November 2025 arXiv papers — page 116

Lyra Hoeben-Kuil, Gijs van Dijck, Jaromir Savelka, Johanna Gunawan

Understanding the legally relevant factual basis of an event and conveying it through text is a key skill of legal professionals. This skill is important for preparing forms (e.g., insurance claims) or other legal documents (e.g., court claims), but often presents a challenge for laypeople. Current AI approaches aim to bridge this gap, but mostly rely on the

Woojae Jeong, Wenhui Cui, Kleanthis Avramidis, Takfarinas Medani

Electroencephalography (EEG) offers detailed access to neural dynamics but remains constrained by noise and trial-by-trial variability, limiting decoding performance in data-restricted or complex paradigms. Data augmentation is often employed to enhance feature representations, yet conventional uniform averaging overlooks differences in trial informativeness

Jiahui Sun, Weining Wang, Mingzhen Sun, Yirong Yang

Sounding Video Generation (SVG) remains a challenging task due to the inherent structural misalignment between audio and video, as well as the high computational cost of multimodal data processing. In this paper, we introduce ProAV-DiT, a Projected Latent Diffusion Transformer designed for efficient and synchronized audio-video generation. To address structu

Rosario Napoli, Gabriele Morabito, Antonio Celesti, Massimo Villari

The rise of graph-structured data has driven major advances in Graph Machine Learning (GML), where graph embeddings (GEs) map features from Knowledge Graphs (KGs) into vector spaces, enabling tasks like node classification and link prediction. However, since GEs are derived from explicit topology and features, they may miss crucial implicit knowledge hidden

Rosario Napoli, Antonio Celesti, Massimo Villari, Maria Fazio

Drones are embedded systems (ES) used across a wide range of fields, from photography to shipments and even during crisis management for searching, rescuing and damage assessment activities. However, their limited battery life and high energy consumption are very important challenges, especially in networked systems where multiple drones must communicate wit

HanYu Zhang, Tomoji Kishi

Code smell is a great challenge in software refactoring, which indicates latent design or implementation flaws that may degrade the software maintainability and evolution. Over the past decades, a variety of refactoring approaches have been proposed, which can be broadly classified into metrics-based, rule-based, and machine learning-based approaches. Recent

Nana Tian, Giorgio Colombo, Victor Schinazi

Early detection of Cognitive Impairment (CI) is critical for timely intervention, preservation of independence, and reducing the burden of dementia. Yet, most screening tools remain lengthy, clinic-based, and poorly suited for large-scale unsupervised deployment. This paper evaluates the test-retest reliability, validity, and usability of mini-SPACE, a short

Nataraju Bodappa, Gregory Jerkiewicz, Peter Grutter

Understanding O2 bubble nucleation and growth during the oxygen evolution reaction (OER) is crucial to comprehend their influences on catalytically active sites in the process. To achieve this goal, mapping the spatial variation of nanoscale dynamic individual steps at the electrocatalytic interfaces is vital, as it further enables a detailed understanding o

Jialang Lu, Shuning Sun, Pu Wang, Chen Wu

Purple fringing, a persistent artifact caused by Longitudinal Chromatic Aberration (LCA) in camera lenses, has long degraded the clarity and realism of digital imaging. Traditional solutions rely on complex and expensive apochromatic (APO) lens hardware and the extraction of handcrafted features, ignoring the data-driven approach. To fill this gap, we introd

Congbin Xu, Yue Yu, Haojie Ren, Zhaojun Wang

Conformal prediction offers a distribution-free framework for constructing prediction sets with finite-sample coverage. Yet, efficiently leveraging multiple conformity scores to reduce prediction set size remains a major open challenge. Instead of selecting a single best score, this work introduces a principled aggregation strategy, COnfidence-Level Allocati

Hiroshi Hirai

In this paper, we address the optimization problem of minimizing $Q(df_x)$ over a Hadamard manifold ${\cal M}$, where $f$ is a convex function on ${\cal M}$, $df_x$ is the differential of $f$ at $x \in {\cal M}$, and $Q$ is a function on the cotangent bundle of ${\cal M}$. This problem generalizes the problem of minimizing the gradient norm $\|\nabla f(x)\|$

Enoch Hyunwook Kang, Hema Yoganarasimhan

Large Language Models (LLMs) have enabled self-improving AI systems that iteratively generate, evaluate, and refine their outcomes. Recent studies show that prompt-optimization-based self-improvement can outperform state-of-the-art reinforcement-learning fine-tuning of LLMs, but performance is typically measured by generation efficiency. However, in many app

Kyunghyun Baek, Seungjin Lee, Joonsuk Huh, Dongkeun Lee

Ground-state estimation lies at the heart of a broad range of quantum simulations. Most near-term approaches are cast as variational energy minimization and thus inherit the challenges of problem-specific energy landscapes. We develop the quantum amplitude-amplification eigensolver (QAAE), which departs from the variational paradigm and instead coherently dr

Zhichen Lai, Hua Lu, Huan Li, Jialiang Li

Trajectory similarity computation is fundamental functionality that is used for, e.g., clustering, prediction, and anomaly detection. However, existing learning-based methods exhibit three key limitations: (1) insufficient modeling of trajectory semantics and hierarchy, lacking both movement dynamics extraction and multi-scale structural representation; (2)

Yinghao Ruan, Wei Pang, Shuaihao Liu, Huili Yang

The advent of smart manufacturing is addressing the limitations of traditional centralized scheduling and inflexible production line configurations in the rubber tyre industry, especially in terms of coping with dynamic production demands. Contemporary tyre manufacturing systems form complex networks of tightly coupled subsystems pronounced nonlinear interac

Brittany Terese Fasy, Maksym Makarchuk, Samuel Micka, David L. Millman

Topological descriptors, such as the Euler characteristic function and the persistence diagram, have grown increasingly popular for representing complex data. Recent work showed that a carefully chosen set of these descriptors encodes all of the geometric and topological information about a shape in R^d. In practice, epsilon nets are often used to find sampl

Ashwin Gerard Colaco, Martin Boissier, Sriram Rao, Shubharoop Ghosh

Physics-based simulators play a critical role in scientific discovery and risk assessment, enabling what-if analyses for events like wildfires and hurricanes. Today, databases treat these simulators as external pre-processing steps. Analysts must manually run a simulation, export the results, and load them into a database before analysis can begin. This line

Sijie Wang, Qiang Wang, Shaohuai Shi

Video generation has been advancing rapidly, and diffusion transformer (DiT) based models have demonstrated remark- able capabilities. However, their practical deployment is of- ten hindered by slow inference speeds and high memory con- sumption. In this paper, we propose a novel pipelining frame- work named PipeDiT to accelerate video generation, which is e

Jiachun Zheng, Yunqing Huang, Nianyu Yi, Yunlei Yang

In this work, we propose data-integrated neural networks (DataInNet) for solving partial differential equations (PDEs), offering a novel approach to leveraging data (e.g., source terms, initial conditions, and boundary conditions). The core of this work lies in the integration of data into a unified network framework. DataInNet comprises two subnetworks: a d

Cuiqun Chen, Qi Chen, Bin Yang, Xingyi Zhang

Cross-view geo-localization (CVGL) matches query images ($\textit{e.g.}$, drone) to geographically corresponding opposite-view imagery ($\textit{e.g.}$, satellite). While supervised methods achieve strong performance, their reliance on extensive pairwise annotations limits scalability. Unsupervised alternatives avoid annotation costs but suffer from noisy ps

Xubo Gu, Xun Huan, Yao Ren, Wenqing Zhou

Monitoring battery health is essential for ensuring safe and efficient operation. However, there is an inherent trade-off between assessment speed and diagnostic depth-specifically, between rapid overall health estimation and precise identification of internal degradation states. Capturing detailed internal battery information efficiently remains a major cha

Aditya Kumar Sahu, Chandan Kumar, Saksham Kumar, Serdar Solak

Steganography and steganalysis are strongly related subjects of information security. Over the past decade, many powerful and efficient artificial intelligence (AI) - driven techniques have been designed and presented during research into steganography as well as steganalysis. This study presents a scientometric analysis of AI-driven steganography-based data

Shaowei Guan, Yu Zhai, Zhengyu Zhang, Yanze Wang

Large Language Models (LLMs) are increasingly vulnerable to adversarial attacks that can subtly manipulate their outputs. While various defense mechanisms have been proposed, many operate as black boxes, lacking transparency in their decision-making. This paper introduces ExplainableGuard, an interpretable adversarial defense framework leveraging the chain-o

Scott Staniewicz, Sara Mirzaee, Heresh Fattahi, Talib Oliver-Cabrera

Operational near-real-time monitoring of Earth's surface deformation using Interferometric Synthetic Aperture Radar (InSAR) requires processing algorithms that efficiently incorporate new acquisitions without reprocessing historical archives. We present sequential phase linking approach using compressed single-look-complex images (SLCs) capable of producing

Jing-Hui Huang, Xiang-Yun Hu

We introduce a hybrid methodology that synergistically combines weak value amplification (WVA) and deep learning to suppress the limiting effects of residual timing jitter in pump-probe interferometry, achieved through simulations of pump-induced time delays at a few-attosecond resolution. The WVA protocol, employing real weak values, amplifies the minute de

Shin Motooka, Noriki Komori, Tomoaki Niiyama, Satoshi Sunada

Ghost imaging (GI) and single-pixel imaging (SPI) techniques enable image reconstruction without spatially resolved detectors, offering unique access to wide spectral ranges and challenging imaging environments. Yet, their adoption has been limited by the slow generation of mask patterns, which constrains achievable frame rates. Here, we demonstrate ultrafas

Saksham Kumar, Ashish Singh, Srinivasarao Thota, Sunil Kumar Singh

Deepfakes are major threats to the integrity of digital media. We propose DeiTFake, a DeiT-based transformer and a novel two-stage progressive training strategy with increasing augmentation complexity. The approach applies an initial transfer-learning phase with standard augmentations followed by a fine-tuning phase using advanced affine and deepfake-specifi

Huimin Cheng, Xiaowei Yu, Shushan Wu, Luyang Fang

Medical images exhibit latent anatomical groupings, such as organs, tissues, and pathological regions, that standard Vision Transformers (ViTs) fail to exploit. While recent work like SBM-Transformer attempts to incorporate such structures through stochastic binary masking, they suffer from non-differentiability, training instability, and the inability to mo

Zheng Hui, Xiaokai Wei, Reza Shirkavand, Chen Wang

Generative recommendation has recently emerged as a powerful paradigm that unifies retrieval and generation, representing items as discrete semantic tokens and enabling flexible sequence modeling with autoregressive models. Despite its success, existing approaches rely on a single, uniform codebook to encode all items, overlooking the inherent imbalance betw

Paloma Sette, Maria Werneck, William Barbosa, Ana Loubacker

The emotional and structural experience of music remains a significant accessibility challenge for the deaf and hard of hearing community. This paper introduces MUSTEM (Multisensorial Emotional Translation), a novel system designed to translate music into a rich, coherent, and scientifically-grounded sensory experience. We present a dual-modality approach ad

Cheng-Chang Tsai, Kai-Wen Cheng, Chun-Shien Lu

Federated learning (FL) has shown success in collaboratively training a model among decentralized data resources without directly sharing privacy-sensitive training data. Despite recent advances, non-IID (non-independent and identically distributed) data poses an inevitable challenge that hinders the use of FL. In this work, we address the issue of non-IID h

Hao Li, Jiajun He, Guangshuo Wang, Dengguo Feng

Retrieval-Augmented Generation (RAG) enhances large language models by integrating external knowledge, but reliance on proprietary or sensitive corpora poses various data risks, including privacy leakage and unauthorized data usage. Membership inference attacks (MIAs) are a common technique to assess such risks, yet existing approaches underperform in RAG du

Soham Bhattacharyya, Omkar Sridhar

Compact binary systems emitting gravitational waves (GWs) can exhibit orbital eccentricity, along with generic spin orientations, leading to the precession of the orbital angular momentum, individual spins, and the orbital plane. While eccentric binaries with aligned spins are well studied, closed form post Newtonian (PN) expressions that simultaneously incl

Shivam Barwey, Pinaki Pal

Super-resolution flow reconstruction using state-of-the-art data-driven techniques is valuable for a variety of applications, such as subgrid/subfilter closure modeling, accelerating spatiotemporal forecasting, data compression, and serving as an upscaling tool for sparse experimental measurements. In the present work, a first-of-its-kind multiscale graph tr

Xinyuan Hu, Changyue Shi, Chuxiao Yang, Minghao Chen

Feed-forward 3D reconstruction from sparse, low-resolution (LR) images is a crucial capability for real-world applications, such as autonomous driving and embodied AI. However, existing methods often fail to recover fine texture details. This limitation stems from the inherent lack of high-frequency information in LR inputs. To address this, we propose \text

Zhen Liu, Xuefan Yin, Andrey Bogdanov, Yujia Nie

Multistability -- the emergence of multiple stable states under identical conditions -- is a hallmark of nonlinear complexity and an enabling mechanism for multilevel optical memory and photonic computing. Its realization in a compact footprint, however, is limited by intrinsically weak optical nonlinearities and the enlarged free spectral range that raises

Satanu Ghosh, Collin Holgate, Neal R. Brodnik, Doug Downey

We apply preference learning to the task of language model-guided design of novel structural alloys. In contrast to prior work that focuses on generating stable inorganic crystals, our approach targets the synthesizeability of a specific structural class: BCC/B2 superalloys, an underexplored family of materials with potential applications in extreme environm

Xiaohao Liu, Xiaobo Xia, Jiaheng Wei, Shuo Yang

Multimodal representation learning harmonizes distinct modalities by aligning them into a unified latent space. Recent research generalizes traditional cross-modal alignment to produce enhanced multimodal synergy but requires all modalities to be present for a common instance, making it challenging to utilize prevalent datasets with missing modalities. We pr

Jiahe Shi, Zhengqi Gao, Ching-Yun Ko, Duane Boning

Recent advances in large language models (LLMs) have demonstrated significant potential in hardware design automation, particularly in using natural language to synthesize Register-Transfer Level (RTL) code. Despite this progress, a gap remains between model capability and the demands of real-world RTL design, including syntax errors, functional hallucinatio

Guotao Liang, Baoquan Zhang, Zhiyuan Wen, Zihao Han

Masked image generation (MIG) has demonstrated remarkable efficiency and high-fidelity images by enabling parallel token prediction. Existing methods typically rely solely on the model itself to learn semantic dependencies among visual token sequences. However, directly learning such semantic dependencies from data is challenging because the individual token

Arun Ramachandran, Ramaswamy Govindarajan, Murali Annavaram, Prakash Raghavendra

With the skyrocketing costs of GPUs and their virtual instances in the cloud, there is a significant desire to use CPUs for large language model (LLM) inference. KV cache update, often implemented as allocation, copying, and in-place strided update for each generated token, incurs significant overhead. As the sequence length increases, the allocation and cop

Jun Zhou, Chi Xu, Kaifeng Tang, Yuting Ge

Estimating the 3D poses of hands and objects from a single RGB image is a fundamental yet challenging problem, with broad applications in augmented reality and human-computer interaction. Existing methods largely rely on visual cues alone, often producing results that violate physical constraints such as interpenetration or non-contact. Recent efforts to inc

Nicholas Tetteh Ofoe, Weilun Wang, Lei Wu

The increasing integration of energy storage systems (ESSs) into power grids has necessitated effective real-time control strategies under uncertain and volatile electricity prices. An important problem of model predictive control of ESSs is identifying the minimum forecast horizon needed to exactly simulate the globally optimal control trajectory. Existing

Anton Kolonin

Quantifying numerical data involves addressing two key challenges: first, determining whether the data can be naturally quantified, and second, identifying the numerical intervals or ranges of values that correspond to specific value classes, referred to as "quantums," which represent statistically meaningful states. If such quantification is feasible, conti

Laurent W. Marcoux, Heydar Radjavi, Yuanhang Zhang

Let $\mathcal{H}$ be a complex, separable Hilbert space (of finite or infinite dimension), and let $\mathcal{U}(\mathcal{H})$ denote the group of unitary operators on $\mathcal{H}$. A symmetry is, by definition, a unitary operator $J$ with $J^2 =I$. Denote by $\text{Sym}_k(\mathcal{H})$ the subset of $\mathcal{U}(\mathcal{H})$ consisting of those operators e

Jeong Hun Yeo, Sangyun Chung, Sungjune Park, Dae Hoe Kim

Long-video understanding remains a significant challenge for Multimodal Large Language Models (MLLMs) due to inherent token limitations and the complexity of capturing long-term temporal dependencies. Existing methods often fail to capture the global context and complex event relationships necessary for deep video reasoning. To address this, we introduce GCA

Rulin Zhou, Wenlong He, An Wang, Jianhang Zhang

Accurate point tracking in surgical environments remains challenging due to complex visual conditions, including smoke occlusion, specular reflections, and tissue deformation. While existing surgical tracking datasets provide coordinate information, they lack the semantic context necessary to understand tracking failure mechanisms. We introduce VL-SurgPT, th

N. T. Dung, N. T. Hang

In this paper, we consider a general class of stochastic Volterra equations with small noise. Our aim is to study the fluctuation of the solution around its deterministic limit. We use the techniques of Malliavin calculus to show that the fluctuation process satisfies central limit theorem and provide an optimal estimate for the rate of convergence. An appli

Shreyas Raorane, Kabir Ram Puri, Anh-Quan Pham

Autonomous robots operating in dynamic environments must balance global path optimality with real-time responsiveness to disturbances. This requires addressing a fundamental trade-off between computationally expensive global planning and fast local adaptation. Sampling-based planners such as RRT* produce near-optimal paths but struggle under perturbations, w

Zheng Qi, Zhang Xueguang, Yuan Qirong

We present a sample of 28 objects at z<0.3 from Data Release 16 of the Sloan Digital Sky Survey (SDSS DR16) with large velocity offset (> 200 km/s) of narrow H$\beta$ and H$\alpha$ emission lines relative to absorption lines. Diagnostic classification via the Baldwin-Phillips-Terlevich diagram indicates that the sample comprises 12 AGNs, 12 composite galaxie

Xianglong Shi, Silin Cheng, Sirui Zhao, Yunhan Jiang

Existing Weakly-Supervised Referring Expression Comprehension (WREC) methods, while effective, are fundamentally limited by a one-to-one mapping assumption, hindering their ability to handle expressions corresponding to zero or multiple targets in realistic scenarios. To bridge this gap, we introduce the Weakly-Supervised Generalized Referring Expression Com

Jyoti, Lalit Kumar Vashisht

In this paper, we study perturbation of Hilbert-Schmidt frames under structured modifications, where the perturbation takes the form of replacing finitely or infinitely many frame elements. We establish explicit criteria under which the perturbed sequence retains the Hilbert-Schmidt frame property. In the finite case, the stability bounds depend quantitative

Shounak Ray Chaudhuri, Arash Jahangiri, Christopher Paolini

Traffic safety analysis at signalized intersections is essential for reducing vehicle and pedestrian collisions, yet traditional crash-based studies are limited by data sparsity and reporting latency. This paper presents a multi-camera computer vision framework for real-time safety assessment through Post-Encroachment Time (PET) computation, demonstrated at

Ji-Yu Song, Guo-Hong Du, Tian-Nuo Li, Ling-Feng Wang

Recently, the combination of the Dark Energy Spectroscopic Instrument (DESI) Data Release 2 (DR2) baryon acoustic oscillation (BAO) data and the Planck cosmic microwave background (CMB) measurements has shown a $\sim$3$\sigma$ preference for a dynamical dark energy model with a phantom-crossing behavior. However, such a phantom-crossing dark energy evolution

Youwu Lin, Xiaoyu Qian, Jinru Wu, Qi Liu

We propose the Modified Mahalanobis Distance Conformal Prediction (MMDCP), a unified framework for multi-class classification and outlier detection under label shift, where the training and test distributions may differ. In such settings, many existing methods construct nonconformity scores based on empirical cumulative or density functions combined with dat

Valeriya Chasova

This work analyses the 1921 article by Erich Bessel-Hagen entitled \"Uber die Erhaltungss\"atze der Elektrodynamik ("On the conservation laws of electrodynamics"). The article is based on Noether's theorems, which were formulated by Emmy Noether in 1918 and concern consequences of symmetries of actions, including conservation laws. Bessel-Hagen firstly exten

Truong Vo, Sanmi Koyejo

Large language models (LLMs) are increasingly deployed in culturally diverse environments, yet existing evaluations of cultural competence remain limited. Existing methods focus on de-contextualized correctness or forced-choice judgments, overlooking the need for cultural understanding and reasoning required for appropriate responses. To address this gap, we

Yan Liang, Dandan Xu, Anowar J. Shajib, Yiping Shu

We investigate potential systematic biases introduced by assumptions regarding stellar orbital anisotropy in joint lensing-dynamics modeling. Our study employs the massive early-type galaxies from the TNG100 simulation at redshifts z = 0.2, 0.5, and 0.7. Based on the simulated galaxies, we generate a self-consistent mock dataset containing both lensing and s

Jiuhua Hu, Daniel Appelo, Yingda Cheng

In this paper, we develop a framework for designing arbitrary high order low-rank schemes for the Lindblad equation with time-dependent Hamiltonians. Our approach is based on nested Picard iterative integrators (NPI) and results in schemes in Kraus form that are completely positive and trace preserving (CPTP). The schemes are amenable to low rank formulation

Sam Buss, Anant Dhayal, Valentine Kabanets, Antonina Kolokolova

We formalize the proof of Reingold's Theorem that SL=L [Rei05] in the theory of bounded arithmetic VL, which corresponds to ``logspace reasoning''. As a consequence, we get that VL=VSL, where VSL is the theory of bounded arithmetic for ``symmetric-logspace reasoning''. This resolves in the affirmative an old open question from Kolokolova [Kol05] (see also Co

Palakorn Achananuparp, Ye Xu, Yao Lu, Xavier Jayaraj Siddarth Ashok

We present a large-scale analysis of career mobility of college-educated U.S. workers using online resume profiles to investigate how gender, race, and job change options are associated with upward mobility. This study addresses key research questions of how the job changes affect their upward career mobility, and how the outcomes of upward career mobility d

Hossein Mohebbi, Mohammed Abdulrahman, Yanting Miao, Pascal Poupart

Recent advances in text-to-image generation have produced strong single-shot models, yet no individual system reliably executes the long, compositional prompts typical of creative workflows. We introduce Image-POSER, a reflective reinforcement learning framework that (i) orchestrates a diverse registry of pretrained text-to-image and image-to-image experts,

Guangchao Yao, Yali Li

The counting of solutions to the N-Queens problem is a classic NP-complete problem with extremely high computational complexity. As of now, the academic community has rigorously verified the number of solutions only up to N <= 26. In 2016, the research team led by PreuBer solved the 27-Queens problem using FPGA hardware, which took approximately one year, th

Yunqi Hong, Johnson Kao, Liam Edwards, Nein-Tzu Liu

AI tools in pathology have improved screening throughput, standardized quantification, and revealed prognostic patterns that inform treatment. However, adoption remains limited because most systems still lack the human-readable reasoning needed to audit decisions and prevent errors. We present RECAP-PATH, an interpretable framework that establishes a self-le

Kai-Wen K. Yang, Andrew Bai, Alexandra Bermudez, Yunqi Hong

Deep learning is transforming microscopy, yet models often fail when applied to images from new instruments or acquisition settings. Conventional adversarial domain adaptation (ADDA) retrains entire networks, often disrupting learned semantic representations. Here, we overturn this paradigm by showing that adapting only the earliest convolutional layers, whi

Xinyu He, Botong Zhao, Bingbing Li, Shujing Lyu

Accurate segmentation and measurement of lithography scanning electron microscope (SEM) images are crucial for ensuring precise process control, optimizing device performance, and advancing semiconductor manufacturing yield. Lithography segmentation requires pixel-level delineation of groove contours and consistent performance across diverse pattern geometri

Ganlin Xu, Zhitao Yin, Linghao Zhang, Jiaqing Liang

Information retrieval (IR) systems play a critical role in navigating information overload across various applications. Existing IR benchmarks primarily focus on simple queries that are semantically analogous to single- and multi-hop relations, overlooking \emph{complex logical queries} involving first-order logic operations such as conjunction ($\land$), di

Shuochen Liu, Pengfei Luo, Chao Zhang, Yuhao Chen

Aiming to identify precise evidence sources from visual documents, visual evidence attribution for visual document retrieval-augmented generation (VD-RAG) ensures reliable and verifiable predictions from vision-language models (VLMs) in multimodal question answering. Most existing methods adopt end-to-end training to facilitate intuitive answer verification.

Tenghao Ji, Eytan Adar

A challenge in fine-tuning text-to-image diffusion models for specific topics is to select good examples. Fine-tuning from image sets of varying quality, such as Wikipedia Commons, will often produce poor output. However, training images that \textit{do} exemplify the target concept (e.g., a \textit{female Mountain Bluebird}) help ensure that the generated i

Eunkyu Park, Wesley Hanwen Deng, Vasudha Varadarajan, Mingxi Yan

Explanations are often promoted as tools for transparency, but they can also foster confirmation bias; users may assume reasoning is correct whenever outputs appear acceptable. We study this double-edged role of Chain-of-Thought (CoT) explanations in multimodal moral scenarios by systematically perturbing reasoning chains and manipulating delivery tones. Spe

Hiroki Ohta, Aaron Merlin Müller, Shunji Tsuchiya

We demonstrate that the ground state of a spin-1 $XXZ$ chain with uniaxial anisotropies, single-ion anisotropy $D$ and Ising-like anisotropy $J$, within the Haldane phase can serve as a resource state for measurement-based quantum computation implementing single-qubit gates. The gate fidelity of both elementary rotation gates and general single-qubit unitary

Zeyu Lu, Peng Zhang, Chun Yong Chong, Shan Gao

Existing fine-grained predictive mutation testing studies predominantly rely on deep learning, which faces two critical limitations in practice: (1) Exorbitant computational costs. The deep learning models adopted in these studies demand significant computational resources for training and inference acceleration. This introduces high costs and undermines the

Abinaya Swaruba Rajamuthukumar, Ruediger Pakmor, Stephen Justham, Aakash Bhat

Type Ia supernovae are thermonuclear explosions of white dwarfs, yet the nature of their progenitor systems remains uncertain. Recent discoveries of hypervelocity stars provide unique constraints, as these stars likely represent the surviving companions of such explosions. Using detailed binary evolution models computed with MESA and population synthesis wit

Paulo Henrique Foganholo Biazetto, Mirko Fiacchini, Christophe Prieur, Gustavo Artur de Andrade

This paper proposes an imitation learning (IL) framework for synthesizing neural network (NN) controllers that achieve boundary stabilization of systems governed by reaction-diffusion partial differential equations (PDEs). The plant is assumed to be actuated through a Dirichlet boundary condition and subject to a Neumann condition on the unactuated side. The

Zitian Wu, Arkaprava Roy, Leo L. Duan

Persistent homology is a cornerstone of topological data analysis, offering a multiscale summary of topology with robustness to nuisance transformations, such as rotations and small deformations. Persistent homology has seen broad use across domains such as computer vision and neuroscience. Most statistical treatments, however, use homology primarily as a fe

Francesco Di Clemente, Alessandro Drago, Lorenzo Formaggio, Claudia Ratti

We study the early Universe trajectory around the QCD transition in lepton-flavor-asymmetric cases with small total lepton asymmetry ($|\ell|\lesssim 10^{-2}$), while allowing large individual lepton asymmetries. For each temperature, we find an upper bound on the baryon chemical potential $\mu_{\mathrm B}(T)$: $\tau$--$\mu$ asymmetric cases exhibit a local

Paulo Henrique Foganholo Biazetto, Gustavo Artur de Andrade, Tiago Roux Oliveira, Miroslav Krstic

This paper addresses the design and analysis of an extremum-seeking (ES) controller for scalar static maps in the context of infinite-dimensional dynamics governed by complex-valued partial differential equations (PDEs) of Schrodinger type. The system is actuated at one boundary, and the map input is defined as a real-valued quadratic functional correspondin

Jiaming Liang, Chi-Man Pun

Despite their wide application, the vulnerabilities of deep neural networks raise societal concerns. Among them, transformation-based attacks have demonstrated notable success in transfer attacks. However, existing attacks suffer from blind spots in parameter optimization, limiting their full potential. Specifically, (1) prior work generally considers low-it

Hung Du, Hy Nguyen, Srikanth Thudumu, Rajesh Vasa

Connected and autonomous vehicles across land, water, and air must often operate in dynamic, unpredictable environments with limited communication, no centralized control, and partial observability. These real-world constraints pose significant challenges for coordination, particularly when vehicles pursue individual objectives. To address this, we propose a

Xiang Ma, Taihua Chen, Pengcheng Wang, Xuemei Li

Time series forecasting is crucial for applications in various domains. Conventional methods often rely on global decomposition into trend, seasonal, and residual components, which become ineffective for real-world series dominated by local, complex, and highly dynamic patterns. Moreover, the high model complexity of such approaches limits their applicabilit

Shaoqi Wang, Lu Yu, Siwei Lou, Feng Yan

The convergence of deep learning and formal mathematics has spurred research in formal verification. Statement autoformalization, a crucial first step in this process, aims to translate informal descriptions into machine-verifiable representations but remains a significant challenge. The core difficulty lies in the fact that existing methods often suffer fro

Songsong Zhang, Chuanqi Tang, Hongguang Zhang, Guijian Tang

Identity-Preserving Personalized Generation (IPPG) has advanced film production and artistic creation, yet existing approaches overemphasize facial regions, resulting in outputs dominated by facial close-ups.These methods suffer from weak visual narrativity and poor semantic consistency under complex text prompts, with the core limitation rooted in identity

Jeffrey Uhlmann

Earlier versions proposed Graded Projection Recursion (GPR) as a deterministic packed-recursion framework for model-honest near-quadratic dense matrix multiplication. This revised version withdraws the exact dense matrix multiplication theorem and the downstream consequences that depended on it with a conservative AMM framework. The local ingredients remain

Weilun Jiang

We report a new oscillatory form in the two coupled dissipative Rydberg atomic chains by modulating its spacing. Such oscillation has $\pi$-phase difference between two neighboring sites, which distinguishes itself from antiferromagnetic-type synchronization in the previous studies. Theoretically, we find a phase with coexisting two types of continuous time

Yi Wang, Shu-Feng Zhou

Bantele and colleagues recently reported that repair of a single CRISPR/Cas9-induced DNA double-strand break (DSB) in the c-MYC topologically associated domain leads to a persistent depletion of chromatin interactions and long-term transcriptional attenuation across multiple generations of human cells. They interpret this observation as evidence for a previo

Jia Li, Shu-Feng Zhou

Dabas et al. in Science 2025 report that approximately 117 human kinases directly phosphorylate the C-terminal domain (CTD) of RNA polymerase II (Pol II), proposing an extensive, direct biochemical bridge between signal transduction and transcriptional control. Such a sweeping claim that one-fourth of the human kinome directly targets the CTD represents a pr

Rana Imran Mushtaq, Chunyang Wang, Shi Zhi, Zengxuan Zhao

In this research paper, we present an exact matrix form analytical solution of the multi-dimensional generalized Langevin equation with quadratic potentials. Our investigation provides detailed expressions for the two-dimensional probability distribution and extends the understanding of the dynamics governed by harmonic potentials. By utilizing the inverse L

Yuan Guo, Wen Chen, Yanze Zhu, Zhendong Li

This paper investigates a novel transmissive reconfigurable intelligent surface (TRIS) transceiver-empowered simultaneous wireless information and power transfer (SWIPT) system with multiple information decoding (ID) and energy harvesting (EH) users. Under the considered system model, we formulate an optimization problem that maximizes the sum-rate of all ID

Zhenhao Guo, Rachit Saluja, Tianyuan Yao, Quan Liu

Fine-grained glomerular subtyping is central to kidney biopsy interpretation, but clinically valuable labels are scarce and difficult to obtain. Existing computational pathology approaches instead tend to evaluate coarse diseased classification under full supervision with image-only models, so it remains unclear how vision-language models (VLMs) should be ad

Debashis Chatterjee

Modern epidemiological analytics increasingly use machine learning models that offer strong prediction but often lack calibrated uncertainty. Bayesian methods provide principled uncertainty quantification, yet are viewed as difficult to integrate with contemporary AI workflows. This paper proposes a unified Bayesian and AI framework that combines Bayesian pr

Louise T. C. Seeyave, Carlton M. Baugh, Angel Chandro-Gomez, Claudia del P. Lagos

We explore how the choice of galaxy formation model affects the predicted properties of high-redshift galaxies. Using the FLARES zoom resimulation strategy, we compare the EAGLE hydrodynamics model and the GALFORM, L-Galaxies, SC-SAM and SHARK semi-analytic models (SAMs) at $5\leq z \leq 12$. The first part of our analysis examines the stellar mass functions

Darko Tanasković, Maksim Makrushin, Petar Mitrić

Building on the recent success of a quantum-classical method for computing transport properties in the Holstein model with a single phonon mode [P. Mitri\'c et al., Phys. Rev. B ${\bf 111}$, L161105 (2025)], we now assess its reliability in more realistic scenarios involving multiple phonon modes in the Holstein model, as well as single- and multi-mode Peier

Yuan Guo, Wen Chen, Xudong Bai, Chong He

A novel transmissive reconfigurable intelligent surface (TRIS) transceiver-empowered simultaneous wireless information and power transfer (SWIPT) framework is proposed. The sum-rate of the information decoding (ID) users is maximized by optimizing the TRIS transceiver's beamforming, subject to the energy harvesting (EH) users' quality-of-harvest and the per-

Md Ahsanul Haque, Md Mahmuduzzaman Kamol, Suresh Kumar Amalapuram, Vladik Kreinovich

Android malware detection systems suffer severe performance degradation over time due to concept drift caused by evolving malicious and benign app behaviors. Although recent methods leverage active learning and hierarchical contrastive loss to address drift, they remain fully supervised, computationally expensive, and ineffective on long-term real-world benc

Hui Huang, Yanping Chen, Ruizhang Huang, Chuan Lin

Generative LLMs typically improve Named Entity Recognition (NER) performance through instruction tuning. They excel at generating entities by semantic pattern matching but lack an explicit, verifiable reasoning mechanism. This "cognitive shortcutting" leads to suboptimal performance and brittle generalization, especially in zero-shot and lowresource scenario

Aditya Khanna

The number of standard Young tableaux possible of shape corresponding to a partition $\lambda$ is called the dimension of the partition and is denoted by $f^{\lambda}$. Partitions with odd dimensions were enumerated by McKay and were further characterized by Macdonald using the theory of 2-core towers. We use the same theory to extend the results to partitio

Zack D. Lasner, Aidan T. Ohl, Nicole M. Albright, Kendall L. Rice

We perform laser spectroscopy of dysprosium monoxide (DyO) to determine the hyperfine structure of the ground X8 and excited [17.1]7 states in the $^{161}$Dy and $^{163}$Dy isotopologues. These dysprosium nuclei have non-zero nuclear spin and dynamical octupole deformation, which gives them high sensitivity to time-reversal-violating new physics via the nucl

Matthew Dickson

This paper examines the model-dependent asymptotic behaviour of the critical threshold intensity for stretched-out random connection models (RCMs) on hyperbolic spaces. The proof uses lace expansion arguments, but has notable qualitative differences to the Euclidean case in how it evaluates spectral radii. The result is applied to the Boolean disc RCM and a

Xinming Gao, Shangzhe Li, Yujin Cai, Wenwu Yu

Offline reinforcement learning (RL) enables policy learning from fixed datasets without further environment interaction, making it particularly valuable in high-risk or costly domains. Extreme $Q$-Learning (XQL) is a recent offline RL method that models Bellman errors using the Extreme Value Theorem, yielding strong empirical performance. However, XQL and it

Sebastião dos Anjos Sousa-Júnior, Julián Faúndez, Rubem Mondaini

We investigate the periodic Anderson model composed of an itinerant $c$-band and a strongly localized $f$-band, featuring on-site electron-electron interactions in the $f$-orbitals. The two bands interact via a hybridization term with spin-orbit coupling, which enables spin-flip processes. In the non-interacting limit, these profoundly alter the electronic s