November 2025 arXiv papers — page 43

Yuanzhe Li, Steffen Müller

Predicting pedestrian crossing intention is crucial for autonomous vehicles to prevent pedestrian-related collisions. However, effectively extracting and integrating complementary cues from different types of data remains one of the major challenges. This paper proposes an attention-guided cross-modal interaction Transformer (ACIT) for pedestrian crossing in

Farid Aliniaeifard, Shu Xiao Li

In a study, published in Nature, researchers from DeepMind and mathematicians demonstrated a general framework using machine learning to make conjectures in pure mathematics. Here, we build upon this framework to develop a method for identifying sufficient conditions that imply a given mathematical statement. As a demonstration, we apply this process to Stan

Sian Heesom-Green, Jonathan Shock, Geoff Nitschke

The Cognitive Buffer Hypothesis (CBH) posits that larger brains evolved to enhance survival in changing conditions. However, larger brains also carry higher energy demands, imposing additional metabolic burdens. Alongside brain size, brain organization plays a key role in cognitive ability and, with suitable architectures, may help mitigate energy challenges

Jinping Wang, Zhiqiang Gao, Zhiwu Xie

Recent studies on Neural Collapse (NC) reveal that, under class-balanced conditions, the class feature means and classifier weights spontaneously align into a simplex equiangular tight frame (ETF). In long-tailed regimes, however, severe sample imbalance tends to prevent the emergence of the NC phenomenon, resulting in poor generalization performance. Curren

Xinchi Huang, Hirofumi Nishi, Yoshifumi Kawada, Tomofumi Zushi

Quantum computing is a promising technology that accelerates the partial differential equations solver for practical problems. The reconstruction of solutions (i.e., the readout of quantum states) remains a crucial problem, although numerous efficient quantum algorithms have been proposed. In this paper, we propose and compare several efficient readout metho

Fumin Wang

Decoded Quantum Interferometry (DQI) promises superpolynomial speedups for structured optimization; however, its practical realization is often hindered by significant sensitivity to hardware noise and spectral dispersion. To bridge this gap, we introduce Kernelized Decoded Quantum Interferometry (k-DQI), a unified framework that integrates spectral engineer

Xiucheng Wang, Tingwei Yuan, Yang Cao, Nan Cheng

Radio maps (RMs) serve as environment-aware electromagnetic (EM) representations that connect scenario geometry and material properties to the spatial distribution of signal strength, enabling localization without costly in-situ measurements. However, constructing high-fidelity indoor RMs remains challenging due to the prohibitive latency of EM solvers and t

Weronika Czerniawska, Ivan Fesenko

The space of two-dimensional geometric adeles of a surface is far from being a locally compact space and there is no translation countably additive invariant nontrivial measure on it. At the same time, certain subquotients of the adeles are direct limits of compact subquotients or inverse limits of discrete subquotients, compatible in a special way. Using th

Minghe Gao, Juncheng Li, Yuze Lin, Xuqi Liu

We contend that embodied learning is fundamentally a lifecycle problem rather than a single-stage optimization. Systems that optimize only one link (data collection, simulation, learning, or deployment) rarely sustain improvement or generalize beyond narrow settings. We introduce Arcadia, a closed-loop framework that operationalizes embodied lifelong learnin

Yuanzhe Li, Hang Zhong, Steffen Müller

Pedestrian crossing intention prediction is essential for autonomous vehicles to improve pedestrian safety and reduce traffic accidents. However, accurate pedestrian intention prediction in urban environments remains challenging due to the multitude of factors affecting pedestrian behavior. In this paper, we propose a multi-context fusion Transformer (MFT) t

Weiyuan Qiu, Lingrui Wang

We constructed Yoccoz puzzle for cosine functions $f(z)=ae^z+be^{-z}$ with bounded post-critical set, and proved that a Fatou component is a Jordan domains if it is bounded and is not eventually a Siegal disk. We proved that $f$ is renormalizable if a critical value escapes to $\infty$. Finally, we obtained the local connectivity of $J(f)$.

Yulong Deng, Zheng Guan, Min He, Xue Wang

Cross-Disciplinary Cold-start Knowledge Tracing (CDCKT) faces a critical challenge: insufficient student interaction data in the target discipline prevents effective knowledge state modeling and performance prediction. Existing cross-disciplinary methods rely on overlapping entities between disciplines for knowledge transfer through simple mapping functions,

Yuanzhe Li, Steffen Müller

Pedestrian crossing intention prediction is essential for the deployment of autonomous vehicles (AVs) in urban environments. Ideal prediction provides AVs with critical environmental cues, thereby reducing the risk of pedestrian-related collisions. However, the prediction task is challenging due to the diverse nature of pedestrian behavior and its dependence

Lingxuan Wu, Zhi Yin

We introduce a spoke-arc decomposition of non-crossing annular pair partitions $NC_2(p,q)$ that records spoke type and orientation, isolates spoke-level contributions, and factorizes the dependence on the ellipticity parameter $\gamma$ into a spoke factor and arc weights. This yields closed-form descriptions of the limiting covariance of Gaussian elliptic ma

Sungjae Kim, Kihyun Na, Jinyoung Choi, Injung Kim

Automatic Pitch Correction (APC) enhances vocal recordings by aligning pitch deviations with intended musical notes. However, existing APC systems either rely on reference pitches, which limits practical applicability, or employ simple pitch estimation algorithms that often fail to preserve expressiveness and naturalness. We propose BERT-APC, a reference-fre

Dan Qiu, Jennifer A. Johnson, Chao Liu, Diogo Souto

We utilized the Stellar LAbel Machine (SLAM), a data-driven model based on Support Vector Regression, to derive stellar parameters ([Fe/H], $T_{\rm eff}$, and $\log{g}$) for SDSS-V M dwarfs using low-resolution optical spectra (R$\sim$2000) obtained with the BOSS spectrographs. These parameters are calibrated using LAMOST F, G or K dwarf companions ([Fe/H]),

Peining Zhang, Hongchen Qin, Haochen Zhang, Ziqi Guo

This work investigates the zero-shot forecasting capability of time series foundation models for Leaf Area Index (LAI) forecasting in agricultural monitoring. Using the HiQ dataset (U.S., 2000-2022), we systematically compare statistical baselines, a fully supervised LSTM, and the Sundial foundation model under multiple evaluation protocols. We find that Sun

Simin Zhu, Satish Ravindran, Alexander Yarovoy, Francesco Fioranelli

Conventional radar segmentation research has typically focused on learning category labels for different moving objects. Although fundamental differences between radar and optical sensors lead to differences in the reliability of predicting accurate and consistent category labels, a review of common radar perception tasks in automotive reveals that determini

Edward Ajayi, Martha Kachweka, Mawuli Deku, Emily Aiken

Mental health challenges and cyberbullying are increasingly prevalent in digital spaces, necessitating scalable and interpretable detection systems. This paper introduces a unified multiclass classification framework for detecting ten distinct mental health and cyberbullying categories from social media data. We curate datasets from Twitter and Reddit, imple

Mingyi Lu, Guowei Liu, Le Liang, Chongtao Guo

Collaborative perception, an emerging paradigm in autonomous driving, has been introduced to mitigate the limitations of single-vehicle systems, such as limited sensor range and occlusion. To improve the robustness of inter-vehicle data sharing, semantic communication has recently further been integrated into collaborative perception systems to enhance overa

Anton Lyubinin

We are extending Popularity Bias Memorization theorem from arXiv:archive/2404.12008 in several directions. We extend it to arbitrary degree distributions and also prove both upper and lower estimates for the alignment with top-k singular hyperspace.

Nikit Phadke

We present a universal framework for similarity-preserving encodings that subsumes all discrete, continuous, algebraic, and learned similarity methods under a single theoretical umbrella. By formulating similarity as functional witness projection over monoids, we prove that \[ O\!\left(\frac{1}{\Delta^{2}}\log N\right) \] encoding complexity with ranking pre

Mihir Sahasrabudhe

Transformers are theoretically reversal-invariant: their function class does not prefer left-to-right over right-to-left mappings. Yet empirical studies on natural language repeatedly report a "reversal curse," and recent work on temporal asymmetry in LLMs suggests that real-world corpora carry their own arrow of time. This leaves an unresolved question: do

Dishanika Denipitiyage, Naveen Karunanayake, Suranga Seneviratne, Sanjay Chawla

We propose RankOOD, a rank-based Out-of-Distribution (OOD) detection approach based on training a model with the Placket-Luce loss, which is now extensively used for preference alignment tasks in foundational models. Our approach is based on the insight that with a deep learning model trained using the Cross Entropy Loss, in-distribution (ID) class predictio

Haibo HU, Lianming Huang, Nan Guan, Chun Jason Xue

Vision-Language Action (VLA) models unify perception, reasoning, and trajectory generation for autonomous driving, but suffer from significant inference latency due to deep transformer stacks. We present DeeAD, a training-free, action-guided early-exit framework that accelerates VLA planning by evaluating the physical feasibility of intermediate trajectories

Tao-tao Qiu, Yifu Cai, Yang Liu, Si-Yu Li

The recently released data of DESI DR2 favors a dynamical dark energy theory, with the equation of state crossing the cosmological constant boundary $w=-1$. In this paper, we briefly review quintom cosmology, especially the quintom bounce. We will give three examples of a quintom bounce and one example of a cyclic universe with quintom matter.

Y. Abe, M. Szymczak, J. Zeler, L. Marciniak

The shift from point-based thermal sensing to filter-free thermal imaging requires luminescent thermometers that exhibit pronounced and thermally driven spectral changes within spectral regions matching the sensitivity profiles of the R, G, and B channels of a digital camera. In this work, we introduce such a system, enabled by the synergistic interplay betw

Adam Kardos, Sven-Olaf Moch, Oliver Schnetz

We present an implementation of algorithms for the symbolic integration of hyperlogarithms multiplied by rational functions in the computer algebra system FORM. This implementation encompasses cases where hyperlogarithms have rational letters or a rational argument. It complements the previous implementation, HyperInt, in MAPLE by Erik Panzer, utilizing the

Meng-Chia Lee, Wen Sheng Lim, Yuan-Hao Chang, Tei-Wei Kuo

The memory capacity in edge devices is often limited due to constraints on cost, size, and power. Consequently, memory competition leads to inevitable page swapping in memory-constrained mixed-criticality edge devices, causing slow storage I/O and thus performance degradation. In such scenarios, inefficient memory allocation disrupts the balance between appl

Yaoli Liu, Ziheng Ouyang, Shengtao Lou, Yiren Song

Reference-guided image generation has progressed rapidly, yet current diffusion models still struggle to preserve fine-grained visual details when refining a generated image using a reference. This limitation arises because VAE-based latent compression inherently discards subtle texture information, causing identity- and attribute-specific cues to vanish. Mo

Fengcheng Liu

Random matrix theory has become a cornerstone in modern statistics and data science, providing fundamental tools for understanding high-dimensional covariance structures. Within this framework, the Wishart matrix plays a central role in multivariate analysis and related applications. This paper investigates both the exact and asymptotic distributions of the

Farhaan Ebadulla, Chiraag Mudlpaur, Shreya Chaurasia, Gaurav BV

Predicting gaze behavior in virtual reality environments remains a significant challenge with implications for rendering optimization and interface design. This paper introduces a multimodal approach to VR gaze prediction that combines temporal gaze patterns, head movement data, and visual scene information. By leveraging a gated fusion mechanism with cross-

Xinyu Wang, Hanwei Wu, Qingchen Hu, Zhenghan Tai

Decoder-only rerankers are central to Retrieval-Augmented Generation (RAG). However, generalist models miss domain-specific nuances in high-stakes fields like finance and law, and naive fine-tuning causes surface-form overfitting and catastrophic forgetting. To address this challenge, we introduce R2R, a domain-aware framework that combines dynamic expert ro

Lianming Huang, Haibo Hu, Qiao Li, Nan Guan

Sparsity is essential for deploying large models on resource constrained edge platforms. However, optimizing sparsity patterns for individual tasks in isolation ignores the significant I/O overhead incurred during frequent task switching. We introduce an on-demand multi-task sparsity framework specifically designed to minimize switching costs by maximizing p

Haoran Zheng, Renchi Yang, Yubo Zhou, Jianliang Xu

Message passing neural networks (MPNNs) have emerged as go-to models for learning on graph-structured data in the past decade. Despite their effectiveness, most of such models still incur severe issues such as over-smoothing and -correlation, due to their underlying objective of minimizing the Dirichlet energy and the derived neighborhood aggregation operati

Pei Zeng, Guo Zheng, Qian Xu, Liang Jiang

Fault tolerance is widely regarded as indispensable for achieving scalable and reliable quantum computing. However, the spacetime overhead required for fault-tolerant quantum computating remains prohibitively large. A critical challenge arises in many quantum algorithms with Clifford + $\varphi$ compiling, where logical rotation gates $R_{Z_L}(\varphi)$ serv

Senhan Yao, Longxu Zhang

Stochastic gradient (SG) methods are fundamental to system identification and machine learning, enabling online parameter estimation in large-scale and streaming-data settings. As a classical identification method, the SG algorithm has been extensively studied for decades. Under non-persistent excitation, the strongest currently available convergence result

Aras Bacho, Aleksei G. Sorokin, Xianjin Yang, Théo Bourdais

Neural operator learning methods have garnered significant attention in scientific computing for their ability to approximate infinite-dimensional operators. However, increasing their complexity often fails to substantially improve their accuracy, leaving them on par with much simpler approaches such as kernel methods and more traditional reduced-order model

Kaike Zhang, Jiakai Tang, Du Su, Shuchang Liu

Recommender systems shape how people discover information, form opinions, and connect with society. Yet, as their influence grows, traditional metrics, e.g., accuracy, clicks, and engagement, no longer capture what truly matters to humans. The workshop on Human-Centered Recommender Systems (HCRS) calls for a paradigm shift from optimizing engagement toward d

Junru Li, Qing Wang, Zhe Yang, Shuo Liu

Distributed storage systems typically maintain strong consistency between data nodes and metadata nodes by adopting ordered writes: 1) first installing data; 2) then updating metadata to make data visible.We propose SwitchDelta to accelerate ordered writes by moving metadata updates out of the critical path. It buffers in-flight metadata updates in programma

Syed Affan Aslam, Areej Ilyas, Jibran Rashid

We prove that the PARITY protocol is optimal for a general class of non-adaptive distillation protocols of all $n$ player nonlocal boxes (NLBs) based on XOR games. The conditional distributions generated by these NLBs are assumed to have trivial local marginals. We also show that already for $n=2$, PARITY is no longer optimal if the local marginals are non-t

Songbo Wang, Renchi Yang, Yurui Lai, Xiaoyang Lin

The emergence of graph neural networks (GNNs) has offered a powerful tool for semi-supervised node classification tasks. Subsequent studies have achieved further improvements through refining the message passing schemes in GNN models or exploiting various data augmentation techniques to mitigate limited supervision. In real graphs, nodes often tend to form t

Yihua Li, Guoyin Chen, Jinhan Guo, Yang Guo

Total solar eclipses (TSEs) provide a unique opportunity to observe the large-scale solar corona. The solar wind plays an important role in forming the large-scale coronal structure and magnetohydrodynamic (MHD) simulations are used to reproduce it for further studying coronal mass ejections (CMEs). We conduct a data-constrained MHD simulation of the global

Wangjie Li, Lin Li, Qingyang Hong

The rapid advancement of speech synthesis and voice conversion technologies has raised significant security concerns in multimedia forensics. Although current detection models demonstrate impressive performance, they struggle to maintain effectiveness against constantly evolving deepfake attacks. Additionally, continually fine-tuning these models using histo

Yun Xing, Xiaobin Hu, Qingdong He, Jiangning Zhang

Recently, Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as an effective approach to incentivizing reasoning capability in Large Multimodal Models (LMMs), while the underlying mechanisms behind this post-training paradigm are poorly understood. We begin by exploring how input activations are affected by RLVR through the perspective of logi

Yu Hu, Chong Cheng, Sicheng Yu, Xiaoyang Guo

Reconstructing dynamic 4D scenes is challenging, as it requires robust disentanglement of dynamic objects from the static background. While 3D foundation models like VGGT provide accurate 3D geometry, their performance drops markedly when moving objects dominate. Existing 4D approaches often rely on external priors, heavy post-optimization, or require fine-t

Eva Bayer-Fluckiger, Bert van Geemen, Matthias Schütt

We determine the Hodge endomorphism algebras of non-projective complex K3 surfaces (and more generally, hyperkähler manifolds). We show that they are either totally real fields or number fields generated by Salem numbers. This is unlike the projective case, where the endomorphism fields are either totally real or CM. We also develop precise existence criteri

Yifan Fan, Le Liang, Peng Liu, Xiao Li

Multi-access point coordination (MAPC) is a key technology for enhancing throughput in next-generation Wi-Fi within dense overlapping basic service sets. However, existing MAPC protocols rely on static, protocol-defined rules, which limits their ability to adapt to dynamic network conditions such as varying interference levels and topologies. To address this

Weizi Shao, Taolin Zhang, Zijie Zhou, Chen Chen

Recent advancements in multi-modal retrieval-augmented generation (mRAG), which enhance multi-modal large language models (MLLMs) with external knowledge, have demonstrated that the collective intelligence of multiple agents can significantly outperform a single model through effective communication. Despite impressive performance, existing multi-agent syste

V. Galkin, O. Pochinka

This paper studies regular topological flows $f^t$ defined on closed {topological} manifolds $M^n$. The chain recurrent set of such a flow consists of a finite number of topologically hyperbolic fixed points and periodic orbits. Like their smooth analogs -- Morse-Smale flows -- regular flows possess a continuous Morse-Bott function that decreases outside the

Tomokage Yoneyama, Tsubasa Tamba, Hirokazu Odaka, Aya Bamba

In X-ray astronomy, most observatories utilize multi-pixel photon-counting devices. When a photon counting device observes a bright source, we face an unavoidable problem called pile-up. Pile-up leads to mistakes in the observational properties of the source, mainly an apparent decrease in the X-ray flux. X-Ray Imaging and Spectroscopy Mission (XRISM) has tw

Yujia Wang, Fenglong Ma, Jinghui Chen

Asynchronous federated learning (FL) has recently gained attention for its enhanced efficiency and scalability, enabling local clients to send model updates to the server at their own pace without waiting for slower participants. However, such a design encounters significant challenges, such as the risk of outdated updates from straggler clients degrading th

Hongji Yang, Yucheng Zhou, Wencheng Han, Runzhou Tao

Recent advances in diffusion models have demonstrated impressive capability in generating high-quality images for simple prompts. However, when confronted with complex prompts involving multiple objects and hierarchical structures, existing models struggle to accurately follow instructions, leading to issues such as concept omission, confusion, and poor comp

Navid Mohammad Mirzaei, Wan Yang

Clemmesen's hook refers to a commonly observed slowdown and rebound in breast cancer incidence around the age at menopause. It suggests a shift in the underlying carcinogenic dynamics, but the mechanistic basis remains poorly understood. Building on our previously developed Extended Multistage Clonal Expansion Tumor (MSCE-T) model, we perform a theoretical a

Changho Choi, Minho Kim, Jinkyu Kim

Despite decades of progress, a truly input-size agnostic visual encoder-a fundamental characteristic of human vision-has remained elusive. We address this limitation by proposing \textbf{MambaEye}, a novel, causal sequential encoder that leverages the low complexity and causal-process based pure Mamba2 backbone. Unlike previous Mamba-based vision encoders th

Manoj Kummini, Abhiram Subramanian

Let $X \subseteq \mathbb{P}^n, n \geq 4$ be a codimension-two subcanonical local complete intersection variety with ideal sheaf $\mathcal{I}_X$. Let $a_X \in \mathbb{Z}$ be such that $ω_X = \mathscr{O}_X(a_X)$. Assume that there exists $\displaystyle j \leq \frac{a_X+n+2}{2}$ such that $Γ(\mathcal{I}_X(j)) \neq 0$. We prove some sufficient conditions on the

Yohann Trivino

I present a three-dimensional Discrete Element Method study of self-gravitation and contact mechanics in cold granular assemblies. The model couples direct Newtonian attraction between every particle pair with a linear visco-elastic normal contact law. Particles are treated as non-cohesive spheres; the normal force is parameterized to reproduce a prescribed

Deepra Ghosh, Sanat K. Sarkar

This paper develops a general framework for controlling the false discovery rate (FDR) in multiple testing of Gaussian means against two-sided alternatives. The widely used Benjamini-Hochberg (BH) procedure provides exact FDR control under independence or conservative control under specific one-sided dependence structures, but its validity for correlated two

Hichem Felouat, Hanrui Wang, Isao Echizen

3D face recognition offers a robust biometric solution by capturing facial geometry, providing resilience to variations in illumination, pose changes, and presentation attacks. Its strong spoof resistance makes it suitable for high-security applications, but protecting stored biometric templates remains critical. We present GFT-GCN, a privacy-preserving 3D f

Tianyi Chen, Michael Solodko, Sen Wang, Jongwoo Ko

Computer Using Agents (CUAs) are increasingly equipped with external tools, enabling them to perform complex and realistic tasks. For CUAs to operate effectively, application selection, which refers to deciding which application to use before invoking fine-grained tools such as APIs, is a fundamental capability. It determines whether the agent initializes th

Meiyu Zhong, Noel Teku, Ravi Tandon

Large Language Models (LLMs), though shown to be effective in many applications, can vary significantly in their response quality. In this paper, we investigate this problem of prompt fairness: specifically, the phrasing of a prompt by different users/styles, despite the same question being asked in principle, may elicit different responses from an LLM. To q

Jinxuan Zhu, Zihao Yan, Yangyu Xiao, Jingxiang Guo

Contact feedback is essential for contact-rich robotic manipulation, as it allows the robot to detect subtle interaction changes and adjust its actions accordingly. Six-axis force-torque sensors are commonly used to obtain contact feedback, but their high cost and fragility have discouraged many researchers from adopting them in contact-rich tasks. To offer

Biplab Pal

Let $B \subset A$ be a depth $2$ inclusion of simple unital $C^*$-algebras with a conditional expectation of index-finite type. We show that the second relative commutant $B' \cap A_1$ carries a canonical structure of a weak $C^*$-Hopf algebra. Furthermore, we construct an action of this weak $C^*$-Hopf algebra on $A$ for which $B$ is precisely the fixed

Haoran Hu, Junren Shi, Shuo Jiang, Kun Cheng

Forward Collision Warning systems are crucial for vehicle safety and autonomous driving, yet current methods often fail to balance precise multi-agent interaction modeling with real-time decision adaptability, evidenced by the high computational cost for edge deployment and the unreliability stemming from simplified interaction models.To overcome these dual

Hongxu Chen, Renjun Duan, Shuangqian Liu

In the paper, we develop spectral theory to analyze the sharp asymptotic behavior of solutions to the Boltzmann equation around global Maxwellians in a three-dimensional infinite layer $\mathbb{R}^2\times (-1,1)$. The isothermal diffuse reflection boundary condition is imposed on two parallel infinite planes at $x_3=\pm 1$. The main difficulties lie in the f

Qingda Hu, Xinjun Yang, Feifei Li, Junru Li

In recent years, resource elasticity and cost optimization have become essential for RDBMSs. While cloud-native RDBMSs provide elastic computing resources via disaggregated computing and storage, storage costs remain a critical user concern. Consequently, data compression emerges as an effective strategy to reduce storage costs. However, existing compression

Chenliang Li, Adel Elmahdy, Alex Boyd, Zhongruo Wang

Reinforcement learning (RL) algorithms such as PPO and GRPO are widely used to train large language models (LLMs) for multi-turn agentic tasks. However, in off-policy training pipelines, these methods often exhibit unstable optimization dynamics and are prone to performance collapse. Through empirical analysis, we identify two fundamental sources of instabil

Bradley E. Schaefer

A popular solution to the Type Ia supernova (SNIa) progenitor problem is that the immediate progenitors are symbiotic star systems. This solution requires that the companion star of the exploding white dwarf must be a red giant star with a heavy stellar wind. This has been tested for 189 normal SNIa, with all tested systems being proven to not have the requi

Hariprasad M

Given l<s<m an upper bound on the s norm is given using l norm and m norm. The result is applied in bounding odd values of zeta function, binomial sums and gamma and beta functions.

Shreyansh Swami, Ishwardeep Singh, Ujjwalpreet Singh, Chinmay Prawah Pant

Polymorphic malware continually alters its structure to evade signature-based defences, challenging both commercial antivirus (AV) and enterprise detection systems. This study introduces a reproducible framework for analysing eight polymorphic behaviours-junk code insertion, control-flow obfuscation, packing, data encoding, domain generation, randomized beac

Yuxuan Wu, Linghan Ma, Ruichen Zhang, Yinqiu Liu

Edge General Intelligence (EGI) represents a paradigm shift in mobile edge computing, where intelligent agents operate autonomously in dynamic, resource-constrained environments. However, the deployment of advanced agentic AI models on mobile and edge devices faces significant challenges due to limited computation, energy, and storage resources. To address t

Wenhao Zhao, Harriet Lau, Stephen Parman, James W. Head

The timing of the Moon's formation is fundamental to understanding the early Earth-Moon system. Ages of lunar magma ocean (LMO) crystallization have long been regarded as a key proxy for that event. Yet returned lunar sample ages cluster near the relatively young age of ~4.35 billion years ago (Ga). These ages are commonly interpreted as recording either a y

Mohammadreza Razvan, Sheida Shahidi

Chaotic bursting behaviors have been observed by many authors in neural dynamics mainly in the transition between different kinds of bursting behavior. As a well-known three-dimensional ODEs model with various bursting solutions, the FitzHugh-Rinzel model has been considered in this paper. The structure of the strange attractor that appears in chaotic transi

Akash Doshi, Pinar Sen, Kirill Ivanov, Wei Yang

Channel coding from 2G to 5G has assumed the inputs bits at the physical layer to be uniformly distributed. However, hybrid automatic repeat request acknowledgement (HARQ-ACK) bits transmitted in the uplink are inherently non-uniformly distributed. For such sources, significant performance gains could be obtained by employing joint source channel coding, aid

Jingchu Gai, Guanning Zeng, Huaqing Zhang, Aditi Raghunathan

It is widely recognized that reinforcement learning (RL) fine-tuning of large language models often leads to diversity collapse, where outputs lack variety. Prior work has proposed a range of heuristics to counteract this effect, but these methods are ad hoc: they frequently trade off correctness for diversity, their effectiveness varies across tasks, and in

Shenjun Zhong, Zhifeng Chen, Zhaolin Chen

Magnetic Resonance Fingerprinting (MRF) leverages transient-state signal dynamics generated by the tunable acquisition parameters, making the design of an optimal, robust sequence a complex, high-dimensional sequential decision problem, such as optimizing one of the key parameters, flip angle. Reinforcement learning (RL) offers a promising approach to automa

Rahul Sharma, Pragnya Ramjee, Kaushik Murali, Mohit Jain

Patients frequently seek information during their medical journeys, but the rising volume of digital patient messages has strained healthcare systems. Large language models (LLMs) offer promise in generating draft responses for clinicians, yet how physicians refine these drafts remains underexplored. We present a mixed-methods study with nine ophthalmologist

Xinyu Xu, Wenqin Yue, Yueli Yu, Yongke Yan

Effective modulation of magnetic permeability plays a vital role in the development of high-performance inductors. Here, phase-field simulations of hard/soft ferrite composites (BaM/NiZn) clarify how exchange coupling and microstructure impact magnetic permeability. We show that particle size, volume fraction, and orientation of the hard phase can effectivel

Peng Zhao, Yu-Hu Yan, Hang Yu, Zhi-Hua Zhou

Universal online learning aims to achieve optimal regret guarantees without requiring prior knowledge of the curvature of online functions. Existing methods have established minimax-optimal regret bounds for universal online learning, where a single algorithm can simultaneously attain $\mathcal{O}(\sqrt{T})$ regret for convex functions, $\mathcal{O}(d \log T

Youngseo Kim, Dohyun Kim, Geonhee Han, Paul Hongsuck Seo

Image diffusion models, though originally developed for image generation, implicitly capture rich semantic structures that enable various recognition and localization tasks beyond synthesis. In this work, we investigate their self-attention maps can be reinterpreted as semantic label propagation kernels, providing robust pixel-level correspondences between r

Songlin Zhao, Michael Pitts, Zhuwei Qin

Large language models (LLMs) are increasingly adapted into domain-specific variants for applications in law, healthcare, and finance. Their scale, however, limits deployment in resource-constrained settings, and existing compression approaches often either degrade after domain adaptation or require substantial additional computation. We introduce EfficientXp

Md Mosharaf Hossan, Rifat Ara Tasnim, Farjana Z Eishita

Serious games for health are designed with specific health objectives and are increasingly being used in mental health interventions. Leveraging sensor equipped handheld devices such as smartphones and smartwatches, these games can provide accessible and engaging therapeutic environments. This study introduces a heart rate (HR) controlled game to aid players

Vaishali Vinay

Large language models (LLMs) are being rapidly integrated into decision-support tools, automation workflows, and AI-enabled software systems. However, their behavior in production environments remains poorly understood, and their failure patterns differ fundamentally from those of traditional machine learning models. This paper presents a system-level taxono

Lidi Zhang, Han Wu, Liyu Zhang, Ruofeng Liu

The 3D bin packing problem, with its diverse industrial applications, has garnered significant research attention in recent years. Existing approaches typically model it as a discrete and static process, while real-world applications involve continuous gravity-driven interactions. This idealized simplification leads to infeasible deployments (e.g., unstable

Kenta Yamamoto, Teruaki Hayashi

Recent advances in machine learning and big data analytics have intensified the demand for high-quality cross-domain datasets and accelerated the growth of data trading across organizations. As data become increasingly recognized as an economic asset, data marketplaces have emerged as a key infrastructure for data-driven innovation. However, unlike mature pr

Mikołaj Sienicki, Krzysztof Sienicki

A recent paper by Aziz and Howl (Nature 2025) argues that, once quantum matter is described at the level of quantum field theory and coupled to a classical gravitational field, higher order processes can generate entanglement between two spatially separated masses. A contemporaneous critical note (Marletto, Oppenheim, Vedral, Wilson, arXiv:2511.07348v1) show

Oleg Viro

Let $X$ be a real algebraic variety with set of complex points $X_{\mathbb C}$ and set of real points $X_{\mathbb R}$. A complex slice of $X$ is a transverse intersection of $X_{\mathbb R}$ with a complex subvariety $V$ of $X_{\mathbb C}$. Complex slices are real algebraic varieties of a very special kind. They are cooriented, realize an integer cohomology c

Janani Kugarajeevan, Thanikasalam Kokul, Amirthalingam Ramanan, Subha Fernando

One-stream Transformer-based trackers have demonstrated remarkable performance by concatenating template and search region tokens, thereby enabling joint attention across all tokens. However, enabling an excessive proportion of background search tokens to attend to the target template tokens weakens the tracker's discriminative capability. Several token prun

Yuika Kajihara, Mitsuru Shibayama

In 1986, Moser showed that for a given area-preserving map, there exists a Hamiltonian system that realizes it on the Poincar\'e section. Using his technique, we show that for any braid, there exists a Hamiltonian system whose orbits realize the given braid. In particular, when the braid is pseudo-Anosov, so is the Poincar\'e map of the corresponding Hamilto

Qiyao Wei, Edward Morrell, Lea Goetz, Mihaela van der Schaar

Evaluating the open-form textual responses generated by Large Language Models (LLMs) typically requires measuring the semantic similarity of the response to a (human generated) reference. However, there is evidence that current semantic similarity methods may capture syntactic or lexical forms over semantic content. While benchmarks exist for semantic equiva

Qi Yan

We study the stochastic Willmore flow and the stochastic surface diffusion flow for closed or non-closed curves on $\mathbb{R}^2$ in this paper. We equivalently formulate them as a stochastic one-phase Stefan problem (or a stochastic free boundary problem) of the curvature, which is parameterized by the arc-length, and the length of the curves. After rewriti

Shaozhen Xu

Using the birational map between a smooth toric variety (adapted to the phase function of the oscillatory integral) and $\mathbb{R}^n\textbackslash\{0\}$, we can effectively carry out the van der Corput-type analysis in higher dimensions. This allows us to give an elegant derivation of the leading term in Varchenko's asymptotic expansion \cite{Var76}. We exp

Arijit Chakraborty

We study Malle's conjecture for the group $C_2 \wr H$ where $H$ is a permutation group. Malle's conjecture for this case was proved by J\"urgen Kl\"uners in \cite{arXiv:1108.5597} under mild conditions for $H$. In this article, we provide an alternative method to obtain the explicit main term and a power-saving error term for $C_2 \wr H$ extensions of an arb

Kun Guo, Yun Shen, Xijun Wang, Chaoqun You

Fast and accurate video object recognition, which relies on frame-by-frame video analytics, remains a challenge for resource-constrained devices such as traffic cameras. Recent advances in mobile edge computing have made it possible to offload computation-intensive object detection to edge servers equipped with high-accuracy neural networks, while lightweigh

Kehan Wang, Tingqiong Cui, Yang Zhang, Yu Chen

Fine-grained image retrieval, which aims to find images containing specific object components and assess their detailed states, is critical in fields like security and industrial inspection. However, conventional methods face significant limitations: manual features (e.g., SIFT) lack robustness; deep learning-based detectors (e.g., YOLO) can identify compone

Yufan Chen, Omar Moured, Ruiping Liu, Junwei Zheng

Conventional document layout analysis (DLA) traditionally depends on empirical priors or a fixed set of learnable queries executed in a single forward pass. While sufficient for early-generation documents with a small, predetermined number of regions, this paradigm struggles with contemporary documents, which exhibit diverse element counts and increasingly c

Noboru Ito, Yuichiro Iwamoto

We determine whether each known generating set of arbitrary oriented Reidemeister moves is minimal. We then provide a complete classification of minimal generating sets that include a coherent Reidemeister move of type II. We also classify all minimal generating sets that include a braid-type Reidemeister move of type III. Beyond these two cases, we identify

Qin Ren, Yufei Wang, Lanqing Guo, Wen Zhang

Diffusion models have become the dominant paradigm in text-to-image generation, and test-time scaling (TTS) improves sample quality by allocating additional computation at inference. Existing TTS methods, however, resample the entire image, while generation quality is often spatially heterogeneous. This leads to unnecessary computation on regions that are al

Thonn Homsnit, Kensuke Kageyama, Tomohisa Kojima

Physics-Informed Neural Networks (PINNs) encounter significant training difficulties when applied to doubly-clamped beam problems, and the underlying causes are not fully understood. This study investigates the PINN loss landscape to identify the failure mechanisms of two primary formulations: the high-order strong formulation and the energy-based formulatio

Jun Jia, Hongyi Miao, Yingjie Zhou, Wangqiu Zhou

With the rapid progress in diffusion models, image synthesis has advanced to the stage of zero-shot image-to-image generation, where high-fidelity replication of facial identities or artistic styles can be achieved using just one portrait or artwork, without modifying any model weights. Although these techniques significantly enhance creative possibilities,