April 2026 arXiv papers — page 137

Firas Rassoul-Agha, Mikhail Sweeney

For stochastic Hamilton-Jacobi (SHJ) equations, instability points are the space-time locations where two eternal solutions with the same asymptotic velocity differ. Another fundamental structure in such equations is shocks, which are the space-time locations where the velocity field is discontinuous. In this work, we study the KPZ fixed point, the central o

Tarek M. Elgindi, Yupei Huang

We consider steady states of the incompressible Euler equation on two-dimensional domains. For non-radial analytic steady states on bounded simply connected domains, it was shown previously that there must be a global functional relationship between the stream function and the vorticity. We show that this does not extend to smooth functions, even under furth

Yash Deshpande, Quirin Vogel, Laura Becker, Kaan Aykurt

Achieving consistent time across devices in distributed systems often involves exchanging timestamped messages over a network. Precise time synchronization is crucial for applications such as cellular networks, industrial automation, and transactional databases. However, delay variation in synchronization packets-often caused by congestion from competing tra

Ding Zhang, Di Zhao, Philipp Braun, Jianqi Chen

This paper investigates the robust stability problem of a feedback system in the presence of uncertainties induced by graphical regions in the plane where the scaled relative graphs (SRGs) reside. Our main results are developed using a novel and intuitive concept, the Davis-Wielandt shell, together with its connection to SRGs and related variants. We first s

Anzhelika V. Buskina, Vladimir A. Zyuzin

In this Letter we theoretically study physical properties of a model of heavy fermion $d-f$ hybrid. In the studied model two species of fermions have dispersions with different masses, one being much heavier than the other. Hybridization between the fermions at the crossing point of their dispersions doesn't open a true insulating gap leaving a heavy fermion

Jiongyu Dai, Raymond Zhao, Farhad Rezazadeh, Lizhong Zheng

Open Radio Access Network (O-RAN) architectures enhance flexibility for 6G and NextG networks. However, it also brings significant challenges in O-RAN testing with evaluating abundant, high-dimensional key performance indicators (KPIs). In this paper, we introduce a novel two-stage framework to learn temporally-aware low-dimensional representations of O-RAN

Rodrigo Itzamná Becerra-Deana, Joseph Lamarre, Raphaël Maltais-Tariant, Adam Zolnai

This paper examines the polarization-dependent output of various types of 3-mode photonic lanterns fabricated using double-clad fibers. We explore the sensitivity of the modal response across several types of photonic lanterns, from the fully symmetric and strongly coupled structure of regular photonic lanterns to the fully asymmetric structure of mode-selec

Jianing Zhao, Zhuoting Cai, Xiang Yin

In this paper, we investigate safety-critical control problem of discrete-time stochastic systems with incomplete information, where safety constraints must be enforced using state estimates obtained from noisy measurements. We develop an output-feedback control barrier function (CBF) framework based on an expectation-based discrete-time barrier condition th

Witold Płecha

We study the almost surely finite random variable $S$ defined by the distributional fixed-point equation \[ S \stackrel{d}{=} 1 + \max\{US', (1-U)S''\}, \qquad U \sim \mathrm{Unif}(0,1), \] where $S'$ and $S''$ are independent copies of $S$, independent of $U$. This random variable arises as the almost sure limit of the normalized worst-case number of key co

Felicitas Hörmann, Anna-Lena Horlemann

Generalized Reed-Solomon (GRS) and Gabidulin codes have been proposed for various code-based cryptosystems, though most such schemes without elaborate disguising techniques have been successfully attacked. Both code classes are prominent examples of the isometric families of (generalized) skew and linearized Reed-Solomon ((G)SRS and (G)LRS) codes which are o

Emmanuel Trinidad, Neil Irwin Bernardo

This paper investigates the fundamental limits of integrated sensing and communication (ISAC) systems with 1-bit receiver quantization. We analyze a Gaussian fading ISAC channel with separate communication and monostatic sensing links, where both communication and sensing receivers are equipped with 1-bit quantizers. When the communication channel state info

Steve Hanneke, Qinglin Meng, Shay Moran, Amirreza Shaeiri

The Sauer-Shelah-Perles Lemma is a cornerstone of combinatorics and learning theory, bounding the size of a binary hypothesis class in terms of its Vapnik-Chervonenkis (VC) dimension. For classes of functions over a $k$-ary alphabet, namely the multiclass setting, the Natarajan dimension has long served as an analogue of VC dimension, yet the corresponding S

Jason Z Wang

The most cited calibration result in deep learning -- post-temperature-scaling ECE of 0.012 on CIFAR-100 (Guo et al., 2017) -- is below the statistical noise floor. We prove this is not a failure of the experiment but a law: the minimax rate for estimating calibration error with model error rate epsilon is Theta((Lepsilon/m)^{1/3}), and no estimator can beat

Zhi-Lei Zhang, Xin Yue, Guo-Jian Qiao, C. P. Sun

In a hybrid system where two quantum dots (QDs) are coupled to a conventional $s$-wave superconductor, Poor Man's Majorana modes (PMMs) have been proposed. Existing theories often idealize the superconductor (SC) as a bulk system or an infinitely long chain, or treat it as another quantum dot with proximity-induced superconductivity, while experiments employ

Seungjoo Lee, Vimal Mollyn, Chris Harrison, Justin Chan

We present GlintMarkers, the first system to perform gaze-driven spatial perception using the inward-facing cameras on XR eyewear. Our key observation is that the cornea acts as a mirror that encodes both gaze direction and visual information about the environment in a small, low-contrast reflection. To extract spatial and semantic information from this refl

Benjamin Stern, Peter Nadel

LLM agents with persistent memory store information as flat factual records, providing little context for temporal reasoning, change tracking, or cross-session aggregation. Inspired by the drawing effect [3], we introduce dual-trace memory encoding. In this method, each stored fact is paired with a concrete scene trace, a narrative reconstruction of the mome

Alonso Hernández-Antón, Josua D. Schär, Aleksandr Grigorev, Guillermo F. Peñas

The tunable interaction between stationary quantum bits and propagating modes of light allows for the encoding of quantum information in the state of itinerant photons. This ability fulfills a central requirement for quantum networking, enabling quantum state transfer between distant quantum devices. Conventionally, a symmetric envelope of the photon wavepac

Hayden Prairie, Zachary Novack, Taylor Berg-Kirkpatrick, Daniel Y. Fu

Traditional fixed-depth architectures scale quality by increasing training FLOPs, typically through increased parameterization, at the expense of a higher memory footprint, or data. A potential alternative is looped architectures, which instead increase FLOPs by sending activations through a block of layers in a loop. While promising, existing recipes for tr

Amar Gahir, Varshil Patel, Shreyank N Gowda

Deep neural networks are typically trained by uniformly sampling large datasets across epochs, despite evidence that not all samples contribute equally throughout learning. Recent work shows that progressively reducing the amount of training data can improve efficiency and generalization, but existing methods rely on fixed schedules that do not adapt during

Yiyang Huang, Yitian Zhang, Yizhou Wang, Mingyuan Zhang

Despite significant progress in video-language modeling, hallucinations remain a persistent challenge in Video Large Language Models (Vid-LLMs), referring to outputs that appear plausible yet contradict the content of the input video. This survey presents a comprehensive analysis of hallucinations in Vid-LLMs and introduces a systematic taxonomy that categor

Yu. A. Pusep, M. A. T. Patricio, M. M. Glazov, V. A. Oliveira

The Wiedemann--Franz law, which determines the universality of the ratio of thermal conductivity to electrical conductivity, is studied in the hydrodynamic electron transport regime, where electron--electron scattering predominates over scattering by disorder. In this case, the different relaxation of electric and thermal currents can lead to a violation of

Dongen Li, Yi Liu, Junqi Liu, Zewen Sun

Achieving real-time Simultaneous Localization and Mapping (SLAM) based on 3D Gaussian splatting (3DGS) in large-scale real-world environments remains challenging, as existing methods still struggle to jointly achieve low-latency pose estimation, continuous 3D Gaussian reconstruction, and long-term global consistency. In this paper, we present a tightly coupl

Felipe A. Asenjo, Swadesh M. Mahajan

New solutions for Bessel electromagnetic beams, propagating along the light cones, are investigated. Of the variety of structures possible in the light cone variables, the one involving a product of Airy functions is discussed in detail. This class of solutions, representing an asymmetry on the light-cone coordinates dependence, is a non-trivial extension to

Tianshuo Zhang, Haoyuan Zhang, Siran Peng, Weisong Zhao

Continual face forgery detection (CFFD) requires detectors to learn emerging forgery paradigms without forgetting previously seen manipulations. Existing CFFD methods commonly rely on replaying a small amount of past data to mitigate forgetting. Such replay is typically implemented either by storing a few historical samples or by synthesizing pseudo-forgerie

Santiago Arenas-Velilla, Axel Munk, Luis-Alberto Rodríguez

Recently, the statistical properties of empirical Entropic Optimal Transport (EOT) have attracted great interest, as this quantity has been shown to be useful for complex data analysis, among other reasons due to its computational efficiency. In several applications, it has been observed that the EOT plan provides valuable information beyond just the optimal

Wu Wang, Yuang Cheng, Fouzi Harrou, Ying Sun

Early and reliable detection of Alzheimer's disease (AD) is crucial for timely clinical intervention and improved patient management. It also supports the evaluation of emerging therapeutic strategies. In this paper, we propose a Low-Rank Mixture of Experts (LoRA-MoE) deep learning framework for Alzheimer's disease diagnosis based on handwriting analysis. Ha

Ao Yang, Kai Zhang, Chen Fang

In Hermitian impurity scattering, each isolated late-time exponential is the fingerprint of a bound state. We show that this correspondence breaks down in non-Hermitian bands. For a single impurity in a non-Hermitian lattice, the late-time signal is controlled by isolated complex frequencies selected by the analytic continuation of the Green's function relev

Darlayne Addabbo

Let $V$ be a grading-restricted vertex algebra and let $A^\infty(V)=U^\infty(V)/Q^\infty(V)$ be the associative algebra constructed by Huang, where $U^\infty(V)$ is the space of column-finite infinite matrices with entries in V and $Q^\infty(V)$ is an ideal of a (nonassociative) algebra structure on $U^\infty(V)$ defined by Huang. Huang introduced families o

Jiachen Li, Hanyu Zhu, Edward Kim, Shihao Li

Hypertrophic Cardiomyopathy (HCM) is a genetic heart disease affecting approximately 1 in 500 people and is the leading cause of sudden cardiac death in young athletes. Current diagnostic methods -- cardiovascular magnetic resonance (CMR), echocardiography, and genetic testing -- are limited by high costs, operator dependency, or insufficient accuracy, while

Yuhan Jin, Nayari Marie Lessa, Mariela De Lucas Alvarez, Melvin Laux

Marine ecosystem degradation necessitates continuous, scientifically selective underwater monitoring. However, most autonomous underwater vehicles (AUVs) operate as passive data loggers, capturing exhaustive video for offline review and frequently missing transient events of high scientific value. Transitioning to active perception requires a causal, online

Andrei Pantea, Luca Biferale, Michele Buzzicotti, Guillaume Charpiat

Recent advances in data-driven modeling have shown that diffusion models can successfully generate synthetic Lagrangian trajectories in turbulent flows. Building on this progress, we extend the method to the joint generation of pairs of Lagrangian velocity trajectories, enabling a fully data-driven representation of turbulent pair dispersion, a long-standing

Jingzhi Hu, Ouya Wang, Geoffrey Ye Li

In generative semantic communication, semantic tokens guide receiver-side generative models to synthesize high-dimensional content. In challenging network environments, however, frequent token erasure distorts the conveyed semantics beyond what receiver-side recovery can restore. In this paper, we propose a token encoding framework (TokCode) for robust seman

Ayce Idil Aytekin, Xu Chen, Zhengyang Shen, Thabo Beeler

We present Grasp in Gaussians (GraG), a fast and robust method for reconstructing dynamic 3D hand-object interactions from a single monocular video. Unlike recent approaches that optimize heavy neural representations, our method focuses on tracking the hand and the object efficiently, once initialized from pretrained large models. Our key insight is that, gi

Chung-Ming Chien, Manu Orsini, Eugene Kharitonov, Neil Zeghidour

Speech-to-speech language models have recently emerged to enhance the naturalness of conversational AI. In particular, full-duplex models are distinguished by their real-time interactivity, including handling of pauses, interruptions, and backchannels. However, improving their factuality remains an open challenge. While scaling the model size could address t

Hilde C. Bjornland, Nicolas Hardy, Dimitris Korobilis

We develop a Quantile Bayesian Vector Autoregression (QBVAR) to forecast real oil prices across different quantiles of the conditional distribution. The model allows predictor effects to vary across quantiles, capturing asymmetries that standard mean-focused approaches miss. Using monthly data from 1975 to 2025, we document three findings. First, the QBVAR i

Akira Shudo

In generic Hamiltonian systems that are neither completely integrable nor fully chaotic, phase space consists of a mixture of regular and chaotic components. In classical dynamics, transitions between different invariant sets in phase space are strictly forbidden, and these sets act as dynamical barriers to one another. In quantum mechanics, in contrast, wav

Jérémie Messud, Yagnik Chatterjee

The LogQ algorithm encodes Quadratic Unconstrained Binary Optimization (QUBO) problems, which are often encountered in the industry (portfolio optimization, fleet optimization, charging stations, etc.). It was developed within the framework of quantum computing, designed as a pragmatic approach to quantum combinatorial optimization that drastically reduces t

Sravan Chittupalli, Ayush Jain, Dong Huang

Resolving real-world human-object interactions in images is a many-to-many challenge, in which disentangling fine-grained concurrent physical contact is particularly difficult. Existing semantic contact estimation methods are either limited to single-human settings or require object geometries (e.g., meshes) in addition to the input image. Current state-of-t

Anthony Mallama

Magnitudes recorded from 2021 through 2026 for Starlink VisorSats reveal brightening of 0.6 magnitudes, while those of OneWeb show dimming of 0.4. Both trends are significant at 3 sigma. This study is based on 1.6 million magnitudes recorded by the MMT9 robotic observatory.

Yunhui He, Leo G Rebholz

We consider nonlinear GMRES (NGMRES) as an acceleration technique for the Navier-Stokes Picard iteration, a direction that has not previously been explored. We identify the optimal norm for the least squares optimization problem arising in the NGMRES algorithm, and establish a convergence analysis for NGMRES with general depth that proves NGMRES scales the P

Jonas Dolezal, Sawood Alam, Mark Graham, Maty Bohacek

The proliferation of AI-generated and AI-assisted text on the internet is feared to contribute to a degradation in semantic and stylistic diversity, factual accuracy, and other negative developments (sometimes subsumed under the Dead Internet Theory). What has hindered answering these questions is that it has not been understood just how much of the internet

Kohei Sato, Hiromasa Watanabe, Takeshi Yamazaki

We propose a variant of the model-independent method for determining meson charge radii from spatial moments of correlation functions on the lattice. Traditional determinations based on fits to the momentum transfer squared dependence of form factors are subject to systematic uncertainties arising from the choice of fit ansatz. By contrast, model-independent

Shi Chen, Jinbo Wen, Jiawen Kang, Tenghui Huang

Web 3.0 is envisioned as a decentralized paradigm, where blockchain serves as a core technology for transparent and tamper-proof data management. Among various blockchain architectures, consortium blockchains have emerged as the preferred platform for enterprise-grade Web 3.0. For consortium blockchains, newly generated blocks are generally propagated to all

Saptarshi Ghosh, Tianyu Jiang

Metonymy and metaphor often co-occur in natural language, yet computational work has studied them largely in isolation. We introduce a framework that transforms a literal sentence into three figurative variants: metonymic, metaphoric, and hybrid. Using this framework, we construct MetFuse, the first dedicated dataset of figurative fusion between metonymy and

Deqing Yang, Yingying Liu, Qicong Wang, Zhi Zeng

Image restoration under adverse conditions, such as underwater, haze or fog, and low-light environments, remains a highly challenging problem due to complex physical degradations and severe information loss. Existing datasets are predominantly limited to a single degradation type or heavily rely on synthetic data without stereo consistency, inherently restri

Fangyu Sun, Fanxing Li, Linzuo Zhang, Yu Hu

Training and transferring learning-based policies for quadrotors from simulation to reality remains challenging due to inefficient visual rendering, physical modeling inaccuracies, unmodeled sensor discrepancies, and the absence of a unified platform integrating differentiable physics learning into end-to-end training. While recent work has demonstrated vari

Sohail Farhangi, Joel Moreira, Rigoberto Zelada

In 2005, Parreau proved that if a measure preserving system is not strongly mixing then it contains a non-trivial factor that is disjoint from every strongly mixing system. Taking this construction as the starting point, we develop the complementary notions of $\mathcal U$-generated and $\mathcal U$-mixing systems, for a set $\mathcal U$ of ultrafilters, and

Xu Chen, Kevin Kluge, Maximilian Basler, Lorenz Dörschel

This work addresses the challenge of ignition timing and load control in homogeneous charge compression ignition engines operating subject to uncertainty from complex combustion dynamics and external disturbances. To handle this issue, we propose a nonlinear stochastic model predictive control approach explicitly incorporating distributional information of u

Ronald Skorobogat, Ameya Prabhu, Matthias Bethge

Multilingual benchmarks guide the development of frontier models. Yet multilingual evaluations reported by frontier models are structured similar to popular reasoning and knowledge benchmarks, but across many languages. We show such benchmarks, and consequently multilingual evaluations, measure mathematical reasoning and factual recall, not multilingual prof

Yifei Yan, Linqi Ye

As reinforcement learning for humanoid robots evolves from single-task to multi-skill paradigms, efficiently expanding new skills while avoiding catastrophic forgetting has become a key challenge in embodied intelligence. Existing approaches either rely on complex topology adjustments in Mixture-of-Experts (MoE) models or require training extremely large-sca

Zijian Song, Qichang Li, Jiawei Zhou, Zhenlong Yuan

At its core, robotic manipulation is a problem of vision-to-geometry mapping ($f(v) \rightarrow G$). Physical actions are fundamentally defined by geometric properties like 3D positions and spatial relationships. Consequently, we argue that the foundation for generalizable robotic control should be a vision-geometry backbone, rather than the widely adopted v

Thea Budde, Marina Kristć Marinković, Joao C. Pinto Barros

Hilbert space fragmentation refers to exponential growth in the number of dynamically disconnected Krylov sectors with system size. It is taken as evidence of ergodicity breaking, since conventional symmetries generate at most a polynomial number of sectors. However, we demonstrate that generalized symmetries can fragment the Hilbert space. Models with highe

John H. Dallyn, Nils T. Otterstrom, Matt Eichenfield, Peter T. Rakich

Stimulated Brillouin scattering (SBS), an optical nonlinearity arising from photon-phonon interactions, has formed the basis for a large class of optical signal processing devices, including Brillouin amplifiers. A limiting factor of such amplifiers is the noise due to thermal-mechanical fluctuations that the phonons imprint on the optical signal. Prior work

Hyeonbeen Lee, Min-Jae Jung, Tae-Kyeong Yeu, Jong-Boo Han

Force and torque (F/T) sensing is critical for robot-environment interaction, but physical F/T sensors impose constraints in size, cost, and fragility. To mitigate this, recent studies have estimated force/wrench sensorlessly from robot internal states. While existing methods generally target relatively slow interactions, tasks involving rapid interactions,

Yikun Liu, Jiangchao Yao, Weidi Xie, Yanfeng Wang

Composed Image Retrieval (CIR) aims to retrieve a target image based on a query composed of a reference image, and a relative caption that specifies the desired modification. Despite the rapid development of CIR models, their performance is not well characterized by existing benchmarks, which inherently contain indeterminate queries degrading the evaluation

Oscar Stenhammar, Gábor Fodor, Carlo Fischione

Machine learning models are increasingly deployed in wireless networks with stringent performance requirements. However, dynamic propagation environments and fluctuating traffic densities introduce concept drift, which complicates the ability to maintain accurate predictive machine learning models. We propose a distributed optimization framework that jointly

Breandan Considine

Many techniques in program synthesis, superoptimization, and array programming require parallel rollouts of general-purpose programs. GPUs, while capable targets for domain-specific parallelism, are traditionally underutilized by such workloads. Motivated by this opportunity, we introduce a pleasingly parallel virtual machine and benchmark its performance by

Georg Frenck

The doubling conjecture predicts that a manifold admits positive scalar curvature with mean convex boundary if and only if its double admits positive scalar curvature. We show that it holds true for manifolds where the inclusion of the boundary satisfies a certain split-condition on fundamental groups. Our proof is based on surgery-techniques for positive sc

Haidong Lu, Gregg S. Gonsalves, Fan Li, Guanyu Tong

Both cluster randomized trials and quasi-experimental designs are used to evaluate the impact of health and social policies and interventions. Stepped-wedge cluster randomized trials randomize a staggered adoption approach, while recent difference-in-differences methods allow analysis of non-randomized settings where similar policies are adopted at different

Yiran Guo, Wei Chen, Cong Yu, Bo Ai

Wireless communications in intelligent rail transit face harsh propagation conditions, including severe penetration loss, frequent blockages, and amplified large-scale fading. Existing leaky coaxial cables (LCX) provide wired-to-wireless conversion and stable coverage, but can be energy- and spectrum-inefficient, particularly at high carrier frequencies. Mot

Chuyang Xiang, Yichen Wei, Jiale Ma, Handing Wang

Large Language Model-based Hyper Heuristic (LHH) has recently emerged as an efficient way for automatic heuristic design. However, most existing LHHs just perform well in optimizing a single function within a pre-defined solver. Their single-layer evolution makes them not effective enough to write a competent complete solver. While some variants incorporate

Francesco Vissani

The term 'neutrinoless' is a cornerstone of modern particle physics, yet it defines a fundamental process by what is missing rather than what is created. We trace the origins of this privative neologism to a 1953 experimental claim and show how a 'sociology of suspicion' transformed Ettore Majorana's affirmative ontology into an agnostic shorthand. By examin

Muhammad Kamran Janjua, Hugo Silva, Di Niu, Bahador Rashidi

Multimodal language models (MLLMs) are increasingly paired with vision tools (e.g., depth, flow, correspondence) to enhance visual reasoning. However, despite access to these tool-generated visual cues, MLLMs often fail to benefit from them. Existing approaches typically feed raw tool outputs into the model, but these dense, pixel-level representations are m

Ulrich Wiedner

Glueballs represent a fascinating aspect of the strong interaction in nature. Gluons that serve as the mediators of the strong interaction are massless particles, but they possess a property unique to the strong interaction called color charge, which is analogous to electric charge in the electromagnetic interaction. Glueballs are composed of multiple gluons

Prashanth Chandran, Daoye Wang, Timo Bolkart

We present CUBE (Control-based Unified B-spline Encoding), a new geometric representation for human faces that combines B-spline volumes with learned features, and demonstrate its use as a decoder for 3D scan registration and monocular 3D face reconstruction. Unlike existing B-spline representations with 3D control points, CUBE is parametrized by a lattice (

Maria Duque-Gijon, Joshua Robertson, Dafydd Owen-Newns, Jack Baker

Neuromorphic photonics is emerging as a powerful platform for fast and efficient optical information processing and sensing. However, future brain-inspired photonic systems require compact and scalable light sources, capable of generating the neuro-mimetic optical signals needed for their operation. This work demonstrates a single-stack laser that delivers o

Shivani P. Shah, Rana Ezzeddine, Erika M. Holmbeck, Alexander P. Ji

The actinides, including thorium (Th), are the heaviest observable elements synthesized in the universe, holding clues to the extremes of the astrophysical and nuclear conditions of $r$-process sites. We present Th abundances based on high-resolution spectroscopy for 47 metal-poor stars, the largest homogeneously analyzed sample to date. The chemical evoluti

Chaoyao Shen, Linfeng Jiang, Yixian Shen, Tao Xu

Deep learning (DL) compilers rely on cost models and auto-tuning to optimize tensor programs for target hardware. However, existing approaches depend on large offline datasets, incurring high collection costs and offering suboptimal transferability across platforms. In this paper, we introduce TCL, a novel efficient and transferable compiler framework for fa

Yifan Du, Zikang Liu, Jinbiao Peng, Jie Wu

Multimodal deep search agents have shown great potential in solving complex tasks by iteratively collecting textual and visual evidence. However, managing the heterogeneous information and high token costs associated with multimodal inputs over long horizons remains a critical challenge, as existing methods often suffer from context explosion or the loss of

Yuhao Huang, Yunqi Fu, Yu Xia, Yuemin Li

Three-dimensional (3D) photonic integrated circuits (PIC) are emerging as an indispensable scheme for high density and multifunctional photonic systems. However, the wafer-scale scaling of PICs towards a 3D configuration is constrained by two key factors: (i) the trade-off between inter-layer taper efficiency and footprint, and (ii) wafer-scale uniformity of

Andrei Atanov, Jesse Allardice, Roman Bachmann, Oğuzhan Fatih Kar

Visual tokenizers map high-dimensional raw pixels into a compressed representation for downstream modeling. Beyond compression, tokenizers dictate what information is preserved and how it is organized. A de facto standard approach to video tokenization is to represent a video as a spatiotemporal 3D grid of tokens, each capturing the corresponding local infor

Juan C. Alzate Cobo, Tobias Henkels, Oliver Weeger

Beam theory has traditionally been restricted to small elastic strains and rigid cross-sections. Relaxing these assumptions within closed-form analytical frameworks remains challenging. In contrast, the cross-sectional warping problem provides a computational approach that enables the derivation of general, nonlinear constitutive relations for beam models, t

Aleksandr Talitckii, Matthew Peet

The attitude dynamics of aircraft and spacecraft exhibit significantly nonlinear behaviour. In spacecraft, torque is generated through reaction wheels and control moment gyros. In aircraft, torque is generated using lift on control surfaces. In both cases, complex geometries, unique configurations, and internal/environmental changes imply that models must be

Liudmyla Vasylenko, Adi Livnat

At the fundamental conceptual level, two alternatives have traditionally been considered for how mutations arise and how evolution happens: 1) random mutation and natural selection, and 2) Lamarckism. Recently, the theory of Interaction-based Evolution (IBE) has been proposed, according to which mutations are neither random nor Lamarckian, but are influenced

Olimjon Eshkobilov, Shirali Kadyrov, Khudoyor Mamayusupov

Let \(H(n)\) denote the Hilbert number, i.e.\ the maximal number of limit cycles of planar polynomial vector fields of degree \(\le n\). A classical lower-bound mechanism for \(H(n)\) is \emph{replication}: one pulls back a vector field by a polynomial map and lifts each existing limit cycle to several disjoint copies while controlling the resulting degree.

Silvaneo V. dos Santos, Layla Parast

Surrogate markers offer the potential to reduce the burden of data collection by replacing costly or invasive primary outcomes with more accessible measurements, provided that they can faithfully indicate the effectiveness of a treatment. However, appropriate evaluation of a surrogate is particularly complex in longitudinal studies, where both outcomes and s

Changshu Liu

Code reasoning tasks are increasingly crucial to evaluating large language models (LLMs). Yet most existing benchmarks rely on simplistic, LLM-generated snippets or human-written solutions to code challenges and often restrict inputs and outputs to primitive types, failing to reflect the structure and dependencies of real-world projects. These simplification

Davide Accadia, Danilo Lewański, Giulio Ruzza

Hurwitz theory provides a large variety of enumerative problems related to algebraic geometry, mathematical physics, and combinatorics. We give a general framework to approach the large genus asymptotics of Hurwitz theory using only elementary methods and apply it to several types of Hurwitz numbers: single, double, or with an arbitrary numbers of fixed rami

Heng Tao, Yiming Zhong, Zemin Yang, Yuexin Ma

Fast grasping is critical for mobile robots in logistics, manufacturing, and service applications. Existing methods face fundamental challenges in impact stabilization under high-speed motion, real-time whole-body coordination, and generalization across diverse objects and scenarios, limited by fixed bases, simple grippers, or slow tactile response capabilit

Jakob Schwerter, Loreen Sabel, Judith Bose, Matthew L. Bernacki

STEM dropout rates remain high at universities, particularly in computer science programs with theory-intensive courses. Digital learning environments now capture rich behavioral data that could help identify struggling students early, yet the generalizability of data-driven prediction models across courses and institutions remains uncertain. Guided by self-

Abhirup Nandy, Nilabhra R. Das

In the academic landscape, scientific research has been primarily conducted through research institutions, which requires a massive influx of funds from various sources. Presently, these funding bodies have been moving from trust-based funding to performance-based evaluation systems for granting funds to the research bodies. This has led to the rise in popul

Chen Qu, Paul L. Houston, Qi Yu, Apurba Nandi

There has been a veritable explosion of methods and software to perform machine-learned regression on datasets of electronic energies and forces to develop high-dimensional machine learned potential energy surfaces (ML-PESs). A major, but not deeply-studied aspect is how well different ML-PESs represent the same dataset on which they are trained, beyond the

Alessandro Perotti

We prove a general Fueter Theorem over real alternative *-algebras. We show that a suitable power of the Laplacian maps Dunkl-regular functions to Dunkl monogenic functions with axial symmetries. Using the embedding of hypercomplex function theories in the class of Dunkl monogenic functions, we subsume several Fueter-type results known in the literature and

Abiodun A. Solanke

The rapid expansion of large language model (LLM) safety evaluation has produced a substantial benchmark ecosystem, but not a correspondingly coherent measurement ecosystem. We present AISafetyBenchExplorer, a structured catalogue of 195 AI safety benchmarks released between 2018 and 2026, organized through a multi-sheet schema that records benchmark-level m

Anne Lee, Gurudutt Hosangadi

The rapid advancement of AI has changed the character of HPC usage such as dimensioning, provisioning, and execution. Not only has energy demand been amplified, but existing rudimentary continual learning capabilities limit ability of AI to effectively manage HPCs. This paper reviews emerging directions beyond monolithic transformers, emphasizing agentic AI

L. A. Ferreira, G. Luchini, H. Malavazzi

We present an integral formulation of classical Yang-Mills theory coupled to fermionic and scalar matter fields in (1+1)-dimensional Minkowski spacetime. By reformulating the local dynamics in terms of loop-space holonomies, we demonstrate that the path independence of the holonomy eigenvalues constitutes a conservation law, yielding an infinite hierarchy of

Jiahua Pei, Yi Liu, Guoping Pan, Yuanhao Jiang

Object Goal Navigation (ObjectNav) refers to an agent navigating to an object in an unseen environment, which is an ability often required in the accomplishment of complex tasks. While existing methods demonstrate proficiency in isolated single object navigation, their limitations emerge in the restricted applicability of lifelong memory representations, whi

David Levin

We consider the problem of reconstructing missing data on a smooth manifold from incomplete and nonuniform samples. While classical methods for manifold approximation typically assume quasi-uniform data, their performance deteriorates significantly in the presence of large gaps or holes. We propose a unified framework for manifold data imputation that reduce

Trayan Gospodinov, Peter Müller, Thibault Dardinier

Many important functional and security properties--including non-interference, determinism, and generalized non-interference (GNI)--are hyperproperties, i.e., properties relating multiple executions of a program. Existing separation logics allow one to reason about specific classes of hyperproperties, e.g., $\forall\forall$-hyperproperties such as non-interf

Riccardo Consonni, Luca Magri

We propose a theory based on dynamical systems to explain and predict the occurrence of extreme events, of which critical transitions form a subset. In fast-slow nonlinear systems, we identify a cascade of events preceding extreme events: (i) a slow regime, in which the fast covariant Lyapunov vectors (CLVs) are both tangent to the fast eigenvectors and rema

Zhichao Lin, Zhichao Liang, Gaoqiang Liu, Meng Xu

As agentic foundation models continue to evolve, how to further improve their performance in vertical domains has become an important challenge. To this end, building upon Tongyi DeepResearch, a powerful agentic foundation model, we focus on the Chinese medical deep search scenario and propose QuarkMedSearch, systematically exploring a full-pipeline approach

Seowung Leem, Lin Gu, Ruogu Fang

Humans readily recognize objects from sparse line drawings, a capacity that appears early in development and persists across cultures, suggesting neural rather than purely learned origins. Yet the computational mechanism by which the brain transforms high-level semantic knowledge into low-level visual symbols remains poorly understood. Here we propose that a

Ethan Ackelsberg, Florian K. Richter

Let $d(\cdot)$ denote the natural density on the positive integers. We characterize all sets $A,B$ with positive density satisfying $d(A+B)=d(A)+d(B)$, under the assumption that the two sets are not both contained in a proper finite union of residue classes. This gives a new inverse theorem for Kneser's sumset inequality in the integers, and provides a parti

Marta Zagorowska, Lukas Ortmann, Giuseppe Belgioioso, Lars Imsland

Online Feedback Optimization leverages properties of optimization algorithms to develop controllers for systems with limited model availability, which is often the case in process control. The interplay between the parameters of the chosen optimization algorithm, as well as lack of direct connection to the characteristics of the underlying process make their

Cankat Tilki, Tobias Breiten, Serkan Gugercin

We develop the interpolatory $\mathcal{H}_2$ optimal model reduction framework for linear control systems posed on infinite dimensional state, input and output spaces. Specifically, we consider linear systems formulated as controlled abstract Cauchy problems on a Banach space and approximate them via Petrov-Galerkin projection onto finite dimensional trial a

Zhikun Zeng, Jiazhao Xu, Hanyu Li, Shiang Zhang

Granular flows are ubiquitous in nature and industrial applications, yet a complete continuum theory remains a long-standing challenge. The leading empirical approach, {\mu}(I) rheology, lacks microscopic foundations and becomes multivalued in dense, slowly sheared flows where nonlocal corrections are required. Exploiting state-of-the-art high-speed X-ray to

Samuel Mathews, Xiaodan Zhu, Andr'e Guerra, Phillip Servio

Gas hydrates are potential candidates in future energy sources while simultaneously providing structures with extensive applications in carbon capture and storage, gas transport, and important separation processes. Prior research in the field considers the dynamics of the water molecule backbone in particular. We investigated the pressure-enthalpy landscape

Yujin Jeong, Seonghyun Jeong

Multivariate mixed-type outcomes are difficult to model jointly, and additional complexity arises when both marginal effects and dependence structures vary with a covariate such as age or time. Existing approaches often impose restrictive dependence assumptions or lack sufficient flexibility to accommodate heterogeneous response types in a unified framework.

Fredrik Hasselgren, Bálint Koczor

Tensor-network simulations of quantum many-body dynamics are fundamentally limited by entanglement build-up, which leads to exponentially growing computational costs. Furthermore, these classical simulation algorithms are inherently sequential as typically a tensor network representation of the quantum state is updated incrementally at each time step. We bui

P. C. Kuo, R. G. Novikov

We consider the Schr\"odinger equation with a multipoint potential of Bethe-Peierls-Thomas-Fermi type. For this singular potential, we develop scattering and inverse scattering at high energies. In particular, in this framework, our results include analogs of the "regular" Born-Faddeev formula for the scattering amplitude and analogs of related "regular" inv