March 2025 arXiv papers — page 210

Souvika Sarkar, Md. Najib Hasan, Santu Karmaker

Bangla, a language spoken by over 300 million native speakers and ranked as the sixth most spoken language worldwide, presents unique challenges in natural language processing (NLP) due to its complex morphological characteristics and limited resources. While recent Large Decoder Based models (LLMs), such as GPT, LLaMA, and DeepSeek, have demonstrated excell

Yimin Tang, Zhenghong Yu, Jiaoyang Li, Sven Koenig

Multi-Agent Path Finding (MAPF) involves finding collision-free paths for multiple agents while minimizing a cost function--an NP-hard problem. Bounded suboptimal methods like Enhanced Conflict-Based Search (ECBS) and Explicit Estimation CBS (EECBS) balance solution quality with computational efficiency using focal search mechanisms. While effective, traditi

Yimin Tang, Xiao Xiong, Jingyi Xi, Jiaoyang Li

Multi-Agent Path Finding (MAPF), which focuses on finding collision-free paths for multiple robots, is crucial for applications ranging from aerial swarms to warehouse automation. Solving MAPF is NP-hard so learning-based approaches for MAPF have gained attention, particularly those leveraging deep neural networks. Nonetheless, despite the community's contin

Maksim Papenkov, Beau Robinette

Risk-averse investors often wish to exclude stocks from their portfolios that bear high credit risk, which is a measure of a firm's likelihood of bankruptcy. This risk is commonly estimated by constructing signals from quarterly accounting items, such as debt and income volatility. While such information may provide a rich description of a firm's credit risk

Kevin Liu, Mei Yin

We consider the descent and flag major index statistics on the colored permutation groups, which are wreath products of the form $\mathfrak{S}_{n,r}=\mathbb{Z}_r\wr \mathfrak{S}_n$. We show that the $k$-th moments of these statistics on $\mathfrak{S}_{n,r}$ will coincide with the corresponding moments on all conjugacy classes without cycles of lengths $1,2,\

Bingqing Song, Boran Han, Shuai Zhang, Hao Wang

Having an LLM that aligns with human preferences is essential for accommodating individual needs, such as maintaining writing style or generating specific topics of interest. The majority of current alignment methods rely on fine-tuning or prompting, which can be either costly or difficult to control. Model steering algorithms, which modify the model output

Yiming Xu, Bin Shi, Zhen Peng, Huixiang Liu

The development and evaluation of graph neural networks (GNNs) generally follow the independent and identically distributed (i.i.d.) assumption. Yet this assumption is often untenable in practice due to the uncontrollable data generation mechanism. In particular, when the data distribution shows a significant shift, most GNNs would fail to produce reliable p

Alan Q. Wang, Fangrui Huang, Bailey Trang, Wei Peng

Designing generative models for 3D structural brain MRI that synthesize morphologically-plausible and attribute-specific (e.g., age, sex, disease state) samples is an active area of research. Existing approaches based on frameworks like GANs or diffusion models synthesize the image directly, which may limit their ability to capture intricate morphological de

Marek Kuchař, Kamila Moriová, Martin Kozák

The interaction between freely propagating electrons and light waves is typically described using an approximation in which we assume that the electron velocity remains approximately the same during the interaction. In this article we analytically describe the dynamics of electrons in an interaction potential generated by an optical beat wave beyond this reg

Roberto Balestri, Guglielmo Pescatore

Serialized TV shows are built on complex storylines that can be hard to track and evolve in ways that defy straightforward analysis. This paper introduces a multi-agent system designed to extract and analyze these narrative arcs. Tested on the first season of Grey's Anatomy (ABC 2005-), the system identifies three types of arcs: Anthology (self-contained), S

Jeonghwan Park, Niall McLaughlin, Ihsen Alouani

Adversarial attacks remain a significant threat that can jeopardize the integrity of Machine Learning (ML) models. In particular, query-based black-box attacks can generate malicious noise without having access to the victim model's architecture, making them practical in real-world contexts. The community has proposed several defenses against adversarial att

Feiran Zhao, Alessandro Chiuso, Florian Dörfler

As the benchmark of data-driven control methods, the linear quadratic regulator (LQR) problem has gained significant attention. A growing trend is direct LQR design, which finds the optimal LQR gain directly from raw data and bypassing system identification. To achieve this, our previous work develops a direct LQR formulation parameterized by sample covarian

Michael Garn, Angus Kan

We perform logical and physical resource estimation for computing binary elliptic curve discrete logarithms using Shor's algorithm on fault-tolerant quantum computers. We adopt a windowed approach to design our circuit implementation of the algorithm, which comprises repeated applications of elliptic curve point addition operations and table look-ups. Unlike

Cindy Xiangrui Kong, Haoyang Zheng, Guang Lin

Traditional methods for system discovery frequently struggle with efficient data usage and uncertainty quantification. Identifying the governing equations of complex dynamical systems from data presents a significant challenge in scientific discovery, especially when high-quality measurements are scarce and expensive to obtain. To overcome these limitations,

Yishun Luo, Martin Zubeldia

We consider a discrete-time parallel service system consisting of $n$ heterogeneous single server queues with infinite capacity. Jobs arrive to the system as an i.i.d. process with rate proportional to $n$, and must be immediately dispatched in the time slot that they arrive. The dispatcher is assumed to be able to exchange messages with the servers to obtai

Paul Fong, Albert Li, Zoe Meadows, Rati Pillai

This paper presents a four-unit, four-component mathematical model of iodine metabolism and its impact on thyroid hormone levels in the body. We focus on the relationships between iodine (I-), triiodothyronine (T3), thyroxine (T4), and thyroid-stimulating hormone (TSH) through the mixer, thyroid, sensor (pituitary gland), and metabolism. Iodine plays a funda

Damian R Sowinski, Sean Kelty, Gourab Ghoshal

Configurational entropy (CE) and configurational complexity (CC) are recently popularized information theoretic measures used to study the stability of solitons. This paper examines their behavior for 2D and 3D lattice Ising Models, where the quasi-stability of fluctuating domains is controlled by proximity to the critical temperature. Scaling analysis lends

Luis Vitor Zerkowski, Zixuan Wang, Ilya Vidrin, Mariel Pettee

Dancing in a duet often requires a heightened attunement to one's partner: their orientation in space, their momentum, and the forces they exert on you. Dance artists who work in partnered settings might have a strong embodied understanding in the moment of how their movements relate to their partner's, but typical documentation of dance fails to capture the

Hongchao Du, Shangyu Wu, Arina Kharlamova, Nan Guan

Large Language Models (LLMs) face challenges for on-device inference due to high memory demands. Traditional methods to reduce memory usage often compromise performance and lack adaptability. We propose FlexInfer, an optimized offloading framework for on-device inference, addressing these issues with techniques like asynchronous prefetching, balanced memory

Ryan D. Boyden, Kimberly L. Emig, Nicholas P. Ballering, Charles J. Law

We present new Atacama Large Millimeter/submillimeter Array observations that, for the first time, detect hydrogen and helium radio recombination lines from a protoplanetary disk. We imaged the Orion Nebula Cluster at 3.1 mm with a spectral setup that covered the $n=42 \rightarrow 41$ transitions of hydrogen (H41$\alpha$) and helium (He41$\alpha$). The unpre

Diyaz Yakubov, David Hästbacka

The increasing demand for real-time data processing in Internet of Things (IoT) devices has elevated the importance of edge computing, necessitating efficient and secure deployment of applications on resource-constrained devices. Kubernetes and its lightweight distributions (k0s, k3s, KubeEdge, and OpenYurt) extend container orchestration to edge environment

Boris N. Slautin, Utkarsh Pratiush, Doru C. Lupascu, Maxim A. Ziatdinov

We introduce a Deep Kernel Learning Variational Autoencoder (VAE-DKL) framework that integrates the generative power of a Variational Autoencoder (VAE) with the predictive nature of Deep Kernel Learning (DKL). The VAE learns a latent representation of high-dimensional data, enabling the generation of novel structures, while DKL refines this latent space by s

Diyaz Yakubov, David Hästbacka

Edge computing environments increasingly rely on lightweight container orchestration platforms to manage resource-constrained devices. This paper provides an empirical analysis of five lightweight kubernetes distributions (KD)(k0s, k3s, KubeEdge, OpenYurt, and Kubernetes (k8s)) focusing on their performance and resource efficiency in edge computing scenarios

Christodoulos Benetatos, Frank Cwitkowitz, Nathan Pruyne, Hugo Flores Garcia

HARP 2.0 brings deep learning models to digital audio workstation (DAW) software through hosted, asynchronous, remote processing, allowing users to route audio from a plug-in interface through any compatible Gradio endpoint to perform arbitrary transformations. HARP renders endpoint-defined controls and processed audio in-plugin, meaning users can explore a

Matthew DosSantos DiSorbo, Harang Ju, Sinan Aral

Large language models (LLMs), initially developed for generative AI, are now evolving into agentic AI systems, which make decisions in complex, real-world contexts. Unfortunately, while their generative capabilities are well-documented, their decision-making processes remain poorly understood. This is particularly evident when testing targeted decision-makin

Kevin Kappelmann, Fabian Huch, Lukas Stevens, Mohammad Abdulaziz

We present a semi-automated framework to construct and reason about programs in a deeply-embedded while-language. The while-language we consider is a simple computation model that can simulate (and be simulated by) Turing Machines with a quadratic time and constant space blow-up. Our framework derives while-programs from functional programs written in a subs

Stefan Trandafir, Adán Cabello

According to Pavi\v{c}i{\'c}, Kochen and Specker's 117-observable set is not a ``Kochen-Specker set''. By the same reason, in arXiv:2502.13787, Pavi\v{c}i{\'c} claims that 10 statements in our paper ``Optimal conversion of Kochen-Specker sets into bipartite perfect quantum strategies'' [Phys. Rev. A 111, 022408 (2025)] are ``wrong''. In all cases, Pavi\v{c}i

Zhuoyue Lyu, Per Ola Kristensson

Everyday object-based interactions (EOIs) and mid-air gesture interactions (MAIs) have been widely explored, yet prior work on their integration often targets narrow use cases or specific technologies, leaving designers and developers with limited guidance that generalizes across diverse EOIs and MAIs. We introduce Objestures ("Obj" + "Gestures") -- five int

Jude Khouja, Lingyi Yang, Karolina Korgul, Simeon Hellsten

Frontier language models demonstrate increasing ability at solving reasoning problems, but their performance is often inflated by circumventing reasoning and instead relying on their expanding knowledge and memorisation capacity. We introduce LINGOLY-TOO, a challenging reasoning benchmark of 1,203 questions and a total of 6,995 sub-questions that counters th

So Young Lee, Russell Scheinberg, Amber Shore, Ameeta Agrawal

This study examines how large language models (LLMs) resolve relative clause (RC) attachment ambiguities and compares their performance to human sentence processing. Focusing on two linguistic factors, namely the length of RCs and the syntactic position of complex determiner phrases (DPs), we assess whether LLMs can achieve human-like interpretations amid th

Jaime Garibay-Rodriguez, Morgan R. Edwards, Ann F. Fink, Zeyneb Magavi

Electrification presents opportunities and challenges for household energy affordability. Without careful coordination, the costs of natural gas systems will be borne by a shrinking customer base, driving up bills for those left behind. This affordability issue is worsened by new pipeline investments, which risk locking communities into carbon-intensive infr

Siqi Ouyang, Xi Xu, Lei Li

Simultaneous translation of unbounded streaming speech remains a challenging problem due to the need for effectively processing the history speech context and past translations so that quality and latency, including computation overhead, can be balanced. Most prior works assume pre-segmented speech, limiting their real-world applicability. In this paper, we

Fatima J. Sarmin, Atiquer R. Rahman, Christopher J. Henry, Noman Mohammed

Sharing of tabular data containing valuable but private information is limited due to legal and ethical issues. Synthetic data could be an alternative solution to this sharing problem, as it is artificially generated by machine learning algorithms and tries to capture the underlying data distribution. However, machine learning models are not free from memori

Seyed Hossein Hosseini DolatAbadi, Sayyed Mohammad Hossein Hashemi, Mohammad Hosseini, Moein-Aldin AliHosseini

The rapid urbanization of cities and increasing vehicular congestion have posed significant challenges to traffic management and safety. This study explores the transformative potential of artificial intelligence (AI) and machine vision technologies in revolutionizing traffic systems. By leveraging advanced surveillance cameras and deep learning algorithms,

Eduardo Abi Jaber, Maxime Guellil

Fourier-based methods are central to option pricing and hedging when the Fourier-Laplace transform of the log-price and integrated variance is available semi-explicitly. This is the case for the Volterra Stein-Stein stochastic volatility model, where the characteristic function is known analytically. However, naive evaluation of this formula can produce disc

M. M. Sharma, Santosh Karki Chhetri, Gokul Acharya, David Graf

Ternary shandite compounds with the general formula T3M2X2 (T = Ni, Co, Rh or Pd; M = Sn, In or Pb and X = S or Se) have emerged as a large pool of topological semimetals. This family of compounds hosts different topological phases for various combinations of T, M and X. This paper reports the observation of quantum oscillations under the high magnetic field

D. N. Voskresensky

Conditions are found, at which in nuclear matter there may appear a spatially nonuniform $p$ wave $\pi^0$ condensate supplemented by a spatially varying spontaneous magnetization. The pion-nucleon interaction and the anomaly contributions to magnetization are taken into account. Response of the system on external magnetic field is also considered. Then the m

Javier Pliego, Yu-Chen Sun, Mengdi Wang

Let $ k,l \geq 2$ be natural numbers, and let $d_k,d_l$ denote the $k$-fold and $l$-fold divisor functions, respectively. We analyse the asymptotic behavior of the sum $\sum_{x<n\leq x+H_1}d_k(n)d_l(n+h)$. More precisely, let $\varepsilon>0$ be a small fixed number and let $\Phi(x)$ be a positive function that tends to infinity arbitrarily slowly as $x\to \i

Thuy Nguyen, Dang Nguyen, Hoang Nguyen, Thuan Luong

We present a challenging benchmark for the Open WorLd VISual question answering (OWLViz) task. OWLViz presents concise, unambiguous queries that require integrating multiple capabilities, including visual understanding, web exploration, and specialized tool usage. While humans achieve 69.2% accuracy on these intuitive tasks, even state-of-the-art VLMs strugg

Atefeh Termehchi, Ekram Hossain, Isaac Woungang

Deep Reinforcement Learning (DRL) is a key machine learning technology driving progress across various scientific and engineering fields, including wireless communication. However, its limited interpretability and generalizability remain major challenges. In supervised learning, generalizability is commonly evaluated through the generalization error using in

Shiyang Chen, Xiang Song, Vasiloudis Theodore, Hang Liu

Graph Neural Networks (GNNs) are a new research frontier with various applications and successes. The end-to-end inference for all nodes, is common for GNN embedding models, which are widely adopted in applications like recommendation and advertising. While sharing opportunities arise in GNN tasks (i.e., inference for a few nodes and training), the potential

Hong kyu Lee, Qiuchen Zhang, Carl Yang, Li Xiong

Graph unlearning aims to remove a subset of graph entities (i.e. nodes and edges) from a graph neural network (GNN) trained on the graph. Unlike machine unlearning for models trained on Euclidean-structured data, effectively unlearning a model trained on non-Euclidean-structured data, such as graphs, is challenging because graph entities exhibit mutual depen

Dylan Huson, Indiana Cowan, Logan Sizemore, Marina Kounkel

We present a new processing of XP spectra for 220 million stars released in Gaia DR3. The new data model is capable of handling objects with Teff between 2000 and 50,000 K, and with log g between 0 and 10, including objects of multitude of masses and evolutionary stages. This includes for the first time ever robust processing of spectroscopic parameters for

Charles Collot, Pierre Germain, Eliot Pacherie

We consider the nonlinear Schr\"odinger equation in dimension one for a generic nonlinearity. We show that ground states do not have embedded eigenvalues in the essential spectrum of their linearized operators.

Keonwoo Oh, Pooja Nilangekar, Amol Deshpande

With ever-increasing volume and heterogeneity of data, advent of new specialized compute engines, and demand for complex use cases, large-scale data systems require a performant catalog system that can satisfy diverse needs. We argue that existing solutions, including recent lakehouse storage formats, have fundamental limitations and that there is a strong m

Olaya Alvarez-Tunon, Yury Brodskiy, Erdal Kayacan

With the rise of deep learning, there is a fundamental change in visual SLAM algorithms toward developing different modules trained as end-to-end pipelines. However, regardless of the implementation domain, visual SLAM's performance is subject to diverse environmental challenges, such as dynamic elements in outdoor environments, harsh imaging conditions in u

Yue Meng, Nathalie Majcherczyk, Wenliang Liu, Scott Kiesel

Multi-agent coordination is crucial for reliable multi-robot navigation in shared spaces such as automated warehouses. In regions of dense robot traffic, local coordination methods may fail to find a deadlock-free solution. In these scenarios, it is appropriate to let a central unit generate a global schedule that decides the passing order of robots. However

Charles Collot, Pierre Germain, Eliot Pacherie

We consider the linearized two-dimensional Gross-Pitaevskii equation around a vortex of degree one, with data in the same equivariance class. Various estimates are proved for the solution; in particular, conditions for optimal decay in $L^\infty$ and boundedness in $L^2$ are identified. The analysis relies on a full description of the spectral resolution of

Avrim Blum, Emily Diana, Kavya Ravichandran, Alexander Williams Tolbert

Ambition and risk-taking have been heralded as important ways for marginalized communities to get out of cycles of poverty. As a result, educational messaging often encourages individuals to strengthen their personal resolve and develop characteristics such as discipline and grit to succeed in ambitious ends. However, recent work in philosophy and sociology

Zhangchen Xu, Yang Liu, Yueqin Yin, Mingyuan Zhou

We introduce KodCode, a synthetic dataset that addresses the persistent challenge of acquiring high-quality, verifiable training data across diverse difficulties and domains for training Large Language Models for coding. Existing code-focused resources typically fail to ensure either the breadth of coverage (e.g., spanning simple coding tasks to advanced alg

Danqing Zhang, Balaji Rama, Jingyi Ni, Shiying He

We introduce LiteWebAgent, an open-source suite for VLM-based web agent applications. Our framework addresses a critical gap in the web agent ecosystem with a production-ready solution that combines minimal serverless backend configuration, intuitive user and browser interfaces, and extensible research capabilities in agent planning, memory, and tree search.

Elena Cáceres, Brian Kent, Harita Palani Balaji

Two modern programs involving analogies between general relativity and electromagnetism, gravito-electromagnetism (GEM) and the classical double copy (CDC), induce electromagnetic potentials from specific classes of spacetime metrics. We demonstrate such electromagnetic potentials are typically gauge equivalent to Killing vectors present in the spacetime, lo

Haz Sameen Shahgir, Chansong Lim, Jia Chen, Evangelos E. Papalexakis

Generating high-quality question-answer pairs for specialized technical domains remains challenging, with existing approaches facing a tradeoff between leveraging expert examples and achieving topical diversity. We present ExpertGenQA, a protocol that combines few-shot learning with structured topic and style categorization to generate comprehensive domain-s

Andrew Kiruluta, Eric Lundy, Andreas Lemos

We present FourierNAT, a novel non-autoregressive Transformer (NAT) architecture that employs Fourier-based mixing in the decoder to generate output sequences in parallel. While traditional NAT approaches often face challenges with capturing global dependencies, our method leverages a discrete Fourier transform to mix token embeddings across the entire seque

Reshma Anna-Thomas, Casey J. Law, Eric W. Koch, Alexa C. Gordon

Fast Radio Bursts (FRBs) are millisecond-duration radio transients that serve as unique probes of ionized extragalactic matter. We report the discovery and localization of two FRBs piercing the Andromeda galaxy (M31) with the realfast transient-detection system at the Very Large Array. These unique sightlines enable constraints on M31's electron density dist

Krishna Dasaratha, Juan Ortner, Chengyang Zhu

Motivated by the prevalence of prediction problems in the economy, we study markets in which firms sell models to a consumer to help improve their prediction. Firms decide whether to enter, choose models to train on their data, and set prices. The consumer can purchase multiple models and use a weighted average of the models bought. Market outcomes can be ex

Nicolas Bridges, Eric Samperton

We show that for any fixed $(2+1)$-dimensional TQFT over $\mathbb{C}$ of either Turaev-Viro-Barrett-Westbury or Reshetikhin-Turaev type, the problem of (exactly) computing its invariants on closed 3-manifolds is either solvable in polynomial time, or else it is $\#\mathsf{P}$-hard to (exactly) contract certain tensors that are built from the TQFT's fusion ca

Shadi Ali Ahmad, Marc S. Klinger

Inclusions and extensions lie at the heart of physics and mathematics. The most relevant kind of inclusion in quantum systems is that of a von Neumann subalgebra, which is the focus of this work. We propose an object intrinsic to a given algebra that indexes its potential extensions into larger algebras. We refer to this object as a spatial Q-system, since i

Alexandre Alouadi, Baptiste Barreau, Laurent Carlier, Huyên Pham

We investigate the generative capabilities of the Schr\"odinger Bridge (SB) approach for time series. The SB framework formulates time series synthesis as an entropic optimal interpolation transport problem between a reference probability measure on path space and a target joint distribution. This results in a stochastic differential equation over a finite h

D. J. D. Santos, T. Shimizu, R. Davies, Y. Cao

With the advent of GRAVITY+, the upgrade to the beam combiner GRAVITY at the Very Large Telescope Interferometer (VLTI), fainter and higher redshift active galactic nuclei (AGNs) are becoming observable, opening an unprecedented opportunity to further our understanding of the cosmic coevolution of supermassive black holes and their host galaxies. To identify

Alfio Bonanno, Kevin Falls, Renata Ferrero

Much like the action, diffeomorphism invariance can be used to fix the form of the path integral measure in quantum gravity. Moreover, since there is a redundancy between what constitutes "the action" and what constitutes "the measure" one can always pick a minimal form of the latter. However, the authors of the recent papers arXiv:2412.14108, arXiv:2412.101

Gaetano Lambiase, Tanmay Kumar Poddar, Luca Visinelli

We assess the effect of the Cosmic Neutrino Background (C$\nu$B) on superradiant instabilities caused by an ultralight scalar field around spinning black holes (BHs). When the scalar couples to neutrinos via a Yukawa interaction, thermal corrections from the C$\nu$B induce a quartic self-interaction and an effective mass term for the scalar. We show that, fo

E. Ceccarelli, D. Massari, F. Aguado-Agelet, A. Mucciarelli

The system of globular clusters (GCs) in the Milky Way (MW) comprises a mixture of both in situ and accreted clusters. Tracing the origin of GCs provides invaluable insights into the formation history of the MW. However, reconciling diverse strands of evidence is often challenging. A notable example is NGC 288, where despite significant efforts in the litera

Yue Pan, Romain Teyssier, Ulrich P. Steinwandel, Alice Pisani

We present a theoretical framework for calculating the volume filling fraction of galactic outflows in cosmic voids by integrating analytical models for the halo mass function (HMF), the halo occupation fraction, the stellar mass-halo mass relation, and outflow sizes. Using RAMSES, we perform a hydrodynamical zoom-in simulation of the central 25 cMpc/h regio

Iason Baldes

Density perturbations have recently been shown to lead to a novel effect in the freeze-out of heavy particles called "acoustically driven freeze-out." This leads to an enhancement in the yield in standard leptogenesis. We extend this calculation to include $2 \to 2$ washout processes in type-I leptogenesis and the Sommerfeld-enhanced $2 \to 2$ gauge annihila

Luca Mantani, Veronica Sanz

The Standard Model Effective Field Theory (SMEFT) offers a systematic approach to study potential deviations from the Standard Model (SM) through higher-dimensional operators that encapsulate new physics effects. In this work, we analyze flavour-blind SMEFT contributions to flavour observables and assess their interplay with high-energy measurements from LEP

Erik Recio-Armengol, Shahnawaz Ahmed, Joseph Bowles

We propose an approach to generative quantum machine learning that overcomes the fundamental scaling issues of variational quantum circuits. The core idea is to use a class of generative models based on instantaneous quantum polynomial circuits, which we show can be trained efficiently on classical hardware. Although training is classically efficient, sampli

S. Molendi, M. Balboni, I. Bartalucci, S. De Grandi

The goal of the work presented in this paper is to use observed entropy profiles to infer constraints on the accretion process in massive halos. We compare entropy profiles from various observational samples with those generated by an updated version of the semi-analytical models developed in the early 2000s, modified to reflect recent advancements in our un

Cédric Delaunay, Seung J. Lee, Roee Ozeri, Gilad Perez

Clocks based on nuclear isomer transitions promise exceptional stability and precision. The low transition energy of the thorium-229 isomer makes it an ideal candidate, as it has been excited by a vacuum-ultraviolet laser and is highly sensitive to subtle interactions. This enables the development of powerful tools for probing new forces, which we call {\it

Yago Bea

The relativistic viscous hydrodynamic description of the quark-gluon plasma by M\"uller-Israel-Stewart formulations has been very successful, but despite this success, these theories present limitations regarding well-posedness and causality. In recent years, a well-behaved version of the relativistic Navier-Stokes equations has been formulated, appearing as

Pablo Bueno, Oscar Lasso Andino, Javier Moreno, Guido van der Velde

As argued in arXiv:2104.10172, introducing a non-minimally coupled scalar field, three-dimensional Einstein gravity can be extended by infinite families of theories which admit simple analytic generalizations of the charged BTZ black hole. Depending on the gravitational couplings, the solutions may describe black holes with one or several horizons and with c

Julian Ingham, Ronny Thomale, Harley D. Scammell

Alongside high-temperature charge order and superconductivity, kagome metals exhibit signatures of time-reversal symmetry breaking and nematicity which appear to depend strongly on external perturbations such as strain and magnetic fields, posing a fundamental challenge for conceptual reconciliation. We develop a theory of vestigial order descending from an

Oliver Zier, Rahul Kannan, Aaron Smith, Ewald Puchwein

We investigate the impact of ionizing external ultraviolet (UV) radiation on low-mass haloes ($M_{h}<10^{10}M_\odot$) at high redshift using $1140M_\odot$ baryonic resolution zoom-in simulations of seven regions from the THESAN-ZOOM project. We compare three simulation sets that differ in the treatment of external UV radiation: one employing a uniform UV bac

Adam M. Dillamore, Jason L. Sanders, Vasily Belokurov, Hanyuan Zhang

Co-moving groups of stars (streams) are well known in the velocity space of the disc near the Sun. Many are thought to arise from resonances with the Galactic bar or spiral arms. In this work, we search for similar moving groups in the velocity space of the halo, at low angular momentum. From the asymmetry of the radial velocity distribution $v_R$, we identi

Sahand Seifnashri, Shu-Heng Shao, Xinping Yang

We provide a general prescription for gauging finite non-invertible symmetries in 1+1d lattice Hamiltonian systems. Our primary example is the Rep(D$_8$) fusion category generated by the Kennedy-Tasaki transformation, which is the simplest anomaly-free non-invertible symmetry on a spin chain of qubits. We explicitly compute its lattice F-symbols and illustra

T. H. Reiprich, A. Veronica, F. Pacaud, P. Stöcker

The Fornax cluster is one of the closest X-ray-bright galaxy clusters. Previous observations of the intracluster medium were limited to less than R500. We aim to significantly extend the X-ray coverage. We used data from 5 SRG/eROSITA all-sky surveys and performed a detailed 1- and 2-dimensional X-ray surface brightness analysis, tracing hot gas emission fro

Qinyu Zhao, Stephen Gould, Liang Zheng

Existing autoregressive (AR) image generative models use a token-by-token generation schema. That is, they predict a per-token probability distribution and sample the next token from that distribution. The main challenge is how to model the complex distribution of high-dimensional tokens. Previous methods either are too simplistic to fit the distribution or

Yue Meng, Chuchu fan

Generating realistic simulations is critical for autonomous system applications such as self-driving and human-robot interactions. However, driving simulators nowadays still have difficulty in generating controllable, diverse, and rule-compliant behaviors for road participants: Rule-based models cannot produce diverse behaviors and require careful tuning, wh

Konstantinos Voudouris, Lucy G. Cheke, Eric Schulz

Researchers are increasingly subjecting artificial intelligence systems to psychological testing. But to rigorously compare their cognitive capacities with humans and other animals, we must avoid both over- and under-stating our similarities and differences. By embracing a comparative approach, we can integrate AI cognition research into the broader cognitiv

Han Xue, Jieji Ren, Wendi Chen, Gu Zhang

Humans can accomplish complex contact-rich tasks using vision and touch, with highly reactive capabilities such as fast response to external changes and adaptive control of contact forces; however, this remains challenging for robots. Existing visual imitation learning (IL) approaches rely on action chunking to model complex behaviors, which lacks the abilit

Purba Mukherjee, Anjan A Sen

We perform a model-independent reconstruction of the angular diameter distance ($D_{A}$) using the Multi-Task Gaussian Process (MTGP) framework with DESI-DR1 BAO and DES-SN5YR datasets. We calibrate the comoving sound horizon at the baryon drag epoch $r_d$ to the Planck best-fit value, ensuring consistency with early-universe physics. With the reconstructed

Siming Huang, Yuliang Xu, Mingmeng Geng, Yao Wan

In this paper, we present a comprehensive analysis and monitoring framework for the impact of Large Language Models (LLMs) on Wikipedia, examining the evolution of Wikipedia through existing data and using simulations to explore potential risks. We begin by analyzing article content and page views to study the recent changes in Wikipedia and assess the impac

Ethan Mendes, Alan Ritter

Collecting ground-truth rewards or human demonstrations for multi-step reasoning tasks is often prohibitively expensive, particularly in interactive domains such as web tasks. We introduce Self-Taught Lookahead (STL), a reward-free framework that improves language model-based value functions by reasoning explicitly about state transitions. STL can be viewed

Marko Medvedev, Kaifeng Lyu, Dingli Yu, Sanjeev Arora

Weak-to-Strong Generalization (Burns et al., 2024) is the phenomenon whereby a strong student, say GPT-4, learns a task from a weak teacher, say GPT-2, and ends up significantly outperforming the teacher. We show that this phenomenon does not require a strong learner like GPT-4. We consider student and teacher that are random feature models, described by two

Dmitry Nechaev, Alexey Pchelnikov, Ekaterina Ivanova

Advancing AI in computational pathology requires large, high-quality, and diverse datasets, yet existing public datasets are often limited in organ diversity, class coverage, or annotation quality. To bridge this gap, we introduce SPIDER (Supervised Pathology Image-DEscription Repository), the largest publicly available patch-level dataset covering multiple

Ke Ji, Jiahao Xu, Tian Liang, Qiuzhi Liu

Improving the reasoning capabilities of large language models (LLMs) typically requires supervised fine-tuning with labeled data or computationally expensive sampling. We introduce Unsupervised Prefix Fine-Tuning (UPFT), which leverages the observation of Prefix Self-Consistency -- the shared initial reasoning steps across diverse solution trajectories -- to

Leixian Shen, Haotian Li, Yifang Wang, Xing Xie

The emergence of generative AI (GenAI) models, including large language models and text-to-image models, has significantly advanced the synergy between humans and AI with not only their outstanding capability but more importantly, the intuitive communication method with text prompts. Though intuitive, text-based instructions suffer from natural languages' am

Jason F. Manning, Christoforos Neofytidis

We determine which closed orientable $3$-manifolds $M$ admit a self-homeomorphism restricting to a pseudo-Anosov map on an incompressible subsurface $\Sigma$, which we call a pseudo-Anosov surface. When $M$ is irreducible, we show that the self-homeomorphism of $M$ is isotopic rel $\Sigma$ to a "partially pseudo-Anosov" homeomorphism, a notion that we will i

Kai Wang, Gabrielle Gilmer, Matheus Candia Arana, Hirotaka Iijima

Diverse organisms exploit the geomagnetic field (GMF) for migration. Migrating birds employ an intrinsically quantum mechanical mechanism for detecting the geomagnetic field: absorption of a blue photon generates a radical pair whose two electrons precess at different rates in the magnetic field, thereby sensitizing cells to the direction of the GMF. In this

Manuel Gutiérrez, Raymond A. Hounnonkpe

We study the influence of the existence of totally geodesic null hypersurface on the properties of a Lorentzian manifold. By coupling the rigging technique with the existence of a null foliation we prove the existence of a Riemann flow structure which allows us to use powerful results to show how curvature conditions on the spacetime restricts its causal str

Shubham S. Ganar, Deepak J., Arindam Das

Droplet impact on solid liquid-infused surfaces (LIS) has been widely explored due to its significant scientific implications and industrial relevance. In most studies, the predominant impact behavior observed is complete droplet rebound. In This study we investigated the influence of octadecyltrichlorosilane (OTS) functionalization and oil coatings on the d

Beepul Bharti, Mary Versa Clemens-Sewall, Paul H. Yi, Jeremias Sulam

As the use of predictive machine learning algorithms increases in high-stakes decision-making, it is imperative that these algorithms are fair across sensitive groups. However, measuring and enforcing fairness in real-world applications can be challenging due to the missing or incomplete sensitive group information. Proxy-sensitive attributes have been propo

M. van der Hulst, R. A. González, K. Classens, P. Tacx

Multivariable parametric models are essential for optimizing the performance of high-tech systems. The main objective of this paper is to develop an identification strategy that provides accurate parametric models for complex multivariable systems. To achieve this, an additive model structure is adopted, offering advantages over traditional black-box model s

Joyce Cahoon, Prerna Singh, Nick Litombe, Jonathan Larson

In this work, we benchmark various graph-based retrieval-augmented generation (RAG) systems across a broad spectrum of query types, including OLTP-style (fact-based) and OLAP-style (thematic) queries, to address the complex demands of open-domain question answering (QA). Traditional RAG methods often fall short in handling nuanced, multi-document synthesis t

Javier Canto, Nico Michele Schiavone, Luis Vega

We investigate the fractional dispersion of solutions to the Helmholtz equation with periodic scattering data. We show that, under appropriate rescaling, the interaction between the different frequencies exhibits the same fluctuating behavior as for the Schr\"{o}dinger equation. To achieve this, we first establish an asymptotic fractional uncertainty princip

Jordan Wilson-Gerow

We study the conservative dynamics of spinless compact objects in a general effective theory of gravity which includes a metric and an arbitrary number of scalar fields, through $\mathcal{O}(G^{3})$. Departures from Einstein gravity, which preserve general coordinate and local Lorentz invariance, are characterized by higher-derivative terms in a Lagrangian w

Amir Farakhor, Iman Askari, Di Wu, Yebin Wang

Large-scale battery energy storage systems (BESS) have found ever-increasing use across industry and society to accelerate clean energy transition and improve energy supply reliability and resilience. However, their optimal power management poses significant challenges: the underlying high-dimensional nonlinear nonconvex optimization lacks computational trac

Bassel El Mabsout, Abdelrahman Abdelgawad, Renato Mancuso

Practitioners designing reinforcement learning policies face a fundamental challenge: translating intended behavioral objectives into representative reward functions. This challenge stems from behavioral intent requiring simultaneous achievement of multiple competing objectives, typically addressed through labor-intensive linear reward composition that yield

Shaina Raza, Mukund Sayeeganesh Chettiar, Matin Yousefabadi, Tahniat Khan

In this paper, we introduce FairSense-AI: a multimodal framework designed to detect and mitigate bias in both text and images. By leveraging Large Language Models (LLMs) and Vision-Language Models (VLMs), FairSense-AI uncovers subtle forms of prejudice or stereotyping that can appear in content, providing users with bias scores, explanatory highlights, and a

Markus Lohrey, Andreas Rosowski

The problem whether a given permutation group contains a permutation with a given cycle type is studied. This problem is known to be NP-complete. In this paper it is shown that the problem can be solved in logspace for a cyclic permutation group and that it is NP-complete for a 2-generated abelian permutation group. In addition it is shown that it is NP-comp