October 2025 arXiv papers — page 222

Alexander Kopte, André Kaup

To manage the complexity of transformers in video compression, local attention mechanisms are a practical necessity. The common approach of partitioning frames into patches, however, creates architectural flaws like irregular receptive fields. When adapted for temporal autoregressive models, this paradigm, exemplified by the Video Compression Transformer (VC

Soroush Zare, Farokhnaz Hosseinifar, Luis Miguel Nieto, Dhruba Jyoti Gogoi

This study delves into the intricate properties of a Schwarzschild black hole enveloped by King dark matter in an isotropic configuration. The thermodynamic characteristics of this black hole are meticulously analyzed, and the dynamics of massive and massless particles in its vicinity are investigated. In examining the trajectories of massless particles, the

Shahriar Kabir Nahin, Hadi Askari, Muhao Chen, Anshuman Chhabra

Test-Time Scaling (TTS) improves LLM reasoning by exploring multiple candidate responses and then operating over this set to find the best output. A tacit premise behind TTS is that sufficiently diverse candidate pools enhance reliability. In this work, we show that this assumption in TTS introduces a previously unrecognized failure mode. When candidate dive

Travis Dillon, Junnosuke Koizumi, Sammy Luo

Littlewood asked for the maximum number $N$ of congruent infinite cylinders that can be arranged in $\mathbb{R}^3$ so that every pair touches. We improve upon the proof of the second author that $N \leq 18$ to show that $N \leq 10$. Together with the lower bound established by Boz\'oki, Lee, and R\'onyai, this shows that $N \in \{7,8,9,10\}$. Our method is b

Mingsong Yan, Charles Kulick, Sui Tang

Continuous-depth graph neural networks, also known as Graph Neural Differential Equations (GNDEs), combine the structural inductive bias of Graph Neural Networks (GNNs) with the continuous-depth architecture of Neural ODEs, offering a scalable and principled framework for modeling dynamics on graphs. In this paper, we present a rigorous convergence analysis

Minh K. Quan, Pubudu N. Pathirana

Cross-slice attack attribution in 6G networks faces the fundamental challenge of distinguishing genuine causal relationships from spurious correlations in shared infrastructure environments. We propose a theoretically-grounded domain-adapted Granger causality framework that integrates statistical causal inference with network-specific resource modeling for r

Olivier Couronné, Sandro Gallo, Leonardo T. Rolla

We consider a long-range percolation model on homogeneous oriented trees with several lengths. We obtain the critical surface as the set of zeros of a specific polynomial with coefficients depending explicitly on the lengths and the degree of the tree. Restricting to the case of two lengths, we obtain new bounds on the critical parameters, monotonicity prope

Yuanzhe Shen, Yide Liu, Zisu Huang, Ruicheng Yin

Large language models (LLMs) demonstrate remarkable performance across diverse tasks, yet their effectiveness frequently depends on costly commercial APIs or cloud services. Model selection thus entails a critical trade-off between performance and cost: high-performing LLMs typically incur substantial expenses, whereas budget-friendly small language models (

Arushi Dashore, Aryan Anumala, Emily Hui, Olivia Yang

Automated tennis stroke analysis has advanced significantly with the integration of biomechanical motion cues alongside deep learning techniques, enhancing stroke classification accuracy and player performance evaluation. Despite these advancements, existing systems often fail to connect biomechanical insights with actionable language feedback that is both a

Nicholas R. Allgood

We develop a mathematical framework for quantum time transfer based on commuting families of Hamiltonians and synchronization observables. The synchronization subspace is defined as the kernel of a difference operator between local clocks, and we show that this subspace is preserved exactly by a commutative $*$-subalgebra of Hamiltonians compatible with the

Ravi Kalluri

Although Agile methodologies emphasize decentralized decision-making and team autonomy, engineering managers continue to be employed in Agile software organizations. This apparent paradox suggests that traditional managerial functions persist despite the theoretical displacement of managerial hierarchy in Agile. This paper explores the persistence of enginee

Matthew Lisondra, Junseo Kim, Glenn Takashi Shimoda, Kourosh Zareinia

Vision algorithms can be executed directly on the image sensor when implemented on the next-generation sensors known as focal-plane sensor-processor arrays (FPSP)s, where every pixel has a processor. FPSPs greatly improve latency, reducing the problems associated with the bottleneck of data transfer from a vision sensor to a processor. FPSPs accelerate visio

Joshua Taylor

We design a model-predictive controller for managing the actuators in sewer networks. It minimizes flooding and combined-sewer overflow during rain and pollution at other times. To make the problem tractable, we use a convex relaxation of the microbial growth kinetics and a physically motivated linearization of the mass flow bilinearities. With these approxi

Vinod Raman, Shenghao Xie, Samson Zhou

Motivated by the predictable nature of real-life in data streams, we study online regression when the learner has access to predictions about future examples. In the extreme case, called transductive online learning, the sequence of examples is revealed to the learner before the game begins. For this setting, we fully characterize the minimax expected regret

Adam J. Burgasser, Eileen C. Gonzales, Samuel A. Beiler, Channon Visscher

The atmospheres of low-temperature brown dwarfs and gas giant planets are expected to contain the phosphine molecule, PH$_3$ However, previous observations have shown much lower abundances of this molecule than predicted by atmospheric chemistry models. We report JWST spectroscopic observations of phosphine in the atmosphere of the brown dwarf Wolf 1130C. Mu

Sagar Bharadwaj, Harrison Williams, Luke Wang, Michael Liang

World-scale augmented reality (AR) applications need a ubiquitous 6DoF localization backend to anchor content to the real world consistently across devices. Large organizations such as Google and Niantic are 3D scanning outdoor public spaces in order to build their own Visual Positioning Systems (VPS). These centralized VPS solutions fail to meet the needs o

Yonnel Chen Kuang Piao, Jean Carlors Paul, Leuson Da Silva, Arghavan Moradi Dakhel

Code refactoring is a fundamental software engineering practice aimed at improving code quality and maintainability. Despite its importance, developers often neglect refactoring due to the significant time, effort, and resources it requires, as well as the lack of immediate functional rewards. Although several automated refactoring tools have been proposed,

Mohammad Amin Abbasi, Hassan Naderi

This study presents PsychoLexTherapy, a framework for simulating psychotherapeutic reasoning in Persian using small language models (SLMs). The framework tackles the challenge of developing culturally grounded, therapeutically coherent dialogue systems with structured memory for multi-turn interactions in underrepresented languages. To ensure privacy and fea

Liyuan Hu, Jitao Wang, Zhenke Wu, Chengchun Shi

This paper focuses on reinforcement learning (RL) with clustered data, which is commonly encountered in healthcare applications. We propose a generalized fitted Q-iteration (FQI) algorithm that incorporates generalized estimating equations into policy learning to handle the intra-cluster correlations. Theoretically, we demonstrate (i) the optimalities of our

Yadav Mahesh Lorik, Kaushik Sarveswaran, Nagaraj Sundaramahalingam, Aravindakumar Venugopalan

Time series anomaly detection forms a very crucial area in several domains but poses substantial challenges. Due to time series data possessing seasonality, trends, noise, and evolving patterns (concept drift), it becomes very difficult to set a general notion of what constitutes normal behavior. Anomalies themselves could be varied, ranging from a single ou

Akhil Padmanabha, Jessie Yuan, Tanisha Mehta, Rajat Kumar Jenamani

Millions of people around the world need assistance with feeding. Robotic feeding systems offer the potential to enhance autonomy and quality of life for individuals with impairments and reduce caregiver workload. However, their widespread adoption has been limited by technical challenges such as estimating bite timing, the appropriate moment for the robot t

Hyelin Nam, Hyojun Go, Byeongjun Park, Byung-Hoon Kim

Human video generation is becoming an increasingly important task with broad applications in graphics, entertainment, and embodied AI. Despite the rapid progress of video diffusion models (VDMs), their use for general-purpose human video generation remains underexplored, with most works constrained to image-to-video setups or narrow domains like dance videos

D. S. Chuchelov, M. I. Vaskovskaya, E. A. Tsygankov, V. V. Vassiliev

We report the observation and characterization of a high-contrast dual-frequency Doppler-free ground-state crossover resonance in the D1 line of 87Rb.The crossover appears at a two-photon detuning exceeding the natural linewidth of the excited state and is formed by the optical pumping effect. Unlike the previously proposed resonance at zero two-photon detun

Ardalan Aryashad, Parsa Razmara, Amin Mahjoub, Seyedarmin Azizi

Autonomous driving perception systems are particularly vulnerable in foggy conditions, where light scattering reduces contrast and obscures fine details critical for safe operation. While numerous defogging methods exist, from handcrafted filters to learned restoration models, improvements in image fidelity do not consistently translate into better downstrea

Hangting Ye, Jinmeng Li, He Zhao, Mingchen Zhuge

Existing anomaly detection (AD) methods for tabular data usually rely on some assumptions about anomaly patterns, leading to inconsistent performance in real-world scenarios. While Large Language Models (LLMs) show remarkable reasoning capabilities, their direct application to tabular AD is impeded by fundamental challenges, including difficulties in process

Md. Atabuzzaman, Andrew Zhang, Chris Thomas

Large Vision-Language Models (LVLMs) have demonstrated impressive performance on vision-language reasoning tasks. However, their potential for zero-shot fine-grained image classification, a challenging task requiring precise differentiation between visually similar categories, remains underexplored. We present a novel method that transforms zero-shot fine-gr

Rana Nameer Hussain Khan, Dawood Wasif, Jin-Hee Cho, Ali Butt

The increasing complexity of cloud-native infrastructure has made Infrastructure-as-Code (IaC) essential for reproducible and scalable deployments. While large language models (LLMs) have shown promise in generating IaC snippets from natural language prompts, their monolithic, single-pass generation approach often results in syntactic errors, policy violatio

Vincent Savaux, Steve Sawadogo, Hyeon Seok Rou, Giuseppe Thadeu Freitas de Abreu

This paper investigates the robustness of affine frequency division multiplexing (AFDM) and orthogonal time frequency space (OTFS) modulation schemes against non-white Gaussian noise, which can model various sources of additive disturbances to the received signal. The proposed approach demonstrates that the performance of these waveforms depends on the abili

Adrian-Dinu Urse, Dumitru-Clementin Cercel, Florin Pop

Natural disaster assessment relies on accurate and rapid access to information, with social media emerging as a valuable real-time source. However, existing datasets suffer from class imbalance and limited samples, making effective model development a challenging task. This paper explores augmentation techniques to address these issues on the CrisisMMD multi

Jinghao Lyu, Kyle J. Ray, James P. Crutchfield

The energy cost of computation has emerged as a central challenge at the intersection of physics and computer science. Recent advances in statistical physics -- particularly in stochastic thermodynamics -- enable precise characterizations of work, heat, and entropy production in information-processing systems driven far from equilibrium by time-dependent con

Lutz Oettershagen, Othon Michail

Balancing resource efficiency and fairness is critical in networked systems that support modern learning applications. We introduce the \emph{Fair Minimum Labeling} (FML) problem: the task of designing a minimum-cost temporal edge activation plan that ensures each group of nodes in a network has sufficient access to a designated target set, according to spec

Nusrat Jahan Lia, Shubhashis Roy Dipta, Abdullah Khan Zehady, Naymul Islam

Detecting media bias is crucial, specifically in the South Asian region. Despite this, annotated datasets and computational studies for Bangla political bias research remain scarce. Crucially because, political stance detection in Bangla news requires understanding of linguistic cues, cultural context, subtle biases, rhetorical strategies, code-switching, im

Miloš Baljozović, Shiladitya Karmakar, André L. Fernandes Cauduro, Mothuku Shyam Sundar

We report the emergence of adsorption-induced magnetism from heterohelicene molecules on a non-magnetic Cu(100) surface. Spin-polarized low-energy electron microscopy (SP-LEEM) measurements reveal spin-dependent electron reflectivity for enantiopure 7,12,17-trioxa[11]helicene (TO[11]H) monolayers, indicating the formation of a spin-polarized state localized

Abdelilah Ganmati, Karim Afdel, Lahcen Koutti

In the era of pervasive cyber threats and exponential growth in digital services, the inadequacy of single-factor authentication has become increasingly evident. Multi-Factor Authentication (MFA), which combines knowledge-based factors (passwords, PINs), possession-based factors (smart cards, tokens), and inherence-based factors (biometric traits), has emerg

Zheng Huang, Mingyu Liu, Xiaoyi Lin, Muzhi Zhu

Vision-Language-Action (VLA) models represent a pivotal advance in embodied intelligence, yet they confront critical barriers to real-world deployment, most notably catastrophic forgetting. This issue stems from their overreliance on continuous action sequences or action chunks, which inadvertently create isolated data silos that disrupt knowledge retention

Antonios Saravanos

The waterfall model, one of the earliest software development methodologies, has played a foundational role in shaping contemporary software engineering practices. This paper provides a historical and critical overview of the model, tracing its conceptual origins in software engineering, its formalization by Royce, and its evolution through decades of indust

Akshay Kudva, Joel A. Paulson

Optimal design under uncertainty remains a fundamental challenge in advancing reliable, next-generation process systems. Robust optimization (RO) offers a principled approach by safeguarding against worst-case scenarios across a range of uncertain parameters. However, traditional RO methods typically require known problem structure, which limits their applic

Zahra Atf, Peter R. Lewis

We present a gamified explainable AI (XAI) system for ethically aware consumer decision-making in the coffee domain. Each session comprises six rounds with three options per round. Two symbolic engines provide real-time reasons: a Kantian module flags rule violations (e.g., child labor, deforestation risk without shade certification, opaque supply chains, un

Shawn Shuoshuo Chen, Daiyaan Arfeen, Minlan Yu, Peter Steenkiste

Allocating resources to distributed machine learning jobs in multi-tenant torus-topology clusters must meet each job's specific placement and communication requirements, which are typically described using shapes. There is an inherent tension between minimizing network contention and maximizing cluster utilization when placing various-shaped jobs. While exis

Jose Garcia-Alonso, Enrique Moguel, Jaime Alvarado-Valiente, Javier Romero-Alvarez

Quantum computing is rapidly progressing from theoretical promise to practical implementation, offering significant computational advantages for tasks in optimization, simulation, cryptography, and machine learning. However, its integration into real-world software systems remains constrained by hardware fragility, platform heterogeneity, and the absence of

Ching-Yu Yao

Modulated symmetries are internal symmetries that are not invariant under spacetime symmetry actions. We propose a general way to describe the lattice translation modulated symmetries in 1+1D, including the non-invertible ones, via the tensor network language. We demonstrate that the modulations can be described by some autoequivalences of the categories. Al

Yutong Li, Yiwen Pan, Wenbin Yan

We find an intriguing relation between the chiral algebra and the mixed Hodge structure of the Coulomb branch of four dimensional $\mathcal{N} = 2$ superconformal field theories. We identify the space of irreducible characters of the $\mathcal{N} = 4$ $SU(N)$ chiral algebra $\mathbb{V}[\mathcal{T}_{SU(N)}]$ by analytically computing the Wilson line Schur ind

Anoy Saha, Mona Ghassemi

The electrification of aircraft is reshaping the foundations of aerospace design by positioning electrical systems at the center of propulsion, control, and onboard functionality. This chapter provides an overview of electrical system architectures for electric and hybrid electric aircraft, highlighting both established principles and emerging design strateg

Seil Kang, Woojung Han, Dayun Ju, Seong Jae Hwang

Starting from flow- and diffusion-based transformers, Multi-modal Diffusion Transformers (MM-DiTs) have reshaped text-to-vision generation, gaining acclaim for exceptional visual fidelity. As these models advance, users continually push the boundary with imaginative or rare prompts, which advanced models still falter in generating, since their concepts are o

Sunghwan Kim, Woojeh Chung, Zhirui Dai, Dwait Bhatt

In this paper, we demonstrate that mobile manipulation policies utilizing a 3D latent map achieve stronger spatial and temporal reasoning than policies relying solely on images. We introduce Seeing the Bigger Picture (SBP), an end-to-end policy learning approach that operates directly on a 3D map of latent features. In SBP, the map extends perception beyond

Said Elnaffar, Farzad Rashidi, Abedallah Zaid Abualkishik

This review examines the role of artificial intelligence (AI) agents in programming education, focusing on how these tools are being integrated into educational practice and their impact on student learning outcomes. An analysis of fifty-eight peer-reviewed studies published between 2022 and 2025 identified three primary categories of AI agents: chatbots, ge

V. A. Vassiliev

The complete list of connected components of the set of Morse functions in the deformations of function singularities of class $J_{10}$ is given. Thus, the isotopy classification of Morse perturbations of parabolic real function singularities is finished.

Jonathan E. Rubin, Justyna Signerska-Rynkowska, Jonathan Touboul

We investigate here various properties of the responses of excitable systems subject to periodic forcing and noise. While the properties of intrinsic oscillators, subject to added periodic signals, are well understood, much less is known about the factors that determine the response precision of excitable units, intrinsically at rest, when activated by perio

William G. P. Mayner, William Marshall, Giulio Tononi

Integrated information theory (IIT) starts from the existence of consciousness and characterizes its essential properties: every experience is intrinsic, specific, unitary, definite, and structured. IIT then formulates existence and its essential properties operationally in terms of cause-effect power of a substrate of units. Here we address IIT's operationa

Yunhao Li, Sijing Wu, Jun Jia, Kang Fu

In recent years, with the rapid development of large multimodal models (LMMs), explainable image quality assessment (IQA) has attracted increasing attention, aiming to understand the perceptual quality problems of images. Existing studies typically construct large-scale instruction tuning datasets to enhance the quality perception capabilities of LMMs, follo

Ajo Babu George, Sreehari J R Ajo Babu George, Sreehari J R Ajo Babu George, Sreehari J R

Aims Late diagnosis of Oral Squamous Cell Carcinoma (OSCC) contributes significantly to its high global mortality rate, with over 50\% of cases detected at advanced stages and a 5-year survival rate below 50\% according to WHO statistics. This study aims to improve early detection of OSCC by developing a multimodal deep learning framework that integrates cli

Niranjan Kumar Ilampooranan, Constantinos Chamzas

The ability to solve motion-planning queries within a fixed time budget is critical for deploying robotic systems in time-sensitive applications. Semi-static environments, where most of the workspace remains fixed while a subset of obstacles varies between tasks, exhibit structured variability that can be exploited to provide stronger guarantees than general

Yunhao Li, Sijing Wu, Yucheng Zhu, Huiyu Duan

With the rapid development of 3D scanning and reconstruction technologies, dynamic digital human avatars based on 4D meshes have become increasingly popular. A high-precision dynamic digital human avatar can be applied to various fields such as game production, animation generation, and remote immersive communication. However, these 4D human avatar meshes ar

Saja Al-Dabet, Sherzod Turaev, Nazar Zaki, Arif O. Khan

Diagnosing ocular-induced abnormal head posture (AHP) requires a comprehensive analysis of both head pose and ocular movements. However, existing datasets focus on these aspects separately, limiting the development of integrated diagnostic approaches and restricting AI-driven advancements in AHP analysis. To address this gap, we introduce PoseGaze-AHP, a nov

Sreedhar Narayanaswamy, Pratikkumar Dilipkumar Patel, Ian Karlin, Apoorv Gupta

This paper presents datacenter power profiles, a new NVIDIA software feature released with Blackwell B200, aimed at improving energy efficiency and/or performance. The initial feature provides coarse-grain user control for HPC and AI workloads leveraging hardware and software innovations for intelligent power management and domain knowledge of HPC and AI wor

Oleg Filatov, Jiangtao Wang, Jan Ebert, Stefan Kesselheim

Despite recent progress in optimal hyperparameter transfer under model and dataset scaling, no unifying explanatory principle has been established. For Adam and Scion optimizers, we discover that joint optimal scaling across model and dataset sizes is conditioned on a single invariant: the operator norm of the output layer. Across models with up to 1.3B para

Nikolaos Kaparinos, Vasileios Mezaris

Generative Adversarial Networks (GANs) achieve excellent performance in generative tasks, such as image super-resolution, but their computational requirements make difficult their deployment on resource-constrained devices. While knowledge distillation is a promising research direction for GAN compression, effectively training a smaller student generator is

Runhao Liu, Ziming Chen, Guangzhen Yao, Peng Zhang

Skin cancer is one of the most prevalent and deadly forms of cancer worldwide, highlighting the critical importance of early detection and diagnosis in improving patient outcomes. Deep learning (DL) has shown significant promise in enhancing the accuracy and efficiency of automated skin disease diagnosis, particularly in detecting and classifying skin lesion

Dalia Ali, Muneeb Ahmed, Hailan Wang, Arfa Khan

Despite AI's promise for addressing global challenges, empirical understanding of AI adoption in mission-driven organizations (MDOs) remains limited. While research emphasizes individual applications or ethical principles, little is known about how resource-constrained, values-driven organizations navigate AI integration across operations. We conducted thema

Yiheng Xie, Wenqi Cui, Adam Wierman

Data center loads have expanded significantly in recent years. Compared to traditional loads, data centers are highly sensitive to voltage deviations and thus their protection mechanisms trip more proactively during voltage fluctuations. During a grid fault, simultaneous tripping of large-scale data centers can further destabilize the transmission system and

Xinwen Zhang, Hongchang Gao

The recently introduced optimizer, Muon, has gained increasing attention due to its superior performance across a wide range of applications. However, its effectiveness in federated learning remains unexplored. To address this gap, this paper investigates the performance of Muon in the federated learning setting. Specifically, we propose a new algorithm, Fed

Wenhao Deng, Long Wei, Chenglei Yu, Tailin Wu

Reinforcement learning with verifiable rewards (RLVR) has recently enhanced the reasoning capabilities of large language models (LLMs), particularly for mathematical problem solving. However, a fundamental limitation remains: as the sampling budget increases, the advantage of RLVR-trained models over their pretrained bases often diminishes or even vanishes,

Sushil Singla

We find expressions for the Gateaux derivative of the matrix norms in operator spaces, and operator systems. Some applications of the results to quantum probability measures, states on C$^*$-algebras, and Birkhoff-James orthogonality are also presented.

Arina Kharlamova, Bowei He, Chen Ma, Xue Liu

Online services rely on CAPTCHAs as a first line of defense against automated abuse, yet recent advances in multi-modal large language models (MLLMs) have eroded the effectiveness of conventional designs that focus on text recognition or 2D image understanding. To address this challenge, we present Spatial CAPTCHA, a novel human-verification framework that l

Nathalia Nascimento, Everton Guimaraes, Paulo Alencar

The rise of large language models (LLMs) has introduced transformative potential in automated code generation, addressing a wide range of software engineering challenges. However, empirical evaluation of LLM-based code generation lacks standardization, with studies varying widely in goals, tasks, and metrics, which limits comparability and reproducibility. I

Xiaoxiao Ma, Jane Ye

Recently, a new local optimality concept for minimax problems, termed calm local minimax points, has been introduced. In this paper, we extend this concept to a general class of nonsmooth, nonconvex nonconcave minimax problems with coupled constraints, where the inner feasible set depends on the outer variable. We derive comprehensive first-order and second-

Faeze Moradi Kalarde, Ben Liang, Min Dong, Yahia A. Eldemerdash Ahmed

In Federated Learning (FL) with over-the-air aggregation, the quality of the signal received at the server critically depends on the receive scaling factors. While a larger scaling factor can reduce the effective noise power and improve training performance, it also compromises the privacy of devices by reducing uncertainty. In this work, we aim to adaptivel

Raghav Sharma, Manan Mehta

Ensuring that critical IoT systems function safely and smoothly depends a lot on finding anomalies quickly. As more complex systems, like smart healthcare, energy grids and industrial automation, appear, it is easier to see the shortcomings of older methods of detection. Monitoring failures usually happen in dynamic, high dimensional situations, especially w

Jyoti Kini, Rohit Gupta, Mubarak Shah

Traditional object detection models are typically trained on a fixed set of classes, limiting their flexibility and making it costly to incorporate new categories. Open-vocabulary object detection addresses this limitation by enabling models to identify unseen classes without explicit training. Leveraging pretrained models contrastively trained on abundantly

Minseo Lee, Byeonghyeon Lee, Lucas Yunkyu Lee, Eunsoo Lee

4D Gaussian Splatting has emerged as a new paradigm for dynamic scene representation, enabling real-time rendering of scenes with complex motions. However, it faces a major challenge of storage overhead, as millions of Gaussians are required for high-fidelity reconstruction. While several studies have attempted to alleviate this memory burden, they still fac

Sanhita Basu, Tomas Fröding, Ali Teymur Kahraman, Dimitris Toumpanakis

Background: Pleural Effusions (PE) is a common finding in many different clinical conditions, but accurately measuring their volume from CT scans is challenging. Purpose: To improve PE segmentation and quantification for enhanced clinical management, we have developed and trained a semi-supervised deep learning framework on contrast-enhanced CT volumes. Mate

Tianlong Nan, Shuvomoy Das Gupta, Garud Iyengar, Christian Kroer

We study the alternating gradient descent-ascent (AltGDA) algorithm in two-player zero-sum games. Alternating methods, where players take turns to update their strategies, have long been recognized as simple and practical approaches for learning in games, exhibiting much better numerical performance than their simultaneous counterparts. However, our theoreti

JingJing Zang, Chuan Yue, Qiang Yuan, Wei Jiang

The Dark Matter Particle Explorer (DAMPE) is a satellite-borne detector designed to detect high-energy cosmic ray particles with its core component being a BGO calorimeter capable of measuring energies from $\sim$GeV to $O(100)$ TeV. The 32 radiation lengths thickness of the calorimeter is designed to ensure full containment of showers produced by cosmic ray

Rui Qian, Xin Yin, Chuanhang Deng, Zhiyuan Peng

We present UGround, a \textbf{U}nified visual \textbf{Ground}ing paradigm that dynamically selects intermediate layers across \textbf{U}nrolled transformers as ``mask as prompt,'' diverging from the prevailing pipeline that leverages the fixed last hidden layer as ``\texttt{<SEG>} as prompt.'' UGround addresses two primary challenges posed by the prevailing

Jianyu Wang, Tianrui Hou, Wenchi Cheng, Hailin Zhang

Magnetic induction (MI) communication is an effective underground emergency communication technique after disasters such as landslides, mine collapses, and earthquakes, due to its advantages in mediums such as soil, concrete, and metals. Based on channel state information (CSI), magnetic beamforming can significantly improve the performance of MI communicati

Ruiying Ma, Chieh-Jan Mike Liang, Yanjie Gao, Francis Y. Yan

Designing system algorithms remains challenging, where the discontinuous nature of the solution space often forces system engineers to rely on generic heuristics at the expense of performance. We study whether LLMs can practically drive algorithm generation, and find that they are biased towards well-known generic designs, rather than making the creative lea

Fernando Darío Almeida García, Francisco Raimundo Albuquerque Parente, Michel Daoud Yacoub, Jose Cândido Silveira Santos Filho

The sum of random variables (RVs) appears extensively in wireless communications, at large, both conventional and advanced, and has been subject of longstanding research. The statistical characterization of the referred sum is crucial to determine the performance of such communications systems. Although efforts have been undertaken to unveil these sum statis

Jihoon Ok, Giovanni Scilla, Bianca Stroffolini

We study partial regularity for nondegenerate parabolic systems of double phase type, where the growth function is given by $H(z,s)=s^p+a(z)s^q$, $z=(x,t)\in\Omega_T$, with $\tfrac{2n}{n+2}<p\le q$ and $a(z)$ a nonnegative $C^{0,\alpha,\frac{\alpha}{2}}$-continuous function for some $\alpha\in(0,1]$. As the main result we prove that if $q< \min \{p+\tfrac{\a

Jianyu Wang, Zhichao Li, Wenchi Cheng, Wei Zhang

Magnetic induction (MI) communication is an effective underground emergency communication technique after disasters such as landslides, mine collapses, and earthquakes, due to its advantages in mediums such as soil, concrete, and metals. However, the propagation mediums in practical MI based underground emergency communications are usually diverse and compos

Raghav Sharma, Manan Mehta

Small language models (SLMs; 1-12B params, sometimes up to 20B) are sufficient and often superior for agentic workloads where the objective is schema- and API-constrained accuracy rather than open-ended generation. We synthesize recent evidence across open and proprietary SLMs (Phi-4-Mini, Qwen-2.5-7B, Gemma-2-9B, Llama-3.2-1B/3B, Ministral-3B/8B, Apple on-d

Payton Howell, Aleksandr Aravkin

We review optimization-based approaches to smoothing nonlinear dynamical systems. These approaches leverage the fact that the Extended Kalman Filter and corresponding smoother can be framed as the Gauss-Newton method for a nonlinear least squares maximum a posteriori loss, and stabilized with standard globalization techniques. We compare the performance of t

Gon Buzaglo, Noah Golowich, Elad Hazan

This paper investigates a class of games with large strategy spaces, motivated by challenges in AI alignment and language games. We introduce the hidden game problem, where for each player, an unknown subset of strategies consistently yields higher rewards compared to the rest. The central question is whether efficient regret minimization algorithms can be d

Vincent Nguyen, Guilherme Herzog, José Cambronero, Marcus Revaj

Manually editing pasted code is a long-standing developer pain point. In internal software development at Google, we observe that code is pasted 4 times more often than it is manually typed. These paste actions frequently require follow-up edits, ranging from simple reformatting and renaming to more complex style adjustments and cross-language translations.

Reza Rahimi Baghbadorani, Peyman Mohajerin Esfahani, Sergio Grammatico

We propose an accelerated algorithm with a Frank-Wolfe method as an oracle for solving strongly monotone variational inequality problems. While standard solution approaches, such as projected gradient descent (aka value iteration), involve projecting onto the desired set at each iteration, a distinctive feature of our proposed method is the use of a linear m

Mario Castro, Benjamín Mancilla, Fabian Wolff, Alvaro S. Nunez

This paper presents a novel approach for generating and controlling spin currents in an antiferromagnetic twisted honeycomb bilayer in response to an elastic deformation. Utilizing a continuum model, closely based upon the seminal Bistritzer-MacDonald model, that captures the essential physics of low-energy moir\'e bands, we calculate the spin current respon

Pranav Sharma, Shivank Garg, Durga Toshniwal

Recent advances in image generation models have led to models that produce synthetic images that are increasingly difficult for standard AI detectors to identify, even though they often remain distinguishable by humans. To identify this discrepancy, we introduce \textbf{Mirage}, a curated dataset comprising a diverse range of AI-generated images exhibiting v

Behraj Khan, Tahir Qasim Syed

When training data are fragmented across batches or federated-learned across different geographic locations, trained models manifest performance degradation. That degradation partly owes to covariate shift induced by data having been fragmented across time and space and producing dissimilar empirical training distributions. Each fragment's distribution is sl

Shen Fan, Przemyslaw Musialski

We propose a simple, data-efficient pipeline that augments an implicit reconstruction network based on neural SDF-based CAD parts with a part-segmentation head trained under PartField-generated supervision. Unlike methods tied to fixed taxonomies, our model accepts meshes with any number of parts and produces coherent, geometry-aligned labels in a single pas

María Aguado-Yáñez, Karl Jansen, Daniel Gómez-Marín, Sergi Jordà

A quantum computing algorithm for rhythm generation is presented, which aims to expand and explore quantum computing applications in the arts, particularly in music. The algorithm maps quantum random walk trajectories onto a rhythmspace -- a 2D interface that interpolates rhythmic patterns. The methodology consists of three stages. The first stage involves d

Yuanjie Lei, Shuangqian Liu, Qinghua Xiao, Huijiang Zhao

We prove global existence of smooth solutions near Maxwellians for the non-cutoff Vlasov-Poisson-Boltzmann system in the weakly collisional regime. To address the weak dissipation of the non-cutoff linearized Boltzmann operator, we develop a refined velocity-weighted energy framework combined with vector-field techniques to control the transport term, nonlin

S. Choo, S. Varshney, J. Shah, A. K. Manjeshwar

Freestanding ferroelectric membranes are promising for flexible electronics, nonvolatile memory, photonics, and spintronics, but their synthesis is challenged by the need for reproducibility with precise stoichiometric control. Here, we demonstrate the adsorption-controlled growth of single-crystalline, epitaxial BaTiO3 films by hybrid molecular beam epitaxy

Shuoyan Wei, Feng Li, Shengeng Tang, Runmin Cong

Continuous space-time video super-resolution (C-STVSR) has garnered increasing interest for its capability to reconstruct high-resolution and high-frame-rate videos at arbitrary spatial and temporal scales. However, prevailing methods often generalize poorly, producing unsatisfactory results when applied to out-of-distribution (OOD) scales. To overcome this

Dániel Gábor Simon

Let $P\subset \R^2$ be a set of $n$ points in general position. A peeling sequence of $P$ is a list of its points, such that if we remove the points from $P$ in that order, we always remove the next point from the convex hull of the remainder of $P$. Using the methodology of Dumitrescu and T\'oth \cite{Dumitrescu}, with a more careful analysis, we improve th

Pedro Ivo da Cruz, Dimitri Silva, Tito Spadini, Ricardo Suyama

Massive multiple-input multiple-output (MMIMO) is essential to modern wireless communication systems, like 5G and 6G, but it is vulnerable to active eavesdropping attacks. One type of such attack is the pilot contamination attack (PCA), where a malicious user copies pilot signals from an authentic user during uplink, intentionally interfering with the base s

Gerd Kortemeyer, Alexander Caspar, Daria Horica

We investigate whether contemporary multimodal LLMs can assist with grading open-ended calculus at scale without eroding validity. In a large first-year exam, students' handwritten work was graded by GPT-5 against the same rubric used by teaching assistants (TAs), with fractional credit permitted; TA rubric decisions served as ground truth. We calibrated a h

Alex Durkin, Jasper Stolte, Mehmet Mercangöz

Start-ups and product grade-changes are critical steps in continuous-process plant operation, because any misstep immediately affects product quality and drives operational losses. These transitions have long relied on manual operation by a handful of expert operators, but the progressive retirement of that workforce is leaving plant owners without the tacit

André Coelho, Pedro Ribeiro, Helder Fontes, Rui Campos

This position paper presents A4FN, an Agentic Artificial Intelligence (AI) architecture for intent-driven automation in Flying Networks (FNs) using Unmanned Aerial Vehicles (UAVs) as access nodes. A4FN leverages Generative AI and Large Language Models (LLMs) to enable real-time, context-aware network control via a distributed agentic system. It comprises two

Yunyun Ma, Jiguang Sun

The computation of scattering poles for a sound-soft obstacle is investigated. These poles correspond to the eigenvalues of two boundary integral operators. We construct novel decompositions of these operators and show that they are Fredholm. Then a Fourier-Galerkin method is proposed for discretization. By establishing the regular convergence of the discret

Pavel Rajmic, Jiří Schimmel, Šimon Cieslar

The speech transmission index (STI) is a popular simple metric for the prediction of speech intelligibility when speech is passed through a transmission channel. Computation of STI from acoustic measurements is described in the IEC 60268-16:2020 standard. Though, reliable implementations of STI are not publicly accessible and are frequently limited to the us

Adam Haroon, Tristan Schuler

High Altitude Balloons (HABs) can leverage stratospheric wind layers for limited horizontal control, enabling applications in reconnaissance, environmental monitoring, and communications networks. Existing multi-agent HAB coordination approaches use deterministic methods like Voronoi partitioning and extremum seeking control for large global constellations,