October 2025 arXiv papers — page 20

Ngoc Phuoc An Vo, Manish Kesarwani, Ruchi Mahindru, Chandrasekhar Narayanaswami

FinOps (Finance + Operations) represents an operational framework and cultural practice which maximizes cloud business value through collaborative financial accountability across engineering, finance, and business teams. FinOps practitioners face a fundamental challenge: billing data arrives in heterogeneous formats, taxonomies, and metrics from multiple clo

Gilbert Bahati, Ryan M. Bena, Meg Wilkinson, Pol Mestres

Robotic systems navigating in real-world settings require a semantic understanding of their environment to properly determine safe actions. This work aims to develop the mathematical underpinnings of such a representation -- specifically, the goal is to develop safety filters that are risk-aware. To this end, we take a two step approach: encoding an understa

Farshid Reza, Beihan Chen, Miaomiao Jin

Al$_x$Ga$_{1-x}$N alloys are essential for high-performance optoelectronic and power devices, yet the role of composition on defect energetics remains underexplored, largely due to the limitations of first-principles methods in modeling disordered alloys. To address this, we employ a machine learning interatomic potential (MLIP) to investigate the structural

Namita Uppal, Konstantinos Tassis, Vasiliki Pavlidou, Vincent Pelgrims

We present a novel technique to decompose line-of-sight (LOS) stellar polarization as a function of distance, aimed at reconstructing three dimensional (3D) plane-of-sky (POS) magnetic structures in the interstellar medium (ISM). The method assumes that the observed polarization arises from discrete, thin dust layers located at varying distances along the LO

Jose A. Morales Escalante

The main ideas behind a research plan to use the Wigner formulation as a bridge between classical and quantum probabilistic algorithms are presented, focusing on a particular case: the Quantum analog of Stochastic Gradient Descent in its continuous-time limit based on the Wigner formulation of Open Quantum Systems.

Davide Belfiori, Sergio Martin-Alvarez, Enrique Lopez-Rodriguez, Rosita Paladino

The interstellar medium (ISM) is permeated by magnetic fields that affect gas dynamics and star formation. These fields correlate with supernova (SN)-driven turbulence, but whether the scaling is universal across galaxy properties, ISM phases, and energy budgets remains unclear. We quantify the dependence of magnetic fields on star formation activity includi

Emily Herron, Junqi Yin, Feiyi Wang

Large language models (LLMs) have demonstrated transformative potential in scientific research, yet their deployment in high-stakes contexts raises significant trustworthiness concerns. Here, we introduce SciTrust 2.0, a comprehensive framework for evaluating LLM trustworthiness in scientific applications across four dimensions: truthfulness, adversarial rob

Yiren Tong, Panagiotis Tsoutsanis

In this paper, we present a multi-dimensional, arbitrary-order hybrid reconstruction framework for compressible flows on unstructured meshes. The method combines the efficiency of linear reconstruction with the robustness of high-order non-oscillatory schemes, activated only where needed through a novel a priori detection strategy. By minimising the use of c

Moncef Derouich

This study proposes a quantitative framework to enhance curriculum coherence through the systematic alignment of Course Learning Outcomes (CLOs) and Program Learning Outcomes (PLOs), contributing to continuous improvement in outcome-based education. Grounded in accreditation standards such as ABET and NCAAA, the model introduces mathematical tools that map e

Frederico Belcavello, Ely Matos, Arthur Lorenzi, Lisandra Bonoto

The use of LLM-based applications as a means to accelerate and/or substitute human labor in the creation of language resources and dataset is a reality. Nonetheless, despite the potential of such tools for linguistic research, comprehensive evaluation of their performance and impact on the creation of annotated datasets, especially under a perspectivized app

Samuel W. Remedios, Aaron Carass, Jerry L. Prince, Blake E. Dewey

The purpose of this study is to present and compare three denoising diffusion probabilistic models (DDPMs) that generate 3D $T_1$-weighted MRI human brain images. Three DDPMs were trained using 80,675 image volumes from 42,406 subjects spanning 38 publicly available brain MRI datasets. These images had approximately 1 mm isotropic resolution and were manuall

Vishnu Kumar, Nishta Arora, Bhargavi B. A., Akshay Naik

Parametric excitation in coupled mechanical systems has enabled advances in sensing, computation, and phonon control. The function of distinct phase modes using parametric driving remains insufficiently explored. Here, we investigate the nonlinear and parametric response of degenerate phase modes in a Double Ended Tuning Fork (DETF) resonator. Our measuremen

Shashi B. Mishra, Elena R. Margine

Superconductivity in compressed H3S arises from the interplay between high-frequency phonons and a pronounced van Hove singularity near the Fermi level. Using first-principles calculations, we investigate the superconducting properties of H3S and D3S at 160 and 200 GPa, explicitly incorporating anharmonic lattice dynamics and first-order vertex corrections t

Denniz Goren, Holger Caesar

The vulnerability of cyclists, exacerbated by the rising popularity of faster e-bikes, motivates adapting automotive perception technologies for bicycle safety. We use our multi-sensor SenseBike research platform to study 3D LiDAR semantic segmentation for bicycles. We introduce the novel BikeScenes-lidarseg Dataset, comprising 3021 consecutive LiDAR scans a

Aristides V. Doumas

The double Dixie cup problem of D.J. Newman and L. Shepp is a well-known variant of the coupon collector problem, where the object of study is the number of coupons that a collector has to buy in order to complete m sets of all N existing different coupons. In this paper we consider the case where the coupons distribution is a mixture of two different distri

Chi Sun, Jacob Linder

We consider theoretically the possibility of coexisting ferroelectric and metallic altermagnetic order, which has recently been predicted in insulating and semiconducting systems via ab initio calculations. Solving self-consistently a mean-field Hubbard model, accounting also for the energy cost of distorting the lattice to produce an electric polarization,

Giovanni Pelliccioli, Rene Poncelet

We achieve for the first time NNLO QCD + NLO EW accuracy for doubly polarised WZ inclusive production at the LHC, in the case of fully leptonic decays. Additionally, we provide estimates for missing higher-order uncertainties in QCD associated with doubly polarised differential cross sections and joint polarisation fractions, obtained both with standard scal

Prune C. August, Robin Wordsworth, Mikayla Huffman, David Brain

Detection of an atmosphere around a terrestrial exoplanet will be a major milestone in the field, but our observational capacities are biased towards to tidally locked, close-in planets orbiting M-dwarf stars. The atmospheres of these planets are vulnerable to atmospheric erosion and collapse due to condensation of volatiles on the nightside. However, these

Gabriele Bolli, Fausto Gozzi

We study a family of stationary Hamilton-Jacobi-Bellman (HJB) equations in Hilbert spaces arising from stochastic optimal control problems. The main difficulties to treat such problems are: the lack of smoothing properties of the linear part of the HJB equation; the presence of unbounded control operators; the presence of state-dependent costs. This features

Michael J. Facci, Qi Sun, Boyce E. Griffith

We present an enhanced immersed interface method for simulating incompressible fluid flows in thin gaps between closely spaced immersed boundaries. This regime, common in engineered structures such as including tribological interfaces and bearing assemblies, poses significant computational challenges because of limitations in grid resolution and the prohibit

Soujanya Hazra, Sanjay Ghosh

Depression is a major cause of global mental illness and significantly influences suicide rates. Timely and accurate diagnosis is essential for effective intervention. Electroencephalography (EEG) provides a non-invasive and accessible method for examining cerebral activity and identifying disease-associated patterns. We propose a novel graph-based deep lear

Harris Hardiman-Mostow, Jack Mauro, Adrien Weihs, Andrea L. Bertozzi

We propose a graph-topological approach to active learning that directly targets the core challenge of exploration versus exploitation under scarce label budgets. To guide exploration, we introduce a coreset construction algorithm based on Balanced Forman Curvature (BFC), which selects representative initial labels that reflect the graph's cluster structure.

Ben Spitz

Let $G$ be a finite group, and $k$ an integer. In this note, we show that for any $G$-Tambara functor $T$ and any subgroups $H_1, H_2 \leq G$, $k$ is a unit in $T(G/H_1)$ if and only if $k$ is a unit in $T(G/H_2)$. In other words, one may speak unambiguously of the localization $T[1/k]$. As a consequence, the norm functors $N_H^G$ commute with inverting $k$.

Tong Ma, Hui Lai, Hui Wang, Zhenhu Tian

ATLAS is a constraint-guided generation framework for structured engineering artifacts whose outputs must satisfy explicit schemas, domain rules, and audit requirements. Rather than treating a large language model as a standalone generator, ATLAS places generation inside a model-driven workflow that separates domain representation, constraint compilation, an

Kang Chen, Zhihao Liu, Tonghe Zhang, Zhen Guo

Vision-Language-Action (VLA) models enable robots to understand and perform complex tasks from multimodal input. Although recent work explores using reinforcement learning (RL) to automate the laborious data collection process in scaling supervised fine-tuning (SFT), applying RL to large-scale flow-based VLAs (\eg, $\pi_0$, $\pi_{0.5}$) remains challenging d

Severin Bunk, Miguel Pino Carmona, C. S. Shahbazi

We prove well-posedness of the analytic Cauchy problem for gradient generalized Ricci solitons on an abelian bundle gerbe and solve the initial data equations on every compact Riemann surface. Along the way, we provide a novel characterization of the self-similar solutions of the generalized Ricci flow by means of families of automorphisms of the underlying

Franc Forstneric, Alfheidur Edda Sigurdardottir

Let $X$ be a smooth open manifold of even dimension, $T$ be a topological space, and $\mathscr{J}=\{J_t\}_{t\in T}$ be a continuous family of smooth integrable Stein structures on $X$. Under suitable additional assumptions on $T$ and $\mathscr{J}$, we prove an Oka principle for continuous families of maps from the family of Stein manifolds $(X,J_t)$, $t\in T

Hritik Gopal Shah, Gregory Giustino, Elli Ntakou

The increasing frequency and severity of High Impact and Low Probability events such as hurricanes and windstorms pose significant challenges to the resilience of electrical power distribution systems, particularly in regions of New England where there is a significant amount of overhead infrastructure in areas where vegetation is predominant. Traditional re

Eitán Sprejer, Fernando Avalos, Augusto Bernardi, Jose Pedro Brito de Azevedo Faustino

Aligning LLM-based judges with human preferences is a significant challenge, as they are difficult to calibrate and often suffer from rubric sensitivity, bias, and instability. Overcoming this challenge advances key applications, such as creating reliable reward models for Reinforcement Learning from Human Feedback (RLHF) and building effective routing syste

Hanie Vatani, Reza Ebrahimi Atani

Federated Learning (FL) enables collaborative model training without sharing raw data but suffers from limited scalability, high communication costs, and privacy risks due to its centralized architecture. This paper proposes FedSelect-ME, a hierarchical multi-edge FL framework that enhances scalability, security, and energy efficiency. Multiple edge servers

Simon Yu, Peilin Yu, Hongbo Zheng, Huajie Shao

We present VISAT, a novel open dataset and benchmarking suite for evaluating model robustness in the task of traffic sign recognition with the presence of visual attributes. Built upon the Mapillary Traffic Sign Dataset (MTSD), our dataset introduces two benchmarks that respectively emphasize robustness against adversarial attacks and distribution shifts. Fo

Christian Dittrich, Jennifer Flygare Kinne

Why do brains and deep networks converge on similar representations? Task-optimized artificial neural networks quantitatively predict primate ventral stream responses despite radically different substrates and optimization dynamics. This convergence demands explanation beyond shared natural image statistics or task structure alone. The Compression Efficiency

Wenhao Yang, Minghui Zhou, Daniel Izquierdo Cortázar, Yehui Wang

Enterprise engagement with open source has evolved from tactical adoption to strategic deep integration, exposing them to a complex risk landscape far beyond mere code. However, traditional risk management, narrowly focused on technical tools, is structurally inadequate for systemic threats like upstream "silent fixes", community conflicts, or sudden license

Sajid Ullah, Vittorio Colao

Our main contributions include proving sufficient conditions for the existence of solution to a second order problem with nonzero nonlocal initial conditions, and providing a comprehensive analysis using fundamental solutions and fixed-point techniques. The theoretical results are illustrated through applications to partial differential equations, including

Auyona Ray

This project began by constructing an index of economic insecurity using multiple socioeconomic indicators. Although poverty alone predicted SNAP participation more accurately than the composite index, its explanatory power was weaker than anticipated, echoing past findings that enrollment cannot be explained by income alone. This led to a shift in focus: id

Saba Ghasemi Naraghi, Tylee Kareck, Lingyun Xiao, Richard Reed

Ethylene is one of the most ubiquitous chemicals and is predominantly produced through steam cracking. However, steam cracking is highly energy- and carbon-intensive, making its decarbonization a priority. Electrifying the steam cracking process is a promising pathway to reduce carbon emissions. However, this is challenged by the intrinsic conflict between t

Katia Ferrière, Ludovic Montier, Jean-Sébastien Carrière

Context. The polarized thermal emission from interstellar dust offers a valuable tool for probing both the dust and the magnetic field in the interstellar medium (ISM). However, existing observations only yield the total amount of dust emission along the line of sight (LoS), with no information on its LoS distribution. Aims. We present a new method designed

Pavel Hubáček, Jan Václavek, Michelle Yeo

The rising importance of cryptocurrencies as financial assets pushed their applicability from an object of speculation closer to standard financial instruments such as loans. In this work, we initiate the study of secure protocols that enable fiat-denominated loans collateralized by cryptocurrencies such as Bitcoin. We provide limited-custodial protocols for

Shmuel Nussinov

There is a vast literature on Dark Matter (DM) with many reviews of specific topics only a small fraction of which will be mentioned. I start with a very brief review of cosmology which underlies much of DM research and some relevant General Relativity (GR). I next discuss Self Interacting Dark Matter (SIDM) models and upper bounds on the mass M(X) of point-

Jason L. Sanders

Stellar abundances of elements with production channels that are metallicity-dependent (most notably aluminium) have provided an empirical route for separating different Galactic components. We present 'single-zone' analytic solutions for the chemical evolution of galaxies when the stellar yields are metallicity-dependent. Our solutions assume a constant sta

Pietro Follia, Bassano Vacchini, Heinz-Peter Breuer

We study the emergence of quantum memory effects in a spin-boson system at finite temperature driven by an external time-periodic force. Quantifying memory effects by the trace-distance based measure for non-Markovianity and performing numerical simulations employing the hierarchical equations of motion approach, we find a pronounced peak structure when plot

Patrick Slane, Ákos Bogdán, David Pooley

The Chandra X-ray Observatory is a mainstay of modern observational astrophysics. With the highest angular resolution of any X-ray facility, its imaging and spectral capabilities in the 0.5-10 keV band have led to both unique and complementary breakthroughs in nearly all areas of the field. Now more than a quarter century into its mission, Chandra continues

Rebecca L. McClain, Adam K. Leroy, Enrico Congiu, Ashley. T. Barnes

We use the full-disk VLT-MUSE mosaic of NGC 253 to identify 2492 HII regions and study their resolved structure. With an average physical resolution of 17 pc, this is one of the largest samples of highly resolved spectrally mapped extragalactic HII regions. Regions of all luminosities exhibit a characteristic emission profile described by a double Gaussian w

PA Crowther, JM Bestenlehner

We present a new spectroscopic pipeline designed to analyse large numbers of hot massive stars homogeneously. The pipeline has been developed to utilise large grids of FASTWIND non-LTE, line blanketed models in which spherical geometry is adopted, and uniquely incorporates model errors. The pipeline has been applied to three contemporary datasets involving V

Arnaud Marsiglietti, James Melbourne

We present an extension of the famous Littlewood-Offord problem when Bernoulli distributions are replaced with discrete log-concave distributions. A variant of the Littlewood-Offord problem for arithmetic progressions, as well as an entropic version, is also discussed. Along the way, we recover and extend a result of Madiman and Woo (2015) on the entropy pow

Chen Bai, Mao Tian Tan, Bastien Lapierre, Shinsei Ryu

We study quantum quench dynamics in (1+1)-dimensional critical systems, starting from thermal pure states called crosscap states, and evolving them under spatially inhomogeneous Hamiltonians. The spatial inhomogeneity is introduced through a deformation of the Hamiltonian, expressed as linear combinations of the generators of the $SL^{(q)}(2,\mathbb{R})$ sub

Xiaoke Huang, Ningsen Wang, Hui Liu, Xianfeng Tang

Large Multimodal Models (LMMs) are increasingly capable of answering medical questions that require joint reasoning over images and text, yet training general medical VQA systems is impeded by the lack of large, openly usable, high-quality corpora. We present MedVLSynther, a rubric-guided generator-verifier framework that synthesizes high-quality multiple-ch

Rafael Aoude, Donal O'Connell, Matteo Sergola, Chris D. White

We describe an electromagnetic system which is related to black hole production with Hawking radiation through the double copy. We consider the scattering of a massless scalar particle through a collapsing electromagnetic background -- the single copy of Vaidya -- and identify the Feynman diagrams that exponentiate in the geometric-optics limit. The Bogoliub

Kedong Wang, Bohui Wan, Cody L. Covington, Kalman Varga

We employ real-space, real-time time-dependent density functional theory (TDDFT) combined with Ehrenfest dynamics to investigate ultrafast intermolecular relaxation following inner-valence ionization in hydrated pyrrole. This time-dependent approach treats electronic and nuclear motions simultaneously, allowing the description of electronic excitation, charg

Daniel Shaffer, Alex Levchenko

Superconducting diode effects (SDE), both in bulk superconductors and in Josephson junctions, have garnered a lot of attention due to potential applications in classical and quantum computing, as well as superconducting sensors. Here we review various mechanisms that have been theoretically proposed for their realization. We first provide a brief historical

Ken Huang, Kyriakos Rock Lambros, Jerry Huang, Yasir Mehmood

This paper introduces the Agentic AI Governance Assurance & Trust Engine (AAGATE), a Kubernetes-native control plane designed to address the unique security and governance challenges posed by autonomous, language-model-driven agents in production. Recognizing the limitations of traditional Application Security (AppSec) tooling for improvisational, machine-sp

Barbara J. Anthony-Twarog, Samantha W. Brunker, Constantine P. Deliyannis, Evan Rich

Precision UBVRI photometry of NGC 7789 is combined with Gaia data to map reddening variations across the cluster face. HYDRA spectra, Gaia astrometry, and isochrone fitting constrain the absolute reddening, apparent modulus, and age to E(B-V) = 0.30 +/- 0.02, (m-M)=12.51 +/- 0.06, and 1.46 +/- 0.02 Gyr for [Fe/H] between -0.2 and solar; the spectroscopic [Fe

Christian Coester, Tze-Yang Poon

The online $k$-taxi problem, introduced in 1990 by Fiat, Rabani and Ravid, is a generalization of the $k$-server problem where $k$ taxis must serve a sequence of requests in a metric space. Each request is a pair of two points, representing the pick-up and drop-off location of a passenger. In the interesting ''hard'' version of the problem, the cost is the t

Xingjian Zhang, Tianhong Gao, Suliang Jin, Tianhao Wang

Large language models (LLMs) are increasingly used as raters for evaluation tasks. However, their reliability is often limited for subjective tasks, when human judgments involve subtle reasoning beyond annotation labels. Thinking traces, the reasoning behind a judgment, are highly informative but challenging to collect and curate. We present a human-LLM coll

Tonny K B, Shikhi M

The concept of mutual visibility in a graph encodes combinatorial information about vertex subsets with prescribed visibility properties and serves as a useful algebraic invariant. In this paper, we derive algebraic conditions for the mutual-visibility number of $(d,2)$-graphs with non-negative defect. We then determine this parameter for $(d,2,-2)$-graphs f

Esteban A. Rodríguez-Mena, Biel Martínez, Ahmad Fouad Kalo, Yann-Michel Niquet

Recent advances in the scaling of spin qubits have led to the development of sparse architectures where spin qubits are distributed across multiple quantum dots. This distributed approach allows for qubit manipulation through hopping and flopping modes and may enable spin shuttling protocols to entangle spins beyond nearest neighbors. Here, we develop a micr

Brooke Elizabeth Kidmose, Andreas Brasen Kidmose, Cliff C. Zou

Modern vehicles remain vulnerable to unauthorized use and theft despite traditional security measures including immobilizers and keyless entry systems. Criminals exploit vulnerabilities in Controller Area Network (CAN) bus systems to bypass authentication mechanisms, while social media trends have expanded auto theft to include recreational joyriding by unde

Minh-Luan Doan, Evan O'Dorney

We show that the heat kernel measures based at the north pole of the spheres $S^{N-1}(\sqrt N)$, with properly scaled radius $\sqrt N$ and adjusted center, converge to a Gaussian measure in $\mathbb R^\infty$, and find an explicit formula for this measure.

Mingyuan Wang, Guus Avis, Stefan Krastanov

Greenberger-Horne-Zeilinger (GHZ) states play a central role in quantum computing and communication protocols, as a typical multipartite entanglement resource. This work introduces an efficient enumeration and simulation method for circuits that preserve and distill noisy GHZ states, significantly reducing the simulation complexity of a gate on $n$ qubits, f

Samuel Liu, Chen-Yu Hu, Ming-Yuan Song, Xinyu Bao

Analog crossbar arrays consisting of emerging memory devices can greatly alleviate the computational strain required by vector matrix multiplications for neural network applications. The ability to produce spin orbit torque-magnetic random-access memory (SOT-MRAM) at wafer-scale positions SOT-MRAM as a strong memory candidate. In this work, we fabricate and

Anton Ilderton, William Lindved, Karthik Rajeev

Gravity and gauge theory are concretely linked by the double copy. Although well-studied at the level of perturbative scattering in vacuum, far less is known about non-perturbative aspects or extensions of the double copy beyond trivial backgrounds. We show here how Hawking radiation in a collapse metric, its associated thermal spectrum, and horizon-dependen

Elisa Damiani, Margherita Marsili, Maria Clelia Righi

Diamond and diamond-like carbon (DLC) coatings are widely employed for their exceptional mechanical, thermal and chemical properties, but their industrial application is often limited by weak adhesion to metallic substrates. In this work, we employ a high-throughput ab initio approach to systematically investigate the adhesion of diamond/metal interfaces, co

Soumavo Ghosh, Elena D'Onghia

The generation of spiral arms and the mechanisms controlling their properties within a realistic cosmological framework - the complete understanding is still beyond our grasp. Using a statistically significant sample of Milky Way- and Andromeda-like (MW/M31) analogs from the high-resolution TNG50 cosmological simulation, we carry out the first systematic inv

Luis A. Anchordoqui, Dieter Lust, Severin Lüst

In this note we introduce some concepts of Species Quantum Mechanics. Specifically, we consider quantum operators that correspond to the species number $N_s$ and the tower mass scale $m_t$ in the context of the swampland distance conjecture. We discuss the commutation relations, a possible wave function, and symplectic duality transformations on the conjugat

Jinchao Zhao, Peizhi Mai, Gaurav Tenkila, Philip W. Phillips

We demonstrate that the Mott transition exhibits universal scaling as a consequence of the breaking of a $\mathbb{Z}_2$ symmetry in momentum space. A direct consequence of this discrete symmetry breaking is the charge or Mott gap itself. From extensive numerics, we proffer that it is the charge compressibility that acts as the underlying order parameter as i

Cassandra Lochhaas, Molly S. Peeples, Brian W. O'Shea, Jason Tumlinson

The circumgalactic medium (CGM) is host to gas flows into and out of galaxies and regulates galaxy growth, but the multiphase, diffuse gas in this region is challenging to observe. We investigate the properties of gas giving rise to O VI emission from the CGM that upcoming missions, such as the Aspera SmallSat, will be able to map in local galaxies. We use t

Jonatan Jacquemin-Ide, Mitchell C. Begelman, Beverly Lowell, Matthew Liska

Magnetically arrested disks (MADs) are a compelling model for explaining variability in low-luminosity active galactic nuclei (AGN), including horizon-scale outbursts like those observed in Sagittarius A*. MADs experience powerful flux eruptions-episodic ejections of magnetic flux from the black hole horizon-that may drive the observed luminosity variations.

Cassandre Notton, Vassilis Apostolou, Agathe Senellart, Anthony Walsh

The Perceval Challenge is an open, reproducible benchmark designed to assess the potential of photonic quantum computing for machine learning. Focusing on a reduced and hardware-feasible version of the MNIST digit classification task or near-term photonic processors, it offers a concrete framework to evaluate how photonic quantum circuits learn and generaliz

Hrant Gharibyan, Mohammed Zuhair Mullath, Nicholas E. Sherman, Vincent P. Su

We design and demonstrate heuristic quantum advantage with peaked circuits (HQAP circuits) on Quantinuum's System Model H2 quantum processor. Through extensive experimentation with state-of-the-art classical simulation strategies, we identify a clear gap between classical and quantum runtimes. Our largest instance involves all-to-all connectivity with 2000 t

Ismail Soudi, Adam Takacs

Jet quenching - the modification of high-energy jets in the quark-gluon plasma - has been extensively studied through weakly coupled scattering amplitudes embedded in parton-shower frameworks. These models, often combined with bulk hydrodynamic evolution, successfully describe a wide range of observables, though they typically rely on assumptions of rapid th

Orion Lee, Qian Cao, Yogesh N. Joglekar, Kater Murch

Unitary and dissipative models of quantum dynamics are linear maps on the space of states or density matrices. This linearity encodes the superposition principle, a key feature of quantum theory. However, this principle can break down in effective non-Hermitian dynamics arising from postselected quantum evolution. We theoretically characterize and experiment

Yonit Hochberg, Majed Khalaf, Alessandro Lenoci, Rotem Ovadia

We show that the scattering rate for any dark matter (DM) interaction with electrons in any target is proportional to several measurable material properties, encapsulated by a single master formula. This generalizes the dielectric function formalism--developed for DM interactions that couple to electron density--to any interaction, incorporating both spin-de

Asher Berlin, Zachary Bogorad, Peter W. Graham, Harikrishnan Ramani

A terrestrial population of millicharged particles that interact significantly with normal matter can arise if they make up a dark matter subcomponent or if they are light enough to be produced in cosmic ray air showers. Such particles thermalize to terrestrial temperatures through repeated scatters with normal matter in Earth's environment. We show that a s

Charlotte L. Jackson, James H. Matthews, Imogen H. Whittam, Matt J. Jarvis

We investigate the relationship between disc winds, radio jets, accretion rates and black hole masses of a sample of $\sim$100k quasars at z $\approx$ 2. Combining spectra from the 17th data release of the Sloan Digital Sky Survey (SDSS) with radio fluxes from the 2nd data release of the Low Frequency ARray (LOFAR) Two-Meter Sky Survey (LoTSS), we statistica

Robin Eappen, Pavel Kroupa

Understanding the diversity of star formation histories (SFHs) of galaxies is key to reconstructing their evolutionary paths. Traditional models often assume parametric forms such as delayed-tau or exponentially declining models, which may not reflect the actual variety of formation processes. We aim to assess what types of SFHs are consistent with the obser

Yiyang Zhang, Xuheng Ding, Lilan Yang, Erini Lambrides

Recent JWST observations have revealed a population of red, compact, high-redshift objects called Little Red Dots (LRDs), whose host components have remained largely unconstrained, possibly due to their extreme compactness. Current morphological studies suggest the presence of extended emission in LRDs at rest-frame ultraviolet wavelengths. However, in the r

Frederick B. Davies, Sarah E. I. Bosman, Valentina D'Odorico, Sofia Campo

The fraction of "dark pixels" in the Ly$\alpha$ and other Lyman-series forests at $z\sim 5-6$ provides a powerful constraint on the end of the reionization process. Any spectral region showing transmission must be highly ionized, while dark regions could be ionized or neutral, thus the dark pixel fraction provides a (nearly) model independent upper limit to

Mitrajyoti Ghosh, Kevin Liguori, Takemichi Okui, Kohsaku Tobioka

Experiments such as MACS and the proposed MACE study muonium-antimuonium conversion by the energies of the final-state $e^\pm$. The $e^+$ and $e^-$ from an antimuonium decay tend to be non-relativistic and relativistic, respectively, and vice versa for muonium. However, these $e^\pm$ can exchange their energies by hard Bhabha scattering, causing muonium to f

Ameya Chavda, Alberto Nicolis, Alessandro Podo, John Staunton

The short-distance singular structure of the two-point function of a free scalar field in curved spacetime has a universal behavior that characterizes well-behaved states (called Hadamard states). This includes a non-analytic term proportional to the Ricci scalar curvature known as the Hadamard tail. This is usually derived by solving a differential equation

Sebastian Cespedes, Zhehan Qin, Dong-Gang Wang

Effective field theories (EFTs) provide a powerful framework to parametrise unknown aspects of possible ultraviolet (UV) physics. For scalar fields in de Sitter space, however, new emergent phenomena can arise when the cut-off scale of the theory lies below the horizon scale $H$, as seen in the stochastic formalism of inflation. In this work, we study EFTs t

Asher Berlin, Zachary Bogorad, Peter W. Graham, Harikrishnan Ramani

A terrestrial population of room-temperature millicharged particles can arise if they make up a dark matter subcomponent or if they are light enough to be produced in cosmic ray air showers. In a companion paper, we showed that a simple electrified shell acts as an efficient accumulator for such particles, parametrically enhancing their local density by many

Pablo Bueno, Robie A. Hennigar, Ángel J. Murcia

Quasi-topological gravities (QTGs) are higher-curvature extensions of Einstein gravity in $D\geq 5$ spacetime dimensions. Throughout the years, different notions of QTGs constructed from analytic functions of polynomial curvature invariants have been introduced in the literature. In this paper, we show that all such definitions may be reduced to three distin

Ameya Chavda, Daniel McLoughlin, Sebastian Mizera, John Staunton

We propose that the broad architecture of the renormalization group flow in quantum field theories is, at least in part, fixed by unitarity. The precise statement is summarized in the Unitarity Flow Conjecture, which states that the non-linear $S$-matrix identities obtained by imposing unitarity imply those needed to derive the renormalization group equation

Avery J. Tishue, Charuhas Shiveshwarkar, Gilbert Holder

Recent reconstructions of the large-scale cosmological velocity field with kinetic Sunyaev Zeldovich (kSZ) tomography have returned an amplitude that is low with respect to the halo model prediction, captured by the kSZ velocity reconstruction bias $b_v <1$. This suggests that common choices for modeling the galaxy-electron cross correlation have systematica

Baolu Li, Yiming Zhang, Qinghe Wang, Liqian Ma

Visual effects (VFX) are crucial to the expressive power of digital media, yet their creation remains a major challenge for generative AI. Prevailing methods often rely on the one-LoRA-per-effect paradigm, which is resource-intensive and fundamentally incapable of generalizing to unseen effects, thus limiting scalability and creation. To address this challen

Nathan Godey, Wissam Antoun, Rian Touchent, Rachel Bawden

We release Gaperon, a fully open suite of French-English-coding language models designed to advance transparency and reproducibility in large-scale model training. The Gaperon family includes 1.5B, 8B, and 24B parameter models trained on 2-4 trillion tokens, released with all elements of the training pipeline: French and English datasets filtered with a neur

Maximilian Willer, Peter Ruckdeschel

flowengineR is an R package designed to provide a modular and extensible framework for building reproducible algorithmic workflows for general-purpose machine learning pipelines. It is motivated by the rapidly evolving field of algorithmic fairness, where new metrics, mitigation strategies, and methods continuously emerge. A central challenge in fairness, bu

Guneet S. Dhillon, Javier González, Teodora Pandeva, Alicia Curth

While generative models, especially large language models (LLMs), are ubiquitous in today's world, principled mechanisms to assess their (in)correctness are limited. Using the conformal prediction framework, previous works construct sets of LLM responses where the probability of including an incorrect response, or error, is capped at a user-defined tolerance

Naoki Kiyohara, Edward Johns, Yingzhen Li

Stochastic differential equations (SDEs) are well suited to modelling noisy and irregularly sampled time series found in finance, physics, and machine learning. Traditional approaches require costly numerical solvers to sample between arbitrary time points. We introduce Neural Stochastic Flows (NSFs) and their latent variants, which directly learn (latent) S

Kush Hari, Ziyang Chen, Hansoul Kim, Ken Goldberg

Surgical suturing is a high-precision task that impacts patient healing and scarring. Suturing skill varies widely between surgeons, highlighting the need for robot assistance. Previous robot suturing works, such as STITCH 1.0 [1], struggle to fully close wounds due to inaccurate needle tracking and poor thread management. To address these challenges, we pre

Sriram Balasubramanian, Samyadeep Basu, Koustava Goswami, Ryan Rossi

Large language models (LLMs) are increasingly used for long-document question answering, where reliable attribution to sources is critical for trust. Existing post-hoc attribution methods work well for extractive QA but struggle in multi-hop, abstractive, and semi-extractive settings, where answers synthesize information across passages. To address these cha

Chuhao Chen, Isabella Liu, Xinyue Wei, Hao Su

Articulated 3D objects are central to many applications in robotics, AR/VR, and animation. Recent approaches to modeling such objects either rely on optimization-based reconstruction pipelines that require dense-view supervision or on feed-forward generative models that produce coarse geometric approximations and often overlook surface texture. In contrast,

Faizuddin Ahmed, Ahmad Al-Badaw, Orhan Donmez, Izzet Sakalli

We present an investigation of quantum-corrected black hole spacetimes coupled with clouds of strings, examining two distinct theoretical models that incorporate quantum gravitational effects through different implementations of correction terms. Our study explores the geodesic structure, focusing on photon sphere properties, black hole shadows, and innermos

Chase Vogeli

We prove an induction theorem for the higher algebraic K-groups of group algebras $kG$ of finite groups $G$ over characteristic $p$ finite fields $k$. For a certain class of finite groups, which we call $p$-isolated, this reduces calculations to calculations for their $p$-subgroups. We do so by showing that the stable module categories of $kH$ as $H$ ranges

M. I. Gornostaev

The radiation-dominated polar emitting region of an accreting X-ray pulsar is simulated numerically in the framework of a three-dimensional (geometrically two-dimensional) model. The radiative transfer within the emitting region and the structure of the latter are calculated with the use of the self-consistent algorithm developed earlier. The magnetic scatte

Chumeng Liang, Jiaxuan You

Diagrams play a central role in research papers for conveying ideas, yet they are often notoriously complex and labor-intensive to create. Although diagrams are presented as images, standard image generative models struggle to produce clear diagrams with well-defined structure. We argue that a promising direction is to generate demonstration diagrams directl

Vanessa Figueiredo, David Elumeze

Large Language Models (LLMs) promise to transform interactive games by enabling non-player characters (NPCs) to sustain unscripted dialogue. Yet it remains unclear whether constrained prompts actually improve player experience. We investigate this question through The Interview, a voice-based detective game powered by GPT-4o. A within-subjects usability stud

Xu Zheng, Zihao Dongfang, Lutao Jiang, Boyuan Zheng

Humans possess spatial reasoning abilities that enable them to understand spaces through multimodal observations, such as vision and sound. Large multimodal reasoning models extend these abilities by learning to perceive and reason, showing promising performance across diverse spatial tasks. However, systematic reviews and publicly available benchmarks for t

Ethan Harvey, Dennis Johan Loevlie, Michael C. Hughes

Multiple instance learning (MIL) is often used in medical imaging to classify high-resolution 2D images by processing patches or classify 3D volumes by processing slices. However, conventional MIL approaches treat instances separately, ignoring contextual relationships such as the appearance of nearby patches or slices that can be essential in real applicati

He Hu, Chiyuan Ma, Qianning Wang, Lin Liu

The shortage of mental health professionals has driven the web to become a primary avenue for accessible psychological support. While Large Language Models (LLMs) offer promise for scalable web-based counseling, existing approaches often lack emotional understanding, adaptive strategies, and long-term memory. These limitations pose risks to digital well-bein