March 2026 arXiv papers — page 59

Belle, Belle II Collaborations, :, M. Abumusabh

We report a search for the rare decay $B^{+} \rightarrow K^{+} τ^{+} τ^{-}$ using $1.2 \times 10^9$ $Υ(4S)$ mesons produced near threshold in electron-positron collisions and collected by the Belle and Belle~II experiments. We fully reconstruct the hadronic decay of one $B$ meson produced in the $Υ(4S)\rightarrow B^{+} B^{-}$ decay, and search for $B^{\pm}\r

Timothy H. Boyer

Two criteria for the spectra of relativistic waves are proposed. Zero-point radiation provides the identity representation of the conformal group in Minkowski spacetime. Thermal radiation provides the irreducible representation of the conformal group in Minkowski spacetime which involves exactly one scaling parameter (the temperature) which is also time-stat

Yunes Alqudsi

Drone light shows (DLShows) represent a rapidly growing application of swarm robotics, creating captivating aerial displays through the synchronized flight of hundreds or thousands of unmanned aerial vehicles (UAVs) as environmentally friendly and reusable alternatives to traditional pyrotechnics. This domain presents unique challenges in optimally assigning

Markó Horváth

The cyclic hoist scheduling problem originates in electroplating lines, where a single or multiple hoists transport parts between processing tanks subject to technological constraints. The objective is typically to determine a cyclic sequence of hoist movements that minimizes the cycle time while satisfying travel and processing constraints. Although the pro

Emir Sezik, Callum Britton, Alex Touma, Gunnar Pruessner

The visit probability, quantifying whether a particle has reached a given point for the first time by a specified time, provides access to various extreme value statistics and serves as a fundamental tool for characterising active matter models. However, previous studies have largely neglected how the visit probability depends on the internal degree of freed

Gyungho Maeng, Subeen Lim, Bonggeun Shong, Yeonghun Lee

As interconnect dimensions continue to shrink, the industry-standard copper faces a critical increase in resistivity, presenting a significant hurdle to overall device performance. To overcome this limitation, this work investigates the potential of ruthenium (Ru)-based compounds, encompassing binary, ternary, and quaternary systems, as viable alternatives t

Lizxandra Flores-Rivera, Natascha Manger, Michiel Lambrechts, Mario Flock

High-resolution ALMA observations have revealed asymmetric dust crescents in several protoplanetary disks, suggesting efficient dust trapping mechanisms potentially linked to gas vortices. While such features have been associated with vortices--whether induced by massive planets, turbulence , or other disk processes--their origin remains unclear. In this stu

Manuel Zahn, Elke Beyreuther, Iuliia Kiseleva, Julius Ratzenberger

The comprehensive description of both the electrical transport along conductive domain walls (CDWs) in lithium niobate (LNO) single crystals and the charge injection at the interfacing metal electrodes, emerged to be a complex challenge. Recently, a heuristic evaluation allowed to postulate the "R2D2" equivalent-circuit model (consisting of two paral

Anooj Thayyil Raveendran, Subham Adak, Artem Shydliukh, Natalja Redinger

Wide-field mid-infrared photothermal (MIP) imaging offers rapid labelfree chemical contrast for biomedical and polymer analysis. However, its field of view (FOV) is limited by the pulse intensity of conventional infrared lasers. Here, we present a wide-field MIP microscope that uses a high-power free-electron laser (FEL) rather than a quantum cascade laser (

Qi-Cheng Wu, Yan-Hui Zhou, Biao-liang Ye, Tong Liu

Exceptional points (EPs) in non-Hermitian systems give rise to enhanced sensitivity and chiral state transfer, which are important for quantum technologies. Although parameter trajectories encircling EPs can control symmetric and chiral state transfer, their robustness against practical perturbations and their role in quantum sensing remain largely unexplore

Nizam Kadir

Monolithic Large Language Models (LLMs) used in educational dialogue often behave as "black boxes," where pedagogical decisions are implicit and difficult to audit, frequently violating instructional constraints by providing answers too early. We introduce the Ensemble of Specialized LLMS (ES-LLMS) architecture that separates decision-making from wor

Junkai Yang, Qirui Wang, Yaoqing Jin, Shuai Ma

Retrieving partially relevant segments from untrimmed videos remains difficult due to two persistent challenges: the mismatch in information density between text and video segments, and limited attention mechanisms that overlook semantic focus and event correlations. We present KDC-Net, a Knowledge-Refined Dual Context-Aware Network that tackles these issues

J. L. Costa, E. Santos, A. Y. M. Tani, J. B. S. Mendes

In this work, we experimentally investigate spin and orbital torque phenomena using the spin-torque ferromagnetic resonance (ST-FMR) technique in a series of bilayer systems composed of different normal metal (NM) materials. Permalloy (Py) and Ni were employed as ferromagnetic (FM) layers to probe the spin and orbital torque responses, respectively. For the

Paul Durbin, Zifei Yin

Surface roughness plays a substantial role in many flows for which Reynolds averaged prediction is needed. The transformation used in the k-omega0 model is extended to rough surfaces by adding an effective origin. The log-layer offset is computed as a function of this effective origin, thereby creating a correspondence between effective origin and equivalent

Stefan Kolb, Milen Yakimov

We prove that the star product for quantum symmetric pair coideal subalgebras is short. We apply this result to obtain new conceptual proofs, from first principles, of several fundamental facts about quantum symmetric pairs. In particular, we establish the existence of the algebra anti-automorphism $σ_τ$ and of the bar involution, without making use of the q

Annyun Das, Kanu Sinha

We develop a microscopic description of the fluctuation-mediated Casimir-Polder (CP) shifts on a 'test' two-level atom placed near a two-dimensional atomic array of two-level atoms. We derive the resonant and off-resonant CP potentials experienced by the excited test atom using fourth-order perturbation theory, under the assumption that the test atom

Eric Gourgoulhon, Alexandre Le Tiec, Marc Casals

The horizon multipole moments of a Kerr black hole are computed from two distinct definitions that have been proposed in the literature. The first one [Ashtekar et al., Class. Quantum Grav. 21, 2549 (2004)] regards axisymmetric isolated horizons, while the second one [Ashtekar et al., J. High Energ. Phys. 2022, 28 (2022)] applies to generic (i.e., not necess

Zheng Zhao, Weifeng Zhuang, Yanwu Gu, Peng Qian

Pre-execution calibration is a major bottleneck for operating superconducting quantum processors, and qubit frequency allocation is especially challenging due to crosstalk-coupled objectives. We establish that the widely-used Snake optimizer is mathematically equivalent to Block Coordinate Descent (BCD), providing a rigorous theoretical foundation for this s

Alexandre Belin, Scott Collier, Lorenz Eberhardt, Diego Liska

We explore the sum over topologies in AdS$_3$ quantum gravity and its relationship with the statistical interpretation of the boundary theory. We formulate a statistical version of the conformal bootstrap that systematizes the universal statistical properties of high-energy CFT$_2$ data. We identify a series of surgery moves on bulk manifolds that precisely

Justin Airas, Bin Zhang

Implicit solvent models (ISMs) promise to deliver the accuracy of explicit solvent simulations at a fraction of the computational cost. However, despite decades of development, their accuracy has remained insufficient for many critical applications, particularly for simulating protein folding and the behavior of intrinsically disordered proteins. Developing

Upamanyu Moitra

We consider the effective theory of heterotic strings in two spacetime dimensions, in a double field theory-inspired formalism, manifestly consistent with $T$-duality in string theory. Restricting the gauge group to a single $\mathrm{U}(1)$, we study the charged black hole solution and perform a precise analysis of the properties of the dual geometry with th

Lorenzo Crippa, Gautam Rai, Dumitru Călugăru, Haoyu Hu

In twisted bilayer graphene, a unified understanding of the mechanisms governing temperature-dependent electronic spectra and thermodynamic properties remains controversial despite extensive theoretical efforts. Here, we present a comprehensive theoretical framework that quantitatively accounts for scanning tunneling spectroscopy, quantum twisting microscopy

Fabio Marino, Sinan Moura Soysüren, Marcus Sperling

A magnetic quiver framework is proposed for studying maximal branches of 3d orthosymplectic Chern--Simons matter theories with $\mathcal{N} \geq 3$ supersymmetry, arising from Type IIB brane setups with O3 planes. These branches are extracted via brane moves, yielding orthosymplectic $\mathcal{N}=4$ magnetic quivers whose Coulomb branches match the moduli sp

Rabsan Galib Ahmed, Adithi Udupa, Giulia Ferrini

We introduce a new family of multi-mode, rotationally symmetric bosonic codes inspired by the group-theoretic framework of [Phys. Rev. Lett. 133, 240603 (2024)]. Such a construction inverts the traditional paradigm of code design by identifying codes from the requirement that a group of chosen logical gates should be implemented by means of physically simple

Marco Fumero, Luca Moschella, Emanuele Rodolà, Francesco Locatello

Neural networks transform high-dimensional data into compact, structured representations, often modeled as elements of a lower dimensional latent space. In this paper, we present an alternative interpretation of neural models as dynamical systems acting on the latent manifold. Specifically, we show that autoencoder models implicitly define a latent vector fi

Takuya Shibayama, Hideaki Imamura, Katsuhiko Nishimra, Kohei Shinohara

Crystal structure prediction (CSP) is crucial for identifying stable crystal structures in given systems and is a prerequisite for computational atomistic simulations. Recent advances in neural network potentials (NNPs) have reduced the computational cost of CSP. However, searching for stable crystal structures across the entire composition space in multicom

Wenhui Huang, Xin-Chi Zhou, Libo Zhang, Jiawei Zhang

Anderson localization physics features three fundamental types of eigenstates: extended, localized, and critical, with the third one exhibiting the exotic properties in-between the former two. Confirming the presence of critical states is challenging, as it typically necessitates either advancing the analysis to the thermodynamic limit or identifying a unive

Yang Sun, Haoyu Wang, Christopher M. Poskitt, Jun Sun

The emergence of Autonomous Vehicles (AVs) has spurred research into testing the resilience of their perception systems, i.e., ensuring that they are not susceptible to critical misjudgements. It is important that these systems are tested not only with respect to other vehicles on the road, but also with respect to objects placed on the roadside. Trash bins,

Yu-Ting Kuang, Jing-Zhi Zhou, Zhe Chang, Di Wu

In June 2023, multiple pulsar timing array (PTA) collaborations provided evidence for the existence of a stochastic gravitational-wave background (SGWB). As a significant source of the SGWBs, scalar-induced gravitational waves (SIGWs) receive extensive attention. We explore the influence of anisotropic primordial power spectra on second-order SIGWs and deriv

Philipp Wesp, Robbie Holland, Vasiliki Sideri-Lampretsa, Sergios Gatidis

Vision foundation models (FMs) achieve state-of-the-art performance in medical imaging. However, they encode information in abstract latent representations that clinicians cannot interrogate or verify. The goal of this study is to investigate Sparse Autoencoders (SAEs) for replacing opaque FM image representations with human-interpretable, sparse features. W

Kaya Alpturer, Constantine Doumanidis, Aviv Zohar

Peer-discovery protocols within P2P networks are often vulnerable: because creating network identities is essentially free, adversaries can eclipse honest nodes or partition the overlay. This threat is especially acute for blockchains, whose security depends on resilient peer connectivity. We present AetherWeave, a stake-backed peer-discovery protocol that t

Zebang Shen, Ya-Ping Hsieh, Niao He

Diffusion models often generate novel samples even when the learned score is only \emph{coarse} -- a phenomenon not accounted for by the standard view of diffusion training as density estimation. In this paper, we show that, under the \emph{manifold hypothesis}, this behavior can instead be explained by coarse scores capturing the \emph{geometry} of the data

Qianlong Lan, Anuj Kaul

Deploying large language models (LLMs) as autonomous browser agents exposes a significant attack surface in the form of Indirect Prompt Injection (IPI). Cloud-based defenses can provide strong semantic analysis, but they introduce latency and raise privacy concerns. We present the Cognitive Firewall, a three-stage split-compute architecture that distributes

Yongseok Jeon, Sara Shashaani

We concern computer model calibration problem where the goal is to find the parameters that minimize the discrepancy between the multivariate real-world and computer model outputs. We propose to solve an approximation using signed residuals that enables a root finding approach and an accelerated search. We characterize the distance of the solutions to the ap

Graham Barnes, Karin Dissauer

Solar flare ribbons, manifesting as transient brightenings in the chromosphere, are believed to trace out the footpoints of magnetic field lines that are reconnecting higher in the solar atmosphere. These field lines lie in a separatrix or quasi-separatrix layer that separates domains of different magnetic connectivity and hence forms a natural location for

Mingqi Gao, Sijie Li, Jungong Han

This technical report explores the MOSEv2 track of the PVUW 2026 Challenge, which targets complex semi-supervised video object segmentation. Built on SAM~3, we develop an automatic re-prompting framework to improve robustness under target disappearance and reappearance, severe transformation, and strong same-category distractors. Our method first applies the

Robin J. Williams, Mahmoud Saad Abouamer, Petar Popovski

Prediction of wireless channels and their statistics is a fundamental procedure for ensuring performance guarantees in wireless systems. Statistical radio maps powered by Gaussian processes (GPs) offer flexible, non-parametric frameworks, but their performance depends critically on the choice of mean and covariance functions. These are typically learned from

Vera C Rubin Observatory Team, Tatiana Acero Cuellar, Emily Acosta, Christina L Adair

We present Rubin Data Preview 1 DP1, the first data from the NSF DOE Vera C Rubin Observatory, comprising raw and calibrated single epoch images, coadds, difference images, detection catalogs, and ancillary data products. DP1 is based on 1792 optical near infrared exposures acquired over 48 distinct nights by the Rubin Commissioning Camera LSSTComCam on the

Ali Akram

Retinal diseases remain among the leading preventable causes of visual impairment worldwide. Automated screening based on fundus image analysis has the potential to expand access to early detection, particularly in underserved populations. This paper presents a reproducible deep learning pipeline for binary retinal disease risk classification from publicly a

Anand Swaroop

Grokking-the phenomenon where validation accuracy of neural networks on modular addition of two integers rises long after training data has been memorized-has been characterized in previous works as producing sinusoidal input weight distributions in transformers and multi-layer perceptrons (MLPs). We find empirically that ReLU MLPs in our experimental settin

Aueaphum Aueawatthanaphisut, Kuepon Auewattanapisut

Adapting large-scale foundation models to new domains with limited supervision remains a fundamental challenge due to latent distribution mismatch, unstable optimization dynamics, and miscalibrated uncertainty propagation. This paper introduces an uncertainty-aware probabilistic latent transport framework that formulates domain adaptation as a stochastic geo

David R. Hatch, Leonhard A. Leppin, Mike T. Kotschenreuther, Saeid Houshmandyar

Global and local linear gyrokinetic simulations of 42 pedestal equilibria from three DIII-D discharges are used to investigate pedestal stability and its impact on pedestal structure and confinement. Microtearing modes (MTMs) and kinetic ballooning modes (KBMs) represent the main ion scale instabilities. For all three discharges, MTMs lie near a stability bo

Nan Cui, Wendy Hui Wang, Yue Ning

Large Language Models (LLMs) have introduced new capabilities to recommender systems, enabling dynamic, context-aware, and conversational recommendations. However, LLM-based recommender systems inherit and may amplify social biases embedded in their pre-training data, especially when demographic cues are present. Existing fairness solutions either require ex

Mohammadreza Narimani, Alireza Pourreza, Ali Moghimi, Parastoo Farajpoor

Accurate and timely crop yield estimation is critical for global food security, agricultural policy, and farm management. The Copernicus Sentinel-2 satellite constellation, with high spatial, temporal, and spectral resolution, has transformed agricultural monitoring by enabling field- and sub-field-scale analysis. This review synthesizes recent advances in S

Fernando Argentieri, Andrea Ulliana

We prove that all ergodic automorphisms of the $N$-dimensional torus with two dimensional center are stably ergodic. This includes all ergodic automorphisms in dimension $N\leq 5$ or $N=7$. This generalizes a previous result of Rodriguez-Hertz, that required an additional algebraic condition on the carachteristic polynomial of the linear automorphism. The co

Jinhao Nie, Guangchi Zhang, Miao Cui, Hao Fu

In this letter, we address spatially non-stationary near-field channel estimation for extremely large-scale multiple-input multiple-output (XL-MIMO) systems with a hybrid combining architecture. One key challenge in the considered problem lies in that conventional channel estimation algorithms typically struggle to effectively identify and adapt to the parti

Anssi Lahtinen

We develop a theory of umkehr maps for twisted generalized homology theories. In this theory, interesting umkehr maps, including generalizations of important classical ones, are induced by cartesian morphisms of a certain category opfibred over the category of spaces and continuous maps, making it possible to access them through universal properties.

Hyun-June Jang, Peuli Nath, Yuqin Wang, Mingoo Kim

A major barrier to decentralized, near-patient diagnostics is the lack of a signal transduction modality that is both analytically precise and accessible at the point of care. Optical readouts remain instrument-dependent and difficult to miniaturize, while compact electrochemical readouts are prone to matrix-derived signal distortion, limiting their biomarke

Likun Cao, Jie Hua, James Evans

This paper investigates the mechanisms underlying scientific stratification in the era of transition from elite to mass science. Existing scholarship has largely examined scientific stratification through the Matthew effect framework at the individual, institutional, and lineage levels, but this theoretical lens has grown limited in today's academic landscap

Daniel Burgarth, Paolo Facchi

A quantum particle on a circle in a quadratic potential exhibits a spectrum that is not harmonic, despite having all algebraic properties of the quantum harmonic oscillator. This raises the question where the usual algebraic argument -- implying integer gaps -- fails. The answer is illuminating and covers a surprisingly rich range of physical phenomena for s

Maxwell Shepherd

Live streaming platforms host interconnected communities of content creators whose audiences overlap and interact in ways that are poorly understood at fine temporal resolution. We present a descriptive longitudinal study of audience behavior within a creator ecosystem, analyzing 2.9 million minute-by-minute viewership observations across 7,762 livestreams f

Md. Kamrul Hossain, Walid Aljoby

Intent-Based Networking (IBN) aims to simplify operating heterogeneous infrastructures by translating high-level intents into enforceable policies and assuring compliance. However, dependable automation remains difficult because (i) realizing intents from ambiguous natural language into controller-ready policies is brittle and prone to conflicts and unintend

Florica C. Cîrstea, Maria Fărcăşeanu

Under sharp conditions, we prove the existence and refined asymptotic behaviour near zero (resp., at infinity) for all positive radial solutions to elliptic equations such as \begin{equation}\label{eq11} \tag{*} \mathbb L_{\rho,\lambda}(u)=\Delta u+ (2-N-2\rho)\, \frac{x\cdot \nabla u}{|x|^2}+ \frac{\lambda}{|x|^2}u=|x|^{\theta}\,u^q\, |\nabla u|^m\quad \mbo

Florian Rein, Stefan. G. Llewellyn Smith, William. R. Young

We investigate the competition between horizontal convection (HC) and Rayleigh-B\'enard convection (RBC) in a fluid layer subject to a uniform destabilizing buoyancy flux at the bottom and a horizontally varying buoyancy distribution at the top. The RBC forcing imposes negative horizontal mean vertical buoyancy gradients at the top and bottom of the fluid la

Patrick Loic Foalem, Leuson Da Silva, Foutse Khomh, Heng Li

Logging plays a central role in ensuring reproducibility, observability, and reliability in machine learning (ML) systems. While logging is generally considered a good engineering practice, poorly designed logging can negatively affect experiment tracking, security, debugging, and system performance. In this paper, we present an empirical study of logging sm

Haoxuan, Zhou, X. Joan Hu, Yi Xiong

In attempt to advance the current practice for assessing and predicting the primary ovarian insufficiency (POI) risk in female childhood cancer survivors, we propose two estimating function based approaches for age-specific logistic regression. Both approaches adapt the inverse probability of censoring weighting (IPCW) strategy and yield consistent estimator

Ning Zhu

Unsupervised medical anomaly detection is severely limited by the scarcity of normal training samples. Existing methods typically train dedicated models for each dataset or disease, requiring hundreds of normal images per task and lacking cross-modality generalization. We propose Semantic Iterative Reconstruction (SIR), a framework that enables a single univ

Pengfei Zhu, Julien Lecompagnon, Mathias Ziegler, Clemente Ibarra-Castanedo

Wavefront-free behavior and the resulting quantification difficulties are intrinsic limitations of vibrothermography due to the diffusive nature of thermal fields. This work proposes a generalized virtual wave reconstruction framework to address the absence of propagation features in thermal diffusion-wave fields and its impact on quantitative defect charact

Bibek Tiwari, Yuanyuan Ni, Xiaoshan Xu

Reliable organic ferroelectrics for memory applications require extreme endurance under repeated electrical switching. Here we demonstrate exceptional fatigue resistance in highly crystalline 2-methylbenzimidazole (MBI) films grown by low-temperature deposition followed by restrained crystallization (LDRC) in a simple Pt/MBI/Pt capacitor geometry. Switching

Majd Latah, Kubra Kalkan

Traditional network experiments focus on validation through either simulation or emulation. Each approach has its own advantages and limitations. In this work, we present a new tool for next-generation network experiments created through Artificial Intelligence (AI) coding agents. This tool facilitates hybrid network experimentation through simulation and em

Peterson Yuhala, Mpoki Mwaisela, Pascal Felber, Valerio Schiavoni

Processing-in-memory (PIM) architectures bring computation closer to data, reducing the processor-memory transfer bottleneck in traditional processor-centric designs. Novel hardware solutions, such as UPMEM's in-memory processing technology, achieve this by integrating low-power DRAM processing units (DPUs) into memory DIMMs, enabling massive parallelism and

Alejandro Ayala, Bruno S. Lopes, Ricardo L. S. Farias, Luis C. Parra

We explore isospin imbalanced strongly interacting matter within the two-flavor Linear Sigma Model with quarks, an effective model for low-energy QCD. At one loop order, including quark, pion, and sigma fluctuations while respecting chiral symmetry, we find that the formation of an isospin condensate necessarily gives rise to a Goldstone mode. This mode enfo

Mohammadjavad Azarm, Rizwan Asif, Alessandra Milloch, Francesco Proietto

Understanding how electronic excitations evolve across space and time is essential for revealing the microscopic processes underlying quantum and optoelectronic materials. However, existing approaches cannot simultaneously resolve ultrafast coherent dynamics with microscopic spatial information: pump-probe microscopy lacks access to quantum coherence, while

Paul Bergold, Giovanni Manfredi, Cesare Tronci

Mixed quantum-classical models are widely used to reduce the computational cost of fully quantum simulations. However, their general applicability across different classes of problems remains an open question. Here, we address this issue for systems featuring spin-orbit coupling. In particular, we study the interaction dynamics of quantum spin-1/2 and classi

Omar Zamzam, Takfarinas Medani, Chinmay Chinara, Richard Leahy

Automated seizure detection from long-term clinical videos can substantially reduce manual review time and enable real-time monitoring. However, existing video-based methods often struggle to generalize to unseen subjects due to background bias and reliance on subject-specific appearance cues. We propose a joint-centric attention model that focuses exclusive

Mohsen Sahraei Ardakani, Rui Song

Curriculum learning improves reinforcement learning (RL) efficiency by sequencing tasks from simple to complex. However, many self-paced curriculum methods rely on computationally expensive inner-loop optimizations, limiting their scalability in high-dimensional context spaces. In this paper, we propose Self-Paced Gaussian Curriculum Learning (SPGL), a novel

Yen-Chia Hsu, Despoina Touska

This report describes a dataset for industrial smoke segmentation, published on a figshare repository (https://doi.org/10.21942/uva.31847188). The dataset is licensed under CC BY 4.0.

Kimia Forghani, Suraj Raval, Lamar Mair, Axel Krieger

Singularities in robotic and dynamical systems arise when the mapping from control inputs to task-space motion loses rank, leading to an inability to determine inputs. This limits the system's ability to generate forces and torques in desired directions and prevents accurate trajectory tracking. This paper presents a control barrier function (CBF) framework

Heechan Yuk, Xinyu Dai, Natalie Kovacevic

AGNs are known to be variable in almost all wavelengths and timescales. The shortest variability timescale of AGNs can be used to probe the smallest scale structures within AGNs. We aim to measure the shortest detected variability timescale, $t_{min,ul}$, of type 1 radio-quiet Seyfert galaxies and analyse their characteristics. We extracted TESS light curves

Amy Damitz, George Burns, Yuan-Yu Jau

We report several technical approaches that significantly improve the performance of a vapor-cell atomic electrometer operating in the quasi-DC frequency domain ($\ll$ 1 kHz). With a very small active volume of approximately 11 mm$^3$ inside the vapor cell, we demonstrated a noise floor for electric field (E-field) sensitivity ranging from 0.2 to 7.7 mV/m$\s

Franck Ndzomga

Evaluating AI agents on comprehensive benchmarks is expensive because each evaluation requires interactive rollouts with tool use and multi-step reasoning. We study whether small task subsets can preserve agent rankings at substantially lower cost. Unlike static language model benchmarks, agent evaluation is subject to scaffold-driven distribution shift, sin

Xinyi Yi, Ioannis Lestas

District heating systems (DHSs) require coordinated economic dispatch and temperature regulation under uncertain operating conditions. Existing DHS operation strategies often rely on disturbance forecasts and nominal models, so their economic and thermal performance may degrade when predictive information or model knowledge is inaccurate. This paper develops

Jordan LeDoux

In a controlled experiment on modular arithmetic ($p = 9973$), varying only example ordering while holding all else constant, two fixed-ordering strategies achieve 99.5\% test accuracy by epochs 487 and 659 respectively from a training set comprising 0.3\% of the input space, well below established sample complexity lower bounds for this task under IID order

A. Koley, P. Sanhueza, A. M. Stutz, P. Saha

Magnetic fields and turbulence may play a crucial role in the evolution of molecular clouds and ultimately in the formation of dense cores and stars. Despite being studied in many molecular clouds, the exact role of magnetic fields and turbulence in star formation is still poorly understood. Here, we report the high resolution plane of sky magnetic field (B_

Filip Rezabek, Dahlia Malkhi, Amir Yahalom

The emergence of decentralized satellite networks creates a pressing need for trust architectures that operate without physical access to hardware, without pre-provisioned vendor secrets, and without dependence on a single manufacturer's attestation service. Terrestrial TEEs are insufficient: hardware-based designs are susceptible to physical attacks, and mo

Thomas Allard, Helmut Bölcskei

We characterize the entropy and minimax risk of a broad class of compact pseudodifferential operators. Under suitable decay and regularity conditions on the symbol, we combine a Weyl-type asymptotic relation between the eigenvalue-counting function and the phase-space volume of the symbol with a general correspondence between spectral quantities, entropy, an

Arpita Pal

Using a closed quantum optical coupled-dipole model, we investigate why sub-sevenfold symmetries are likely absent in the stacked-ring scaffolds of light-harvesting 2 (LH2) complexes in purple photosynthetic bacteria.

Alex Rodrigo dos Santos Sousa, Vitor Ribas Perrone

We consider the statistical problem of estimating constituent curves from observations of their aggregated curves, referred to as aggregated functional data, in models with additive errors. A typical model arises in chemometrics via the Beer-Lambert law. The package FunctionalCalibration provides functions to estimate individual curves from aggregated curves

Lautaro Kogan, María Victoria Ríos

Automated detection and classification of cervical cells in conventional Pap smear images can strengthen cervical cancer screening at scale by reducing manual workload, improving triage, and increasing consistency across readers. However, it is challenged by severe class imbalance and frequent nuclear overlap. We present our approach to the RIVA Cervical Cyt

Dale R. Worley

We restate a process presented by Stanley as a technique to prove that there exists exactly one $d$-differential distributive lattice for any positive integer $d$. This process can be trivially extended to apply to distributive finitary lattices that have a variety of differential poset structures. It can be viewed as an algorithm for constructing such latti

Jingyuan Wang, Zhenisbek Tagay, Liyu Shi, Jiahao Liang

The strange metal behavior in cuprate superconductors - characterized by linear in temperature resistivity and anomalous Hall transport - stands in stark contrast to the expectation of conventional Fermi liquid (FL) theory. Remarkably, the similar transport behavior has also been observed in the heavy fermion metal CeCoIn$_5$, whose d-wave superconducting gr

Adrianna Tassone, Eden J. Hennessey, Anastasia Smolina, Skye Hennessey

Harmful experiences such as harassment and discrimination continue to push many people out of science. To better understand identities and experiences of harm among physicists, we conducted Canadian Physics Counts, the first comprehensive national survey examining equity, diversity, and inclusion within Canada's physics community. To better understand identi

ARC Prize Foundation

We introduce ARC-AGI-3, an interactive benchmark for studying agentic intelligence through novel, abstract, turn-based environments in which agents must explore, infer goals, build internal models of environment dynamics, and plan effective action sequences without explicit instructions. Like its predecessors ARC-AGI-1 and 2, ARC-AGI-3 focuses entirely on ev

Jingyi Tian, Lin Cai

The fast motion of Low Earth Orbit (LEO) satellites causes the propagation channel to vary rapidly, and its behavior is strongly shaped by the surrounding environment, especially at low elevation angles where signals are highly susceptible to terrain blockage and other environmental effects. Existing studies mostly rely on assumed statistical channel distrib

Chuanning Wei, Kin Fung Li, Dionysis Kalogerias, Margaret P. Chapman

We formulate and solve a discrete-time linear-quadratic regulation (LQR) problem in a finite horizon that penalizes temporal variability and stochastic variability of the state trajectory. Our approach enables the user to strike a balance between regulating the state and reducing temporal variability, with explicit sensitivity to risk. We achieve this by ext

Tristan Luca Saidi, Gonzalo Mena, Larry Wasserman, Florian Gunsilius

Many scientific systems, such as cellular populations or economic cohorts, are naturally described by probability distributions that evolve over time. Predicting how such a system would have evolved under different forces or initial conditions is fundamental to causal inference, domain adaptation, and counterfactual prediction. However, the space of distribu

Romain Leroux, Andre Estevez-Torres, Raphael Voituriez, Ananyo Maitra

Morphogenesis emerges from dynamic feedback among geometry, mechanics, and chemistry; however, disentangling these contributions in living systems remains challenging. Here, we focus on the interplay between geometry and mechanics by developing a minimal in vitro model in which purified microtubules and kinesin motor clusters self-organize into a two-dimensi

Yixin He, Quanyu Tang, Teng Zhang

We determine the optimal universal growth scale for the integral means of normalized logarithmic derivatives in the Carath\'eodory class. This resolves a problem of D.~K.~Thomas.

Duosi Dai, Pavithren V S Pakianathan, Gunnar Treff, Mahdi Sareban

Wearables and mobile health applications are increasingly adopted for self-management of chronic illnesses; yet the data feels overwhelming for older adults with cardiovascular disease (CVD). This study explores how they make sense of self-tracked data and identifies design opportunities for Large Language Model (LLM)-enabled support. We conducted a seven-da

Diego Macrini

Democracy is not a single mechanism. It is a space of possible configurations -- a spectrum stretching from pure direct participation to full delegation of authority. The systems we live under today occupy a narrow band of that spectrum, chosen centuries ago under constraints that no longer apply, and rarely questioned since. Votiverse is a platform for expl

Sheehan Olver

This paper constructs polynomial bases that capture the structure of the de Rham complex with boundary conditions in disks and cylinders (both periodic and finite) in a way that respects rotational symmetry. The starting point is explicit constructions of vector and matrix orthogonal polynomials on the unit disk that are analogous to the (scalar) generalised

Marwan Najjar

M-theory geometric engineering on manifolds of special holonomy yields a rich class of novel field theories. In this paper, we construct new 3d $\mathcal{N}=2^{\ast}$ and $\mathcal{N}=4^{\ast}$ gauge theories, realized as mass-deformations of theories with 16 supercharges, within this framework. These arise from non-compact 8d geometries given by fibrations

Sneha Paul, Zachary Patterson, Nizar Bouguila

Point cloud foundation models demonstrate strong generalization, yet adapting them to downstream tasks remains challenging in low-data regimes. Full fine-tuning often leads to overfitting and significant drift from pre-trained representations, while existing parameter-efficient fine-tuning (PEFT) methods mitigate this issue by introducing additional trainabl

Xinyao Wu, Zhe Xu, Cheng Chen, Jiawei Ma

Class-incremental learning (CIL) in medical image-guided diagnosis requires retaining prior diagnostic knowledge while adapting to newly emerging disease categories, which is critical for scalable clinical deployment. This problem is particularly challenging due to heterogeneous data and privacy constraints that prevent memory replay. Although pretrained fou

Wanqing Zhang, Hengyue Zhu, Yide Zhang

Three-dimensional (3D) photoacoustic computed tomography (PACT) is a powerful noninvasive biomedical imaging modality that provides volumetric data for structural and functional assessment \textit{in vivo}. To maximize angular coverage and mitigate limited-view artifacts, modern 3D PACT systems frequently employ hemispherical transducer arrays. While substan

Mohamed Nennouche, Mohammad-Ali Khalighi, Alexis Alfredo Dowhuszko, Djamal Merad

Underwater observatories have recently emerged as an efficient solution for marine biodiversity monitoring. The primary objective of this work is to enable efficient and cost-effective data muling from underwater sensors by investigating the use of optical wireless communications to transmit data from the underwater sensors to an aerial node close to the wat

Martin N. Danka, Jessica K. Bone, George B. Ploubidis, Richard J. Silverwood

Inverse probability of treatment weighting (IPTW) is widely used to estimate causal effects, but guidance is limited for count exposures. It is also unclear how IPTW performs when combined with multiple imputation in this context. In this study, we evaluated five IPTW methods applied to count exposures: multinomial binning, parametric and non-parametric cova

Anna Soukhovei, Carson Kohlbrenner, Caleb Escobedo, Alexander Gholmieh

We introduce a low-cost method for mounting sensors onto robot links for large-area sensing coverage that does not require the sensor's positions or orientations to be calibrated before use. Using computer aided design (CAD), a robot skin covering, or skin unit, can be procedurally generated to fit around a nondevelopable surface, a 3D surface that cannot be

Behnam Pourhassan, Izzet Sakalli, Houcine Aounallah, Fabiano F. Santos

We study non-perturbative quantum gravitational corrections to the thermodynamics and quantum work distribution of the $n$-dimensional Schwarzschild--Tangherlini--Anti-de Sitter black hole. Starting from the corrected entropy $S = S_0 + \eta\, e^{-S_0}$, where $S_0$ is the Bekenstein--Hawking entropy, we derive the modified specific heat, internal energy, He

Nadav Kunievsky

This paper examines how age at migration affects cultural assimilation by studying convergence in gender role attitudes between immigrants and the UK-born population. Although cultural values are central to policy debates about integration and social cohesion, most work on migration timing focuses on economic outcomes, leaving effects on values and beliefs f