December 2025 arXiv papers — page 94

Steven Owen, Nathan Brown, Nikos Chrisochoides, Rao Garimella

Artificial intelligence is beginning to reduce the manual effort in the CAD-to-mesh pipeline. Written for meshing and geometry practitioners with limited AI background, this survey organizes recent work by workflow step. We cover part classification and segmentation, mesh quality prediction, and defeaturing. We review AI guidance for unstructured meshing, bl

Wyatt Gibbons, Teng Zhang, Kevin Barrow, Tyler Lindemann

In superconducting quantum interference devices (SQUIDs), the superconducting diode effect may be generated by interference of multiple harmonic components in the current-phase relationships (CPRs) of different branches forming SQUID loops. Through the inclusion of two gate-tunable Josephson junctions in series in each interference branch of a double-loop SQ

Turja Kundu, Sanjukta Bhowmick

Graph neural networks (GNNs) excel on homophilic graphs where connected nodes share labels, but struggle with heterophilic graphs where edges do not imply similarity. Moreover, iterative message passing limits scalability due to neighborhood expansion overhead. We introduce ATLAS (Adaptive Topology-based Learning at Scale), a propagation-free framework that

Ghaith Hiary, Tianyu Zhao

We make explicit a result of Selberg on the argument of Dirichlet $L$-functions averaged over non-principal characters modulo a prime $q$. As a corollary, we show for all sufficiently large prime $q$ that the height of the lowest non-trivial zero of the corresponding family of $L$-functions is less than $1075\cdot \frac{2\pi}{\log q}$. Here the scaling facto

Javier Reynoso-Cordova, Daniele Gaggero, Marco Regis, Marco Taoso

The Large Magellanic Cloud (LMC) is the largest satellite galaxy of the Milky Way and provides a unique laboratory for high-energy astrophysics and dark matter studies. In this work, we develop an end-to-end numerical description of cosmic-ray transport and the associated non-thermal emission in the LMC, extending the public DRAGON and HERMES codes. Within t

N. Reyes, A. Weiss, S. J. C. Yates, A. M. Baryshev

The thermal emission at sub-millimeter wavelengths carries unique information in many astronomical applications ranging from disks and planet formation around young stars, to galaxy evolution studies at cosmological distances. Advancing on the mapping speed to detect this faint emission in ground-based astronomy has been a technical challenge for decades. Th

Marc Kegel, Isacco Nonino, Monika Yadav

In this article, we define the contact surgery distance of two contact 3-manifolds $(M,\xi)$ and $(M',\xi')$ as the minimal number of contact surgeries needed to obtain $(M,\xi)$ from $(M',\xi')$. Our main result states that the contact surgery distance between two contact $3$-manifolds is at most $5$ larger than the topological surgery distance between the

Matthew Heaney, Olive Healy, Jason Wyse, Arthur White

Sleep difficulties in children are heterogeneous in presentation, yet conventional assessment tools like the Children's Sleep Habits Questionnaire (CSHQ) reduce this complexity to a single cumulative score, obscuring distinct patterns of sleep disturbance that require different interventions. Latent Class Regression (LCR) models offer a principled approach t

Francesca Gomez, Adam Buick, Leah Ferentinos, Haelee Kim

Frontier AI developers operate at the intersection of rapid technical progress, extreme risk exposure, and growing regulatory scrutiny. While a range of external evaluations and safety frameworks have emerged, comparatively little attention has been paid to how internal organizational assurance should be structured to provide sustained, evidence-based oversi

Yuuki Ishiwatari, Takahiro Yokoyama, Tomoya Kojima, Taisuke Banno

Predicting self-assembly in multi-component amphiphilic systems is challenging due to the complexity of intercomponent interactions and the combinatorial growth of possible formulations. In this study, we develop a unified machine-learning framework that directly predicts self-assembly behavior from the molecular structures of constituent components, indepen

Marius Fischer, Peter Vang Uttenthal

By extending the notion of spin of prime ideals, we show that a short character sum conjecture implies that the set of primes raising the level of a certain even Galois representation has density 2/3, as conjectured by Ramakrishna in 1998.

Petar Mitrić, Veljko Janković, Darko Tanasković, Nenad Vukmirović

We assess the accuracy of the cumulant expansion (CE) method, combined with the independent-particle approximation (IPA), for calculating charge mobility in electron-phonon systems. As representative testbeds, we consider the Peierls and Fr\"ohlich models, which serve as simplified frameworks where accurate or numerically exact benchmarks are available. Thes

J. Sumaya-Martinez

We develop a quantum metrological framework for resonant nanophotonic sensors based on subwavelength Fabry--Perot slit cavities. Building on classical Fisher-information analyses of resonant transmission sensors, we model parameter encoding as a phase-and-loss quantum channel embedded in one arm of a Mach-Zehnder interferometer. We derive the quantum Fisher

María Benito, Annaliina Aavik, Giuseppina Battaglia, Salvador Cardona-Barrero

The chemical dichotomy in the [$\alpha$/Fe]-[Fe/H] plane is a consequence of the complex processes underlying the formation and evolution of disc galaxies such as observed in the stellar Milky Way disc. We determine what can drive an $\alpha$-bimodality of the disc in a zoom-in hydrodynamical simulated galaxy which has had no major mergers and negligible rad

Niklas Lauffer, Xiang Deng, Srivatsa Kundurthy, Brad Kenstler

A popular paradigm for training LM agents relies on imitation learning, fine-tuning on expert trajectories. However, we show that the off-policy nature of imitation learning for multi-turn LM agents suffers from the fundamental limitation known as covariate shift: as the student policy's behavior diverges from the expert's, it encounters states not present i

Xiaojie Tao, Yaoyu Fan, Zhaoyi Ye, Rajit Gadh

The integration of heavy-duty electric vehicles (EVs) with Vehicle-to-Grid (V2G) capability can enhance primary frequency response and improve stability in power systems with high renewable penetration. This study evaluates the technical potential of heavy-duty EV fleets to support the California power grid under three practical charging strategies: immediat

Reza Mohammadkhani, Azad Azizzadeh, Seyed Vahab Al-Din Makki, John Thompson

Low-resolution quantization is essential to reduce implementation cost and power consumption in massive multiple-input multiple-output (MIMO) systems for 5G and 6G. While most existing studies assume perfect channel state information (CSI), we model the impact of coarse quantization noise on both channel estimation and data transmission, yielding a more real

S. M. Yousuf Iqbal Tomal, Abdullah Al Shafin

Quantum state tomography faces exponential scaling with system size, while recent neural network approaches achieve polynomial scaling at the cost of losing the geometric structure of quantum state space. We introduce geometric latent space tomography, combining classical neural encoders with parameterized quantum circuit decoders trained via a metric-preser

Karina I. Mazzitello, Daniel G. Zarlenga, Constancio M. Arizmendi

We investigate the transport properties of particles driven by colored noise in a flashing ratchet potential, focusing on both non-interacting and single-file interacting regimes. The model incorporates memory effects via a non-Markovian friction kernel, leading to superdiffusive dynamics and enhanced currents in the absence of interactions. However, when pa

Chase Wilson

Mubayi and Verstraete conjectured that if $T$ is a tree on $t + 1$ vertices, then any $n$-vertex graph $G$ with average degree $d$ contains at least \[ n d(d - 1) \cdots (d - t + 1) \] labeled copies of $T$ as long as $d$ is sufficiently large compared to $t$. We prove this is true and show that when the diameter of $T$ is at least $3$, equality holds iff $G

Kaustav Mitra, Frank C. van den Bosch, Josephine Baggen, Johannes U. Lange

We develop a novel technique to probe the $S_8$ tension, using information from the smallest scales of galaxy redshift survey data. Specifically, we use Basilisk, a Bayesian hierarchical tool for forward modeling the kinematics and abundance of satellite galaxies extracted from spectroscopic data, to first constrain the galaxy-halo connection precisely and a

Firas Bayram, Bestoun S. Ahmed, Erik Hallin

This paper introduces a novel end-to-end framework that efficiently integrates data quality assessment with machine learning (ML) model operations in real-time production environments. While existing approaches treat data quality assessment and ML systems as isolated processes, our framework addresses the critical gap between theoretical methods and practica

Massimiliano Fadda, Enrico Motta, Francesco Osborne, Diego Reforgiato Recupero

News sources play a central role in democratic societies by shaping political and social discourse through specific topics, viewpoints and voices. Understanding these dynamics is essential for assessing whether the media landscape offers a balanced and fair account of public debate. In earlier work, we introduced a pipeline that, given a news corpus, i) uses

Jing Huang, Ying Ying Ye

Chordal graphs and chordal bigraphs enjoy beautiful characterizations, in terms of forbidden subgraphs, vertex/edge orderings, vertex/edge separating sets, and tree-like representations. In this paper, we introduce chordal signed graphs and chordal signed bigraphs. Interestingly, chordal signed graphs are equivalent to strict chordal digraphs studied by Hell

Patrick Müller, Andrei Tretiakov, Amanda Younes, Nicole Halawani

We present a spectroscopic investigation of $^{169}\mathrm{Tm}^+$ that provides two key foundations for its use as a platform for advanced quantum applications. First, we establish the complete spectroscopic road map for optical cycling (including laser cooling) by performing high-resolution spectroscopy on $^{169}\mathrm{Tm}^+$ ions in an ion trap. We chara

Ilya Trofimov, Daria Voronkova, Alexander Mironenko, Anton Dmitriev

We introduce a topological feedback mechanism for the Travelling Salesman Problem (TSP) by analyzing the divergence between a tour and the minimum spanning tree (MST). Our key contribution is a canonical decomposition theorem that expresses the tour-MST gap as edge-wise topology-divergence gaps from the RTD-Lite barcode. Based on this, we develop a topologic

Huzheng Yang, Katherine Xu, Andrew Lu, Michael D. Grossberg

Creating new visual concepts often requires connecting distinct ideas through their most relevant shared attributes -- their vibe. We introduce Vibe Blending, a novel task for generating coherent and meaningful hybrids that reveals these shared attributes between images. Achieving such blends is challenging for current methods, which struggle to identify and

Lindsey S. Wiser, Alexander Roth, Vivien Parmentier, Michael R. Line

Inferring the properties of transiting exoplanet atmospheres relies on comparing models to spectroscopic observations. Atmosphere models, however, make a range of assumptions, from one-dimensional (1D, varying with altitude) radiative-convective equilibrium (RCE) to three-dimensional (3D) general circulation models (GCMs). The goal of this investigation is t

Nguyen Binh, Arianna S. Long, Jacqueline Antwi-Danso, David C. Andrews

High-redshift ($z > 3$), massive quiescent galaxies (QGs) offer a significant window into early Universe galaxy formation. Previous works have predicted miscellaneous properties for these quiescents, from an overdensity of neighbors to elevated quenched fractions among such neighbors (i.e. galactic conformity). However, due to a scarcity in highly-resolved d

Darrin O' Brien, Dhikshith Gajulapalli, Eric Xia

Results in interpretability suggest that large vision and language models learn implicit linear encodings when models are biased by in-context prompting. However, the existence of similar linear representations in more general adaptation regimes has not yet been demonstrated. In this work, we develop the concept of a task matrix, a linear transformation from

Jingwei Chen

Self-referential learning -- training a model on data it generated itself -- promises boundless scalability but chronically suffers from model collapse: language models degenerate into repetitive text, GANs drop modes, and reinforcement-learning policies over-exploit. Although practitioners employ ad~hoc fixes such as real-data mixing, entropy bonuses, knowl

Wenshuo Li, Majid Mirmehdi, Tilo Burghardt

Biologists have long combined visuals with textual field notes to re-identify (Re-ID) animals. Contemporary AI tools automate this for species with distinctive morphological features but remain largely image-based. Here, we extend Re-ID methodologies by incorporating precise dermatoglyphic textual descriptors-an approach used in forensics but new to ecology.

Conor Rowan

Recently, the explicit constraint force method (ECFM) was introduced as a principled approach to solution reconstruction in the presence of missing physics. In solution reconstruction, parameters of a physical model are estimated from sparse measurement data as a means to obtain the full solution field. In contrast, inverse problems target the missing parame

Daniel Perkins, Davis Hunter, Dhrumil Patel, Galen Flanagan

The recent surge in large language models has automated translations of spoken and written languages. However, these advances remain largely inaccessible to American Sign Language (ASL) users, whose language relies on complex visual cues. Isolated sign language recognition (ISLR) - the task of classifying videos of individual signs - can help bridge this gap

Satyaki Chowdhury, Jakub Mielczarek

Nielsen's geometric approach offers a powerful framework for quantifying the complexity of unitary transformations. In this formulation, complexity is defined as the length of the minimal geodesic in a suitably constructed geometric space associated with the Lie group of relevant operators. Despite its conceptual appeal, determining geodesic distances on Lie

Sahibpreet Singh, Shikha Dhiman

The integration of generative Artificial Intelligence into the digital ecosystem necessitates a critical re-evaluation of Indian criminal jurisprudence regarding computational forensics integrity. While algorithmic efficiency enhances evidence extraction, a research gap exists regarding the Digital Personal Data Protection Act, 2023's compatibility with adve

Michele Perna, Santiago Arribas, Luca Costantin, Pablo G. Pérez-González

Collisional ring galaxies are a rare class of interacting systems, making up only ~0.01% of galaxies in the local Universe. Their formation is typically attributed to a head-on collision of a massive galaxy with a compact satellite (intruder), triggering density waves that, propagating outward, produce the characteristic ring morphology. Here, we present the

Sam Jeong, Hae Yong Kim

Fourier Analysis Network (FAN) was recently proposed as a simple way to improve neural network performance by replacing part of Rectified Linear Unit (ReLU) activations with sine and cosine functions. Although several studies have reported small but consistent gains across tasks, the underlying mechanism behind these improvements has remained unclear. In thi

Rachael Hill, Casey Kovesdi, Torrey Mortenson, Madelyn Polzin

Accelerator control systems often represent relatively complex and safety-sensitive human-machine interfaces within process control industries. These systems are technically robust and reflect the cumulative integration of solutions built and adapted across decades. One of the regular, unfortunate casualties of provisional accelerator control system updates

Tadej Starčič

We compute and analyze isotropy subgroups of the complex orthogonal group with respect to the similarity transformation on itself and on skew-symmetric matrices. Their group structure is related to a group of certain nonsingular block matrices whose blocks are rectangular block Toeplitz.

Dan Ben-Ami, Gabriele Serussi, Kobi Cohen, Chaim Baskin

Video Large Language Models (Video-LLMs) are improving rapidly, yet current Video Question Answering (VideoQA) benchmarks often admit single-cue shortcuts, under-testing reasoning that must integrate evidence across time. We introduce HERBench, a benchmark designed to make multi-evidence integration unavoidable: each question requires at least three non-over

Riley Acker, Aman Desai, Garrett Kenyon, Frank Barrows

We present an oscillatory neuromorphic primitive implemented with networks of coupled Wien bridge oscillators and tunable resistive couplings. Phase relationships between oscillators encode patterns, and a local Hebbian learning rule continuously adapts the couplings, allowing learning and recall to emerge from the same ongoing analog dynamics rather than fr

F. Mireles, E. Ortiz

The Zitterbewegung phenomenon in multiband electronic systems is known to be subtly related to the charge conductivity, Berry curvature and the Chern number. Here we show that some spin-dependent properties as the optical spin conductivity, and intrinsic spin Hall conductivity are also entangled with the Zitterbewegung amplitudes. We also show that in multib

William Meng, Benjamin Lee, Hong Wang

KV cache offloading enables long-context LLM inference by storing caches in CPU DRAM, but PCIe bandwidth limitations create severe bottlenecks. In this paper, we develops an analytical framework that derives $\kappa_{\text{crit}}$, the critical cached-to-prefill token ratio where execution becomes memory-bound and show typical workloads exceed this threshold

Christian Hornhuber, Mohammad Ful Hossain Seikh, Mark Stockham, Scott Voigt

Current experiments seeking first-ever observation of Ultra-High Energy Neutrinos (UHEN) typically utilize radio frequency (RF) receiver antennas deployed in cold, radio-transparent polar ice, to measure the coherent RF signals resulting from neutrino interactions with ice molecules. Accurate calibration of the receiver response, sampling the full range of p

Advait Gosai, Tyler Vuong, Utkarsh Tyagi, Steven Li

End-to-end (E2E) spoken dialogue systems are increasingly replacing cascaded pipelines for voice-based human-AI interaction, processing raw audio directly without intermediate transcription. Existing benchmarks primarily evaluate these models on synthetic speech and single-turn tasks, leaving realistic multi-turn conversational ability underexplored. We intr

Pavel A. Makarov, Vladimir I. Shcheglov

This paper analyzes the accuracy of the standard Yee finite-difference time-domain (FDTD) scheme for simulating normal incidence of harmonic plane waves on planar interfaces between lossless, linear, homogeneous, isotropic media. Unlike prior analyses limited to dielectric interfaces, we provide a unified treatment encompassing both dielectric and magnetic m

Ronnie de Souza Santos, Maria Teresa Baldassarre, Cesar França

Quantum software testing introduces new challenges that differ fundamentally from those in classical software engineering. Aims: This study investigates how the quantum software industry defines testing roles and what skills are expected from professionals in these positions. Method: We analyzed 110 job postings from organizations involved in quantum softwar

Alessandro Casadei, Clemens Grupp, Sreyoshi Bhaduri, Lu Guo

This work introduces a methodology to adjust forecasts based on node-specific cost function asymmetry. The proposed model generates savings by dynamically incorporating the cost asymmetry into the forecasting error probability distribution to favor the least expensive scenario. Savings are calculated and a self-regulation mechanism modulates the adjustments

Viet K. Nguyen, Mohammad I. Husain

Agentic AI introduces security vulnerabilities that traditional LLM safeguards fail to address. Although recent work by Unit 42 at Palo Alto Networks demonstrated that ChatGPT-4o successfully executes attacks as an agent that it refuses in chat mode, there is no comparative analysis in multiple models and frameworks. We conducted the first systematic penetra

Mattia Capuano, Livia Ferro, Tomasz Lukowski, Alessandro Palazio

In this paper we explore the mathematical properties of wavefunction coefficients in power-law FRW cosmologies, and establish their relation to cluster algebras. We focus on the particular contributions to the wavefunction coefficient coming from the path Feynman graphs, and show that the singularities of the wavefunction associated with a $n$-site path grap

Le Chen, Ian Ruau, Wenxian Shen

We study, in Part I of this series, boundedness and global existence of positive classical solutions to a parabolic-elliptic chemotaxis system with signal-dependent sensitivity and a logistic-type source on a bounded smooth domain $\Omega\subset\mathbb{R}^N$: \begin{equation*} \begin{cases} \displaystyle u_t=\Delta u-\chi_0\nabla\cdot\left(\frac{u^m}{(1+v)^\

Munki Jeong, Alexander Strang

Skew-symmetric functions are a class of functions defined on a product space $M \times M$ that are antisymmetric with respect to the order of their inputs. In [13], the authors proved that non-deterministic skew-symmetric Gaussian fields cannot be stationary or isotropic and proposed an alternative notion: stationarity (isotropy) in each component space. Our

Biao Zhang, Paul Suganthan, Gaël Liu, Ilya Philippov

We introduce T5Gemma 2, the next generation of the T5Gemma family of lightweight open encoder-decoder models, featuring strong multilingual, multimodal and long-context capabilities. T5Gemma 2 follows the adaptation recipe (via UL2) in T5Gemma -- adapting a pretrained decoder-only model into an encoder-decoder model, and extends it from text-only regime to m

Faisal Chowdhury, Sola Shirai, Sarthak Dash, Nandana Mihindukulasooriya

A data product is designed to address a specific business need by transforming raw data into a curated, usable asset that delivers actionable insights. Despite practical advances in related areas like text-to-SQL and ELT pipelines, there is no comprehensive benchmark for evaluating the end-to-end process of automatically generating such data products from hi

Mahmuda Sharmin, Taihao Han, Jie Huang, Narayanan Neithalath

Machine learning (ML) has been increasingly applied in concrete research to optimize performance and mixture design. However, one major challenge in applying ML to cementitious materials is the limited size and diversity of available databases. A promising solution is the development of multi-modal databases that integrate both numerical and graphical data.

Pouria Mazloumi, Xiaofeng Xu

In this paper, we explore the cluster algebras for symbol letters or singularities of cosmological correlators in a conformally coupled scalar field theory. We show that the symbol letters for tree-level n-site ladder cosmological correlators are governed by A_{2(n-1)} cluster algebras. Additionally, we demonstrate that the symbol letters for one-loop bubble

George Georgiou, Dimitrios Zoakos

We propose a new class of holographic dualities between certain, generically non supersymmetric, defect conformal field theories (dCFTs) and their gravity duals. Our construction interpolates between the 1/2-BPS D3-D3 system and its field theory dual at one end, and the holographic duality presented in arXiv: 2506.14505 at the other. On the gravity side, the

Sorin Dascalescu, Constantin Nastasescu, Laura Nastasescu, Paul Rebenciuc

If $A$ is a finite-dimensional algebra graded by a group $G$, and $\sigma \in G$, we define a variant of paratrophic matrix associated with $A$ and $\sigma$, and we use it to characterize the $\sigma$-graded Frobenius property for $A$. We discuss the invertibility of such paratrophic matrices, and then use them to check whether certain graded algebras are $\

Aslak Djupskås, Alexander Johannes Stasik, Signe Riemer-Sørensen

Reliable uncertainty estimation is crucial for machine learning models, especially in safety-critical domains. While exact Bayesian inference offers a principled approach, it is often computationally infeasible for deep neural networks. Monte Carlo dropout (MCD) was proposed as an efficient approximation to Bayesian inference in deep learning by applying neu

Carlos M. O. Bastos, Emanuel J. A. dos Santos, José A. dos S. Laranjeira, Kleuton A. L. Lima

Two-dimensional (2D) metallic lattices with kagome topology provide a unique platform for exploring the interplay between geometric frustration, reduced coordination, and lattice stability in elemental systems. Motivated by the recent experimental realization of atomically thin gold layers and kagome goldene, we present a first-principles investigation of fr

Thibault D. Décoppet, Benjamin Haïoun

We investigate invariants of 4-dimensional 2-handlebodies associated to the Temperley-Lieb category in characteristic $p>2$ and at a primitive fourth root of unity. These invariants depend additionally on a height parameter $n$, and we focus on the case $n=2$. Provided that $p>3$, we show that the height $n=2$ invariant associated to the Temperley-Lieb categ

Claudio Lopez, Emilio Donoso, Mariano Javier de L. Dominguez Romero

Modern wide-field surveys require robust spatial masks to excise bright-star halos, bleed trails, poor-quality regions, and user-defined geometry at scale. We present Skykatana, an open source pipeline that builds and combines boolean HEALPix/HEALSparse maps into science-ready masks and engineered for low-memory operation. Skykatana can efficiently construct

Jonathan Granot, Hagai B. Perets, Ramandeep Gill, Paz Beniamini

GRB$\;$250702B is the longest GRB recorded so far, with multiple gamma-ray emission episodes spread over a duration exceeding $25\;$ks and a weaker soft X-ray pre-peak $\sim1\;$day gradually rising emission. It is offset from its host galaxy center by $\sim5.7\;$kpc, and displays a long-lived afterglow emission in radio to X-ray. Its true nature is unclear,

Arth Bhardwaj, Sia Godika, Yuvam Loonker

Traditional, centralized security tools often miss adaptive, multi-vector attacks. We present the Multi-Agent LLM Cyber Defense Framework (MALCDF), a practical setup where four large language model (LLM) agents-Detection, Intelligence, Response, and Analysis-work together in real time. Agents communicate over a Secure Communication Layer (SCL) with encrypted

Rose Albu Mustaf, Sajilesh K. P., Sanu Mishra, Junze Deng

We report the experimental discovery of bulk superconductivity in two kagome lattice compounds, YRu$_3$B$_2$ and LuRu$_3$B$_2$, which were predicted through machine learning-accelerated high-throughput screening combined with first principles calculations. These materials crystallize in the hexagonal CeCo$_3$B$_2$-type structure with planar kagome networks f

Gard Olav Helle, Tommaso Benacchio, Anna Bomme Ousager, Jørgen Ellegaard Andersen

We present a quantum algorithm for the simulation of the linear advection-diffusion equation based on block encodings of high order finite-difference operators and the quantum singular value transform. Our complexity analysis shows that the higher order methods significantly reduce the number of gates and qubits required to reach a given accuracy. The theore

Soutick Saha, Sean Fancher, Andrew Mugler

Bacteria track chemical gradients using a biased random walk, a process called chemotaxis. Experiments suggest that bacteria also communicate during this process. Using a mathematical model, we find that sufficiently strong communication succeeds in keeping a population of bacteria together but slows down chemotaxis. However, if the secretion of the communic

Brian Batell, Akshay Ghalsasi, Wenjie Huang, Matthew Low

$N$-naturalness is a novel solution to the electroweak hierarchy problem which posits $N$ copies of the Standard Model with varying Higgs mass-squared parameters. Reheating proceeds through a "reheaton" particle that deposits most of its energy density into the Standard Model and small but potentially measurable fractions into the other copies. Typically the

Thomas Williams, Christopher B. Prior, David MacTaggart, D. Shaun Bloomfield

Solar flares are a primary driver of space weather, and forecasting their occurrence remains a significant challenge. This paper presents a novel flare prediction model based on topologically derived photospheric magnetic parameters. We employ the \texttt{ARTop} framework to compute the time-dependent input rates of magnetic winding and helicity across more

Antonio Herrero-Brocal, Javier Perez-Soler, Avelino Vicente

The gauge principle is a cornerstone of particle-physics model building. Nevertheless, many constructions leave certain global $U(1)$ redundancies ungauged. In this work, we take the gauge principle to its logical extreme by promoting all $SU(N)$ symmetries to $U(N)$. We focus on a model based on local $U(3)\times U(2)$ invariance. This framework accounts fo

Marianne Moore, Stefano Profumo

Standard thermal freeze-out scenarios with QCD-scale interaction rates predict a $uuddss$ sexaquark relic abundance many orders of magnitude below the observed dark matter density, representing a key challenge for sexaquark dark matter models. Additionally, if the maximum post-inflationary temperature never exceeds the QCD confinement scale, the usual therma

Raghuveer Garani, Chris Kouvaris, Michel H. G. Tytgat, Jérôme Vandecasteele

We investigate hydrostatic configurations of asymmetric dark matter (DM) spheres in scenarios where fermionic DM can propagate into extra spatial dimensions, while Standard Model fields remain confined to ordinary three dimensions. As the number of extra dimensions increases, the effective equation of state for non-relativistic matter softens, making even mo

Felix Forner, Felix Tellander

Quantum field theories containing fields with the same quantum numbers allow for mixed kinetic terms in the Lagrangian, leading to off-diagonal elements in the tree-level two-point function. After removing the mixing by a field rotation, the off-diagonal UV divergences cannot be subtracted by a counterterm, still one can show that the theory is renormalizabl

Alejandro Cruz-Osorio, Claudio Meringolo, Christian M. Fromm, Yosuke Mizuno

The recent 230 GHz observations by the Event Horizon Telescope have resolved the innermost structure of the M87 galaxy, revealing a ring-like feature consistent with thermal synchrotron emission from a magnetized torus surrounding a rotating supermassive black hole. Moreover, Global Millimeter VLBI Array observations at 86 GHz have revealed a larger-scale, e

Samuel Schlegel, Borivoje Dakić, Flavio Del Santo

Entanglement is often regarded as an inherently quantum feature. We show that this does not have to be the case: under restricted operational access, classical correlations can appear nonseparable when expressed in the formalism of quantum mechanics. If an observer is limited to a constrained set of measurements and transformations, certain classical phase-s

Pallabi Dey, Debasish Banerjee, Emilie Huffman

The fermion sign problem poses a formidable challenge to the use of Monte Carlo methods for lattice gauge theories with dynamical fermionic matter fields. A meron cluster algorithm recently formulated for gauge fields represented as spin-$\frac{1}{2}$ quantum links coupled to a single flavour of staggered fermions samples only two of the exponentially many G

A. A. Chrimes, N. Sarin, D. Coppejans, P. J. Groot

Luminous Fast Blue Optical Transients (LFBOTs) are a class of extragalactic transient of uncertain origin. Several hypotheses have been put forward which could feasibly be consistent with the sample number of events discovered thus far, including tidal disruption events around intermediate mass black holes, failed supernovae and mergers of stars with black h

Zacharie Van Herstraeten, Jack Davis, Nuno C. Dias, João N. Prata

Providing an operational characterization of the Wigner-positive states (WPS), i.e., the set of quantum states with non-negative Wigner function, is a longstanding open problem. For pure states, the only WPS are Gaussian states, but the situation is considerably more subtle for mixed states. Here, we approach the problem using convex geometry, reducing the q

Caterina Zerba, Sarang Gopalakrishnan, Michael Knap

We explore the information-theoretic phases of monitored quantum circuits subject to dynamics that conserves both charge and dipole moment, as well as measurements of the local charge density. Explicitly, both charge and dipole-moment conservation are strong symmetries, but under the dynamics they can be spontaneously broken to weak symmetries: this spontane

Qi-Yuan Mao, H. Lu

We consider Einstein-Maxwell gravity in diverse dimensions and construct the small charge perturbation to the extremal rotating black holes with all equal angular momenta in odd $D=2n+1$ dimensions. Exact solutions exist at the next-to-leading order (NLO), and they are analytic, allowing us to obtain the charge corrections to thermodynamic quantities at this

Giovani Dalla Valle Garcia, Juan Herrero-García, Joel Jones-Pérez, Javier Silva-Malpartida

Sub-GeV dark matter (DM) has emerged as a particularly compelling target in light of the persistent null results from conventional DM searches. While s-wave annihilating DM candidates with masses below the GeV are strongly constrained by indirect-detection bounds, inelastic scenarios can naturally evade these limits. In this work, we show that parity violati

Sreemayee Aditya, Xhek Turkeshi, Piotr Sierant

Quantum many-body dynamics generate nonclassical correlations naturally described by quantum resource theories. Quantum magic resources (or nonstabilizerness) capture deviation from classically simulable stabilizer states, while coherence and fermionic non-Gaussianity measure departure from the computational basis and from fermionic Gaussian states, respecti

Stephan Foldes, Russ Woodroofe

We show that in a rank supersolvable lattice that is graded by a bounded real interval, any antichain cutset is a level set for some appropriately constructed grading. As a consequence, given an antichain cutset in any of the measurable Boolean lattice, a continuous partition lattice, or a continuous projective geometry, we may find a grading in which the cu

Olivia Curtis, Bryanne McDonough, Tereasa Brainerd

We study void galaxies in the TNG300 simulation between redshifts $z=3$ and $z=0$. Cosmic void catalogs were constructed using a watershed-based void-finding algorithm, and we define four populations of field galaxies for our investigation: [1] galaxies that are members of a watershed void, [2] galaxies that are located within a radius $r \leq 0.8 R_{\rm eff

Obada Nairat, John F. Beacom, Kevin J. Kelly, Shirley Weishi Li

Matter-induced neutrino flavor mixing (the Mikheyev-Smirnov-Wolfenstein, or MSW, effect) is a central prediction of the neutrino mixing framework, but it has not been conclusively observed. Direct observation of the energy-dependent MSW transition in the solar electron-neutrino survival probability would solve this, but backgrounds have been prohibitive. We

L. Bisigello, G. Gandolfi, A. Grazian, G. Rodighiero

The dust content of star-forming galaxies is generally positively correlated with their stellar mass. However, some recent JWST studies have shown the existence of a population of dwarf galaxies with an unexpectedly large dust attenuation. Using the Cosmic Evolution Early Release Science Survey (CEERS) data, we identified a sample of 1361 highly extincted lo

Ping He, Jing Shu, Bin Xu, Jincheng Xu

We identify symmetric Dicke states as the optimal quantum probes for distributed sensing of wave-like dark-matter fields. Within an ensemble-averaged quantum-metrological framework that incorporates the field's random phases and finite coherence, they maximize the Fisher information for short-baseline arrays with $N_d$ sensors and realize a robust $N_d^2$ en

Nicolas J. Cerf, Ulysse Chabaud, Jack Davis, Nuno C. Dias

For Hilbert spaces $\mathcal H\subseteq L^2(\mathbb R)$ we consider the convex sets $\mathcal D_+(\mathcal H)$ of Wigner-positive states (WPS), i.e.~density matrices over $\mathcal H$ with non-negative Wigner function. We investigate the topological structure of these sets, namely concerning closure, compactness, interior and boundary (in a relative topology

Julien Pinske, Jan Sperling, Klaus Mølmer

There are processes that cannot generate entanglement but may, nevertheless, amplify entanglement already present in a system. Here, we show that a non-entangling operation can increase the Schmidt number of a quantum state only if it can generate entanglement with some non-zero probability. This is in stark contrast to the case where the parties of a quantu

Ema Šipková, Alex Kemp, Dario Fritzewski, Andrew Tkachenko

Space-based photometry has substantially increased the number of pulsating stars found in binary systems by more than four orders of magnitude. Combined with high-resolution spectroscopy, high-precision photometry offers model-independent constraints on stellar parameters and internal processes. The advent of space-based photometric surveys has given us acce

Marcus Högås, Edvard Mörtsell, Harry Desmond, Adam Riess

We revisit the local distance ladder measurement of the Hubble constant in models where gravity is modified by a fifth force, an additional long-range interaction. In many such theories the force is screened; suppressed in dense environments but potentially active in galaxies used for distance calibration. We model this environmental dependence using three q

Arnau Bas i Beneito, Ajdin Palavrić, Andrea Sainaghi

In the Standard Model, baryon number is an accidental symmetry, whose violation would constitute unambiguous evidence of new physics, with proton decay providing its most prominent experimental signature. At the same time, the peculiar structure of flavor can serve as a guiding principle for exploring possible new-physics effects. In this work, we present a

Ali Fatemiabhari, Horatiu Nastase, Carlos Nunez, Dibakar Roychowdhury

We investigate holographic Krylov complexity in fully top-down AdS$_3$ and AdS$_2$ supergravity backgrounds dual to two-dimensional linear-quiver SCFTs and one-dimensional conformal quantum mechanics. In these geometries, the warp factors, dilaton and other fields depend non-trivially on the 'quiver coordinate' (denoted by $\eta$ in this paper). This $\eta$-

Aaron Z. Goldberg

The defining feature of ideal Gottesman-Kitaev-Preskill (GKP) states is that they are unchanged by stabilizers, which allow them to detect and correct for common errors without destroying the quantum information encoded in the states. Given this property, can one use the amount to which a state is unchanged by the stabilizers as a proxy for the quality of a

Semih Tuna, Brian D. Metzger, Yan-Fei Jiang, Andrea Antoni

The formation of a compact accretion disk following a tidal disruption event (TDE) requires that the shocked stellar debris cool efficiently as it settles toward the black hole. While recent simulations suggest that stream dissipation occurs rapidly, how the weakly bound debris subsequently loses its thermal energy to assemble a compact disk near the circula

Sihui Ji, Xi Chen, Shuai Yang, Xin Tao

The core challenge for streaming video generation is maintaining the content consistency in long context, which poses high requirement for the memory design. Most existing solutions maintain the memory by compressing historical frames with predefined strategies. However, different to-generate video chunks should refer to different historical cues, which is h

Jun Zhang, Teng Wang, Yuying Ge, Yixiao Ge

This paper does not introduce a novel method but instead establishes a straightforward, incremental, yet essential baseline for video temporal grounding (VTG), a core capability in video understanding. While multimodal large language models (MLLMs) excel at various video understanding tasks, the recipes for optimizing them for VTG remain under-explored. In t

Yue Zhao, Hanwen Jiang, Zhenlin Xu, Chutong Yang

Non-parametric quantization has received much attention due to its efficiency on parameters and scalability to a large codebook. In this paper, we present a unified formulation of different non-parametric quantization methods through the lens of lattice coding. The geometry of lattice codes explains the necessity of auxiliary loss terms when training auto-en

Zihan Wang, Jiashun Wang, Jeff Tan, Yiwen Zhao

We introduce CRISP, a method that recovers simulatable human motion and scene geometry from monocular video. Prior work on joint human-scene reconstruction relies on data-driven priors and joint optimization with no physics in the loop, or recovers noisy geometry with artifacts that cause motion tracking policies with scene interactions to fail. In contrast,