December 2025 arXiv papers — page 56

Huimin Liu, Tong Yang, Xiongfang Liu, Shengwei Zeng

Understanding the electronic properties of doped copper-oxygen planes remains a significant challenge in condensed matter physics and is crucial to unraveling the mechanisms behind high-temperature superconductivity in cuprates. Recently, the observation of charge transfer and interfacial polarons in superconducting interface has aroused extensive research i

Ran Li, Pan Xiao, Kaushik Dutta, Youdong Guo

Fluorescence Microcopy Calcium Imaging is a fundamental tool to in-vivo record and analyze large scale neuronal activities simultaneously at a single cell resolution. Automatic and precise detection of behaviorally relevant neuron activity from the recordings is critical to study the mapping of brain activity in organisms. However a perpetual bottleneck to t

Yuan Fang, Fabian Waschkowski, Maximilian Reissmann, Richard D. Sandberg

Computational Fluid Dynamics (CFD)-driven training combines machine learning (ML) with CFD solvers to develop physically consistent closure models with improved predictive accuracy. In the original framework, each ML-generated candidate model is embedded in a CFD solver and evaluated against reference data, requiring hundreds to thousands of high-fidelity si

Ivan Daunis

Building deployment-ready LLM agents requires complex orchestration of tools, data sources, and control flow logic, yet existing systems tightly couple agent logic to specific programming languages and deployment models. We present a declarative system that separates agent workflow specification from implementation, enabling the same pipeline definition to e

Haoshuai Wang, Zhaoqi Dong, Lei Chen

Collective motion is a fundamental phenomenon in biological swarms. As a framework for studying synchronization in motions, the Vicsek model is simple and efficient, assuming isotropic interactions with a complete field of view. Drawing inspiration from natural swarms, we incorporate realistic constraints into the model. By analysing the interaction structur

Ali Zeytoon-Nejad

Business growth is a goal of great importance for its both private and social benefits. Many firms view business growth as an imperative for their survival, stability, and long-term success. Business growth can be socially beneficial, too, as it enables businesses to expand into new territories where they can stimulate economic growth and development, create

Nelson Abdiel Colón Vargas, Carlos Ortiz Marrero

Computing topological invariants of 3-manifolds is generally intractable, yet specialized algebraic structures can enable efficient algorithms. For Witten-Reshetikhin-Turaev (WRT) invariants of torus bundles, we exploit the non-commutative torus structure to embed the skein algebra of the closed torus into its symmetric subalgebra at roots of unity. This yie

Ariana Azarbal, Victor Gillioz, Vladimir Ivanov, Bryce Woodworth

Developers often struggle to specify correct training labels and rewards. Perhaps they don't need to. We propose recontextualization, which reduces how often language models "game" training signals, performing misbehaviors those signals mistakenly reinforce. We show recontextualization prevents models from learning to 1) prioritize evaluation metrics over ch

Connor Kilrain, David Carlyn, Julia Chae, Sara Beery

The rise of personalized generative models raises a central question: how should we evaluate identity preservation? Given a reference image (e.g., one's pet), we expect the generated image to retain precise details attached to the subject's identity. However, current generative evaluation metrics emphasize the overall semantic similarity between the referenc

Xiaowei Wang

Since transformer was firstly published in 2017, several works have been proposed to optimize it. However, the major structure of transformer remains unchanged, ignoring one of its main intrinsic limitations, which is the same static value is used for every query in a head. Transformer itself tries to solve this problem by implementing multi-head attentions,

Hengrui Jia, Taoran Li, Jonas Guan, Varun Chandrasekaran

Machine unlearning aims to remove specific data influences from trained models, a capability essential for adhering to copyright laws and ensuring AI safety. Current unlearning metrics typically measure success by monitoring the model's performance degradation on the specific unlearning dataset ($D_u$). We argue that for Large Language Models (LLMs), this ev

Xu Liu, Yu Liu, Hanshuo Qiu, Yang Qirong

Vision-Language Navigation (VLN) enables agents to navigate in complex environments by following natural language instructions grounded in visual observations. Although most existing work has focused on ground-based robots or outdoor Unmanned Aerial Vehicles (UAVs), indoor UAV-based VLN remains underexplored, despite its relevance to real-world applications

Haijun Li

Operator regular variation of a multivariate distribution can be decomposed into the operator tail dependence of the underlying copula and the regular variation of the univariate marginals. In this paper, we introduce operator tail densities for copulas and show that an operator-regularly-varying density can be characterized through the operator tail density

Haoze Li, Jie Zhang, Guoying Zhao, Stephen Lin

Face Presentation Attack Detection (PAD) demands incremental learning (IL) to combat evolving spoofing tactics and domains. Privacy regulations, however, forbid retaining past data, necessitating rehearsal-free IL (RF-IL). Vision-Language Pre-trained (VLP) models, with their prompt-tunable cross-modal representations, enable efficient adaptation to new spoof

Sihao Lin, Zerui Li, Xunyi Zhao, Gengze Zhou

Despite remarkable progress in Vision-Language Navigation (VLN), existing benchmarks remain confined to fixed, small-scale datasets with naive physical simulation. These shortcomings limit the insight that the benchmarks provide into sim-to-real generalization, and create a significant research gap. Furthermore, task fragmentation prevents unified/shared pro

Zelin Zhao, Xinyu Gong, Bangya Liu, Ziyang Song

Achieving precise camera control in video generation remains challenging, as existing methods often rely on camera pose annotations that are difficult to scale to large and dynamic datasets and are frequently inconsistent with depth estimation, leading to train-test discrepancies. We introduce CETCAM, a camera-controllable video generation framework that eli

Ayana Hussain, Ricky Fang

Deep learning models can reveal sensitive information about individual training examples, and while differential privacy (DP) provides guarantees restricting such leakage, it also alters optimization dynamics in poorly understood ways. We study the training dynamics of neural networks under DP by comparing Gradient Descent (GD), and Adam to their privacy-pre

Muhammad Usman Tariq, Abhinav Jangda, Angelica Moreira, Madan Musuvathi

Advancements in large language models (LLMs) are showing promising impact in software development and programming assistance. However, these models struggle when operating on low-level backend code. This challenge is exacerbated in the domain of GPU kernels, where performance-critical details are coupled to rapidly evolving hardware characteristics and avail

Peng Yu, Han Fu, William F. Schiela, William Strickland

The superconducting diode effect (SDE) has recently been observed in various systems, sparking interest in novel superconducting devices and offering a new platform to probe intrinsic material properties. Josephson junctions with strong Rashba spin-orbit coupling have exhibited nonreciprocal critical currents under applied magnetic fields. In this work, we i

Liming Lu, Xiang Gu, Junyu Huang, Jiawei Du

Large Language Models (LLMs) are increasingly used in agentic systems, where their interactions with diverse tools and environments create complex, multi-stage safety challenges. However, existing benchmarks mostly rely on static, single-turn assessments that miss vulnerabilities from adaptive, long-chain attacks. To fill this gap, we introduce DREAM, a fram

Klaus Deckelnick, Hans-Christoph Grunau, Robert Nürnberg, Glen Wheeler

The free boundary free elastic flow is the steepest descent gradient flow for the elastic energy of curves meeting parallel lines perpendicularly. In this article we prove that the straight line has, measured in Euler's scale-invariant bending energy, a basin of attraction at least to the level $1.9615\, π$. We show that our method of proof cannot be pus

Saizo Aoyagi, Ryoma Okazaki, Seishiro Hara, Fumiya Ikeda

Since the COVID-19 pandemic, online lectures have spread rapidly and many students are satisfied with them. However, one challenge remains the loss of concentration due to the lack of students' copresence. Our previous work suggests that presenting 3D characters with appropriate actions has the potential to improve concentration in online lectures. Neverthel

E. Salahinejad, M. Jafari Baghjeghaz

In this work, diopside (CaMgSi2O6) was doped with fluoride at a level of 1 mol.%, without the formation of any second phase, by a wet chemical precipitation method. The sintered structure of the synthesized nanopowders was studied by X-ray diffraction, Fourier transform infrared spectroscopy and field-emission scanning electron microscopy. Also, the samples'

Seongkyu Jung, Namyoon Lee, Jeonghun Park

This paper addresses precoder design for secure multiple-input multiple-output (MIMO) integrated sensing and communications (ISAC) systems. We introduce a MIMO channel with a multiple-antenna eavesdropper and a multiple-antenna sensing receiver (MIMO-ME-MS) and analyze the fundamental performance limits of this tripartite tradeoff. Using sensing mutual infor

Shijian Ma, Yunqi Huang, Yan Lin

Drama script continuation requires models to maintain character consistency, advance plot coherently, and preserve dramatic structurecapabilities that existing benchmarks fail to evaluate comprehensively. We present DramaBench, the first large-scale benchmark for evaluating drama script continuation across six independent dimensions: Format Standards, Narrat

Devi Yuliarti, Ravi Prakash, Hiu Ching Cheung, Amy Strong

The tactile properties of tissue, such as elasticity and stiffness, often play an important role in surgical oncology when identifying tumors and pathological tissue boundaries. Though extremely valuable, robot-assisted surgery comes at the cost of reduced sensory information to the surgeon, with vision being the primary. Sensors proposed to overcome this se

Shaun Khoo, Jessica Foo, Roy Ka-Wei Lee

Agentic AI systems present both significant opportunities and novel risks due to their capacity for autonomous action, encompassing tasks such as code execution, internet interaction, and file modification. This poses considerable challenges for effective organizational governance, particularly in comprehensively identifying, assessing, and mitigating divers

Yijun Dong, Paul Schwerdtner, Benjamin Peherstorfer

The Dirac-Frenkel variational principle is a widely used building block for using nonlinear parametrizations in the context of model reduction and numerically solving partial differential equations; however, it typically leads to time-dependent least-squares problems that are poorly conditioned. This work introduces a randomized time stepping scheme that sol

Fei Chen, Shang Li

In the seminal paper of Borel and Tits about reductive groups, they show some fundamental results about Bruhat cells with respect to a minimal parabolic subgroup, e.g., relative Bruhat decomposition and its geometrization, relative Bruhat order and the relation of Zariski closure and topological closure. In this paper, we show analogous results for Bruhat ce

Konstantin Kaulen, Tobias Ladner, Stanley Bak, Christopher Brix

This report summarizes the 6th International Verification of Neural Networks Competition (VNN-COMP 2025), held as a part of the 8th International Symposium on AI Verification (SAIV), that was collocated with the 37th International Conference on Computer-Aided Verification (CAV). VNN-COMP is held annually to facilitate the fair and objective comparison of sta

Nikita Gaianov, Anastasia Parusnikova

An algebraic $q$-difference equation is considered. A sufficient condition for the existence of a formal power-logarithmic expansion of a solution to such an equation in the neighborhood of zero is proposed. An example of applying this sufficient condition for constructing a formal expansion of a solution to a certain $q$-difference analogue of the fifth Pai

Navin Chhibber, Amber Rastogi, Ankur Mahida, Vatsal Gupta

Another threat is the development of large quantum computers, which have a high likelihood of breaking the high popular security protocols because it can use both Shor and Grover algorithms. In order to fix this looming threat, quantum-resistant cryptographic systems, otherwise known as post-quantum cryptography (PQC), are being formulated to protect cyberse

Farjana Yesmin, Romana Akter

Post-disaster aid allocation in developing nations often suffers from systematic biases that disadvantage vulnerable regions, perpetuating historical inequities. This paper presents a fairness-aware artificial intelligence framework for prioritizing post-flood aid distribution in Bangladesh, a country highly susceptible to recurring flood disasters. Using re

Tongyuan Miao, Gary Huang, Kai Jun Han, Annie Jiang

Diffusion Large Language Models (DLLMs) enable fully parallel token decoding but often remain impractical at inference time due to the many denoising iterations required to refine an information-free, fully masked initialization into coherent text. Most existing acceleration methods focus on traversing this generative trajectory more efficiently via improved

Mokshay Madiman, James Melbourne, Cyril Roberto

We study the behavior of log-supermodular functions under convolution. In particular we show that log-concave product densities preserve log-supermodularity, confirming in the special case of the standard Gaussian density, a conjecture of Zartash and Robeva. Additionally, this stability gives a ``conditional'' entropy power inequality for log-supermodular ra

Mokshay Madiman, James Melbourne, Cyril Roberto

The entropy power inequality for independent random vectors is a foundational result of information theory, with deep connections to probability and geometric functional analysis. Several extensions of the entropy power inequality have been developed for settings with dependence, including by Takano, Johnson, and Rioul. We extend these works by developing a

Lingjie Zhao, Xue Yu, Yongzhi Qi, Hao Hu

As the pursuit of synergy between Artificial Intelligence (AI) and Operations Research (OR) gains momentum in handling complex inventory systems, a critical challenge persists: how to effectively reconcile AI's adaptive perception with OR's structural rigor. To bridge this gap, we propose a novel OR-Guided "Pretrain-then-Reinforce" framework. To provide stru

Suresh Kumar

We investigate a phenomenological extension of the standard $\Lambda$CDM framework, the $\Omega_1\Omega_2$-$\Lambda$CDM model, in which the total energy density of the universe is expanded in powers of $1+z$. This parameterization recovers $\Lambda$CDM and introduces two additional terms, $\Omega_1(1+z)$ and $\Omega_2(1+z)^2$, in the dark energy sector along

Haozhe Jia

Capsule endoscopy has enabled minimally invasive gastrointestinal imaging, but its clinical utility is limited by the inherently low resolution of captured images due to hardware, power, and transmission constraints. This limitation hampers the identification of fine-grained mucosal textures and subtle pathological features essential for early diagnosis. Thi

Jinyan Liu, Zikang Chen, Qinchuan Wang, Tan Xie

When applied directly in an end-to-end manner to medical follow-up tasks, Large Language Models (LLMs) often suffer from uncontrolled dialog flow and inaccurate information extraction due to the complexity of follow-up forms. To address this limitation, we designed and compared two follow-up chatbot systems: an end-to-end LLM-based system (control group) and

Jianan Zhu, Tianruo Zhang, Diana Silver, Ellicott Matthay

Matching is one of the most widely used causal inference frameworks in observational studies. However, all the existing matching-based causal inference methods are designed for either a single treatment with general treatment types (e.g., binary, ordinal, or continuous) or factorial (multiple) treatments with binary treatments only. To our knowledge, no exis

Chong Liu, Ming Zhang, Fei Li, Hao Zhou

Automated front-end engineering drastically reduces development cycles and minimizes manual coding overhead. While Generative AI has shown promise in translating designs to code, current solutions often produce monolithic scripts, failing to natively support modern ecosystems like React, Vue, or Angular. Furthermore, the generated code frequently suffers fro

Cheng Yaw Low, Heejoon Koo, Jaewoo Park, Meeyoung Cha

Taxonomic classification of ecological families, genera, and species underpins biodiversity monitoring and conservation. Existing computer vision methods typically address fine-grained recognition and long-tailed learning in isolation. However, additional challenges such as spatiotemporal domain shift, hierarchical taxonomic structure, and previously unseen

Xiaotong Xu, Bei Jiang, Runze Wang, Zhibin Qiu

Transition metal dichalcogenides (TMDs) enable magnetic property engineering via intercalation, but stoichiometry-structure-magnetism correlations remain poorly defined for Fe-intercalated $\mathrm{NbSe_2}$. Here, we report a systematic study of $\mathrm{Fe}_{x}\mathrm{NbSe_2}$ across an extended composition range $0.05 \le x \le 0.38$, synthesized via chemi

Kshitiz K. Mallick, Doris Arzoumanian, Satoko Takahashi, Ray S. Furuya

We present an analysis of polarised dust emission at 850 micron for a parsec long filament in the northern part of the Orion B molecular cloud. The region was observed by the JCMT SCUBA-2/POL-2 polarimeter. The filament has a line mass (~80 Msun/pc) larger than the critical (magnetic) line mass (~37 Msun/pc); and hosts one starless, three prestellar, and fou

Gyeongrok Oh, Youngdong Jang, Jonghyun Choi, Suk-Ju Kang

Dominant paradigms for 4D LiDAR panoptic segmentation are usually required to train deep neural networks with large superimposed point clouds or design dedicated modules for instance association. However, these approaches perform redundant point processing and consequently become computationally expensive, yet still overlook the rich geometric priors inheren

Guozhen Li, Xiaoyue Li, Xuerong Mao, Guoting Song

This paper is to investigate if the solution of a hybrid stochastic functional differential equation (SFDE) with infinite delay can be approximated by the solution of the corresponding hybrid SFDE with finite delay. A positive result is established for a large class of highly nonlinear hybrid SFDEs with infinite delay. Our new theory makes it possible to num

Youzhi Zhang

The United Nations' 2030 Agenda for Sustainable Development requires that all countries collaborate to fight adversarial factors to achieve peace and prosperity for humans and the planet. This scenario can be formulated as an adversarial team game in AI literature, where a team of players play against an adversary. However, previous solution concepts for thi

Yanding Yang, Weitao Zhou, Jinhai Wang, Xiaomin Guo

Autonomous buses run on fixed routes but must operate in open, dynamic urban environments. Disengagement events on these routes are often geographically concentrated and typically arise from planner failures in highly interactive regions. Such policy-level failures are difficult to correct using conventional imitation learning, which easily overfits to spars

Zui Oporto, Gonzalo Marcelo Ramírez-Ávila

We present a dynamical framework for modeling the motion of point-like charged particles, with or without mass, in general external electromagnetic fields. A key feature of this formulation is the treatment of time coordinate as a dynamical variable. The framework applies to the relativistic regime while consistently admitting a nonrelativistic limit. We als

Ryosuke Korekata, Quanting Xie, Yonatan Bisk, Komei Sugiura

In this study, we address the problem of open-vocabulary mobile manipulation, where a robot is required to carry a wide range of objects to receptacles based on free-form natural language instructions. This task is challenging, as it involves understanding visual semantics and the affordance of manipulation actions. To tackle these challenges, we propose Aff

Kun Zhao, Siyuan Dai, Yingying Zhang, Guodong Liu

Early detection of Alzheimer's disease (AD) requires models capable of integrating macro-scale neuroanatomical alterations with micro-scale genetic susceptibility, yet existing multimodal approaches struggle to align these heterogeneous signals. We introduce R-GenIMA, an interpretable multimodal large language model that couples a novel ROI-wise vision trans

Amlan Sinha, Ryne Beeson

In this paper, we present a bi-level optimal control framework for designing low-thrust spacecraft trajectories with robustness against missed-thrust-events. The upper-level (UL) problem generates a nominal trajectory assuming full control authority, while each lower-level (LL) problem computes the optimal recovery maneuver following a missed-thrust-event al

Jinfeng Liang, Shanshan Zhu, Yang Li, Qionglin Dai

We report a spectrum of exotic frequency-locked states in a ring of phase oscillators with pure three-body interactions. For identical oscillators, the system hosts a vast multiplicity of stable quantized frequency-locked states without phase coherence. Introducing frequency heterogeneity broadens each quantized level into a continuous band and drives an ext

Tuo Wu, Kai-Kit Wong, Jie Tang, Junteng Yao

Fluid antenna system (FAS) represents the concept of treating antenna as a reconfigurable physical-layer resource to broaden system design and network optimization and inspire next-generation reconfigurable antennas. FAS can unleash new degree of freedom (DoF) via antenna reconfigurations for novel spatial diversity. Reconfigurable intelligent surfaces (RISs

Jiaji Ren, Ye Tian, Baiyang Liu, Tuo Wu

Direction of Arrival (DOA) estimation serves as a critical sensing technology poised to play a vital role in future intelligent and ubiquitous communication systems. Despite the development of numerous mature super-resolution algorithms, the inherent end-fire effect problem in fixed antenna arrays remains inadequately addressed. This work proposed a novel ar

Wei Peng, Jianchen Hu, Kang Liu, Qiaozhu Zhai

Bayesian optimization is an effective method for solving expensive black-box optimization problems. Most existing methods use Gaussian processes (GP) as the surrogate model for approximating the black-box objective function, it is well-known that it can fail in high-dimensional space (e.g., dimension over 500). We argue that the reliance of GP on precise num

Liang Guoqiang, Sun Jian, Lin Gege, Zhang Shuo

The advancement of science is inherently dependent on the recombination of existing knowledge, and innovative research typically relies on the atypical recombination of established knoweldge bases. This study introduces a Knowledge Eccentricity to enable timely assessment of the novelty of research outputs by quantifying their degree of deviation from the ex

Zheng Wang, Zhiye Yang, Yang Yang, Avik Ranjan Adhikary

Doppler-resilient complementary sequence (DRCS) sets play a vital role in modern communication and sensing systems, particularly in high-mobility environments. This work makes two primary contributions. First, we refine the definition of quasi-Florentine rectangles to a more general form,termed generalized quasi-Florentine rectangles, and propose a systemati

Baiyang Chen, Zhong Yuan, Zheng Liu, Dezhong Peng

Outlier detection is a critical task in data mining, aimed at identifying objects that significantly deviate from the norm. Semi-supervised methods improve detection performance by leveraging partially labeled data but typically overlook the uncertainty and heterogeneity of real-world mixed-attribute data. This paper introduces a semi-supervised outlier dete

Baiyang Chen, Zhong Yuan, Dezhong Peng, Xiaoliang Chen

Outlier detection aims to find samples that behave differently from the majority of the data. Semi-supervised detection methods can utilize the supervision of partial labels, thus reducing false positive rates. However, most of the current semi-supervised methods focus on numerical data and neglect the heterogeneity of data information. In this paper, we pro

Jie Fan, Xiangdi Huang

In 1995, Kazhikhov and Vaigant introduced a particular class of isentropic compressible Navier-Stokes equations with variable viscosity coefficients and, for the first time, established the existence of global smooth solutions for arbitrarily large initial data in bounded two-dimensional domains. This result was subsequently extended and refined to accommoda

Pu Zhao, Arash Akbari, Xuan Shen, Zhenglun Kong

Recently, Large Language Models (LLMs) have undergone a significant transformation, marked by a rapid rise in both their popularity and capabilities. Leading this evolution are proprietary LLMs like GPT-4 and GPT-o1, which have captured widespread attention in the AI community due to their remarkable performance and versatility. Simultaneously, open-source L

Stanislav Jabuka, Ehsan Mirbagheri

We introduce the {\em pair-cut cone $PCUT_n$} of metrics on sets with $n\ge 3$ elements, that correspond to linear combinations with non-negative coefficients of the cut-metrics resulting from cuts that are pairs. Given a metric, we fully characterize membership in the pair-cut cone in terms of quantities computed from the metric directly. We also prove a ne

M. Prado, A. Manjavacas, F. A. Pinheiro, W. J. M. Kort-Kamp

Harnessing structural correlations in near-field plasmonic and polaritonic systems hold untapped potential for controlling light-matter interactions at the nanoscale. By tuning these correlations, one can reshape mode localization, coupling, and spectral distribution which are properties central to manipulating energy transport and field enhancement in nanop

Elizabeth Himwich, Monica Pate

A universal class of light-ray operators formed from null integrals of the stress tensor is constructed in generic interacting Lorentzian conformal field theories in four spacetime dimensions. This class of light-ray operators generates the wedge algebra of ${\rm w}_{1+\infty}$, which was recently identified among the asymptotic symmetries of asymptotically

Chaoyang Luo, Yuji Zhang, Minyu Feng, Attila Szolnoki

Individual cooperative strategy influences the surrounding dynamic population, which in turn affects cooperative strategy. To better model this phenomenon, we develop a Markov decision chain based game transitions model and examine the dynamic transitions in game states of individuals within a network and their impact on the strategy's evolution. Additionall

Shuntuo Xu, Zhou Yu, Jian Huang

Identifying low-dimensional sufficient structures in nonlinear sufficient dimension reduction (SDR) has long been a fundamental yet challenging problem. Most existing methods lack theoretical guarantees of exhaustiveness in identifying lower dimensional structures, either at the population level or at the sample level. We tackle this issue by proposing a new

Tuo Wu, Xiazhi Lai, Maged Elkashlan, Naofal Al-Dhahir

The synergy of fluid antenna systems (FAS) and reconfigurable intelligent surfaces (RIS) is poised to unlock robust Vehicle-to-Everything (V2X) communications. However, a critical gap persists between theoretical predictions and real-world performance. Existing analyses predominantly rely on the Central Limit Theorem (CLT), an assumption valid only for a lar

Cheng-Hong Chang, Pei-Hsuan Tsai

Object recognition has become prevalent across various industries. However, most existing applications are limited to identifying objects alone, without considering their associated states. The ability to recognize both the state and object simultaneously remains less common. One approach to address this is by treating state and object as a single category d

Tianle Lu, Ke Chen, Yuping Duan

We introduce a novel formulation for curvature regularization by penalizing normal curvatures from multiple directions. This total normal curvature regularization is capable of producing solutions with sharp edges and precise isotropic properties. To tackle the resulting high-order nonlinear optimization problem, we reformulate it as the task of finding the

Jian You, Xiangfeng Li, Erwan Zerhouni

Conventional automatic speech recognition (ASR) models typically produce outputs as normalized texts lacking punctuation and capitalization, necessitating post-processing models to enhance readability. This approach, however, introduces additional complexity and latency due to the cascaded system design. In response to this challenge, there is a growing tren

Yuwon Yoon, Kevin Iwan, Madeleine Zwart, Xiaohan Qin

Planning for an upcoming project iteration (sprint) is one of the key activities in Scrum planning. In this paper, we present our work in progress on exploring the applicability of Large Language Models (LLMs) for solving this problem. We conducted case studies with manually created data sets to investigate the applicability of OpenAI models for supporting t

Guandong Li, Yijun Ding

Recent tuning-free identity customization methods achieve high facial fidelity but often overlook visual context, such as lighting, skin texture, and environmental tone. This limitation leads to ``Semantic-Visual Dissonance,'' where accurate facial geometry clashes with the input's unique atmosphere, causing an unnatural ``sticker-like'' effect. We propose *

Yoshihiko Hasegawa

Traditional thermodynamic trade-off relations usually apply to quantities that depend linearly on probability distributions. In contrast, many important information-theoretic measures, such as entropies, are nonlinear and therefore difficult to analyze with existing frameworks. Motivated by replica methods in quantum information and spin-glass theory, we int

Alonso Tapia, Enrique Muñoz

In this work, we explore the possible emergence of superconducting phases in a multi-Weyl semimetal. In particular, we show that the presence of a pair of Weyl nodes with chirality $|\nu| \ge 1$ leads to an effective description of the intra-nodal pairings in terms of monopole harmonics, in contrast to inter-nodal pairings that preserve the angular dependenc

Weiqian Zhao, Wenzhao Huang, Zifu Su, Fangyuan Li

The deep integration of quantum communication and fiber-optic sensing is pivotal for the development of next-generation multifunctional and highly reliable secure information infrastructure. Here, we present a Sagnac-loop integrated system (SLIS) that, for the first time, combines ring-based quantum key distribution (QKD) with fiber-based weak measurement (W

Zihan Wang

We propose a scalar-mediated Self-Interacting Dark Matter (SIDM) model to address small-scale structure anomalies such as the core-cusp and diversity problems. The model is composed by a leptophilic scalar mediator and a pseudo-Dirac dark matter candidate with a mass splitting of 100ev.We imposed a dark discrete $\mathbb{Z}_2$ symmetry forbids tree-level ela

Masafumi Shimojo, Satoshi Ishihara, Hironobu Kataoka, Atsuko Matsukawa

We study the correlation functions between the dynamical variables and between their conjugate momenta at sites of a harmonic lattice in the $d$-dimensional Euclidean space. We show that at the thermodynamic limit, they can be expressed in terms of Lauricella's C-type hypergeometric series. Furthermore, using these expressions, we explicitly demonstrate that

Debamita Ghosh, George K. Atia, Yue Wang

In many real-world settings, reinforcement learning systems suffer performance degradation when the environment encountered at deployment differs from that observed during training. Distributionally robust reinforcement learning (DR-RL) mitigates this issue by seeking policies that maximize performance under the most adverse transition dynamics within a pres

Yizhi Wang, Linan Yue, Min-Ling Zhang

Large Reasoning Models (LRMs) have demonstrated remarkable performance on complex reasoning tasks through long Chain-of-Thought (CoT) reasoning. Extending these successes to multimodal reasoning remains challenging due to the increased complexity of integrating diverse input modalities and the scarcity of high-quality long CoT training data. Existing multimo

Prosper Torsu

We develop a spectral low-mode reduced solver for second-order elliptic boundary value problems with spatially varying diffusion coefficients. The approach projects standard finite difference or finite element discretization onto a global coarse space spanned by the lowest Dirichlet Laplacian eigenmodes, yielding an analytic reduced model that requires no tr

Kang-Min Hu, Min Namkung, Hyang-Tag Lim

Quantum computers have the potential to deliver speed-ups for solving certain important problems that are intractable for classical counterparts, making them a promising avenue for advancing modern computation. However, many quantum algorithms require deep quantum circuits, which are challenging to implement on current noisy devices. To address this limitati

Saman Forouzandeh, Wei Peng, Parham Moradi, Xinghuo Yu

We present MACLA, a framework that decouples reasoning from learning by maintaining a frozen large language model while performing all adaptation in an external hierarchical procedural memory. MACLA extracts reusable procedures from trajectories, tracks reliability via Bayesian posteriors, selects actions through expected-utility scoring, and refines procedu

Michael Neilan, Hongzhi Wan

We construct and analyze a TraceFEM discretization for the surface biharmonic problem. The method utilizes standard quadratic Lagrange finite element spaces defined on a three-dimensional background mesh and a symmetric $C^0$ interior penalty formulation posed on a second-order polyhedral approximation of the surface. Stability is achieved through a combinat

Wojciech Bielas

Under the assumption $\frak{c}\geqslant \omega_2$, we give an example of a dense plastic subset $X\subseteq\mathbb{R}$ of cardinality $|X|<\frak{c}$. This answers Problem 1 of arXiv:2510.10537.

Li Yan, Bolun Liu, Chao Li, Jing Liang

Dynamic multimodal multiobjective optimization presents the dual challenge of simultaneously tracking multiple equivalent pareto optimal sets and maintaining population diversity in time-varying environments. However, existing dynamic multiobjective evolutionary algorithms often neglect solution modality, whereas static multimodal multiobjective evolutionary

Yunhan Mou, Scott Hummel, Yuan Huang

Composite endpoints are frequently used as primary or secondary analyses in cardiovascular clinical trials to increase clinical relevance and statistical efficiency. Alternatively, the Win Ratio (WR) and other Win Statistics (WS) analyses rely on a strict hierarchical ordering of endpoints, assigning higher priority to clinically important endpoints. However

Pablo Granizo, Yuri Oku, Kentaro Nagamine

Numerical galaxy formation simulations are sensitive to numerical methods and sub-grid physics models, making code comparison projects essential for quantifying uncertainties. Here, we evaluate GADGET4-OSAKA within the AGORA project framework by conducting a systematic comparison with its predecessor. We perform an isolated disk galaxy and a cosmological zoo

Chun Sum Brian Pang, Bruce A. Davidson, Fengmiao Li, Mohamed Oudah

Two-dimensional electron gases (2DEGs) at the surfaces of KTaO3 have become an exciting platform for exploring strong spin-orbit coupling, Rashba physics, and low-carrier-density superconductivity. Yet, a large fraction of reported KTaO3-based 2DEGs has been realized through chemically complex overlayers that both generate carriers and can obscure the native

Arnaud Brothier, Ryan Seelig

We construct an explicit infinite family of pairwise non-isomorphic infinite simple groups of type $\mathrm{F}_\infty$ (in particular, they are finitely presented) that act faithfully on the circle by orientation-preserving homeomorphisms, but that admit no non-trivial piecewise affine nor piecewise projective actions on the projective line. Our examples are

Alexander Kruchkov

We investigate fundamental bounds on the curvature of quantum correlation functions in imaginary time. Focusing first on topological phases, we show that quantum geometry can qualitatively modify the imaginary-time decay of correlations, leading to nontrivial curvature behavior beyond simple exponential scaling. More generally, we show a universal bound on c

Andrea Addazi

We explore the paradigm of pre-geometric gravity, where spacetime geometry and the gravitational field are not fundamental but emerge from the spontaneous symmetry breaking (SSB) of a larger gauge symmetry. Specifically, we consider a gauge theory based on the de Sitter $SO(1,4)$ or anti-de Sitter $SO(3,2)$ group, formulated on a manifold without a prior met

Xinchen Jiang, Yuejiang Shi, Yueng-Kay Martin Peng, Shaodong Song

The non-inductive current start-up by multi-harmonic electron cyclotron wave has been systematically investigated in the EXL-50U spherical torus. Significant enhancements of the driven current with increasing number of resonance layers have been demonstrated by variation of the number of harmonic resonance layers of the ECW through adjustment of the magnetic

Wen-Long Jin

Large Language Models (LLMs) execute complex multi-turn interaction protocols but lack formal specifications to verify execution against designer intent. We introduce FASTRIC, a Prompt Specification Language that makes implicit Finite State Machines (FSMs) explicit in natural language prompts, enabling conformance verification through execution trace analysi

Haixia Dong, Ziqing Xie, Jiwei Zhang

This paper presents the development and analysis of an asymptotically compatible (AC) unfitted finite element method for one-dimensional nonlocal elliptic interface problems. The proposed method achieves optimal error estimates through three principal contributions: (i) an extended maximum principle, coupled with an asymptotic consistency analysis of the flu

Yadong Liu, Jianwei Liu, He Liang, Dimitrios Kanoulas

Quadruped mobile manipulators offer strong potential for agile loco-manipulation but remain difficult to control and transfer reliably from simulation to reality. Reinforcement learning (RL) shows promise for whole-body control, yet most frameworks are proprietary and hard to reproduce on real hardware. We present an open pipeline for training, benchmarking,

Joost Jorritsma, Pascal Maillard, Peter Mörters

We describe the critical window for percolation in the universality class of sparse growing random graphs. In our models, vertices arrive sequentially and connect independently to each earlier vertex $v$ with probability proportional to a nonpositive power of the arrival time of $v$, continuing until the graph has $n$ vertices. This class includes uniformly

Nikhil Garg, Anxiong Song, Niklas Plessnig, Nathan Savoia

Electroencephalography (EEG)-based brain-computer interfaces (BCIs) are strongly affected by non-stationary neural signals that vary across sessions and individuals, limiting the generalization of subject-agnostic models and motivating adaptive and personalized learning on resource-constrained platforms. Programmable memristive hardware offers a promising su

Ryan Spencer, Roey Yaari, Ritvik Vemavarapu, Joyce Yang

Multimodal large language models (MLLMs) are proficient in perception and instruction-following, but they still struggle with spatial reasoning: the ability to mentally track and manipulate objects across multiple views and over time. Spatial reasoning is a key component of human intelligence, but most existing benchmarks focus on static images or final outp