October 2025 arXiv papers — page 144

Yangyu Lu, Yifei Huang, Dong An, Qi Zhao

Adiabatic evolution is a central paradigm in quantum physics. Digital simulations of adiabatic processes are generally viewed as costly, since algorithmic errors typically accumulate over the long evolution time, requiring exceptionally deep circuits to maintain accuracy. This work demonstrates that digital adiabatic evolution is intrinsically accurate and r

Laura K. Currie, Chris A. Jones

A mechanism by which the surface zonal flows of giant planets can be gradually attenuated with depth is explored. The zonal flow is driven by an imposed forcing in a thin layer near the surface. A meridional circulation is set up, analogous to the Ferrel-like cells observed in Jupiter's atmosphere. Acting on a stably stratified thin surface layer, the meridi

Tiancheng Yang, Tao Wang, Ke Xu, Hanwen Sun

Recent JWST observations have revealed a large population of quiescent galaxies (QGs) at high redshift ($z \sim 4-8$), challenging current models of early galaxy formation and quenching. Accurate number density estimates are crucial but remain uncertain. We present a systematic study of QGs at $0.5 < z < 8$ using a mass-complete sample from the JWST/PRIMER s

Paloma E S Pellegrini, Francisco T Orlandini, Silvia V G Nista, Stéphane Lanteri

Thermal scanning probe lithography offers high resolution and versatility, making it a promising alternative for fabricating photonic devices. Here, we introduce a new method that expands its applications by enabling direct fabrication of arbitrary perforated patterns on a silver film.

Dongsen Zhang, Zekun Li, Xu Luo, Xuannan Liu

The Model Context Protocol (MCP) standardizes how large language model (LLM) agents discover, describe, and call external tools. While MCP unlocks broad interoperability, it also enlarges the attack surface by making tools first-class, composable objects with natural-language metadata, and standardized I/O. We present MSB (MCP Security Benchmark), the first

Bowen Fan, Zhilin Guo, Xunkai Li, Yihan Zhou

Graph Neural Networks (GNNs) have become a pivotal framework for modeling graph-structured data, enabling a wide range of applications from social network analysis to molecular chemistry. By integrating large language models (LLMs), text-attributed graphs (TAGs) enhance node representations with rich textual semantics, significantly boosting the expressive p

Jonathan Dransfeld, Marvin Künnemann, Mirza Redzic, Marcus Wunderlich

The \emph{Dominating $H$-Pattern} problem generalizes the classical $k$-Dominating Set problem: for a fixed \emph{pattern} $H$ and a given graph $G$, the goal is to find an induced subgraph $S$ of $G$ such that (1) $S$ is isomorphic to $H$, and (2) $S$ forms a dominating set in $G$. Fine-grained complexity results show that on worst-case inputs, any signific

Victor Besnier, David Hurych, Andrei Bursuc, Eduardo Valle

Recent advances in image and video generation have raised significant interest from both academia and industry. A key challenge in this field is improving inference efficiency, as model size and the number of inference steps directly impact the commercial viability of generative models while also posing fundamental scientific challenges. A promising directio

Arup Chattopadhyay, Supratim Jana

We present the notion of the nearly dual compressed shift-invariant subspaces of the orthogonal complement of the model space and obtain their structure using Hitt&#39;s algorithm \cite{DH}.

Shunsuke Onoo, Yoshihiro Nagano, Yukiyasu Kamitani

Sensory representation is typically understood through a hierarchical-causal framework where progressively abstract features are extracted sequentially. However, this causal view fails to explain misrepresentation, a phenomenon better handled by an informational view based on decodable content. This creates a tension: how does a system that abstracts away de

Roberto Flores, Jerome Daquin, Mauro Pontani, Hadi Susanto

Uncontrolled geostationary satellites abandoned near an unstable equilibrium point of the equator experience irregular transitions between dynamical states (continuous circulation, long and short libration). They are caused by the interaction between the longitudinal dynamics, governed by the tesseral harmonics of the geopotential, and the orbital precession

Pengcheng Li, Manzi Nan, Haojie Zhang, Junhuai Xu

Revealing the neutron density distribution in the nucleus is one of the crucial tasks of nuclear physics. Within the framework of the ultrarelativistic quantum molecular dynamic model followed by a correlation afterburner program, we investigate the effects of the initial neutron density distribution on the charged-pion yield ratio $\pi^{-}/\pi^{+}$, the two

Hritik Bansal, Devendra Singh Sachan, Kai-Wei Chang, Aditya Grover

Recent advances in vision-language models (VLMs) have made them highly effective at reasoning tasks. However, the principles underlying the construction of performant VL reasoning training datasets remain poorly understood. In this work, we introduce several data curation approaches and study their impacts on VL reasoning capabilities by carefully controllin

Yuechun Yu, Han Ying, Haoan Jin, Wenjian Jiang

The reliable evaluation of large language models (LLMs) in medical applications remains an open challenge, particularly in capturing the complexity of multi-turn doctor-patient interactions that unfold in real clinical environments. Existing evaluation methods typically rely on post hoc review of full conversation transcripts, thereby neglecting the dynamic,

Arup Chattopadhyay, Supratim Jana

We introduce the notion of the Dual Truncated Hankel Operator (DTHO) and provide several operator equation characterizations using the dual compressed shift operator. These characterizations are similar to classical results concerning Hankel operators and align with recent findings related to Truncated Hankel Operators (THO) \cite{GM}. Additionally, our work

Jack Harrison, Junxiong Hu, Charles Godfrey, Jheng-Cyuan Lin

Antiferromagnetic materials are promising platforms for the development of ultra-fast spintronics and magnonics due to their robust magnetism, high-frequency relativistic dynamics, low-loss transport, and the ability to support topological textures. However, achieving deterministic control over antiferromagnetic order in thin films is a major challenge, due

Xue-Mei Li, Xianfeng Ren

We study the singular stochastic wave equation on $\mathbb T^2$, with a cubic nonlinearity and Gaussian rough Mat\'ern forcing (a Fourier multiplier of order $\alpha>0$ applied to space-time white noise) and establish local well-posedness for $\alpha < \tfrac{3}{8}$. This extends [GKO18] beyond white noise and strengthens the quadratic-case result [OO21] ($\

Hanru Bai, Weiyang Ding, Difan Zou

Diffusion models have achieved impressive success in high-fidelity image generation but suffer from slow sampling due to their inherently iterative denoising process. While recent one-step methods accelerate inference by learning direct noise-to-image mappings, they sacrifice the interpretability and fine-grained control intrinsic to diffusion dynamics, key

Vu Tram Anh Khuong, Luu Tu Nguyen, Thi Bich Phuong Man, Thanh Ha Le

Micro-expressions are brief, involuntary facial movements that typically last less than half a second and often reveal genuine emotions. Accurately recognizing these subtle expressions is critical for applications in psychology, security, and behavioral analysis. However, micro-expression recognition (MER) remains a challenging task due to the subtle and tra

Hyunji Min, Sangwon Jung, Junyoung Sung, Dosung Lee

Current approaches rely on zero-shot evaluation due to the absence of training data; while proprietary models such as GPT-4 exhibit strong reasoning capabilities, smaller open-source models remain ineffective at complex tool use. To address this limitation, we propose a novel training framework GOAT, that enables fine-tuning LLM agents without human annotati

Ali Mekky, Omar El Herraoui, Preslav Nakov, Yuxia Wang

Large language models (LLMs) are increasingly deployed across high-impact domains, from clinical decision support and legal analysis to hiring and education, making fairness and bias evaluation before deployment critical. However, existing evaluations lack grounding in real-world scenarios and do not account for differences in harm severity, e.g., a biased d

Wen-Jing Zhang, Xin Liu

We perform an improved perturbative QCD study of the decays $B_c^+ \to \eta_c L^+$,where $L$ denotes the light ground-state pseudoscalar, vector mesons and the corresponding $p$-wave scalar, axial-vector, and tensor ones, and predict their branching ratios (BRs) associated with relative ratios at leading order in the strong coupling $\alpha_s$. Our results $

Chanwoo Kim, Jihwan Yoon, Hyeonseong Kim, Taemoon Jeong

Mobile robot navigation in dynamic human environments requires policies that balance adaptability to diverse behaviors with compliance to safety constraints. We hypothesize that integrating data-driven rewards with rule-based objectives enables navigation policies to achieve a more effective balance of adaptability and safety. To this end, we develop a frame

Tao Xie, Zexi Tan, Haoyi Xiao, Binbin Sun

Early Time Series Classification (ETSC) is critical in time-sensitive medical applications such as sepsis, yet it presents an inherent trade-off between accuracy and earliness. This trade-off arises from two core challenges: 1) models should effectively model inherently weak and noisy early-stage snippets, and 2) they should resolve the complex, dual require

Giuseppe Greco, Thomas Boch, Pierre Fernique, Manon Marchand

Context. The Multi-Order Coverage map (MOC) is a widely adopted standard promoted by the International Virtual Observatory Alliance (IVOA) to support data sharing and interoperability within the Virtual Observatory (VO) ecosystem. This hierarchical data structure efficiently encodes and visualizes irregularly shaped regions of the sky, enabling applications

Mi-Ryang Kim, Jeong-Eun Lee, Contreras Peña Carlos, Gregory Herczeg

Time-domain studies of mid-infrared and submillimeter variability have shown that at least half of protostars are variable. We present a statistical analysis of mid-infrared variability among young stellar objects (YSOs) in the distant, massive star-forming region W51 using NEOWISE data. From a catalog of 81 protostars, 527 disk objects, and 37,687 other sou

Junfei Tan, Yuxin Chen, An Zhang, Junguang Jiang

Recent breakthroughs in large language models (LLMs) have fundamentally shifted recommender systems from discriminative to generative paradigms, where user behavior modeling is achieved by generating target items conditioned on historical interactions. Yet current generative recommenders still suffer from two core limitations: the lack of high-quality negati

Yakun Song, Xiaobin Zhuang, Jiawei Chen, Zhikang Niu

Recent attempts to interleave autoregressive (AR) sketchers with diffusion-based refiners over continuous speech representations have shown promise, but they remain brittle under distribution shift and offer limited levers for controllability. We introduce DISTAR, a zero-shot text-to-speech framework that operates entirely in a discrete residual vector quant

Yiming Zhang, Chester Holtz, Gal Mishne, Alex Cloninger

Learning with noisy labels remains challenging because over-parameterized networks memorize corrupted supervision. Meta-learning-based sample reweighting mitigates this by using a small clean subset to guide training, yet its behavior and training dynamics lack theoretical understanding. We provide a rigorous theoretical analysis of meta-reweighting under la

Muammer Bay, Timo von Marcard, Dren Fazlija

Recent advances in generative AI, particularly in computer vision (CV), offer new opportunities to optimize workflows across industries, including logistics and manufacturing. However, many AI applications are limited by a lack of expertise and resources, which forces a reliance on general-purpose models. Success with these models often requires domain-speci

Oliver Thim, Andreas Ekström, Christian Forssén

We extend previous studies of the deuteron and triton ground-state energies to next-to-next-to-leading order (N$^2$LO) in chiral effective field theory, employing a power counting in which subleading interactions are treated perturbatively. Triton calculations are performed using the no-core shell model, and we demonstrate converged perturbative results for

Pin-Lun Chen, Chi-Hsi Kung, Che-Han Chang, Wei-Chen Chiu

Evaluating the safety of autonomous vehicles (AVs) requires diverse, safety-critical scenarios, with collisions being especially important yet rare and unsafe to collect in the real world. Therefore, the community has been focusing on generating safety-critical scenarios in simulation. However, controlling attributes such as collision type and time-to-accide

Himel Ghosh, Sayak Chatterjee, Antik Ganguly, Shreetama Karmakar

The most startling of the contemporary problems is the sleepiness of chauffeur which causes lots of car accidents. Prevention of those impending accidents by detecting and alerting the sleepy chauffeur is vital, otherwise that would lead to loss of lives and various traumas along with severe injuries. The slumber or sleep may be caused by huge stress, pressu

Zhen Du, Jingjing Xu, Yifeng Xiong, Jie Wang

Integrated sensing and communications (ISAC) is considered an innovative technology in sixth-generation (6G) wireless networks, where utilizing orthogonal frequency division multiplexing (OFDM) communication signals for sensing provides a cost-effective solution for implementing ISAC. However, the sensing performance of matched and mismatched filtering schem

Christoph Federrath, Stella Offner

We review recent advances in the numerical modeling of turbulent flows and star formation. An overview of the most widely used simulation codes and their core capabilities is provided. We then examine methods for achieving the highest-resolution magnetohydrodynamical turbulence simulations to date, highlighting challenges related to numerical viscosity and r

Ehsan Shahoseini

In this chapter, we want to have an overview of the Taylor--Wiles patching method. For this purpose, at the first, we recall Mazur's theory of deforming Galois representations and study both local and global deformation problems. Then, we go through the subject of Taylor-Wiles primes and examine the role that they play on the Galois side and the modular (aut

Xiaoxue Ren, Penghao Jiang, Kaixin Li, Zhiyong Huang

Web applications are prime targets for cyberattacks as gateways to critical services and sensitive data. Traditional penetration testing is costly and expertise-intensive, making it difficult to scale with the growing web ecosystem. While language model agents show promise in cybersecurity, modern web applications demand visual understanding, dynamic content

Dong Le, Wade Shipley, Alexandria Do, Liya Bi

Organic surface ligands are integral components of nanocrystals and nanoparticles that have a strong influence on their physicochemical properties, their interaction with the environment, and their ability to self-assemble and order into higher-order structures. These hybrid nanomaterials are tunable with applications in catalysis, directed self-assembly, ne

Aakash Bhat, Rüdiger Pakmor, Ken J. Shen, Evan B. Bauer

A significant number of hypervelocity stars with velocities between $1500-2500$ km/s have recently been observed. The only plausible explanation so far is that they have been produced through thermonuclear supernovae in white dwarf binaries. Since these stars are thought to be surviving donors of Type Ia supernovae, a surprising finding was that these stars

Tian-yao Fang, Ming-Chung Chu

While ultra-light bosonic dark matter (ULDM) in a Bose-Einstein condensate (BEC) state could naturally account for the central core in some galaxies and resolve the core-cusp problem, the dark matter density distribution in the outer regions of galaxies remains less explored. We propose a trial wavefunction to model the ULDM distribution beyond the BEC core.

Petr Samoldekin, Christian Schulz, Henning Woydt

Process mapping asks to assign vertices of a task graph to processing elements of a supercomputer such that the computational workload is balanced while the communication cost is minimized. Motivated by the recent success of GPU-based graph partitioners, we propose two GPU-accelerated algorithms for this optimization problem. The first algorithm employs hier

Zeyu Yang, Satoshi Nakamura

Simultaneous speech translation requires accurate segmentation to balance translation quality and latency. Recent studies such as SHAS have introduced pretrained segmentation models, achieving stronger performance than heuristic rules. However, segmentation models such as SHAS, though pretrained and more robust than heuristic methods, are still constrained b

Linyi Yang, Yixuan Weng

Current deep-research agents run in a ''fire-and-forget'' mode: once started, they give users no way to fix errors or add expert knowledge during execution. We present ResearStudio, the first open-source framework that places real-time human control at its core. The system follows a Collaborative Workshop design. A hierarchical Planner-Executor writes every

Martin Beneke, Hiromasa Takaura

A long-standing problem concerns the question how to consistently combine perturbative expansions in QCD with power corrections in the context of the operator product expansion (OPE), since the former exhibit ambiguities due to infrared renormalons, which are of the same order as the power corrections. We propose to use the gradient flow time $1/\sqrt{t}$ as

Xi Cheng, Pingfa Feng, Mingyu Fan, Zhichao Liao

Freehand sketches exhibit unique sparsity and abstraction, necessitating learning pipelines distinct from those designed for images. For sketch learning methods, the central objective is to fully exploit the effective information embedded in sketches. However, there is limited research on what constitutes effective sketch information, which in turn constrain

Orit E. Raz

We extend the proximity technique of Solymosi and Zahl [J. Combin. Theory, Ser. A (2024)] to the setting of trivariate polynomials. In particular, we prove the following result: Let $f(x,y,z)=(x-y)^2+(\varphi(x)-z)^2$, where $\varphi(x)\in \mathbb{R}[x]$ has degree at least 3. Then, for every finite $A,B,C\subset \mathbb{R}$ each of size $n$, one has $|f(A,B

Shingo Yokoi, Kento Sasaki, Yu Yamaguchi

Recent advances in end-to-end (E2E) autonomous driving have been enabled by training on diverse large-scale driving datasets, yet autonomous driving models still struggle in out-of-distribution (OOD) scenarios. The COOOL benchmark targets this gap by encouraging hazard understanding beyond closed taxonomies, and the 2COOOL challenge extends it to generating

Ryuji Hashimoto, Takehiro Takayanagi, Masahiro Suzuki, Kiyoshi Izumi

In real-world stock markets, certain chart patterns -- such as price declines near historical highs -- cannot be fully explained by fundamentals alone. These phenomena suggest the presence of path dependence in price formation, where investor decisions are influenced not only by current market conditions but also by the trajectory of prices leading up to the

Pylyp Cherevan

For the paraboloid decomposition $F=\sum_{\Theta} F_{\Theta}$ with $\Theta\subset{|\xi|\sim\lambda}$ and radius $r=\lambda^{-2/3}$, we prove a log-free estimate $|F|{L^{6}(Q{\lambda})}\lesssim \lambda^{\Sigma_{\lambda}} D^{\Sigma_{D}} \big(\sum_{\Theta}|F_{\Theta}|{L^{6}}^{2}\big)^{1/2}$ as $\lambda\to\infty$, where $D=\lambda^{1/12}$. Key components: (i) br

Hao Zhang, Kexin Li, Wenlin Qiu

This paper presents a spatial two-grid (STG) compact difference scheme for a two-dimensional (2D) nonlinear diffusion-wave equation with variable exponent, which describes, e.g., the propagation of mechanical diffusive waves in viscoelastic media with varying material properties. Following the idea of the convolution approach, the diffusion-wave model is fir

I. Stepanov, M. Ersfeld, A. V. Poshakinskiy, M. Lepsa

We show that the initialization of an ensemble of electrons in the same spin state in strained n-InGaAs subject to a perpendicular magnetic field triggers an AC electric current at GHz frequencies. The AC current emerges in the absence of any driving force and survives until the coherent precession of the electron spins is lost. The current amplitude increas

Yun Peng, Kisub Kim, Linghan Meng, Kui Liu

Code review is an essential process to ensure the quality of software that identifies potential software issues at an early stage of software development. Among all software issues, security issues are the most important to identify, as they can easily lead to severe software crashes and service disruptions. Recent research efforts have been devoted to autom

Jiayu Yao, Shenghua Liu, Yiwei Wang, Rundong Cheng

Large Audio Language Models (LALMs) are increasingly applied to audio understanding and multimodal reasoning, yet their ability to locate when events occur remains underexplored. We present the first systematic study of temporal bias in LALMs, revealing a key limitation in their timestamp prediction. For example, when asked "At which second does the lecturer

Jiwan Kim, Kibum Kim, Sangwoo Seo, Chanyoung Park

Recently, efficient Multimodal Large Language Models (MLLMs) have gained significant attention as a solution to their high computational complexity, making them more practical for real-world applications. In this regard, the knowledge distillation (KD) approach has emerged as a promising alternative, which transfers the rich visual and linguistic knowledge f

Zhentao Shi, Yishu Wang

We leverage an ensemble of many regressors, the number of which can exceed the sample size, for economic prediction. An underlying latent factor structure implies a dense regression model with highly correlated covariates. We propose the L2-relaxation method for estimating the regression coefficients and extrapolating the out-of-sample (OOS) outcomes. This f

Yifeng Yao, Yike Yun, Jing Wang, Huishuai Zhang

Multimodal Large Language Models (MLLMs) have demonstrated significant capabilities in image understanding, but long-video are constrained by context windows and computational cost. Uniform frame sampling often leads to substantial information loss. Meanwhile existing keyframe selection methods such as text-frame retrieval or RL-based frame optimization typi

Youngju Yoo, Seho Kim, Changick Kim

3D instance segmentation is crucial for understanding complex 3D environments, yet fully supervised methods require dense point-level annotations, resulting in substantial annotation costs and labor overhead. To mitigate this, box-level annotations have been explored as a weaker but more scalable form of supervision. However, box annotations inherently intro

Chengrui Xiang, Tengfei Ma, Xiangzheng Fu, Yiping Liu

Drug repurposing plays a critical role in accelerating treatment discovery, especially for complex and rare diseases. Biomedical knowledge graphs (KGs), which encode rich clinical associations, have been widely adopted to support this task. However, existing methods largely overlook common-sense biomedical concept knowledge in real-world labs, such as mechan

Mo Zhou, Haosheng Zhou, Ruimeng Hu

We propose the Mean-Field Actor-Critic (MFAC) flow, a continuous-time learning dynamics for solving mean-field games (MFGs), combining techniques from reinforcement learning and optimal transport. The MFAC framework jointly evolves the control (actor), value function (critic), and distribution components through coupled gradient-based updates governed by par

Abdullahi Mohammad, Bdah Eya, Bassant Selim

Impulsive noise poses a significant challenge to the reliability of wireless communication systems, necessitating accurate estimation of its statistical parameters for effective mitigation. This paper introduces a multitask learning (MTL) framework based on a CNN-LSTM architecture enhanced with an attention mechanism for the joint estimation of impulsive noi

Abdulhady Abas Abdullah, Arkaitz Zubiaga, Seyedali Mirjalili, Amir H. Gandomi

This review surveys the rapid evolution of Meta AI's LLaMA (Large Language Model Meta AI) series - from LLaMA 1 through LLaMA 4 and the specialized parameter-efficient fine-tuning (PEFT) methods developed for these models. We first describe the LLaMA family of foundation models (7B-65B to 288B parameters), their architectures (including native multimodal and

Anthony J. Pahl, Alice Shapley, Naveen A. Reddy, Ryan Sanders

We present ionizing photon production efficiencies (${\xi}_{\rm ion}$) for 63 z=1.5-6.9 star-forming galaxies using precise nebular dust attenuation corrections from the JWST/AURORA survey. A subset of objects within AURORA have individually-determined nebular dust attenuation curves, which vary significantly in shape and normalization, resulting in reduced

Matthew T. Darby, Muhammad Saleh, Marialore Sulpizi, Clotilde S. Cucinotta

Platinum step edges dominate electrocatalytic activity in fuel cells and electrolysers, yet their atomistic electrochemical behaviour remains poorly understood. Here, we employ \textit{ab initio} molecular dynamics under controlled electrode potentials to model a realistic stepped Pt--water interface incorporating experimentally observed (111)$\times$(111) a

Junnuo Wang

Recent advances in diffusion-based generative models have enabled high-quality text-to-audio synthesis, but fine-grained acoustic control remains a significant challenge in open-source research. We present Audio Palette, a diffusion transformer (DiT) based model that extends the Stable Audio Open architecture to address this "control gap" in controllable aud

Yusen Xie, Zhenmin Huang, Jianhao Jiao, Dimitrios Kanoulas

In this paper, we propose UniGS, a unified map representation and differentiable framework for high-fidelity multimodal 3D reconstruction based on 3D Gaussian Splatting. Our framework integrates a CUDA-accelerated rasterization pipeline capable of rendering photo-realistic RGB images, geometrically accurate depth maps, consistent surface normals, and semanti

Hung Pham, Viet Vo, Tien Tuan Anh Dinh, Duc Tran

Stream processing systems are important in modern applications in which data arrive continuously and need to be processed in real time. Because of their resource and scalability requirements, many of these systems run on the cloud, which is considered untrusted. Existing works on securing databases on the cloud focus on protecting the data, and most systems

Joseph Breen, Agniva Roy, Luya Wang

We characterize regularity of Lagrangian submanifolds in Weinstein Lefschetz fibrations, establishing a conjecture of Giroux and Pardon. Our main result is the Weinstein analogue of a closed symplectic Lefschetz pencil result of Auroux, Mu\~noz, and Presas. As an application, given a Legendrian link in tight $S^3$ and an exact filling which is part of an arb

Akshay Naik, William R. Norris, Dustin Nottage, Ahmet Soylemezoglu

Autonomous ground vehicles operating off-road must plan curvature-feasible paths while accounting for spatially varying soil strength and slope hazards in real time. We present a continuous state--cost metric that combines a Bekker pressure--sinkage model with elevation-derived slope and attitude penalties. The resulting terrain cost field is analytic, bound

Yoshiaki Koma, Miho Koma

The dual Ginzburg-Landau (DGL) theory is one of the nonperturbative effective field theories of quantum chromodynamics (QCD). The DGL theory describes the QCD vacuum as a dual superconductor and possesses electric flux-tube solutions via the dual Meissner effect, which applies to the quark confinement mechanism. We demonstrate a powerful numerical method for

Minghan Wang, Thuy-Trang Vu, Ehsan Shareghi, Gholamreza Haffari

Inference-time scaling through multiple sample generation in combination with Process- or Outcome-Reward Model (PRM or ORM) re-ranking has proven effective for text-based reasoning in large language models. This paper investigates whether such established techniques can be successfully adapted to reasoning in the continuous space, using COCONUT (Hao et al. 2

Kenneth Weiss, Thomas M. Stitt, Daryl Hawkins, Olga Pearce

Due to the increasing diversity of high-performance computing architectures, researchers and practitioners are increasingly interested in comparing a code's performance and scalability across different platforms. However, there is a lack of available guidance on how to actually set up and analyze such cross-platform studies. In this paper, we contend that th

Oleg Butkovsky

These are lecture notes for a mini-course on stochastic sewing, taught at the University of Edinburgh and Beijing Institute of Technology in Spring/Summer 2025. The aim is to introduce the reader to stochastic sewing techniques and to show how they can be successfully applied to study various problems in stochastic analysis, including: regularization by nois

Ziqi Wang, Boye Niu, Zipeng Gao, Zhi Zheng

With the increasing capabilities of Large Language Models (LLMs), parallel reasoning has emerged as a new inference paradigm that enhances reasoning robustness by concurrently exploring multiple lines of thought before converging on a final answer. It has become a significant trend to explore parallel reasoning to overcome the fragility of standard sequentia

Kaisarbek Omirzakhov, Ali Pirmoradi, Geun Ho Ahn, Amirreza Shoobi

Optical interconnects are essential for data centers and AI systems. Given the limited energy production, ultra-low energy and dense optical interconnects are required to support the exponential growth of AI systems. Here we report the demonstration of a monolithically integrated optical transmitter where use of power efficient architecture and devices such

Katrin FÄssler, Enrico Le Donne, Sebastiano Nicolussi Golo, Alessandro Ottazzi

We study conditions under which quasi-conformal homeomorphisms are quasi-isometries. We show that if two nilpotent geodesic Lie groups are quasi-conformally homeomorphic, then they are quasi-isometrically equivalent. We also give more general results beyond the nilpotent case. In particular, we show that quasi-conformal homeomorphisms between geodesic Lie gr

Liangwei Nathan Zheng, Wenhao Liang, Wei Emma Zhang, Miao Xu

Pseudo-Alignment is a pervasive challenge in many large language models for time series (LLM4TS) models, often causing them to underperform compared to linear models or randomly initialised backbones. However, there is limited discussion in the community for the reasons that pseudo-alignment occurs. In this work, we conduct a thorough investigation into the

Jiahuan Zhou, Kai Zhu, Zhenyu Cui, Zichen Liu

Recently, pre-trained state space models have shown great potential for video classification, which sequentially compresses visual tokens in videos with linear complexity, thereby improving the processing efficiency of video data while maintaining high performance. To apply powerful pre-trained models to downstream tasks, prompt learning is proposed to achie

Ziyuan Gao, Philippe Morel

One-shot medical image segmentation faces fundamental challenges in prototype representation due to limited annotated data and significant anatomical variability across patients. Traditional prototype-based methods rely on deterministic averaging of support features, creating brittle representations that fail to capture intra-class diversity essential for ro

Kevin Hsu

We study the fair division of indivisible items. In the general model, the goal is to allocate $m$ indivisible items to $n$ agents while satisfying fairness criteria such as MMS, EF1, and EFX. We also study a recently-introduced graphical model that represents the fair division problem as a multigraph, in which vertices correspond to agents and edges to item

Andrea Collevecchio, Gabor Lugosi, Adrian Vetta, Rui-Ray Zhang

A long-standing open problem in algorithmic game theory asks whether or not there is a polynomial time algorithm to compute a Nash equilibrium in a random bimatrix game. We study random win-lose games, where the entries of the $n\times n$ payoff matrices are independent and identically distributed (i.i.d.) Bernoulli random variables with parameter $p=p(n)$.

Zhongwei Yu, Wannian Xia, Xue Yan, Bo Xu

Advanced large language models (LLMs) frequently reflect in reasoning chain-of-thoughts (CoTs), where they self-verify the correctness of current solutions and explore alternatives. However, given recent findings that LLMs detect limited errors in CoTs, how reflection contributes to empirical improvements remains unclear. To analyze this issue, in this paper

Hyemi Song, Matthew Johnson, Kirsten Whitley, Eric Krokos

Embodiment shapes how users verbally express intent when interacting with data through speech interfaces in immersive analytics. Despite growing interest in Natural Language Interaction (NLI) for visual analytics in immersive environments, users' speech patterns and their use of embodiment cues in speech remain underexplored. Understanding their interplay is

Masaki Kashima

A pseudo 2-factor of a graph is a spanning subgraph such that each component is $K_1$, $K_2$, or a cycle. This notion was introduced by Bekkai and Kouider in 2009, where they showed that every graph $G$ has a pseudo 2-factor with at most $\alpha(G)-\delta(G)+1$ components that are not cycles. For a graph $G$ and a set of vertices $S$, let $\delta_G(S)$ denot

Jiepeng Fang, Xuhua He

Let $\mathbf{U}$ be a quantum group of symmetric type. We introduce the {\it thickening realization} to realize (a suitable approximation of) the tensor product ${^{\omega}\Lambda_{\lambda_1}}\otimes \Lambda_{\lambda_2}$ of a simple integrable lowest weight module and a highest weight module as a subquotient of the Verma module of a larger quantum group $\ti

Minhao Qiao, Hai Dong, Iqbal Gondal

Cross chain interoperability in blockchain systems exposes a fundamental tension between user privacy and regulatory accountability. Existing solutions enforce an all or nothing choice between full anonymity and mandatory identity disclosure, which limits adoption in regulated financial settings. We present VeilAudit, a cross chain auditing framework that in

Gang Chen, Chaoran Liu, Yangwen Zhang

This paper establishes optimal error estimates in the $L^2$ for the non-symmetric Nitsche method in an unfitted interface finite element setting. Extending our earlier work, we give a complete analysis for the Poisson interface model and, by formulating a tailored dual problem that restores adjoint consistency, derive the desired bounds.

Jiahuan Zhou, Chao Zhu, Zhenyu Cui, Zichen Liu

Continual Test-Time Adaptation (CTTA) aims to quickly fine-tune the model during the test phase so that it can adapt to multiple unknown downstream domain distributions without pre-acquiring downstream domain data. To this end, existing advanced CTTA methods mainly reduce the catastrophic forgetting of historical knowledge caused by irregular switching of do

Nguyen Quang Loc, Nguyen Cong Minh, Phan Thi Thuy

In this paper, we shall provide explicit formulas for the extremal Betti numbers of $R/I$, where $I$ is the defining ideal of certain weighted hyperplanes in $\Bbb{P}^{n-1}$ and $R$ is the polynomial ring in $n$ indeterminates over a field. As a consequence, we completely classify such ideals which are pseudo-Gorenstein as in sense of V. Ene, J. Herzog, T. H

Yuki Yasuda, Ryo Onishi

This study employs a neural network that represents the solution to a Schr\"odinger bridge problem to perform super-resolution of 2-m temperature in an urban area. Schr\"odinger bridges generally describe transformations between two data distributions based on diffusion processes. We use a specific Schr\"odinger-bridge model (SM) that directly transforms low

Xindan Zhang, Jianping Zhao, Yanren Hou

In this paper, we investigate optimal control problems governed by the parabolic interface equation, in which the control acts on the interface. The solution to this problem exhibits low global regularity due to the jump of the coefficient across the interface and the control acting on the interface. Consequently, the traditional finite element method fails

Muhammad Talha, Abdullah Mohiuddin, Sehrish Javed, Ahmed Jawad Qureshi

The paper presents a comparative user study between an Augmented Reality-based Computer-Aided Design (AR-CAD) system and a traditional computer-based CAD modeling software, SolidWorks. Twenty participants of varying skill levels performed 3D modeling tasks using both systems. The results showed that while the average task completion time is comparable for bo

Bibhu Prasad Tripathy, Asutosh Satapathy, Utkal Keshari Dutta, Bijan Kumar Patel

Let $\mathcal{P}_{n}$ be the $n$-th Padovan number, $E_{n}$ be the $n$-th Perrin number and $N_{n}$ be the $n$-th Narayana's cows number. Let $b$ be a positive integer such that $b \geq 2$. In this paper, we study the Diophantine equations \[ \mathcal{P}_{n} = (b \pm 1)\cdot b^{l} \pm 1, \] \[ E_{n} = (b \pm 1)\cdot b^{l} \pm 1, \] and \[ N_{n} = (b \pm 1)\c

Ali Parsaee, Bei Jiang, Zachary Friggstad, Russell Greiner

Standard supervised learners attempt to learn a model from a labeled dataset. Given a small set of labeled instances, and a pool of unlabeled instances, a budgeted learner can use its given budget to pay to acquire the labels of some unlabeled instances, which it can then use to produce a model. Here, we explore budgeted learning in the context of survival d

Harsh Kasyap, Minghong Fang, Zhuqing Liu, Carsten Maple

Federated learning (FL) is a privacy-preserving machine learning technique that facilitates collaboration among participants across demographics. FL enables model sharing, while restricting the movement of data. Since FL provides participants with independence over their training data, it becomes susceptible to poisoning attacks. Such collaboration also prop

Srinjana Routh, Shaonwita Pal, Dibyendu Nandy, Subhamoy Chatterjee

Polar fields at the minimum of a sunspot cycle -- which are a manifestation of the radial component of the Sun's poloidal field -- are deemed to be the best indicator of the strength of the toroidal component, and hence the amplitude of the future sunspot cycle. However, the Sun's polar magnetic fields are difficult to constrain with ground-based or space-ba

Sabine Muzellec, Yousif Kashef Alghetaa, Simon Kornblith, Kohitij Kar

Human core object recognition depends on the selective use of visual information, but the strategies guiding these choices are difficult to measure directly. We present MAPS (Masked Attribution-based Probing of Strategies), a behaviorally validated computational tool that tests whether explanations derived from artificial neural networks (ANNs) can also expl

Yonghao Liu, Yajun Wang, Chunli Guo, Wei Pang

Graph few-shot learning has attracted increasing attention due to its ability to rapidly adapt models to new tasks with only limited labeled nodes. Despite the remarkable progress made by existing graph few-shot learning methods, several key limitations remain. First, most current approaches rely on predefined and unified graph filters (e.g., low-pass or hig

Yanzhao Jia, Zhaobo Wu, Zheyi Cao, Shihao Ji

This paper introduces a novel disk array architecture, designated RAID-0e (Resilient Striping Array), designed to superimpose a low-overhead fault tolerance layer upon traditional RAID 0 (striping). By employing a logically and physically separate parity domain to protect a primary data domain, RAID-0e mitigates the risk of array-wide data loss from common,

Ziyu Dong, Jaehoon Jeong, Alex Pomarol

Theories with pseudoscalars that couple through anomalies (such as axion models) are of particular phenomenological interest. We carry out a comprehensive analysis of all bounds obtainable from bootstrapping the amplitudes when a pseudoscalar couples to photons and gravitons. This allows us to find new cutoff scales of theories with anomalies that are more r

Shihao Ji, Zihui Song, Jiajie Huang

Large Language Models (LLMs) hallucinate, generating factually incorrect yet confident assertions. We argue this stems from the Transformer's Softmax function, which creates "Artificial Certainty" by collapsing ambiguous attention scores into a single probability distribution, discarding uncertainty information at each layer. To fix this, we introduce the Cr