May 2025 arXiv papers — page 5

Qiming Zeng, Hao Luo, Yuhao Lin, Yicheng Jin

By retrieving contexts from knowledge graphs, graph-based retrieval-augmented generation (GraphRAG) enhances large language models (LLMs) to generate quality answers for user questions. Many GraphRAG methods have been proposed and reported inspiring performance in answer quality. However, we observe that the current answer evaluation framework for GraphRAG h

Yujie Zhu, Aiden Ross, Xiangwei Guo, Venkatraman Gopalan

An analytical model is developed to predict the temporal evolution of the lattice polarization in ferroelectric nanomembranes upon the excitation by a terahertz (THz) electromagnetic pulse of an arbitrary waveform, and the concurrent transmission of the THz pulse in both the linear and the nonlinear regimes. It involves the use of the perturbation method to

Pintu Debnath, Sayan Goswami, Chunlin Liu

The concept of Central sets, introduced by Furstenberg through the framework of topological dynamics, has played a pivotal role in combinatorial number theory. Furstenberg's Central Sets Theorem highlighted their rich combinatorial structure. Later, De, Hindman, and Strauss strengthen this theorem using the algebraic framework of the Stone--\v{C}ech compacti

Ngoc Long Pham, Tri Nhu Do

Neural network (NN)-based end-to-end (E2E) communication systems, in which each system component may consist of a portion of a neural network, have been investigated as potential tools for developing artificial intelligence (Al)-native E2E systems. In this paper, we propose an NN-based bitwise receiver that improves computational efficiency while maintaining

Andrew Laeuger, Colin Weller, Dongjun Li, Yanbei Chen

Recent studies have shown that far-field perturbations to the curvature potential of a black hole spacetime may destabilize its quasinormal mode (QNM) spectrum while only mildly affecting time-domain ringdown signals. In this work, we study the QNM spectrum and ringdown behavior of a Schwarzschild black hole with a far-field perturbation to its physical envi

Mafiz Uddin

EPR showed that two particles emitted from a source can be entangled by a shared wavefunction where two non-commuting observables (position, momentum) can be simultaneously real, leading to a contradiction with quantum mechanics (two non-commuting variables can not be simultaneously real). John Bell derived an inequality where any local hidden variables pred

Ngoc Tuyen Do, Tri Nhu Do

In the surveillance and defense domain, multi-target detection and classification (MTD) is considered essential yet challenging due to heterogeneous inputs from diverse data sources and the computational complexity of algorithms designed for resource-constrained embedded devices, particularly for Al-based solutions. To address these challenges, we propose a

Samuel R. Hedemann

We present new generalizations of Cauchy-Schwarz (CS) inequalities to multiple vectors and use them to derive multi-operator quantum uncertainty relations and propose multi-operator squeezing.

Zi-Chao Chi, Xing-Gang He, Zhi-Yi Wu

A distinctive problem of harmonic analysis on $\R$ with respect to a Borel probability measure $\mu$ is identifying all $t\in\R$ such that both \[\left\{e^{-2\pi i\lambda x}: \lambda\in\Lambda\right\}\quad\text{and}\quad \left\{e^{-2\pi i\lambda x}: \lambda\in t\Lambda\right\}\] form orthonormal bases of the space $L^2(\mu)$. Currently, this phenomenon has b

Yubai Wei, Jiale Han, Yi Yang

Text embedding models play a cornerstone role in AI applications, such as retrieval-augmented generation (RAG). While general-purpose text embedding models demonstrate strong performance on generic retrieval benchmarks, their effectiveness diminishes when applied to private datasets (e.g., company-specific proprietary data), which often contain specialized t

Hoang T. Nguyen, Priya L. Donti

Efficiently solving constrained optimization problems is crucial for numerous real-world applications, yet traditional solvers are often computationally prohibitive for real-time use. Machine learning-based approaches have emerged as a promising alternative to provide approximate solutions at faster speeds, but they struggle to strictly enforce constraints,

Jiawang Li

In this communication, two novel low-cost single-layer filtering antennas (filtennas) are proposed for millimeter wave (mmWave) applications. The proposed filtennas consists of a compact circular substrate integrated waveguide (SIW) cavity, a metal post close to the center of the cavity for power feeding, a metal post in the center for modes controlling, and

Teng Fang, Binzhou Xia

For nonempty subsets $X$ and $Y$ of a group $G$, we say that $(X,Y)$ is a tiling of $G$ if every element of $G$ can be uniquely expressed as $xy$ for some $x\in X$ and $y\in Y$. In 1966, Rothaus and Thompson studied whether the symmetric group $S_n$ with $n\geq3$ admits a tiling $(T_n,Y)$, where $T_n$ consists of the identity and all the transpositions in $S

Jie Ren, Zhenwei Dai, Xianfeng Tang, Yue Xing

Although Large Language Models (LLMs) have demonstrated impressive capabilities across a wide range of tasks, growing concerns have emerged over the misuse of sensitive, copyrighted, or harmful data during training. To address these concerns, unlearning techniques have been developed to remove the influence of specific data without retraining from scratch. H

Sarthak Kumar Maharana, Saksham Singh Kushwaha, Baoming Zhang, Adrian Rodriguez

While recent audio-visual models have demonstrated impressive performance, their robustness to distributional shifts at test-time remains not fully understood. Existing robustness benchmarks mainly focus on single modalities, making them insufficient for thoroughly assessing the robustness of audio-visual models. Motivated by real-world scenarios where shift

Chengkun Dong, Matthew R. Chua, Rasna Maruthiyodan Veetil, T. Thu Ha Do

Dynamic control of light, and in particular beam steering, is pivotal in various optical applications, including telecommunications, LiDAR, and biomedical imaging. Traditional approaches achieve this by interfacing a tunable modulating device with an external light source, facing challenges in achieving compact devices. Here, we introduce a dynamic photolumi

Rorry Brenner, Evan Davis, Rushi Chaudhari, Rowan Morse

Perforated Backpropagation is a neural network optimization technique based on modern understanding of the computational importance of dendrites within biological neurons. This paper explores further experiments from the original publication, generated from a hackathon held at the Carnegie Mellon Swartz Center in February 2025. Students and local Pittsburgh

Yixuan Li, Hongbo Xu, Ming Zeng, Yuanwei Liu

In this letter, we investigate a novel pinching antenna (PA)-aided wireless powered communication network (WPCN), in which multiple PAs are activated along a waveguide to establish robust line-of-sight links with multiple devices. Both time division multiple access (TDMA) and non-orthogonal multiple access (NOMA) protocols are considered in the PA-WPCN. More

Jovana Kondic, Pengyuan Li, Dhiraj Joshi, Zexue He

Chart-to-code reconstruction -- the task of recovering executable plotting scripts from chart images -- provides important insights into a model's ability to ground data visualizations in precise, machine-readable form. Yet many existing multimodal benchmarks largely focus primarily on answering questions about charts or summarizing them. To bridge this gap,

Qianyi Wang, Ben Wang, Jun Wang, Lijian Zhang

Quantum mechanics imposes a lower bound on the time required for a quantum system to reach certain given targets. In this paper, from a geometric perspective, we introduce a new quantum speed limit (QSL) based on the Bloch angle and derive the condition for it to saturate. Experimentally, we demonstrate the feasibility of measuring this QSL using a photonic

Gerald Dugan, Andrew J. Lankford, Benno List, Shinichiro Michizono

The International Linear Collider was conceived as a global project for an energy-frontier electron-positron collider.It employs superconducting RF and nano-beam technologies with a center-of-mass energy of 500 GeV. Its cost was estimated in 2013, based on the Technical Design Report published in 2013.Japan's high-energy community proposed to host the ILC in

Chinkit Patel, Kee Siong Ng

Many large enterprises that operate highly governed and complex ICT environments have no efficient and effective way to support their Data and AI teams in rapidly spinning up and tearing down self-service data and compute infrastructure, to experiment with new data analytic tools, and deploy data products into operational use. This paper proposes a key piece

Lin Yang, Huu-Thiet Nguyen, Chen Lv, Domenico Campolo

In this work, we explore how conventional motion planning algorithms can be reapplied to contact-rich manipulation tasks. Rather than focusing solely on efficiency, we investigate how manipulation aspects can be recast in terms of conventional motion-planning algorithms. Conventional motion planners, such as Rapidly-Exploring Random Trees (RRT), typically co

Xueyuan Chen, Dongchao Yang, Wenxuan Wu, Minglin Wu

Dysarthric speech reconstruction (DSR) aims to convert dysarthric speech into comprehensible speech while maintaining the speaker's identity. Despite significant advancements, existing methods often struggle with low speech intelligibility and poor speaker similarity. In this study, we introduce a novel diffusion-based DSR system that leverages a latent diff

Chau Nguyen, Son Nguyen, Dora Woodruff

We give a new formula for the Littlewood--Richardson coefficients in terms of peelable tableaux compatible with shuffle tableaux, in the same fashion as Remmel--Whitney rule. This gives an efficient way to compute generalized Littlewood--Richardson coefficients for Temperley--Lieb immanants of Jacobi--Trudi matrices. We will also show that our rule behaves w

Shivam Shorewala, Zihao Yang

Knowledge of accurate relative skills in any competitive system is essential, but foundational approaches such as ELO discard extremely relevant performance data by concentrating exclusively on binary outcomes. While margin of victory (MOV) extensions exist, they often lack a definitive method for incorporating this information. We introduce Margin of Victor

Qianyi Wang, Ben Wang, Jun Wang, Lijian Zhang

The quantum geometric tensor (QGT) is a fundamental quantity for characterizing the geometric properties of quantum states and plays an essential role in elucidating various physical phenomena. The traditional QGT, defined only for pure states, has limited applicability in realistic scenarios where mixed states are common. To address this limitation, we gene

Hao Chen, Alfio Borzi

The Lindblad equation is a widely used quantum master equation to model the dynamical evolution of open quantum systems whose states are described by density matrices. This equation is also a fundamental building block to design optimal control functions. In this paper we develop full- and low-rank exponential midpoint integrators for solving both the forwar

Haibo Hu, Guodong Bian, Ailun Yi, Chunhui Jiang

Quantum defects in solids have emerged as a transformative platform for advancing quantum technologies. A key requirement for these applications is achieving high-fidelity single-spin readout, particularly at room temperature for quantum biosensing. Here, we demonstrate through ab initio simulations of a primary quantum defect in 4H silicon carbide that stra

Sungjae Lee, Hoyoung Kim, Jeongyeon Hwang, Eunhyeok Park

Scaling test-time computation--generating and analyzing multiple or sequential outputs for a single input--has become a promising strategy for improving the reliability and quality of large language models (LLMs), as evidenced by advances in uncertainty quantification and multi-step reasoning. A key shared component is semantic clustering, which groups outpu

Mustafa Chasmai, Alexander Shepard, Subhransu Maji, Grant Van Horn

We present the iNaturalist Sounds Dataset (iNatSounds), a collection of 230,000 audio files capturing sounds from over 5,500 species, contributed by more than 27,000 recordists worldwide. The dataset encompasses sounds from birds, mammals, insects, reptiles, and amphibians, with audio and species labels derived from observations submitted to iNaturalist, a g

Jonathan Gillot, Joannes Barbarat, Charles Philippe, Hector Alvarez-Martinez

We report on the effect of a weak magnetic field applied on an iodine cell used to frequency stabilize a laser. A 1.5$~\mu$m laser is frequency tripled in order to excite the molecular transitions at 0.51$~\mu$m and frequency locked on a hyperfine line. With this frequency reference, we report short-term stability about $3\,\times\,10^{-14}~\tau^{-1/2}$, wit

C. Selvaraju, S. Sabari, O. T. Lekeufack, F. Kenmogne

This study investigates the emergence of chaotic dynamics in Bose-Einstein condensates (BECs) subjected to both alternating (AC) and constant (DC) components of the interaction strength, modeled through the scattering length. We systematically explore how the interplay of AC and DC nonlinearities affect the dynamical evolution of the condensate under a tilte

Liqiang Zhu, Zhan Gao, Weiyao Ke, Hanzhong Zhang

Jet quenching is studied in a background magnetic field and a finite baryon chemical potential. The production of energetic partons is calculated using the next-to-leading order (NLO) perturbative Quantum Chromodynamics (pQCD) parton model, while the parton energy loss formula is obtained from the AdS/CFT correspondence incorporating the magnetic field and b

Peng-Cheng Zhang, Pei-Pin Yang, Ting-Ting Duan, Hailong Zhu

The transverse momentum ($p_T$) spectra of identified light charged hadrons, specifically bosons ($\pi^{\pm}$ and $K^{\pm}$) as well as fermions [$p(\bar p)$], produced in small collision systems, namely deuteron-gold (d+Au) and proton-proton (p+p) collisions at the top energy of the Relativistic Heavy Ion Collider (RHIC) with a center-of-mass energy of $\sq

Peter Neigel, David Antony Selby, Shota Arai, Benjamin Tag

Wearable devices offer detailed sleep-tracking data. However, whether this information enhances our understanding of sleep or simply quantifies already-known patterns remains unclear. This work explores the relationship between subjective sleep self-assessments and sensor data from an Oura ring over 4--8 weeks in-the-wild. 29 participants rated their sleep q

Yifan Peng, Shakeel Muhammad, Yui Sudo, William Chen

The Open Whisper-style Speech Models (OWSM) project has developed a series of fully open speech foundation models using academic-scale resources, but their training data remains insufficient. This work enhances OWSM by integrating YODAS, a large-scale web-crawled dataset with a Creative Commons license. However, incorporating YODAS is nontrivial due to its w

Ming Hu, Jianfu Yin, Mingyu Dou, Yuqi Wang

The automatic classification of medical time series signals, such as electroencephalogram (EEG) and electrocardiogram (ECG), plays a pivotal role in clinical decision support and early detection of diseases. Although Transformer based models have achieved notable performance by implicitly modeling temporal dependencies through self-attention mechanisms, thei

Kevin P. Thompson

The existence of excircles and an Apollonius circle for a triangle in taxicab geometry are connected to the concept of inscribed triangles.

Hugo Díaz, Arvind K. Saibaba, Srinivas Eswar, Vishwas Rao

We consider optimal experimental design (OED) for Bayesian inverse problems, where the experimental design variables have a certain multiway structure. Given $d$ different experimental variables with $m_i$ choices per design variable $1 \le i\le d$, the goal is to select $k_i \le m_i$ experiments per design variable. Previous work has related OED to the colu

Jingyang He, Shuai Wang, Ang Li

Selection bias, arising from the systematic inclusion or exclusion of certain samples, poses a significant challenge to the validity of causal inference. While Bareinboim et al. introduced methods for recovering unbiased observational and interventional distributions from biased data using partial external information, the complexity of the backdoor adjustme

Gerard Christopher Yeo, Kokil Jaidka

Datasets used for emotion recognition tasks typically contain overt cues that can be used in predicting the emotions expressed in a text. However, one challenge is that texts sometimes contain covert contextual cues that are rich in affective semantics, which warrant higher-order reasoning abilities to infer emotional states, not simply the emotions conveyed

Mingxuan Liu, Tyler L. Hayes, Massimiliano Mancini, Elisa Ricci

Open-vocabulary object detection models allow users to freely specify a class vocabulary in natural language at test time, guiding the detection of desired objects. However, vocabularies can be overly broad or even mis-specified, hampering the overall performance of the detector. In this work, we propose a plug-and-play Vocabulary Adapter (VocAda) to refine

James Afful

As machine learning systems are increasingly deployed in high-stakes domains such as criminal justice, finance, and healthcare, the demand for interpretable and trustworthy models has intensified. Despite the proliferation of local explanation techniques, including SHAP, LIME, and counterfactual methods, there exists no standardized, reproducible framework f

Svetlana Churina, Akshat Gupta, Insyirah Mujtahid, Kokil Jaidka

Code-mixing involves the seamless integration of linguistic elements from multiple languages within a single discourse, reflecting natural multilingual communication patterns. Despite its prominence in informal interactions such as social media, chat messages and instant-messaging exchanges, there has been a lack of publicly available corpora that are author

Boyi Zhang, Zhuo Liu, Hangfeng He

In real practice, questions are typically complex and knowledge-intensive, requiring Large Language Models (LLMs) to recognize the multifaceted nature of the question and reason across multiple information sources. Iterative and adaptive retrieval, where LLMs decide when and what to retrieve based on their reasoning, has been shown to be a promising approach

Muhammad Adnan, Nithesh Kurella, Akhil Arunkumar, Prashant J. Nair

Diffusion Transformers (DiTs) achieve state-of-the-art results in text-to-image, text-to-video generation, and editing. However, their large model size and the quadratic cost of spatial-temporal attention over multiple denoising steps make video generation computationally expensive. Static caching mitigates this by reusing features across fixed steps but fai

Kourosh Shahnazari, Seyed Moein Ayyoubzadeh, Mohammadali Keshtparvar

The quest for interpretable reinforcement learning is a grand challenge for the deployment of autonomous decision-making systems in safety-critical applications. Modern deep reinforcement learning approaches, while powerful, tend to produce opaque policies that compromise verification, reduce transparency, and impede human oversight. To address this, we intr

Shreshth Saini, Ru-Ling Liao, Yan Ye, Alan C. Bovik

Despite recent advancements in latent diffusion models that generate high-dimensional image data and perform various downstream tasks, there has been little exploration into perceptual consistency within these models on the task of No-Reference Image Quality Assessment (NR-IQA). In this paper, we hypothesize that latent diffusion models implicitly exhibit pe

Jingde Cheng, Gennaro Notomista

This paper proposes a novel control framework for robotic swarms capable of turning a musical input into a painting. The approach connects the two artistic domains, music and painting, leveraging their respective connections to fundamental emotions. The robotic units of the swarm are controlled in a coordinated fashion using a heterogeneous coverage policy t

Long Xu, Peng Gao, Wen-Jia Tang, Fei Wang

Although deep learning-based visual tracking methods have made significant progress, they exhibit vulnerabilities when facing carefully designed adversarial attacks, which can lead to a sharp decline in tracking performance. To address this issue, this paper proposes for the first time a novel adversarial defense method based on denoise diffusion probabilist

Jisoo Jeong, Hong Cai, Jamie Menjay Lin, Fatih Porikli

Conventional training for optical flow and stereo depth models typically employs a uniform loss function across all pixels. However, this one-size-fits-all approach often overlooks the significant variations in learning difficulty among individual pixels and contextual regions. This paper investigates the uncertainty-based confidence maps which capture these

Takuzo Okada

We show that any quasismooth Fano threefold weighted complete intersections of type $(12, 14)$ in $\mathbb{P} (1, 2, 3, 4, 7, 11)$ is birationally solid.

Sofiane Mahiou, Amir Dizche, Reza Nazari, Xinmin Wu

We propose dpmm, an open-source library for synthetic data generation with Differentially Private (DP) guarantees. It includes three popular marginal models -- PrivBayes, MST, and AIM -- that achieve superior utility and offer richer functionality compared to alternative implementations. Additionally, we adopt best practices to provide end-to-end DP guarante

Shaun Ee, Chris Covino, Cara Labrador, Christina Krawec

As AI-enabled cyber capabilities become more advanced, we propose "differential access" as a strategy to tilt the cybersecurity balance toward defense by shaping access to these capabilities. We introduce three possible approaches that form a continuum, becoming progressively more restrictive for higher-risk capabilities: Promote Access, Manage Access, and D

Ren-Xin Zhao

Natural Language Processing (NLP) faces challenges in the ability to quickly model polysemous words. The Grover's Algorithm (GA) is expected to solve this problem but lacks adaptability. To address the above dilemma, a Quantum Text Pre-training Network (QTP-Net) is proposed to improve the performance of NLP tasks. First, a Quantum Enhanced Pre-training Featu

Xiao Yu, Baolin Peng, Ruize Xu, Michel Galley

Recent progress in reasoning with large language models (LLMs), such as DeepSeek-R1, demonstrates impressive capabilities in domains like mathematics and coding, by exhibiting complex cognitive behaviors such as verification, goal decomposition, and self-reflection. However, it is unclear what behavior is effective and what behavior is missing for long-horiz

Yufei Tian, Jiao Sun, Nanyun Peng, Zizhao Zhang

As language models evolve to tackle complex, multifaceted tasks, their evaluation must adapt to capture this intricacy. A granular, skill-specific understanding of model capabilities can empower researchers to make informed model development plans. In this paper, we introduce SkillVerse, an unsupervised tree-structured diagnosis framework for understanding m

Sara Ghazanfari, Francesco Croce, Nicolas Flammarion, Prashanth Krishnamurthy

Recent work has shown that eliciting Large Language Models (LLMs) to generate reasoning traces in natural language before answering the user's request can significantly improve their performance across tasks. This approach has been extended to multimodal LLMs, where the models can produce chains-of-thoughts (CoT) about the content of input images and videos.

Alisha Ukani, Hamed Haddadi, Alex C. Snoeren, Peter Snyder

We present a study of how local frames (i.e., iframes loading content like "about:blank") are mishandled by a wide range of popular Web security and privacy tools. As a result, users of these tools remain vulnerable to the very attack techniques against which they seek to protect themselves, including browser fingerprinting, cookie-based tracking, and data e

Atul Ganju, Shashaank Aiyer, Ved Sriraman, Karthik Sridharan

We present a new active learning framework for multiclass classification based on surrogate risk minimization that operates beyond the standard realizability assumption. Existing surrogate-based active learning algorithms crucially rely on realizability$\unicode{x2014}$the assumption that the optimal surrogate predictor lies within the model class$\unicode{x

Mahmoud Elgenedy

Complexity of Neural Networks is increasing rapidly due to the massive increase in model parameters. Specifically, in Large Language Models (LLMs), the number of model parameters has grown exponentially in the past few years, for example, from 1.5 billion parameters in GPT2 to 175 billion in GPT3. This raises a significant challenge for implementation, espec

Sima Farokhnejad, Angélica S. da Mata, Mariana Macedo, Ronaldo Menezes

Commodities, including livestock, flow through trade networks globally, with trajectories that can be effectively captured using mobility pattern modelling approaches similar to those used in human mobility studies. However, documenting these movements comprehensively presents significant challenges; it can be unrealistic, costly, and may conflict with data

Yuxiang Xu, Wenjuan Yu, Yuqian Wan, Zhongming Zhang

This study addresses critical challenges in managing the transportation of spent nuclear fuel, including inadequate data transparency, stringent confidentiality requirements, and a lack of trust among collaborating parties, issues prevalent in traditional centralized management systems. Given the high risks involved, balancing data confidentiality with regul

Hyangsuk Min, Yuho Lee, Minjeong Ban, Jiaqi Deng

Evaluation frameworks for text summarization have evolved in terms of both domain coverage and metrics. However, existing benchmarks still lack domain-specific assessment criteria, remain predominantly English-centric, and face challenges with human annotation due to the complexity of reasoning. To address these, we introduce MSumBench, which provides a mult

Nuojin Cheng, Leonard Papenmeier, Stephen Becker, Luigi Nardi

Bayesian optimization is a widely used method for optimizing expensive black-box functions, with Expected Improvement being one of the most commonly used acquisition functions. In contrast, information-theoretic acquisition functions aim to reduce uncertainty about the function's optimum and are often considered fundamentally distinct from EI. In this wo

Jamme Omar A. Biscocho, Kristian Hauser A. Villegas

We investigate the quantum geometric tensor, which is comprised of the Berry curvature and quantum metric, in a generalized Dirac two-band system with non-integer dispersion $E(\mathbf{k})\sim k^α$. Our analysis reveals that this type of dispersion introduces significant and novel effects on quantum band geometry. We calculate the Berry curvature and observe

Robert E. Gompf

We show that tori in Engel 4-manifolds behave analogously to knots in contact 3-manifolds: Every torus with trivial normal bundle is isotopic to infinitely many distinct transverse tori, distinguished locally (and globally in the nullhomologous case) by their formal invariants. (Few examples of transverse tori were previously known.) We classify the formal i

Hideaki Joko, Faegheh Hasibi

A systematic, reliable, and low-cost evaluation of Conversational Information Access (CIA) systems remains an open challenge. Existing reference-based evaluation methods are proven insufficient for evaluating the dynamic nature of information access conversations, while existing LLM-based reference-free methods suffer from evaluation bias and limited general

Nicolaas Weideman, Sima Arasteh, Mukund Raghothaman, Jelena Mirkovic

Data-flow analysis is a critical component of security research. Theoretically, accurate data-flow analysis in binary executables is an undecidable problem, due to complexities of binary code. Practically, many binary analysis engines offer some data-flow analysis capability, but we lack understanding of the accuracy of these analyses, and their limitations.

Brendan Sands, Yining Wang, Chenhao Xu, Yuxuan Zhou

Large language models (LLMs) have been prominent in various tasks, including text generation and summarisation. The applicability of LLMs to the generation of product reviews is gaining momentum, paving the way for the generation of movie reviews. In this study, we propose a framework that generates movie reviews using three LLMs (GPT-4o, DeepSeek-V3, and Ge

Ankit Kumar, Hajime Sotani

We investigate the structural, dynamical, and oscillatory properties of neutron stars admixed with dark matter, modeled via a single-fluid formalism where dark matter interacts with nuclear matter through an effective Higgs-portal coupling. Employing three relativistic mean-field nuclear matter equations of state-IOPB-I, BigApple, and NL3- we incorporate a p

Shayantani Kar, B. Shresth Bhimrajka, Aditya Kumar, Sahil Gupta

Rapid spread of false images and videos on online platforms is an emerging problem. Anyone may add, delete, clone or modify people and entities from an image using various editing software which are readily available. This generates false and misleading proof to hide the crime. Now-a-days, these false and counterfeit images and videos are flooding on the int

Monika Sanoria, Gema Malet-Engra, Giorgio Scita, Nir Gov

The collective chemotaxis of multicellular clusters is an important phenomenon in various physiological contexts, ranging from embryonic development to cancer metastasis. Such clusters often display interesting shape dynamics and instabilities, but their physical origin, functional benefits, and role in overall chemotactic migration remain unclear. Here, we

Ruonan Wang, Runxi Wang, Yunwen Shen, Chengfeng Wu

Large Language Models (LLMs) have shown impressive performance on a range of educational tasks, but are still understudied for their potential to solve mathematical problems. In this study, we compare three prominent LLMs, including GPT-4o, DeepSeek-V3, and Gemini-2.0, on three mathematics datasets of varying complexities (GSM8K, MATH500, and MIT Open Course

Hayoung Jung, Shravika Mittal, Ananya Aatreya, Navreet Kaur

Understanding the prevalence of misinformation in health topics online can inform public health policies and interventions. However, measuring such misinformation at scale remains a challenge, particularly for high-stakes but understudied topics like opioid-use disorder (OUD)--a leading cause of death in the U.S. We present the first large-scale study of OUD

John Harvill, Ziwei Fan, Hao Wang, Luke Huan

Existing work on prompt compression for Large Language Models (LLM) focuses on lossy methods that try to maximize the retention of semantic information that is relevant to downstream tasks while significantly reducing the sequence length. In this paper, we introduce a task-agnostic lossless compression technique similar to LZ77 that makes it possible to redu

Chathura R. Sumanasiri, Tulsi Ram Sahu, Aditya G. Nair

Flutter in lightweight airfoils under unsteady flows presents a critical challenge in aeroelastic stability and control. This study uncovers phase-localized mechanisms that drive the onset and suppression of flutter in a freely pitching airfoil at low Reynolds number. By introducing targeted impulsive stiffness perturbations, we identify critical phases that

Antonello Paolino, Gabriele Nava, Fabio Di Natale, Fabio Bergonti

Robots with multi-modal locomotion are an active research field due to their versatility in diverse environments. In this context, additional actuation can provide humanoid robots with aerial capabilities. Flying humanoid robots face challenges in modeling and control, particularly with aerodynamic forces. This paper addresses these challenges from a technol

Payal Mohapatra, Akash Pandey, Xiaoyuan Zhang, Qi Zhu

Unvoiced electromyography (EMG) is an effective communication tool for individuals unable to produce vocal speech. However, most prior methods rely on paired voiced and unvoiced EMG signals, along with speech data, for EMG-to-text conversion, which is not practical for such individuals. Given the rise of large language models (LLMs) in speech recognition, we

Yongshun Hu, Dan Wen

We explore the time-dependent Casimir-Polder-like quantum gravitational interaction between a nonpointlike object and a gravitational Dirichlet boundary, i.e., the dynamical gravitational Casimir-Polder interaction, based on the theory of linearized quantum gravity. We demonstrate that the dynamical interaction potential is nonzero prior to the radiation, wh

Can Polat, Erchin Serpedin, Mustafa Kurban, Hasan Kurban

Most materials science datasets are limited to atomic geometries (e.g., XYZ files), restricting their utility for multimodal learning and comprehensive data-centric analysis. These constraints have historically impeded the adoption of advanced machine learning techniques in the field. This work introduces MultiCrystalSpectrumSet (MCS-Set), a curated framewor

Narcicegi Kiran, Tiago Pereira

We address the inverse problem of reconstructing both the structure and dynamics of a network from mean-field measurements, which are linear combinations of node states. This setting arises in applications where only a few aggregated observations are available, making network inference challenging. We focus on the case when the number of mean-field measureme

E. N. Bashmakova, S. B. Korolev, T. Yu. Golubeva

In the paper, we develop a bosonic quantum error correction code based on squeezed Fock states. We compare our proposed code with one based on squeezed Schrodinger's cat states using the Knill-Laflamme cost function and the Petz map fidelity. We demonstrate that squeezed Fock states are competitive in protecting information in a channel with particle loss an

Purvish Jajal, Nick John Eliopoulos, Benjamin Shiue-Hal Chou, George K. Thiruvathukal

Diffusion models are state-of-the-art generative models, yet their samples often fail to satisfy application objectives such as safety constraints or domain-specific validity. Existing techniques for alignment require gradients, internal model access, or large computational budgets resulting in high compute demands, or lack of support for certain objectives.

M. Archipley, A. Hryciuk, L. E. Bleem, K. Kornoelje

Context. The South Pole Telescope third-generation camera (SPT-3G) has observed over 10,000 square degrees of sky at 95, 150, and 220 GHz (3.3, 2.0, 1.4 mm, respectively) and will significantly overlap the ongoing 14,000 square-degree Euclid Wide Survey. The Euclid collaboration recently released Euclid Deep Field South (EDF-S) observations of 23 square degr

Fanglei Xue, Andrew Kubaney, Zhichun Guo, Joseph K. Min

Protein sequence design methods have demonstrated strong performance in sequence generation for de novo protein design. However, as the training objective was sequence recovery, it does not guarantee designability--the likelihood that a designed sequence folds into the desired structure. To bridge this gap, we redefine the training objective by steering sequ

Zifan Wang, Christina Q. Knight, Jeremy Kritz, Willow E. Primack

Large Language Model (LLM) safeguards, which implement request refusals, have become a widely adopted mitigation strategy against misuse. At the intersection of adversarial machine learning and AI safety, safeguard red teaming has effectively identified critical vulnerabilities in state-of-the-art refusal-trained LLMs. However, in our view the many conferenc

Manav Nitin Kapadnis, Atharva Naik, Carolyn Rose

Reinforcement learning (RL) to improve code review comment generation requires handling unstructured outputs, making reinforcement learning (RL) feedback challenging. The two main RL approaches, namely RL with Verifiable Feedback (RLVR) and RL with AI Feedback (RLAIF), offer trade-offs: RLVR provides reliable feedback for structured tasks like code generatio

Reggie C. Pantig

We explore analytically the implications of a curvature-modified extended uncertainty principle (EUP) derived in a rotating G\"odel spacetime and apply it to the construction of a semiclassical black hole model. Adapting techniques from corpuscular black hole frameworks, we reinterpret the G\"odel-type uncertainty relation as an effective energy bound, leadi

Luis Felipe Strano Moraes, Ignacio Becker, Pavlos Protopapas, Guillermo Cabrera-Vives

We apply pre-trained Vision Transformers (ViTs), originally developed for image recognition, to the analysis of astronomical spectral data. By converting traditional one-dimensional spectra into two-dimensional image representations, we enable ViTs to capture both local and global spectral features through spatial self-attention. We fine-tune a ViT pretraine

Samantha E. Smith, Santiago Bermudez, Pavan Chaitanya, Zoe Boekelheide

Magnetic hyperthermia treatment (MHT) utilizes heat generated from magnetic nanoparticles (MNPs) under an alternating magnetic field (AMF) for therapeutic applications. Gadolinium silicide (Gd5Si4) has emerged as a promising MHT candidate due to its self-regulating heating properties and potential biocompatibility. However, the impact of high-dose X-ray irra

Hemant Sharma, Kenneth Goodenough, Johannes Borregaard, Filip Rozpędek

Graph states are a powerful class of entangled states with numerous applications in quantum communication and quantum computation. Local Clifford (LC) operations that map one graph state to another can alter the structure of the corresponding graphs, including changing the number of edges. Here, we tackle the associated edge-minimisation problem: finding gra

Linh Pham

There are few code switching datasets, labeled or unlabled, that exist today. As a result, ASR requires new methods to utilize the vast monolingual data and models that exist. This paper uses OpenAI's open source ASR model, Whisper, which has been pre-trained on 680K hours of audio to perform monolingual ASR tasks. In Part 1, this paper examines how exploiti

Tianqi Chen, Shujian Zhang, Mingyuan Zhou

This paper introduces DLM-One, a score-distillation-based framework for one-step sequence generation with continuous diffusion language models (DLMs). DLM-One eliminates the need for iterative refinement by aligning the scores of a student model's outputs in the continuous token embedding space with the score function of a pretrained teacher DLM. We investig

Chenjing Bu

Joyce vertex algebras are vertex algebra structures defined on the homology of certain $\mathbb{C}$-linear moduli stacks, and are used to express wall-crossing formulae for Joyce's homological enumerative invariants. This paper studies the generalization of this construction to settings that come from non-linear enumerative problems. In the special case of o

Ahmed Elhady, Eneko Agirre, Mikel Artetxe

Continued pretraining (CPT) is a popular approach to adapt existing large language models (LLMs) to new languages. When doing so, it is common practice to include a portion of English data in the mixture, but its role has not been carefully studied to date. In this work, we show that including English does not impact validation perplexity, yet it is critical

Ryan E. Dunagin, Robbie Mears, Dario Bueno-Baques, Vasyl S. Tyberkevych

Brillouin light scattering (BLS) is a powerful experimental tool that can be used to get insights into the fundamental and applied properties of matter, like dispersions of quasiparticles in a solid, as well as their spatio-temporal dynamics. Many applications of light scattering favor the use of optical fibers in place of free-space optics. In this work, we

Muhammad Suhail Saleem, Rishi Veerapaneni, Maxim Likhachev

Heuristic search solvers like RTDP-Bel and LAO* have proven effective for computing optimal and bounded sub-optimal solutions for Partially Observable Markov Decision Processes (POMDPs), which are typically formulated as belief MDPs. A belief represents a probability distribution over possible system states. Given a parent belief and an action, computing bel

D. C. Jacobsen

In this work we present a constructive proof that pure SU(3) Yang-Mills theory on R^4 exists as a nontrivial Wightman quantum field theory and exhibits a strictly positive mass gap. Our approach embeds the four-dimensional gauge theory as the zero-mode sector of a five-dimensional orbifold regulator that preserves gauge invariance and reflection positivity.