetic field sensor based on a coupled system of an active microwave cavity and yttrium iron garnet (YIG), with t

Peifeng Zhang, Zice Qiu, Donghua Yu, Shilei Cao

In continual visual question answering (VQA), existing Continual Learning (CL) methods are mostly built for symmetric, unimodal architectures. However, modern Vision-Language Models (VLMs) violate this assumption, as their trainable components are inherently asymmetric. This structural mismatch renders VLMs highly prone to catastrophic forgetting when learni

Ignacy Czajkowski, Robert Paluch

Source localization in complex networks is a rapidly advancing field with numerous real-world applications, including determining the source of misinformation. In this work, we model information spread across several real-world and synthetic complex networks using our Generalized Fractional Susceptible-Infected-Recovered (GFSIR) model, which incorporates the

Kun Yan, Yizhi Hu, Wei-Hua Xiao, Xiaolong Zou

Altermagnet-based heterojunctions have demonstrated magnetoresistive effects in experiments, however, a predictive theoretical model for non-ferromagnetic structures has remained elusive. In this work, we develop a tunneling-based spin-transport theory that explicitly incorporates the transverse-wavevector ($\bf{k}_\|$)-dependent spin polarization of an alte

Charles Elbar

We study a one-dimensional cross-diffusion system for two interacting populations on the torus, with a linear pressure law and different mobilities. For arbitrary bounded non-negative initial data, we show that any good approximation scheme, yields existence of global weak solutions. More precisely, we introduce a notion of \textit{admissible approximation s

Hang Su, Zequn Liu, Chen Hu, Xuesong Lu

While LLMs have demonstrated remarkable potential in Question Answering (QA), evaluating personalization remains a critical bottleneck. Existing paradigms predominantly rely on lexical-level similarity or manual heuristics, often lacking sufficient data-driven validation. We address this by mining Community-Individual Preference Divergence (CIPD), where indi

Maksym Prodius, Mateusz Łącki, Jakub Zakrzewski

The plethora of possible ground states of spinor bosons placed in an external lattice and a cavity is revisited. We discuss the simplest case when the external lattice nodes coincide with the antinodes of the cavity field. We analyze the problem within the grand-canonical mean-field approach, considering both the homogeneous system and the nonhomogeneous cas

Jose M. Sojo-Gordillo, Alex Rodriguez-Iglesias, Dominik M. Koch, Arianna Nigro

Phononic silicon structures have emerged as an integrable and scalable nanosystem for tailoring thermal transport. However, their widespread adoption has been limited by their complex fabrication pathways. Alongside, the reliable characterization of thermal properties in suspended nanostructured films remains challenging, as thermal contact resistances often

Fu Feng, Yucheng Xie, Ruixiao Shi, Jing Wang

The pre-training and fine-tuning paradigm has become the dominant approach for model adaptation. However, conventional pre-training typically yields models at a fixed scale, whereas practical deployment often requires models of varying sizes, exposing its limitations when target model scales differ from those used during pre-training. To address this, we pro

Zhuo Wang, Zhuo Zhang, Yafu Li, Yu Cheng

Large Language Models (LLMs) exhibit strong mathematical reasoning when trained on high-quality Chain-of-Thought (CoT) that articulates intermediate steps, yet costly CoT curation hinders further progress. While existing remedies such as distillation from stronger LLMs and self-synthesis based on test-time search alleviate this issue, they often suffer from

Ali Bagheri Nejad, Mahdi Aliyari-Shoorehdeli, Abolfazl Hasanzadeh

Preventing machine failure is inherently superior to reactive remediation, particularly for critical assets like gas turbines, where early fault detection (FD) is a cornerstone of industrial sustainability. However, modern deep learning-based FD models often face a significant trade-off between architectural complexity and real-time operational constraints,

Mathias Dus

We present a geometric framework for Reinforcement Learning (RL) that views policies as maps into the Wasserstein space of action probabilities. First, we define a Riemannian structure induced by stationary distributions, proving its existence in a general context. We then define the tangent space of policies and characterize the geodesics, specifically addr

Yiting Cai, Haiyan Guo, Hong-Jian Lai, Bo Zhou

The Hamiltonicity and related subjects of split graphs, and in particular $K_{1,r}$-free split graphs with $r\ge 3$ received much attention. Dai et al. [Discrete Math. 345 (2022) 112826] conjectured that every $(r-1)$-connected $K_{1,r}$-free split graph is Hamiltonian. They proved the case when $r=4$, and earlier Renjith and Sadagopan [Int. J. Found. Comput

Jordan Shipard, Arnold Wiliem, Kien Nguyen Thanh, Wei Xiang

Generalized Category Discovery (GCD) challenges methods to identify known and novel classes using partially labeled data, mirroring human category learning. Unlike prior GCD methods, which operate within a single modality and require dataset-specific fine-tuning, we propose a modality-agnostic GCD approach inspired by the human brain's abstract category form

E. Russeil, R. Lunnan, J. Peloton, S. Schulze

Superluminous supernovae (SLSNe) are one of the most luminous stellar explosions known, yet they remain poorly understood. Because they are intrinsically rare, efficiently identifying them in the large alert streams produced by modern time-domain surveys is essential for enabling spectroscopic follow-up. We present NOMAI, a machine learning classifier design

Tatsuro Kawakami

We establish Grauert--Riemenschneider vanishing for $F$-pure threefolds over a perfect field $k$ of characteristic $p>5$. We apply this to prove Steenbrink vanishing for three-dimensional sharply $F$-pure pairs in characteristic $p>5$. As a consequence, we obtain the logarithmic extension for one-forms in this setting.

Jaehun Jeong, Donghwa Ji, Hyunjun Jang, Kabgyun Jeong

Sequential Quantum State Discrimination (SQSD) can be naturally framed as a sequential decision-making problem: at each time step, an agent must decide whether to perform an additional measurement to gather more information or to conclude with an optimal decision based on the current belief. In this paper, we formally cast SQSD into a static-hidden-state Par

Matías Neto, Pablo Marquet, Mara Freilich, Luis Martí

While the classical Critical Depth Hypothesis (CDH) effectively explains the onset of blooms as transient instabilities, it does not fully capture the seasonal decoupling of biological rates and the long-term persistence of phytoplankton communities in fluctuating thermal environments. To address these limitations, we introduce a parsimonious framework that

Udayan Rathore, Ashish Singh

We leverage a quasi natural experiment from India on introduction of free bus schemes for women across five states to study it's impact on women's workforce participation. We use two rounds of the representative Time Use Survey and a triple difference estimation strategy, complemented by an event study framework to identify the causal relationship of interes

Kaustav Chatterjee, Ulrik Lund Andersen

We introduce a general witness-based framework for quantifying and operationally activating continuous-variable (CV) resources into discrete-variable (DV) bipartite entanglement or Einstein- Podolsky-Rosen (EPR) steering. For the three standard CV resource theories associated with Wigner negativity (WN), genuine non-Gaussianity (GNG), and standard non-Gaussi

Nori Nakata, Jingxiao Liu, Guodong Chen, Rie Nakata

Subsurface properties are essential for hazard assessment, energy and environmental management, and infrastructure resilience, but direct observations are sparse and uneven, motivating the use of surface observations as indirect constraints. Here we explore whether AlphaEarth embeddings can be applied to subsurface estimation despite indirect and non-unique

Yanguang Sun, Hengmin Zhang, Jianjun Qian, Jian Yang

Early identification and removal of polyps can reduce the risk of developing colorectal cancer. However, the diverse morphologies, complex backgrounds and often concealed nature of polyps make polyp segmentation in colonoscopy images highly challenging. Despite the promising performance of existing deep learning-based polyp segmentation methods, their percep

Santiago Folgueras

A review of recent multiboson and vector boson scattering (VBS) measurements from the ATLAS and CMS Collaborations at the LHC is presented. Results are reported from precision diboson cross-section measurements, novel CP-sensitive and polarisation observables in $W\gamma$ production, VBS observations in semileptonic and fully leptonic final states including

Adrian Edin, Michel Kieffer, Mikael Johansson, Zheng Chen

The communication bottleneck in federated learning (FL) has spurred extensive research into techniques to reduce the volume of data exchanged between client devices and the central parameter server. In this paper, we systematically classify gradient and model compression schemes into three categories based on the type of correlations they exploit: structural

Midan Shim, Seokju Hwang, Kaehyun Um, Kyong-Ho Lee

Large language models still struggle with faithfulness and hallucinations despite their remarkable reasoning abilities. In Knowledge Graph Question Answering (KGQA), semantic parsing-based approaches address the limitations by understanding constraints in a user's question and converting them into a logical form to execute on a knowledge graph. However, exis

Jonas Sommerfeldt, Nick M. Mayer, Anna Maiorova, Wilko Middents

We present a theoretical study of Compton scattering of X- and $\gamma$-rays by a $K$-shell electron. Special attention is paid to the double-differential cross section and polarization of the scattered photons for linearly polarized incident photons. To investigate these observables, we employ the scattering matrix (S-matrix) approach based on relativistic

Jana Vráblíková, Ezio Malis, Laurent Busé

Solving non-linear least-squares problem for pose estimation (rotation and translation) is often a time consuming yet fundamental problem in several real-time computer vision applications. With an adequate rotation parametrization, the optimization problem can be reduced to the solution of a~system of polynomial equations and solved in closed form. Recent ad

Zhaoxing Li, Hai-Feng Zhang, Xiaoming Zhang

Conventional Graph Contrastive Learning (GCL) on Text-Attributed Graphs (TAGs) relies on blind stochastic augmentations, inadvertently entangling task-relevant signals with noise. We propose SDM-SCR, a robust framework anchored in Approximate Orthogonal Decomposition. First, the Semantic Decoupling Module (SDM) leverages the instruction-following capability

Hy Nguyen, Thanh Nguyen Pham, Helen Yuliana Angmalisang, Liam Wigney

In many real-world settings, problem instances that need to be solved are quite similar, and knowledge from previous optimization runs can potentially be utilized. We explore this for the Traveling Salesperson problem with time windows (TSPTW), which often arises in settings where the travel-time matrix is fixed but time-window constraints change across rela

Y. F. Li, Biao Chen, Y. Wu, Y. Liu

Exceptional points (EPs) in non-Hermitian photonic systems enable unconventional control of wave amplitude and phase. However, identifying the EPs in a multidimensional parameter space of a system can be nontrivial and, in some cases, even infeasible. Here we propose an inverse-design method to efficiently locate the scattering EPs for a two-port resonant sy

Pascal Bégout, Jesús Ildefonso Díaz

We study the complex Ginzburg-Landau equation posed on possibly unbounded domains, including some singular and saturated nonlinear damping terms. This model interpolates between the nonlinear Schr{\"o}dinger equation and dissipative parabolic dynamics through a complex timederivative prefactor, capturing the interplay between dispersion and dissipation. As a

Daichi Arimatsu

Collino proved that the fundamental group of a certain Zariski open set of the symmetric square of a hyperelliptic curve is isomorphic to the integral Heisenberg group. We compute the mixed Hodge structure on this fundamental group, and show that the second extension class is expressed by the Abel-Jacobi invariant of the canonical class and the marked points

Jinpeng Zhang, Qingjun Jin

Operator mixing is a generic and unavoidable feature in realistic quantum field theories, including QCD and effective field theories. Yet existing methods for computing anomalous dimensions in the presence of mixing remain limited by intricate sub-divergence subtractions and escalating computational complexity. In this work, we extend the recently developed

Zi-Shen Li, Yuxiang Yang

The quantum Mpemba effect (QMpE) describes an anomalous thermalization phenomenon in which quantum states initially far from equilibrium can approach thermal equilibrium faster than states that begin closer to it. While this effect has been extensively studied in various frameworks, its practical implications for quantum information processing remain largely

Jan Niklas Lettner, Hadeer El Ashhab, Veit Hagenmeyer, Benjamin Schäfer

Large-scale renewable energy deployment introduces pronounced volatility into the electricity system, turning grid operation into a complex stochastic optimization problem. Accurate electricity price forecasting (EPF) is essential not only to support operational decisions, such as optimal bidding strategies and balancing power preparation, but also to reduce

Esther Brown, Victoria Dean, Finale Doshi-Velez

Consumer wearables enable continuous measurement of physiological data related to stress and recovery, but turning these streams into actionable, personalized stress-management recommendations remains a challenge. In practice, users often do not know how a given intervention, defined as an activity intended to reduce stress, will affect heart rate (HR), hear

Mong-Wen Gu, Aizhan Sabitova, Taner Esat, Christian Wagner

Advances in nanotechnology now allow the creation of artificial atoms - engineered structures whose electronic states closely mimic those of real atoms. Understanding how these artificial atoms interact and bond is key to designing new materials with tailored electronic properties. Here, we use scanning tunnelling microscopy to visualise the bound states of

Peiyu Wang, Hong Wang, Yaru Fu, Rongfang Song

To meet the urgent demands for spectral efficiency and multi-user access in high-frequency application scenario for the sixth-generation wireless communication, this paper investigates a rate splitting multiple access (RSMA) system assisted by pinching antennas (PAs) with multiple waveguides and multiple carriers, aiming to maximize the overall system sum ra

Jiyun Lee, You Kyoung Chung, Joonsuk Huh

Computational molecular design requires binding arrangements that are not only energetically favorable but also chemically realizable. However, computational methods remain limited in directly recovering fragment pose pairs that can later be connected into a single molecule. To address this problem, we formulated the simultaneous placement of two fragments a

Una M. Kelly, Luuk J. Spreeuwers, Raymond N. J. Veldhuis

Morphing is a challenge to face recognition (FR) for which several morphing attack detection solutions have been proposed. We argue that face recognition and differential morphing attack detection (D-MAD) in principle perform very similar tasks, which we support by comparing an FR system with two existing D-MAD approaches. We also show that currently used de

Liang Qin, Weiwei Wan, Kensuke Harada

Regrasp planning is often required when one pick-and-place cannot transfer an object from an initial pose to a goal pose while maintaining grasp feasibility. The main challenge is to reason about shared-grasp connectivity across intermediate poses, where discrete search becomes brittle. We propose an implicit multi-step regrasp planning framework based on di

Runze Li, Hongyin Zhang, Junxi Jin, Qixin Zeng

Vision-Language-Action (VLA) models have emerged as a promising paradigm for building embodied agents that ground perception and language into action. However, most existing approaches rely on direct action prediction, lacking the ability to reason over long-horizon trajectories and evaluate their consequences, which limits performance in complex decision-ma

Kang An, Chenhao Si, Shiqian Ma, Ming Yan

Physics-Informed Neural Networks (PINNs) often suffer from slow convergence, training instability, and reduced accuracy on challenging partial differential equations due to the anisotropic and rapidly varying geometry of their loss landscapes. We propose a lightweight curvature-aware optimization framework that augments existing first-order optimizers with a

J. Fransson

Indirect long range interactions between localized magnetic moments are in metals mediated by itinerant electrons. In insulators and semi-conductor, such interactions need to be small, if not negligible, due to the absence of mediating carriers. The existence of magnetically ordered insulators, for instance, metal-oxides, is therefore an everlasting source f

Kazuma Okamoto, Takumi Imura, Naruto Nakamura, Satoshi Abo

A novel approach for measuring the electrical resistivity of non-magnetic materials using magnetic force microscopy (MFM) is discussed. In this method, MFM detects magnetic fields generated by eddy currents induced by the oscillation of a magnetized probe tip. To enhance measurement sensitivity, it is essential to increase the magnitude of these eddy current

Fabrizio Catanese, Ciro Ciliberto, Concettina Galati

This is now an expository note about the following classical problem. Let $(X, \bf 0)$ be the germ of a hypersurface in $(\mathbb C^n,\bf 0)$ with an ordinary singularity of multiplicity $m$ at the origin $\bf 0$. A natural question to ask is whether $X$ and its tangent cone at the origin are analytically isomorphic. The answer is negative in general, in vie

Laura Antonucci, A. Bonvalet, X. Solinas, M. Joffre

Arbitrary Detuning ASynchronous OPtical Sampling (ADA-SOPS) is an emerging technique for extending standard pump--probe experiments performed with two femtosecond lasers to multitimescale experiments, which are of great interest for the study of complex systems. Although no specific requirements are needed for laser repetition rates, their ratio determines t

Jiaqi Zhu, Shaofeng Cai, Jie Chen, Fang Deng

Online anomaly detection (OAD) plays a pivotal role in real-time analytics and decision-making for evolving data streams. However, existing methods often rely on costly retraining and rigid decision boundaries, limiting their ability to adapt both effectively and efficiently to concept drift in dynamic environments. To address these challenges, we propose Dy

Seokwon Lee, Jaeyoung Sim, Sihyun Kim, Yuhsing Li

Recent advances in query optimization have shifted from traditional rule-based and cost-based techniques towards machine learning-driven approaches. Among these, reinforcement learning (RL) has attracted significant attention due to its ability to optimize long-term performance by learning policies over query planning. However, existing RL-based query optimi

Badri N. Patro, Vijay S. Agneeswaran

Vision State Space Models (SSMs) like Vim, VMamba, and SiMBA rely on complex scanning strategies to adapt sequential SSMs to process 2D images, introducing computational overhead and architectural complexity. We propose HAMSA, a scanning-free SSM operating directly in the spectral domain. HAMSA introduces three key innovations: (1) simplified kernel paramete

Sarmad Sohail, Ghufran Haider

Large language models are increasingly used as natural-language interfaces to enterprise software, but their direct use as system operators remains unsafe. Model errors can propagate into unauthorized actions, malformed requests, cross-workspace execution, and other costly failures. We argue this is primarily an execution architecture problem. We present a b

Yuval Ran-Milo, Hila Ofek, Shahar Mendel

Transformers commonly exhibit an attention sink: disproportionately high attention to the first position. We study this behavior in GPT-2-style models with learned query biases and absolute positional embeddings. Combining structural analysis with causal interventions, validated across natural-language, mathematical, and code inputs, we find that the sink ar

K. Ton, G. Kestler, D. A. Steck, J. T. Barreiro

The Casimir-Polder (CP) effect -- the force between a neutral atom and an uncharged conducting plate in empty space -- is an intriguing consequence of quantum vacuum fluctuations. The typically attractive CP potential crosses over from a scaling of $z^{-3}$ at short separations to $z^{-4}$ at long distances, where retardation effects due to the finite speed

David Exler, Nils Friederich, Martin Krüger, John Jbeily

Myotubes are multinucleated muscle fibers serving as key model systems for studying muscle physiology, disease mechanisms, and drug responses. Mechanistic studies and drug screening thereby rely on quantitative morphological readouts such as diameter, length, and branching degree, which in turn require precise three-dimensional instance segmentation. Yet est

Haoran Li, Zhiyuan Wang, Zhipeng Yu, Xingpeng Du

Orbital angular momentum (OAM) multiplexing offers a promising approach to high-capacity optical communication by harnessing the orthogonality of vortex beams. However, its practical deployment is severely limited in real-world settings where dynamic scattering media, such as turbulent atmosphere, distort multiplexed fields into random speckles and disrupt O

Xiao-Liang Qi

This article argues that the most important significance of the AI revolution, especially the rise of large language models, lies not simply in automation, but in a fundamental change in how complex information and human know-how are carried, replicated, and shared. From this perspective, AI for Science is especially important because it may transform not on

Krti Tallam

Persistent language-model agents increasingly combine tool use, tiered memory, reflective prompting, and runtime adaptation. In such systems, behavior is shaped not only by current prompts but by mutable internal conditions that influence future action. This paper introduces layered mutability, a framework for reasoning about that process across five layers:

Xudong Yu

BESIII has accumulated 4.5 fb$^{-1}$ of $e^+e^-$ collision data in the 4.6 to 4.7 GeV energy range, corresponding to the world's largest sample of $\Lambda_c^+\bar{\Lambda}_c^-$ pairs. This paper summarizes recent BESIII results on charmed-baryon decays, including the observation of the rare semi-leptonic decay $\Lambda_c^+\to ne^+\nu_e$ using a Graph Neural

Cheng Bi, Hong-Quan Li

On generalized Heisenberg-type groups $\mathbb{G}(2n,m,\mathbb{U},\mathbb{W})$, we give uniform volume estimates for the ball defined by a large class of Carnot-Carath\'{e}odory distances, and establish weak (1, 1) $O(C^m \, n)$-estimates for associated centered Hardy-Littlewood maximal functions, extending the results in \cite{BLZ25}. As a by-product, we es

Ratnangshu Das, Negar Monir, Youssef Ait Si, Adnane Saoud

In this paper, we present a novel framework for quantifying a lower bound on resilience in continuous-time (non)linear systems subject to external disturbances while ensuring satisfaction of signal temporal logic specifications. Unlike robustness, which evaluates how well a system satisfies a specification under a given disturbance, resilience measures the m

Yongwei Huang, Zhenhui Huang, Sergiy A. Vorobyov, Zhi-Quan Luo

The globally optimal robust adaptive beamforming (RAB) solution is studied for worst-case signal-to-interference-plus-noise ratio (SINR) maximization (the maximin SINR problem) under convex and closed uncertainty sets for the desired signal covariance and interference-plus-noise covariance (INC) matrices, considering a general-rank signal model. First, the c

Xin Xie, Dongyun Xue, Wuguannan Yao, Mingxiao Feng

LLM-powered systems require complex multi-step decision-making abilities to solve real-world tasks, yet current planning approaches face a trade-off between the high latency of inference-time search and the limited generalization of supervised fine-tuning. To address this limitation, we introduce \textbf{SGA-MCTS}, a framework that casts LLM planning as non-

Saif ur Rehman Khan, Imad Ahmed Waqar, Arooj Zaib, Saad Ahmed

Structural damage detection is essential for maintaining the safety and reliability of civil infrastructure. However, accurately identifying different types of structural damage from images remains challenging due to variations in damage patterns and environmental conditions. To address these challenges, this paper proposes MS-SSE-Net, a novel deep learning

Jiyoung Lim, Heejae Yang, Jee-Hyong Lee

Composed Image Retrieval (CIR) aims to retrieve target images by integrating a reference image with a corresponding modification text. CIR requires jointly considering the explicit semantics specified in the query and the implicit semantics embedded within its bi-modal composition. Recent training-free Zero-Shot CIR (ZS-CIR) methods leverage Multimodal Large

Fan Cui, Hongyuan Hou, Zizhang Luo, Chenyun Yin

Existing benchmarks for hardware design primarily evaluate Large Language Models (LLMs) on isolated, component-level tasks such as generating HDL modules from specifications, leaving repository-scale evaluation unaddressed. We introduce HWE-Bench, the first large-scale, repository-level benchmark for evaluating LLM agents on real-world hardware bug repair ta

Camelia Jana, Santabrata Das

We study global transonic solution for a relativistic, magnetized, viscous advective accretion flow around a rotating black hole, incorporating the effects of mass and angular momentum loss through winds. Our model considers dominant toroidal magnetic fields with synchrotron radiation as the primary cooling mechanism. To self-consistently model mass loss, th

Kunlin Wu, Yanning Wang, Haofeng Tan, Boyi Chen

Recent image-to-audio models have shown impressive performance on object-centric visual scenes. However, their application to satellite imagery remains limited by the complex, wide-area semantic ambiguity of top-down views. While satellite imagery provides a uniquely scalable source for global soundscape generation, matching these views to real acoustic envi

Yi He, Tao Wang, Yi Jin, Congyan Lang

Recent advances in 3D Gaussian Splatting (3DGS) have enabled highly efficient and photorealistic novel view synthesis. However, segmenting objects accurately in 3DGS remains challenging due to the discrete nature of Gaussian representations, which often leads to aliasing and artifacts at object boundaries. In this paper, we introduce NG-GS, a novel framework

Yuliya Tsybina, Evgenia Antonova, Sergey Shchanikov, Vsevolod Kulagin

Biological agents navigate complex environments by combining long-term memory of successful actions with short-term suppression of recently visited locations-a capability that remains difficult to replicate in artificial systems, especially under partial observability. Inspired by the complementary timescales of neural and astrocytic dynamics, we introduce a

Rongchao Xu, Lin Jiang, Dahai Yu, Ximiao Li

Human activity traces (HATs) are critical for many applications, including human mobility modeling and point-of-interest (POI) recommendation. However, growing privacy concerns have severely limited access to authentic large-scale HAT datasets. Recent advances in generative AI provide new opportunities to synthesize realistic and privacy-preserving HATs for

Lorenzo Foscolo, Mark Haskins, Johannes Nordström

We prove existence, uniqueness and structure results for complete noncompact 7-dimensional G2-holonomy metrics with ALC (asymptotically locally conical) asymptotics. We regard such spaces as G2-analogues of ALF gravitational instantons in 4-dimensional hyperk\"ahler geometry. Our main results include the existence of a G2-analogue of the Atiyah-Hitchin metri

Songlin Li, Zhiqing Guo, Dan Ma, Changtao Miao

Although some existing image manipulation localization (IML) methods incorporate authenticity-related supervision, this information is typically utilized merely as an auxiliary training signal to enhance the model's sensitivity to manipulation artifacts, rather than being explicitly modeled as localization evidence opposing the manipulated regions. Consequen

Satwik Bathula, Anand A. Joshi

Multiplicative gating is widely used in neural architectures and has recently been applied to attention layers to improve performance and training stability in large language models. Despite the success of gated attention, the mathematical implications of gated attention mechanisms remain poorly understood. We study attention through the geometry of its repr

Shilong Zhang, Meng Zhang, Qilin Luo, Zihao Tao

Ruddlesden-Popper nickelates offer a new route to high-temperature superconductivity beyond the cuprates and iron-pnictides. However, the electronic reorganization that enables superconductivity in bilayer nickelates remain unresolved, largely due to the difficulty of directly probing the superconducting phase. Here, we overcome this limitation by stabilizin

Kaustubh Mhatre, Vedant Tewari, Aditya Ray, Farhan Khan

Topology optimization is a computational method used to determine the optimal material distribution within a prescribed design domain, aiming to minimize structural weight while satisfying load and boundary conditions. For critical infrastructure applications, such as structural health monitoring of bridges and buildings, particularly in digital twin context

Marco Monaci, Duncan A. Forbes, Jonah S. Gannon, Bärbel S. Koribalski

Using the first data release of the Five-hundred-meter Aperture Spherical radio Telescope (FAST) All-Sky HI survey (FASHI), we compile a catalogue of 70 dark galaxy candidates (DGCs) within 50 Mpc. We select DGCs without an identified optical counterpart at a limiting g-band magnitude of ~ 28 mag arcsec^-2 in the DESI Legacy Survey, using both automatic cros

David Muñoz-Lahoz

Let $X$ be a Dedekind complete Banach lattice, and let $P\colon X\to X$ be a positive projection for which the largest central operator below $P$ is $α\operatorname{id}_X$, for some $α\ge 0$. Wickstead conjectured that $α$ must either be $0$ or $1/n$, for some $n \in \mathbb{N}$, and proved it for finite-dimensional $X$. In this paper, we show that the conje

Masahiko Saito, Tomoe Kishimoto, Junichi Tanaka

Ensuring the reproducibility of physics results is one of the crucial challenges in high-energy physics (HEP). In this study, we develop a proof-of-concept system that uses large language models (LLMs) to extract analysis procedures from HEP publications and generate executable analysis code for reproducing published results. Our method consists of two stage

Vengatesan Ganapathy, Pranay Patil, Ajit C. Balram

Understanding how the interaction range and various types of disorder affect the level statistics of many-body quantum systems and lead to the emergence of many-body localization (MBL) is a challenging open frontier. We study the level statistics of a variant of the spin-$1/2$ Haldane-Shastry model with $1/r^{\alpha}$ interactions, where $\alpha{\geq}0$ para

Giovanni M. Di Liberto

Encoding models enable measurement of how our brains represent sensory inputs using electro-and magneto-encephalography (MEEG). Evaluating how closely encoding models reflect the underlying brain functions is a crucial premise for model interpretation and hypothesis testing. However, the ground-truth neural activity is unknown, preventing model evaluation wi

Ole König, James F. Steiner, Niek Bollemeijer, Riley M. T. Connors

The 2023/24 NICER monitoring campaign of the 7 Crab bright black hole X-ray binary Swift J1727.8-1613 covered the outburst in almost all accretion states. High-quality data are available in the high-Eddington-fraction hard-intermediate state, hard-to-soft transition, the soft state, and the poorly studied back-transition to the dim hard state, making it an i

Adelekun Oluwademilade, Ademola Adedamola, Abiola Abdulhakeem, Akinpelu Azeezat

Speech Emotion Recognition (SER) is the use of machines to detect the emotional state of humans based on the speech, which is gaining importance in natural human-computer interaction. Speech is a very valuable source of information, as emotions modify the patterns of speech; pitch, energy and even timing. Nonetheless, SER is not an easy task because speakers

Xiangning Yu, Yuwei Guo, Yuqi Hou, Xiao Xue

LLM-empowered agent simulations are increasingly used to study social emergence, yet the micro-to-macro causal mechanisms behind macro outcomes often remain unclear. This is challenging because emergence arises from intertwined agent interactions and meso-level feedback and nonlinearity, making generative mechanisms hard to disentangle. To this end, we intro

Niangen Ye, Jiawen Zhu, Baojun Chen, Dong Wang

Communication is pivotal in LLM training, and a thorough analysis of the communication efficiency of AI data center (AIDC) network is essential for guiding the design of these capital-intensive clusters. However, conventional metrics are inadequate for such analysis, as they do not directly link network activity to computational progress and lack granularity

Hao Luo, Umut Demirhan, Ahmed Alkhateeb

This paper explores an integrated sensing and communication (ISAC) system with backscattering RFID tags. In this setup, an access point employs communication beams to serve communication users while leveraging a sensing beam to interrogate RFID tags. Under the total transmit power constraint of the system, our objective is to design a joint sensing and commu

Shiuli Chatterjee, Andrzej Hryczuk

Freeze-in of multi-component dark sectors is governed not only by the interaction with the thermal plasma, but also by their internal dynamics. Full thermalisation within the dark sector is not guaranteed, raising the question of impact of departures from local thermal equilibrium onto the evolution and ultimately relic abundance and momentum distribution of