December 2025 arXiv papers — page 33

Yongwoo Choi, Ahmad Jafar Arifi, Ho-Meoyng Choi, Chueng-Ryong Ji

We present a self-consistent light-front quark model (LFQM) for the kaon based on the Bakamjian--Thomas (BT) construction and apply it to the electromagnetic and scalar form factors, as well as the full set of unpolarized T-even transverse-momentum-dependent distributions (TMDs) and their collinear parton distribution functions (PDFs). A uniform implementati

Jiahong Yu, Ziqi Wang, Hailiang Zhao, Wei Zhai

Understanding natural-language references to objects in dynamic 3D driving scenes is essential for interactive autonomous systems. In practice, many referring expressions describe targets through recent motion or short-term interactions, which cannot be resolved from static appearance or geometry alone. We study temporal language-based 3D grounding, where th

Duo Chai, Zizhen Liu, Shuhuai Wang, Songwei Pei

Large language models (LLMs) are highly compute- and memory-intensive, posing significant demands on high-performance GPUs. At the same time, advances in GPU technology driven by shrinking transistor sizes and lower operating voltages have made these devices increasingly susceptible to soft errors. While prior work has examined GPU reliability, most studies

Nicholas G. Polson, Daniel Zantedeschi

Predictive inference requires balancing statistical accuracy against informational complexity, yet the choice of complexity measure is usually imposed rather than derived. We treat econometric objects as predictive rules, mappings from information to reported predictive distributions, and impose three structural requirements on evaluation: locality, strict p

Jagaran Chakma, Zhiguang Zhou, Badhan Chakma

This research develops and evaluates machine learning models to predict the mechanical properties of steel-polypropylene fiber-reinforced high-performance concrete (HPC). Three model families were investigated: Extra Trees with XGBoost (ET-XGB), Random Forest with LightGBM (RF-LGBM), and Transformer with XGBoost (Transformer-XGB). The target properties inclu

Mutiara Shabrina, Nova Kurnia Putri, Jefri Satria Ferdiansyah, Sabita Khansa Dewi

Text-driven image manipulation often suffers from attribute entanglement, where modifying a target attribute (e.g., adding bangs) unintentionally alters other semantic properties such as identity or appearance. The Predict, Prevent, and Evaluate (PPE) framework addresses this issue by leveraging pre-trained vision-language models for disentangled editing. In

Maedeh Rassekh, Martin Gmitra

We present a first-principles and quantum transport study of proximity-induced spin-orbit torque (SOT) in graphene on a trigonal CrSBr monolayer. Density functional theory combined with nonequilibrium Green's function calculations shows that the CrSBr substrate induces spin polarization and a sizable exchange splitting in the graphene Dirac states. The resul

Chengxu Yang, Jingling Yuan, Siqi Cai, Jiawei Jiang

Hallucinations in large language models (LLMs) are commonly regarded as errors to be minimized. However, recent perspectives suggest that some hallucinations may encode creative or epistemically valuable content, a dimension that remains underquantified in current literature. Existing hallucination detection methods primarily focus on factual consistency, st

Jiaxi Huang, Daniel Tataru

The skew mean curvature flow is an evolution equation for $d$ dimensional ma\-nifolds embedded in $\mathbb{R}^{d+2}$ (or more generally, in a Riemannian manifold). It can be viewed as a Schr\"odinger analogue of the mean curvature flow, or alternatively as a quasilinear version of the Schr\"odinger Map equation. In this article, we prove large data local wel

Qiuqi Li, Yiting Liu, Jin Zhao, Wencan Zhu

Parametric partial differential equations (PDEs) are fundamental for modeling a wide range of physical and engineering systems influenced by uncertain or varying parameters. Traditional neural network-based solvers, such as Physics-Informed Neural Networks (PINNs) and Deep Galerkin Methods, often face challenges in generalization and long-time prediction eff

Sandeep Kumar Kataria

Recent JWST observations claim the existence of a significant fraction of bars in the kinematically hotter and thicker disk at high redshift Universe. These observations challenge the current understanding of disk stability in galaxies similar to the Milky Way. The analytical work and N-body simulations suggest that the kinematically hot (dispersion-dominate

Iryna Kashuba, Alexander Molev

We apply the recently introduced idempotents for the Sergeev superalgebra to construct quantum immanants for the queer Lie superalgebra ${\mathfrak q}_N$ as central elements of its universal enveloping algebra. We prove universal odd and even Capelli identities for ${\mathfrak q}_N$ and use them to calculate the images of the quantum immanants under the acti

Manali Dutta, Rahul Singh, Shalabh Bhatnagar

We study a remote estimation setup with an autoregressive (AR) Markov process, a sensor, and a remote estimator. The sensor observes the process and sends encoded observations to the estimator as packets over an unreliable communication channel modeled as the Gilbert-Elliot (GE) channel. We assume that the sensor gets to observe the channel state by the ACK/

Paul Ben Ishai, Roshlin Kiruba, Amos Bardea

Organic Magnetoresistance is defined as the change of resistance in an organic material, such as a conducting polymer, as a function of an imposed magnetic field. We demonstrate this effect in a Polypyrrole/ Polydimethylsiloxane complex by using a novel magnetic pulse system. The frequency spectrum of the current flowing through the sample reveals equally sp

Junde Liu, Liwen Su, Pei Liu, Hui Liu

Nonvolatile control of the Mott transition is a central goal in correlated-electron physics, offering access to fascinating emergent states and great potential for technological applications. Compared to chemical or mechanical approaches, ultrafast optical excitation further promises a path to create and manipulate novel non-equilibrium phases with ultimate

Botao Ren, Junjun Hu, Xinda Xue, Minghua Luo

Lifelong embodied navigation requires agents to accumulate, retain, and exploit spatial-semantic experience across tasks, enabling efficient exploration in novel environments and rapid goal reaching in familiar ones. While object-centric memory is interpretable, it depends on detection and reconstruction pipelines that limit robustness and scalability. We pr

Hong Xie, Haoran Gu, Yanying Huang, Tao Tan

This paper proposes a variant of multiple-play stochastic bandits tailored to resource allocation problems arising from LLM applications, edge intelligence, etc. The model is composed of $M$ arms and $K$ plays. Each arm has a stochastic number of capacities, and each unit of capacity is associated with a reward function. Each play is associated with a priori

Xinyu Tang, Yuliang Zhan, Zhixun Li, Wayne Xin Zhao

Large reasoning models (LRMs) are typically trained using reinforcement learning with verifiable reward (RLVR) to enhance their reasoning abilities. In this paradigm, policies are updated using both positive and negative self-generated rollouts, which correspond to distinct sample polarities. In this paper, we provide a systematic investigation into how thes

Takahide Suzuki, Kazuki Nakanishi, Takashi Fujiwara, Hideyuki Shimizu

Therapeutic discovery remains a formidable challenge, impeded by the fragmentation of specialized domains and the execution gap between computational design and physiological validation. Although generative AI offers promise, current models often function as passive assistants rather than as autonomous executors. Here, we introduce OrchestRA, a human-in-the-

Nidhi Nidhi, Ambesh Kumar Pandey, K. Sreenadh

In this article, we investigate normalized solutions for nonlinear problems involving variable exponents. To the best of our knowledge, normalized solutions have not been previously studied in this setting, and our results appear to be new. A key difficulty is that the standard scaling argument, which is important in the classical normalized solution approac

Masaaki Fujii

Financial firms and institutional investors are routinely evaluated based on their performance relative to their peers. These relative performance concerns significantly influence risk-taking behavior and market dynamics. While the literature studying Nash equilibrium under such relative performance competitions is extensive, its effect on asset price format

Zhiyuan Liu, Daocheng Fu, Pinlong Cai, Lening Wang

High-fidelity and controllable 3D simulation is essential for addressing the long-tail data scarcity in Autonomous Driving (AD), yet existing methods struggle to simultaneously achieve photorealistic rendering and interactive traffic editing. Current approaches often falter in large-angle novel view synthesis and suffer from geometric or lighting artifacts d

Zhiwen Yang, Jinglin Xu, Yuxin Pen

Few-shot fine-grained visual categorization (FS-FGVC) focuses on identifying various subcategories within a common superclass given just one or few support examples. Most existing methods aim to boost classification accuracy by enriching the extracted features with discriminative part-level details. However, they often overlook the fact that the set of suppo

Rongpei Hong, Jian Lang, Ting Zhong, Yong Wang

Multimodal Large Language Model (MLLM) Personalization is a critical research problem that facilitates personalized dialogues with MLLMs targeting specific entities (known as personalized concepts). However, existing methods and benchmarks focus on the simple, context-agnostic visual identification and textual replacement of the personalized concept (e.g., "

Guopeng Li, Haisheng Tan, Chi Zhang, Hongqiu Ni

Training deep learning recommendation models (DLRMs) on edge workers brings several benefits, particularly in terms of data privacy protection, low latency and personalization. However, due to the huge size of embedding tables, typical DLRM training frameworks adopt one or more parameter servers to maintain global embedding tables, while leveraging the edge

Ze Tao, Ke Xu, Fujun Liu

Physics-Informed Neural Networks (PINNs) have demonstrated considerable success in solving complex fluid dynamics problems. However, their performance often deteriorates in regimes characterized by steep gradients, intricate boundary conditions, and stringent physical constraints, leading to convergence failures and numerical instabilities. To overcome these

Zhishuai Zhang, Xintian Li, Shilong Liu, Aodong Zhang

In this paper, we propose AMS-IO-Agent, a domain-specialized LLM-based agent for structure-aware input/output (I/O) subsystem generation in analog and mixed-signal (AMS) integrated circuits (ICs). The central contribution of this work is a framework that connects natural language design intent with industrial-level AMS IC design deliverables. AMS-IO-Agent in

Jagaran Chakma, Zhiguang Zhou, Jyoti Chakma, Cao YuSen

This study presents a data-driven, multi-objective approach to predict the mechanical performance, flow ability, and porosity of Ultra-High-Performance Concrete (UHPC). Out of 21 machine learning algorithms tested, five high-performing models are selected, with XGBoost showing the best accuracy after hyperparameter tuning using Random Search and K-Fold Cross

Zaizhe Zhang, Xi Chen, Kenji Watanabe, Takashi Taniguchi

Graphene-based moire superlattices exhibit novel quantum phenomena driven by pronounced interactions, leading to topological corrected states like orbital Chern insulators exhibiting quantum anomalous Hall effect (QAHE). Typically, intrinsic Chern insulators are stabilized at odd moir\'e fillings, as even fillings often result in valley-balanced, topological

Gaofeng Fan, Yu Meng, Chuan Liu, Zhaofeng Liu

We present a systematic lattice calculation of the vector and axial vector form factors $V$ and $A_i~(i=0,1,2)$ for the $D_s \to \phi \ell \nu_\ell$ semileptonic decay using (2+1)-flavor Wilson-clover fermion configurations generated by the CLQCD collaboration. Seven gauge ensembles with different lattice spacings, from $0.052~\text{fm}$ to $0.105~\text{fm}$

Jiaqun Jiang

We introduce the shell formula-a framework that unifies the description of partition functions whose pole structures are classified by Young diagrams of arbitrary dimension. The formalism yields explicit closed-form expressions and recursion relations for a wide range of physical systems, including instanton partition functions of 5d pure super Yang-Mills th

Yihan Wang, Domenico Bongiovanni, Dario Jukić, Sihong Lei

Angular momentum conservation stands as one of the most fundamental and robust laws of physics. In discrete lattices, however, its realization can deviate markedly from the continuous case, especially in the presence of nontrivial momentum-space band touchings. Here, we investigate angular momentum conservation associated with quadratic band-touching points

Alan A. Dzhioev, Andrey V. Yudin, Natalia V. Dunina-Barkovskaya, Andrey I. Vdovin

Reliable predictions of (anti)neutrino spectra and luminosities are essential for assessing the feasibility of detecting pre-supernova neutrinos. Using the stellar evolution code MESA, we calculate the (anti)neutrino spectra and luminosities under realistic conditions of temperature, density, and electron fraction. Our study includes (anti)neutrinos produced

Toshiya Yurikusa

We study the $g$-fan associated with a skew-symmetrizable matrix in the sense of cluster algebras. We show that a skew-symmetrizable matrix is of finite type if and only if its $g$-fan is complete; equivalently (as we show), its support contains all lattice points.

Haolian Chi, Kunrui Cao, Zhou Su, Lei Zhou

As a novel member of flexible antennas, the pinching antenna (PA) is realized by integrating small dielectric particles on a waveguide, offering unique regulatory capabilities on constructing line-of-sight (LoS) links and enhancing transceiver channels, reducing path loss and signal blockage. Meanwhile, non-orthogonal multiple access (NOMA) has become a pote

Bintao He, Yiran Cheng, Hongjia Li, Xiang Gao

Understanding protein flexibility and its dynamic interactions with other molecules is essential for studying protein function. Although cryogenic electron microscopy(cryo-EM) provides an opportunity to observe macromolecular dynamics directly, computational analysis of datasets mixing continuous and discrete structural states remains a formidable challenge.

Jian Lang, Rongpei Hong, Ting Zhong, Leiting Chen

The proliferation of harmful memes on online media poses significant risks to public health and stability. Existing detection methods heavily rely on large-scale labeled data for training, which necessitates substantial manual annotation efforts and limits their adaptability to the continually evolving nature of harmful content. To address these challenges,

Si-Yi Jiang, Wenbin Zhao, Gui-Jun Ding

We investigate inflation models governed by the Siegel modular group $Sp(4,\mathbb{Z})$. The $Sp(4,\mathbb{Z})$ group extends the $SL(2,\mathbb{Z})$ framework from one modulus to three moduli while preserving the hyperbolic geometry of the K\"ahler potential, allowing for the construction of cosmological $\alpha$-attractor models. In this context, we use gen

Peixin Wang, Jianhao Bai, Min Zhang, C. -H. Luke Ong

Probabilistic programming provides a high-level framework for specifying statistical models as executable programs with built-in randomness and conditioning. Existing inference techniques, however, typically compute posterior distributions over program states at fixed time points, most often at termination, thereby failing to capture the temporal evolution o

Yinfu Feng, Yanjing Wu, Rong Xiao, Xiaoyi Zen

Item-to-Item (I2I) recommendation models are widely used in real-world systems due to their scalability, real-time capabilities, and high recommendation quality. Research to enhance I2I performance focuses on two directions: 1) model-centric approaches, which adopt deeper architectures but risk increased computational costs and deployment complexity, and 2)

Takuro Kutsuna

Diffusion models have become a central tool in deep generative modeling, but standard formulations rely on a single network and a single diffusion schedule to transform a simple prior, typically a standard normal distribution, into the target data distribution. As a result, the model must simultaneously represent the global structure of the distribution and

Shuo Sun, Shixin Zhang, Jiwei Yan, Jun Yan

Python's dynamic typing mechanism, while promoting flexibility, is a significant source of runtime type errors that plague large-scale software, which inspires the automatic type inference techniques. Existing type inference tools have achieved advances in type inference within isolated code snippets. However, repository-level type inference remains a signif

Yun Gao

Let $I$ be a homogeneous ideal in the polynomial ring $R = k[z_1, \cdots, z_n]$ , where $k$ is an algebraically closed field of characteristic zero. Macaulay's Theorem provides constraints on the Hilbert function of $I$ or $R/I$ from one degree to the next. Nowadays, the standard quotation of Macaulay's theorem is $H_{R/I}(d + 1) \le H_{R/I}(d)^{\langle d\ra

Masaaki Yamauchi, Yiyuan Liang, Hiroko Hara, Hideyuki Shimonishi

Smart home automation that adapts to a user's emotional state can enhance psychological safety in daily living environments. This study proposes an emotion-aware automation framework guided by the emotional Biologically Inspired Cognitive Architecture (eBICA), which integrates appraisal, somatic responses, and behavior selection. We conducted a proof-of-conc

Sudheer Mishra, Sundararajan Natarajan, Natarajan E

This work presents a new conforming stabilized virtual element method for the generalized Boussinesq equation with temperature-dependent viscosity and thermal conductivity. A gradient-based local projection stabilization method is introduced in the discrete formulation to circumvent the violation of the discrete inf-sup condition. The well-posedness of the c

Ze Zheng, Yuegang Li, Hang Xu, Jingzheng Huang

Spatial photonic Ising machines offer a novel optical platform for optimization and spin-model simulation, but existing diffraction-based schemes rely on auxiliary spins or multiplexing to encode high-rank couplings and external fields, reducing either speed or spin count. We demonstrate an amplitude-only, rank-free spatial photonic Ising machine in which ar

Xin Liu, Haoran Li, Dongbin Zhao

Humans can efficiently extract knowledge and learn skills from the videos within only a few trials and errors. However, it poses a big challenge to replicate this learning process for autonomous agents, due to the complexity of visual input, the absence of action or reward signals, and the limitations of interaction steps. In this paper, we propose a novel,

Lijun Bo, Dongfang Yang, Shihua Wang

This paper studies the dynamic pricing mechanism for data products in demand-driven markets through a game-theoretic framework. We develop a three-tier Stackelberg game model to capture the hierarchical strategic interactions among key market entities: a single data buyer, an intermediary broker, and a competitive seller group. To characterize the temporal d

Linxuan Fan, Juntao Jiang, Weixuan Liu, Zhucun Xue

Skin lesion segmentation is a crucial step in dermatology for guiding clinical decision-making. However, existing methods for accurate, robust, and resource-efficient lesion analysis have limitations, including low performance and high computational complexity. To address these limitations, we propose UltraLBM-UNet, a lightweight U-Net variant that integrate

Zelin Zang, Wenyi Gu, Siqi Ma, Dan Yang

With the rapid growth of large language models (LLMs) and vision-language models (VLMs) in medicine, simply integrating clinical text and medical imaging does not guarantee reliable reasoning. Existing multimodal models often produce hallucinations or inconsistent chains of thought, limiting clinical trust. We propose a diagnostic framework built upon LLaVA

Shinnosuke Hirano, Yuiga Wada, Kazuki Matsuda, Seitaro Otsuki

We focus on the automatic evaluation of image captions in both reference-based and reference-free settings. Existing metrics based on large language models (LLMs) favor their own generations; therefore, the neutrality is in question. Most LLM-free metrics do not suffer from such an issue, whereas they do not always demonstrate high performance. To address th

M. A. Bunin, I. I Geguzin

Within the framework of the local electron density functional theory, an ab-initio method is proposed that takes into account the self-interaction energy correction (SIC) for the crystal potential. The principle of dividing the unit cell into regions remained the same as for the ground-state potential. The expression for the self-consistent muffin-tin-SIC po

Alexander Podolskiy, Semen Molokov, Timofey Gerasin, Maksim Titov

We present Gamayun, a 1.5B-parameter multilingual language model trained entirely from scratch on 2.5T tokens. Designed for efficiency and deployment in resource-constrained environments, Gamayun addresses the lack of research on small non-English-centric LLMs by adopting a novel two-stage pre-training strategy: balanced multilingual training for cross-lingu

Ernest Fokoué

Ensemble learning is traditionally justified as a variance-reduction strategy, explaining its strong performance for unstable predictors such as decision trees. This explanation, however, does not account for ensembles constructed from intrinsically stable estimators-including smoothing splines, kernel ridge regression, Gaussian process regression, and other

K. De Commer, G. Schrader, A. Shapiro, C. Voigt

We construct the first examples of purely continuous, $q$-deformed Lie type locally compact quantum groups in higher rank. They arise from Drinfeld-Jimbo quantization, at unimodular deformation parameter, of the totally positive part of higher rank split real Lie groups in type $A$. Our techniques are based on quantum cluster theory, in particular as develop

Sudhanshu Garg, Andrew Wang, Chaitanya Kulkarni, Ali Sahami

We present the development and optimization of PayPal's Commerce Agent, powered by NEMO-4-PAYPAL, a multi-agent system designed to revolutionize agentic commerce on the PayPal platform. Through our strategic partnership with NVIDIA, we leveraged the NeMo Framework for LLM model fine-tuning to enhance agent performance. Specifically, we optimized the Search a

Haoyi Zhou, Shuo Li, Tianyu Chen, Qi Song

While large vision-language models (VLMs) demonstrate strong long-context understanding, their prevalent small branches fail on linguistics-photography alignment for a limited window size. We discover that knowledge distillation improves students' capability as a complement to Rotary Position Embeddings (RoPE) on window sizes (anchored from large models). Bu

Hajime Murata, Hiroaki Ishizuka

Chiral magnetic states give rise to rich phenomena, from the anomalous Hall effect and the nonlinear electrical current to multiferroics and magnetochiral dichroism. Most of the studies on electrical transport so far have focused on the cases where the magnetic moments are well approximated by classical local moments. Here, we reveal that the coexistence of

Ahilan Ayyachamy Nadar Ponnusamy, Karthic Chandran, M Maruf Hossain

The scaling trend in Large Language Models (LLMs) has prioritized increasing the maximum context window to facilitate complex, long-form reasoning and document analysis. However, managing this expanded context introduces severe computational overhead. This paper investigates the critical trade-off between system performance and model quality when dense trans

Ammar El Falou

The integration of non-terrestrial networks (NTNs) into 6G systems is crucial for achieving seamless global coverage, particularly in underserved and disaster-prone regions. Among NTN platforms, unmanned aerial vehicles (UAVs) are especially promising due to their rapid deployability. However, this shift from fixed, wired base stations (BSs) to mobile, wirel

Israk Hasan Jone, D. M. Rafiun Bin Masud, Promit Sarker, Sayed Fuad Al Labib

Shrimp is one of the most widely consumed aquatic species globally, valued for both its nutritional content and economic importance. Shrimp farming represents a significant source of income in many regions; however, like other forms of aquaculture, it is severely impacted by disease outbreaks. These diseases pose a major challenge to sustainable shrimp produ

Zhangzheng Tu, Kailun Su, Shaolong Zhu, Yukun Zheng

Recovering world-coordinate human motion from monocular videos with humanoid robot retargeting is significant for embodied intelligence and robotics. To avoid complex SLAM pipelines or heavy temporal models, we propose a lightweight, engineering-oriented framework that leverages SAM 3D Body (3DB) as a frozen perception backbone and uses the Momentum HumanRig

Anthony Bolton, Wuyang Zhou, Zehua Chen, Giorgos Iacovides

Financial time series forecasting is particularly challenging for transformer-based time series foundation models (TSFMs) due to non-stationarity, heavy-tailed distributions, and high-frequency noise present in data. Low-rank adaptation (LoRA) has become a popular parameter-efficient method for adapting pre-trained TSFMs to downstream data domains. However,

Hui Guo, Qihang Zheng, Chenghai Huo, Dongliang Guo

The efficient deployment of large language models (LLMs) is hindered by memory architecture heterogeneity, where traditional compilers suffer from fragmented workflows and high adaptation costs. We present nncase, an open-source, end-to-end compilation framework designed to unify optimization across diverse targets. Central to nncase is an e-graph-based term

Giona Fieni, Joschua Wüthrich, Marc-Philippe Neumann, Mohammad M. Moradi

This paper presents two complementary frameworks to optimize Formula 1 race strategies, jointly accounting for energy allocation, tire wear and pit stop timing. First, the race scenario is modeled using lap time maps and a dynamic tire wear model capturing the main trade-offs arising during a race. Then, we solve the problem by means of a mixed-integer nonli

Xiaobin Ren, Kaiqi Zhao, Katerina Taškova, Patricia Riddle

Spatio-temporal kriging is a fundamental problem in sensor networks, driven by the sparsity of deployed sensors and the resulting missing observations. Although recent approaches model spatial and temporal correlations, they often under-exploit two practical characteristics of real deployments: the sparse spatial distribution of locations and the heterogeneo

Zhong-Heng Li

A unified equation is employed to analytically investigate the scattering of massless spin particles by a Schwarzschild-type medium black hole. It is found that for spin particles, curved spacetime induces an effective complex potential analogous to a Coulomb field. While the real part of this potential contributes a real logarithmic term to the phase, the i

Changzhi Sun, Xiangyu Chen, Jixiang Luo, Dell Zhang

External memory is a key component of modern large language model (LLM) systems, enabling long-term interaction and personalization. Despite its importance, memory management is still largely driven by hand-designed heuristics, offering little insight into the long-term and uncertain consequences of memory decisions. In practice, choices about what to read o

Lei Gao, Dingding Liang, Jiawei Gao, Chulun Lin

Multimodal vital sign monitoring and speech detection hold significant importance in medical health, public safety, and other fields. This study proposes a broadband tunable microwave photonic radar system that can simultaneously monitor respiration, heartbeat, and speech. The system works by generating broadband radar signals to detect subtle skin displacem

Takaaki Ito

In tropical geometry, there are several important classes of ideals and congruences such as tropical ideals, bend congruences, and the congruences of the form $\mathbf E(Z)$. Although they are analogues of the concept of ideals of rings, it is not well known whether they are finitely generated. In this paper, we study whether the congruences of the form $\ma

Zhong-Heng Li

This study investigates the quasinormal modes of Kerr-Newman-de Sitter black holes for massless spin particles using the unified equation. We derive analytical expressions for both the quasinormal mode frequencies and the radial wave functions. The frequencies are determined exclusively by the black hole parameters and the quantum numbers $n$ and $m$, while

Patrick Yubeaton, Sarthak Gupta, M. Salman Asif, Chinmay Hegde

The design of novel algorithms for solving inverse problems in signal processing is an incredibly difficult, heuristic-driven, and time-consuming task. In this short paper, we the idea of automated algorithm discovery in the signal processing context through meta-learning tools such as Neural Architecture Search (NAS). Specifically, we examine the Iterative

Yihang Duan

While the technology for detecting AI-Generated Content (AIGC) images has advanced rapidly, the field still faces two core issues: poor reproducibility and insufficient gen eralizability, which hinder the practical application of such technologies. This study addresses these challenges by re viewing 7 key papers on AIGC detection, constructing a lightweight

Xiaoming Chen, Haoze Chen, Fei Xu, Meifeng Gao

With the rapid development of quantum computing, classical cryptography systems are increasingly vulnerable to security threats, thereby highlighting the urgency of constructing architectures that are resilient to quantum computing attacks. While Quantum Key Distribution (QKD) offers security with information-theoretic guarantees, its relatively low key gene

Unnati Saraswat, Tarun Rao, Namah Gupta, Shweta Swami

Intelligent image editing increasingly relies on advances in computer vision, multimodal reasoning, and generative modeling. While vision-language models (VLMs) and diffusion models enable guided visual manipulation, existing work rarely ensures that inserted objects are \emph{contextually appropriate}. We introduce two new tasks for advertising and digital

Jiajun Lu, Mu Tian, Chaoxi Cui, Zhi-Ming Yu

We propose a fractionally quantized polarization induced by interlayer sliding in bilayer altermagnets, unveiling a previously unrecognized multiferroic phase termed sliding fractional quantum multiferroicity (SFQM). This unconventional magnetic phase uniquely integrates sliding ferroelectricity with fractional quantum ferroelectricity, enabling highly effic

Yejian Hu, Zhenhang Pu, Xiangru Chen, Yuxiang Xi

Non-Hermitian topological phase, with the novel concepts such as exceptional points and skin effect, has opened up a new paradigm beyond Hermitian topological physics. Exceptional ring semimetal, featured by a stable ring of exceptional points in three dimensions, exhibits first-order topological properties, including topological surface states and surface-d

Chi Zhang, Jun Liu, Dengyin Wang

In this paper, we introduce the weakly nilpotent hypergroups with giving some new properties, and then establish several structural characterizations of these hypergroups. Some results obtained in this paper answer the two questions raised by the first author and W. Guo.

Qi Hu, Jiangchao Liu, Xin Yu, Lin Zhang

As the complexity of mobile applications grows exponentially and the fragmentation of user device environments intensifies, ensuring online application stability faces unprecedented challenges. Traditional methods, such as static logging and post-crash analysis, lack real-time contextual information, rendering them ineffective against "ghost bugs" that only

Pierluigi Benevieri, Guglielmo Feltrin

In this paper we propose some continuation theorems for the periodic problem \begin{equation*} \begin{cases} \, x_{i}' = g_{i}(t,x_{i+1}), &i=1,\ldots,n-1, \\ \, x_{n}' = h(t,x_{1},\ldots,x_{n}), \\ \, x_{i}(0)=x_{i}(T), &i=1,\ldots,n, \end{cases} \end{equation*} providing a unified framework that improves and extends earlier contributions by Jean Mawhin and

Chunyu Qu

This article examines how legacy lending relationships shape the allocation of emergency credit under severe information frictions. Using a novel dataset linking Small Business Administration (SBA) loan records with Dun and Bradstreet microdata for over 26 million U.S. firms, I investigate whether prior participation in the SBA 7(a) program acted as a gatewa

Hua Shen

Artificial intelligence (AI) is transforming education, offering unprecedented opportunities to personalize learning, enhance assessment, and support educators. Yet these opportunities also introduce risks related to equity, privacy, and student autonomy. This chapter develops the concept of bidirectional human-AI alignment in education, emphasizing that tru

Hua Shen, Tiffany Knearem, Divy Thakkar, Pat Pataranutaporn

The rapid integration of generative AI into everyday life underscores the need to move beyond unidirectional alignment models that only adapt AI to human values. This workshop focuses on bidirectional human-AI alignment, a dynamic, reciprocal process where humans and AI co-adapt through interaction, evaluation, and value-centered design. Building on our past

Somayeh Bandari, Raheleh Jafari

We investigate the Gauss algebra for squarefree Veronese algebras generated in degree $3$. For small dimensions not exceeding $7$, we determine the Gauss algebra by specifying its generators and show in particular that it is normal and Cohen-Macaulay.

Uday Kumar Nidadala, Venkata Bhumika Guthi

Horizon reduction is a common design strategy in offline reinforcement learning (RL), used to mitigate long-horizon credit assignment, improve stability, and enable scalable learning through truncated rollouts, windowed training, or hierarchical decomposition (Levine et al., 2020; Prudencio et al., 2023; Park et al., 2025). Despite recent empirical evidence

Hiroki Matsui

We present a detailed evaluation of constrained minisuperspace path integrals in Jackiw-Teitelboim (JT) gravity and in biaxial Bianchi IX quantum cosmology, employing the Gelfand-Yaglom theorem to compute the relevant functional determinants. In both settings, integrating out the dilaton or a minisuperspace variable produces a functional delta that enforces

Xinliang An, Haoyang Chen, Fulin Qi, Wenze Su

In this paper, we prove the finite-time shock formation for the compressible Euler equations on the two-dimensional sphere $\mathbb{S}^2$. In contrast to the flat Euclidean case $\mathbb{R}^2$, the geometry of $\mathbb S^2$ imposes new difficulties, and the fluid dynamics are affected by the curved background. To overcome these challenges, we modify the exis

Chunyu Qu

The Paycheck Protection Program (PPP) was the largest targeted business support program in the United States, yet its firm-level effects remain contested. I link administrative PPP and SBA 7(a) records to a near-universe panel of U.S. employer firms from Dun and Bradstreet, covering roughly 30 million establishments, and evaluate short-run impacts on employm

Wenyu Gao, Fan Xu

In this paper, we investigate the relationships between Harder-Narasimhan filtrations and derived Hall algebras. We extend several results from abelian categories to triangulated categories, including Reineke inversions, wall-crossing formulas, and Joyce's elements $\epsilon_\gamma$. The results in triangulated categories can be summarized via a diagram of t

Xinru Wen, Weizhong Lin, Xuan Xiao

Accurate identification of antiviral peptides (AVPs) is critical for accelerating novel drug development. However, current computational methods struggle to capture intricate sequence dependencies and effectively handle ambiguous, hard-to-classify samples. To address these challenges, we propose AVP-Fusion, a novel two-stage deep learning framework integrati

Yuzhen Lin, Hongyi Chen, Xuanjing Chen, Shaowen Wang

Generative recommendation models often struggle with two key challenges: (1) the superficial integration of collaborative signals, and (2) the decoupled fusion of multimodal features. These limitations hinder the creation of a truly holistic item representation. To overcome this, we propose CEMG, a novel Collaborative-Enhaned Multimodal Generative Recommenda

Dongchen Han, Tianyu Li, Ziyi Wang, Gao Huang

The self-attention mechanism has been a key factor in the advancement of vision Transformers. However, its quadratic complexity imposes a heavy computational burden in high-resolution scenarios, restricting the practical application. Previous methods attempt to mitigate this issue by introducing handcrafted patterns such as locality or sparsity, which inevit

Ping Zhao, Zhenyu Liu, Dan Zhuang

Testing high-dimensional quantile regression coefficients is crucial, as tail quantiles often reveal more than the mean in many practical applications. Nevertheless, the sparsity pattern of the alternative hypothesis is typically unknown in practice, posing a major challenge. To address this, we propose an adaptive test that remains powerful across both spar

Yanhao Li, Lu Ma, Jiaran Zhang, Lexiang Tang

Existing approaches typically rely on fixed length penalties, but such penalties are hard to tune and fail to adapt to the evolving reasoning abilities of LLMs, leading to suboptimal trade-offs between accuracy and conciseness. To address this challenge, we propose Leash (adaptive LEngth penAlty and reward SHaping), a reinforcement learning framework for eff

Igor V. Ovchinnikov

It was recently established that the formalism of the generalized transfer operator (GTO) of dynamical systems (DS) theory, applied to stochastic differential equations (SDEs) of arbitrary form, belongs to the family of cohomological topological field theories (TFT) -- a class of models at the intersection of algebraic topology and high-energy physics. This

Eugene H. Lin, Yishun Zhou, Hsin-Yi Hung, Luke Moon

Orb-weaving spiders primarily sense leg vibrations to detect and locate prey caught on their wheel-shaped webs. Biological experiments and computational modeling elucidated the physics of how these spiders use long-timescale web-building behaviors, which occur before prey capture, to modulate vibration sensing of prey by controlling web geometry, materials,

Yuan Li, Qiaoyuan Cheng, Engui Fan

We establish the global existence of solutions to the Fokas-Lenells equation for any initial data in a weighted Sobolev space $H^{3}(\mathbb{R})\cap H^{2,1}(\mathbb{R})$.This result removes all spectral restrictions on the initial data required in our previous work. The proof primarily relies on the inverse scattering transform formulated as new Riemann-Hilb

Clemens Bannwart

The well groups were introduced by Edelsbrunner, Morozov, and Patel to measure the robustness of geometric features of a function with respect to perturbations. Roughly speaking, the $r$-th well group measures the number of features that cannot be removed by perturbing the function by at most $r$. The Shrinking Wellness Lemma states that the rank of these gr

Heng-Yu Chen, Yasuaki Hikida, Yasutaka Koga

The region near a black hole horizon may be modified by quantum gravity effects that resolve the singularity. Such geometry may be represented by an exotic compact object. Because the horizon is enclosed by a photon sphere, it is difficult to probe this region directly. In this paper, we develop a method to study the region inside the photon sphere by applyi

Ernest Fokoué

Nature rarely reveals her secrets bluntly, yet in the Fibonacci sequence she grants us a glimpse of her quiet architecture of growth, harmony, and recursive stability \citep{Koshy2001Fibonacci, Livio2002GoldenRatio}. From spiral galaxies to the unfolding of leaves, this humble sequence reflects a universal grammar of balance. In this work, we introduce \emph