May 2025 arXiv papers — page 92

Zhenglin Hua, Jinghan He, Zijun Yao, Tianxu Han

Large vision-language models (LVLMs) have achieved remarkable performance on multimodal tasks. However, they still suffer from hallucinations, generating text inconsistent with visual input, posing significant risks in real-world applications. Existing approaches to address this issue focus on incorporating external knowledge bases, alignment training, or de

Qian Li, Yuyi Wang

We prove that any Turing machine running on inputs of arbitrary length can be simulated by a constant bit-size transformer, as long as the context window is sufficiently long. This improves previous works, which require scaling up either the model's precision or the number of parameters on longer inputs. Furthermore, we prove that the complexity class SPACE$

Arghya Datta, Philippe Gagnon, Florian Maire

Probabilistic principal component analysis (PCA) and its Bayesian variant (BPCA) are widely used for dimension reduction in machine learning and statistics. The main advantage of probabilistic PCA over the traditional formulation is allowing uncertainty quantification. The parameters of BPCA are typically learned using mean-field variational inference, and i

Ming Yang, Haoran Li

6DoF object pose estimation is fundamental to robotic grasp tasks. While recent learning-based methods achieve high accuracy, their computational demands hinder deployment on resource-constrained mobile platforms. In this work, we revisit the classical keypoint matching paradigm and propose GMatch, a lightweight, geometry-constrained keypoint matcher that ca

Mike Hensler, Hannah Klawa

An integral domain $R$ is an $i$-domain if for every overring $S$ of $R$, $\text{Spec}(S) \rightarrow \text{Spec}(R)$ is injective and is a mated integral if for every overring $S$ of $R$ and prime ideal $P$ of $R$ such that $PS \neq S$, there exists exactly one prime ideal $Q$ of $S$ such that $Q \cap R = P$. In this paper, we explore graded notions of $i$-

Shicheng Xu, Liang Pang, Yunchang Zhu, Jia Gu

Distilling reasoning paths from teacher to student models via supervised fine-tuning (SFT) provides a shortcut for improving the reasoning ability of smaller Large Language Models (LLMs). However, the reasoning paths generated by teacher models often reflect only surface-level traces of their underlying authentic reasoning. Insights from cognitive neuroscien

Jingwu Tang, Jiahao Zhang, Fei Fang, Zhiwei Steven Wu

Bayesian persuasion, a central model in information design, studies how a sender, who privately observes a state drawn from a prior distribution, strategically sends a signal to influence a receiver's action. A key assumption is that both sender and receiver share the precise knowledge of the prior. Although this prior can be estimated from past data, such a

Joseph Pollard, Richard G. Morris

Liquid crystals formed of bent-core molecules are exotic materials that exhibit the twist-bend nematic phase. This arises when an energetic preference for nonzero local bend distortion is accommodated via twist in the texture, resulting in properties synonymous with both smectics and cholesterics. Here we describe how the frustration inherent to the twist-be

Heqiang Wang, Xiang Liu, Xiaoxiong Zhong, Lixing Chen

The Internet of Things (IoT) ecosystem generates vast amounts of multimodal data from heterogeneous sources such as sensors, cameras, and microphones. As edge intelligence continues to evolve, IoT devices have progressed from simple data collection units to nodes capable of executing complex computational tasks. This evolution necessitates the adoption of di

Qi Duan, Ehab Al-Shaer

The emergence of cloud computing gives huge impact on large computations. Cloud computing platforms offer servers with large computation power to be available for customers. These servers can be used efficiently to solve problems that are complex by nature, for example, satisfiability (SAT) problems. Many practical problems can be converted to SAT, for examp

Yuke Zhang

This study introduces an interpretable machine learning (ML) framework to extract macroeconomic alpha from global news sentiment. We process the Global Database of Events, Language, and Tone (GDELT) Project's worldwide news feed using FinBERT -- a Bidirectional Encoder Representations from Transformers (BERT) based model pretrained on finance-specific langua

Jeffrey Seely, Yuki Imajuku, Tianyu Zhao, Edoardo Cetin

Existing reasoning benchmarks for large language models (LLMs) frequently fail to capture authentic creativity, often rewarding memorization of previously observed patterns. We address this shortcoming with Sudoku-Bench, a curated benchmark of challenging and unconventional Sudoku variants specifically selected to evaluate creative, multi-step logical reason

Mikhail Menschikov, Alexander Kharitonov, Maiia Kotyga, Vadim Porvatov

Large Language Models (LLMs) exhibit position bias systematically underweighting information based on its location in the context but how this bias varies across languages and models remains unclear. We conduct a multilingual study across five typologically diverse languages (English, Russian, German, Hindi, Vietnamese) and five model architectures, analyzin

Jinyu Guo, Xunlei Chen, Qiyang Xia, Zhaokun Wang

Retrieval-Augmented Generation (RAG) encounters efficiency challenges when scaling to massive knowledge bases while preserving contextual relevance. We propose Hash-RAG, a framework that integrates deep hashing techniques with systematic optimizations to address these limitations. Our queries directly learn binary hash codes from knowledgebase code, eliminat

Haohan Wang, Xu Shi, Hengyu Zhang, Yashuai Cao

Channel knowledge map (CKM) has emerged as a crucial technology for next-generation communication, enabling the construction of high-fidelity mappings between spatial environments and channel parameters via electromagnetic information analysis. Traditional CKM construction methods like ray tracing are computationally intensive. Recent studies utilizing neura

Nathan Brady, David Tennyson, Thomas Vandermeulen

In this paper, we apply both supervised and unsupervised machine learning algorithms to the study of the string landscape and swampland in 6-dimensions. Our data are the (almost) anomaly-free 6-dimensional $\mathcal{N} = (1,0)$ supergravity models, characterised by the Gram matrix of anomaly coefficients. Our work demonstrates the ability of machine learning

Zehong Wang, Zheyuan Zhang, Tianyi Ma, Chuxu Zhang

Graph neural networks (GNNs) have been predominantly driven by message-passing, where node representations are iteratively updated via local neighborhood aggregation. Despite their success, message-passing suffers from fundamental limitations -- including constrained expressiveness, over-smoothing, over-squashing, and limited capacity to model long-range dep

Hyang Cui

Recent studies have applied large language models (LLMs) to machine translation quality estimation (MTQE) by prompting models to assign numeric scores. Nonetheless, these direct scoring methods tend to show low segment-level correlation with human judgments. In this paper, we propose a generation-based evaluation paradigm that leverages decoder-only LLMs to

Yue Zhou, Barbara Di Eugenio

Despite LLMs' explicit alignment against demographic stereotypes, they have been shown to exhibit biases under various social contexts. In this work, we find that LLMs exhibit concerning biases in how they associate solution veracity with demographics. Through experiments across five human value-aligned LLMs on mathematics, coding, commonsense, and writing p

Zhaoge Bi, Linghan Huang, Haolin Jin, Qingwen Zeng

Electricity price forecasting is a critical component of modern energy-management systems, yet existing approaches heavily rely on numerical histories and ignore contemporaneous textual signals. We introduce NSW-EPNews, the first benchmark that jointly evaluates time-series models and large language models (LLMs) on real-world electricity-price prediction. T

Farshid Farhadi Khouzani, Abdolreza Mirzaei, Paul La Plante, Laxmi Gewali

Evolutionary optimization algorithms often face defects and limitations that complicate the evolution processes or even prevent them from reaching the global optimum. A notable constraint pertains to the considerable quantity of function evaluations required to achieve the intended solution. This concern assumes heightened significance when addressing costly

Yu Zhang, Bing-Zhao Li

With the growing demand for non-Euclidean data analysis, graph signal processing (GSP) has gained significant attention for its capability to handle complex time-varying data. This paper introduces a novel sampling method based on the joint time-vertex fractional Fourier transform (JFRFT), enhancing signal representation in time-frequency analysis and GSP. T

Koffka Khan

University students and working professionals are increasingly encountering generative artificial intelligence (AI) in education and practice, yet their approaches and outcomes differ markedly. This paper proposes an academic study contrasting novice over-reliance on AI with expert augmentation of AI, grounded in two real-world narratives. In one, a universi

Kotaro Yoshida, Konstantinos Slavakis

Invariant risk minimization (IRM) aims to enable out-of-distribution (OOD) generalization in deep learning by learning invariant representations. As IRM poses an inherently challenging bi-level optimization problem, most existing approaches -- including IRMv1 -- adopt penalty-based single-level approximations. However, empirical studies consistently show tha

Hyopil Shin, Sangah Lee, Dongjun Jang, Wooseok Song

We introduce KoBALT (Korean Benchmark for Advanced Linguistic Tasks), a comprehensive linguistically-motivated benchmark comprising 700 multiple-choice questions spanning 24 phenomena across five linguistic domains: syntax, semantics, pragmatics, phonetics/phonology, and morphology. KoBALT is designed to advance the evaluation of large language models (LLMs)

Alireza Arbabi, Florian Kerschbaum

The growing deployment of large language models (LLMs) has amplified concerns regarding their inherent biases, raising critical questions about their fairness, safety, and societal impact. However, quantifying LLM bias remains a fundamental challenge, complicated by the ambiguity of what "bias" entails. This challenge grows as new models emerge rapidly and g

Jinyuan Chang, Chenlong Li, Cheng Yong Tang, Zhengtian Zhu

We propose a novel multiple testing methodology for controlling the false discovery rate (FDR) in high-dimensional linear models that integrates model-X knockoff techniques with debiased penalized regression estimators. At the foundation of our methodology, we construct and study two sets of naturally paired high-dimensional test statistics and the associate

Zekun Zhao, Qingqian Kang, Shoukang Chang, Teng Zhao

The accuracy of quantum measurements can be effectively improved by using both photon-added non-Gaussian operations and Kerr nonlinear phase shifters. Here, we employ coherent state mixed photon-added squeezed vacuum state as input into a Mach-Zehnder interferometer with parity detection, thereby achieving a significant enhancement in phase measurement accur

Junhong Lin, Xinyue Zeng, Jie Zhu, Song Wang

Large Language Models (LLMs) have achieved remarkable success in complex reasoning tasks, but their inference remains computationally inefficient. We observe a common failure mode in many prevalent LLMs, overthinking, where models generate verbose and tangential reasoning traces even for simple queries. Recent work has tried to mitigate this by enforcing fix

Robin Scheibler, John R. Hershey, Arnaud Doucet, Henry Li

We consider the problem of single-channel audio source separation with the goal of reconstructing $K$ sources from their mixture. We address this ill-posed problem with FLOSS (FLOw matching for Source Separation), a constrained generation method based on flow matching, ensuring strict mixture consistency. Flow matching is a general methodology that, when giv

Haotian Lan, Yao Gao, Yujun Cheng, Wei Yuan

Social media's rise establishes user-generated content (UGC) as pivotal for travel decisions, yet analytical methods lack scalability. This study introduces a dual-method LLM framework: unsupervised expectation extraction from UGC paired with survey-informed supervised fine-tuning. Findings reveal leisure/social expectations drive engagement more than founda

Yang Yu, André Erpenbeck, Dominika Zgid, Guy Cohen

The investigation of quantum impurity models plays a crucial role in condensed matter physics because of their wide-ranging applications, such as embedding theories and transport problems. Traditional methods often fall short of producing accurate results for multi-orbital systems with complex interactions and off-diagonal hybridizations. Recently, tensor-tr

Amr Hegazy, Mostafa Elhoushi, Amr Alanwar

Controlling undesirable Large Language Model (LLM) behaviors, such as the generation of unsafe content or failing to adhere to safety guidelines, often relies on costly fine-tuning. Activation steering provides an alternative for inference-time control, but existing methods typically lack fine-grained, adaptive mechanisms. We introduce a novel approach using

Feng Dai, Eero Saksman, Dachun Yang, Wen Yuan

Let $\Lambda_s$ denote the Lipschitz space of order $s\in(0,\infty)$ on $\mathbb{R}^n$, which consists of all $f\in\mathfrak{C}\cap L^\infty$ such that, for some constant $L\in(0,\infty)$ and some integer $r\in(s,\infty)$, \begin{equation*} \label{0-1}\Delta_r f(x,y): =\sup_{|h|\leq y} |\Delta_h^r f(x)|\leq L y^s, \ x\in\mathbb{R}^n, \ y \in(0, 1]. \end{equa

Aditya T. Vadlamani, Anutam Srinivasan, Pranav Maneriker, Ali Payani

Conformal Prediction (CP) is a popular method for uncertainty quantification with machine learning models. While conformal prediction provides probabilistic guarantees regarding the coverage of the true label, these guarantees are agnostic to the presence of sensitive attributes within the dataset. In this work, we formalize \textit{Conformal Fairness}, a no

Naiqi Li, Peiyuan Liu, Zheng Liu, Tao Dai

Solving puzzles in natural language poses a long-standing challenge in AI. While large language models (LLMs) have recently shown impressive capabilities in a variety of tasks, they continue to struggle with complex puzzles that demand precise reasoning and exhaustive search. In this paper, we propose Logic-of-Thought (Logot), a novel framework that bridges

Panagiotis Lymperopoulos, Vasanth Sarathy

Modern Large Language Models (LLMs) often require external tools, such as machine learning classifiers or knowledge retrieval systems, to provide accurate answers in domains where their pre-trained knowledge is insufficient. This integration of LLMs with external tools expands their utility but also introduces a critical challenge: determining the trustworth

Homer A. Riva-Cambrin, Rahul Singh, Sanju Lama, Garnette R. Sutherland

Cryptography underpins the security of modern digital infrastructure, from cloud services to health data. However, many widely deployed systems will become vulnerable after the advent of scalable quantum computing. Although quantum-safe cryptographic primitives have been developed, such as lattice-based digital signature algorithms (DSAs) and key encapsulati

Ma Zhenhua, Jiang Lining

The primary contribution of this study lies in proposing a new concept termed $2$-tuples of noncommutative Orlicz sequence spaces $\bigoplus\limits_{j=1}^{2}S_{\varphi_{j},p}$, where $S_{\varphi_{j}}$ denotes a noncommutative Orlicz sequence space. By leveraging the three-line theorem, we establish the Riesz-Thorin interpolation theorem for $\bigoplus\limits

Ian Jacob Cabansag, Paul Ntegeka

Spotify's streaming charts offer a real-time lens into music popularity, driving discovery, playlists, and even revenue potential. Understanding what influences a song's rise in ranks on these charts-especially early on-can guide marketing efforts, investment decisions, and even artistic direction. In this project, we developed a classification pipeline to p

Pingxu Hu, Yinqin Li, Dachun Yang, Wen Yuan

Let $X$ be a ball Banach function space on $\mathbb{R}^n$, $k\in\mathbb{N}$, $h\in\mathbb{R}^n$, and $\Delta^k_h$ denote the $k${\rm th} order difference. In this article, under some mild extra assumptions about $X$, the authors prove that, for both parameters $q$ and $\gamma$ in \emph{sharp} ranges which are related to $X$ and for any locally integrable fun

Jiale Chen, Bo He, Maofa Wang

In this paper, we investigate the $r$-summing Carleson embeddings on weighted Fock spaces $F^p_{\alpha,w}$. By using duality arguments, translating techniques and block diagonal operator skills, we completely characterize the $r$-summability of the natural embeddings $I_d:F^p_{\alpha,w}\to L^p_{\alpha}(\mu)$ for any $r\geq1$ and $p>1$, where $w$ is a weight

Peng Yu, Yuan Zhong, Ziqi Wang, Hui Wang

In this work, we present two brane-world-type solutions in a two-dimensional (2D) dilaton gravity model with singular space-time backgrounds. By employing a first-order superpotential formalism, we first construct the 2D analogues of the thick brane solution previously given by Gremm and analyze the corresponding linear scalar perturbations. We show that for

Bo Li, Gexiang Fang, Wei Ye, Zhenghua Xu

Recent research in information extraction (IE) focuses on utilizing code-style inputs to enhance structured output generation. The intuition behind this is that the programming languages (PLs) inherently exhibit greater structural organization than natural languages (NLs). This structural advantage makes PLs particularly suited for IE tasks. Nevertheless, ex

Xiaoshan Chen, Chen Yang, Zhou Yang

Data assets are data commodities that have been processed, produced, priced, and traded based on actual demand. Reasonable pricing mechanism for data assets is essential for developing the data market and realizing their value. Most existing literature approaches data asset pricing from the seller's perspective, focusing on data properties and collection cos

Robert Gerbicz

On May 14, 2025, DeepMind announced that AlphaEvolve, a large language model applied to a set of mathematical problems, had matched or exceeded the best known bounds on several problems. In the case of the sum and difference of sets problem, AlphaEvolve, using a set of $54265$ integers, improved the known lower bound of $\theta=1.14465$ to $\theta=1.1584$. I

Yue Li, Xin Yi, Dongsheng Shi, Gerard de Melo

With the increasing size of Large Vision-Language Models (LVLMs), network pruning techniques aimed at compressing models for deployment in resource-constrained environments have garnered significant attention. However, we observe that pruning often leads to a degradation in safety performance. To address this issue, we present a novel and lightweight approac

Monirul Islam Mahmud

Keylogger detection involves monitoring for unusual system behaviors such as delays between typing and character display, analyzing network traffic patterns for data exfiltration. In this study, we provide a comprehensive analysis for keylogger detection with traditional machine learning models - SVC, Random Forest, Decision Tree, XGBoost, AdaBoost, Logistic

Yash Kumar Atri, Thomas H Shin, Thomas Hartvigsen

While bariatric and metabolic surgery (MBS) is considered the gold standard treatment for severe and morbid obesity, its therapeutic efficacy hinges upon active and longitudinal engagement with multidisciplinary providers, including surgeons, dietitians/nutritionists, psychologists, and endocrinologists. This engagement spans the entire patient journey, from

Leasly A. Campa-Raymundo, Luis Franco-Pérez

In this study, we present a rigorous analytical proof of the uniqueness of central configurations for the five-body problem, assuming that all five masses are equal and positioned at the vertices of a planar polygon. We consider configurations in which the bodies are equally spaced in angular position relative to the center of mass, and aim to determine whet

Zifeng Wang, Benjamin Danek, Jimeng Sun

Validating scientific hypotheses is a central challenge in biomedical research, and remains difficult for artificial intelligence (AI) agents due to the complexity of real-world data analysis and evidence interpretation. In this work, we present BioDSA-1K, a benchmark designed to evaluate AI agents on realistic, data-driven biomedical hypothesis validation t

Ziyi Zhou, Nicholas Stern, Julien Laasri

Much research has been done to analyze the stock market. After all, if one can determine a pattern in the chaotic frenzy of transactions, then they could make a hefty profit from capitalizing on these insights. As such, the goal of our project was to apply reinforcement learning (RL) to determine the best time to buy a stock within a given time frame. With o

Akihiro Fukuda, Yohei Nakayama, Shoichi Toyabe

Biological molecular motors are high-performance nanomachines that convert chemical energy into mechanical motion via chemomechanical coupling. Their reaction cycles typically comprise a series of intermediate chemical states between the initial and final primary states. However, the influence of these intermediate states on motor performance has not yet bee

Damien Ferbach, Katie Everett, Gauthier Gidel, Elliot Paquette

We investigate scaling laws for stochastic momentum algorithms with small batch on the power law random features model, parameterized by data complexity, target complexity, and model size. When trained with a stochastic momentum algorithm, our analysis reveals four distinct loss curve shapes determined by varying data-target complexities. While traditional s

Zifeng Wang, Jiacheng Lin, Qiao Jin, Junyi Gao

Developing artificial intelligence (AI) for clinical research requires a comprehensive data foundation that supports model training and rigorous evaluation. Here, we introduce TrialPanorama, a large-scale structured resource that aggregates 1.6M clinical trial records from fifteen global registries and links them with biomedical ontologies and associated lit

Seoyoung Ko, Hyunjeong Shim, Wanju Doh, Sungmin Yun

Retrieval-Augmented Generation (RAG) is crucial for improving the quality of large language models by injecting proper contexts extracted from external sources. RAG requires high-throughput, low-latency Approximate Nearest Neighbor Search (ANNS) over billion-scale vector databases. Conventional DRAM/SSD solutions face capacity/latency limits, whereas special

Emanuel Onica, Claudiu-Nicu Bărbieru, Andrei Arusoaie, Oana-Otilia Captarencu

We believe that leveraging real-time blockchain operational data is of particular interest in the context of the current rapid expansion of rollup networks in the Ethereum ecosystem. Given the compatible but also competing ground that rollups offer for applications, stream-based monitoring can be of use both to developers and to EVM networks governance. In t

Ziqing Wang, Kexin Zhang, Zihan Zhao, Yibo Wen

Large language models (LLMs) are introducing a paradigm shift in molecular discovery by enabling text-guided interaction with chemical spaces through natural language, symbolic notations, with emerging extensions to incorporate multi-modal inputs. To advance the new field of LLM for molecular discovery, this survey provides an up-to-date and forward-looking

Jiaxin Zhang

We study multiple chordal SLE$(\kappa)$ systems in a simply connected domain $\Omega$, where $z_1, \ldots, z_n \in \partial \Omega$ are boundary starting points and $q \in \partial \Omega$ is an additional marked boundary point. As a consequence of the domain Markov property and conformal invariance, we show that the presence of the marked boundary point $q$

C. L. Chang, Y. -Y. Chang, M. Garcia-Sciveres, W. Guo

Solid state phonon detectors used in the search for dark matter and coherent neutrino nucleus interactions (CE$\nu$NS) require excellent energy resolution (eV-scale or below) and low backgrounds. An unknown source of phonon bursts, the low energy excess (LEE), dominates other above-threshold backgrounds and generates excess shot noise from sub-threshold burs

Jiaxin Zhang

We develop a general theory of multiple chordal $\mathrm{SLE}(0)$ systems of type $(n, m)$ for positive integers $n$ and $m$ with $m \leq \lfloor n/2 \rfloor$, extending the construction of~\cite{ABKM20} beyond the previously studied case $n = 2m$. By applying integrals of motion associated with the Loewner evolution, we show that, in the $\mathbb{H}$-unifor

Jinpei Guo, Yifei Ji, Zheng Chen, Kai Liu

Pretrained latent diffusion models have shown strong potential for lossy image compression, owing to their powerful generative priors. Most existing diffusion-based methods reconstruct images by iteratively denoising from random noise, guided by compressed latent representations. While these approaches have achieved high reconstruction quality, their multi-s

Dominick Kubica, Dylan T. Gordon, Nanami Emura, Derleen Saini

As of 2025, Generative Artificial Intelligence (GenAI) has become a central tool for productivity across industries. Beyond text generation, GenAI now plays a critical role in coding, data analysis, and research workflows. As large language models (LLMs) continue to evolve, it is essential to assess the reliability and accuracy of their outputs, especially i

Aayushi Dangol, Robert Wolfe, Daeun Yoo, Arya Thiruvillakkat

As generative artificial intelligence (genAI) increasingly mediates how children learn, communicate, and engage with digital content, understanding children's hopes and fears about this emerging technology is crucial. In a pilot study with 37 fifth-graders, we explored how children (ages 9-10) envision genAI and the roles they believe it should play in their

Gagan Bhatia, Maxime Peyrard, Wei Zhao

Modern BPE tokenizers often split calendar dates into meaningless fragments, e.g., 20250312 $\rightarrow$ 202, 503, 12, inflating token counts and obscuring the inherent structure needed for robust temporal reasoning. In this work, we (1) introduce a simple yet interpretable metric, termed date fragmentation ratio, that measures how faithfully a tokenizer pr

Duy-Nam Bui, Manh Duong Phung, Hung Pham Duy

This study proposes an event-based reconfiguration control to navigate a robot swarm through challenging environments with narrow passages such as valleys, tunnels, and corridors. The robot swarm is modeled as an undirected graph, where each node represents a robot capable of collecting real-time data on the environment and the states of other robots in the

Ming Shen, Raphael Shu, Anurag Pratik, James Gung

We have seen remarkable progress in large language models (LLMs) empowered multi-agent systems solving complex tasks necessitating cooperation among experts with diverse skills. However, optimizing LLM-based multi-agent systems remains challenging. In this work, we perform an empirical case study on group optimization of role-based multi-agent systems utiliz

Md Mahbub Alam, Amilcar Soares, José F. Rodrigues-Jr, Gabriel Spadon

Accurate vessel trajectory prediction is crucial for navigational safety, route optimization, traffic management, search and rescue operations, and autonomous navigation. Traditional data-driven models lack real-world physical constraints, leading to forecasts that disobey vessel motion dynamics, such as in scenarios with limited or noisy data where sudden c

Patrick Gerard, Hans W. A. Hanley, Luca Luceri, Emilio Ferrara

Political discourse has grown increasingly fragmented across different social platforms, making it challenging to trace how narratives spread and evolve within such a fragmented information ecosystem. Reconstructing social graphs and information diffusion networks is challenging, and available strategies typically depend on platform-specific features and beh

M. Caussé, L. Toraille, G. Geneste, P. Loubeyre

Reaching pressures in the 100 GPa range enables the synthesis of hydrogen-rich compounds, with nontraditional H stoichiometries and H sublattices, called superhydrides. Record-breaking superconductivity temperature in some superhydrides have attracted great interest. A crucial next step is to stabilize superhydrides outside of high-pressure environments, lea

Melika Babakan, Fabio Benatti, Laleh Memarzadeh

In the following, we study the dissipative time-evolution of a quantum chain consisting of three coupled harmonic oscillators, the first and third of which weakly interact quadratically with two independent thermal baths in equilibrium at different temperatures. Due to the quadratic form of the total Hamiltonian, the unitary dynamics of the compound system i

Gabriel Rivière, Lasse L. Wolf

For a large class of symplectic integer matrices, the action on the torus extends to a symplectic $\mathbb{Z}^r$-action with $r\geq 2$. We apply this to the study of semiclassical measures for joint eigenfunctions of the quantization of the symplectic matrices of the $\mathbb{Z}^r$-action. In the irreducible setting, we prove that the resulting probability m

Yiyi Zhu

In this paper, we use the twisted regular representation theory of vertex operator algebras to construct bimodules over twisted Zhu algebras, extending Haisheng Li's work in untwisted scenarios. Moreover, a conjecture of Dong and Jiang on bimodule theory is confirmed.

Jos Zuijderwijk, Iris Beerepoot, Thomas Martens, Eva Knies

Government transparency, widely recognized as a cornerstone of open government, depends on robust information management practices. Yet effective assessment of information management remains challenging, as existing methods fail to consider the actual working behavior of civil servants and are resource-intensive. Using a design science research approach, we

Ken Chen, Zi-Gao Dai

Relativistic jets can be produced within the accretion disk of an active galactic nucleus (AGN), leading to distinct thermal emission as they propagate through a dense disk environment. In this paper, we present a comprehensive study of dynamical evolution of jets embedded in an AGN disk and their associated observational properties, focusing on scenarios in

Marc Brooks, Gabriel Durham, Kihyuk Hong, Ambuj Tewari

Recent advances in generative artificial intelligence (GenAI) models have enabled the generation of personalized content that adapts to up-to-date user context. While personalized decision systems are often modeled using bandit formulations, the integration of GenAI introduces new structure into otherwise classical sequential learning problems. In GenAI-powe

Anya Chaturvedi, Joshua J. Daymude, Andréa W. Richa

Algorithms for mutual exclusion aim to isolate potentially concurrent accesses to the same shared resources. Motivated by distributed computing research on programmable matter and population protocols where interactions among entities are often assumed to be isolated, Daymude, Richa, and Scheideler (SAND`22) introduced a variant of the local mutual exclusion

Patomporn Payoungkhamdee, Pume Tuchinda, Jinheon Baek, Samuel Cahyawijaya

Multi-step reasoning is essential for large language models (LLMs), yet multilingual performance remains challenging. While Chain-of-Thought (CoT) prompting improves reasoning, it struggles with non-English languages due to the entanglement of reasoning and execution. Program-of-Thought (PoT) prompting separates reasoning from execution, offering a promising

Richard Aguiar Maduro, Amanda Kronhardt Fritsch, Sonja Franke-Arnold

Various methods have been introduced to measure the orbital angular momentum (OAM) of light, from fork holograms to Dove prism interferometers, from tilted lenses to triangular apertures - each with their own benefits and limitations. Here we demonstrate how simple knife-edge diffraction can be used to identify the OAM of an optical phase vortex from the for

Emanuele Carlini, Domenico Di Gangi, Vinicius Monteiro de Lira, Hanna Kavalionak

Seaports play a crucial role in the global economy, and researchers have sought to understand their significance through various studies. In this paper, we aim to explore the common characteristics shared by important ports by analyzing the network of connections formed by vessel movement among them. To accomplish this task, we adopt a bottom-up network cons

Yannik Zemp, Ehsan Hassanpour, Jan Gerrit Horstmann, Yusuke Tokunaga

In many multiferroics, rare-earth and transition-metal orders exist side by side. For analyzing their interaction and its consequences for the multiferroic state, the associated domain patterns and their spatial correlation can give valuable insight. Unfortunately, this is often hampered by the lack of access to the domains of the rare-earth order. Here, we

Zewei Zhang, Chenhao Li, Takahiro Miki, Marco Hutter

Reinforcement learning (RL)-based motion imitation methods trained on demonstration data can effectively learn natural and expressive motions with minimal reward engineering but often struggle to generalize to novel environments. We address this by proposing a hierarchical RL framework in which a low-level policy is first pre-trained to imitate animal motion

Jiahuan Long, Wenzhe Zhang, Ning Wang, Tingsong Jiang

Physical field reconstruction (PFR) aims to predict the state distribution of physical quantities (e.g., velocity, pressure, and temperature) based on limited sensor measurements. It plays a critical role in domains such as fluid dynamics and thermodynamics. However, existing deep learning methods often fail to capture long-range temporal dependencies, resul

Renato Berlinghieri, Yunyi Shen, Jialong Jiang, Tamara Broderick

Scientists often want to make predictions beyond the observed time horizon of "snapshot" data following latent stochastic dynamics. For example, in time course single-cell mRNA profiling, scientists have access to cellular transcriptional state measurements (snapshots) from different biological replicates at different time points, but they cannot access the

Kma Solaiman

We present BiasLab, a dataset of 300 political news articles annotated for perceived ideological bias. These articles were selected from a curated 900-document pool covering diverse political events and source biases. Each article is labeled by crowdworkers along two independent scales, assessing sentiment toward the Democratic and Republican parties, and en

Jiayue Liu, Zhongchao Yi, Zhengyang Zhou, Qihe Huang

Discovering regularities from spatiotemporal systems can benefit various scientific and social planning. Current spatiotemporal learners usually train an independent model from a specific source data that leads to limited transferability among sources, where even correlated tasks requires new design and training. The key towards increasing cross-domain knowl

Connor Mattes, Esha Datta, Ali Pinar

Subgraph densities play a crucial role in network analysis, especially for the identification and interpretation of meaningful substructures in complex graphs. Localized subgraph densities, in particular, can provide valuable insights into graph structures. Distinguishing between mathematically-determined and domain-driven subgraph density features, however,

Lujun Li, Lama Sleem, Niccolo' Gentile, Geoffrey Nichil

With the emergence of ChatGPT, Transformer models have significantly advanced text classification and related tasks. Decoder-only models such as Llama exhibit strong performance and flexibility, yet they suffer from inefficiency on inference due to token-by-token generation, and their effectiveness in text classification tasks heavily depends on prompt quali

Jinhua Liang, Yuanzhe Chen, Yi Yuan, Dongya Jia

Editing sound with precision is a crucial yet underexplored challenge in audio content creation. While existing works can manipulate sounds by text instructions or audio exemplar pairs, they often struggled to modify audio content precisely while preserving fidelity to the original recording. In this work, we introduce a novel editing approach that enables l

Miguel A. López-Santamaría, Y. D. Mayya, Luis Lomelí-Núñez, L. Rodríguez-Merino

We here report the results from spectroscopic observations of a sample of 26 globular cluster (GC) and 21 faint fuzzy (FF) candidates in the lenticular galaxy NGC 1023 using the 10.4-m Gran Telescopio Canarias. Using the recessional velocities and stellar absorption features, we determine that 18 and 9 of the observed candidates are bona fide GCs and FFs, re

Bart Kosko, Olaoluwa Adigun

We present the new bidirectional variational autoencoder (BVAE) network architecture. The BVAE uses a single neural network both to encode and decode instead of an encoder-decoder network pair. The network encodes in the forward direction and decodes in the backward direction through the same synaptic web. Simulations compared BVAEs and ordinary VAEs on the

Jie Gong, Jiajie Huang

In recent years, there has been an increasing focus on real-time mobile applications, such as news updates and weather forecast. In these applications, data freshness is of significant importance, which can be measured by age-of-synchronization (AoS). At the same time, the reduction of carbon emission is increasingly required by the communication operators.

Iona Clemente, Eleanor K. Sansom, Hadrien A. R. Devillepoix, Taichi Kawamura

This exploratory study investigates whether seismic signals can be used to infer fragmentation during a fireball event. Re-entry objects, particularly sample return capsules (SRCs) such as the one from the Hayabusa2 mission, behave similarly to slow meteors during atmospheric entry and provide valuable insights into natural fireball events. In this study, we

Daniel Flood, Matthew England, Beate Grawemeyer

This study is part of a larger project focused on measuring, understanding, and improving student engagement in programming education. We investigate whether synthetic data generation can help identify at-risk students earlier in a small, imbalanced dataset from an introductory programming module. The analysis used anonymised records from 379 students, with

David Stefanyszyn, Xi Tong, Yuhang Zhu

Cosmological correlation functions of inflaton and graviton perturbations are the fundamental observables of early universe cosmology and remain a primary target for observations. In this work, we ask the following question: are these observables independent of one another? We find that in the parity-odd sector of inflationary perturbation theory, the answer

Milad Kabirifar, Biswarup Mukherjee, S. Gokul Krishnan, Charalambos Konstantinou

The diversity of prosumers' resources in energy communities can provide significant technical and economic benefits to both prosumers and the distribution system operator (DSO). To maximize these benefits, a coordination framework is required to address all techno-economic constraints as well as the objectives of all agents. This paper presents a fully distr

Michal Golovanevsky, William Rudman, Michael Lepori, Amir Bar

Multimodal Large Language Models (MLLMs) perform well on tasks such as visual question answering, but it remains unclear whether their reasoning relies more on memorized world knowledge or on the visual information present in the input image. To investigate this, we introduce Visual CounterFact, a new dataset of visually-realistic counterfactuals that put wo

Sergei M. Gorbunov

We consider the convergence of additive functionals under the determinantal point process with the confluent hypergeometric kernel, corresponding to a sufficiently smooth function $f(x/R)$, as $R\to\infty$. We show that these functionals approach Gaussian distribution and give an estimate on the Kolmogorov-Smirnov distance. To obtain these results, we derive

Jay Yu, Austin Bennett, Billy Gao, Rebecca Joseph

Retroactive Public Goods Funding (RetroPGF) rewards blockchain projects based on proven impact rather than future promises. This paper reviews voting mechanisms for Optimism's RetroPGF, where "badgeholders" allocate rewards to valuable projects. We explore Optimism's previous schemes for RetroPGF voting, including quadratic, mean, and median voting. We prese

Maxon Rubin-Toles, Maya Gambhir, Keshav Ramji, Aaron Roth

Language models are increasingly being used in important decision pipelines, so ensuring the correctness of their outputs is crucial. Recent work has proposed evaluating the "factuality" of claims decomposed from a language model generation and applying conformal prediction techniques to filter out those claims that are not factual. This can be effective for