October 2025 arXiv papers — page 156

Zhiwen Ruan, Yixia Li, He Zhu, Yun Chen

Large language models (LLMs) primarily rely on supervised fine-tuning (SFT) as a key method to adapt pre-trained models to domain-specific tasks such as mathematical reasoning. However, standard SFT uniformly penalizes all tokens, neglecting that only a small subset of critical tokens determines reasoning correctness. This uniform supervision often causes re

Sanchit Sinha, Oana Frunza, Kashif Rasul, Yuriy Nevmyvaka

The capabilities of Large Vision-Language Models (LVLMs) have reached state-of-the-art on many visual reasoning tasks, including chart reasoning, yet they still falter on out-of-distribution (OOD) data, and degrade further when asked to produce their chain-of-thought (CoT) rationales, limiting explainability. We present Chart-RVR, a general framework that fi

Liqun Qi, Chunfeng Cui, Haibin Chen, Yi Xu

In this paper, we systemically introduce completely positive biquadratic (CPB) tensors and copositive biquadratic tensors. We show that all weakly CPB tensors are sum of squares tensors, the CPB tensor cone and the copositive biquadratic tensor cone are dual cone to each other. We also show that the outer product of two completely positive matrices is a CPB

Yejin Lee, Hyeseon Ahn, Yo-Sub Han

Hate speech remains prevalent in human society and continues to evolve in its forms and expressions. Modern advancements in internet and online anonymity accelerate its rapid spread and complicate its detection. However, hate speech datasets exhibit diverse characteristics primarily because they are constructed from different sources and platforms, each refl

Runyu Yang, Ivan V. Bajić

Mainstream image and video coding standards -- including state-of-the-art codecs like H.266/VVC, AVS3, and AV1 -- adopt a block-based hybrid coding framework. While this framework facilitates straightforward optimization for Peak Signal-to-Noise Ratio (PSNR), it struggles to effectively optimize perceptually-aligned metrics such as Multi-Scale Structural Sim

Zeteng Lin, Xingxing Li, Wen You, Xiaoyang Li

Existing Vision Language Models (VLMs) often struggle to preserve logic, entity identity, and artistic style during extended, interleaved image-text interactions. We identify this limitation as "Multimodal Context Drift", which stems from the inherent tendency of implicit neural representations to decay or become entangled over long sequences. To bridge this

Tanuj Khattar, Noah Shutty, Craig Gidney, Adam Zalcman

Decoded Quantum Interferometry (DQI) provides a framework for superpolynomial quantum speedups by reducing certain optimization problems to reversible decoding tasks. We apply DQI to the Optimal Polynomial Intersection (OPI) problem, whose dual code is Reed-Solomon (RS). We establish that DQI for OPI is the first known candidate for verifiable quantum advant

Santanu S. Dey, Frédéric Meunier, Diego Moran Ramirez

Geoffrion's theorem is a fundamental result from mathematical programming assessing the quality of Lagrangian relaxation, a standard technique to get bounds for integer programs. An often implicit condition is that the set of feasible solutions is finite or described by rational linear constraints. However, we show through concrete examples that the conclusi

Jidong Li, Lingyong Fang, Haodong Zhao, Sufeng Duan

Multimodal large language models (MLLMs) have witnessed astonishing advancements in recent years. Despite these successes, MLLMs remain vulnerable to flase premise problems. However, existing benchmarks targeting this issue are limited in scope: they often lack fine-grained categorization, exhibit insufficient coverage, and thus fail to provide a rigorous ev

Junhyuck Kim, Ethan Ewer, Taehong Moon, Jongho Park

While 4-bit quantization has emerged as a memory-optimal choice for non-reasoning models and zero-shot tasks across scales, we show that this universal prescription fails for reasoning models, where the KV cache rather than model size can dominate memory. Through systematic experiments across 1,700 inference scenarios on AIME25 and GPQA-Diamond, we find a sc

Junchao Gong, Jingyi Xu, Ben Fei, Fenghua Ling

Weather prediction is a critical task for human society, where impressive progress has been made by training artificial intelligence weather prediction (AIWP) methods with reanalysis data. However, reliance on reanalysis data limits the AIWPs with shortcomings, including data assimilation biases and temporal discrepancies. To liberate AIWPs from the reanalys

Zhuo Li, Yuege Feng, Dandan Guo, Jinpeng Hu

The reward model (RM) plays a crucial role in aligning Large Language Models (LLMs) with human preferences through Reinforcement Learning, where the Bradley-Terry (BT) objective has been recognized as simple yet powerful, specifically for pairwise preference learning. However, BT-based RMs often struggle to effectively distinguish between similar preference

Wei Huang, Yue Liao, Yukang Chen, Jianhui Liu

Mixture-of-Experts (MoE) effectively scales large language models (LLMs) and vision-language models (VLMs) by increasing capacity through sparse activation. However, preloading all experts into memory and activating multiple experts per input introduces significant computational and memory overhead, making the expert module a major contributor to model size

Dong Hu, Fenqing Hu, Lidong Yang, Chao Huang

Ensuring safety in autonomous driving (AD) remains a significant challenge, especially in highly dynamic and complex traffic environments where diverse agents interact and unexpected hazards frequently emerge. Traditional reinforcement learning (RL) methods often struggle to balance safety, efficiency, and adaptability, as they primarily focus on reward maxi

Xiaoyun Zhang, Xiaojian Yuan, Di Huang, Wang You

Reasoning ability has become a defining capability of Large Language Models (LLMs), with Reinforcement Learning with Verifiable Rewards (RLVR) emerging as a key paradigm to enhance it. However, RLVR training often suffers from policy entropy collapse, where the policy becomes overly deterministic, hindering exploration and limiting reasoning performance. Whi

Praveen Jayakumar, Tao Zeng, Artur F. Izmaylov

Exact unitary transformations play a central role in the analysis and simulation of many-body quantum systems, yet the conditions under which they can be carried out exactly and efficiently remain incompletely understood. We show that exact transformations arise whenever the adjoint action of a unitary's generator defines a linear map within a finite-dimensi

Zhiqiang Yuan, Wenjun Mao, Zhuo Chen, Xiyue Shang

Translating C code into safe Rust is an effective way to ensure memory safety. Compared to rule-based approaches, which often produce largely unsafe Rust code, LLM-based methods generate more idiomatic and safer Rust by leveraging extensive training on human-written code. Despite their promise, existing LLM-based approaches still struggle with project-level

Yu Cui, Feng Liu, Jiawei Chen, Canghong Jin

Recent years have witnessed a surge of research on leveraging large language models (LLMs) for sequential recommendation. LLMs have demonstrated remarkable potential in inferring users' nuanced preferences through fine-grained semantic reasoning. However, they also exhibit a notable limitation in effectively modeling collaborative signals, i.e., behavioral c

Maral Doctorarastoo, Katherine A. Flanigan, Mario Bergés, Christopher McComb

The capacity to predict human spatial preferences within built environments is instrumental for developing Cyber-Physical-Social Infrastructure Systems (CPSIS). A significant challenge in this domain is the generalizability of preference models, particularly their efficacy in predicting preferences within environmental configurations not encountered during t

Qing Zhu, Xian Yu, Yu-Li Huang

We consider a real-world chemotherapy scheduling template design problem, where we cluster patient types into groups and find a representative time-slot duration for each group to accommodate all patient types assigned to that group, aiming to minimize the total expected idle time and overtime. From Mayo Clinic's real data, most patients' treatment durations

Xi Mao, Zhendong Wang, Jingyu Li, Lingchao Mao

Early detection of Alzheimer's disease (AD) is crucial because its neurodegenerative effects are irreversible, and neuropathologic and social-behavioral risk factors accumulate years before diagnosis. Identifying higher-risk individuals earlier enables prevention, timely care, and equitable resource allocation. We predict cognitive performance from social de

Eitan Klinger, Vivaan Wadhwa, Jungyeul Park

This article presents a curated resource and evaluation suite for punctuation-aware treebank binarization. Standard binarization pipelines drop punctuation before head selection, which alters constituent shape and harms head-child identification. We release (1) a reproducible pipeline that preserves punctuation as sibling nodes prior to binarization, (2) der

Quan Zhao, Guilai Liu

This paper studies the associated Levi-Civita products of a Leibniz algebra with a nondegenerate skew-symmetric $2$-cocycle. Such products form into the notion of an anti-pre-Leibniz algebra, which is characterized as a Leibniz-admissible algebra which renders a representation of the sub-adjacent Leibniz algebra through the negative multiplication operators.

Xuyao Deng, Yanjie Sun, Yong Dou, Kele Xu

Scaling laws have profoundly shaped our understanding of model performance in computer vision and natural language processing, yet their application to general audio representation learning remains underexplored. A key challenge lies in the multifactorial nature of general audio representation-representation quality is jointly influenced by variables such as

Namhoon Kim, Sara Fridovich-Keil

Generative models have shown strong potential as data-driven priors for solving inverse problems such as reconstructing medical images from undersampled measurements. While these priors improve reconstruction quality with fewer measurements, they risk hallucinating features when test images lie outside the training distribution. Existing uncertainty quantifi

Onil Boussim

This paper provides a nonparametric framework for causal inference with categorical outcomes under binary treatment and binary instrument settings. I decompose the observed joint probability of outcomes and treatment into marginal probabilities of potential outcomes and treatment, and association parameters that capture selection bias due to unobserved heter

Haishan Ye, Xiangyu Chang, Xi Chen

We propose a new framework for analyzing zeroth-order optimization (ZOO) from the perspective of \emph{oblivious randomized sketching}.In this framework, commonly used gradient estimators in ZOO-such as finite difference (FD) and random finite difference (RFD)-are unified through a general sketch-based formulation. By introducing the concept of oblivious ran

Xiaoming Shi, Yunli Li, Xiaodan Shao, Jie Xu

This paper presents a cost-effective and easily-deployable flexible-sector six-dimensional movable antenna (6DMA) architecture for future wireless communication networks, which enables flexible antenna configurations to match users' spatial distribution for capacity enhancement. Different from conventional sectorized base station (BS) with fixed-position ant

Nilima Rao, Jagriti Srivastava, Pradeep Kumar Sharma, Hritvik Shrivastava

Modern enterprises manage vast knowledge distributed across heterogeneous systems such as Jira, Git repositories, Confluence, and wikis. Conventional retrieval methods based on keyword search or static embeddings often fail to answer complex queries that require contextual reasoning and multi-hop inference across artifacts. We present a modular hybrid retrie

Jason E. Williams, Nicholas P. Konidaris

Atmospheric scintillation is one of the largest sources of error in ground-based spectrophotometry, reducing the precision of astrophysical signals extracted from the time-series of bright objects to that of much fainter objects. Relative to the fundamental Poisson noise, scintillation is not effectively reduced by observing with larger telescopes, and alter

Dakang Cen, Wenlong Zhang, Zhidong Zhang

This work investigates the inverse drift problem in the one-dimensional parabolic equation with the final time data. The authors construct an operator first, whose fixed points are the unknown drift, and then apply it to prove the uniqueness. The proof of uniqueness contains an iteration converging to the drift, which inspires the numerical algorithm. To han

Jiajun Wu, Xuanqi Wang, Chengyu Zhang, Chenghong Li

We present a prism-coupled packaging strategy for whispering-gallery mode resonators (WGMRs). Utilizing an all-solid-state optical adhesive process with active temperature control and hermetic sealing, the package exhibits exceptional long-term stability and environmental robustness. A standalone WGMR module was characterized, demonstrating a temperature sen

Yuda Bi, Ying Zhu, Vince D Calhoun

We present a theoretical framework that extends classical information theory to finite and structured systems by redefining redundancy as a fundamental property of information organization rather than inefficiency. In this framework, redundancy is expressed as a general family of informational divergences that unifies multiple classical measures, such as mut

Qizhou Peng, Yang Zheng, Yu Wen, Yanna Wu

Reinforcement learning (RL) has been an important machine learning paradigm for solving long-horizon sequential decision-making problems under uncertainty. By integrating deep neural networks (DNNs) into the RL framework, deep reinforcement learning (DRL) has emerged, which achieved significant success in various domains. However, the integration of DNNs als

Anirudh Ganesh, Jayavardhan Reddy

We present a reproducibility study of the state-of-the-art neural architecture for sequence labeling proposed by Ma and Hovy (2016)\cite{ma2016end}. The original BiLSTM-CNN-CRF model combines character-level representations via Convolutional Neural Networks (CNNs), word-level context modeling through Bi-directional Long Short-Term Memory networks (BiLSTMs),

Marshall J. Basson, Eric Biddulph-West, Caitlyn Holl, Will Lankenau

Over the past several decades, dozens of tests have sought the 132 Lorentz-violating degrees of freedom in the nonrelativistic limit of the minimal matter sector of the Standard-Model Extension, yet 43 remained unconstrained. In this Letter, we limit all previously unconstrained degrees of freedom and make improvements on 13 prior limits. The approach introd

Jiahong Chen, Jinghao Wang, Zi Wang, Ziwen Wang

6D pose estimation of textureless objects is valuable for industrial robotic applications, yet remains challenging due to the frequent loss of depth information. Current multi-view methods either rely on depth data or insufficiently exploit multi-view geometric cues, limiting their performance. In this paper, we propose DKPMV, a pipeline that achieves dense

Zonghuan Xu, Jiayu Li, Yunhan Zhao, Xiang Zheng

Vision-Language-Action (VLA) models map multimodal perception and language instructions to executable robot actions, making them particularly vulnerable to behavioral backdoor manipulation: a hidden trigger introduced during training can induce unintended physical actions while nominal task performance remains intact. Prior work on VLA backdoors primarily st

SHengjie Ma, Chenlong Deng, Jiaxin Mao, Jiadeng Huang

While reinforcement learning (RL) enhances their ability to plan and reason across retrieval steps, we identify a critical failure mode in this setting: Tool-Call Hacking. Unlike execution-based tools (e.g., code or math), whose effects are directly observable, the weak observability of causal dependencies between retrieved evidence and reasoning under forma

Junjie Luo, Changjun Wang

We analyze an infinite-horizon deterministic joint replenishment model from a non-cooperative game-theoretical approach. In this model, a group of retailers can choose to jointly place an order, which incurs a major setup cost independent of the group, and a minor setup cost for each retailer. Additionally, each retailer is associated with a holding cost. Ou

Cunyuan Jiang

Interpreting the vibrational properties of amorphous solids beyond Debye's theory is challenging due to the presence of inhomogeneity on the mesoscopic scale. In this work, we model this inhomogeneity by real-space fluctuating elasticity with a spatially correlated distribution and calculate the dynamical properties using an exact real-space field theoretica

Yawen Yang, Fukun Ma, Shiao Meng, Aiwei Liu

In biomedical fields, one named entity may consist of a series of non-adjacent tokens and overlap with other entities. Previous methods recognize discontinuous entities by connecting entity fragments or internal tokens, which face challenges of error propagation and decoding ambiguity due to the wide variety of span or word combinations. To address these iss

Yuanzhao Zhang, Sean P. Cornelius

Feedback control is an effective strategy for stabilizing a desired state and has been widely adopted in maintaining the stability of systems such as flying birds and power grids. By default, this framework requires continuous control input to offset deviations from the desired state, which can be invasive and cost a considerable amount of energy. Here, we i

Hengyuan Zhang, Shiping Yang, Xiao Liang, Chenming Shang

Training student models on synthetic data generated by strong teacher models is a promising way to distilling the capabilities of teachers. However, recent studies show that stronger models are not always optimal teachers, revealing a mismatch between teacher outputs and student learnability. To address this issue, we propose PerSyn (Personalized data Synthe

Max Charles, Louis Desdoigts, Benjamin Pope, Peter Tuthill

Flying on board the James Webb Space Telescope (JWST) above Earth's turbulent atmosphere, the Aperture Masking Interferometer (AMI) on the NIRISS instrument is the highest-resolution infrared interferometer ever placed in space. However, its performance was found to be limited by non-linear detector systematics, particularly charge migration - or the Brighte

Xuyao Deng, Yong Dou, Kele Xu

Direction-of-Arrival (DOA) estimation in sensor arrays faces limitations under demanding conditions, including low signal-to-noise ratio, single-snapshot scenarios, coherent sources, and unknown source counts. Conventional beamforming suffers from sidelobe interference, adaptive methods (e.g., MVDR) and subspace algorithms (e.g., MUSIC) degrade with limited

Xiaohan Chen, Man I Lam, Yingying Zhou, Hongrui Gu

Slitless spectroscopy eliminates the need for slits, allowing light to pass directly through a prism or grism to generate a spectral dispersion image that encompasses all celestial objects within a specified area. This technique enables highly efficient spectral acquisition. However, when processing CSST slitless spectroscopy data, the unique design of its f

Yi Yu, Zhenxing Hu

Explainable recommendation through counterfactual reasoning seeks to identify the influential aspects of items in recommendations, which can then be used as explanations. However, state-of-the-art approaches, which aim to minimize changes in product aspects while reversing their recommended decisions according to an aggregated decision boundary score, often

Alice Ho, Jashan Singhal, Deji Akinwande, Huili Grace Xing

The importance of electrical contact resistance and thermal boundary resistance has increased dramatically as devices are scaled to atomic limits. The use of a rich range of materials with various bandstructures (e.g. parabolic, conical), and in geometries exploiting various dimensionalities (e.g. 1D wires, 2D sheets, and 3D bulk) will increase in the future

Geon Yeong Park, Inhwa Han, Serin Yang, Yeobin Hong

The exponential growth of the global makeup market has paralleled advancements in virtual makeup simulation technology. Despite the progress led by GANs, their application still encounters significant challenges, including training instability and limited customization capabilities. Addressing these challenges, we introduce DreamMakup - a novel training-free

Xiao Chen, Haechan Park, Silas Hoffman, Shuanglong Liu

Spin decoherence poses a significant challenge in molecular magnets, with the nuclear spin bath serving as a prominent source. Intriguingly, spin qubits at the clock transition exhibit remarkable insensitivity to the surrounding nuclear spins. Recent experimental studies have unveiled a correlation between the decoherence time and the density of spin qubits,

Wendi Di, Zheng Guo, Cai Heng Li

A characterization is given of finite groups $H$ that have skew-morphisms of order coprime to the order $|H|$, and their skew-morphisms. A complete classification is then given of the automorphism groups and the underlying graphs of vertex-rotary core-free Hall Cayley maps.

Hanchang Cheng, Weimin Mu, Fan Liu, Weilin Zhu

Time series anomaly detection(TSAD) is a critical task in signal processing field, ensuring the reliability of complex systems. Reconstruction-based methods dominate in TSAD. Among these methods, VAE-based methods have achieved promising results. Existing VAE-based methods suffer from the limitation of single-window feature and insufficient leveraging of lon

Jiajie Qiu, Dakota Thompson, Kamal Youcef-Toumi, Amro M. Farid

The electrification of transportation represents a critical challenge in the global transition toward net-zero emissions, as the sector often accounts for more than one-quarter of national energy consumption. Achieving this transformation requires not only widespread adoption of electric vehicles (EVs) but also their seamless integration into interdependent

Ki Jung Seo, Sehun Lim, Taeuk Kim

Recent progress in large language models (LLMs) has enabled them to communicate their confidence in natural language, improving transparency and reliability. However, this expressiveness is often accompanied by systematic overconfidence, whose underlying causes remain poorly understood. In this work, we analyze the dynamics of verbalized confidence estimatio

Xinyu Shao, Yanzhe Tang, Pengwei Xie, Kaiwen Zhou

Many language-guided robotic systems rely on collapsing spatial reasoning into discrete points, making them brittle to perceptual noise and semantic ambiguity. To address this challenge, we propose RoboMAP, a framework that represents spatial targets as continuous, adaptive affordance heatmaps. This dense representation captures the uncertainty in spatial gr

Yerin Hong, Juhwan Lim, Jinhong Min, Nishkarsh Agarwal

Molybdenum disulfide (MoS2) is a widely studied layered material for electronic, optical, and catalytic applications. It can host lithium ions between the van der Waals layers, which triggers a phase transition between the semiconducting 2H phase and metallic 1T phase. While lithium insertion triggers a phase transition to the 1T phase, the phase behavior up

Honghui Yuan, Keiji Yanai

With the rapid development of diffusion models, style transfer has made remarkable progress. However, flexible and localized style editing for scene text remains an unsolved challenge. Although existing scene text editing methods have achieved text region editing, they are typically limited to content replacement and simple styles, which lack the ability of

Daoyu Wang, Mingyue Cheng, Shuo Yu, Zirui Liu

Understanding and reasoning on the large-scale scientific literature is a crucial touchstone for large language model (LLM) based agents. However, existing works are mainly restricted to tool-free tasks within single papers, largely due to the lack of a benchmark that evaluates cross-paper reasoning and multi-tool orchestration in authentic research scenario

Yalan Wei, Shifang Li, Yuke Song, Chaoyu He

The discovery of flat-bands in magic-angle twisted bilayer graphene has underscored the potential of moire engineering for correlated states, but such phases are notoriously difficult to realize and highly fragile against perturbations. Here, we propose an alternative route to flat-bands by introducing sp3 hybridization in twisted graphite. Instead of relyin

Paige Bright, Alexander Ortiz, Dmitrii Zakharov

We prove a sharp continuum Beck-type theorem for hyperplanes. Our work is inspired by foundational work of Beck on the discrete problem, as well as refinements due to Do and Lund. The inductive proof uses recent breakthrough results in projection theory by Orponen--Shmerkin--Wang and Ren, who proved continuum Beck-type theorems for lines in $\mathbb{R}^2$ an

Wenyun Li, Zheng Zhang, Dongmei Jiang, Xiangyuan Lan

Large language models (LLMs) have garnered significant interest in AI community. Despite their impressive generation capabilities, they have been found to produce misleading or fabricated information, a phenomenon known as hallucinations. Consequently, hallucination detection has become critical to ensure the reliability of LLM-generated content. One primary

T. Shimizu, T. Kurosawa, S. Tsuchiya, R. Tobise

Understanding the interplay between superconductivity and the pseudogap phase is essential for elucidating the mechanism of high-temperature superconductivity in cuprates. Here we provide direct spatial evidence that these two states are locally and intrinsically correlated. Using spatially and temporally resolved measurements of photoinduced quasiparticle d

Hongyu Lin, Haolin Pan, Haoran Luo, Yuchen Li

Compiler optimization is crucial for enhancing program performance by transforming the sequence of optimization passes while maintaining correctness. Despite the promising potential of large language models (LLMs)-based agent for software optimization, automating compiler optimization remains challenging due to: (1) semantic misalignment between abstract pro

Ben DalFavero, Ryan LaRose

We present a method for quantum error mitigation on partially error-corrected quantum computers - i.e., computers with some logical qubits and some noisy qubits. Our method is inspired by the error cancellation method and is implemented via a circuit for convex combinations of channels which we introduce in this work. We show how logical ancilla qubits can a

Giacomo Lanfiuti Baldi, Andrea Nigri, Han Lin Shang

Understanding and modeling mortality patterns, especially differences in mortality rates between populations, is vital for demographic analysis and public health planning. We compare three statistical models within the age-period framework to examine differences in death counts. The models are based on the double Poisson, bivariate Poisson, and Skellam distr

Shuanghao Bai, Wenxuan Song, Jiayi Chen, Yuheng Ji

Embodied intelligence has witnessed remarkable progress in recent years, driven by advances in computer vision, natural language processing, and the rise of large-scale multimodal models. Among its core challenges, robot manipulation stands out as a fundamental yet intricate problem, requiring the seamless integration of perception, planning, and control to

Sleem Abdelghafar, Maryam Aliakbarpour, Chris Jermaine

Disclosing information via the publication of a machine learning model poses significant privacy risks. However, auditing this disclosure across every datapoint during the training of Large Language Models (LLMs) is computationally prohibitive. In this paper, we present Gradient Uniqueness (GNQ), a principled, attack-agnostic metric derived from an informati

Ziad Ghanem

Classical linear ciphers, such as the Hill cipher, operate on fixed, finite-dimensional modules and are therefore vulnerable to straightforward known-plaintext attacks that recover the key as a fully determined linear operator. We propose a symmetric-key cryptosystem whose linear action takes place instead in the Burnside ring $A(G)$ of a compact Lie group $

Jyotiranjan Beuria

We explore the structure of the parameter space in the Singlet Scalar Dark Matter (SSDM) model and the Next-to-Two Higgs Doublet Model (N2HDM) with $\tan\beta = 5$ and $\tan\beta = 45$. Parameter points are classified as allowed or excluded based on compatibility with the Higgs observation constraints. Using a combined framework of Topological Data Analysis

Zhishui Hu, Liang Dong

In this paper, we study a class of unbalanced step-reinforced random walks that unifies the elephant random walk, the positively step-reinforced random walk, and the negatively step-reinforced random walk. By establishing a connection with bond percolation on random recursive trees, these processes can be represented as randomly weighted sums of independent

Shumin Li, Xiaoyun Lai

While the global integration of artificial intelligence (AI) into veterinary medicine is accelerating, its adoption dynamics in major markets such as China remain uncharacterized. This paper presents the first exploratory analysis of AI perception and adoption among veterinary professionals in China, based on a cross-sectional survey of 455 practitioners con

Benjamin Anwasia, Diogo Arsénio

We consider the description of a Fermi gas of free electrons given by the Boltzmann--Fermi--Dirac equation, and aim at providing a precise mathematical understanding of the Fermi ground state and its first-order approximation of excited states on the Fermi sphere. In order to achieve that, using the framework of hydrodynamic limits in collisional kinetic the

You-Tian Zou, Tong-Pu Yu

Actinide nuclei provide a suitable platform for studying the laser-assisted nuclear $\alpha$ decay, with potential applications in nuclear transmutation, nuclear radiotherapy, and nuclear battery regulation. In the present work, we develop a deformed one-parameter model to quantitatively study the influence of ultra-intense laser fields on the $\alpha$ decay

Maria Vasilyeva, James Brannick, Ben S. Southworth

We present multiscale graph-based reduction algorithms for upscaling heterogeneous and anisotropic diffusion problems. The proposed coarsening approaches begin by constructing a partitioning of the computational domain into a set of balanced local subdomains, resulting in a standard type of domain decomposition. Given this initial decomposition, general coar

Dikshant Shehmar, Matthew E. Taylor, Ehsan Hashemi

The transition of control from autonomous systems to human drivers is critical in automated driving systems, particularly due to the out-of-the-loop (OOTL) circumstances that reduce driver readiness and increase reaction times. Existing takeover strategies are based on fixed time-based transitions, which fail to account for real-time driver performance varia

Bukunmi Gabriel Odunlami, Marcos Netto

We propose a novel framework for estimating the parameters of an aggregated distributed energy resources (der_a) model. First, we introduce a rigorous method to determine whether all model parameters are estimable. When they are not, our approach identifies the subset of parameters that can be estimated. The proposed framework offers new insights into the nu

Jinxin Xiong, Yanting Huang, Yingxiao Wang, Linxin Yang

Security-Constrained Unit Commitment is a fundamental optimization problem in power systems operations. The primary computational bottleneck arises from the need to solve large-scale Linear Programming (LP) relaxations within branch-and-cut. Conventional simplex and barrier methods become computationally prohibitive at this scale due to their reliance on exp

Yu Chao, Siyu Lin, xiaorong wang, Zhu Zhang

We introduce LLM x MapReduce-V3, a hierarchically modular agent system designed for long-form survey generation. Building on the prior work, LLM x MapReduce-V2, this version incorporates a multi-agent architecture where individual functional components, such as skeleton initialization, digest construction, and skeleton refinement, are implemented as independ

Junwon You, Dasol Kang, Jae-Hun Jung

Contrastive Vision-Language Models (VLMs) have demonstrated strong zero-shot capabilities. However, their cross-modal alignment remains biased toward English due to limited multilingual multimodal data. Recent multilingual extensions have alleviated this gap but enforce instance-level alignment while neglecting the global geometry of the shared embedding spa

IlKwon Sohn, Changyeol Lee, Wooyeong Song, Kwangil Bae

Achieving reliable performance on early fault-tolerant quantum hardware will depend on protocols that manage noise without incurring prohibitive overhead. We propose a novel framework that integrates quantum computation with the functionality of classical error correction. In this approach, quantum computation is performed within the codeword subspace define

Lakshana Iruni Assalaarachchi, Zainab Masood, Rashina Hoda, John Grundy

Software practitioners are discussing GenAI transformations in software project management openly and widely. To understand the state of affairs, we performed a grey literature review using 47 publicly available practitioner sources including blogs, articles, and industry reports. We found that software project managers primarily perceive GenAI as an "assist

Yashom Dighe, Youngjin Kim, Karthik Dantu

Autonomous racing requires tight integration between perception, planning and control to minimize latency as well as timely decision making. A standard autonomy pipeline comprising a global planner, local planner, and controller loses information as the higher-level racing context is sequentially propagated downstream into specific task-oriented context. In

Jiajing Guo, Kenil Patel, Jorge Piazentin Ono, Wenbin He

Large language models (LLMs) are increasingly powering Text-to-SQL (Text2SQL) systems, enabling non-expert users to query industrial databases using natural language. While test-time scaling strategies have shown promise in LLM-based solutions, their effectiveness in real-world applications, especially with the latest reasoning models, remains uncertain. In

Thiago Holleben

In 2018, Cook, Harbourne, Migliore and Nagel introduced the concept of unexpected hypersurfaces, which connects the study of Lefschetz properties of artinian algebras defined by powers of linear forms, to a family of interpolation problems. In this paper, inspired by the theory of unexpected hypersurfaces, we introduce the concept of unexpected systems of pa

Ali Fallah, Shun Nakamura, Steven van de Par

Ambisonics is a method for capturing and rendering a sound field accurately, assuming that the acoustics of the playback room does not significantly influence the sound field. However, in practice, the acoustics of the playback room may lead to a noticeable degradation in sound quality. We propose a recording and rendering method based on Ambisonics that uti

Ruchit Rawal, Jeffrey Yang Fan Chiang, Chihao Shen, Jeffery Siyuan Tian

AI coding assistants powered by large language models (LLMs) have transformed software development, significantly boosting productivity. While existing benchmarks evaluate the correctness and security of LLM-generated code, they are typically limited to single-turn tasks that do not reflect the iterative nature of real-world development. We introduce MT-Sec,

Riley Thornton

We build on work of Elek and Zucker and develop a topological analogue of the theory of weak containment. We show that definitions in terms of local patterns, containment in ultra(co)products, and continuous model theory are all equivalent, just as in ergodic theory. And, for actions on Cantor space, we show these are all equivalent to approximate conjugacy.

Lina Yan, Jeffrey Huy Khong, Aleksandar Kostadinov, Wen-Jun Chen

Emergent behavior in complex systems arises from nonlinear interactions among components, yet the intricate nature of self-organization often obscures the underlying causal relationships, long regarded as the "holy grail" of complexity research. To address this challenge, we adopted an inductive, mechanism-agnostic approach to characterize how diseased biolo

Zhaofang Qian, Hardy Chen, Zeyu Wang, Li Zhang

Vision-language models (VLMs) have advanced rapidly, yet their capacity for image-grounded geolocation in open-world conditions, a task that is challenging and of demand in real life, has not been comprehensively evaluated. We present EarthWhere, a comprehensive benchmark for VLM image geolocation that evaluates visual recognition, step-by-step reasoning, an

Fei Xu

This paper proposes an efficient algorithm for solving the Hartree--Fock equation combining a multilevel correction scheme with an adaptive refinement technique to improve computational efficiency. The algorithm integrates a multilevel correction framework with an optimized implementation strategy. Within this framework, a series of linearized boundary value

Chenrui Wang, Junyi Shu, Billy Chiu, Yu Li

The rapid development of LLMs has raised concerns about their potential misuse, leading to various watermarking schemes that typically offer high detectability. However, existing watermarking techniques often face trade-off between watermark detectability and generated text quality. In this paper, we introduce Learning to Watermark (LTW), a novel selective w

Zheng Cao, Xingran Shao, Yuheng Yan, Helyette Geman

We propose a novel model, the Hyped Log-Periodic Power Law Model (HLPPL), to the problem of quantifying and detecting financial bubbles, an ever-fascinating one for academics and practitioners alike. Bubble labels are generated using a Log-Periodic Power Law (LPPL) model, sentiment scores, and a hype index we introduced in previous research on NLP forecastin

Shutong Lin, Zhengkang Xiang, Jianzhong Qi, Kourosh Khoshelham

Real-world point cloud datasets have made significant contributions to the development of LiDAR-based perception technologies, such as object segmentation for autonomous driving. However, due to the limited number of instances in some rare classes, the long-tail problem remains a major challenge in existing datasets. To address this issue, we introduce a nov

Hong Chen, Siddhartha Sahi

In a widely circulated manuscript from the 1980s, now available on the arXiv, I.~G.~Macdonald introduced certain multivariable hypergeometric series ${}_pF_q(x)= {}_pF_q(x;\alpha)$ and ${}_pF_q(x,y)= {}_pF_q(x,y;\alpha)$ in one and two sets of variables $x=(x_1,\dots x_n)$ and $y=(y_1,\dots y_n)$. These two series are defined by explicit expansions in terms

Se-jin Oh, Travis Scrimshaw

We construct a higher level analogue of Dorey's rule, which describe certain surjective morphisms between Kirillov--Reshetikhin (KR) modules over quantum affine algebras. Building on this, we establish a generalized T-system of short exact sequences and prove the denominator formula between KR modules in all nonexceptional types, except with only mild ambigu

Jixiang Yang, Omid Sharifi Sedeh, Chiho Yoon, Shenyong Ye

Spin-polarized superconductors offer a rare platform for studying electronic correlations, but few candidate systems have been experimentally confirmed to date. Here, we report the observation of a spin-polarized superconducting state, denoted SC5, in WSe2-proximitized rhombohedral trilayer graphene. At in-plane magnetic field B|| = 0 T, SC5 has a critical t

Sumukh Pinge, Ashkan Moradifirouzabadi, Keming Fan, Prasanna Venkatesan Ravindran

The rapid expansion of mass spectrometry (MS) data, now exceeding hundreds of terabytes, poses significant challenges for efficient, large-scale library search - a critical component for drug discovery. Traditional processors struggle to handle this data volume efficiently, making in-storage computing (ISP) a promising alternative. This work introduces an IS

Yudam Seo, Tsvi Tlusty, Junghyo Jo

The origin and organizing principles of the genetic code remain fundamental puzzles in life science. The vanishingly low probability of the natural codon-to-amino acid mapping arising by chance has spurred the hypothesis that its structure is a solution optimized for robustness against mutations and translational errors. For the construction of effective mol

Chenze Li, Subhadeep Paul

We propose a method for transfer learning in nonparametric regression using a random forest (RF) with distance covariance-based feature weights, assuming the unknown source and target regression functions are sparsely different. Our method obtains residuals from a source domain-trained Centered RF (CRF) in the target domain, then fits another CRF to these re