October 2025 arXiv papers — page 170

Eddy Collin

Electromagnetism is at the heart of the Standard Model, but despite all the successes of modern theory, our basic description of light traveling in free space remains unsatisfactory. The four bosons that compose light are introduced in a rather trivial way, simply by quantizing the (scalar and vector) potential amplitudes. This leads to quite a few conceptua

Ruben Pascual, Mikel Sesma-Sara, Aranzazu Jurio, Daniel Paternain

The audiovisual industry is undergoing a profound transformation as it is integrating AI developments not only to automate routine tasks but also to inspire new forms of art. This paper addresses the problem of producing a virtually unlimited number of novel characters that preserve the artistic style and shared visual traits of a small set of human-designed

Zhenhailong Wang, Jiateng Liu, Amin Fazel, Ritesh Sarkhel

Modern conversational agents like ChatGPT and Alexa+ rely on predefined policies specifying metadata, response styles, and tool-usage rules. As these LLM-based systems expand to support diverse business and user queries, such policies, often implemented as in-context prompts, are becoming increasingly complex and lengthy, making faithful adherence difficult

Jisu Han, Wonjun Hwang

Test-time adaptation paradigm provides flexibility towards domain shifts by performing immediate adaptation on unlabeled target data from the source model. Vision-Language Models (VLMs) leverage their generalization capabilities for diverse downstream tasks, and test-time prompt tuning has emerged as a prominent solution for adapting VLMs. In this work, we e

Manuel Vargas Guzmán, Jakub Szymanik, Maciej Malicki

Despite the remarkable progress in neural models, their ability to generalize, a cornerstone for applications such as logical reasoning, remains a critical challenge. We delineate two fundamental aspects of this ability: compositionality, the capacity to abstract atomic logical rules underlying complex inferences, and recursiveness, the aptitude to build int

Ines Altemir Marinas, Anastasiia Kucherenko, Alexander Sternfeld, Andrei Kucharavy

The performance of Large Language Models (LLMs) is determined by their training data. Despite the proliferation of open-weight LLMs, access to LLM training data has remained limited. Even for fully open LLMs, the scale of the data makes it all but inscrutable to the general scientific community, despite potentially containing critical data scraped from the i

Hans van Haren

Turbulence is indispensable to redistribute nutrients for all life forms larger than microbial, on land and in the ocean. Yet, the development of deep-sea turbulence was not studied in three dimensions to date. As a disproportionate laboratory, an array of nearly 3000 high-resolution temperature sensors had been installed for three years on the flat 2500-m d

Bharath Muppasani, Ritirupa Dey, Biplav Srivastava, Vignesh Narayanan

Multi-agent pathfinding (MAPF) remains a critical problem in robotics and autonomous systems, where agents must navigate shared spaces efficiently while avoiding conflicts. Traditional centralized algorithms with global information provide high-quality solutions but scale poorly in large-scale scenarios due to the combinatorial explosion of conflicts. Conver

Alma L. Cavallin, Oliver Thim, Christian Forssén

We study the connection between accidental symmetries in the nuclear interaction and spin entanglement in two-nucleon scattering. Specifically, we incorporate different levels of Wigner $SU(4)$ and Serber symmetries into leading-order potentials derived from chiral effective field theory. We conduct a quantitative analysis by computing the full $S$ matrix, d

Patrick Gerard, Luca Luceri, Leonardo Blas, Emilio Ferrara

Online narratives spread unevenly across platforms, with content emerging on one site often appearing on others, hours, days or weeks later. Existing cross-platform information diffusion models often treat platforms as isolated systems, disregarding cross-platform activity that might make these patterns more predictable. In this work, we frame cross-platform

Jing-Yi Jia, Da-Chun Qiang, Lin-Yu Li, Hao Wei

Fast radio bursts (FRBs) can provide a measure of the Hubble constant $H_0$ that is independent of the constraints set by the cosmic microwave background (CMB) and the type Ia supernovae (SNIa), thereby arbitrating the Hubble tension. In the literature, the methodology proposed by Macquart et al. has been widely used, in which the contributions to the disper

Mikhail Terekhov, Alexander Panfilov, Daniil Dzenhaliou, Caglar Gulcehre

AI control protocols serve as a defense mechanism to stop untrusted LLM agents from causing harm in autonomous settings. Prior work treats this as a security problem, stress testing with exploits that use the deployment context to subtly complete harmful side tasks, such as backdoor insertion. In practice, most AI control protocols are fundamentally based on

Benzheng Yuan, Chaojie Zhang, Chuanbing Han, Shuya Wang

In superconducting quantum circuits, decoherence errors in qubits constitute a critical factor limiting quantum gate performance. To mitigate decoherence-induced gate infidelity, rapid implementation of quantum gates is essential. Here we propose a scheme for rapid controlled-Z (CZ) gate implementation through energy-level engineering, which leverages Rabi o

Jingyi Huang, Yuyi Yang, Mengmeng Ji, Charles Alba

The COVID-19 infodemic calls for scalable fact-checking solutions that handle long-form misinformation with accuracy and reliability. This study presents SAFE (system for accurate fact extraction and evaluation), an agent system that combines large language models with retrieval-augmented generation (RAG) to improve automated fact-checking of long-form COVID

Alexandra Blessing, Dirk Blömker

We analyze stochastic partial differential equations (SPDEs) with quadratic nonlinearities close to a change of stability. To this aim we compute finite-time Lyapunov exponents (FTLEs), observing a change of sign based on the interplay between the distance towards the bifurcation and the noise intensity. A technical challenge is to provide a suitable control

Ralf Römer, Adrian Kobras, Luca Worbis, Angela P. Schoellig

Imitation learning (IL) with generative models, such as diffusion and flow matching, has enabled robots to perform complex, long-horizon tasks. However, distribution shifts from unseen environments or compounding action errors can still cause unpredictable and unsafe behavior, leading to task failure. Early failure prediction during runtime is therefore esse

Pierre Botteron

This thesis explores foundational aspects of quantum information theory and quantum cryptography. First, we investigate quantum correlations in interactive settings, including the CHSH and graph isomorphism games. We aim to distinguish quantum correlations from non-signaling correlations by leveraging the principle of communication complexity. To this end, w

Anna Honeycutt, Hailey Murray, Eric Chitambar

Quantum masking is a special type of secret sharing in which some information gets reversibly distributed into a multipartite system, leaving the original information inaccessible to each subsystem. This paper proposes a dynamical extension of quantum masking to the level of quantum channels. In channel masking, the identity of a channel becomes locally hidd

Ivo Düntsch, Wojciech Dzik

The purpose of this note is to shed some light on the preservation of unification types of locally finite varieties of interior algebras and varieties of Heyting algebras under the functors presented by W. Blok in his dissertation.

Chanaprom Cholsuk, Furkan Ağlarcı, Daniel K. L. Oi, Serkan Ateş

Quantum key distribution (QKD) relies on single photon sources (SPSs), e.g. from solid-state systems, as flying qubits, where security strongly requires sub-Poissonian photon statistics with low second-order correlation values (\$g^{(2)}(0)\$). However, achieving such low \$g^{(2)}(0)\$ remains experimentally challenging. We therefore propose a decoy-like QK

Nicholas King, Eugene Pashkovski, Reid Patterson, Paige Rockwell

Large Amplitude Oscillatory Shear (LAOS) is a key technique for characterizing nonlinear viscoelasticity in a wide range of materials. Most research to date has focused on the shear stress response to an oscillatory strain input. However, for highly elastic materials such as polymer melts, the time-varying first normal stress difference $N_1(t;\omega,\gamma_

Antoine Luciano

We give a concise historical background to Montmort's matching problem and its modern variants such as the hat-check problem, then develop a unified counting framework for fixed-point-free allocations. Using elementary recurrence and inclusion-exclusion arguments, we derive closed forms for derangements, rectangular injections, and partial l-matchings, and w

Faried Abu Zaid, Tim Katzke, Emmanuel Müller, Daniel Neider

Unsupervised anomaly detection is often framed around two widely studied paradigms. Deep one-class classification, exemplified by Deep SVDD, learns compact latent representations of normality, while density estimators realized by normalizing flows directly model the likelihood of nominal data. In this work, we show that uniformly scaling flows (USFs), normal

Jingyi Feng, Xiang Feng

Understanding the encoding and decoding mechanisms of dynamic neural responses to different visual stimuli is an important topic in exploring how the brain represents visual information. Currently, hierarchically deep neural networks (DNNs) have played a significant role as tools for mining the core features of complex data. However, most methods often overl

Andreas Dedner, Jan Giesselmann, Kiwoong Kwon, Tristan Pryer

This work provides reliable a posteriori error estimates for Runge-Kutta discontinuous Galerkin approximations of nonlinear convection-diffusion systems. The classes of systems we study are quite general with a focus on convection-dominated and degenerate parabolic problems. Our a posteriori error bounds are valid for a family of discontinuous Galerkin spati

Miquel Cueca, Antonio Maglio, Fabricio Valencia

In this work, we study symplectic structures on graded manifolds and their global counterparts, higher Lie groupoids. We begin by introducing the concept of graded manifold, starting with the degree 1 case, and translating key geometric structures into classical differential geometry terms. We then extend our discussion to the degree 2 case, presenting sever

Álvaro Álvarez-Domínguez

This thesis applies techniques from quantum field theory in curved spacetimes to study particle creation in external fields, focusing on the Schwinger effect (i.e., the production of particle-antiparticle pairs by intense electric fields). Although experimental verification remains out of reach, theoretical analysis advances our understanding of this phenome

J. F. Silva Neto, D. S. M. Alencar, L. T. Brito, G. A. Alves

We investigate the critical properties of kinetic continuous opinion dynamics using deep learning techniques. The system consists of $N$ continuous spin variables in the interval $[-1,1]$. Dense neural networks are trained on spin configuration data generated via kinetic Monte Carlo simulations, accurately identifying the critical point on both square and tr

Ashkan Sebghati, S. Hassan HosseinNia

In this paper, a robust nonlinear control scheme is designed for the motion control of a class of piezo-actuated nano-positioning systems using frequency-domain analysis. The hysteresis, the nonlinearity in the piezoelectric material, degrades the precision in tracking references with high frequency contents and different travel ranges. The hysteresis compen

Cana Elgvin, Fredrik S. Hage, Khairi F. Elyas, Katja Höflich

Functional nanomaterials, including 2D materials and their heterostructures are expected to impact fields ranging from catalysis, optoelectronics to nanophotonics. To realize their potential, novel experimental approaches need to be developed to characterize the combined materials and their components. Techniques using fast electrons, such as electron energy

Raffaele Cristodaro, Benjamin Kraner, Claudio J. Tessone

This paper investigates the impact of sanctions on Tornado Cash, a smart contract protocol designed to enhance transaction privacy. Following the U.S. Department of the Treasury's sanctions against Tornado Cash in August 2022, platform activity declined sharply. We document a significant and sustained reduction in transaction volume, user diversity, and over

Moritz Hauck, Axel Målqvist, Malin Mosquera

We propose a multiscale method for mixed-dimensional elliptic problems with highly heterogeneous coefficients arising, for example, in the modeling of fractured porous media. The method is based on the Localized Orthogonal Decomposition (LOD) framework and constructs locally supported, problem-adapted basis functions on a coarse mesh that does not need to re

Edvin Olofsson, Evan Lovelle Fulton, Rezvan Tahouri, Mattias Bertolino

We study the nonlinear and resonant process of two-photon ionization of atoms (He and H) in a pump-probe scheme. The pump pulse prepares the quantum system in a superposition of the ground state and an excited bound state. By varying the phase difference between the pulses, we show how it is possible to coherently control the dressed-state population during

Rituparno Chowdhury, Alistair Inglis, Lucy E. Walker, Petri Murto

We report a family of luminescent alternant diradicals which, at room temperature, support a ground-state spin-triplet, near-unity photoluminescence quantum yields, and optical spin addressability. These diradicals comprise trityl groups meta-linked via pyridyl or phenyl groups, enabling optically bright triplet-to-triplet and singlet-to-singlet transitions.

Fanfan Lin, Peter Wilson, Xinze Li, Alan Mantooth

Artificial intelligence (AI) is rapidly transforming power electronics, with AI-related publications in IEEE Power Electronics Society selected journals increasing more than fourfold from 2020 to 2025. However, the ethical dimensions of this transformation have received limited attention. This article underscores the urgent need for an ethical framework to g

Jin-Chuan Shi, Chengye Su, Jiajun Wang, Ariel Shamir

Editing 4D scenes reconstructed from monocular videos based on text prompts is a valuable yet challenging task with broad applications in content creation and virtual environments. The key difficulty lies in achieving semantically precise edits in localized regions of complex, dynamic scenes, while preserving the integrity of unedited content. To address thi

Ehsan Eftekhari-Zadeh, Mikhail Gyrdymov, Parysatis Tavana, Robert Loetzsch

Long-living, hot and dense plasmas generated by ultra-intense laser beams are of critical importance for laser-driven nuclear physics, bright hard X-ray sources, and laboratory astrophysics. We report the experimental observation of plasmas with nanosecond-scale lifetimes, near-solid density, and keV-level temperatures, produced by irradiating periodic array

Rolando Gonzales Martinez

This paper introduces a novel framework for measuring multidimensional inequality based on a statistical physics reinterpretation of centrifugal and centripetal forces in rotating systems. Inspired by the mechanics of the Gravitron and extended via the conceptual AntiGravitron, this study proposes a new class of inequality metrics grounded in multivariate mi

Hyunin Lee, Yong Zhang, Hoang Vu Nguyen, Xiaoyi Liu

Cross-domain sequential recommendation (CDSR) aims to align heterogeneous user behavior sequences collected from different domains. While cross-attention is widely used to enhance alignment and improve recommendation performance, its underlying mechanism is not fully understood. Most researchers interpret cross-attention as residual alignment, where the outp

Xixi Wang, Jordanka Kovaceva, Miguel Costa, Shuai Wang

Free-text crash narratives recorded in real-world crash databases have been shown to play a significant role in improving traffic safety. However, large-scale analyses remain difficult to implement as there are no documented tools that can batch process the unstructured, non standardized text content written by various authors with diverse experience and att

Raffaele Cristodaro, Benjamin Kraner, Claudio J. Tessone

Tornado Cash is a decentralised mixer that uses cryptographic techniques to sever the on-chain trail between depositors and withdrawers. In practice, however, its anonymity can be undermined by user behaviour and operational quirks. We conduct the first cross-chain empirical study of Tornado Cash activity on Ethereum, BNB Smart Chain, and Polygon, introducin

Mats Vroon, Hans L. Bodlaender

A stable cutset is a set of vertices $S$ of a connected graph, that is pairwise non-adjacent and when deleting $S$, the graph becomes disconnected. Determining the existence of a stable cutset in a graph is known to be NP-complete. In this paper, we introduce a new exact algorithm for Stable Cutset. By branching on graph configurations and using the $O^*(1.3

Thomas Koopman, Sven-Bodo Scholz

Quickhull is an algorithm for computing the convex hull of points in a plane that performs well in practice, but has poor complexity on adversarial input. In this paper we show the same holds for the numerical stability of Quickhull.

Hidde Jense, Marc Viña, Erminia Calabrese, J. Colin Hill

We compare cosmological parameters from different Planck sky maps and likelihood pipelines, assessing robustness of cosmological results with respect to the choice of the latest Planck maps-likelihood combination. We show that, for the Planck multipole range retained in combination with ground-based observations, different products give very similar cosmolog

Ben Adenbaum, Emily Barnard, Max Hlavacek, Bryson Kagy

The poset of maximal tubings of a graph generalizes several well-known and remarkable partial orders. Notable examples include the weak Bruhat order and the Tamari lattice, posets of maximal tubings for the complete graph and the path graph, respectively. It is an open problem to characterize graphs for which the poset of maximal tubings is a lattice. In thi

E. Abasov, E. Boos, V. Bunichev, P. Volkov

This study explores possible manifestations of Dark Matter (DM) in processes involving multiple top quarks at the LHC. We analyze both the associated production of scalar and pseudoscalar DM Mediators with up to four top quarks, as well as their resonant production in top-rich final states, within a simplified model framework. Cross sections were calculated

Francesco Di Colandrea, Lorenzo Marrucci, Filippo Cardano

Skyrmionic patterns of optical fields have recently emerged across diverse photonic platforms. Here, we show that such textures also arise in the polarization eigenstates of light propagation through flat dielectric devices with an engineered, space-dependent optic-axis orientation. We focus on two-dimensional periodic structures, where propagation through m

Minjun Kim, Hyeonseok Lim, Hangyeol Yoo, Inho Won

This work presents the first large-scale investigation into constructing a fully open bilingual large language model (LLM) for a non-English language, specifically Korean, trained predominantly on synthetic data. We introduce KORMo-10B, a 10.8B-parameter model trained from scratch on a Korean-English corpus in which 68.74% of the Korean portion is synthetic.

Omer Ben-Porat, Gur Keinan, Rotem Torkan

We study an online stochastic matching problem in which an algorithm sequentially matches $U$ users to $K$ arms, aiming to maximize cumulative reward over $T$ rounds under budget constraints. Without structural assumptions, computing the optimal matching is NP-hard, making online learning computationally infeasible. To overcome this barrier, we focus on sing

Nizar El Ghazal, Antoine Caubrière, Valentin Vielzeuf

This paper presents a comparative study of context management strategies for end-to-end Spoken Dialog State Tracking using Speech-LLMs. We systematically evaluate traditional multimodal context (combining text history and spoken current turn), full spoken history, and compressed spoken history approaches. Our experiments on the SpokenWOZ corpus demonstrate t

Yankun Han

Weight initialization governs signal propagation and gradient flow at the start of training. This paper offers a theory-grounded and empirically validated study across two regimes: compact ReLU multilayer perceptrons and GPT-2-style transformers. First, a logarithmic sweep of the initial standard deviation maps vanishing and exploding regimes and identifies

Yi Zhang, Yiting Duan, Xiangjun Wang, Zhikun Zhang

Stochastic dynamical systems provide essential mathematical frameworks for modeling complex real-world phenomena. The Fokker-Planck-Kolmogorov (FPK) equation governs the evolution of probability density functions associated with stochastic system trajectories. Developing robust numerical methods for solving the FPK equation is critical for understanding and

Max B. Zhao, Fei Li

We propose and evaluate a quantum-inspired algorithm for solving Quadratic Unconstrained Binary Optimization (QUBO) problems, which are mathematically equivalent to finding ground states of Ising spin-glass Hamiltonians. The algorithm employs Matrix Product States (MPS) to compactly represent large superpositions of spin configurations and utilizes a discret

Victor Morand, Josiane Mothe, Benjamin Piwowarski

Named entities are fundamental building blocks of knowledge in text, grounding factual information and structuring relationships within language. Despite their importance, it remains unclear how Large Language Models (LLMs) internally represent entities. Prior research has primarily examined explicit relationships, but little is known about entity representa

Han Hu, Wenjie Wan, Feiyu Chen, Xiaoyu Liu

With the increasing complexity of power systems,accurately identifying critical states (the states corresponding to minimal cut sets) and assessing system reliability have become crucial tasks. In this paper, a mathematical lattice structure is employed to represent and partition the state space of power system. Based on this structure, a novel recursive met

María Hernández Ruiz, Christopher Kiehl, Vito Giovanni Lucivero, Morgan W. Mitchell

We demonstrate an optically pumped magnetometer that monitors spin polarization using Pound Drever Hall (PDH) technique. The instrument exhibits a noise floor of 22.2 pT/sqrt(Hz) limited by optical photon shot noise, short-term instability of 30.8 pT/sqrt(Hz)/sqrt({\tau}) for averaging times {\tau} < 0.2 s , instability below 70 pT for 0.2 s < {\tau} < 20 s

Timothy Tran, William Schiesser

This research investigates the feasibility of producing affordable, functional acoustic guitars using 3D printing, with a focus on producing structural designs with proper tonal performance. Conducted in collaboration with William Schiesser, the study uses a classical guitar model, chosen for its lower string tension, to evaluate the tonal characteristics of

Yavuz Durmazkeser, Patrik Okanovic, Andreas Kirsch, Torsten Hoefler

We introduce LLM SELECTOR, the first framework for active model selection of Large Language Models (LLMs). Unlike prior evaluation and benchmarking approaches that rely on fully annotated datasets, LLM SELECTOR efficiently identifies the best LLM with limited annotations. In particular, for any given task, LLM SELECTOR adaptively selects a small set of queri

Antonio Martínez-Ibarra, Aurora González-Vidal, Adrián Cánovas-Rodríguez, Antonio F. Skarmeta

The Mar Menor, Europe's largest hypersaline coastal lagoon, located in southeastern Spain, has undergone severe eutrophication crises, with devastating impacts on biodiversity and water quality. Monitoring chlorophyll-a, a proxy for phytoplankton biomass, is essential to anticipate harmful algal blooms and guide mitigation strategies. Traditional in situ mea

Thomas Koopman, Jordy Aaldering, Bernard van Gastel, Sven-Bodo Scholz

Finding the convex hull is a fundamental problem in computational geometry. Quickhull is a fast algorithm for finding convex hulls. In this paper, we present VQhull, a fast parallel implementation of Quickhull that exploits vector instructions, and coordinates CPU cores in a way that minimizes data movement. This implementation obtains a sequential runtime i

Qianyou Sun, Jiexin Zheng, Bohan Jin, Lihua Chen

Identifying inter-firm relationships such as supply and competitive ties is critical for financial analysis and corporate governance, yet remains challenging due to the scale, sparsity, and contextual dependence of corporate data. Graph-based methods capture structure but miss semantic depth, while large language models (LLMs) excel at text but remain limite

Beige Jerry Jin, Leila Wehbe

Current non-invasive neuroimaging techniques trade off between spatial resolution and temporal resolution. While magnetoencephalography (MEG) can capture rapid neural dynamics and functional magnetic resonance imaging (fMRI) can spatially localize brain activity, a unified picture that preserves both high resolutions remains an unsolved challenge with existi

Michael Lindner, Julian Geis, Toni Seibold, Tom Brown

Germany has set an ambitious target of reaching net zero greenhouse gas emissions by 2045. We explore how integrated cross-sectoral planning can reduce costs compared to existing national plans. Our new linear optimization model PyPSA-DE simulates the electricity and hydrogen transmission networks, as well as supply, demand, and storage in all sectors of the

Alejandro Castro, Ignacio Sanchez-Burgos, Nuria H. Espejo, Adiran Garaizar

Glyphosate, the most widely used herbicide worldwide, crystallizes through complex intermolecular interactions that are strongly influenced by synthesis-derived impurities. Understanding this process at the molecular scale is critical for optimizing production, ensuring product quality, and assessing environmental impact. Here, we employ direct coexistence m

Pedro L. Contreras E

In this manuscript, we find an inhomogeneous stripes phase in a numerical model for an unconventional superconductor with time-reversal-breaking symmetry and triplet odd pairing. We contrast a robust well known homogeneous phase with dilute disorder characterized by a unitary resonance and a tiny gap that resembles an s-wave superconductor, with a new inhomo

Amir Bahador Javadi, Philip Pong

This study explores data-driven modeling techniques to capture the dynamics of a grid-forming converter-based infinite bus system, critical for renewable-integrated power grids. Using sparse identification of nonlinear dynamics and deep symbolic regression, models were generated from synthetic data simulating key disturbances in active power, reactive power,

Yuichi Yokoyama, Kohei Yamagami, Yuta Sumiya, Hayaru Shouno

Deep learning has revolutionized computer vision, yet a major gap persists between complex, data-hungry deep learning models and the practical demands of state-of-the-art scientific measurements. To fundamentally bridge this gap, we propose deep prior-based denoising, a robust deep learning model that requires no training data. We demonstrate its effectivene

Chongxiao Cai, Yan Zhu, Min Sheng, Jiandong Li

Low-Earth-orbit (LEO) satellites assist observation satellites (OSs) to compress and backhaul more time-determined images (TDI) has become a new paradigm, which is used to enhance the timeout caused by the limited computing resources of OSs. However, how to capture the time-varying and dynamic characteristics of multi-dimensional resources is challenging for

Idris Dag, Serkan Uğurluoğlu, Nihat Adar

The purpose of this paper is to propose a new algorithm for obtaining approximate solutions to the Burgers' equation (BE). Integration in time by a quadratic B-spline collocation method is shown. To the best of our knowledge, B-splines have not previously been used to integrate partial differential equations in both time and space. First, the BE is integrate

Sahab Zandi, Kamesh Korangi, Juan C. Moreno-Paredes, María Óskarsdóttir

Small and Medium-sized Enterprises (SMEs) are known to play a vital role in economic growth, employment, and innovation. However, they tend to face significant challenges in accessing credit due to limited financial histories, collateral constraints, and exposure to macroeconomic shocks. These challenges make an accurate credit risk assessment by lenders cru

Sanghyun Kim, Gihyeon Jeon, Seungwoo Hwang, Jiho Lee

While diffusion models are attracting increasing attention for the design of novel materials, their ability to generate low-energy structures in unexplored chemical spaces has not been systematically assessed. Here, we evaluate the performance of two diffusion models, MatterGen and DiffCSP, against three databases: a ternary oxide set (constructed by a genet

Jindong Tian, Yifei Ding, Ronghui Xu, Hao Miao

Weather forecasting is a fundamental task in spatiotemporal data analysis, with broad applications across a wide range of domains. Existing data-driven forecasting methods typically model atmospheric dynamics over a fixed short time interval, e.g., 6 hours, and rely on naive autoregression-based rollout for long-term forecasting, e.g., 5 days. However, this

Christian Bluethgen, Dave Van Veen, Daniel Truhn, Jakob Nikolas Kather

Building agents, systems that perceive and act upon their environment with a degree of autonomy, has long been a focus of AI research. This pursuit has recently become vastly more practical with the emergence of large language models (LLMs) capable of using natural language to integrate information, follow instructions, and perform forms of "reasoning" and p

J. Ablinger, J. Blümlein, A. De Freitas, A. von Manteuffel

We calculate the two-mass three-loop contributions to the unpolarized and polarized massive operator matrix elements $\tilde{A}_{Qg}^{(3)}$ and $\Delta \tilde{A}_{Qg}^{(3)}$ in $x$-space for a general mass ratio by using a semi-analytic approach. We also compute Mellin moments up to $N = 2000 (3000)$ by an independent method, to which we compare the results

Guillaume Bal, Anjali Nair

Wavefield speckle patterns are generated by interference of randomly scattered coherent light. In the weak-coupling regime of the It\^o-Schr\"odinger paraxial model for long-distance wave propagation, we show the following multiscale character: a macroscopic envelope solves a deterministic diffusion equation while the local wavefield (the speckle) is describ

Matthew R. Williams, F. Hunter McGuire, Terrance D. Savitsky

Parameter estimation and inference from complex survey samples typically focuses on global model parameters whose estimators have asymptotic properties, such as from fixed effects regression models. The central challenge is to both mitigate bias induced from potentially unbalanced samples and to incorporate adjustments for differences in effective sample siz

He Jiang, Yufu Wang, Hao Lin, Peiyu Zou

Large Language Models (LLMs) have shown strong performance in automated source-to-target code translation through pretraining on extensive code corpora. However, mainstream LLM-based code translation methods suffer from two critical limitations. First, they are highly sensitive to language-specific features, which often introduce source-language syntax or le

Hayato Imori, Taketo Sano, Kouki Sato, Masaki Taniguchi

This paper is a continuation of our previous work, where we defined an embedded cobordism map on the instanton cube complex that recovers the cobordism maps both in Khovanov homology and singular instanton theory. In this paper, we extend this construction to immersed cobordisms, where we define an immersed cobordism map on Khovanov homology and prove that i

Zhichao Guo, Rik A. H. van Herk, Edgar J. D. Vredenbregt, Servaas J. J. M. F. Kokkelmans

We present a novel acousto-optic lens (AOL) design for neutral atom quantum computing. This approach enhances atom rearrangement in optical tweezer arrays and addresses the speed limitations imposed by the cylindrical lensing effect of acousto-optic deflectors (AODs). By combining a double-pass AOD configuration for dynamic focal tuning with a standard pair

Runkang Feng

In this paper, we study a class of simple OZ-type vertex operator algebras $V$ generated by simple Virasoro vectors $\omega^{ij}=\omega^{ji}$, $1\leq i<j\leq n$, $n\geq 3$. We prove that $V$ is uniquely determined by its Griess algebra $V_2$. The automorphism group of $V$ is also determined. Furthermore, we give the necessary conditions for $V$ to be unitary

Sajad Khatiri, Francisco Eli Vina Barrientos, Maximilian Wulf, Paolo Tonella

Ensuring robust robotic navigation in dynamic environments is a key challenge, as traditional testing methods often struggle to cover the full spectrum of operational requirements. This paper presents the industrial adoption of Surrealist, a simulation-based test generation framework originally for UAVs, now applied to the ANYmal quadrupedal robot for indust

Ziyu Zheng, Yaming Yang, Ziyu Guan, Wei Zhao

The ``pre-train, prompt" paradigm, designed to bridge the gap between pre-training tasks and downstream objectives, has been extended from the NLP domain to the graph domain and has achieved remarkable progress. Current mainstream graph prompt-tuning methods modify input or output features using learnable prompt vectors. However, existing approaches are conf

Dakai Zhai, Jiong Gao, Boya Du, Junwei Xu

Accurately predicting conversion rates (CVR) for low-activity users remains a fundamental challenge in large-scale e-commerce recommender systems. Existing approaches face three critical limitations: (i) reliance on noisy and unreliable behavioral signals; (ii) insufficient user-level information due to the lack of diverse interaction data; and (iii) a syste

Dong-Gil Im, Seung-Yeun Yoo, Chung-Hyun Lee, Jongheon Suh

At the heart of recent breakthroughs in quantum imaging and spectroscopy utilizing undetected photons lies the quantum optical effect known as induced coherence without induced emission. This fundamental quantum interference effect has unlocked new possibilities in accessing challenging wavelength regimes for advanced imaging and spectroscopic analysis. Desp

Isaac Robledo, Yiqing Li, Guy Y. Cornejo Maceda, Rodrigo Castellanos

The Hybrid Genetic Optimisation framework (HYGO) is introduced to meet the pressing need for efficient and unified optimisation frameworks that support both parametric and functional learning in complex engineering problems. Evolutionary algorithms are widely employed as derivative-free global optimisation methods but often suffer from slow convergence rates

Mert İnan, Anthony Sicilia, Alex Xie, Saujas Vaduguru

Establishing shared goals is a fundamental step in human-AI communication. However, ambiguities can lead to outputs that seem correct but fail to reflect the speaker's intent. In this paper, we explore this issue with a focus on the data visualization domain, where ambiguities in natural language impact the generation of code that visualizes data. The availa

Jerome Sieber, Antonio Orvieto, Melanie N. Zeilinger, Carmen Amo Alonso

Deep sequence models, ranging from Transformers and State Space Models (SSMs) to more recent approaches such as gated linear RNNs, fundamentally compute outputs as linear combinations of past value vectors. To draw insights and systematically compare such architectures, we develop a unified framework that makes this output operation explicit, by casting the

Per Sebastian Skardal, Federico Battiston, Maxime Lucas, Matthew S Mizuhara

Understanding how higher-order interactions affect collective behavior is a central problem in nonlinear dynamics and complex systems. Most works have focused on a single higher-order coupling function, neglecting other viable choices. Here we study coupled oscillators with dyadic and three different types of higher-order couplings. By analyzing the stabilit

Zhenlin Zhang, Wei Hu, Enrico Perfetto, Gianluca Stefanucci

In open quantum systems hosting excitons, dissipation mechanisms critically shape the excitonic dynamics, band-structure and topological properties. A microscopic understanding of excitons in such non-Hermitian settings demands a first-principles generalization of the Bethe-Salpeter equation (BSE). Building on a recently introduced nonequilibrium Green's fun

Yu Sun, Bo Chen, Peng Gao, Qiuyi Li

This paper is concerned with time domain forward scattering and inverse scattering problems with a single moving point source as the emitter. Approximate solutions are provided for the forward scattering problem with a moving emitter. Regarding the inverse problem, in addition to a basic indicator function based on the approximate solutions, a novel indicato

Takeo Sasai, Giacomo Borraccini, Yue-Kai Huang, Hideki Nishizawa

Optical link tomography (OLT) is a rapidly evolving field that allows the multi-span, end-to-end visualization of optical power along fiber links in multiple dimensions from network endpoints, solely by processing signals received at coherent receivers. This paper has two objectives: (1) to report the first field trial of OLT, using a commercial transponder

Qi Yan, Xiang-Dong Li

We are concerned with a stochastic mean curvature flow of graphs with extra force over a periodic domain of any dimension. Based on compact embedding method of variational SPDE, we prove the existence of martingale solution. Moreover, we derive the small perturbation limit of the stochastic weighted mean curvature flow.

Arthur C. R. Dutra, Ties-A. Ohst, Hai-Chau Nguyen, Otfried Gühne

Measurements that can be implemented via local operations and classical communication (LOCC) constitute a class of operations that is available in future quantum networks in which parties share entangled resource states. We characterise the different classes of measurements implementable with LOCC, where communication is restricted to a single round with a f

Reza Sedghi, Anand Subramoney, David Kappel

Efficient inference with transformer-based models remains a challenge, especially in vision tasks like object detection. We analyze the inherent sparsity in the MLP layers of DETR and introduce two methods to exploit it without retraining. First, we propose Static Indicator-Based Sparsification (SIBS), a heuristic method that predicts neuron inactivity based

Natalie Abreu, Nikhil Vyas, Sham Kakade, Depen Morwani

Recent efforts to accelerate LLM pretraining have focused on computationally-efficient approximations that exploit second-order structure. This raises a key question for large-scale training: how much performance is forfeited by these approximations? To probe this question, we establish a practical upper bound on iteration complexity by applying full Gauss-N

David Rønne Sallingboe, Sofie Marie Koksbang

One of the most important discoveries in cosmology is the accelerated expansion of the Universe. Yet, the accelerated expansion has only ever been measured {\em in}directly. Redshift drift offers a direct observational probe of the Universe's expansion history, with its sign revealing whether there has been acceleration or deceleration between source and obs

Shijun Liao, Shijie Qin

McMullen et al. [1] comment that the numerical simulations that explicitly include random velocity fluctuations ``should exhibit a thermal-fluctuation-dominated range'' consistent with the literature, so that our results (J. Fluid Mech. 1008, R2, 2025) [2] ``contradict other results in the literature''. First of all, we would give an opposite example against

Adnan Ghribi

We present an open-source pipeline for generating a \emph{living review} of artificial intelligence (AI) and machine learning (ML) applications in accelerator physics and technologies. Traditional review articles provide static snapshots that are quickly outdated by the rapid pace of research. The presented system automatically harvests publications from mul

Vinayak Vinayak, Melike Lakadamyali, Vivek B Shenoy

Nanoscale chromatin domains, variously termed nucleosome clutches, nanodomains, or packing domains, have emerged as fundamental architectural units of the mammalian genome during interphase and mitosis. Unlike cohesin-dependent loops or TADs, these 50-200 nm structures persist in the absence of loop extrusion, pointing to a distinct organizing principle shap

Razi Iqbal

Vehicular Ad-hoc Networks (VANETs) have seen significant advancements in technology. Innovation in connectivity and communication has brought substantial capabilities to various components of VANETs such as vehicles, infrastructures, passengers, drivers and affiliated environmental sensors. Internet of Things (IoT) has brought the notion of Internet of Vehic