October 2025 arXiv papers — page 159

Yuma Ichikawa, Shuhei Kashiwamura, Ayaka Sakata

Quantized neural network training optimizes a discrete, non-differentiable objective. The straight-through estimator (STE) enables backpropagation through surrogate gradients and is widely used. While previous studies have primarily focused on the properties of surrogate gradients and their convergence, the influence of quantization hyperparameters, such as

Paul-Niklas Ken Kandora, Simon Caspar Zeller, Aaron Jeremias Elsing, Elena Kuss

Reformulating nonlinear optimization problems into solver-ready linear optimization problems is often necessary for practical applications, but the process is often manual and requires domain expertise. We propose LinearizeLLM, an agent-based LLM framework that produces solver-ready linear reformulations of nonlinear optimization problems. Agents first detec

M. M. Sheikh-Jabbari, V. Taghiloo

Freelance holography program is an extension of the gauge/gravity correspondence in which the boundary theory can reside on any timelike codimension-one surface in AdS space, and the boundary conditions on the bulk fields can be chosen arbitrarily. Freelance holography provides the framework for a systematic study of various boundary conditions and associate

Xuankai Zhang, Junjin Xiao, Qing Zhang

This paper presents a unified framework that allows high-quality dynamic Gaussian Splatting from both defocused and motion-blurred monocular videos. Due to the significant difference between the formation processes of defocus blur and motion blur, existing methods are tailored for either one of them, lacking the ability to simultaneously deal with both of th

Abdurakhmon Sadiev, Peter Richtárik, Ilyas Fatkhullin

Heavy-tailed noise is pervasive in modern machine learning applications, arising from data heterogeneity, outliers, and non-stationary stochastic environments. While second-order methods can significantly accelerate convergence in light-tailed or bounded-noise settings, such algorithms are often brittle and lack guarantees under heavy-tailed noise -- precise

Caorui Li, Yu Chen, Yiyan Ji, Jin Xu

Recent advances in multimodal large language models (MLLMs) have demonstrated substantial potential in video understanding. However, existing benchmarks fail to comprehensively evaluate synergistic reasoning capabilities across audio and visual modalities, often neglecting either one of the modalities or integrating them in a logically inconsistent manner. T

Kamal Diki

The Bargmann-Fock space(or Fock space for short) is a fundamental example of reproducing kernel Hilbert spaces that has found fascinating applications across multiple fields of current interest, including quantum mechanics, time-frequency analysis, mathematical analysis, and stochastic processes. In recent years, there has been increased interest in studying

Jun Chen, Shichao Hu, Jiuxin Lin, Wenjie Li

In-car multi-zone speech separation, which captures voices from different speech zones, plays a crucial role in human-vehicle interaction. Although previous SpatialNet has achieved notable results, its high computational cost still hinders real-time applications in vehicles. To this end, this paper proposes LSZone, a lightweight spatial information modeling

Yuanquan Liu, Thomas Speck, Isabella Fiorello

Plants are increasingly becoming a source of inspiration for robotics and engineers to develop bioinspired, adaptive, and multifunctional machines. In this study, we propose a bioinspired aquatic machine that mimics the fruit of the water caltrop (Trapa natans L.). Among various plant species, T. natans produces unique woody fruits that can disperse passivel

Teodora Maria Matei, Cristian Croitoru, Tiberiu Harko

We consider the implications of the modified dispersion relations, due to the noncommutativity of the spacetime, for a photon gas filling the early Universe in the framework of the Big Bang Nucleosynthesis (BBN) processes, during the period of light elements formation. We consider three types of deformations present in the dispersion relations for the radiat

Zeyu Cai

This paper investigates the exponential Diophantine equation of the form $a^x+b=c^y$, where $a, b, c$ are given positive integers with $a,c \ge 2$, and $x,y$ are positive integer unknowns. We define this form as a "Type-I transcendental diophantine equation." A general solution to this problem remains an open question; however, the ABC conjecture implies tha

Santhosh M, Jorge Dukelsky, Gerardo Ortiz

We present and analyze an exactly solvable interacting fermionic pairing model, which features interactions that entangle states at momenta $\mathbf{k}$ and $-\mathbf{k}$. These interactions give rise to novel correlated ground states, leading to a rich phase diagram that includes superconducting, multiple metallic, and Mott-insulating phases. At finite inte

Hussein Nassar

Holding a shell in their hands, one can apply six loads: three by pulling and shearing, and three by bending and twisting. Here, it is shown that the shell resists exactly three load cases and comply with the other three, provided the shell is simply connected, meaning it has no holes and no handles. Formally, it is shown that the space of homogeneous membra

Taicheng Guo, Hai Wang, ChaoChun Liu, Mohsen Golalikhani

Multi-turn Text-to-SQL aims to translate a user's conversational utterances into executable SQL while preserving dialogue coherence and grounding to the target schema. However, most existing systems only regard this task as a simple text translation task and follow a short-horizon paradigm, generating a query per turn without execution, explicit verification

Xinyu Yang, Zheheng Jiang, Feixiang Zhou, Yihang Zhu

Action understanding, encompassing action detection and anticipation, plays a crucial role in numerous practical applications. However, untrimmed videos are often characterized by substantial redundant information and noise. Moreover, in modeling action understanding, the influence of the agent's intention on the action is often overlooked. Motivated by thes

Zichun Yu, Chenyan Xiong

High-quality data is a cornerstone of large language model (LLM) pretraining, yet its growth has not kept pace with the needs of frontier models. In this paper, we introduce RePro, a novel web recycling method that trains a relatively small LM with reinforcement learning to generate effective and faithful rephrasings of pretraining data. Specifically, we des

Nassim Athmouni

We investigate discrete fractional Laplacians defined on the half-lattice in several dimensions, allowing possibly different fractional orders along each coordinate direction. By expressing the half-lattice operator as a boundary restriction of the full-lattice one plus a bounded correction that is relatively compact with respect to it, we show that both ope

Yuxiang Luo, Qing Xu, Hai Huang, Yuqi Ouyang

The 3D multi-modal brain tumor segmentation is critical to multi-modal healthcare, and it requires accurate identification of distinct internal anatomical subregions. While the recent prompt-based segmentation paradigms enable interactive experiences for clinicians, existing methods ignore cross-modal correlations and rely on labor-intensive category-specifi

Jørgen Ellegaard Andersen, Li Han, Yong Li, William Elbæk Mistegård

Let $X$ be a general Seifert fibered integral homology $3$-sphere with $r\ge3$ exceptional fibers. For every root of unity $\zeta\not=1$, we show that the SU(2) WRT invariant of $X$ evaluated at $\zeta$ is (up to an elementary factor) the non-tangential limit at $\zeta$ of the GPPV invariant of $X$, thereby generalizing a result from [Andersen-Mistegard 2022

Zhuowei Chen, Bowei Zhang, Nankai Lin, Tian Hou

Recent advances in LLMs have enhanced AI capabilities, but also increased the risk posed by malicious requests, highlighting the need for effective LLM safeguards to detect such queries. Existing approaches largely rely on classifier-based methods that lack interpretability and perform poorly on low-resource languages. To address these limitations, we propos

Deven Panchal

Generative Agentic AI systems are emerging as a powerful paradigm for automating complex, multi-step tasks. However, many existing frameworks for building these systems introduce significant complexity, a steep learning curve, and substantial boilerplate code, hindering rapid prototyping and deployment. This paper introduces simpliflow, a lightweight, open-s

Marco Origlia, Emanuele Parente, Marco Secondini

In continuous-variable quantum key distribution, information reconciliation is required to extract a shared secret key from correlated random variables obtained through the quantum channel. Reverse reconciliation (RR) is generally preferred, since the eavesdropper has less information about Bob's measurements than about Alice's transmitted symbols. When disc

Filippo Calderoni, Adam Clay

We analyze the classification problem for finitely generated orderable groups from the viewpoint of descriptive set theory. We analyze the standard Borel space of finitely generated left-orderable groups, and the subspace of finitely generated bi-orderable groups using spaces of relative cones. We use this setup to show that the isomorphism relation on finit

Md. Nahid Hasan, Sujana Azmi Polin, Saiful Islam, Chandra Nath Podder

The spreading and controlling of rumors have great impacts on our society. The transmission of infectious diseases and the spreading of rumors have some common scenarios. Like cross-infection propagation of diseases, two or many kinds of rumors or information may spread at the same time. In this paper, we propose a novel I2E2S2R rumor-spreading model in a ho

Jinxuan Li, Chaolei Tan, Haoxuan Chen, Jianxin Ma

Image-Language Foundation Models (ILFMs) have demonstrated remarkable success in vision-language understanding, providing transferable multimodal representations that generalize across diverse downstream image-based tasks. The advancement of video-text research has spurred growing interest in extending image-based models to the video domain. This paradigm, t

Yu Li, Menghan Xia, Gongye Liu, Jianhong Bai

Recent Text-to-Video (T2V) models have demonstrated powerful capability in visual simulation of real-world geometry and physical laws, indicating its potential as implicit world models. Inspired by this, we explore the feasibility of leveraging the video generation prior for viewpoint planning from given 4D scenes, since videos internally accompany dynamic s

Emmanuel Giroux

We prove that, for any Morse function on a compact manifold and any adapted gradient satisfying the Morse-Smale condition, there is a homotopically unique complex-valued symplectic Lefschetz fibration on the cotangent bundle whose restriction to the zero-section is the given function, whose imaginary part is the evaluation of covectors on the gradient, and w

Xiang Wang, Yuqing Zhang, Zhimin Zhang

This paper develops novel natural superconvergence and ultraconvergence structures for the bi-$k$-order finite volume element (FVE) method on rectangular meshes. These structures furnish tunable and possibly asymmetric superconvergence and ultraconvergence points. We achieve one-order-higher superconvergence for both derivatives and function values, and two-

Ligong Bian, Hongxin Wang, Yang Xiao, Ji-Chong Yang

The computation of bounce action in a phase transition involves solving partial differential equations, inherently introducing non-negligible numerical uncertainty. Deriving characteristic temperatures and properties of this transition necessitates both differentiation and integration of the action, thereby exacerbating the uncertainty. In this work, we fit

Tao Yu, Zhengbo Zhang, Zhiheng Lyu, Junhao Gong

Efficiently solving real-world problems with LLMs increasingly hinges on their ability to interact with dynamic web environments and autonomously acquire external information. While recent research like Search-R1 and WebDancer demonstrates strong performance in solving web tasks, they heavily rely on additional tools to convert the interactive web environmen

Qiang Xu, Xiangyu Wen, Changran Xu, Zeju Li

The advent of powerful Large Language Models (LLMs) has ushered in an ``Age of the Agent,'' enabling autonomous systems to tackle complex goals. However, the transition from prototype to production is hindered by a pervasive ``crisis of craft,'' resulting in agents that are brittle, unpredictable, and ultimately untrustworthy in mission-critical applications

Ilias Diakonikolas, Chao Gao, Daniel M. Kane, John Lafferty

We study the task of noiseless linear regression under Gaussian covariates in the presence of additive oblivious contamination. Specifically, we are given i.i.d.\ samples from a distribution $(x, y)$ on $\mathbb{R}^d \times \mathbb{R}$ with $x \sim \mathcal{N}(0,\mathbf{I}_d)$ and $y = x^\top \beta + z$, where $z$ is drawn independently of $x$ from an unknow

Henryk Witała

We investigate the pure polarization states of the outgoing neutron deuteron pair in elastic polarized neutron polarized deuteron scattering, as well as the pure polarization states of the three free nucleons produced in the corresponding deuteron breakup reaction. Our aim is to provide clear evidence of entanglement in their spin degrees of freedom. These f

Gaojian Wang, Feng Lin, Tong Wu, Zhisheng Yan

With abundant, unlabeled real faces, how can we learn robust and transferable facial representations to boost generalization across various face security tasks? We make the first attempt and propose FS-VFM, a scalable self-supervised pre-training framework, to learn fundamental representations of real face images. We introduce three learning objectives, name

Fabio Bagarello, Hiroshi Inoue, Camillo Trapani, Salvatore Triolo

Motivated by some recent results, we consider the notion of eigenstate (and eigenvalue) for an element $X$ of a CQ*-algebras and the consequences on algebraic quantum dynamics and on its related derivations are investigated.

Omid Reza Heidari, Siobhan Reid, Yassine Yaakoubi

LLMs have advanced text-to-SQL generation, yet monolithic architectures struggle with complex reasoning and schema diversity. We propose AGENTIQL, an agent-inspired multi-expert framework that combines a reasoning agent for question decomposition, a coding agent for sub-query generation, and a refinement step for column selection. An adaptive router further

Hao Shan, Ruikai Li, Han Jiang, Yizhe Fan

As one of the fundamental modules in autonomous driving, online high-definition (HD) maps have attracted significant attention due to their cost-effectiveness and real-time capabilities. Since vehicles always cruise in highly dynamic environments, spatial displacement of onboard sensors inevitably causes shifts in real-time HD mapping results, and such insta

Thomas Schweser, Michael Stiebitz, Bjarne Toft

In 1934 L. R\'edei published his famous theorem that the number of Hamiltonian paths in a tournament is odd. In fact it is a corollary of a stronger theorem in his paper. Stronger theorems were also obtained in the early 1970s by G.A. Dirac in his lectures at Aarhus University and by C. Berge in his monographs on graphs and hypergraphs. We exhibit the strong

Guy Mor-Lan, Tamir Sheafer, Shaul R. Shenhav

While media bias is widely studied, the epistemic strategies behind factual reporting remain computationally underexplored. This paper analyzes these strategies through a large-scale comparison of CNN and Fox News. To isolate reporting style from topic selection, we employ an article matching strategy to compare reports on the same events and apply the FactA

Luiz P. de Oliveira, Alexandre P. S. Souza, Carlos G. S. Santos, Iberê R. S. Júnior

The multipurpose nature of nuclear research reactors has been investigated. This class of reactors has gained momentum within the international community, given the multiple benefits of nuclear technology in medicine, agriculture, industry, and the development of new materials, being characterized by the simultaneous use of several applications. In this cont

Mengqing Zhang, Shiyi Li, Dongmi Luo, Jianxian Qiu

In this paper, a uniformly high-order discontinuous Galerkin gas kinetic scheme (DG-HGKS) is proposed to solve the Euler equations of compressible flows. The new scheme is an extension of the one-stage compact and efficient high-order GKS (CEHGKS, Li et al. , 2021. J. Comput. Phys. 447, 110661) in the finite volume framework. The main ideas of the new scheme

Sheikh Azizul Hakim, Kowshic Roy, M Saifur Rahman

Large pretrained language models have transformed natural language processing, and their adaptation to protein sequences -- viewed as strings of amino acid characters -- has advanced protein analysis. However, the distinct properties of proteins, such as variable sequence lengths and lack of word-sentence analogs, necessitate a deeper understanding of protei

Seyed Mohammad Hassan Erfani, Kara Lamb, Susanne Bauer, Kostas Tsigaridis

Interactive composition simulations in Earth System Models (ESMs) are computationally expensive as they transport numerous gaseous and aerosol tracers at each timestep. This limits higher-resolution transient climate simulations with current computational resources. ESMs like NASA GISS-ModelE3 (ModelE) often use pre-computed monthly-averaged atmospheric comp

Sebastian Schmidt, Julius Körner, Stephan Günnemann

For high-stakes applications, like autonomous driving, a safe operation is necessary to prevent harm, accidents, and failures. Traditionally, difficult scenarios have been categorized into corner cases and addressed individually. However, this example-based categorization is not scalable and lacks a data coverage perspective, neglecting the generalization to

Kang Lu, Weiqiang Wang, Alex Weekes

In a prequel we introduced the shifted iYangians ${}^\imath Y_μ$ associated to quasi-split Satake diagrams of type ADE and even spherical coweights $μ$, and constructed the iGKLO representations of ${}^\imath Y_μ$, which factor through truncated shifted iYangians ${}^\imath Y_μ^λ$. In this paper, we show that ${}^\imath Y_μ$ quantizes the involutive fixed po

Mohammad Hassan, Mads R. Almassalkhi

Coordination of distributed energy resources (DERs) can engender flexibility necessary to improve grid reliability. Packetized Energy Management (PEM) is a method for coordinating DERs, such as thermostatically controlled loads (TCLs) and electric vehicles, within customer quality-of-service (QoS) limits. In PEM, a DER uses local information to offer flexibi

Peiyin Chen, Zhuowei Yang, Hui Feng, Sheng Jiang

Audio-driven talking-head generation has advanced rapidly with diffusion-based generative models, yet producing temporally coherent videos with fine-grained motion control remains challenging. We propose DEMO, a flow-matching generative framework for audio-driven talking-portrait video synthesis that delivers disentangled, high-fidelity control of lip motion

Can Xie, Ruotong Pan, Xiangyu Wu, Yunfei Zhang

Reinforcement Learning with Verifiable Rewards (RLVR) has shown significant promise for enhancing the reasoning capabilities of large language models (LLMs). However, prevailing algorithms like GRPO broadcast a uniform advantage signal across all tokens in a sequence. This coarse-grained approach overlooks the pivotal role of uncertain, high-stakes decisions

Chenlong He, Zhijian Hao, Leilei Huang, Xiaoyang Zeng

Just Noticeable Distortion (JND)-guided pre-filter is a promising technique for improving the perceptual compression efficiency of image coding. However, existing methods are often computationally expensive, and the field lacks standardized benchmarks for fair comparison. To address these challenges, this paper introduces a twofold contribution. First, we de

Xixiang Zhang, Busheng Wang, Yanfeng Ge, Yong Liu

Inspired by the recently synthesized hexagonal layered phase of FeF$_2$, we studied the magnetic properties of the 1T-FeF$_2$ monolayer and its Janus Fe$X$F ($X$ = O, S) derivatives by first-principles calculations. Our results confirm that these materials are antiferromagnetic semiconductors, and that anion substitution effectively tunes their material prop

Michal Sadowski, Tadija Radusinović, Maria Wyrzykowska, Lukasz Sztukiewicz

Retrosynthesis is one of the domains transformed by the rise of generative models, and it is one where the problem of nonsensical or erroneous outputs (hallucinations) is particularly insidious: reliable assessment of synthetic plans is time-consuming, with automatic methods lacking. In this work, we present RetroTrim, a retrosynthesis system that successful

Yi Zhang, Yushen Long, Yun Ni, Liping Huang

Online ride-hailing platforms aim to deliver efficient mobility-on-demand services, often facing challenges in balancing dynamic and spatially heterogeneous supply and demand. Existing methods typically fall into two categories: reinforcement learning (RL) approaches, which suffer from data inefficiency, oversimplified modeling of real-world dynamics, and di

Wu-Long Xu, Jin Min Yang, Jun Zhao

In this work we consider the scattering between non-relativistic particles with different finite sizes. We first calculate their interaction potential and apply the partial wave method to obtain their scattering cross section. Our findings show that the particle size can significantly affect the scattering between non-relativistic particles. Then we apply su

Piyush Pant, Marcellius William Suntoro, Ayesha Siddiqua, Muhammad Shehryaar Sharif

This paper presents EA-GeoAI, an integrated framework for demand forecasting and equitable hospital planning in Germany through 2030. We combine district-level demographic shifts, aging population density, and infrastructure balances into a unified Equity Index. An interpretable Agentic AI optimizer then allocates beds and identifies new facility sites to mi

Haemin Choi, Gayathri Nadarajan

Although student learning satisfaction has been widely studied, modern techniques such as interpretable machine learning and neural networks have not been sufficiently explored. This study demonstrates that a recent model that combines boosting with interpretability, automatic piecewise linear regression(APLR), offers the best fit for predicting learning sat

Aminata Sow, Tidiane Diallo

This article explores the application of various artificial intelligence techniques to the analysis of near-infrared (NIR) spectra of paracetamol, within the spectral range of 900 nm to 1800 nm. The main objective is to evaluate the performance of several dimensionality reduction algorithms; namely, Principal Component Analysis (PCA), Kernel PCA (KPCA), Spar

Haoyu Zhao, Cheng Zeng, Linghao Zhuang, Yaxi Zhao

The scalability of robotic learning is fundamentally bottlenecked by the significant cost and labor of real-world data collection. While simulated data offers a scalable alternative, it often fails to generalize to the real world due to significant gaps in visual appearance, physical properties, and object interactions. To address this, we propose RoboSimGS,

Viktoria Brandt, Michele Pugini, Nikolas Kaltsoyannis, Gustavo Garcia

We present an investigation of one-photon valence-shell photoelectron spectroscopy and photoelectron circular dichroism (PECD) for the chiral molecule (1R,4R)-3-(heptafluorobutyryl)-(+)-camphor (HFC) and its europium complex Eu(III) tris[3-(heptafluorobutyryl)-(1R,4R)-camphorate] (Eu-HFC$_{3}$), the latter of which constitutes the heaviest organometallic mol

Dengke Chen, Zeying Zhong, Sanli Huang, Jiahao Sun

Efficient and reliable light coupling between optical fibers and photonic integrated circuits has arguably been the most essential issue in integrated photonics for optical interconnects, nonlinear signal conversion, neuromorphic computing, and quantum information processing. A commonly used approach is to use inverse tapers interfacing with lensed fibers, p

Shaoning Li, Le Zhuo, Yusong Wang, Mingyu Li

Developing effective representations of protein structures is essential for advancing protein science, particularly for protein generative modeling. Current approaches often grapple with the complexities of the SE(3) manifold, rely on discrete tokenization, or the need for multiple training objectives, all of which can hinder the model optimization and gener

Jiabao Shi, Minfeng Qi, Lefeng Zhang, Di Wang

Multimodal text-to-image generation remains constrained by the difficulty of maintaining semantic alignment and professional-level detail across diverse visual domains. We propose a multi-agent reinforcement learning framework that coordinates domain-specialized agents (e.g., focused on architecture, portraiture, and landscape imagery) within two coupled sub

Zhao-Fan Cai, Tao Liu

The well-established non-Bloch band theory predicts exponential localization of skin-mode eigenstates in one-dimensional (1D) non-Hermitian systems. Recent studies, however, have uncovered anomalous algebraic localization in higher dimensions. Here, we extend these ideas to Hermitian bosonic quadratic Hamiltonians incorporating quantum squeezing, offering a

Zhaolin Hu, Kun Li, Hehe Fan, Yi Yang

Linear attention mechanisms have emerged as efficient alternatives to full self-attention in Graph Transformers, offering linear time complexity. However, existing linear attention models often suffer from a significant drop in expressiveness due to low-rank projection structures and overly uniform attention distributions. We theoretically prove that these p

Hao Zhuang

This note is a follow-up to our previous work arXiv:2505.14496. For any (4n+2)-dimensional closed symplectic manifold, we find that the dimension of the even-degree part of its 1-filtered cohomology is even, similar to the vanishing property of the classical Euler characteristic of an odd-dimensional closed manifold. We prove our result by constructing and t

Vladislav Popkov, Mario Salerno

We demonstrate that Liouvillian exceptional points (LEPs), previously explored only in continuous Lindbladian dynamics, also emerge in discrete brickwork completely positive trace-preserving (CPTP) circuits. By analytically solving a minimal two-qubit brickwork model, we identify the conditions under which discrete-time LEPs arise and show that they retain t

Guang Zhao, Yue Chang, Jinxian Zhang, Linghui Wu

Particle identification (PID) is essential for future particle physics experiments such as the Circular Electron-Positron Collider and the Future Circular Collider. A high-granularity Time Projection Chamber (TPC) not only provides precise tracking but also enables dN/dx measurements for PID. The dN/dx method estimates the number of primary ionization electr

Guy Mor-Lan, Tamir Sheafer, Shaul R. Shenhav

How is a factual claim made credible? We propose the novel task of Epistemic Appeal Identification, which identifies whether and how factual statements have been anchored by external sources or evidence. To advance research on this task, we present FactAppeal, a manually annotated dataset of 3,226 English-language news sentences. Unlike prior resources that

M. T. Xia, J. R. Long, X. X. Xiang

The existence of subnormal solutions of following three difference equations with Schwarzian derivative $$\omega(z+1)-\omega(z-1)+a(z)(S(\omega,z))^n=R(z,\omega(z)),$$ $$\omega(z+1)\omega(z-1)+a(z)S(\omega,z)=R(z,\omega(z)),$$ and $$(\omega(z)\omega(z+1)-1)(\omega(z)\omega(z-1)-1)+a(z)S(\omega,z)=R(z,\omega(z))$$ are studied by using Nevanlinna theory, where

Yuval Golbari, Navve Wasserman, Gal Vardi, Michal Irani

Determining which data samples were used to train a model, known as Membership Inference Attack (MIA), is a well-studied and important problem with implications on data privacy. SotA methods (which are black-box attacks) rely on training many auxiliary reference models to imitate the behavior of the attacked model. As such, they rely on assumptions which rar

Ahmed J. Abdelmaksoud, Cristian Sestito, Shiwei Wang, Themis Prodromakis

Transformers are at the core of modern AI nowadays. They rely heavily on matrix multiplication and require efficient acceleration due to their substantial memory and computational requirements. Quantization plays a vital role in reducing memory usage, and can be exploited for computations by designing reconfigurable architectures that enhance matrix multipli

Kaito Ohtani, Hiroki Kawabe, Kentaro Yaji, Kikuo Fujita

Triply Periodic Minimal Surface (TPMS) structures are attracting growing attention as promising geometries for next-generation high-performance heat exchangers (HXs), due to their continuous flow paths and high surface-area-to-volume ratio that enhance heat transfer performance. Among these, graded TPMS structures with spatially varying thickness have emerge

Hanbing Liu, Yanru Wu, Yang Li, Ercan E. Kuruoglu

Lithium-ion batteries are becoming increasingly omnipresent in energy supply. However, the durability of energy storage using lithium-ion batteries is threatened by their dropping capacity with the growing number of charging/discharging cycles. An accurate capacity prediction is the key to ensure system efficiency and reliability, where the exploitation of b

Chenyu Jiang, Zhenkun Cai, Ye Tian, Zhen Jia

Context parallelism has emerged as a key technique to support long-context training, a growing trend in generative AI for modern large models. However, existing context parallel methods rely on static parallelization configurations that overlook the dynamic nature of training data, specifically, the variability in sequence lengths and token relationships (i.

Maximos Kaliakatsos-Papakostas, Gregoris Bastas, Dimos Makris, Dorien Herremans

Guitar tablature transcription consists in deducing the string and the fret number on which each note should be played to reproduce the actual musical part. This assignment should lead to playable string-fret combinations throughout the entire track and, in general, preserve parsimonious motion between successive combinations. Throughout the history of guita

Bowei He, Lihao Yin, Huiling Zhen, Shuqi Liu

Post-training compression has been a widely employed approach to scale down large language model (LLM) and facilitate efficient inference. In various proposed compression methods, including pruning and quantization, calibration data plays a vital role by informing the weight importance and activation dynamic ranges. However, how calibration data impacts the

Bahadur Yadav, Sanjay Kumar Mohanty

Forecasting stock prices remains challenging due to the volatile and non-linear nature of financial markets. Despite the promise of deep learning, issues such as mode collapse, unstable training, and difficulty in capturing temporal and feature level correlations have limited the applications of GANs in this domain. We propose a GRU-based Encoder-Decoder GAN

Matan Solomon, Ofra Amir, Omer Ben-Porat

Reinforcement learning agents are often updated with human feedback, yet such updates can be unreliable: reward misspecification, preference conflicts, or limited data may leave policies unchanged or even worse. Because policies are difficult to interpret directly, users face the challenge of deciding whether an update has truly helped. We propose that asses

Hongjie Dong, Haigang Li, Yan Zhao

This paper studies field concentration between two nearly touching conductors separated by imperfect low-conductivity interfaces, modeled by Robin boundary conditions. It is known that for any sufficiently small interfacial bonding parameter $\gamma > 0$, the gradient remains uniformly bounded with respect to the separation distance $\varepsilon$. In contras

Robert G. Cowell

A probabilistic clustering algorithm is proposed for the analysis of forensic DNA mixtures in which individual cells are isolated and short tandem repeats are amplified using the polymerase chain reaction to generate single cell electropherograms. The task of the algorithm is to use the peak height information in the electropherograms to group the cells acco

Di Wu, Shuaidong Pan

This paper proposes a modeling framework for dynamic topic evolution based on temporal large language models. The method first uses a large language model to obtain contextual embeddings of text and then introduces a temporal decay function and an attention mechanism. These components allow the model to adjust the importance of semantic units according to ti

Felix Duelmer, Mohammad Farid Azampour, Nassir Navab

Traditional ultrasound simulation methods solve wave equations numerically, achieving high accuracy but at substantial computational cost. Faster alternatives based on convolution with precomputed impulse responses remain relatively slow, often requiring several minutes to generate a full B-mode image. We introduce UltraScatter, a probabilistic ray tracing f

Heng Zhang, Yuling Shi, Xiaodong Gu, Zijian Zhang

Recent advances in large language model-powered multi-agent systems have demonstrated remarkable collective intelligence through effective communication. However, existing approaches face two primary challenges: (i) \textit{Ineffective group collaboration modeling}, as they rely on pairwise edge representations in graph structures, limiting their ability to

M. Yin, D. D. Vvedensky

Triply periodic minimal surfaces (TPMS) are discovered to conform to surfaces of given charge density distributions embedded in crystals [Z. Kristallogr. \textbf{170}, 138 (1985)]. Based on our previous work [Phys. Rev. Mater. \textbf{9}, 073802 (2025)], we discovered that crystals can have surfaces of a given charge density converging to TPMS. We also disco

Yiting Lu, Fengbin Guan, Yixin Gao, Yan Zhong

Current visual evaluation approaches are typically constrained to a single task. To address this, we propose OmniQuality-R, a unified reward modeling framework that transforms multi-task quality reasoning into continuous and interpretable reward signals for policy optimization. Inspired by subjective experiments, where participants are given task-specific in

Danil A. Vassiliev

We describe rank 2 Gieseker semistable sheaves $E$ on the Fano threefold $X_5$ of index 2 and degree 5 with maximal third Chern class $c_3(E)$ for all possible low values of discriminant $\overline{\Delta}_H(E)\le 40$. The work uses the theory of tilt-stability and Bridgeland stability conditions on smooth projective threefolds. We also make a conjecture abo

Jaime E. Pineda, Rachel K. Friesen, Erik Rosolowsky, Ana Chacón-Tanarro

We present an overview of the final data release (DR2) from the Green Bank Ammonia Survey (GAS). GAS is a Large Program at the Green Bank Telescope to map all Gould Belt star-forming regions with $A_\mathrm{V} \gtrsim 7$~mag visible from the northern hemisphere in emission from NH$_3$ and other key molecular tracers. This final release includes the data for

Zhen Huang, Hong Wang, Wenkai Yang, Muxi Tang

Thermal management in 3D ICs is increasingly challenging due to higher power densities. Traditional PDE-solving-based methods, while accurate, are too slow for iterative design. Machine learning approaches like FNO provide faster alternatives but suffer from high-frequency information loss and high-fidelity data dependency. We introduce Self-Attention U-Net

Yuqi Liu, Liangyu Chen, Jiazhen Liu, Mingkang Zhu

Post-training Large Vision-and-Language Models (LVLMs) typically involves Supervised Fine-Tuning (SFT) for knowledge injection or Reinforcement Learning with Verifiable Rewards (RLVR) for performance enhancement. However, SFT often leads to sub-optimal performance, while RLVR remains constrained by the model's internal knowledge base. While a sequential SFT

MohammadHossein Bateni, Hossein Esfandiari, Samira HosseinGhorban, Alireza Mirrokni

Building learning models frequently requires evaluating numerous intermediate models. Examples include models considered during feature selection, model structure search, and parameter tunings. The evaluation of an intermediate model influences subsequent model exploration decisions. Although prior knowledge can provide initial quality estimates, true perfor

Yuheng Chen, Dingkun Liu, Xinyao Yang, Xinping Xu

Brain-computer interfaces (BCIs) provide potential for applications ranging from medical rehabilitation to cognitive state assessment by establishing direct communication pathways between the brain and external devices via electroencephalography (EEG). However, EEG-based BCIs are severely constrained by data scarcity and significant inter-subject variability

WenTao Liu, Siyu Song, Hao Hao, Aimin Zhou

In recent years, large language models (LLMs) have made remarkable progress, with model optimization primarily relying on gradient-based optimizers such as Adam. However, these gradient-based methods impose stringent hardware requirements, demanding high-concurrency, high-memory GPUs. Moreover, they require all neural network operations to be differentiable,

Zhengyi Zhong, Wenzheng Jiang, Weidong Bao, Ji Wang

Conventional federated learning (FL) assumes a closed world with a fixed total number of clients. In contrast, new clients continuously join the FL process in real-world scenarios, introducing new knowledge. This raises two critical demands: detecting new knowledge, i.e., knowledge discovery, and integrating it into the global model, i.e., knowledge adaptati

Zhuoheng Gao, Jiyao Zhang, Zhiyong Xie, Hao Dong

Most robotic grasping systems rely on converting sensor data into explicit 3D point clouds, which is a computational step not found in biological intelligence. This paper explores a fundamentally different, neuro-inspired paradigm for 6-DoF grasp detection. We introduce SpikeGrasp, a framework that mimics the biological visuomotor pathway, processing raw, as

Dorian Martino, Tristan Rivière

We prove that any weak immersion in the critical Sobolev space $W^{\frac{n}{2}+1,2}(\mathbb{R}^n;\mathbb{R}^d)$ in even dimension $n\geq 4$, has global harmonic coordinates if its second fundamental form is small in the Sobolev space $W^{\frac{n}{2}-1,2}(\mathbb{R}^n;\mathbb{R}^d)$. This is a generalization to arbitrary even dimension $n\ge 4$ of a famous re

Gang Liu

We study rigidity on certain K\"ahler manifolds with nonnegative Ricci curvature. Among others things, we show that a complete noncompact K\"ahler surface with nonnegative Ricci curvature, Euclidean volume growth and quadratic curvature decay is biholomorphic to resolution of an affine algebraic variety.

Bahadur Yadav, Sanjay Kumar Mohanty

In this study, we propose a novel model framework that integrates deep neural networks with the Ridgelet Transform. The Ridgelet Transform on Borel measurable functions is used for arbitrage detection on high-dimensional sparse structures. This transform also enhances the expressive power of neural networks, enabling them to capture complex and high-dimensio

Karin Ikeda

In 1997, B\'aez-Duarte gave a probabilistic proof of the asymptotic formula for the partition function, which had originally been proved by Hardy-Ramanujan. Based on the probabilistic approach, this paper proves an asymptotic formula for the coefficients of the elliptic modular $j$-function using various expressions in terms of modular functions having simpl

David Rodríguez-Martínez, C. J. Pérez del Pulgar

Improving robotic navigation is critical for extending exploration range and enhancing operational efficiency. Vision-based navigation relying on traditional CCD or CMOS cameras faces major challenges when complex illumination conditions are paired with motion, limiting the range and accessibility of mobile planetary robots. In this study, we propose a novel

Ruolan Cheng, Yong Deng, Serafín Moral, José Ramón Trillo

Random permutation set (RPS) is a recently proposed framework designed to represent order-structured uncertain information. Measuring the distance between permutation mass functions is a key research topic in RPS theory (RPST). This paper conducts an in-depth analysis of distances between RPSs from two different perspectives: random finite set (RFS) and tran

Hisakazu Matsuki, Guang Yang, Jiahui Xu, Vitaly N. Golovach

A change in a materials electrical resistance with magnetic field (magnetoresistance) results from quantum interference effects and, or spin-dependent transport, depending on materials properties and dimensionality. In disordered conductors, electron interference leads to weak localization or anti-localization; in contrast, ferromagnetic conductors support s