May 2025 arXiv papers — page 51

Jihao Jia, Felix Tebbenjohanns, Thomas Hoinkes, Jürgen Volz

Tapered optical fibers (TOFs) with sub-wavelength-diameter waists, known as optical nanofibers, are powerful tools for interfacing quantum emitters and nanophotonics. These applications demand stable polarization of the fiber-guided light field. However, the linear birefringence resulting from \r{A}ngstr\"om-scale deviations in the nanofiber's ideally circul

Shiping Zhou, Juntao Huang, Andrew J. Christlieb

Shallow-water equations and low-order shallow-water moment models use vertically coarse representations and therefore cannot, in general, resolve the thin wall-affected region produced by a no-slip bottom. Enforcing the pointwise wall value on a low-order global polynomial reconstruction can introduce stiff relaxation and distort the resolved interior veloci

Polad Geidarov

This paper presents an algorithm for analytically calculating the weights and thresholds of convolutional neural networks (CNNs) without using standard training procedures. The algorithm enables the determination of CNN parameters based on just 10 selected images from the MNIST dataset, each representing a digit from 0 to 9. As part of the method, the number

Lanke Fu, Eduardo Rodriguez, Rory Conlin, Amitava Bhattacharjee

Quasisymmetry (QS) is a property of special magnetic configurations, where the magnetic field strength, but not necessarily the full vector field, has a direction of symmetry. QS leads to reduced neoclassical transport and thus can be a desirable property in stellarator design. The Garren-Boozer (GB) conundrum has been interpreted to mean that globally quasi

Yi Zhang, Zhenzhen Zhang

The growing aging population has significantly increased demand for efficient home health care (HHC) services. This study introduces a Vehicle Routing and Appointment Scheduling Problem (VRASP) to simultaneously optimize caregiver routes and appointment times, minimizing costs while improving service quality. We first develop a deterministic VRASP model and

Selena Ling, Merlin Nimier-David, Alec Jacobson, Nicholas Sharp

Neural fields are a highly effective representation across visual computing. This work observes that fitting these fields is greatly improved by incorporating spatial stochasticity during training, and that this simple technique can replace or even outperform custom-designed hierarchies and frequency space constructions. The approach is formalized as implici

Ghassan Al-Mahmood, Mohsen Amini, Ebrahim Ghanbari-Adivi, Morteza Soltani

We present an exact analytical investigation of the topological properties and edge states of the Haldane model defined on a honeycomb lattice with zigzag edges. By exploiting translational symmetry along the ribbon direction, we perform a dimensional reduction that maps the two-dimensional model into a family of effective one-dimensional systems parametrize

Chenghao Qian, Wenjing Li, Yuhu Guo, Gustav Markkula

In this work, we present WeatherEdit, a novel weather editing pipeline for generating realistic weather effects with controllable types and severity in 3D scenes. Our approach is structured into two key components: weather background editing and weather particle construction. For weather background editing, we introduce an all-in-one adapter that integrates

David O'Neill, Daniel J. D'Orazio, Martin E. Pessah

We present a study of equal-mass hyperbolic encounters, embedded in a uniform gaseous medium. Using linear perturbation theory, we calculate the density wakes excited by these perturbers and compute the resulting forces exerted on them by the gas. We compute the changes to orbital energy, orbital angular momentum and apsidal precession across a wide range of

Lei Tian, Xiaomin Li, Liqian Ma, Hao Yin

Recent advances in 3D reconstruction techniques and vision-language models have fueled significant progress in 3D semantic understanding, a capability critical to robotics, autonomous driving, and virtual/augmented reality. However, methods that rely on 2D priors are prone to a critical challenge: cross-view semantic inconsistencies induced by occlusion, ima

Cédric Lorcé, Asmita Mukherjee, Ravi Singh, Ho-Yeon Won

We discuss in detail the relativistic spatial distribution of transverse angular momentum, including both orbital and intrinsic spin contributions. Using the quantum phase-space formalism, we begin with the definition of the three-dimensional spatial distributions of transverse orbital angular momentum and intrinsic spin in a generic Lorentz frame. By integr

Tolga Altinoluk, Guillaume Beuf, Jamal Jalilian-Marian

We calculate the one-loop corrections to the quark TMD in the light-cone gauge using the background field formalism, with the Mandelstam-Leibbrandt (ML) prescription for the extra singularity present in the light-cone gauge propagator. We use the pure rapidity regulator for rapidity divergences. The Collins-Soper-Sterman (CSS) evolution equations are indeed

P. S. Kesavan, Pontus Nordenfelt

Smart microscopy represents a paradigm shift in biological imaging, moving from passive observation tools to active collaborators in scientific inquiry. Enabled by advances in automation, computational power, and artificial intelligence, these systems are now capable of adaptive decision-making and real-time experimental control. Here, we introduce a theoret

Lorenzo Lucchese, Mikko S. Pakkanen, Almut E. D. Veraart

The expected signature maps a collection of data streams to a lower dimensional representation, with a remarkable property: the resulting feature tensor can fully characterize the data generating distribution. This "model-free" embedding has been successfully leveraged to build multiple domain-agnostic machine learning (ML) algorithms for time series and seq

Samuel Rhys Cox, Rune Møberg Jacobsen, Niels van Berkel

Self-disclosure, the sharing of one's thoughts and feelings, is affected by the perceived relationship between individuals. While chatbots are increasingly used for self-disclosure, the impact of a chatbot's framing on users' self-disclosure remains under-explored. We investigated how a chatbot's description of its relationship with users, particularly in te

Ulrich Haisch, Aparna Sankar, Giulia Zanderighi

We calculate the corrections to the Higgs wave-function renormalization constant arising from modified cubic, quartic, and quintic Higgs self-couplings up to the two-loop level. Using our analytic results, we derive two-dimensional constraints on the modifications of the considered Higgs self-interactions that could potentially be set from precision measurem

R. S. Giles, T. K. Greathouse, R. W. Ebert, W. S. Kurth

The highly elliptical polar orbit of the Juno mission provides a unique opportunity to simultaneously measure the compression state of Jupiter's magnetosphere and the total power emitted by the planet's ultraviolet aurora, using a single spacecraft. This allows us to study how Jupiter's aurora respond to a compression event. In this paper, we present a case

Ruiqi Wu, Xinjie Wang, Liu Liu, Chunle Guo

We present DIPO, a novel framework for the controllable generation of articulated 3D objects from a pair of images: one depicting the object in a resting state and the other in an articulated state. Compared to the single-image approach, our dual-image input imposes only a modest overhead for data collection, but at the same time provides important motion in

Dimitrios Giataganas

The integration of high-energy degrees of freedom along the renormalization group (RG) flow in Poincar\'e-invariant theories can be captured by a monotonic c-function. For such theories, holographic monotonic c-functions have been constructed using entanglement entropy. However, in theories with broken Lorentz invariance, such constructions generally fail, r

B. Berlitz, E. Daum, S. Deck, A. V. Ustinov

Superconducting qubits show great promise to realize practical quantum computers from micro-fabricated integrated circuits. However, their solid-state architecture bears the burden of parasitic modes in qubit materials and the control circuitry which cause decoherence and interfere with qubits. Here, we present evidence that wirebonds, which are used to cont

Zhiyuan Zhang, Amir Vaxman, Stefanos-Aldo Papanicolopulos, Kartic Subr

Elliptic Partial Differential Equations (PDEs) play a central role in computing the equilibrium conditions of physical problems (heat, gravitation, electrostatics, etc.). Efficient solutions to elliptic PDEs are also relevant to computer graphics since they encode global smoothness with local control leading to stable, well-behaved solutions. The Poisson equ

Rafael Valente da Silva, Onel L. Alcaraz López, Richard Demo Souza

Federated learning (FL) faces significant challenges in Internet of Things (IoT) networks due to device limitations in energy and communication resources, especially when considering the large size of FL models. From an energy perspective, the challenge is aggravated if devices rely on energy harvesting (EH), as energy availability can vary significantly ove

Roman G. Smirnov

In their seminal 1928 work, Charles Cobb and Paul Douglas empirically validated the Cobb-Douglas production function through statistical analysis of U.S. economic data from 1899 to 1923. While this established the function's theoretical foundation for growth models like Solow-Swan and its extensions, it simultaneously revealed a fundamental limitation: their

Matthew Hong, Anthony Liang, Kevin Kim, Harshitha Rajaprakash

We hand the community HAND, a simple and time-efficient method for teaching robots new manipulation tasks through human hand demonstrations. Instead of relying on task-specific robot demonstrations collected via teleoperation, HAND uses easy-to-provide hand demonstrations to retrieve relevant behaviors from task-agnostic robot play data. Using a visual track

Reid Graves, Anthony Zhou, Amir Barati Farimani

Accurate prediction of blast pressure fields is essential for applications in structural safety, defense planning, and hazard mitigation. Traditional methods such as empirical models and computational fluid dynamics (CFD) simulations offer limited trade offs between speed and accuracy; empirical models fail to capture complex interactions in cluttered enviro

Mohammad S. Mirakhor, Stephen A. Walker

We present an analysis of the hard X-ray emission from the central region of Abell 3667 using deep NuSTAR observations. While previous studies on the nature of the hard X-ray excess have been controversial, our analysis of the central region suggests that the excess is primarily thermal, best described by a two-temperature (2T) model, with the high-temperatu

Hao Zhao, Rong Pan

Multiple change point (MCP) detection in non-stationary time series is challenging due to the variety of underlying patterns. To address these challenges, we propose a novel algorithm that integrates Active Learning (AL) with Deep Gaussian Processes (DGPs) for robust MCP detection. Our method leverages spectral analysis to identify potential changes and empl

Sahana Ramnath, Anurag Mudgil, Brihi Joshi, Skyler Hallinan

Today, large language models are widely used as judges to evaluate responses from other language models. Hence, it is imperative to benchmark and improve these LLM-judges on real-world language model usage: a typical human-assistant conversation is lengthy, and shows significant diversity in topics, intents, and requirements across turns, e.g. social interac

Sandip Roy, Anirudh Prabhu, Christopher Thompson, Samuel J. Witte

Axion dark matter passing through the magnetospheres of magnetars can undergo hyper-efficient resonant mixing with low-energy photons, leading to the production of narrow spectral lines that could be detectable on Earth. Since this is a resonant process triggered by the spatial variation in the photon dispersion relation, the luminosity and spectral properti

Jamal El Qars, Ismail Essaoudi, Abdelmajid Ainane

Quantum steering is a form of quantum correlation that exhibits an inherent asymmetry, distinguishing it from entanglement and Bell nonlocality. It is now understood that quantum steering plays a pivotal role in asymmetric quantum information tasks. In this work, we propose a scheme to generate asymmetric steering between two mechanical modes by transferring

D. Shiga, S. Inoue, T. Kanda, N. Hasegawa

We investigated the origin of collective electronic phase transitions induced at the heterointerface between monoclinic insulating VO2 and rutile metallic electron-doped VO2 layers using in situ soft x-ray photoemission spectroscopy (PES) and x-ray absorption spectroscopy (XAS) on nanoscale VO2/V0.99W0.01O2 (001)R bilayers. Thanks to the surface sensitivity

Hemant K. Mishra, Ludovico Lami, Mark M. Wilde

The Barnum-Knill theorem states that the optimal success probability in the multiple state discrimination task is not more than the square root of the success probability when the pretty good or square-root measurement is used for this task. An assumption of the theorem is that the underlying ensemble consists of finitely many quantum states over a finite-di

Tal Gonen, Itai Pemper, Ilan Naiman, Nimrod Berman

Generative modeling of time series is a central challenge in time series analysis, particularly under data-scarce conditions. Despite recent advances in generative modeling, a comprehensive understanding of how state-of-the-art generative models perform under limited supervision remains lacking. In this work, we conduct the first large-scale study evaluating

Zhaolin Li, Jan Niehues

With approximately 7,000 languages spoken worldwide, current large language models (LLMs) support only a small subset. Prior research indicates LLMs can learn new languages for certain tasks without supervised data. We extend this investigation to speech recognition, investigating whether LLMs can learn unseen, low-resource languages through in-context learn

Haoran Li, Yingjie Qin, Baoyuan Ou, Lai Xu

Vision-Language Models (VLMs) have made significant progress in multimodal tasks. However, their performance often deteriorates in long-context scenarios, particularly long videos. While Rotary Position Embedding (RoPE) has been widely adopted for length generalization in Large Language Models (LLMs), extending vanilla RoPE to capture the intricate spatial-t

Kalliopi Petraki, Anna Socha, Christiana Vasilaki

In many Abelian and non-Abelian theories, standard calculations of radiative bound-state formation violate partial-wave unitarity - even at arbitrarily small couplings - when capture into excited states is considered. Recent work demonstrated that unitarity can be restored by the proper resummation of squared inelastic processes in the self-energy of the inc

Oliver Morsch, G. Massimo Palma, Davide Rossini

In this review we give a brief overview of quantum simulation as applied to the study of complex systems. In particular, we cover the basic ideas of quantum simulation, neuromorphic computation, the Sachdev-Ye-Kitaev model, as well as applications to quantum batteries.

Shihong Pan, Dimitri Monokandylos, Bing Qi

In continuous-variable (CV) QKD with optical coherent detection, the widely adopted \textit{trusted detector noise} model improves both the secret key rate and the transmission distance. This model assumes that detector noise is inherently random and inaccessible to an adversary. While substantial research has focused on shielding the detector, it is far mor

Juan Burbano-Gallegos, Carlos Pérez-Arancibia, Catalin Turc

This paper introduces a novel class of indirect boundary integral equation (BIE) formulations for the solution of electromagnetic scattering problems involving smooth perfectly electric conductors (PECs) in three-dimensions. These combined-field-type BIE formulations rely exclusively on classical Helmholtz boundary operators, resulting in provably well-posed

Patrick LaChance, Rupert A. C. Croft, Tiziana Di Matteo, Yihao Zhou

We present simulated counterparts of the ``Little Red Dot'' (LRD) galaxies observed with JWST, using the large cosmological hydrodynamic simulation, ASTRID. We create mock observations of the galaxies ($5 \leq z \leq 8$) in ASTRID, and find seventeen which fit the color and size criteria of LRDs. These LRDs are galaxies with high stellar masses ($\rm log(M_*

Jingyu Liu, Ce Zhang

The growing demand for efficient Large Language Model (LLM) inference requires a holistic optimization on algorithms, systems, and hardware. However, very few works have fundamentally changed the generation pattern: each token needs one forward pass and one KV cache. This can be sub-optimal because we found that LLMs are extremely capable of self-identifying

Dirk Becherer, Yuchen Sun

We study solutions to backward differential equations that are driven hybridly by a deterministic discontinuous rough path $W$ of finite $q$-variation for $q \in [1, 2)$ and by Brownian motion $B$. To distinguish between integration of jumps in a forward- or Marcus-sense, we refer to these equations as forward- respectively Marcus-type rough backward stochas

Maarten Golterman, Yigal Shamir

Within the symmetric mass generation (SMG) approach to the construction of lattice chiral gauge theories, one attempts to use interactions to render mirror fermions massive without symmetry breaking, thus obtaining the desired chiral massless spectrum. If successful, the gauge field can be turned on, and thus a chiral gauge theory can be constructed in the p

Aideen Fay, Inés García-Redondo, Qiquan Wang, Haim Dubossarsky

Existing interpretability methods for Large Language Models (LLMs) predominantly capture linear directions or isolated features. This overlooks the high-dimensional, relational, and nonlinear geometry of model representations. We apply persistent homology (PH) to characterize how adversarial inputs reshape the geometry and topology of internal representation

Abdalla G. M. Ahmed, Matt Pharr, Victor Ostromoukhov, Hui Huang

Low-discrepancy sequences have seen widespread adoption in computer graphics thanks to their superior convergence rates. Since rendering integrals often comprise products of lower-dimensional integrals, recent work has focused on developing sequences that are also well-distributed in lower-dimensional projections. To this end, we introduce a novel constructi

Masha Naslidnyk, Siu Lun Chau, François-Xavier Briol, Krikamol Muandet

Embedding probability distributions into reproducing kernel Hilbert spaces (RKHS) has enabled powerful nonparametric methods such as the maximum mean discrepancy (MMD), a statistical distance with strong theoretical and computational properties. At its core, the MMD relies on kernel mean embeddings to represent distributions as mean functions in RKHS. Howeve

Abylay Bissekenov, Xiaoying Pang, Albrecht Kamlah, M. B. N. Kouwenhoven

Young star clusters can inherit bulk rotation from the molecular clouds from which they have formed. This rotation can affect the long-term evolution of a star cluster and its constituent stellar populations. In this study, we aim to characterize the effects of different degrees of initial rotation on star clusters with primordial binaries. The simulations a

Qimin Chen, Yuezhi Yang, Wang Yifan, Vladimir G. Kim

We introduce a 3D detailizer, a neural model which can instantaneously (in <1s) transform a coarse 3D shape proxy into a high-quality asset with detailed geometry and texture as guided by an input text prompt. Our model is trained using the text prompt, which defines the shape class and characterizes the appearance and fine-grained style of the generated det

T. Hagen, K. S. Dawson, Z. Zheng, J. Aguilar

We measure the projected two-point correlation functions of emission-line galaxies (ELGs) from the Dark Energy Spectroscopic Instrument (DESI) One-Percent Survey and model their dependence on stellar mass and [OII] luminosity. We select $\sim$180,000 ELGs with redshifts of $0.8 < z < 1.6$ and define 27 samples according to cuts in redshift and both galaxy pr

Shuhao Guan, Moule Lin, Cheng Xu, Xinyi Liu

This paper introduces PreP-OCR, a two-stage pipeline that combines document image restoration with semantic-aware post-OCR correction to enhance both visual clarity and textual consistency, thereby improving text extraction from degraded historical documents. First, we synthesize document-image pairs from plaintext, rendering them with diverse fonts and layo

Angelina A. Aquino, Lester James V. Miranda, Elsie Marie T. Or

This paper presents UD-NewsCrawl, the largest Tagalog treebank to date, containing 15.6k trees manually annotated according to the Universal Dependencies framework. We detail our treebank development process, including data collection, pre-processing, manual annotation, and quality assurance procedures. We provide baseline evaluations using multiple transfor

Adrien Houge, Anders Johansen, Edwin Bergin, Fred J. Ciesla

The largest reservoir of carbon in protoplanetary discs is stored in refractory organics, which thermally decompose into the gas-phase at the organics line, well interior to the water iceline. Because this region is so close to the host star, it is often assumed that the released gaseous material is rapidly accreted and plays little role in the evolution of

Yolo Y. Tang, Pinxin Liu, Zhangyun Tan, Mingqian Feng

Understanding perspective is fundamental to human visual perception, yet the extent to which multimodal large language models (MLLMs) internalize perspective geometry remains unclear. We introduce MMPerspective, the first benchmark specifically designed to systematically evaluate MLLMs' understanding of perspective through 10 carefully crafted tasks across t

Raktim Gautam Goswami, Prashanth Krishnamurthy, Yann LeCun, Farshad Khorrami

Visual imitation learning enables robotic agents to acquire skills by observing expert demonstration videos. In the one-shot setting, the agent generates a policy after observing a single expert demonstration without additional fine-tuning. Existing approaches typically train and evaluate on the same set of tasks, varying only object configurations, and stru

Jian Zhang, Hanbo Zhang, Anxing Xiao, David Hsu

Operating home appliances, among the most common tools in every household, is a critical capability for assistive home robots. This paper presents ApBot, a robot system that operates novel household appliances by "reading" their user manuals. ApBot faces multiple challenges: (i) infer goal-conditioned partial policies from their unstructured, textual descrip

Julio de la Torre-Vanegas, Miguel Soriano-Garcia, Israel Becerra, Diego Mercado-Ravell

Landing safely in crowded urban environments remains an essential yet challenging endeavor for Unmanned Aerial Vehicles (UAVs), especially in emergency situations. In this work, we propose a risk-aware approach that harnesses semantic segmentation to continuously evaluate potential hazards in the drone's field of view. By using a specialized deep neural netw

Arvindh Arun, Sumit Kumar, Mojtaba Nayyeri, Bo Xiong

Knowledge Graph Foundation Models (KGFMs) have shown promise in enabling zero-shot reasoning over unseen graphs by learning transferable patterns. However, most existing KGFMs rely solely on graph structure, overlooking the rich semantic signals encoded in textual attributes. We introduce SEMMA, a dual-module KGFM that systematically integrates transferable

Mengfei Liu, Yue Chang, Zhecheng Wang, Peter Yichen Chen

Discontinuities in spatial derivatives appear in a wide range of physical systems, from creased thin sheets to materials with sharp stiffness transitions. Accurately modeling these features is essential for simulation but remains challenging for traditional mesh-based methods, which require discontinuity-aligned remeshing -- entangling geometry with simulati

Mu-En Liu, Kai-Siang Chen, Chung-Yun Hsieh, Gelo Noel M. Tabia

Generic high-dimensional bipartite pure states are overwhelmingly likely to be highly entangled. Remarkably, this ubiquitous phenomenon can already arise in finite-dimensional systems. However, unlike the bipartite setting, the entanglement of generic multipartite pure states, and specifically their multipartite marginals, is far less understood. Here, we sh

Hersh Singh

We construct a family of Ginsparg-Wilson Hamiltonians with improved chiral properties, starting from a construction of Creutz-Horvath-Neuberger that provides a doubler-free Hamiltonian lattice regularization for Dirac fermions in even spacetime dimensions. We use a higher-order generalization of the Ginsparg-Wilson relation due to Fujikawa, which yields an o

Meng Cao, Shuyuan Zhang, Xiao-Wen Chang, Doina Precup

Reinforcement Learning from Human Feedback (RLHF) is a widely used technique for aligning Large Language Models (LLMs) with human preferences, yet it often suffers from sparse reward signals, making effective credit assignment challenging. In typical setups, the reward model provides a single scalar score for an entire generated sequence, offering little ins

Zihong Chen, Wanli Jiang, Jinzhe Li, Zhonghang Yuan

Fine-tuning for large language models (LLMs) typically requires substantial amounts of high-quality supervised data, which is both costly and labor-intensive to acquire. While synthetic data generation has emerged as a promising solution, existing approaches frequently suffer from factual inaccuracies, insufficient long-tail coverage, simplistic knowledge st

Xingwei Tan, Marco Valentino, Mahmud Akhter, Maria Liakata

Large language models (LLMs) have shown strong performance in many reasoning benchmarks. However, recent studies have pointed to memorization, rather than generalization, as one of the leading causes for such performance. LLMs, in fact, are susceptible to content variations, demonstrating a lack of robust planning or symbolic abstractions supporting their re

Royden Wagner, Omer Sahin Tas, Felix Hauser, Marlon Steiner

Motion forecasts of road users (i.e., agents) vary in complexity depending on the number of agents, scene constraints, and interactions. In particular, the output space of joint trajectory distributions grows exponentially with the number of agents. Therefore, we decompose multi-agent motion forecasts into (1) marginal distributions for all modeled agents an

James Sunseri, Alexandra Amon, Jo Dunkley, Nicholas Battaglia

We present the first joint analysis of the kinetic Sunyaev-Zeldovich (kSZ) effect with galaxy-galaxy lensing (GGL) for CMASS galaxies in the Baryon Oscillation Spectroscopic Survey (BOSS). We show these complementary probes can disentangle baryons from dark matter in the outskirts of galactic halos by alleviating model degeneracies that are present when fitt

Guido Pagano, Wojciech Adamczyk, Visal So

Trapped atomic ions are among the most advanced platforms for quantum simulation, computation, and metrology, offering long coherence times and precise, individual control over both internal and motional degrees of freedom. In this review, we present a pedagogical introduction to trapped-ion systems, covering the physics of ion trapping, qubit encodings, and

Ibragim Badertdinov, Alexander Golubev, Maksim Nekrashevich, Anton Shevtsov

LLM-based agents have shown promising capabilities in a growing range of software engineering (SWE) tasks. However, advancing this field faces two critical challenges. First, high-quality training data is scarce, especially data that reflects real-world SWE scenarios, where agents must interact with development environments, execute code and adapt behavior b

Xiaoyi Liu, Harvey S. Reall, Jorge E. Santos, Toby Wiseman

We consider Lorentzian General Relativity in a cavity with a timelike boundary, with conformal boundary conditions and also a generalization of these boundary conditions. We focus on the linearized gravitational dynamics about the static empty cavity whose boundary has spherical spatial geometry. It has been recently shown that there exist dynamical instabil

Adrián del Río

The classical symmetry of the source-free Maxwell equations under electric-magnetic duality rotations leads to a conserved Noether charge, corresponding to the circular polarization of light. We show that, in quantum field theory, the vacuum expectation value of this charge is no longer time-independent inside a long, cylindrical waveguide undergoing both li

Zohreh Davoudi, Chung-Chun Hsieh, Saurabh V. Kadam

We present a digital quantum computation of two-hadron scattering in a $Z_2$ lattice gauge theory in 1+1 dimensions. We prepare well-separated single-particle wave packets with desired momentum-space wavefunctions, and simulate their collision through digitized time evolution. Multiple hadronic wave packets can be produced using the efficient, systematically

Minjie Lei, S. E. Clark, Rudy Morel, E. Allys

Unraveling the multi-phase structure of the diffuse interstellar medium (ISM) as traced by neutral hydrogen (HI) is essential to understanding the lifecycle of the Milky Way. However, HI phase separation is a challenging and under-constrained problem. The neutral gas phase distribution is often inferred from the spectral line structure of HI emission. In thi

M. Schuyler Moss, Roeland Wiersema, Mohamed Hibat-Allah, Juan Carrasquilla

Variational Monte Carlo simulations have been crucial for understanding quantum many-body systems, especially when the Hamiltonian is frustrated and the ground-state wavefunction has a non-trivial sign structure. In this paper, we use recurrent neural network (RNN) wavefunction ans\"{a}tze to study the triangular-lattice antiferromagnetic Heisenberg model (T

Lorenzo Baraldi, Davide Bucciarelli, Federico Betti, Marcella Cornia

Instruction-based image editing models offer increased personalization opportunities in generative tasks. However, properly evaluating their results is challenging, and most of the existing metrics lag in terms of alignment with human judgment and explainability. To tackle these issues, we introduce DICE (DIfference Coherence Estimator), a model designed to

Sha Yi, Xueqian Bai, Adabhav Singh, Jianglong Ye

For robot manipulation, both the controller and end-effector design are crucial. Soft grippers are generalizable by deforming to different geometries, but designing such a gripper and finding its grasp pose remains challenging. In this paper, we propose a co-design framework that generates an optimized soft gripper's block-wise stiffness distribution and its

Andrea Bulgarelli, Michele Caselle, Alessandro Nada, Marco Panero

$\mathrm{O}(N)$ vector models in three dimensions, when defined in a geometry with a compact direction and tuned to criticality, exhibit long-range fluctuations which induce a Casimir effect. The strength of the resulting interaction is encoded in the excess free-energy density, which depends on a universal coefficient: the Casimir amplitude. We present a hi

Clemente Smarra, Lodovico Capuano, Adrien Kuntz

Scalar-tensor theories with a scalar field coupled to the Gauss-Bonnet invariant can evade no-hair theorems and allow for non-trivial scalar profiles around black holes. This coupling is characterized by a length scale $\lambda$, which, in an effective field theory perspective, sets the threshold below which deviations from General Relativity become signific

Brandon Carhuas, Ricardo Castillo, Ricardo Freire, Alex Lira

In this work, we establish optimal conditions concerning the global and nonglobal existence of solutions of a semilinear parabolic equations governed by a mixed local-nonlocal operator. Furthermore, our findings recover the Fujita exponent recently derived by Biagi, Punzo and Vecchi, as well as by Del Pezzo and Ferreira.

Nikolay Bobev, Hynek Paul

We use the AdS/CFT correspondence to explicitly calculate some of the three-point functions in the planar limit of the 4d $\mathcal{N}=1$ Leigh-Strassler SCFT. This strongly interacting CFT can be obtained as a mass deformation of the 4d $\mathcal{N}=4$ SYM theory and admits a dual description in terms of an AdS$_5$ background of type IIB supergravity. Our a

Benedikt Diemer

Hydrodynamics is a difficult subject to teach in the classroom because most relevant problems must be solved numerically rather than analytically. While there are numerous public hydrodynamics codes, the complexity of production-level software obscures the underlying physics and can be overwhelming to first-time users. Here we present ULULA, an ultra-lightwe

Pok Man Tam, Hao Chen, Biao Lian

Quantum Hall edge states in proximity to a superconductor (SC) usually acquire a non-quantized electron-to-hole conversion probability in transport, due to non-universal SC couplings and disorders. With counter-propagating modes, we show that the situation can be the opposite in the $\nu=2/3$ fractional quantum Hall (FQH) edge states with SC proximity, where

Emre Can Sertöz, Joël Ouaknine, James Worrell

A 1-period is a complex number given by the integral of a univariate algebraic function, where all data involved -- the integrand and the domain of integration -- are defined over algebraic numbers. We give an algorithm that, given a finite collection of 1-periods, computes the space of all linear relations among them with algebraic coefficients. In particul

Sebastian Trojanowski, Lei Zu

The impact of dark matter-neutrino ($\nu$DM) interactions on cosmological perturbations has regained attention, spurred by indications of non-zero couplings from high-multipole cosmic microwave background data, weak lensing, and Lyman-$\alpha$ observations. We demonstrate that a similar observational preference is obtained if $\nu$DM interactions are primari

Ferruccio Feruglio, Antonio Marrone, Alessandro Strumia, Arsenii Titov

We show that solutions to the strong CP problem based on modular invariance can be extended to incorporate features that appear in string compactifications: quarks with mostly positive modular weights and non-trivial gauge kinetic functions. This requires assuming that singularities and zeroes only appear at special points, such as decompactification limits.

Christopher Boettner

Gaussian processes (GPs) have become a common tool in astronomy for analysing time series data, particularly in exoplanet science and stellar astrophysics. However, choosing the appropriate covariance structure for a GP model remains a challenge in many situations, limiting model flexibility and performance. This work provides an introduction to recent advan

Ming-Yang Zhuang, Junyao Li, Yue Shen, Xiaojing Lin

We present a spectroscopic sample of 23 broad-line AGNs (BLAGNs) at $3\lesssim z\lesssim 6$ selected using F322W2+F444W NIRCam/WFSS grism spectroscopy of the central 100 ${\rm arcmin^2}$ area of the NEXUS survey. Among these BLAGNs, 15 are classified as Little Red Dots (LRDs) based on their rest-frame UV-optical spectral slopes and compact morphology. The nu

Francesco Salvestrini, Simone Bianchi, Edvige Corbelli

Molecular gas is the key ingredient of the star formation cycle, and tracing its dependencies on other galaxy properties is essential for understanding galaxy evolution. In this work, we explore the relation between the different phases of the interstellar medium (ISM), namely molecular gas, atomic gas, and dust, and galaxy properties using a sample of nearb

Aris Tritsis

The formation of stars is governed by the intricate interplay of nonideal magnetohydrodynamic (MHD) effects, gravity, and turbulence. Computational challenges have hindered a comprehensive 3D exploration of this interplay, posing a longstanding challenge in our understanding of clouds and cores. Our objective was to study the spatial features and time evolut

Yasunori Nomura, Tomonori Ugajin

We study how meaningful physical predictions can arise in nonperturbative quantum gravity in a closed Lorentzian universe. In such settings, recent developments suggest that the quantum gravitational Hilbert space is one-dimensional and real for each $\alpha$-sector, as induced by spacetime wormholes. This appears to obstruct the conventional quantum-mechani

Damiano F. G. Fiorillo, Georg G. Raffelt

In neutrino-dense astrophysical environments, these particles exchange flavor through a coherent weak field, forming a collisionless neutrino plasma with collective flavor dynamics. Instabilities, which grow and affect the environment, may arise from neutrino-neutrino refraction alone (fast limit), vacuum energy splittings caused by masses (slow limit), or n

Carlos Nunez, Dibakar Roychowdhury

We investigate the concept of time-like entanglement entropy (tEE) within the framework of holography. We introduce a robust top-down prescription for computing tEE in higher-dimensional QFTs, both conformal and confining, eliminating the ambiguities typically associated with analytic continuation from Euclidean to Lorentzian signatures. We present accurate

Julian Schuhmacher, Guo-Xian Su, Jesse J. Osborne, Anthony Gandon

Scattering experiments are at the heart of high-energy physics (HEP), breaking matter down to its fundamental constituents, probing its formation, and providing deep insight into the inner workings of nature. In the current huge drive to forge quantum computers into complementary venues that are ideally suited to capture snapshots of far-from-equilibrium HEP

Thomas Steingasser, Mark P. Hertzberg, David I. Kaiser

We revisit perturbative unitarity in scalar field inflation with a nonminimal coupling, with Higgs inflation serving as the most prominent example. Although such models are phenomenologically successful, it is critical to examine whether or not unitarity violations spoil their theoretical self-consistency. The analysis of these issues has so far typically re

Hao Geng, Ling-Yan Hung, Yikun Jiang

We provide a derivation of the Ryu-Takayanagi (RT) formula in 3D gravity for generic boundary subregion--including RT surface phase transitions--directly from the dual two-dimensional conformal field theory (CFT). Our approach relies on the universal statistics of the algebraic conformal data and the large-$c$ behavior of conformal blocks with Cardy boundari

Po-Shen Hsin, Ryohei Kobayashi

Hall conductivities are important characterizations of phases of matter. It is known that nonzero Hall conductivities are difficult to realize in local commuting projector lattice models due to no-go theorems in (2+1)D. In this work we construct local commuting projector models in (2+1)D and (3+1)D with nonzero generalized Hall conductivities for ordinary an

Elena Pinetti, Veronika Vodeb, Aurelio Amerio, Alessandro Cuoco

The Cherenkov Telescope Array Observatory (CTAO) will be a ground-based Cherenkov telescope performing wide-sky surveys, ideal for anisotropy studies such as cross-correlations with tracers of the cosmic large-scale structure. Cross-correlations can shed light on high-energy $\gamma$-ray sources and potentially reveal exotic signals from particle dark matter

Pierre Guilmin, Pierre Rouchon, Antoine Tilloy

The theory of continuous quantum measurement allows to reconstruct the state $\rho_t$ of a system from a continuous stochastic measurement record $I_t$. However, this truly continuous-time signal $I_t$ is never available in practice. In experiments, one generally has access to its digitization, i.e., to a series of time averages $I_k$ over finite intervals o

Jeonghun Baek, Kazuki Egashira, Shota Onohara, Atsuyuki Miyai

Manga, or Japanese comics, is a richly multimodal narrative form that blends images and text in complex ways. Teaching large multimodal models (LMMs) to understand such narratives at a human-like level could help manga creators reflect on and refine their stories. To this end, we introduce two benchmarks for multimodal manga understanding: MangaOCR, which ta

Qinyu Zhao, Jaskirat Singh, Ming Xu, Akshay Asthana

An increasing number of autoregressive models, such as MAR, FlowAR, xAR, and Harmon adopt diffusion sampling to improve the quality of image generation. However, this strategy leads to low inference efficiency, because it usually takes 50 to 100 steps for diffusion to sample a token. This paper explores how to effectively address this issue. Our key motivati

Jiahao Lu, Ziwei Xu, Mohan Kankanhalli

Large Language Models (LLMs) have demonstrated impressive reasoning abilities through test-time computation (TTC) techniques such as chain-of-thought prompting and tree-based reasoning. However, we argue that current reasoning LLMs (RLLMs) lack the ability to systematically explore the solution space. This paper formalizes what constitutes systematic problem

Michael Kirchhof, Luca Füger, Adam Goliński, Eeshan Gunesh Dhekane

The common approach to communicate a large language model's (LLM) uncertainty is to add a percentage number or a hedging word to its response. But is this all we can do? Instead of generating a single answer and then hedging it, an LLM that is fully transparent to the user needs to be able to reflect on its internal belief distribution and output a summary o