November 2025 arXiv papers — page 42

Min Zhao, Hongzhou Zhu, Yingze Wang, Bokai Yan

Despite advances, video diffusion transformers still struggle to generalize beyond their training length, a challenge we term video length extrapolation. We identify two failure modes: model-specific periodic content repetition and a universal quality degradation. Prior works attempt to solve repetition via positional encodings, overlooking quality degradati

Ximing Chen, Pui Ieng Lei, Yijun Sheng, Yanyan Liu

Diffusion models (DMs) have recently gained significant interest for their exceptional potential in recommendation tasks. This stems primarily from their prominent capability in distilling, modeling, and generating comprehensive user preferences. However, previous work fails to examine DMs in recommendation tasks through a rigorous lens. In this paper, we fi

Somsubhra De, Harsh Kumar, Arun Prakash A

Grammatical error correction (GEC) is an important task in Natural Language Processing that aims to automatically detect and correct grammatical mistakes in text. While recent advances in transformer-based models and large annotated datasets have greatly improved GEC performance for high-resource languages such as English, the progress has not extended equal

Ilaria Carleo, Grzegorz Nowak, Felipe Murgas, Enric Pallé

As part of the KESPRINT collaboration, we present the discovery and characterization of three exoplanets in the sub-Neptune to super-Neptune regime, spanning key regions of the exo-Neptunian landscape. TOI-1472c and TOI-1648b are newly discovered sub-Neptunes, while TOI-1472b is a previously known super-Neptune for which we provide an improved mass measureme

Rémy Degenne, David Ledvinka, Etienne Marion, Peter Pfaffelhuber

Brownian motion is a building block in modern probability theory. In this paper, we describe a formalization of Brownian motion using the Lean theorem prover. We build on the existing measure-theoretic foundations in Lean's mathematical library, Mathlib, and we develop several key components needed for the construction of Brownian motion, including the Carat

Min Xu, Xuejiao Han, Kai Wan, Gennian Ge

Secure aggregation (SA) is fundamental to privacy preservation in federated learning (FL), enabling model aggregation while preventing disclosure of individual user updates. This paper addresses hierarchical secure aggregation (HSA) against relay and user collusion in homogeneous networks, where each user connects to $n$ relays and each relay serves $m$ user

Johannes Brandt, Maulik Chevli, Rickmer Braren, Georgios Kaissis

Lung cancer risk estimation is gaining increasing importance as more countries introduce population-wide screening programs using low-dose CT (LDCT). As imaging volumes grow, scalable methods that can process entire lung volumes efficiently are essential to tap into the full potential of these large screening datasets. Existing approaches either over-rely on

Johannes Kerber, Helmut Ritsch, Laurin Ostermann

The configuration space, i.e. the Hilbert space, of compound quantum systems grows exponentially with the number of its subsystems: its dimensionality is given by the product of the dimensions of its constituents. Therefore a full quantum treatment is rarely possible analytically and can be carried out numerically for fairly small systems only. Fortunately,

Ikeda Yuya

In this paper, we introduce the framework of a generalized design, which represents any linear operator as a finite sum of local linear maps attached to finitely many points, thereby abstracting the core of design theory without employing integration. We then construct such a design on the space of sections of the tautological bundle over the complex project

Xiaojing Yan, Carlo Fischione

For digital over-the-air computation, the ChannelComp framework has recently been proposed to design digital modulations to compute any arbitrary function over a multiple access channel. To reduce modulation design complexity while increasing computation reliability, this paper integrates a bit-partitioning procedure into ChannelComp. The key process is to p

Deng Fucheng, Wang Wanjie, Gong Ao, Wang Xiaoqi

Focusing on the practical configuration needs of optimization algorithms in deep learning, this article concentrates on five major algorithms: SGD, Mini-batch SGD, Momentum, Adam, and Lion. It systematically analyzes the core advantages, limitations, and key practical recommendations of each algorithm. The research aims to gain an in-depth understanding of t

Yuki Amano, Yuken Ohshiro, Hiromasa Suzuki, Kotaro Fukushima

We present a spatially resolved high-resolution X-ray spectroscopy of the supernova remnant DEM L71 using the Reflection Grating Spectrometer (RGS) aboard XMM-Newton. Because of the large dispersion angle of the RGS, we are able to resolve individual emission lines and examine their spatial distributions within this moderately extended remnant. We derive lin

Ben Bals, Joakim Blikstad, Greg Bodwin, Daniel Dadush

For many popular graph metric sparsifiers, such as spanners, emulators, and preservers, simple and elegant greedy algorithms are known that achieve state-of-the-art or existentially optimal tradeoffs between size and quality. The goal of this paper is to develop and analyze comparable greedy algorithms for nearby objects in graph metric augmentation. We show

Michal Feldman, Yoav Gal-Tzur, Tomasz Ponitka, Maya Schlesinger

We study multi-agent contract design with combinatorial actions, under budget constraints, and for a broad class of objective functions, including profit (principal's utility), reward, and welfare. Our first result is a strong impossibility: For submodular reward functions, no randomized poly-time algorithm can approximate the optimal budget-feasible value w

Hyeonjae Kim, Chenyue Li, Wen Deng, Mengxi Jin

Climate science demands automated workflows to transform comprehensive questions into data-driven statements across massive, heterogeneous datasets. However, generic LLM agents and static scripting pipelines lack climate-specific context and flexibility, thus, perform poorly in practice. We present ClimateAgent, an autonomous multi-agent framework that orche

Miled Alam, Abdul Karim Gizzini, Laurent Clavier

In this paper, we investigate the secrecy energy-efficiency (SEE) of a multi-user downlink non-orthogonal multiple access (NOMA) system assisted by multiple ambient backscatter communications (AmBC) in the presence of a passive eavesdropper. We analyze both the trade-off and the ratio between the achievable secrecy sum-rate and total power consumption. In th

Xingfeng Li, Xiaohan Shi, Junjie Li, Yongwei Li

This study introduces EM2LDL, a novel multilingual speech corpus designed to advance mixed emotion recognition through label distribution learning. Addressing the limitations of predominantly monolingual and single-label emotion corpora \textcolor{black}{that restrict linguistic diversity, are unable to model mixed emotions, and lack ecological validity}, EM

Grzegorz Dudek, Mateusz Kasprzyk, Paweł Pełka

This study investigates the application of the Light Gradient Boosting Machine (LGBM) model for both deterministic and probabilistic forecasting of Bitcoin realized volatility. Utilizing a comprehensive set of 69 predictors -- encompassing market, behavioral, and macroeconomic indicators -- we evaluate the performance of LGBM-based models and compare them wi

Craig Dickson

Prior work has shown that fine-tuning models on a narrow domain with misaligned data can lead to broad misalignment - a phenomenon termed "emergent misalignment" (Betley et al. 2025). While all tested models were susceptible to emergent misalignment, some models showed more resistance than others. Specifically the Qwen-2.5 family proved to be relatively resi

André Dehne, Juri Zach, Peer Stelldinger

The MARWIN robot operates at the European XFEL to perform autonomous radiation monitoring in long, monotonous accelerator tunnels where conventional localization approaches struggle. Its current navigation concept combines lidar-based edge detection, wheel/lidar odometry with periodic QR-code referencing, and fuzzy control of wall distance, rotation, and lon

Abishek Karthik, Pandiyaraju V

The healthcare industry has been revolutionized significantly by novel imaging technologies, not just in the diagnosis of cardiovascular diseases but also by the visualization of structural abnormalities like cardiomegaly. This article explains an integrated approach to the use of deep learning tools and attention mechanisms for automatic detection of cardio

Xinguo Zhu, Shaohui Peng, Jiaming Guo, Yunji Chen

Developing high-performance GPU kernels is critical for AI and scientific computing, but remains challenging due to its reliance on expert crafting and poor portability. While LLMs offer promise for automation, both general-purpose and finetuned LLMs suffer from two fundamental and conflicting limitations: correctness and efficiency. The key reason is that e

Lei Huang, Rui Zhang, Jiaming Guo, Yang Zhang

Large language models (LLMs) have shown promising capabilities in hardware description language (HDL) generation. However, existing approaches often rely on free-form natural language descriptions that are often ambiguous, redundant, and unstructured, which poses significant challenges for downstream Verilog code generation. We treat hardware code generation

N. Chug, S. D. Bass, E. Y. Beyene, C. Curceanu

A direct test of the CPT symmetry is performed for the electromagnetic decays of ortho-positronium using the J-PET tomograph. We present the precise measurement of the CPT-sensitive angular correlation entailing the positronium spin and the momenta of its annihilation photons, surpassing previous studies utilizing the same detection system. Positrons origina

Babak Mohammadian

Superconducting parametric amplifiers (SPAs) are critical components for ultralow-noise qubit readout in quantum computing, addressing the critical challenge of amplifying weak quantum signals without introducing noise that degrades coherence and computational fidelity. Unlike classical amplifiers, SPAs can achieve or closely approach quantum-limited perform

Sharjeel Ahmed, Daim Armaghan, Fatima Naweed, Umair Yousaf

There have been many recent developments in the use of Deep Learning Neural Networks for fire detection. In this paper, we explore an early warning system for detection of forest fires. Due to the lack of sizeable datasets and models tuned for this task, existing methods suffer from missed detection. In this work, we first propose a dataset for early identif

Guangfeng Jiang, Yueru Luo, Jun Liu, Yi Huang

Recent years have witnessed remarkable progress in world models, which primarily aim to capture the spatio-temporal correlations between an agent's actions and the evolving environment. However, existing approaches often suffer from tight runtime coupling or depend on offline reward signals, resulting in substantial inference overhead or hindering end-to-end

Giovanni Spitale, Federico Germani

Advances in artificial intelligence now make it possible to simulate the dead through chatbots, voice clones, and video avatars trained on a person's digital traces. These "digital ghosts" are moving from fiction to commercial reality, reshaping how people mourn and remember. This paper offers a conceptual and ethical analysis of AI-mediated digital afterliv

Ana M. Valencia, Theresa Kuechle, Maximiliam Tomoscheit, Sarah Jasmin Finkelmeyer

Hybrid heterostructures combining transition metal dichalcogenides (TMDs) with light-harvesting dyes are promising materials for next-generation optoelectronics. Yet, controlling and understanding interfacial charge transfer mechanisms in these complex systems remains a major challenge. Here, we investigate the microscopic origin of photoluminescence (PL) qu

Q. Kral, J. Wang, J. Kammerer, S. Lacour

Direct astrometric detection of exomoons remains unexplored. This study presents the first application of high-precision astrometry to search for exomoons around substellar companions. We investigate whether the orbital motion of the companion HD 206893 B exhibits astrometric residuals consistent with the gravitational influence of an exomoon or binary plane

Zizhang Luo, Fan Cui, Kexing Zhou, Runlin Guo

Repairing RTL bugs is crucial for hardware design and verification. Traditional automatic program repair (APR) methods define dedicated search spaces to locate and fix bugs with program synthesis. However, they heavily rely on fixed templates and can only deal with limited bugs. As an alternative, Large Language Models with the ability to understand code sem

Oleg S. Ugolnikov, Ilya S. Yankovsky, Nikolay N. Pertsev, Vladimir I. Perminov

During the summer season of 2025, noctilucent clouds (NLC) were observed at the latitudes 55-60N from the late May until the late August. A distinct 5-day periodicity in their occurrence emerged following the summer solstice. Analysis of EOS Aura/MLS satellite data revealed that this effect was driven by a westward 5-day planetary wave, the amplitude of whic

Arianna Stropeni, Valentina Zaccaria, Francesco Borsatti, Davide Dalle Pezze

In recent years, Visual Anomaly Detection (VAD) has gained significant attention due to its ability to identify defects using only normal images during training. Many VAD models work without supervision but are still able to provide visual explanations by highlighting the anomalous regions within an image. However, although these visual explanations can be h

Bin Sun, Rengang Zhang, Zhiteng Chao, Zizhen Liu

Automatic test pattern generation (ATPG) is a crucial process in integrated circuit (IC) design and testing, responsible for efficiently generating test patterns. As semiconductor technology progresses, traditional ATPG struggles with long execution times to achieve the expected fault coverage, which impacts the time-to-market of chips. Recent machine learni

Marco Battiston, Yu Luo

Bayesian additive regression trees (BART) are popular Bayesian ensemble models used in regression and classification analysis. Under this modeling framework, the regression function is approximated by an ensemble of decision trees, interpreted as weak learners that capture different features of the data. In this work, we propose a generalization of the BART

Duc Anh Vu, Thong Nguyen, Cong-Duy Nguyen, Viet Anh Nguyen

With the advancement of large language models (LLMs), their performance on multiple-choice question (MCQ) tasks has improved significantly. However, existing approaches face key limitations: answer choices are typically presented to LLMs without contextual grounding or explanation. This absence of context can lead to incomplete exploration of all possible an

Chujie Wang, Zhiyuan Luo, Ruiqi Liu, Can Ran

The current remote sensing image analysis task is increasingly evolving from traditional object recognition to complex intelligence reasoning, which places higher requirements on the model's reasoning ability and the flexibility of tool invocation. To this end, we propose a new multimodal agent framework, Vision-Interleaved Chain-of-Thought Framework (VICoT)

Mariana M. Garcez Duarte, Dwi P. A. Nugroho, Georges Tod, Evert Bevernage

The increasing use of Internet-of-Things (IoT) sensors in moving objects has resulted in vast amounts of spatiotemporal streaming data. To analyze this data in situ, real-time spatiotemporal processing is needed. However, current stream processing systems designed for IoT environments often lack spatiotemporal processing capabilities, and existing spatiotemp

Mario de Jesus da Graca, Jörg Dahlkemper, Peer Stelldinger

Accurate single cell detection in brightfield microscopy is crucial for biological research, yet data scarcity and annotation bottlenecks limit the progress of deep learning methods. We investigate the use of unconditional models to generate synthetic brightfield microscopy images and evaluate their impact on object detection performance. A U-Net based diffu

James Delfeld, Gian Marti, Chris Dick

We propose a method for channel estimation in multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) wireless communication systems. The method exploits the band-sparsity of wireless channels in the delay-beamspace domain by solving a regularized optimization problem in a reproducing kernel Hilbert space (RKHS). A suitable re

Chu Chen, Aitor Artola, Yang Liu, Se Weon Park

Chemical Exchange Saturation Transfer (CEST) MRI enables molecular-level visualization of low-concentration metabolites by leveraging proton exchange dynamics. However, its clinical translation is hindered by inherent challenges: spatially varying noise arising from hardware limitations, and complex imaging protocols introduce heteroscedasticity in CEST data

Priyanka Singh, Sebastian Von Mammen

Current mental-health conversational systems are usually based on fixed, generic dialogue patterns. This paper proposes an adaptive framework based on large language models that aims to personalize therapeutic interaction according to a user's psychological state, quantified with the Acceptance of Illness Scale (AIS). The framework defines three specialized

E. Levin

In this paper we propose an approach in high energy QCD, which allows us to calculate the inclusive quarkonia production at ultra high energies. This approach is based on $t$-channel unitarity and on the expressions for dipole densities from the procedure of summing of large Pomeron loops which we have developed in our previous papers. In the framework of th

Yuan Gao, Xi Jin, Manshu Khanna

We study allocation problems with reserve systems under minimum beneficiary-share guarantees, requirements that targeted matches constitute at least a specified percentage of total matches. While such mandates promote targeted matches, they inherently conflict with maximizing total matches. We characterize the complete non-domination frontier using minimal c

Henry Förster, Giacomo Ortali, Lena Schlip

Octilinear graph drawings are a standard paradigm extending the orthogonal graph drawing style by two additional slopes (+1 and -1). We are interested in two constrained drawing problems where the input specifies a so-called representation, that is: a planar embedding; the angles occurring between adjacent edges; the bends along each edge. In Orthogonal Real

George Ruck, Jean-Baptiste Aubin, Alexandre Delétang, Didier Neuzeret

The need for effective biomonitoring in wastewater has become clear due to the impracticality of continuously tracking all chemicals and emerging contaminants in the aquatic exposome. Effect-based biomonitoring provides a cost-effective solution. The ToxMate device, which uses videotracking of locomotor behavior in aquatic invertebrates, has proven efficient

J. M. Almenara, A. Leleu, T. Guillot, R. Mardling

We report the confirmation and initial characterization of a compact and dynamically rich multiple giant planet system orbiting the solar analog TOI-7510. The system was recently identified as a candidate two-planet system in a machine-learning search of the TESS light curves. Using TESS data and photometric follow-up observations with ASTEP, CHEOPS, and Eul

Jinghan Zhao, Yifei Huang, Feng Lu

Learning procedural-aware video representations is a key step towards building agents that can reason about and execute complex tasks. Existing methods typically address this problem by aligning visual content with textual descriptions at the task and step levels to inject procedural semantics into video representations. However, due to their high level of a

Xiaopeng Li, Yuanjin Zheng, Wanyu Wang, wenlin zhang

Personalized Large Language Models (PLLMs) aim to align model outputs with individual user preferences, a crucial capability for user-centric applications. However, the prevalent approach of fine-tuning a separate module for each user faces two major limitations: (1) storage costs scale linearly with the number of users, rendering the method unscalable; and

Giacomo Canevari, Kirill Cherednichenko, Arghir Zarnescu

We consider Laplace's equation in a periodically perforated domain with Robin boundary conditions on the holes, where the Robin coefficient is scaled proportionally to the inverse total surface area of the performations. We identify a regime in which surface and bulk effects contribute at the same order and show that the homogenised equation contains an addi

Dimple Rani Goyal, Dinesh Khurana

In 1977, Hartwig and Luh asked if $a$ is an element of a Dedekind-finite ring $S$, then does $aS = a^2S$ imply $Sa = Sa^2$. This question was answered negatively by Dittmer, Khurana, and Nielsen in 2014. On the other hand, Dittmer et al. proved that the question of Hartwig and Luh has a positive answer for Dedekind-finite exchange rings. We explore the quest

Dáire Healy, Ilaria Prosdocimi, Isadora Antoniano-Villalobos

We study the spatio-temporal features of extremal sub-daily precipitation data over the Piave river basin in northeast Italy using a rich database of observed hourly rainfall. Empirical evidence suggests that both the marginal and dependence structures for extreme precipitation in the area exhibit seasonal patterns, and spatial dependence appears to weaken a

Simon Damm, Jonas Ricker, Henning Petzka, Asja Fischer

Autoregressive (AR) image generation has recently emerged as a powerful paradigm for image synthesis. Leveraging the generation principle of large language models, they allow for efficiently generating deceptively real-looking images, further increasing the need for reliable detection methods. However, to date there is a lack of work specifically targeting t

Marta Sumyk, Oleksandr Kosovan

Computer Use Agents (CUAs) are designed to autonomously operate digital interfaces, yet they often fail to reliably determine whether a given task has been completed. We present an autonomous evaluation and feedback framework that uses vision-language models to assess task completion directly from screenshots and task descriptions. Our dataset covers 42 buil

Bhavya Sukhija, Lenart Treven, Carmelo Sferrazza, Florian Dörfler

We address the challenge of efficient exploration in model-based reinforcement learning (MBRL), where the system dynamics are unknown and the RL agent must learn directly from online interactions. We propose Scalable and Optimistic MBRL (SOMBRL), an approach based on the principle of optimism in the face of uncertainty. SOMBRL learns an uncertainty-aware dyn

Xiaoge Zhang, Zijie Wu, Mingtao Feng, Zichen Geng

Point cloud compression methods jointly optimize bitrates and reconstruction distortion. However, balancing compression ratio and reconstruction quality is difficult because low-frequency and high-frequency components contribute differently at the same resolution. To address this, we propose FLaTEC, a frequency-aware compression model that enables the compre

Jean Van Schaftingen

We show that limits of sequences of smooth maps between compact Riemannian manifolds with equi-integrable $W^{1, p}$-Sobolev energy can always be strongly approximated by smooth maps, giving a counterpart of Hang's density result in $W^{1, 1}$ for the Sobolev space $W^{1, p}$ with integer $p \ge 2$. Our result extends to higher-order Sobolev spaces and is st

Athanase Papadopoulos, Susumu Tanabé

This is an essay on the relation of Andr{\'e} and Simone Weil with Indian culture and Sanskrit literature, especially the Bhagavad G{\=i}t{\=a}, a Hindu scripture which they knew well, which they quoted extensively, and which guided them in making important life decisions. In addressing this question, we will also talk about the life paths of the two Weils,

Corentin Fierobe, Vadim Kaloshin, Alfonso Sorrentino

We prove that a a strongly convex planar domain (Birkhoff table) with dihedral symmetry, which is sufficiently close in a finitely smooth topology to an ellipse, is deformationally spectrally rigid within the class of domains preserving this symmetry. More precisely, any smooth one-parameter family of such domains that preserves the length spectrum (i.e., th

Hugues Draelants

A central challenge in social science is to generate rich qualitative hypotheses about how diverse social groups might interpret new information. This article introduces and illustrates a novel methodological approach for this purpose: sociological persona simulation using Large Language Models (LLMs), which we frame as a "qualitative laboratory". We argue t

Kazuki Okigami, Satoru Hayami

We study the finite-temperature stability of skyrmion crystals in frustrated magnets by analyzing the momentum-space exchange interaction of a classical Heisenberg model on a triangular lattice. Our analysis identifies two key momentum-space features that play a crucial role in stabilizing the skyrmion crystal phase. The first is the sixfold anisotropy in th

Daniel A. Goldston, Ade Irma Suriajaya

Montgomery in 1973 introduced the pair correlation method to study the vertical distribution of Riemann zeta-function zeros. This work assumed the Riemann Hypothesis (RH). One striking application was a short proof that at least 2/3 of zeta-zeros are simple zeros, the first result of its type. Over the last 50 years, most work on pair correlation of zeta-zer

Mingyang Ou, Haojin Li, Yifeng Zhang, Ke Niu

Self-supervised monocular depth estimation serves as a key task in the development of endoscopic navigation systems. However, performance degradation persists due to uneven illumination inherent in endoscopic images, particularly in low-intensity regions. Existing low-light enhancement techniques fail to effectively guide the depth network. Furthermore, solu

Hugo Spring-Ragain

This paper develops a spatial-dynamic framework to analyze the theoretical and quantitative effects of a Land Value Tax (LVT) on urban land markets, capital accumulation, and spatial redistribution. Building upon the Georgist distinction between produced value and unearned rent, the model departs from the static equilibrium tradition by introducing an explic

Geertrui Van de Voorde, Ferdinando Zullo

In this paper, we continue the study of linear sets with complementary weights. We find criteria to determine the set of points of any fixed weight and use this to present particular linear sets with few points of weight more than one. We also present a product-type construction for linear sets of complementary type arising from any linear set, allowing us t

Heman Shakeri

This paper offers a phenomenological reading of contemporary machine learning through Heideggerian concepts, aimed at enriching practitioners' reflexive understanding of their own practice. We argue that this philosophical lens reveals three insights invisible to purely technical analysis. First, the algorithmic Entwurf (projection) is distinctive in being a

Huiyu Bai, Runze Wang, Zhuoyun Du, Yiyang Zhao

Personalized Visual Language Models (VLMs) are gaining increasing attention for their formidable ability in user-specific concepts aligned interactions (e.g., identifying a user's bike). Existing methods typically require the learning of separate embeddings for each new concept, which fails to support real-time adaptation during testing. This limitation beco

Tianyou Ren, Zhenglong Ban, Yaobin Hua, Rong-Jia Yang

We investigate black hole solutions in quadratic $f(R,T)=R+kT^2$ gravity coupled with nonlinear electrodynamics $\mathcal{L}(F)=α- F/(4π) + γF^2$. Solving the field equations yields an exact magnetically charged (anti-)de Sitter black hole metric featuring novel $r^{-6}$ and $r^{-14}$ correction terms. The horizon structure can exhibit up to four horizons de

Yuneil Yeo, Jaewoong Lee, Scott Moura, Maria Laura Delle Monache

This article proposes a nonlinear microscopic dynamical model for autonomous electric vehicles (A-EVs) that considers battery energy efficiency in the car-following dynamics. The model builds upon the Optimal Velocity Model (OVM), with the control term based on the battery dynamics to enable thermally optimal and energy-efficient driving. We rigorously prove

Sandipan Dutta, Krishnendu Gongopadhyay, Rahul Mondal

We describe the equicontinuity regions of cyclic subgroups of the quaternionic projective linear group $\mathrm{PSL}(n+1,\mathbb{H})$. We show that these regions depend solely on the dynamical type of the generator $g$, i.e. whether $g$ is elliptic, parabolic, loxodromic or loxoparabolic. This yields an analytic interpretation of the dynamical classification

Hans-Peter Piepho, Emlyn Williams

Row-column designs play an important role in applications where two orthogonal sources of error need to be controlled for by blocking. Field or greenhouse experiments, in which experimental units are arranged as a rectangular array of experimental units are a prominent example. In plant breeding, the amount of seed available for the treatments to be tested m

Sergei M. Kuzenko, Jonah Ruhl

As a generalisation of the recent construction by Russo and Townsend, we propose a new approach to generate $\mathsf{U}(1)$ duality-invariant models for nonlinear electrodynamics. It is based on the use of two building blocks: (i) a fixed (but otherwise arbitrary) model for self-dual nonlinear electrodynamics with Lagrangian $L(F_{\mu\nu};g)$ depending on a

Yan Li, Yingzhao Li, Gim Hee Lee

In this paper, we present an active exploration framework for high-fidelity 3D reconstruction that incrementally builds a multi-level uncertainty space and selects next-best-views through an uncertainty-driven motion planner. We introduce a hybrid implicit-explicit representation that fuses neural fields with Gaussian primitives to jointly capture global str

Yushuai Ji, Sheng Wang, Zhiyu Chen, Yuan Sun

Diverse types of edge data, such as 2D geo-locations and 3D point clouds, are collected by sensors like lidar and GPS receivers on edge devices. On-device searches, such as k-nearest neighbor (kNN) search and radius search, are commonly used to enable fast analytics and learning technologies, such as k-means dataset simplification using kNN. To maintain high

Zixiao Huang, Wen Zeng, Tianyu Fu, Tengxuan Liu

LLM-based search agents achieve strong performance but suffer from severe latency, as each step requires serialized LLM reasoning followed by action of tool execution. We revisit this bottleneck through the lens of speculation. While traditional predict-verify speculation paradigm can break serial execution, its benefit remains limited, as it retains the ful

Andrey Kupavskii, Janos Pach

A plank is the part of space between two parallel planes. The following open problem, posed 45 years ago, can be viwed as the converse of Tarski's plank problem (Bang's theorem): Is it true that if the total width of a collection of planks is sufficiently large, then the planks can be individually translated to cover a unit ball $B$? A translative covering o

Hao Fu

An important process in tropical cyclone formation is the development of a deep, warm core, which corresponds to the growth of a barotropic cyclone. Persistent convective activity is known to be crucial for the growth of barotropic vorticity. However, it remains unclear whether the fluctuating component of convective activity, such as that caused by the diur

Hui-Jia Zhao, Jie Lu, Yunqing Jiang, Xiao-Ping Lu

Blind Super-Resolution (BSR) in planetary remote sensing constitutes a highly ill-posed inverse problem, characterized by unknown degradation patterns and a complete absence of ground-truth supervision. Existing unsupervised approaches often struggle with optimization instability and distribution shifts, relying on greedy strategies or generic priors that fa

PengYu Chen, Xiaohou Shi, Yuan Chang, Yan Sun

Anomaly prediction (AP) in multivariate time series (MTS) is crucial to ensure system dependability. Existing methods either focus solely on whether an anomaly is imminent without providing precise predictions for the future anomaly, or performing predictions directly on historical data, which is easily drowned out by the normal patterns. To address the chal

Youzhe Yang, Hafiz Majid Hussain, Juha Haakana, Pedro Nardelli

A vital strategy for ports to mitigate the environmental impact of the maritime industry, while complying with frameworks such as the European Green Deal and the Sustainable Development Goals (SDGs), entails the systematic implementation of comprehensive energy management solutions. This paper provides a baseline evaluation of the energy management systems (

Petr Molodyk, Jaemoo Choi, David W. Romero, Ming-Yu Liu

In recent years, point cloud generation has gained significant attention in 3D generative modeling. Among existing approaches, point-based methods directly generate point clouds without relying on other representations such as latent features, meshes, or voxels. These methods offer low training cost and algorithmic simplicity, but often underperform compared

Jun-Yi Lu, Kai Wang, Jin-Long Jiao

The formation of ion-Weibel-mediated collisionless shocks (IW-CSs) in ultraintense-laser-produced counter-streaming plasmas is investigated using particle-in-cell simulations. Analysis of the underlying microphysics reveals that a shear-flow ion-Weibel instability generates magnetic fields, which isotropize the incoming flow and mediate shock formation. An a

Adam Bobrowski, Andrey Pilipenko

We provide a detailed description of all possible Feller processes on infinite} star graphs with finite number of edges, processes that while away from the graph's center behave like a one-dimensional Brownian motion. The description can be seen as a continuation of the seminal paper by It\^o and McKean (devoted to Brownian motions on the half-line), recast

Hongjian Jiang, Michael Hahn, Georg Zetzsche, Anthony Widjaja Lin

Hard attention Chain-of-Thought (CoT) transformers are known to be Turing-complete. However, it is an open problem whether softmax attention Chain-of-Thought (CoT) transformers are Turing-complete. In this paper, we prove a stronger result that length-generalizable softmax CoT transformers are Turing-complete. More precisely, our Turing-completeness proof go

Mykola Makhortykh, Tobias Rohrbach, Maryna Sydorova

Information retrieval systems, such as search engines, increasingly shape the representation of the past and present states of social reality. Despite their importance, these systems face challenges in dealing with the ethical aspects of representation due to various forms of bias, including aesthetic bias that perpetuates hegemonic patterns of representatio

Bowen Zhi

The integration of Supernumerary Limbs (SLs) on humanoid robots poses a significant stability challenge due to the dynamic perturbations they introduce. This thesis addresses this issue by designing a novel hierarchical control architecture to improve humanoid locomotion stability with SLs. The core of this framework is a decoupled strategy that combines lea

Fangyang Zheng

In this short essay, we will survey on two conjectures in non-K\"ahler geometry: the constant holomorphic sectional curvature conjecture and the Fino-Vezzoni conjecture. We aim at the broad audience and assume no expertise in non-K\"ahler geometry. We will discuss the history and recent developments on these two typical conjectures in the field.

Xingyue Lin, Shuai Peng, Xiangyu Xie, Jianhua Zhu

Image vectorization aims to convert raster images into editable, scalable vector representations while preserving visual fidelity. Existing vectorization methods struggle to represent complex real-world images, often producing fragmented shapes at the cost of semantic conciseness. In this paper, we propose COVec, an illumination-aware vectorization framework

Satoshi Jinnouchi

In this paper, we show that if a holomorphic vector bundle is slope polystable with respect to a K\"{a}hler class, then it admits a Hermitian-Yang-Mills metric with respect to a suitable K\"{a}hler current with singularities in higher codimension which represents the K\"{a}hler class. Most parts of the proof remains valid for closed positive $(1,1)$-currents

Jianfei Zhao, Feng Zhang, Xin Sun, Chong Feng

Visual attention serves as the primary mechanism through which MLLMs interpret visual information; however, its limited localization capability often leads to hallucinations. We observe that although MLLMs can accurately extract visual semantics from visual tokens, they fail to fully leverage this advantage during subsequent inference. To address this limita

Yuexun Huang, Delaney Smith, Pei Zeng, Debayan Bandyopadhyay

Scaling quantum networks to continental distances requires physical infrastructure capable of overcoming both exponential attenuation and severe phase decoherence. While the concept of vacuum beam guide (VBG) has recently emerged as a promising low-loss solution, we move beyond it by proposing a rigorous physical-layer architectural blueprint anchored by emp

Haoran Zheng, Renchi Yang, Hongtao Wang, Jianliang Xu

Multimodal Attributed Graphs (MMAGs) are an expressive data model for representing the complex interconnections among entities that associate attributes from multiple data modalities (text, images, etc.). Clustering over such data finds numerous practical applications in real scenarios, including social community detection, medical data analytics, etc. Howev

I. Baragaña, F. Puerta, I. Zaballa

Given a controllable system $(F,G)$, a local parametrization is obtained for the set of feedback gain matrices $K$ such that the state matrix, $F+GK$, of the closed loop system is in a prescribed similarity class. It is shown that this set can be endowed with the structure of a differentiable manifold whose dimension is also computed. Then a local parametriz

Guillaume Letellier, Siddharth Srivastava, Frédéric Jurie, Gaurav Sharma

Foundation models pre-trained with self-supervised learning (SSL) on large-scale datasets have become powerful general-purpose feature extractors. However, their immense size and computational cost make them prohibitive for deployment on edge devices such as robots and AR/VR headsets. Existing compression techniques like standard knowledge distillation creat

Mona Al Batrouni, Florian Bertrand

We construct group-invariant CR maps from the unit sphere in $\mathbb{C}^3$ and provide sharp bounds for the gap termination in this setting.

Roman Vanlaere

We provide a precise description of the bottom of the spectrum in the semiclassical limit of a harmonic-type Schr\"odinger operator with an inverse square potential. By exploiting the connection between the eigenfunctions of these operators and the Kummer and Whittaker functions, we derive accurate localization results for the non-asymptotic zeros of these f

Xiaoran Li, Jie Qiu, Liang Hao, Chen Wang

Broadband lasers have become a key strategy for mitigating laser plasma instabilities in inertial confinement fusion, yet their impact on collisional inverse bremsstrahlung (IB) heating remains unclear. Using one-dimensional collisional particle-in-cell simulations, we systematically examine the effect of bandwidth-induced temporal incoherence on IB absorpti

Farzan Byramji, Russell Impagliazzo

We prove lower bounds for proofs of the bit pigeonhole principle (BPHP) and its generalizations in bounded-depth resolution over parities (Res$(\oplus)$). For weak BPHP$_n^m$ with $m = cn$ pigeons (for any constant $c>1$) and $n$ holes, for all $\epsilon>0$, we prove that any depth $N^{1.5 - \epsilon}$ proof in Res$(\oplus)$ must have exponential size, where

Seungjun Yu, Seonho Lee, Namho Kim, Jaeyo Shin

Recent advancements in multimodal large language models (MLLMs) have shown strong understanding of driving scenes, drawing interest in their application to autonomous driving. However, high-level reasoning in safety-critical scenarios, where avoiding one traffic risk can create another, remains a major challenge. Such reasoning is often infeasible with only

Sjoerd Hermes, Joost van Heerwaarden, Fred van Eeuwijk, Pariya Behrouzi

Traditional statistical approaches primarily aim to model associations between variables, but many scientific and practical questions require causal methods instead. These approaches rely on assumptions about an underlying structure, often represented by a directed acyclic graph (DAG). When all variables are measured at the same level, causal structures can