March 2026 arXiv papers — page 60

Shaonan Liu, Yuichiro Iwashita, Soichiro Nakako, Masakazu Iwamura

Electronic health records (EHRs) are invaluable for clinical research, yet privacy concerns severely restrict data sharing. Synthetic data generation offers a promising solution, but EHRs present unique challenges: they contain both numerical and categorical features that evolve over time. While diffusion models have demonstrated strong performance in EHR sy

Prateek Mantri, Michael S. Bullock, Aditya Tripathi, Robert Kwolek

Recent comparisons of quantum repeater protocols have highlighted the strong near-term potential of multiplexed two-way architectures for long-distance quantum communication. At the same time, advances in hollow-core fiber (HCF) technology motivate a re-examination of the physical transmission medium as an architectural lever in quantum network design. In th

Trevor Oliveira-Smith

In this paper, we standardize a homotopy $4$-sphere constructed by Dunfield and Gong. As a corollary, we show that the $18$-crossing knot $18_{\text{nh}00000601}$, which is not known to be ribbon, is slice in the standard $4$-ball. Thus, $18_{\text{nh}00000601}$ serves as a potential counterexample to the Slice-Ribbon Conjecture. In addition, we show that th

Slobodan Mitrović, Srikkanth Ramachandran, Ronitt Rubinfeld, Mihir Singhal

In this work, we focus on designing an efficient Local Computation Algorithm (LCA) for the set cover problem, which is a core optimization task. The state-of-the-art LCA for computing $O(\log \Delta)$-approximate set cover, developed by Grunau, Mitrovi\'c, Rubinfeld, and Vakilian [SODA '20], achieves query complexity of $\Delta^{O(\log \Delta)} \cdot f^{O(\l

Jerin George Mathew, Sumayya Taher, Anindita Kundu, Denilson Barbosa

Large language models have recently been proposed as tools for automated essay scoring, but their agreement with human grading remains unclear. In this work, we evaluate how LLM-generated scores compare with human grades and analyze the grading behavior of several models from the GPT and Llama families in an out-of-the-box setting, without task-specific trai

Luke B. Handley, Andrew W. Howard, Fei Dai, Ryan A. Rubenzahl

The angle between stellar spin axes and planetary orbits -- stellar obliquity -- probes the dynamics of planetary migration and evolution. The obliquities of giant planets have been extensively studied because they are the most easily measured. Smaller planets, while more difficult to measure, have the advantage of better reflecting the dynamics of planetary

Djardiel da S. Gomes, Alexandre F. Fonseca, Marcelo L. Pereira

The search for carbon-based materials with tailored dimensionality and properties remains an important topic in materials science, particularly for applications in electronics, photonics, and nanomechanics. Among the emerging platforms in this context, graphyne (GY) represents a class of two-dimensional (2D) carbon allotropes composed of benzene rings connec

Morui Zhu, Yongqi Zhu, Song Fu, Qing Yang

Autonomous trucking poses unique challenges due to articulated tractor-trailer geometry, and time-varying sensor poses caused by the fifth-wheel joint and trailer flex. Existing perception and calibration methods assume static baselines or rely on high-parallax and texture-rich scenes, limiting their reliability under real-world settings. We propose dCAP (dy

Duo Lu, Helena Caminal, Manos Chatzakis, Yannis Papakonstantinou

Filtered Vector Search (FVS) is critical for supporting semantic search and GenAI applications in modern database systems. However, existing research most often evaluates algorithms in specialized libraries, making optimistic assumptions that do not align with enterprise-grade database systems. Our work challenges this premise by demonstrating that in a prod

Luis Cid, Marcelo Veloso

In this paper, we study the isotropy groups of locally finite derivations of the polynomial ring $\mathbb{K}[X,Y]$, using Van den Essen's classification of locally finite derivations in two variables. We compare the isotropy group of a locally finite derivation with that of its associated exponential automorphism, showing that they coincide in the locally ni

Anton Freund, Nicholas Pischke

We provide quantitative convergence results for continuous-time dynamical systems in metric spaces that satisfy a continuous-time analog of quasi-Fej\'er monotonicity. More precisely, we provide a (strong) convergence result for such dynamical systems over compact metric spaces which is quantitatively outfitted with a continuous-time rate of metastability, w

Yanxin Liu, Kenneth Q. Zhou

In the aftermath of the COVID-19 pandemic, empirical data have revealed that large-scale health crises not only cause immediate disruptions in mortality dynamics but also have persistent effects that may last for several years. Existing mortality models largely assume that mortality shocks are transitory and overlook how their effects can be long-lasting and

A. Arbieto, L. Segantim, J. Siqueira

In this paper, we define and study weak expansive and expansive measures for pseudogroups, these two notions appear when analyzing the role of the generating set. We investigate the relations between such properties. We also provide a criterion for a measure to be weak expansive through the positivity of its entropy, generalizing the work of Arbieto and Mora

Renbo Tu, Ali SaraerToosi, Nicholas S. Conroy, Gennady Pekhimenko

The Event Horizon Telescope (EHT) delivered the first image of a black hole by capturing the light from its surrounding accretion flow, revealing structure but not dynamics. Simulations of black hole accretion dynamics are essential for interpreting EHT images but costly to generate and impractical for inference. Motivated by this bottleneck, BHCast presents

Rayen Tan, Viswanath Nagarajan

The $k$-of-$n$ testing problem involves performing $n$ independent tests sequentially, in order to determine whether/not at least $k$ tests pass. The objective is to minimize the expected cost of testing. This is a fundamental and well-studied stochastic optimization problem. However, a key limitation of this model is that the success/failure probability of

Humberto Híjar, Apala Majumdar

We use nematic Multi-particle Collision Dynamics (N-MPCD) simulations to study confined nematic liquid crystals in square domains, with three distinct mean-field potentials: the classical Maier-Saupe and Marrucci-Greco models, and a more recent model due to Ilg, Karlin, and \"Ottinger. These potentials incorporate diverse physical features, including spatial

Nicholas Cassar, Akhil Chauhan, Carleton DeTar, Aida El-Khadra

We present a lattice-QCD calculation of the hadronic form factors for $B$-meson semileptonic decays computed using the highly improved staggered quark action for both valence and sea quarks on the MILC collaborations 2+1+1-flavor ensembles with lattice spacing ranging from 0.09 fm to 0.03 fm, many with physical pion masses On our finest ensembles, we compute

Rui Wei, Rui Du, Hanfei Yu, Devesh Tiwari

In Large Language Model (LLM) inference, early-exit refers to stopping computation at an intermediate layer once the prediction is sufficiently confident, thereby reducing latency and cost. However, recent LLMs adopt improved pretraining recipes and architectures that reduce layer redundancy, potentially limiting early-exit opportunities. We re-evaluate laye

W. Cosyn, C. Weiss

We develop the theoretical framework for semi-inclusive deep-inelastic scattering on a polarized spin-1 target and apply it to scattering on the polarized deuteron with spectator nucleon tagging. In Part I (previous article) we present the general form of the semi-inclusive cross section and polarization observables for the spin-1 target. In Part II (this ar

W. Cosyn, C. Weiss

We develop the theoretical framework for semi-inclusive deep-inelastic scattering on a polarized spin-1 target and apply it to scattering on the polarized deuteron with spectator nucleon tagging. In Part I (this article) we present the general form of the semi-inclusive cross section and polarization observables for the spin-1 target. A relativistically cova

Mahdi Jafari, Rahul Sharma, Sami Naim, Christopher Gerking

Software architecture models capture early design decisions that strongly influence system quality attributes, including security. However, architecture-level security assessment and feedback are often absent in practice, allowing security weaknesses to propagate into later phases of the software development lifecycle and, in some cases, to remain undiscover

Bhargav Kulkarni, Henry Whiting, Pavel Panchekha

Rasterization is the process of determining the color of every pixel drawn by an application. Powerful rasterization libraries like Skia, CoreGraphics, and Direct2D put exceptional effort into drawing, blending, and rendering efficiently. Yet applications are still hindered by the inefficient sequences of operations that they ask these libraries to perform.

Marko Lela

This paper proves a conditional structural uniqueness theorem for induced weight on robust record sectors within an admissible Hilbert record layer. Its theorem target and additive carrier differ from those of the standard Born-rule routes: additivity is not placed on the full projector lattice, but on disjoint admissible continuation bundles through an exte

Maxine E. Calle, Fangji Liu

We show that the classifying space of the flow category of a \emph{tame} Morse function on a smooth, closed manifold $M$ recovers the homotopy type of $M$, thereby addressing a claim in a preprint of Cohen--Jones--Segal. The tameness assumption is that the compactified moduli spaces of broken gradient trajectories are locally contractible, ensuring the flow

Mohyeu Hussain, David Koblah, Reiner Dizon-Paradis, Domenic Forte

Analog-mixed-signal (AMS) circuits are highly non-linear and operate on continuous real-world signals, making them far more difficult to model with data-driven AI than digital blocks. To close the gap between structured design data (device dimensions, bias voltages, etc.) and real-world performance, we propose a causal-inference framework that first discover

Eytan Kats, Christoph Grossbroehmer, Ziad Al-Haj Hemidi, Fenja Falta

Medical image registration is a fundamental task in medical image analysis, enabling the alignment of images from different modalities or time points. However, intensity inconsistencies and nonlinear tissue deformations pose significant challenges to the robustness of registration methods. Recent approaches leveraging self-supervised representation learning

Motahhare Mirhosseini, Swathi Kadaba, Allison Swyt, David L. Carroll

Two-dimensional topological insulators feature helical edge states that are remarkably resistant to disorder, making them appeal for energy-efficient electronics and quantum information technologies. In this study, we develop a Te-rod-templated solution growth method to create Bi2Te3 nanoplates with a Corbino geometry. The resulting few-quintuple-layer hexag

Moustafa Tadj, Ahmed Mohammed Cherif, Fethi Latti

In this work, we extend the concepts of $p$-biharmonic maps and $p$-biharmonic hypersurfaces to provide a broader characterization of $(p,q)$-harmonic hypersurfaces and $(p,q)$-harmonic curves in Riemannian manifolds, including Einstein spaces. Moreover, we present new explicit examples of proper $(p,q)$-harmonic hypersurfaces and $(p,q)$-harmonic curves in

MicroBooNE collaboration, P. Abratenko, D. Andrade Aldana, L. Arellano

Scintillation light, produced alongside ionisation charge from particle interactions, plays a critical role in liquid argon time projection chamber (LArTPC) detectors. A detailed understanding of its production and detection mechanisms is essential for robust calibration, systematic uncertainty evaluation, and physics analysis. This article describes the Mic

Jiangtao Shuai, Marvin Carl May, Sonja Schimmler, Manfred Hauswirth

Modern manufacturing under High-Mix-Low-Volume requirements increasingly relies on flexible and adaptive matrix production systems, which depend on interconnected heterogeneous devices and rapid task reconfiguration. To address these needs, we present ROSCell, a ROS2-based framework that enables the flexible formation and management of a computing continuum

Radhakrishnan Srinivasan, Tae-hyeon Shin, Anja von der Linden, Ricardo Herbonnet

The orientation of triaxial galaxy clusters with respect to the line-of-sight is expected to be one of the prime sources of scatter and potential bias in optical observables (e.g., richness and weak-lensing signal) of galaxy clusters. In this work, we use the observed shape of the central Brightest Cluster Galaxy (BCG) as proxy for the orientation along the

Oyebayo Ridwan Olaniran, Diana Shamsutdinova, Sarah Markham, Felix Zimmer

Background: Determining an adequate sample size is essential for developing reliable and generalisable clinical prediction models, yet practical guidance on selecting appropriate methods remains limited. Existing analytical and simulation-based approaches often rely on restrictive assumptions and focus on mean-based criteria. We present and validate pmsims,

Arlen Dean, Zijin Zhang, Stefanus Jasin, Yuqing Liu

Deploying multiple large language models (LLMs) in parallel to classify an unknown ground-truth label is a common practice, yet the problem of optimally allocating queries across heterogeneous models remains poorly understood. In this paper, we formulate a robust, offline query-planning problem that minimizes total query cost subject to statewise error const

Marco Frasca, Anish Ghoshal

We analyze various gravity theories involving de-Sitter, quadratic $\mathcal{R}^2$ and non-minimally coupled scalar in the light of application of the Dyson-Schwinger technique involving exact background solution of the Green's function. We denote specific set of solutions for the metric to move towards a quantum analysis of the theory. This kind of solution

Yiran Qiao, Yiren Lu, Yunlai Zhou, Rui Yang

3D Gaussian Splatting (3DGS) is increasingly recognized as a powerful paradigm for real-time, high-fidelity 3D reconstruction. However, its per-scene optimization pipeline limits scalability and generalization, and prevents efficient inference. Recently emerged feed-forward 3DGS models address these limitations by enabling fast reconstruction from a few inpu

Armin Catovic

Recent advances in generative AI systems have dramatically reduced the cost of digital production, fueling narratives that widespread participation in software creation will yield a proliferation of viable companies. This paper challenges that assumption. We introduce the Builder Saturation Effect, formalizing a model in which production scales elastically b

Nikolai Warner, Cameron Ethan Taylor, Irfan Essa, Apaar Sadhwani

Text-motion retrieval systems learn shared embedding spaces from motion-caption pairs via contrastive objectives. However, each caption is not a deterministic label but a sample from a distribution of valid descriptions: different annotators produce different text for the same motion, mixing motion-recoverable semantics (action type, body parts, directionali

Timo Böhme, Simone Göttlich, Andreas Neuenkirch

We extend our recently introduced stochastic nonlocal traffic flow model to more general random perturbations, including Markovian noise derived from a discretized Jacobi-type stochastic differential equation. Invoking a deterministic stability estimate, we show that the arising random weak entropy solutions are measurable, ensuring that quantities such as t

Licol Zeinfeld, Alona Strugatski, Ziva Bar-Dov, Ron Blonder

The rapid adoption of large language models (LLMs) in education raises profound challenges for assessment design. To adapt assessments to the presence of LLM-based tools, it is crucial to characterize the strengths and weaknesses of LLMs in a generalizable, valid and reliable manner. However, current LLM evaluations often rely on descriptive statistics deriv

Marek Skrzypczyk

The quadratic embedding property of graphs consisting of three paths (theta graphs) is fully characterised. For this aim, a theorem by Winkler (1985) is utilized. An alternative proof of that result using the RKHS technique is presented.

Nur Afsa Syeda, Mohamed Elmahallawy, Luis Fernando de la Torre, John Miller

Agriculture remains a cornerstone of global health and economic sustainability, yet labor-intensive tasks such as harvesting high-value crops continue to face growing workforce shortages. Robotic harvesting systems offer a promising solution; however, their deployment in unstructured orchard environments is constrained by inefficient perception-to-action pip

Manjushree B. Aithal, Ph. D., Alexander Kotz, James Mitchell

Large Language Models (LLMs) offer transformative solutions across many domains, but healthcare integration is hindered by strict data privacy constraints. Clinical narratives are dense with ambiguous acronyms, misinterpretation these abbreviations can precipitate severe outcomes like life-threatening medication errors. While cloud-dependent LLMs excel at Ac

Shreen Gul, Mohamed Elmahallawy, Ardhendu Tripathy, Sanjay Madria

Deep learning models are increasingly deployed in safety-critical applications, where reliable out-of-distribution (OOD) detection is essential to ensure robustness. Existing methods predominantly rely on the penultimate-layer activations of neural networks, assuming they encapsulate the most informative in-distribution (ID) representations. In this work, we

Ashish Malik, Caleb Lowe, Aayam Shrestha, Stefan Lee

We study long-horizon planning in 3D environments from under-specified natural-language goals using only visual observations, focusing on multi-step 3D box rearrangement tasks. Existing approaches typically rely on symbolic planners with brittle relational grounding of states and goals, or on direct action-sequence generation from 2D vision-language models (

Lahcen Khammich, Driss Kiouach

The main purpose of this paper is to study the Dynamical behaviors of a stochastic SIS epidemic model using mean-reverting inhomogeneous geometric brownian motion process. First we demonstrate the existence of a global-in-time solution and establish that is unique and remains positive. Then we derive a sufficient condition for exponential extinction of infec

Lucas H. Ueda, João G. T. Lima, Paula D. P. Costa

Speech Emotion Recognition (SER) in real-world scenarios remains challenging due to severe class imbalance and the prevalence of spontaneous, natural speech. While recent approaches leverage self-supervised learning (SSL) representations and multimodal fusion of speech and text, most existing methods apply supervision only at the final classification layer,

Michał Dymek

We study compact embeddings of Sobolev, Besov, and Triebel-Lizorkin spaces with variable exponents on both bounded and unbounded metric measure spaces. We establish sufficient conditions for compactness, and under additional assumptions, we show that they are also necessary. Moreover, we investigate the influence of isometry group actions on the compactness

Jian Sheng Wang

Multi-chain ecosystems suffer from fragmented identity, siloed liquidity, and bridge-dependent token transfers. We present n-VM, a Layer-1 architecture that hosts n heterogeneous virtual machines as co-equal execution environments over shared consensus and shared state. The design combines three components: a dispatcher that routes transactions by opcode pre

Mohammad Saleh Vahdatpour, Yanqing Zhang

The rapid deployment of machine learning across platforms from milliwatt-class TinyML devices to large language models has made energy efficiency a primary constraint for sustainable AI. Across these scales, performance and energy are increasingly limited by data movement and memory-system behavior rather than by arithmetic throughput alone. This work review

Sean Liu, Ankur Mehta, Wenzhong Yan

Origami-inspired robots offer rapid, accessible design and manufacture with diverse functionalities. In particular, origami robots without conventional electronics have the unique advantage of functioning in extreme environments such as ones with high radiation or large magnetic fields. However, the absence of sophisticated control systems limits these robot

Alexander Figotin

We establish that the dispersion relations of any physical system composed of two coupled subsystems, governed by a space-time homogeneous Lagrangian, admit a factorized form G_{1}G_{2}=\gamma G_{\mathrm{c}}, where G_{1} and G_{2} are the subsystem dispersion functions, G_{\mathrm{c}} is the coupling function, and \gamma is the coupling parameter. The result

Karl D. Gordon, Petia Yanchulova Merica-Jones, Geoffrey C. Clayton, Ralph Bohlin

Dust extinction curves provide one of the main avenues to understanding the detailed nature of dust grains and accounting for the effects of dust on observations of many astrophysical objects. For the first time, spectroscopic ultraviolet (UV) extinction curves are measured in M33 expanding the sample of Local Group galaxies with such measurements to five. T

Hannes Leonhard, Stefano Carlo Lambertenghi, Andrea Stocco

Advanced driver assistance systems (ADAS) often rely on deep neural networks to interpret driving images and support vehicle control. Although reliable under nominal conditions, these systems remain vulnerable to input variations and out-of-distribution data, which can lead to unsafe behavior. To this aim, this tool paper presents the architecture and functi

Marc-Antoine Provost, Nejc Ilenic, Christopher Solinas, Philippe Beardsell

We introduce GTO Wizard Benchmark, a public API and standardized evaluation framework for benchmarking algorithms in Heads-Up No-Limit Texas Hold'em (HUNL). The benchmark evaluates agents against GTO Wizard AI, a state-of-the-art superhuman poker agent that approximates Nash Equilibria, and defeated Slumbot, the 2018 Annual Computer Poker Competition champio

Weilun Xu, Alexander Rusnak, Frederic Kaplan

When large language models make ethical judgments, do their internal representations distinguish between normative frameworks, or collapse ethics into a single acceptability dimension? We probe hidden representations across five ethical frameworks (deontology, utilitarianism, virtue, justice, commonsense) in six LLMs spanning 4B--72B parameters. Our analysis

Abhijit Chowdhary, Elizabeth Newman, Deepanshu Verma

Gradient boosting, a method of building additive ensembles from weak learners, has established itself as a practical and theoretically-motivated approach to approximate functions, especially using decision tree weak learners. Comparable methods for smooth parametric learners, such as neural networks, remain less developed in both training methodology and the

Daniel Halliday, Izidor Benedičič, Andela Zivanovic, Masahiro Naritsuka

We investigate the electronic structure at the surface of the correlated oxide Ca$_3$Ru$_2$O$_7$, a low-symmetry ruthenate oxide which hosts an unconventional polar-metal phase. From a combination of angle-resolved photoemission spectroscopy and scanning tunneling spectroscopy measurements, we demonstrate that the surface hosts an insulating phase, a distinc

Mael Duverger, Judith Rousseau

In this paper, we study semiparametric inference for linear multivariate Hawkes processes, a class of point processes widely used to describe self and mutually exciting phenomena. We establish a convolution theorem giving the best limiting distribution for a regular estimator of smooth functional. Then, in the Bayesian setting, we prove a semiparametric Bern

Badr M. Abdullah, Israel Abebe Azime, Atnafu Lambebo Tonja, Jesujoba O. Alabi

We present Ethio-ASR, a suite of multilingual CTC-based automatic speech recognition (ASR) models jointly trained on five Ethiopian languages: Amharic, Tigrinya, Oromo, Sidaama, and Wolaytta. These languages belong to the Semitic, Cushitic, and Omotic branches of the Afroasiatic family, and remain severely underrepresented in speech technology despite being

Allan Berele, Stefan Catoiu

A variable line through the centroid G of a triangle divides the triangle into two parts each of whose lengths as a fraction of the perimeter fills a closed interval [m,1-m], with m between 0 and 1/2. We show that the range of m taken over all triangles is the interval (3/10,4/9], with 3/10 approached by scales of the triangles approaching the 5-4-1 triangle

Agustín Séttimo, Cristian Sottile, Cecilia Manzino

System I is a recently introduced simply-typed lambda calculus with pairs where isomorphic types are considered equal. In this work we propose a variant of System I with the type Top, and present a complete formalization of this calculus in Agda, which includes the proofs of progress and strong normalization.

Gian Luca Spitzer, Ion Nechita

Quantum graphs have been introduced by Duan, Severini, and Winter to describe the zero-error behaviour of quantum channels. Since then, quantum graph theory has become a field of study in its own right. A substantial source of difficulty in working with quantum graphs compared to classical graphs stems from the fact that they are no longer discrete objects.

Sayan Das, Evgeni Dimitrov, Zongrui Yang

We study a symmetrized (half-space) version of geometric last passage percolation with a boundary parameter $c$ that interpolates between subcritical, critical, and supercritical behavior. This model gives rise to a family of interlacing random curves, or a line ensemble, which encode both the usual last passage time and its higher-rank analogues. Although t

Masoumeh Chapariniya, Aref Farhadipour, Sarah Ebling, Volker Dellwo

We present our system for the BLEMORE Challenge at FG 2026 on blended emotion recognition with relative salience prediction. Our approach combines six encoder families through late probability fusion: an S4D-ViTMoE face encoder adapted with soft-label KL training, frozen layer-selective Wav2Vec2 audio features, finetuned body-language encoders (TimeSformer,

Praveen Kumar Ranjan, Abhinav Sinha, Yongcan Cao

We address target interception in contested environments in the presence of multiple defenders whose interception capability is limited by finite ranges. Conventional methods typically impose conservative stand-off constraints based on maximum engagement distance and neglect the interceptors' actuator limitations. Instead, we formulate safety constraints usi

Sungjoo Chung, Ying Zhang

Adversarial training is a defense method that trains machine learning models on intentionally perturbed attack inputs, so they learn to be robust against adversarial examples. This paper develops a robust voltage control framework for distribution networks with high penetration of distributed energy resources (DERs). Conventional voltage control methods are

Fatih Uenal

While recent work has benchmarked large language models on Swiss legal translation (Niklaus et al., 2025) and academic legal reasoning from university exams (Fan et al., 2025), no existing benchmark evaluates frontier model performance on applied Swiss regulatory compliance tasks. I introduce Swiss-Bench SBP-002, a trilingual benchmark of 395 expert-crafted

Gregory J. Gilbert, Erik A. Petigura, Paige M. Entrican

The dynamical history of a planetary system is recorded in the present day architecture of its constituent planets' sizes, orbital periods, and eccentricities. Studying the relationships between these quantities for large populations provides a window into the processes by which planetary systems form and evolve. Recently, Gilbert, Petigura, and Entrican (20

Jameson Cahill, Joseph W. Iverson, Dustin G. Mixon, Nathan Willey

Bilipschitz invariant theory concerns low-distortion embeddings of orbit spaces into Euclidean space. To date, embeddings with the smallest-possible distortion are known for only a few cases, to include: (a) planar rotations, (b) real phase retrieval, and (c) finite reflection groups. Here, we prove that for all three of these cases, the smallest possible di

Dev Mistry, Feng Qiu, Bo Chen, Feng Liu

Reliable photovoltaic defect identification is essential for maintaining energy yield, ensuring warranty compliance, and enabling scalable inspection of rapidly expanding solar fleets. Although recent advances in computer vision have improved automated defect detection, most existing systems operate as opaque classifiers that provide limited diagnostic insig

Nicolas Scepi, Christian Knigge, Amin Mosallanezhad, Knox S. Long

Disc winds from active galactic nuclei (AGN) can be launched by radiation pressure acting on spectral lines. However, launching a line-driven wind in the X-ray rich environment of AGN is challenging, as the wind easily gets over-ionized. Previous simulations suggested that X-ray self-shielding could enable line driving, though it remained unclear whether thi

Nina Brandl, Mykyta Cherniak, Johannes Kofler, Richard Kueng

We present a comprehensive and self-contained framework for the efficient classical simulation of Clifford circuits acting on $d$-dimensional qudits, including realistic Pauli/Weyl noise via stochastic simulation. Our approach uses the stabilizer tableau formalism for qudits of arbitrary dimension and tracks both stabilizer and destabilizer generators under

Frank Heyen, Michael Sedlmair

We contribute two design studies for augmented reality visualizations that support learning musical instruments. First, we designed simple, glanceable encodings for drum kits, which we display through a projector. As second instrument, we chose guitar and designed visualizations to be displayed either on a screen as an augmented mirror or as an optical see-t

Peiyu Xu, Xin Sun, Krishna Mullia, Raymond Fei

Ray-tracing-based 3D Gaussian splatting (3DGS) methods overcome the limitations of rasterization -- rigid pinhole camera assumptions, inaccurate shadows, and lack of native reflection or refraction -- but remain slower due to the cost of sorting all intersecting Gaussians along every ray. Moreover, existing ray-tracing methods still rely on rasterization-sty

Marc K. Ritter

Tensor trains (TTs), also known as matrix product states (MPS), are compressed representations of high-dimensional data that can be efficiently manipulated to perform calculations on the data. In many applications, such as TT-based solvers for nonlinear partial differential equations, the most expensive step is an elementwise multiplication or similar elemen

Kate Azar, Lamia Ateshian, Mallika T. Randeria, Renée DePencier Piñero

Fluxonium superconducting qubits have demonstrated long coherence times and high single- and two-qubit gate fidelities, making them a favorable building block for superconducting quantum processors. We investigate the dominant limitations to fluxonium qubit energy relaxation time $T_1$ using a set of eight planar, aluminum-on-silicon qubits. We find that a c

Gokce Basar, Maneesha Pradeep, Mikhail Stephanov

The fluctuations of proton multiplicity in heavy-ion collisions are the key observables in the search for the QCD critical point. In this work we present an approach to constraining the cumulants of proton number based on the analytical properties of the QCD equation of state in the vicinity of the critical point. We show that, under the assumption of local

Laurens Smulders, Johannes Noller, Sergi Sirera

We show that dynamical dark energy theories can imprint $O(1)$ modifications on the quasi-normal mode (QNM) spectrum characterising black hole ringdown. The time dependence of dynamical dark energy naturally gives rise to cosmological 'hair' around a black hole. Taking the cubic Galileon as a concrete example, which admits the only known stable solution of t

Cheng Cheng

Large language models can generate runnable software artifacts, but their security remains difficult to evaluate end to end. This study examines that problem through a Detect--Repair--Verify (DRV) workflow, in which vulnerabilities are detected, repaired, and then rechecked with security and functional tests. It addresses four gaps in current evidence: the l

M. Pinterić, M. Dressel, M. Wenzel, P. Puphal

We present a temperature-dependent reflectivity study of single crystals of the ternary intermetallic compounds GdCu$_2$Ge$_2$ and GdCuAl$_3$ over a broad spectral range (100-18000 cm$^{-1}$, equivalent to 12 meV-2.23 eV) down to 13 K. Below 2000 cm$^{-1}$, the optical spectra are dominated by the response of itinerant charge carriers exhibiting two distinct

Frank Heyen, Michael Sedlmair

Musicians mostly have to rely on their ears when they want to analyze what they play, for example to detect errors. Since hearing is sequential, it is not possible to quickly grasp an overview over one or multiple recordings of a whole piece of music at once. We therefore propose various visualizations that allow analyzing errors and stylistic variance. Our

Stefan Friedl, Tobias Hirsch, Marc Kegel

In this short expository note, we give a detailed proof of Markov's theorem on the unsolvability of the homeomorphism problem and of the existence of unrecognizable manifolds in all dimensions larger than 3.

Md Mahbubur Rahman, Arjun Guha, Harshitha Menon

Code LLMs often default to particular programming languages and libraries under neutral prompts. We investigate whether these preferences are encoded as approximately linear directions in activation space that can be manipulated at inference time. Using a difference-in-means method, we estimate layer-wise steering vectors for five language/library pairs and

Vanessa Brzić, Dmitry Grinko, Michał Studziński, Marco Túlio Quintino

State cloning and state transposition are fundamental transformations which, despite being desirable, cannot be perfectly realised due to two conceptually distinct constraints of quantum theory: cloning is forbidden by linearity, while transposition is ruled out by complete positivity. In this work, we show that, despite these different constraints, the best

Yurii Laba, Yaryna Mohytych, Ivanna Rohulia, Halyna Kyryleyza

This study presents a benchmark for evaluating the Visual Word Sense Disambiguation (Visual-WSD) task in Ukrainian. The main goal of the Visual-WSD task is to identify, with minimal contextual information, the most appropriate representation of a given ambiguous word from a set of ten images. To construct this benchmark, we followed a methodology similar to

Zhuo-Yang Song, Hua Xing Zhu

Large language models (LLMs) are increasingly deployed as optimization modules in agentic systems, yet the fundamental limits of such LLM-mediated improvement remain poorly understood. Here we propose a theory of LLM information susceptibility, centred on the hypothesis that when computational resources are sufficiently large, the intervention of a fixed LLM

Zeinab Dehghani, Rameez Raja Kureshi, Koorosh Aslansefat, Faezeh Alsadat Abedi

Artificial intelligence (AI) is increasingly being explored in health and social care to reduce administrative workload and allow staff to spend more time on patient care. This paper evaluates a voice-enabled Care Home Smart Speaker designed to support everyday activities in residential care homes, including spoken access to resident records, reminders, and

Amani Maina-Kilaas, Roger Levy

Digging-in effects, where disambiguation difficulty increases with longer ambiguous regions, have been cited as evidence for self-organized sentence processing, in which structural commitments strengthen over time. In contrast, surprisal theory predicts no such effect unless lengthening genuinely shifts statistical expectations, and neural language models ap

Amaia Razquin, Karin Dissauer, Astrid M. Veronig, Graham Barnes

Coronal dimmings are transient depletions of coronal plasma observed in extreme ultraviolet and soft X-rays, interpreted as low-corona signatures of coronal mass ejections (CMEs). Their evolution is closely linked to CME dynamics, flare reconnection, and large-scale coronal magnetic restructuring. In May 2024, active region (AR) 13664 produced 66 > M-class f

Damir Kinzebulatov

We obtain Hardy inequality for non-local diffusion operator with singular drift, in the case when the strength of attraction to the origin by the drift takes the critical value.

Teresa O. Miranda, Lara Sousa

We develop a semi-analytical model to describe the cosmological evolution of networks of cosmic strings with small-scale structure, by extending the velocity-dependent one-scale model to include an additional lengthscale describing the typical interkink density. We study the impact of the different physical processes involved in the production and removal of

Ranga Kulathunga, Diluka Galappaththige, Gayan Aruma Baduge, Chintha Tellambura

Existing cell-free integrated sensing and communication (CF-ISAC) beamforming algorithms predominantly rely on classical optimization techniques, which often entail high computational complexity and limited scalability. Meanwhile, recent learning-based approaches have difficulty capturing the global interactions and long-range dependencies among distributed

Alexandre Symeonidis-Herzig, Jianhe Low, Ozge Mercanoglu Sincan, Richard Bowden

Sign language production requires more than hand motion generation. Non-manual features, including mouthings, eyebrow raises, gaze, and head movements, are grammatically obligatory and cannot be recovered from manual articulators alone. Existing 3D production systems face two barriers to integrating them: the body-model fittings they train on retain a facial

Rafał Świętek, Maksymilian Kliczkowski, Miroslav Hopjan, Lev Vidmar

Recent work has proposed fading ergodicity as a mechanism for many-body ergodicity breaking. Here, we show that two paradigmatic random matrix ensembles -- the Rosenzweig-Porter model and the ultrametric model -- fall within the same universality class of ergodicity breaking when embedded in a many-body Hilbert space of spins-1/2. By calibrating the paramete

Yu. V. Kalyuzhnyi, T. Patsahan

We study a simplified model of monoclonal antibodies confined in a patchy random porous medium. Antibodies are represented as Y-shaped particles composed of seven tangential hard spheres with attractive patches on the terminal beads, while the matrix consists of randomly distributed hard-sphere obstacles bearing adhesive sites. The model captures antibody be

Ravin Ravi, Dylan Bradshaw, Stefano Ruberto, Gunel Jahangirova

Large Language Models (LLMs) are showing remarkable performance in generating source code, yet the generated code often has issues like compilation errors or incorrect code. Researchers and developers often face wasted effort in implementing checks and refining LLM-generated code, frequently duplicating their efforts. This paper presents LLMLOOP, a framework

Danny Laghi, Nicola Tamanini, Alberto Sesana, Jonathan Gair

We present the first joint gravitational-wave cosmological inference with LISA extreme mass-ratio inspirals at $z\lesssim1$ (galaxy redshifts) and massive black hole binaries at $z\gtrsim1$ (electromagnetic counterparts). Combining these standard sirens reduces cosmological degeneracies and yields competitive constraints on the Hubble constant $H_0$ and the

Steven Cho, Stefano Ruberto, Valerio Terragni

Automated testing is essential for evaluating and improving the reliability of Large Language Models (LLMs), yet the lack of automated oracles for verifying output correctness remains a key challenge. We present LLMORPH, an automated testing tool specifically designed for LLMs performing NLP tasks, which leverages Metamorphic Testing (MT) to uncover faulty b

Yenchia Feng, Chirag Sharma, Karime Maamari

Although large language models (LLMs) have advanced rapidly, robust automation of complex software workflows remains an open problem. In long-horizon settings, agents frequently suffer from cascading errors and environmental stochasticity; a single misstep in a dynamic interface can lead to task failure, resulting in hallucinations or trial-and-error. This p

Rajnandini Mukherjee, Maxwell T. Hansen

The rigorous treatment of four-particle intermediate and final states poses a major challenge for lattice calculations of scattering and decay amplitudes, as well as long-distance matrix elements. As a step towards addressing these challenges, we present a new formalism that perturbatively relates two- and four-particle finite-volume energies and matrix elem