April 2026 arXiv papers — page 2

Muhammad Qasim, Farrukh Javed

In this paper, we study norm-based regularization methods for neural networks. We compare existing penalization approaches and introduce two regularization strategies that extend classical ridge- and lasso-type penalties to neural network models. The first strategy modifies weight decay by incorporating the covariance structure of the input features into a r

Michelle L. Hill, Stephen R. Kane, Bradford J. Foley, Laura K. Schaefer

With recent advances in exoplanet observational techniques enabling the discovery of increasingly smaller planets, a crucial question emerges in the search for habitable planets: how small can a planet be and still maintain an atmosphere? We present results from the Smaller Than Earth Habitability Model (STEHM) which examines how small a planet can be and st

Zifan Zhang, Dianwei Chen, Anjun Gao, Manhua Wang

Network digital twins (NDTs) are transforming network management by offering precise virtual replicas of physical network systems. However, their reliance on diverse and sensitive data introduces significant challenges related to data management, regulatory compliance, and user privacy. In scenarios where selective data removal is necessary, such as device d

Gianni Cassoni

In microgravity, a partially filled cylindrical tank is generally bounded by a curved equilibrium meniscus rather than by an almost flat free surface. This modifies both the bulk liquid inertia and the capillary restoring force, so flat-interface sloshing frequencies can become inaccurate even in the linear regime. This effect matters once the Bond number is

Lucas Becerra, Monica Malen Denham, Alejandro B. Kolton, Karina Laneri

Wildfires are among the most severe disturbances affecting forest ecosystems, with over 50,000 hectares burned in Patagonia, Argentina, during 2025 alone. This study implements a Reaction-Diffusion-Convection (RDC) model to simulate wildfire spread in the Steffen and Martin Lakes area, a region severely impacted by fires. By integrating high-resolution maps

Ankur Dhar, Mohamed A. K. Othman, Valery A. Dolgashev

We demonstrate a new 11.424 GHz SLED-type RF pulse compressor for powering high-gradient X-band photoinjectors with pulse lengths around 20 ns. RF pulse compression provides a practical path to higher peak power at the cost of pulse length for various applications such as RF deflectors for electron beam diagnostics on free electron lasers. Our new compact pu

Robert Cornea

This work explores the geometry of stable wild Vafa-Witten bundles over the complex projective plane $\mathbb{P}^2$. Specifically, we consider stable rank-two pairs $(E,\Phi)$, with $E\to\mathbb{P}^2$ a rank-two holomorphic vector bundle and $\Phi\in H^0(\mathbb{P}^2,\mathrm{End}_0E\otimes\mathcal{O}(d))$ for $d\geq0$, and compute the dimension of the moduli

Malik H. Walker, Robert C. Allen, George C. Ho, Glenn M. Mason

During the propagation of interplanetary coronal mass ejections (ICMEs), evolution of the ICME-driven shock along with interactions with other solar wind structures, planetary bodies, and general changes to their morphology can alter particle acceleration efficiency and transport effects at their associated shocks. While the underlying mechanisms for these p

Argyro Sasli, Karish Seebaluck, Chris Colpitts, Michael Coughlin

Modulated ion acoustic waves (IAWs) -- including triggered ion acoustic waves (TIAWs) and frequency-dispersed ion acoustic waves (FDIAWs) -- are increasingly recognized as efficient drivers of electron heating in the solar wind through nonlinear wave-particle interactions. Identification of these events in the Parker Solar Probe (PSP) FIELDS burst-mode archi

Omar Mrani-Zentar, Serdar Yuksel

Decentralized stochastic control problems are difficult to study due to information structure dependent subtleties, which prevent many classical methods in stochastic control from being applicable. In this paper we consider such problems with general standard Borel spaces under two related information structures. (a) the one-step delayed information sharing

Kaiyan Zhao, Borong Zhang, Yiming Wang, Xingyu Liu

In reinforcement learning (RL) for robotic manipulation, the Decision Transformer (DT) has emerged as an effective framework for addressing long-horizon tasks. However, DT's performance depends heavily on the coverage of collected experiences. Without an active exploration mechanism, standard DT relies on uniform replay, which leads to poor sample efficiency

Jaewon Jeong, Pan-Yang Su, S. Shankar Sastry, Anil Aswani

Many incentive design problems must contend with information asymmetries due to non-observation of efficiency (adverse selection) or non-observation of effort (moral hazard). And although a growing body of literature considers incentive design in control systems, the problem of designing incentives for control systems under information asymmetries has been l

Lubashan Pathirana

We develop a product-level trace-Dobrushin theory for finite-dimensional quantum channel products and apply it to deterministic and stationary random inhomogeneous matrix product states in left-canonical CPTP gauge. For a product of channels, the centered trace-Dobrushin coefficient quantifies the residual dependence on the input state, and its decay is the

Nitin Choudhury, Nikhil Kumar, Aditya Kumar Sinha, Abhijeet Anand

Wide exploration on robocall surveillance research is hindered due to limited access to public datasets, due to privacy concerns. In this work, we first curate Robo-SAr, a synthetic robocall dataset designed for robocall surveillance research. Robo-SAr comprises of ~200 unwanted and ~1200 legitimate synthetic robocall samples across three realistic adversari

Ankur Gupta

We present a class and home problem in graduate transport phenomena and electrochemical engineering that clarifies a common misconception: electroneutrality implies the electric field is constant. Starting with one-dimensional Poisson--Nernst--Planck equations for a silver electroplating cell, students obtain concentration and potential profiles. A companion

Kimon Antonios Provatas, Avery Self, Ioannis Mouratidis, Ilias Georgakopoulos-Soares

Agentic AI scientists equipped with domain-specific tools are rapidly entering scientific workflows across disciplines, with especially strong uptake in the life sciences where they can be used for literature synthesis, sequence analysis, and experimental planning support. While these systems accelerate biological research, they also introduce risks for dual

James T. Sikora, Joe Llama, Rachael M. Roettenbacher, Elisabeth M. Brann

MASCARA-5~b/TOI-1431~b is an ultra-hot Jupiter \citep[$P_{\rm orb}=2.650237\pm0.000003\,{\rm d}$, $T_{\rm eq}=2370\pm70\,{\rm K}$, $M_{\rm p}=3.12\pm0.18\,M_{\rm Jup}$, $R_{\rm p}=1.49\pm0.05\,R_{\rm Jup}$;][]{addison2021} orbiting a bright Am star ($V=8.0\,{\rm mag}$). Recent time-series observations obtained with PEPSI@LBT during the planet's post-eclipse

Elijah Kane, Chris Albert, Andrew Beyer, Charles

The Probe far-Infrared Mission for Astrophysics (PRIMA) is a far-infrared (24-261 micron wavelengths) probe-class space observatory currently under Phase A study, which promises orders-of-magnitude improvement in mapping speed over its predecessors. PRIMA will field exquisitely sensitive kilopixel arrays of kinetic inductance detectors (KIDs) for the Far-Inf

Lukas Schoenlinner

The main result of this article is a Llarull-type rigidity statement for scalar curvature on Riemannian spin manifolds with cone-like singularities in odd dimensions. The even dimensional analog was proven in an earlier work together with Simone Cecchini, Bernhard Hanke and Thomas Schick using index theory and the analysis of abstract cone operators, which a

S. Pirogov, E. Zhizhina

The paper deals with spectral analysis of non-local operators arising in population dynamics models. We consider negative periodic perturbations of non-local operators of the convolution type. Such operators describe evolutions of the first correlation function in the stochastic birth and death dynamcis in the presence of suppression forces that increase mor

Csaba Fábri, Gábor J. Halász, Lorenz S. Cederbaum, Ágnes Vibók

The Pauli principle has far-reaching consequences in quantum physics. Here, we investigate, for the first time, its implications, together with nuclear spin isomerism, in polaritonic chemistry. The theory is developed for a single and a few molecules as well as for an ensemble of molecules. As an explicit and detailed example we first present an accurate num

S. Pirogov, E Zhizhina

Can a local disaster lead to extinction? We answer this question in this work. In the paper \cite{PZ-PPI} we considered contact processes on locally compact metric spaces with state dependent birth and death rates and formulated sufficient conditions on the rates that ensure the existence of invariant measures. One of the crucial conditions in \cite{PZ-PPI}

Bala Prenith Reddy Gopu, Patrick Quinn, George M. Nehma, Madhur Tiwari

On-orbit inspection imagery is crucial as it enables characterization of non-cooperative resident space objects, providing the geometry and structural condition essential for active debris removal and on-orbit servicing mission planning. However, most existing neural implicit surface reconstruction methods have been confined to synthetic or hardware-in-the-l

Udayanga G. W. K. N. Gamage, Yan Zeng, Cesar Cadena, Matteo Fumagalli

Real-time object detection on energy-constrained platforms is critical for applications such as UAV-based inspection, autonomous navigation, and mobile robotics. Spiking neural networks (SNNs) on neuromorphic hardware are believed to be significantly more energy-efficient than conventional artificial neural networks (ANNs). In this work, we present a compreh

Vladislav Sandul, Marco Durante, Thomas Friedrich

Treatment-related lymphopenia is a frequent and clinically significant consequence of cancer therapy that can compromise immune-mediated tumor control and worsen patient outcomes. Despite its importance, no mechanistic framework exists to accurately predict the severity of lymphopenia from patient-specific data. Here, we present a biokinetic model that quant

Yuxi Ma, Yongqian Peng, Junchen Lyu, Chi Zhang

Humor is a fundamental cognitive phenomenon in which humans derive pleasure from the expectation violations and their resolution, exemplifying the brain's dynamic capacity for predictive processing. Classical humor theories emphasize semantic incongruity as the primary driver of amusement, yet overlook temporal dynamics despite comedians' intuition that "tim

Jonathan Unger, Georg Hoffstaetter

We investigate the application of the Achromatic Telescopic Squeezing (ATS) scheme to the Electron Storage Ring (ESR) of the Electron-Ion Collider (EIC) as a method to improve dynamic aperture and momentum acceptance. A comparative study is performed between conventional sextupole correction schemes and ATS-based optics using both a simplified test lattice a

M. A. Khrystik

Let $f_W(n)$ be the number of different factors of length $n$ appearing in $W$. A classical result of Morse and Hedlund, stated in 1938, asserts that an infinite word $W$ is ultimately periodic if and only if $f_W(n)\leq n$ for some $n\in \mathbb N$. In this paper, we describe the form of finite words that satisfy the condition $f_W(n)\leq n$. We study relat

Steven Redolfi

The Receiver Operating Characteristic (ROC) curve of a binary classifier has often been utilized to measure the performance of the classifier. The area beneath this curve is used in particular because of its quoted probabilistic interpretation as being equal to the probability that the classifier will rank a random positive observation above a random negativ

YiFeng Wang, Zhun Sun, Keisuke Sakaguchi

We present Activation Residual Hessian Quantization (ARHQ), a post-training weight splitting method designed to mitigate error propagation in low-bit activation-weight quantization. By constructing an input-side residual Hessian from activation quantization residuals (G_x), ARHQ analytically identifies and isolates error-sensitive weight directions into a hi

F. A. Mashurov, B. K. Sartayev

Let $(P,\cdot,d)$ be a differential perm algebra over a field of characteristic $0$, i.e. an associative algebra satisfying $(ab)c=(ba)c$ equipped with a derivation $d$. We investigate polynomial identities in the algebras obtained from $d$ by the derived operations \[ a\prec b=ab',\quad a\succ b=a'b,\quad a\blacklozenge b=ab'+ba',\quad a\bullet b=a'b+ab',\q

Kirill Shakhmatov

Given a $\mathbb{G}_\mathrm{a}$-action on an affine variety $X$, we show that the complement of the union of all principal invariant cylinders in $X$ is equal to the zero locus of the plinth ideal of the corresponding locally nilpotent derivation.

Yan Cui, Jacob S. Leiby, Wenhui Lei, Dokyoon Kim

Integrating molecular, morphological, and clinical data is essential for basic and translational biomedical research, yet systematic frameworks for jointly modeling these modalities remain limited. Here we present Haiku, a tri-modal contrastive learning model trained on multiplexed immunofluorescence (mIF). It comprises 26.7 million spatial proteomics patche

Alexandru Golic, Egor Babaev, Johan Carlström

Fermionic condensation typically occurs via pairing. In recent decades, however, a fundamental question has emerged: whether alternative forms of order exist, such as condensates of fermion quadruplets. These states--including ``charge-4e" superconductors and ``charge-0" counterflow condensates--lie beyond the standard Bardeen-Cooper-Schrieffer framework, an

Kaituo Zhang, Zhen Xiong, Mingyu Zhong, Zhimeng Jiang

Tool-augmented reasoning has become a popular direction for LLM-based agents, and it is widely assumed to improve reasoning and reliability. However, we demonstrate that this consensus does not always hold: in the presence of semantic distractors, tool-augmented reasoning does not necessarily outperform native CoT. To explain this performance gap, we propose

Julian B. Voits, Ulrich S. Schwarz

For irreducible, time-homogeneous Markov networks, mutual linearity has recently been established for both occupation probabilities and network currents in the stationary regime as well as in the non-stationary regime in Laplace space. The derivation of this property for the stationary distribution utilized the Markov chain tree theorem, which also allows fo

Tenma Wakasugi, Zhaohong Sun, Kei Kimura, Makoto Yokoo

We study two-sided matching markets under hereditary constraints, which extend beyond simple capacity limits and arise in applications such as diversity requirements and refugee resettlement. In these settings, fairness and non-wastefulness are often incompatible, and existing approaches typically address this tension by prioritizing one property at the expe

Debasis Dwibedy, Avyay Nishtala, Pranathi Mukku, D Snehaja

Modern crop advisory systems exhibit a critical limitation termed \textit{economic blindness}. These systems primarily optimize for biological yield, often overlooking market price, which can lead farmers toward agronomically sound yet financially unviable decisions. In this paper, we develop Kisan AI, a smart profit-aware crop advisory system that resolves

Janis Nötzel, Florian Seitz

Shared randomness is the central ingredient for stabilizing symmetrizable communication systems against arbitrarily varying jammers. Given the presence of the jammer, however, the question arises how this precious resource could have been distributed. Several works discuss the use of external sources for this task. In this work, we show, based on the most st

Andreas Hagn, Jan Krause, Moritz Stargalla, Lorenza Moreno

This paper studies a variant of the Set Covering Routing Problem (SCRP) motivated by post-disaster humanitarian logistics. We consider a hybrid distribution concept in which the majority of transportation is performed by helicopters, while ground transport is limited to the last mile, addressing severe accessibility constraints in disaster-affected regions.

Huayu Li, ZhengXiao He, Xiwen Chen, Jingjing Wang

Learning meaningful representations from medical time series (MedTS) such as ECG or EEG signals is a critical challenge. These signals are often high-dimensional, variable-length and rife with noise. Existing self-supervised approaches, such as Masked Autoencoders (MAEs) are highly effective for pre-training general-purpose encoders. However, they do not exp

Saeed Haddadi

We provide a critical assessment of a recent study applying quantum information concepts, including noisy channels and teleportation fidelity, to hyperon-antihyperon pairs produced in $e^{+}e^{-} \to Y\bar Y$ reactions at BESIII. While the spin density matrix reconstructed from experimental data provides a physically meaningful description of production corr

Saeed Haddadi

We critically examine recent claims [Phys. Rev. D 113, 016024 (2026)] regarding quantum coherence, steering, and non-Markovian dynamics in the hyperon-antihyperon system produced in the process $e^{+} e^{-} \rightarrow \Lambda \bar{\Lambda}$. We argue that the theoretical framework employed in the analyzed work suffers from fundamental physical inconsistenci

Arnaud Zinflou

Generative models for time-series imputation achieve strong reconstruction accuracy, yet provide no finite-sample reliability guarantees, a critical limitation in power systems where imputed values inform dispatch and planning. We introduce SPLICE (Self-supervised Predictive Latent Inpainting with Conformal Envelopes), a modular framework coupling latent gen

Ryan J. Cooke, James W. Johnson, Pasquier Noterdaeme, Max Pettini

We report the discovery of two metastable neutral helium (He I*) absorbers in the Milky Way, and use the upgraded CRyogenic InfraRed Echelle Spectrograph on the Very Large Telescope to determine the helium isotope ratio, $^{3}$He/$^{4}$He, along these sightlines. We have also obtained deeper observations of a third sightline to report a $\lesssim4\%$ precisi

Miguel Saavedra-Ruiz, Charlie Gauthier, Kumaraditya Gupta, Shima Shahfar

We have seen tremendous recent progress in our ability to build "spatio-semantic" representations that enable robots to perform complex reasoning across geometry and semantics. However, the vast majority of these methods lack any ability to perform reasoning across time. This is a desirable property in situations where a robot repeatedly observes an environm

Himanshu Singhal, Suresh Sundaram

Online signature verification (OSV) requires distinguishing skilled forgeries from genuine samples under high intra-class variability and with very few enrollment samples. Existing deep learning methods operate directly on raw temporal sequences, restricting them to 1D architectures and preventing the use of pretrained 2D vision backbones. We bridge this gap

Muhammad Dehan Al Kautsar, Saeed Almheiri, Momina Ahsan, Bilal Elbouardi

There is a significant gap in evaluating cultural reasoning in LLMs using conversational datasets that capture culturally rich and dialectal contexts. Most Arabic benchmarks focus on short text snippets in Modern Standard Arabic (MSA), overlooking the cultural nuances that naturally arise in dialogues. To address this gap, we introduce ArabCulture-Dialogue,

Andrew Woods, Chi-Ren Shyu

Multitenancy increases throughput and reduces costs in cloud-based quantum computing, but concurrent job execution introduces security risks through inter-circuit crosstalk. We characterize the structural predictability of these interference patterns across seven IBM superconducting processors, spanning Heron (r1-r3) and Nighthawk (r1) architectures and five

Nhung Thi-Hong Duong, Mai Ngoc Ho, Tin Van Huynh, Kiet Van Nguyen

In this article, we introduce ViLegalNLI, the first large-scale Vietnamese Natural Language Inference (NLI) dataset specifically constructed for the legal domain. The dataset consists of 42,012 premise-hypothesis pairs derived from official statutory documents and annotated with binary inference labels (Entailment and Non-entailment). It covers multiple lega

Marta F. Zamoro, Álvaro Lozano-Onrubia, Luca Merlo, Samuel Rosende Herrero

Axion-like particles (ALPs) are typically identified as pseudoscalars whose couplings are shift-symmetry invariant with the exception of their couplings to gauge bosons and their mass term. Additionally, the ALP mass $m_a$ is usually assumed to be (much) smaller than the ALP decay constant $f_a$. The latter condition is conservative, at best, and excludes pa

Shurui Zhang, Remo Ruffini

We derive a compact, covariant expression for the relative Lorentz factor of two particles in curved spacetime and apply it to particle decay in Kerr spacetime. This allows us to show that energy conservation in the local center-of-mass frame requires the rest-mass loss of the parent particle to be converted into kinetic energy of the decay products. We veri

Ishan Gupta, Pavlo Buryi

We examine if frontier chat-based large language models (LLMs) adjust their outputs based on neurodivergence (ND) context in system prompts and describe the nature of these adjustments. Specifically, we propose NDBench, a 576-output benchmark involving two frontier models, three system prompt types (baseline, ND-profile assertion, and ND-profile assertion wi

Leonard Barboza Navarro, Maria Carolina Volpato, Alisson Ronieri Cadore, Pierre-Louis de Assis

Electro-optic modulators are key components for photonic quantum computing, particularly in fully cryovenic integrated platforms where low loss and compactness are critical. We present a systematic theoretical investigation of compact dual-layer graphene (DSLG) electro-optic phase modulators integrated on silicon nitride waveguides, with emphasis on cryogeni

Sundas Iqbal, Qing Tian, Danish Ali, Jianping Gou

Person re-identification (Re-ID) aims to match images of the same individual across non-overlapping camera views and remains challenging due to domain shifts caused by variations in illumination, background, camera characteristics, and population distributions. Although supervised models perform well under matched training and testing conditions, their perfo

Gregor Flüchter

We consider nonnegative radially symmetric solutions of the parabolic-elliptic Keller-Segel system \begin{align*} \left\lbrace \begin{array}{r@{}l@{\quad}l} &u_t=\Delta u-\nabla \cdot \big(u\nabla v\big),\\ &0=\Delta v -\mu + u , \\ \end{array}\right. \end{align*} where $\mu$ is the spatial average of $u$, under homogeneous Neumann boundary conditions in a b

Sejal Agarwal, Delara Forghani, Brandon Lit, Thomas Driscoll

Human-Computer Interaction (HCI) is a diverse field bringing together theories and methods from fields such as computer science, psychology, and human factors. Historically, HCI has focused on the human through ``user'' or ``human'' centered design, where the focus was either on information processing or understanding people and their concerns with respect t

Geoff Boeing

Urban planners need up-to-date, global, and consistent street network models and indicators to measure resilience and performance, model accessibility, and target local quality-of-life interventions. This article presents up-to-date street network models and indicators for every urban area in the world. It uses 2025 urban area boundaries from the Global Huma

Emma Andrews, Prabhat Mishra

Quantum machine learning integrates the strengths of quantum computing and machine learning, enabling models to learn complex features using fewer parameters than their classical counterparts. Due to the increasing complexity of quantum machine learning models, it is necessary to verify that the implementation of these models satisfy the design specification

Arend-Jan Quist, Marc Farreras Bartra, Alexis de Colnet, John van de Wetering

Tensor networks and circuits are widely used data structures to represent pseudo-Boolean functions. These two formalisms have been studied primarily in separate communities, and this paper aims to establish equivalences between them. We show that some classes of tensor networks that are appealing in practice correspond to classes of circuits with specific pr

M. Fogliardi, M. Meneghetti, C. Giocoli, L. Moscardini

Galaxy clusters are the most massive gravitationally bound structures in the Universe and key probes of cosmic evolution. The large data volume expected from upcoming surveys requires efficient automated analysis methods for tens of thousands of clusters. We present a study using Convolutional Neural Networks (CNNs) to infer cluster structural parameters fro

Edward Hardy, Noelia Sánchez González, Henry Stubbs, Lorenzo Tranchedone

The growth of adiabatic density perturbations during an era of early matter domination induces $\mathcal{O}(1)$ fluctuations in pre-inflationary QCD axion dark matter across a broad, string-theory-motivated parameter space. Remarkably, at $\Lambda$CDM matter-radiation equality the scale of these perturbations coincides with the quantum Jeans scale, so they c

Lisa-Marie Zessner, Robert H. Cameron, Sami K. Solanki, Damien Przybylski

Solar prominences are cool and dense plasma structures floating in the hot solar corona. They are ubiquitous features in the solar atmosphere, but their formation mechanism is still unclear. Here we perform comprehensive fully three-dimensional numerical simulations of prominence formation including the physics necessary to describe all atmospheric layers of

Ding Gu, Zhanpeng Fu, Zhong Wang

Nonreciprocity can profoundly alter the spectra and dynamics of open quantum systems, yet its impact on the long-time steady-state phases of matter has remained largely unexplored. Here we show that the interplay of nonreciprocity, symmetry defects, and spatial boundaries can generate phases beyond the standard spontaneous-symmetry-breaking paradigm. We demo

Sebastian Zieba, Laura Kreidberg, Brandon P. Coy, Aaron Bello-Arufe

JWST has opened a new era in the study of rocky exoplanets, enabling direct characterization of their surfaces with mid-infrared spectroscopy. Different types of rock have distinct spectral features that are diagnostic of the chemical composition and other physical properties like surface texture. Measurements of these features can provide valuable clues abo

Jonas Jäger, Paolo Braccia, Pablo Bermejo, Manuel G. Algaba

Despite rapid recent advances in quantum machine learning, the field is in many ways stuck. Existing approaches can exhibit serious limitations, and we still lack learning frameworks that are simple, interpretable, scalable, and naturally suited to quantum data. To address this, here we introduce quantum Gaussian processes, a Bayesian framework for learning

Cheng Li, Juxiang Li, Shufang Su, Wei Su

The 2HDM+S is a singlet extension of the Two-Higgs-Doublet Model (2HDM), which offers rich collider phenomenology. In this paper, we parametrize the 2HDM+S with the Higgs masses and mixing angles, which provide a model-independent framework to study the collider signature. Under five benchmark scenarios, we obtain the 95\% C.L. exclusion regions in the Type-

Shengda Liu, Yu-Zhe Liu, Keyu Tao

We define and study ordinary differential equations (ODEs) for functions valued in a Banach module $V$ over a finite-dimensional $\Bbbk$-algebra $\mathitΛ$ by using the tensor of Banach modules. Furthermore, we show that the solution space of a homogeneous linear ODE as above is shown to be a finitely generated $\mathitΛ$-submodule.

Sakshi Bahamnia, Thomas Bilitewski

We study the generation of metrologically useful entanglement in a three-level (spin-1) system naturally realized in arrays of dipole-interacting Rydberg atoms confined in optical tweezers. In the spin-quadrupolar operator basis, the interaction Hamiltonian decomposes into effective SU(2) subspaces, within which quench dynamics from product initial states ge

Arvind Rajaraman, Alexander Stewart, Tim M. P. Tait

We present an explicit model of leptogenesis via annihilogenesis in which two right-handed Majorana neutrinos couple to the Standard Model lepton doublets and Higgs, and acquire a large mass shift during a strong first-order phase transition of an additional scalar singlet. As bubbles of true vacuum expand, the $\chi_a$ are reflected off the walls and confin

Heiko Georg Menzler, Rafał Świętek, Mari Carmen Bañuls, Fabian Heidrich-Meisner

We study the onset of weak ergodicity violations in closed quantum many-body systems and focus on cases in which they occur through a transition that is controlled by a model parameter. Our analysis is based on representing quantum systems in Fock space and utilizes graph-theoretical measures. As a main result, we show that the recently introduced graph-ener

David Izquierdo-Villalba, Melanie Habouzit, Matteo Bonetti, Silvia Bonoli

In the hierarchical paradigm of galaxy formation, central massive black holes (MBHs) are expected to coalesce after the merger of their host galaxies. One of the main goals of the Laser Interferometer Space Antenna (LISA) is to constrain the origin and growth of MBHs through their merger rates and mass distribution. Predicting MBH merger rates requires not o

Achilleas Lazarides

Local kinetic constraints in quantum many-body systems can generate slow dynamics or complete many-body localisation. Here we focus on a modification of the quantum East model: Inspired by random matrix theory, we randomise the connectivity in Fock space (rendering it nonlocal in real space) while preserving its organisation into neighbouring magnetisation s

Martijn Janse, M. Luisa Mattana, Julian van Doorn, Eli van der Bent

Levitation of microscopic objects in vacuum combines exceptional environmental isolation with precise control of their dynamics, pushing the limits of sensing and macroscopic quantum physics. In particular, magnetic levitation allows a large range of particle sizes, while avoiding detrimental effects from high-intensity optical trapping beams and electric fi

Bruno Bucciotti, Paolo Creminelli, Alessandro Longo, Warin Patrick McBlain

It is usually assumed that a healthy EFT should not allow superluminal propagation. In the presence of gravity, however, the notion of superluminality becomes subtle, since there is no invariant way to compare with an underlying Minkowski light cone. One can instead resort to an asymptotic criterion: whether the EFT can induce signal propagation faster than

Zhi Li, Raz Firanko, Timothy H. Hsieh

We propose a unifying framework for characterizing pure and mixed state phases of matter across equilibrium, non equilibrium, and metastable regimes. We introduce the concept of locally stable states, defined by the operational property that any local operation (including post selection) can be reversed by a local channel. We prove that local stability is eq

Xin Zhou, Dingkang Liang, Xiwu Chen, Feiyang Tan

Driving world models serve as a pivotal technology for autonomous driving by simulating environmental dynamics. However, existing approaches predominantly focus on future scene generation, often overlooking comprehensive 3D scene understanding. Conversely, while Large Language Models (LLMs) demonstrate impressive reasoning capabilities, they lack the capacit

Furkan Akbaba, Olcay Plevne, Timur Şahin, Sena Aleyna Şentürk

$\omega$~Centauri, the most massive globular cluster in the Milky Way, exhibits a level of stellar population complexity that has long resisted a unified chemical characterisation. We exploit high-resolution near-infrared spectroscopy from the Milky Way Mapper survey (MWM DR19) to construct one of the largest homogeneously analysed samples of $\omega$~Cen me

Hao Geng, Moritz Merz, Lisa Randall

The Karch-Randall braneworld concerns the physics of an AdS$_{d}$ brane embedded in an ambient gravitational AdS$_{d+1}$ spacetime. The gravitational theory induced on the AdS$_{d}$ brane has a very light but massive graviton. It has been established that the zero graviton mass limit of the $d$-dimensional graviton propagator is smooth at tree-level. Further

Vinayak Gupta, Chih-Hao Lin, Shenlong Wang, Anand Bhattad

Reconstructing 3D scenes from sparse, unposed images remains challenging under real-world conditions with varying illumination and transient occlusions. Existing methods rely on scene-specific optimization using appearance embeddings or dynamic masks, which requires extensive per-scene training and fails under sparse views. Moreover, evaluations on limited s

Hao Chen, Jiaming Liu, Zhonghao Yan, Nuowei Han

Robotic foundation models require reasoning over complex visual scenes to execute adaptive actions in dynamic environments. While recent studies on latent-reasoning Vision-Language-Action (VLA) models have demonstrated the capability to capture fine-grained physical dynamics, they remain predominantly confined to static imitation learning, severely limiting

Sebastian J. Morris, Martin Gazo, Simon M. Fischer, Haoyu Zhang

Relaxation of far-from-equilibrium quantum fluids, intimately related to the emergence of long-range order, is theoretically associated with the decay of a turbulent isotropic tangle of vortex lines. We observe and study such decaying quantum turbulence in a homogeneous 3D atomic Bose gas. Using matter-wave techniques to magnify the gas density distribution,

Jiawei Yang, Zhengyang Geng, Xuan Ju, Yonglong Tian

We show that Fr\'echet Distance (FD), long considered impractical as a training objective, can in fact be effectively optimized in the representation space. Our idea is simple: decouple the population size for FD estimation (e.g., 50k) from the batch size for gradient computation (e.g., 1024). We term this approach FD-loss. Optimizing FD-loss reveals several

George K. Leontaris, Pramod Shukla

In this review, we discuss the relevance and impact of studying Calabi-Yau threefolds in the context of global model building in string phenomenology. First, taking a phenomenologist-friendly approach, we review how the topologies of the various divisors and curves of the compactifying CY threefolds play a crucial role for generating the various ``suitable"

Iván Salvador-García, Gianluca Calcagni

This is a first study of the cosmology of classical fractional gravity, a nonlocal proposal endowed with self-adjoint fractional d'Alembertian operators which serves as the basis for an ultraviolet-complete theory of quantum gravity. We derive the classical covariant nonlocal equations of motion for an arbitrary fractional exponent $\gamma$ and reduce them t

J. E. Ebot, Lorenzo Pizzino, Sam Mardazad, Johannes S. Hofmann

Boosting superconductivity by metallic reservoirs is the essence of Kivelson's bilayer proposal. One layer provides pairing to the electrons, while the weakly coupled metal provides additional phase coherence to those pairs by mediating extended-range pair-pair coupling. Demonstrating significant and unambiguous performance gains with strong-coupling methods

Mingyang Liu, Gabriele Farina, Asuman Ozdaglar

Most familiar equilibrium concepts, such as Nash and correlated equilibrium, guarantee only that no single player can improve their utility by deviating unilaterally. They offer no guarantees against profitable coordinated deviations by coalitions. Although the literature proposes solution concepts that provide stability against multilateral deviations (\emp

Rishi G. Gopalakrishnan, Srimayi Korrapati, Sumanta Tewari

We investigate the intrinsic anomalous thermal Hall effect in d-wave altermagnets, where a transverse heat current is generated by a longitudinal temperature gradient in the absence of a magnetic field, with the leading response proportional to $(\nabla T)^3$. In these systems, the intrinsic Berry curvature-driven linear and thermal quantum-metric-driven sec

Matthias Roeper, Robin Buschbeck, Jakob Wetzel, Tobias Ritschel

Multiferroic domain walls in functional oxides exhibit properties distinct from the bulk and are increasingly exploited as active elements in nanoelectronic and photonic devices. Deterministic control of domain populations has typically remained limited to local control, or removal with temperature. Here we demonstrate continuous, reversible manipulation of

Eyon Jang, Damon Falck, Joschka Braun, Nathalie Kirch

Reinforcement learning (RL) has become essential to the post-training of large language models (LLMs) for reasoning, agentic capabilities and alignment. Successful RL relies on sufficient exploration of diverse actions by the model during training, which creates a potential failure mode: a model could strategically alter its exploration during training to in

Tao Ge, Baolin Peng, Hao Cheng, Jianfeng Gao

Realistic long-horizon productivity work is strongly conditioned on user-specific computer environments, where much of the work context is stored and organized through directory structures and content-rich artifacts. To scale synthetic data creation for such productivity scenarios, we introduce Synthetic Computers at Scale, a scalable methodology for creatin

Himanshu Pandey, Ratikanta Behera

In recent years, physics-informed neural networks (PINNs) have gained significant attention for solving differential equations, although they suffer from two fundamental limitations, namely, spectral bias inherent in neural networks and loss imbalance arising from multiscale phenomena. This paper proposes an adaptive wavelet-based PINN (AW-PINN) to address t

Andrea Dunn Beltran, Daniel Rho, Aarav Mehta, Xinqi Xiong

Bronchoscopic navigation relies on registering endoscopic video to a preoperative CT scan, but respiratory motion deforms the airway by 5-20 mm, creating CT-to-body divergence that limits localization accuracy. In practice, this is mitigated through breath-hold protocols, which attempt to match the intraoperative anatomy to a static CT, but are difficult to

Lincan Li, Zheng Chen, Yushun Dong

Electroencephalogram (EEG) signals are vital for automated seizure detection, but their inherent noise makes robust representation learning challenging. Existing graph construction methods, whether correlation-based or learning-based, often generate redundant or irrelevant edges due to the noisy nature of EEG data. This significantly impairs the quality of g

Emma Andrews, Sahan Sanjaya, Prabhat Mishra

Machine learning models can learn from data samples to carry out various tasks efficiently. When data samples are adversarially manipulated, such as by insertion of carefully crafted noise, it can cause the model to make mistakes. Quantum machine learning models are also vulnerable to such adversarial attacks, especially in image classification using variati

Haidong Zhao, Nikolaos Georgantas

Machine learning (ML) inference serving systems host deep neural network (DNN) models and schedule incoming inference requests across deployed GPUs. However, limited support for task prioritization and insufficient latency estimation under concurrent execution may restrict their applicability in on-premises scenarios. We present \emph{Strait}, a serving syst

Susanna F. de Rezende, David Engström, Yassine Ghannane, Kilian Risse

We prove superpolynomial length lower bounds for the semantic tree-like Frege refutation system with bounded line size. Concretely, for any function $n^{2-\varepsilon} \leq s(n) \leq 2^{n^{1-\varepsilon}}$ we exhibit an explicit family $\mathcal{A}$ of $n$-variate CNF formulas $A$, each of size $|A| \le s(n)^{1+\varepsilon}$, such that if $A$ is chosen unifo

Sichang Steven He, Calvin Ardi, Ramesh Govindan, Harsha V. Madhyastha

Many recent news reports have claimed that content generated by large language models (LLMs) is taking over the web. However, these claims are typically not based on a representative sample of the web and the methodology underlying them is often opaque. Moreover, when aiming to minimize the chances of falsely attributing human-authored content to LLMs, we fi

Alexander Chunikhin

A new class of Semantic Numeration Systems, namely, positive rational Semantic Numeration Systems is introduced. For cardinal semantic operators, differences in the formation of carry (common carry) and remainders are defined. The properties of positive rational Semantic Numeration Systems as dynamical systems are formulated and illustrated through analytica

Alberto Cavallo, Irena Matkovič

We advance Matkovi\v{c} ideas, originally applied to complete the classification of tight structures on small Seifert fibred $L$-spaces, to show the existence of contact structures on Brieskorn spheres which are tight and zero-twisting. This uncovers a phenomenon that has never appeared in literature before: namely, that a contact structure $\xi$ on a 3-mani