October 2025 arXiv papers — page 194

Joan Salvà Soler, Günther R. Raidl

Denoising diffusion models (DDMs) offer a promising generative approach for combinatorial optimization, yet they often lack the robust exploration capabilities of traditional metaheuristics like evolutionary algorithms (EAs). We propose a Denoising Diffusion-based Evolutionary Algorithm (DDEA) framework that synergistically integrates these paradigms. It uti

Michael D. Atkinson, Michael D. White, Adam J. Plowman, Pratheek Shanthraj

The development of next-generation structural materials for harsh environments requires rapid assessment of mechanical performance and its dependence on microstructure. While full-field crystal plasticity (CP) models provide detailed insights, the high computational cost limits their use with uncertainty quantification workflows and in component-scale simula

Yu Liu, Wenwen Li, Yifan Dou, Guangnan Ye

As artificial intelligence (AI) enters the agentic era, large language models (LLMs) are increasingly deployed as autonomous agents that interact with one another rather than operate in isolation. This shift raises a fundamental question: how do machine agents behave in interdependent environments where outcomes depend not only on their own choices but also

Ayesha Afzal, Anna Kahler, Georg Hager, Gerhard Wellein

Molecular dynamics simulations are essential tools in computational biophysics, but their performance depend heavily on hardware choices and configuration. In this work, we presents a comprehensive performance analysis of four NVIDIA GPU accelerators -- A40, A100, L4, and L40 -- using six representative GROMACS biomolecular workloads alongside two synthetic

Fangzhou Zhao, Yao Sun, Jianglin Lan, Lan Zhang

Effective path planning is fundamental to the coordination of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) systems, particularly in applications such as surveillance, navigation, and emergency response. Combining UAVs' broad field of view with UGVs' ground-level operational capability greatly improve the likelihood of successfully achi

Roope Anttila, Sylvester Eriksson-Bique, Aleksi Pyörälä

We study quasisymmetric maps on two variants of the classical fractal percolation model: the fat and dense fractal percolations. We show that, almost surely conditioned on non-extinction, the Hausdorff dimension of the fat fractal percolation cannot be lowered with a quasisymmetry and the Hausdorff dimension of the dense fractal percolation cannot be lowered

Shuangshuang Li, Kai Zhao

Significant progress has been made in ultrasonic guided wave (UGW) technology for pipe signal processing and defect imaging recently. However, developing a defect localization and imaging algorithm that requires fewer parameters, offers a wide imaging range, and achieves high positioning accuracy remains a considerable challenge. Traditional direction-of-arr

P. Arévalo, P. Sánchez-Sáez, B. Sotomayor, P. Lira

(Abridged)We explore the potential of optical variability selection methods to identify AGN, including those challenging to detect with conventional techniques. Using the unprecedented combination of depth, sky coverage, and cadence of the ZTF survey, we target even starlight-dominated AGN, known for their redder colours, weaker variability signals, and diff

Prosenjit Biswas, Pervez Shaik, Abhinav Thorat, Ravi Kolla

Large Language Models (LLMs) have advanced recommendation capabilities through enhanced reasoning, but pose significant challenges for real-world deployment due to high inference costs. Conversely, while Small Language Models (SLMs) offer an efficient alternative, their reasoning capabilities for recommendation remain underexplored. Existing systems often us

Ashis Saha, Rabin Banerjee, Sunandan Gangopadhyay

We start from a Lorentzian action in a deformed light-cone background and applying the method of null reduction leads to a Carrollian action in one lower spacetime dimensions. We also identify the correct light-cone definitions of the symmetry generators and their dynamical forms in terms of the fields and take the $c\rightarrow0$ limit. It is observed that

Freya Johnson, Jan Zemen, Helena Knowles, Lesley F. Cohen

Unconventional magnetic materials including non-collinear antiferromagnets, p-wave magnets and altermagnets, are an emerging frontier for quantum spintronics and hybrid quantum devices. Critical to the application of these materials is control over the magnetic domain state, as their unique, symmetry-driven properties vanish in a multi-domain limit. However,

Tsofar Maniv, Vladimir Zhuravlev

The phenomenon of field-induced superconductor to insulator transition (SIT) in disordered 2D electron systems has been a subject of controversy since its discovery in the early 1990s. Here we present a phenomenological quantitative theory of this phenomenon which is not based exclusively on the boson-vortex duality used commonly in the field. Within a new l

Somasekhara Goud Sontti, Arnab Atta

We have developed a coupled level set and volume of fluid-based computational fluid dynamics model to analyze the droplet formation mechanism in a square flow-focusing microchannel. We demonstrate a flexible manipulation of droplet formation and flow regime based on the modified flow-focusing microchannel with a constricted orifice. Furthermore, we have syst

Ruixiang Tang, Huaian Diao, Hongyu Liu, Weisheng Zhou

Stress concentration in bubble-elastic scattering scenarios has significant applications in engineering blasting and medical treatments. This study provides a comprehensive mathematical analysis of stress concentration in bubbly-elastic structures, induced by the quasi-Minnaert resonance. The quasi-Minnaert resonance manifests as two distinct wave patterns n

Jorge González Cázares, David Kramer-Bang, Aleksandar Mijatović

We prove that the norm of a $d$-dimensional L\'evy process possesses a finite second moment if and only if the convex distance between an appropriately rescaled process at time $t$ and a standard Gaussian vector is integrable in time with respect to the scale-invariant measure $t^{-1} dt$ on $[1,\infty)$. We further prove that under the standard $\sqrt{t}$-s

Zaid Alyafeai, Maged S. Al-Shaibani, Bernard Ghanem

Metadata plays a critical role in indexing, documenting, and analyzing scientific literature, yet extracting it accurately and efficiently remains a challenging task. Traditional approaches often rely on rule-based or task-specific models, which struggle to generalize across domains and schema variations. In this paper, we present MeXtract, a family of light

Arkadeep Acharya, Akash Ghosh, Pradeepika Verma, Kitsuchart Pasupa

With the increasing use of RetrievalAugmented Generation (RAG), strong retrieval models have become more important than ever. In healthcare, multimodal retrieval models that combine information from both text and images offer major advantages for many downstream tasks such as question answering, cross-modal retrieval, and multimodal summarization, since medi

Bouthaina Slika, Fadi Dornaika, Fares Bougourzi, Karim Hammoudi

Lung infections, particularly pneumonia, pose serious health risks that can escalate rapidly, especially during pandemics. Accurate AI-based severity prediction from medical imaging is essential to support timely clinical decisions and optimize patient outcomes. In this work, we present a novel method applicable to both CT scans and chest X-rays for assessin

Manuel Mancini, Giuseppe Metere, Federica Piazza

The aim of this article is to investigate internal actions and split extensions in the variety of hoops. We provide a characterization of split extensions with strong section in terms of strong external actions. Beyond the general setting of hoops, the study is extended to the subvarieties of basic hoops, Wajsberg hoops, G\"odel hoops and product hoops. With

Marco Antonio Martínez-Cañadas, Pablo Torres-Sánchez, Javier Praena, Ignacio Porras

Background: The $^{35}Cl(n, p)^{35}S$ reaction is of special interest in three different applications. First, in Boron Neutron Capture Therapy due to the presence of $^{35}Cl$ in brain and skin tissue. Second, it is involved in the creation of $^{36}S$, whose astrophysical origin remains unresolved. Third, in the designing of fast nuclear reactors of new gen

Isabel Copsey, Quentin Hanley, Jack Sutton

Road traffic accidents remain a major public health challenge worldwide, with urbanisation and population density identified as key factors influencing risk. This study analyses monthly accident data from 2009 to 2023 across 632 parliamentary constituencies in England, Wales, and Scotland, using an area-normalised approach based on population density. Segmen

Rahul Gulia, Amlan Ganguly, Michael E. Kuhl, Ehsan Rashedi

Accurate and real-time prediction of wireless channel conditions, particularly the Signal-to-Interference-plus-Noise Ratio (SINR), is a foundational requirement for enabling Ultra-Reliable Low-Latency Communication (URLLC) in highly dynamic Industry 4.0 environments. Traditional physics-based or statistical models fail to cope with the spatio-temporal comple

Messaoud Guesba, Ismail Lakehal, Sid Ahmed Ould Ahmed Mahmoud

In this paper, we introduce and study a new class of bounded linear operators on complex Hilbert spaces, which we call 2-C-normal operators. This class is inspired by and closely related to the notion of 2-normal operators, with additional structure imposed via conjugation. We investigate various fundamental properties of 2-C-normal operators, including alge

Boris Sedlak, Philipp Raith, Andrea Morichetta, Víctor Casamayor Pujol

Edge devices have limited resources, which inevitably leads to situations where stream processing services cannot satisfy their needs. While existing autoscaling mechanisms focus entirely on resource scaling, Edge devices require alternative ways to sustain the Service Level Objectives (SLOs) of competing services. To address these issues, we introduce a Mul

Felipe Rincón, Andreas Fichtner, Mattia Aleardi, Andrea Tognarelli

We present a Fourier neural operator network, designed to correct dispersion errors in numerical wave simulations. The neural dispersion corrector enables the replacement of a computationally expensive high-accuracy simulation by a less expensive low-accuracy simulation. In contrast to neural network surrogates that fully replace a wave equation, the neural

Zhiyu Wang, Sonia Koszut, Pietro Liò, Francesco Ceccarelli

The integration of multi-omics single-cell data remains challenging due to high-dimensionality and complex inter-modality relationships. To address this, we introduce MoRE-GNN (Multi-omics Relational Edge Graph Neural Network), a heterogeneous graph autoencoder that combines graph convolution and attention mechanisms to dynamically construct relational graph

Natascha Hey, Eyal Neuman, Sturmius Tuschmann

We introduce an offline nonparametric estimator for concave multi-asset propagator models based on a dataset of correlated price trajectories and metaorders. Compared to parametric models, our framework avoids parameter explosion in the multi-asset case and yields confidence bounds for the estimator. We implement the estimator using both proprietary metaorde

Daria Ozerova, Ekaterina Trofimova

Iterative refinement has been a promising paradigm to enable large language models (LLMs) to resolve difficult reasoning and problem-solving tasks. One of the key challenges, however, is how to effectively search through the enormous search space of possible refinements. Existing methods typically fall back on predefined heuristics, which are troubled by the

Luke Besley, P. S. Jørgensen, A. Diaz, C. Detlefs

Coherent imaging techniques such as ptychography offer powerful capabilities for 3D resolution of nanoscale structures. By application in grazing incidence, such techniques may achieve exceptional surface sensitivity as demonstrated by grazing incidence small angle scattering. This requires however an extension of the conventional analysis based on the Disto

Samir Abou Haidar, Alexandre Chariot, Mehdi Darouich, Cyril Joly

LiDAR semantic segmentation is crucial for autonomous vehicles and mobile robots, requiring high accuracy and real-time processing, especially on resource-constrained embedded systems. Previous state-of-the-art methods often face a trade-off between accuracy and speed. Point-based and sparse convolution-based methods are accurate but slow due to the complexi

Tanvir Khan, Md Hasan Shahriar Rifat, M. Ibrahim, J. H. Abir

LaMg2H7 is a ternary wide band gap semiconductor that is a member of the hydride family. The bulk physical characteristics of the LaMg2H7 compound, including its structural, electronic band structure, elastic, thermal, and optical characteristics, have been examined in this work utilizing density functional theory (DFT). The elastic constants indicate that {

Antonio Capolupo, Sante Carloni, Luca Fabbri, Simone Monda

We consider a classical field in square torsion theory as a source of torsion for a quantum fermion field in FLRW metric. In the framework of QFT, we obtain vacuum contributions to the energy-momentum tensor and to the axial current that modify the dynamics of the classical field and the field equations as back-reaction. These contributions lead to a modifie

Haoting Zhang, Qiuhan Jia, Zhennan Zhang, Yijie Zhu

Large-scale molecular dynamics simulations with high accuracy have been increasingly popular for their capability to bridge the gap between atomistic modeling and mesoscale phenomena. Both machine learning potentials and enhanced sampling approaches offer substantial improvements in high-accuracy simulation efficiency, which can be further boosted through GP

Frederic Gmeiner, Kenneth Holstein, Nikolas Martelaro

Recent advancements in multimodal generative AI (GenAI) enable the creation of personal context-aware real-time agents that, for example, can augment user workflows by following their on-screen activities and providing contextual assistance. However, prototyping such experiences is challenging, especially when supporting people with domain-specific tasks usi

Huahui Yi, Kun Wang, Qiankun Li, Miao Yu

Multimodal Large Reasoning Models (MLRMs) demonstrate impressive cross-modal reasoning but often amplify safety risks under adversarial or unsafe prompts, a phenomenon we call the \textit{Reasoning Tax}. Existing defenses mainly act at the output level and do not constrain the reasoning process, leaving models exposed to implicit risks. In this paper, we pro

Yining Wang, Jinman Zhao, Chuangxin Zhao, Shuhao Guan

Reinforcement Learning with Human Feedback (RLHF) has been the dominant approach for improving the reasoning capabilities of Large Language Models (LLMs). Recently, Reinforcement Learning with Verifiable Rewards (RLVR) has simplified this paradigm by replacing the reward and value models with rule-based verifiers. A prominent example is Group Relative Policy

Tim Harris, Harvey B. Meyer

In previous work, we determined the improvement coefficients $c_\mathrm{V}$ and $c_{\tilde{\mathrm{V}}}$ required for the massless $\mathrm{O}(a)$-improvement of the local and point-split discretizations of the non-singlet vector current for $N_\mathrm{f}=3$ non-perturbatively $\mathrm{O}(a)$-improved Wilson fermions and the L\"uscher-Weisz gauge action, usi

Didrik Bergström, Deniz Gündüz, Onur Günlü

We consider image transmission via deep joint source-channel coding (DeepJSCC) over multi-hop additive white Gaussian noise (AWGN) channels by training a DeepJSCC encoder-decoder pair with a pre-trained deep hash distillation (DHD) module to semantically cluster images, facilitating security-oriented applications through enhanced semantic consistency and imp

Diana A. Chisholm, G. Massimo Palma, Luca Innocenti

Quantum systems achieve objectivity by redundantly encoding information about themselves into the surrounding environment, through a mechanism known as quantum Darwinism. When this happens, observes measure the environment and infer the system to be in one of its pointer states. We study the emergence of objectivity whenever the Hamiltonian of the system and

Hyeonggeun Han, Sehwan Kim, Hyungjun Joo, Sangwoo Hong

Despite their impressive generative capabilities, text-to-image diffusion models often memorize and replicate training data, prompting serious concerns over privacy and copyright. Recent work has attributed this memorization to an attraction basin-a region where applying classifier-free guidance (CFG) steers the denoising trajectory toward memorized outputs-

Wafaa Mohammed, Vlad Niculae, Chrysoula Zerva

Large language models (LLMs) have emerged as strong contenders in machine translation.Yet, they still struggle to adequately handle discourse phenomena, such as pronoun resolution and lexical cohesion at the document level. In this study, we thoroughly investigate the discourse phenomena performance of LLMs in context-aware translation. We demonstrate that d

Ivan Arraut, Abhishek Kumar Mehta

We show that black-hole remnant scenario naturally arises in the original computations of Hawking without extra assumptions.

Qizhao Chen

In modern financial markets, news plays a critical role in shaping investor sentiment and influencing stock price movements. However, most existing studies aggregate daily news sentiment into a single score, potentially overlooking important variations in topic content and relevance. This simplification may mask nuanced relationships between specific news th

Jiheon Seong, Anindita Bera, Beatrix C. Hiesmayr, Dariusz Chruscinski

Entanglement witnesses (EWs) are a versatile tool to detect entangled states and characterize related properties of entanglement in quantum information theory. A witness $W$ corresponds to an observable satisfying $\mathrm{tr}[W\sigma_{\mathrm{sep}}]\geq 0$ for all separable states $\sigma_{\mathrm{sep}}$; entangled states are detected once the inequality is

Abidemi Orimogunje, Kyeong-Ju Cha, Hyunwoo Park, Abdulahi A. Badrudeen

Precise user localization and tracking enhances energy-efficient and ultra-reliable low latency applications in the next generation wireless networks. In addition to computational complexity and data association challenges with Kalman-filter localization techniques, estimation errors tend to grow as the user's trajectory speed increases. By exploiting mmWave

Olayiwola Arowolo, Jochen L. Cremer

AC Optimal Power Flow (ACOPF) is computationally intensive for large-scale grids, often requiring prohibitive solution times with conventional solvers. Machine learning offers significant speedups, but existing models struggle with scalability and topology flexibility. To address these challenges, we propose a Hybrid Heterogeneous Message Passing Neural Netw

Santiago Gómez Cobos, Michael Ruzhansky

Given a smooth, closed Riemannian manifold $(M,g)$ equipped with a linear connection $\nabla$ (not necessarily metric), we develop the holomorphic functional calculus for operators belonging to the global pseudo-differential classes $\Psi_{\rho, \delta}^m\left(\Omega^\kappa, \nabla, \tau\right)$ introduced by Safarov. As a consequence of our main result, we

Adrien Dorise, Marjorie Bellizzi, Adrien Girard, Benjamin Francesconi

With increasing processing power, deploying AI models for remote sensing directly onboard satellites is becoming feasible. However, new constraints arise, mainly when using raw, unprocessed sensor data instead of preprocessed ground-based products. While current solutions primarily rely on preprocessed sensor images, few approaches directly leverage raw data

Qi Guo, Jianing Wang, Jianfei Zhang, Deyang Kong

Autoformalization addresses the scarcity of data for Automated Theorem Proving (ATP) by translating mathematical problems from natural language into formal statements. Efforts in recent work shift from directly prompting large language models to training an end-to-end formalizer model from scratch, achieving remarkable advancements. However, existing formali

Chen Jiang, Tianqi Zhang, Yu Zou

We show that the anti-canonical volume of a canonical weak Fano $3$-fold is at most $72$. This upper bound is optimal.

Hyungrok Jung, Daneul Kim, Seunggyun Lim, Jeany Son

Generic Event Boundary Detection (GEBD) aims to interpret long-form videos through the lens of human perception. However, current GEBD methods require processing complete video frames to make predictions, unlike humans processing data online and in real-time. To bridge this gap, we introduce a new task, Online Generic Event Boundary Detection (On-GEBD), aimi

Sergey V. Gusev, Olga B. Sapir

A limit variety is a variety that is minimal with respect to being non-finitely based. We present a new limit variety of aperiodic monoid. We also show that if there exists any other limit variety of aperiodic monoids, then it is contained in the joint of the variety $\mathbb B^1$ of all idempotent monoids and certain finitely generated variety $\mathbb E^1$

Anatole Storck, Harley Katz, Julien Devriendt, Adrianne Slyz

We present results of Pop. III formation in the MEGATRON suite of simulations, which self-consistently follows radiation and non-equilibrium chemistry, and resolves gas at near-pc resolution of a Milky Way-mass halo at Cosmic Dawn. While the very first Pop. III stars form in halos with masses well below the atomic cooling limit, whose cooling is dominated by

Zuherman Rustam, Sri Hartini, Sardar M. N. Islam, Fevi Novkaniza

Context: Financial system stability is determined by the condition of the banking system. A bank failure can destroy the stability of the financial system, as banks are subject to systemic risk, affecting not only individual banks but also segments or the entire financial system. Calculating the probability of a bank going bankrupt is one way to ensure the b

Xinzhao Wang, Yuxin Zhang, Soumyabrata Hazra, Tongyang Li

We introduce the first randomized algorithms for Quantum Singular Value Transformation (QSVT), a unifying framework for many quantum algorithms. Standard implementations of QSVT rely on block encodings of the Hamiltonian, which are costly to construct, requiring a logarithmic number of ancilla qubits, intricate multi-qubit control, and circuit depth scaling

Longteng Chen

In this work, we consider a perturbation of an asymptotically conical gradient expanding K\"ahler-Ricci soliton metric $g$ in the same K\"ahler class. We demonstrate that, under suitable assumptions, the normalized K\"ahler-Ricci flow starting from the initial perturbed metric exists for all time and converges uniformly to an asymptotically conical gradient

Jonathan Allcock, Joao F. Doriguello, Gábor Ivanyos, Miklos Santha

Bell sampling is a simple yet powerful tool based on measuring two copies of a quantum state in the Bell basis, and has found applications in a plethora of problems related to stabiliser states and measures of magic. However, it was not known how to generalise the procedure from qubits to $d$-level systems -- qudits -- for all dimensions $d > 2$ in a useful

Pontakorn Trakuekul, Attapol T. Rutherford, Jullajak Karnjanaekarin, Narongkorn Panitsrisit

We introduce OpenJAI-v1.0, an open-source large language model for Thai and English, developed from the Qwen3-14B model. Our work focuses on boosting performance on practical tasks through carefully curated data across three key use cases: instruction following, long-context understanding, and tool use. Evaluation results show that OpenJAI-v1.0 improves on t

Jasem Hamoud, Duaa Abdullah

In this paper, we establishe the extremal bounds of the topological indices -- Sigma index -- focusing on analyzing the sharp upper bounds and the lower bounds of the Sigma index, which is known $\sigma(G)=\sum_{uv\in E(G)}(d_G(u)-d_G(v))^2$. We establish precise lower and upper bounds for the Sigma index, leveraging a non-increasing degree sequence $\mathsc

Nicole Hoess, Carlos Paradis, Rick Kazman, Wolfgang Mauerer

Context: Mining software repositories is a popular means to gain insights into a software project's evolution, monitor project health, support decisions and derive best practices. Tools supporting the mining process are commonly applied by researchers and practitioners, but their limitations and agreement are often not well understood. Objective: This st

Kanglei Zhou, Qingyi Pan, Xingxing Zhang, Hubert P. H. Shum

Action Quality Assessment (AQA) quantifies human actions in videos, supporting applications in sports scoring, rehabilitation, and skill evaluation. A major challenge lies in the non-stationary nature of quality distributions in real-world scenarios, which limits the generalization ability of conventional methods. We introduce Continual AQA (CAQA), which equ

Giorgos Filandrianos, Orfeas Menis Mastromichalakis, Wafaa Mohammed, Giuseppe Attanasio

Gender bias in machine translation (MT) systems has been extensively documented, but bias in automatic quality estimation (QE) metrics remains comparatively underexplored. Existing studies suggest that QE metrics can also exhibit gender bias, yet most analyses are limited by small datasets, narrow occupational coverage, and restricted language variety. To ad

Stefano F. Stefenon, João P. Matos-Carvalho, Valderi R. Q. Leithardt, Kin-Choong Yow

Convolutional neural networks (CNNs) and transformer architectures offer strengths for modeling temporal data: CNNs excel at capturing local patterns and translational invariances, while transformers effectively model long-range dependencies via self-attention. This paper proposes a hybrid architecture integrating convolutional feature extraction with a temp

Thomas Dedieu

This text is a presentation of a set of formulae, first found by Vainsencher (for $\delta \leq 6$) and shortly after improved by Kleiman and Piene, counting $\delta$-nodal curves in a complete linear system on a smooth surface, if $\delta \leq 8$ and the corresponding line bundle is sufficiently positive. We also discuss a complement by Qviller, and related

Elena Senger, Yuri Campbell, Rob van der Goot, Barbara Plank

Automatic Term Extraction (ATE) is a critical component in downstream NLP tasks such as document tagging, ontology construction and patent analysis. Current state-of-the-art methods require expensive human annotation and struggle with domain transfer, limiting their practical deployment. This highlights the need for more robust, scalable solutions and realis

Abhishek Setty

We present a systematic pathway for solving differential equations within the quantum linear systems framework by combining block encoding with Quantum Singular Value Transformation (QSVT). The approach is demonstrated on a complex tridiagonal linear system and extended to problems in computational fluid dynamics: the heat equation with mixed boundary condit

Jesús Bautista, Héctor García de Marina

This paper presents a geometric control framework on the Lie group SO(3) for 3D source-seeking by robots with first-order attitude dynamics and constant translational speed. By working directly on SO(3), the approach avoids Euler-angle singularities and quaternion ambiguities, providing a unique, intrinsic representation of orientation. We design a proportio

Hongjian Chen, Changyun Wen, Xiaolei Li

In this paper, we consider the resilient multi-dimensional consensus and distributed optimization problems of multi-agent systems (MASs) in the presence of both agent-based and denial-of-service (DoS) attacks. The considered agent-based attacks can cover malicious, Byzantine, and stubborn agents. The links between agents in the network can be blocked by DoS

Vasileios Titopoulos, Kosmas Alexandridis, Giorgos Dimitrakopoulos

Attention is a core operation in numerous machine learning and artificial intelligence models. This work focuses on the acceleration of attention kernel using FlashAttention algorithm, in vector processors, particularly those based on the RISC-V instruction set architecture (ISA). This work represents the first effort to vectorize FlashAttention, minimizing

Maryam Abdollahi Shamami, Babak Teimourpour, Farshad Sharifi

Drug-drug interactions (DDIs) are a leading cause of preventable adverse events, often complicating treatment and increasing healthcare costs. At the same time, knowing which drugs do not interact is equally important, as such knowledge supports safer prescriptions and better patient outcomes. In this study, we propose an interpretable and efficient framewor

Danylo Bohomolov, Vita Ivanova, Ulrich T. Schwarz

LED degradation is usually associated with defects in the active region. Whereby the noise analysis can be a strong instrument to reveal them. The results of optical noise measurements for commercially available blue LED samples in a wide frequency range from kHz to MHz are reported. Noise spectra were decomposed into components according to the presented th

Nicholas Crawford, Vesna Iršič Chenoweth

The game of Cops and Robbers on graphs is a well-studied pursuit--evasion model whose central parameter, the cop number, captures the minimum number of pursuers required to guarantee capture of an adversary on a given graph. While the cop number has been determined for many classical graph families, relatively little is known about the important class of par

Syed Shazaib Shah, Daoliang Tan

Alarm data is pivotal in curbing fault behavior in Wind Turbines (WTs) and forms the backbone for advancedpredictive monitoring systems. Traditionally, research cohorts have been confined to utilizing alarm data solelyas a diagnostic tool, merely indicative of unhealthy status. However, this study aims to offer a transformativeleap towards preempting alarms,

Falko Ziebert, Igor M. Kulić

There is this old, eternal question: Why don't animals have wheels? In this perspective we show that they actually do. And they do so in a physically extraordinary way -- by combining incompatible elasticity, differential geometry and dissipative self-organization. Nature's wheel -- the ``wheel-within'' -- has been mysteriously concealed in plain sight, yet

Mikihiro Ikura, Arren Glover, Masayoshi Mizuno, Chiara Bartolozzi

Line segment extraction is effective for capturing geometric features of human-made environments. Event-based cameras, which asynchronously respond to contrast changes along edges, enable efficient extraction by reducing redundant data. However, recent methods often rely on additional frame cameras or struggle with high event rates. This research addresses r

Michael Keiblinger

In recent years, attention-like mechanisms have been used to great success in the space of large language models, unlocking scaling potential to a previously unthinkable extent. "Attention Is All You Need" famously claims RNN cells are not needed in conjunction with attention. We challenge this view. In this paper, we point to existing proofs that architectu

Jaeseok Jeong, Junho Kim, Gayoung Lee, Yunjey Choi

In the domain of text-to-image generation, diffusion models have emerged as powerful tools. Recently, studies on visual prompting, where images are used as prompts, have enabled more precise control over style and content. However, existing methods often suffer from content leakage, where undesired elements of the visual style prompt are transferred along wi

Kaixiang Mo, Yuxin Shi, Weiwei Weng, Zhiqiang Zhou

Large language models (LLMs) are typically developed through large-scale pre-training followed by task-specific fine-tuning. Recent advances highlight the importance of an intermediate mid-training stage, where models undergo multiple annealing-style phases that refine data quality, adapt optimization schedules, and extend context length. This stage mitigate

Nisar Ahmed, Muhammad Imran Zaman, Gulshan Saleem, Ali Hassan

Benchmarks for large language models (LLMs) often rely on rubric-scented prompts that request visible reasoning and strict formatting, whereas real deployments demand terse, contract-bound answers. We investigate whether such "evaluation scent" inflates measured performance without commensurate capability gains. Using a single open-weights model (GPT-OSS-20B

Chenpeng Wang, Xiaojie Cheng, Chunye Wang, Linfeng Yang

Tool-augmented language models have demonstrated strong capabilities, but their reliance on live API access creates scalability and reliability challenges during training and deployment. We propose MTR, a simulation-first training framework for tool-augmented reasoning. Instead of relying on live APIs, MTR learns from complete ReAct traces with schema-valida

Riku Mochizuki, Shusuke Komatsu, Souta Noguchi, Kazuto Ataka

We analyze answers generated by generative engines (GEs) from the perspectives of citation publishers and the content-injection barrier, defined as the difficulty for attackers to manipulate answers to user prompts by placing malicious content on the web. GEs integrate two functions: web search and answer generation that cites web pages using large language

Sauvik Roy, Nirmalya Ghosh, Ayan Banerjee, Subhasish Dutta Gupta

Critical coupling has emerged as a prominent area of research in recent years. However, most theoretical models are based on scalar theories (and occasionally coupled mode theories), which inadequately account for the polarization states of the incident light. To bridge this gap, we revisit the concept of critical coupling in planar multilayer structures usi

Antti Haimi, Lukas Odelius, José Luis Romero

We study the zeros and critical points of different indices of the standard Gaussian entire function on the complex plane (whose zero set is stationary). We provide asymptotics for the second order correlations of all the corresponding number statistics on small observation disks, showing various rates of local repulsion. The results have consequences for si

Mitchell Keren Taraday, Shahaf Wagner, Chaim Baskin

Multimodal retrieval still leans on embedding-based models like CLIP for fast vector search over pre-computed image embeddings. Yet, unlike text retrieval, where joint-encoder rerankers are standard, comparable vision-language rerankers are largely absent. We find that seminal joint encoders such as BLIP are severely bottlenecked by an expensive visual featu

Giovanni Donghi, Daniele Zambon, Luca Pasa, Cesare Alippi

Catastrophic forgetting is one of the main obstacles for Online Continual Graph Learning (OCGL), where nodes arrive one by one, distribution drifts may occur at any time and offline training on task-specific subgraphs is not feasible. In this work, we explore a surprisingly simple yet highly effective approach for OCGL: we use a fixed, randomly initialized e

Borko Stosic

The recently introduced concept of generalized thermodynamics is explored here in the context of 1d, 2d and 3d data analysis, performed on samples drawn from a 3d X-ray soil sample image. Different threshold levels are used to binarize the 3d sample, wherefrom relative frequencies of binary patterns are found and then used to address finite size scaling beha

Gian Maria Dall'Ara

Let $\Gamma_d$ be the largest constant such that every finite collection of cubes in $\mathbb{R}^d$ whose sides are parallel to the coordinate axes admits a disjoint sub-collection occupying a fraction $\Gamma_d$ of its volume. Vitali's greedy algorithm shows that $\Gamma_d\geq 3^{-d}$, and cutting a cube into its $2^d$ dyadic sub-cubes gives $\Gamma_d\leq 2

Russell Beale

As the world continues to change, more and more knowledge workers are embracing remote work. Yet this comes with its challenges for their productivity, and while many Task Management applications promise to improve the productivity of remote workers, it remains unclear how effective they are. Based on existing frameworks, this study investigated the producti

Simon Mack, Morten Overgaard, Dennis Dobler

The pseudo-observation method is regularly applied to time-to-event data. However, to date such analyses have relied on not formally verified statements or ad-hoc methods regarding covariance estimation. This paper strives to close this gap in the literature. To begin with, we demonstrate that the usual Huber-White estimator is not consistent for the limitin

M. D. K. Lee, Qi Zhao, Qin Qichen, Zhao Zhang

We report precision measurement of the unperturbed ${^{3}}D_1$ microwave transition frequencies in $^{176}\mathrm{Lu}^+$ to a fractional uncertainty of $4\times10^{-14}$. We find the $|F,m_F\rangle=|8,0\rangle$ to $|7,0\rangle$ hyperfine transition frequency to be $10\,491\,519\,945.228\,82(38)\,$Hz and the $|7,0\rangle$ to $|6,0\rangle$ transition frequency

Giorgio Di Russo, Anna Tokareva

Microscopic charged black holes can provide possibilities to test the consistency of the effective field theory (EFT) corrections to Einstein-Maxwell theory. A particularly interesting result is fixing the sign of a certain combination of EFT couplings from the requirement that all charged black holes should be able to evaporate (Weak Gravity Conjecture). In

Anubhav Shrimal, Aryan Jain, Soumyajit Chowdhury, Promod Yenigalla

Structured information extraction from unstructured text is critical for emerging Software 3.0 systems where LLM agents autonomously interact with APIs and tools. Recent approaches apply large language models directly to extraction tasks using existing JSON schemas, often with constraint decoding or reinforcement learning approaches to ensure syntactic valid

M. Zannese, P. Guillard, A. Abergel, E. Habart

H2 is the most abundant molecule in the interstellar medium and is a useful tool to study photodissociation regions, where radiative feedback from massive stars on molecular clouds is dominant. The James Webb Space Telescope, with its high spatial resolution, sensitivity, and wavelength coverage provides unique access to the detection of most of H2 lines and

Philipp Mondorf, Mingyang Wang, Sebastian Gerstner, Ahmad Dawar Hakimi

The Circuit Localization track of the Mechanistic Interpretability Benchmark (MIB) evaluates methods for localizing circuits within large language models (LLMs), i.e., subnetworks responsible for specific task behaviors. In this work, we investigate whether ensembling two or more circuit localization methods can improve performance. We explore two variants:

Yuwei Yu, Jin Zhang

Aims. The accretion disk of low-luminosity active galactic nuclei (LLAGNs) is a radiatively inefficient accretion flow (RIAF). Our goal is to find evidence of RIAF radiation from LLAGNs with jets and analyze their radiation properties, which also adds samples to future research on LLAGNs. Methods. Weconducted an analysis of the X-ray data obtained from NuSTA

Jeppe Jon Cederholm, Zhian Xu, Yanfeng Guo, Martin Ovesen

We use spherical neutron polarimetry to determine the ground state magnetic structure of Mn3Sn. We find that Mn3Sn adopts an inverse triangular structure with spins parallel to <100> (Type III) rather than spins parallel to <110> (Type IV). Density functional theory calculations reveal no energy difference between these two structures, suggesting that the se

Carlos Uriarte, Pablo Rodriguez-Lopez, Nagi Khalil

A continuous-opinion model accounting for the social compromise propensity is theoretically and numerically analysed. An agent's opinion is represented by a real number that can be changed through social interactions with her neighbours. The proposed dynamics depends on two fundamental parameters, $\alpha\in[-1,1]$ and $\beta\ge 0$. If an interaction takes p

Vuong Bui

We provide a short and elementary proof that the growth constant of polyiamonds is at most $1+2z+3z^2$ for the unique real root $z$ of the equation $2z^3+z^2-1=0$. This coincidentally suffices to recover the best known upper bound $3.6108$. Unlike the previous proof of this bound, which relied on computer-assisted technical arguments and the counts of polyia

André Greiner-Petter, Maik Fröbe, Jan Philip Wahle, Terry Ruas

The generative plagiarism detection task at PAN 2025 aims at identifying automatically generated textual plagiarism in scientific articles and aligning them with their respective sources. We created a novel large-scale dataset of automatically generated plagiarism using three large language models: Llama, DeepSeek-R1, and Mistral. In this task overview paper

Thomas Kramer, Benjamin Marussig, Martin Schanz

An isogeometric boundary element method (BEM) is presented to solve scattering problems in an isotropic homogeneous medium. We consider wave problems governed by the scalar wave equation as in acoustics and the Lam\'e-Navier equations for elastodynamics considering the theory of linear elasticity. The underlying boundary integral equations imply time-depende