April 2026 arXiv papers — page 32

Amean Asad, Patrick McClurg, João Andrade

This paper presents C8s, a confidential computing architecture for Kubernetes that provides cryptographically rooted confidentiality, integrity, and verifiability guarantees for Kubernetes clusters from infrastructure operators. These guarantees are cryptographically provable to any independent third party verifier. The architecture is built on hardware Trus

Lucas Liuzzo, Wenwen Wei, Andrew R. Poppe, Christina O. Lee

We present case studies of two impulsive solar energetic electron (SEE) events during which particles at energies from 1-600 keV were detected by THEMIS-ARTEMIS orbiting the Moon, Wind at Earth's first Lagrange point, and (for one event) STEREO-A located at 1 AU, off the Sun-Earth line. The SEEs were initially highly anisotropic, traveling outward along the

Katalin Gyarmati

Starting with Ramanujan's famous taxicab problem, we can study the solvability of the equations $p^n+q^n=r^n+s^n$ and, more generally, $p_1^{k_1}+\dots+p_m^{k_m}=0$ among polynomials.

Hassan Abouelgheit, Hendrik Lens

The assessment of reactive power demand plays an instrumental role in power system planning. This paper presents a methodology for calculating reactive power demand based on a two-step approach. Unlike existing methodologies in the literature that focus primarily on optimization of reactive power compensation equipment placement and sizing through single-sim

Razwan Ahmed Tanvir, Greg Speegle

Collaborative blockchain ecosystems allow diverse groups to cooperate on tasks while providing properties such as decentralization and transaction security. We provide a model that uses a repository blockchain to manage hard forks within a collaborative system such that a single process (assuming that it has knowledge of the requirements of each fork) can ac

Ashish Gurung, Jordan Gutterman, Danielle R. Thomas, Mingyu Feng

Measures of disengagement provide insights into unproductive use of learning opportunities. Although measures of active disengagement, such as gaming the system and mind-wandering, are well studied, loss of practice time due to outright task avoidance remains relatively understudied. The current study addresses this gap by extending existing within-task meas

F. Moreno-Insertis, E. R. Priest, D. Nóbrega-Siverio

Aims. We seek to (a) study 1D Cartesian ambipolar diffusion near null points; (b) characterise the nonlinear eigenmodes for ambipolar diffusion; (c) propose tests for ambipolar diffusion solvers in MHD codes. Methods. (a) Direct analysis is used to find analytical solutions for ambipolar diffusion. (b) To study the eigenmodes, we solve the ODE for self-simil

Shiyi Du, Jiayuan Liu, Weihua Du, Yue Huang

Automated agentic workflow design currently relies on per-task iterative search, which is computationally prohibitive and fails to reuse structural knowledge across tasks. We observe that optimized workflows converge to a small family of domain-specific topologies, suggesting that this combinatorial search is largely redundant. Building on this insight, we p

Dan Shi, Zhuowen Han, Simon Ostermann, Renren Jin

Reinforcement learning (RL)-based post-training often improves the reasoning performance of large language models (LLMs) beyond the training domain, while supervised fine-tuning (SFT) frequently leads to general capabilities forgetting. However, the mechanisms underlying this contrast remain unclear. To bridge this gap, we present a feature-level mechanistic

Sricharan Raghavan-Chitra, Arghadip Koner, Joel Yuen-Zhou

Classical optical frameworks such as the discrete dipole approximation (DDA) assume that the linear spectrum of coupled quantum emitters can be computed solely from the linear susceptibilities of individual constituents. However, recent polariton studies show that cavity linear response can encode nonlinear optical susceptibilities. Here, we demonstrate that

Rafael Boto, Thi Nhung Dao, Felix Egle, Karim Elyaouti

We present the first version of the new scanning tool NMSSMScanner that allows to perform efficient scans in the complex multi-parameter space of the Next-to-Minimal Supersymmetric extension of the Standard Model (NMSSM) while taking into account all relevant constraints. As a proof of concept we apply it to the search for NMSSM parameter configurations that

Parmida Valiahdi, Niloofar Mehrnia, Walid Saad, Sinem Coleri

Ultra-reliable and low-latency communication (URLLC) will play a key role in fifth-generation (5G) and beyond networks, enabling mission-critical applications. Meeting the stringent URLLC requirements, characterized by extremely low packet error rates and minimal latency, calls for advanced statistical modeling to accurately capture rare events in wireless c

Jhon Manuel Portella Delgado, Ankit Goel

This paper presents a constraint-lifting control framework for designing stabilizing controllers that guarantee the forward invariance of a prescribed safe set. State-of-the-art safety-enforcing methods, such as control barrier functions (CBFs) and model predictive control (MPC), typically rely on solving constrained optimization problems in real time and th

Alessandro Morescalchi

We study the resonance spectrum of the multiflow induced on a flag manifold by the action, through multiplication by the exponential map, of the Cartan subalgebra of the underlying Lie group. We give a definition of joint resonance for the flow, then prove its discreteness and existence of resonant states. We conclude by explicit characterization of the spec

John Greenlees

We give a general description of the spectral space of conjugacy classes of subgroups of Sp(2): it is a disjoint union of finitely many blocks, each dominated by a subgroup: of these blocks, 26 are of dimension 1, 6 are of dimension 2 and the remainder are isolated points. On each of these blocks there is a sheaf of polynomial rings and a component structure

Alexander Keshavarzi, Daisuke Nomura, Thomas Teubner, Aidan Wright

We present a general and systematic framework to quantify uncertainties arising from imperfectly known systematic correlations in data combinations. Formulated at the level of the combined data, the method enables controlled variation of the correlation structure, leading to the construction of covariance matrices directly on the resulting combination and th

Maximiliano Armesto, Christophe Kolb

Recent work has framed intelligence in verifiable tasks as reducing time-to-solution through learned structure and test-time search, while systems work has explored learned runtimes in which computation, memory and I/O migrate into model state. These perspectives do not explain why capable models remain difficult to deploy in open institutions. We propose in

Ali Keshavarzi, Quentin Bouniot, Benjamin M. Smith, Elsa Angelini

Thoracic Computed Tomography (CT) scans offer detailed insights into the intricate branching network of the airway tree, which is essential for understanding various respiratory diseases. Airway bifurcations, where airway branches split, are crucial landmarks for understanding lung physiology, disease mechanisms and lesion localization. Despite the significa

Fei Cao, Roberto Cortez

In this work, we propose and analyze a novel Schelling-type metapopulation model that examines how random relocations of families between neighborhoods can lead to segregation. The model consists of a large number of houses organized into $N$ neighborhoods with $L$ houses each, without any spatial structure. Houses can be occupied by either a blue or a red f

Mohamad Zamini, Diksha Shukla

Open-vocabulary semantic segmentation requires assigning pixel-level semantic labels while supporting an open and unrestricted set of categories. Training-free CLIP-based approaches preserve strong zero-shot generalization but typically rely on a single inference mechanism, limiting their ability to jointly address unreliable local tokens and insufficient sp

Bo Ni, Haowei Fu, Qinwen Ge, Franck Dernoncourt

As Large Language Models (LLMs) advance, personalization has become a key mechanism for tailoring outputs to individual user needs. However, most existing methods rely heavily on dense interaction histories, making them ineffective in cold-start scenarios where such data is sparse or unavailable. While external signals (e.g., content of similar users) can of

Fahimeh Bayeh, Linh Dinh, Dongho Lee, Scott Wesley

This paper describes the design and implementation of a two-day quantum hackathon for underrepresented high school students in Nova Scotia, Canada. The first day of the hackathon is spent introducing students to quantum computing through hands-on activities, whereas the second day teaches students to apply this knowledge through guided challenges. Both days

Shrisudhan Govindarajan, Daniel Rebain, Dor Verbin, Kwang Moo Yi

We introduce a differentiable 3D representation that unifies the ray tracing capabilities of foam-based ray tracing with the efficiency of modern rasterization pipelines. While prior foam representations enable constant-time ray traversal through an explicit volumetric partition of space, their potentially unbounded cells hinder efficient tile-based rasteriz

Miao Lin, MD Saifur Rahman Mazumder, Feng Yu, Daniel Takabi

Randomized Smoothing (RS) offers formal $\ell_2$ guarantees for arbitrary base classifiers but faces two key practical bottlenecks: (i) it often relies on noise-augmented training to achieve nontrivial certificates, which increases training cost, can reduce clean accuracy, and weakens RS as a genuinely post-hoc defense; and (ii) certification is computationa

Iván Gallo-Méndez, Adolfo F. Viñas, Pablo S. Moya

Non-Maxwellian distributions and their origins in space plasma have attracted significant attention due to their prevalence and impact on various astrophysical and space-related phenomena. This paper presents a theoretical study of the consequences of incorporating a Skew-Kappa distribution to describe the non-thermal electron distribution in the solar wind.

Simon Casassus, Miguel Carcamo, Oriana Dominguez-Jamett, Yuhiko Aoyama

The radio emission mechanisms from accreting protoplanets, and their variability, link observations and physical properties. We revisit the variability of the ~343GHz (ALMA Band7) flux density from PDS70c (F_B7). The subtraction of the extended time-averaged signal may enable the measurement of the flux density from variable and embedded point sources. Visib

Martin Spitznagel, Janis Keuper

Stephen Wolfram proclaimed in his 2003 seminal work "A New Kind Of Science" that simple recursive programs in the form of Cellular Automata (CA) are a promising approach to replace currently used mathematical formalizations, e.g. differential equations, to improve the modeling of complex systems. Over two decades later, while Cellular Automata have still bee

Jhon Manuel Portella Delgado, Ankit Goel

This paper presents a constraint-enforcing control framework for a class of discrete-time strict-feedback nonlinear systems. The objective is to guarantee closed-loop stability while ensuring forward invariance of a prescribed safe set defined by state constraints. The proposed approach transforms the constrained control problem into an equivalent unconstrai

Mohamed El-Hadedy

Electromagnetic signals propagating across interstellar distances are subject to simultaneous distortion by seven distinct physical mechanisms: relativistic Doppler shift, stellar aberration, interstellar medium dispersion, special relativistic time dilation, general relativistic gravitational time dilation, cosmological redshift, and atmospheric transmissio

Seok Hwan Song, Azher Ahmed Efat, Wallapak Tavanapong

Chart-to-table translation converts chart images into structured tabular data. Accurate translation is crucial for Multimodal Language Model (MLM) to answer complex queries. We observe imbalances in the number of images across different aspects of the y-axis information in public chart datasets. Such imbalances can introduce unintended biases, causing uneven

Alexander I. Suciu

We develop a Koszul-theoretic framework for comparing classical Alexander-type invariants with infinitesimal invariants arising from finite-type commutative differential graded algebra models. The central mechanism is Koszul linearization, which replaces nonlinear equivariant constructions with functorial algebraic objects defined from a CDGA. To a connected

Jhon Manuel Portella Delgado, Ankit Goel

This paper develops an adaptive tracking controller for a class of nonlinear systems with parametric uncertainty subject to state constraints. The system is characterized by a strict-feedback structure with unknown parameters entering both the drift and input channels. The objective is to design a control law, without knowledge of the unknown parameters, tha

Mohsen Fathi, Faizuddin Ahmed

We investigate the strong-field phenomenology of a static and spherically symmetric regular black hole supported by an Einasto dark matter (DM) distribution. For the exponential Einasto profile, the geometry is controlled by a single dimensionless halo parameter $a$, and we restrict the analysis to the black hole branch $0<a\leq a_{\rm crit}\simeq0.388$. We

Miles Q. Li, Benjamin C. M. Fung, Boyang Li, Radin Hamidi Rad

Existing white-box jailbreak attacks against aligned LLMs typically append discrete adversarial suffixes to the user prompt, which visibly alters the prompt and operates in a combinatorial token space. Prior work has avoided directly optimizing the embeddings of the original prompt tokens, presumably because perturbing them risks destroying the prompt's sema

Drew Dillon, Kasyap Varanasi

AI coding agents powered by large language models can read codebases and produce functional code, but they routinely violate team-specific product decisions that are invisible in the source code alone. We introduce a controlled benchmark measuring decision compliance, the rate at which an AI coding agent follows established product, design, and engineering d

Keyu Yao, Jinghui Cheng, Jin L. C. Guo

Designers hold primary responsibility for shaping the user interface (UI) and user experience (UX) of a product. This role goes beyond aesthetics and usability, extending to the privacy outcomes of user experience, which often emerge through collaboration with other stakeholders such as developers, product managers, and marketing teams. Previous studies on e

Rozhan Hozhabri Nezhad, Jin L. C. Guo, Jinghui Cheng

Open source software (OSS) often prioritizes technical functionality over usability and UX design. This imbalance limits OSS adoption among broader, non-technical users. Key underlying factors contributing to this issue are the shortage of design expertise in OSS and a dominant developer-centric mindset. To address these persistent issues, we explore the pot

Gustavo Estay, Daisuke Noto, Hugo N. Ulloa

Water exhibits an anomalous nonlinear density equation of state (EOS) as it approaches freezing, along with an increase in viscosity ($μ$) and a decrease in thermal conductivity ($k$). Here we ask: how do these temperature-dependent material properties affect thermal convection in coldwater? We examine these effects within the canonical Rayleigh-Bénard conve

Prafulla Kumar Choubey, Kung-Hsiang Huang, Pranav Narayanan Venkit, Jiaxin Zhang

Enterprise deep research often fails to produce decision-ready reports due to uneven information coverage, context explosion, and premature stopping. We propose a scalable Enterprise Deep Research (EDR) architecture to address these failures. Our system (i) decomposes requests into coverage-driven objectives via outline generation with reflection, (ii) local

Bo Ni, Leyao Wang, Yu Wang, Branislav Kveton

User simulation has long played a vital role in computer science due to its potential to support a wide range of applications. Language, as the primary medium of human communication, forms the foundation of social interaction and behavior. Consequently, simulating conversational behavior has become a key area of study. Recent advancements in large language m

Adam Z. Kaczmarek, Johann Gil, Zygmunt Bąk, Ewa A. Drzazga-Szczȩśniak

Recent theoretical studies propose that Hawking radiation may not emerge strictly at the event horizon but rather from the spatially extended region surrounding a black hole, commonly referred to as the quantum atmosphere. In this work, we explore how this concept influences nonlocal quantum correlations in a bosonic bipartite system located at certain dista

Allen Jue

Learned index structures achieve high performance by modeling the cumulative distribution function (CDF) of keys, but this reliance on data distributions introduces potential vulnerability to adversarial manipulation. Prior work has explored both static data poisoning and dynamic algorithmic complexity attacks (ACA), though evaluations are typically limited

Chao Jiang, Dugang Liu, Cheng Wen, Zhiwu Xu

Large language models have transformed AI-assisted software engineering, but current research remains biased toward high-resource languages such as Python, with weaker performance in languages like Rust and OCaml. Since real-world systems are inherently polyglot, robust multilingual code intelligence is crucial. This survey focuses on two key tasks: multilin

Chamani Shiranthika, Parvaneh Saeedi

Federated learning enables collaborative model training across medical institutions without sharing raw data, but its performance is often limited by domain heterogeneity across clients. Existing approaches to address this challenge fall into two main paradigms: model-side personalization, which adapts model parameters to each client, and data-side harmoniza

Evanthia Patliaka, Christos G. Tsagas

We present a unified analysis of the linear evolution of peculiar-velocity perturbations in the distribution of pressureless matter after recombination. Our study is carried out within the framework of full general relativity and encompasses both the earlier Einstein-de Sitter epoch and the subsequent $\Lambda$-dominated phase. Starting from a non-interactin

Debora Ramacciotti, Martin Steinbach, Bence Temesi, Andreea-Iulia Lefterovici

Sparse quantum state preparation is a common subroutine in quantum algorithms, where classical data with few nonzero entries must be loaded into a quantum state. In this work, we consider the Grover-Rudolph algorithm, which has recently been shown to efficiently prepare sparse states, and we propose two improvements. First, we extend an existing gate-merging

Jun Li, Mingxuan Liu, Jiazhen Pan, Che Liu

Clinical abnormality grounding for rare diseases is often hindered by data scarcity, making supervised fine-tuning impractical and single-pass inference highly unstable. We propose Dynamic Decision Learning (DDL), a framework that enables frozen large vision-language models (LVLMs) to refine their decisions across both language and visual spaces by optimizin

Ishan Patel, Ishan Joshi

We present PolyKV, a system in which multiple concurrent inference agents share a single, asymmetrically compressed KV cache pool. Rather than allocating a separate KV cache per agent -- the standard paradigm -- PolyKV writes a compressed cache once and injects it into N independent agent contexts via HuggingFace DynamicCache objects. Compression is asymmetr

Anthony Mezzacappa

Core collapse supernova modeling has advanced considerably since the first numerical simulations were performed sixty years ago. In particular, the last decade has brought us sophisticated three-dimensional models with significant predictive capabilities -- e.g., for core collapse supernova gravitational wave emission. The six decades of modeling have shown

Nathan Haut, Ilya Basin, Ruchika Gupta, Marzieh Kianinejad

The Beagle framework, through GPU-based Genetic Programming, enables population dynamics previously unattainable (within practical time frames) by CPU-constrained Genetic Programming systems. This work explores how GPU-enabled population sizes impact the success of training for symbolic regression problems. Specifically, when using constant population sizes,

Praveen K. Bommineni, Junwei Wang, Nicolas Vogel, Michael Engel

Monodisperse spherical colloidal particles confined within emulsion droplets can crystallize into icosahedral clusters. Experimentally it was observed that a few large colloidal particles added as defects preferentially migrate to the vertices of the icoshedral clusters. To understand this structure formation phenomenon, we simulate the confined self-assembl

Oscar Delaney, Sambhav Maheshwari, Joe O'Brien, Theo Bearman

Frontier AI companies first deploy their most advanced models internally, for weeks or months of safety testing, evaluation, and iteration, before a possible public release. For example, Anthropic recently developed a new class of model with advanced cyberoffense-relevant capabilities, Mythos Preview, which was available internally for at least six weeks bef

Eric F. Bell, Richard D'Souza, Monica Valluri, Katya Gozman

We investigate the role of hierarchical assembly in the angular momentum (AM) evolution of galaxies using a sample of 471 Milky Way-mass galaxies from the TNG-50 simulation. While galaxy orientation is often attributed to tidal torques and the cooling of gas within halos, we demonstrate that galaxy reorientation (tilting) is a common consequence of satellite

Lawrence Keunho Jang, Jing Yu Koh, Daniel Fried, Ruslan Salakhutdinov

Existing web agent benchmarks have largely converged on short, single-site tasks that frontier models are approaching saturation on. However, real world web use consists of long-horizon, multi-site workflows. Common web navigation tasks, such as comparing products across different domains, planning trips across multiple services, or summarizing information f

Nicholas Nelson, Philip Chang

Although well studied, our understanding of the mass ejection mechanisms of cataclysmic variables remains incomplete. Recent work suggests that binary interaction plays an important role in driving and shaping this mass ejection and may affect the long-term evolution of the system. In this paper, we perform a three-dimensional moving-mesh hydrodynamic simula

Andreea-Iulia Lefterovici, Lara Lelakowski, Michael Perk

The maximum flow problem asks to find the largest possible flow from a source to a sink in a capacitated network. It arises frequently in scheduling, project selection, and as a core subroutine in broader optimisation tasks. Classically, it can be efficiently solved using Dinic's algorithm, which repeatedly performs breadth-first search (BFS) and blocking fl

Jonathan Zhang, Christopher Thompson

We build a self-consistent model of a warm scattering corona near an accreting black hole in Kerr geometry, in the regime of slow ($\sim 0.01$ Eddington) mass accretion. An iterative Monte Carlo procedure is developed that incorporates self-consistently the effects of Compton scattering and electron-positron pair creation, as well as general relativistic len

Ritwick Mishra, Diksha Gupta, Achla Marathe, Krista Danielle Yu

This study examines the global impacts of a localized disruption in Qatar's gas sector using a multi-regional input-output framework and scenario-based analysis. While the direct impacts of this disruption on importing countries are clear, indirect and cascading impacts are not well understood. We use a Multiregional input-output (MRIO) model to assess the i

Dongze Wu, Linglingzhi Zhu, Yao Xie

Learning matrix-valued distributions from high-dimensional and possibly incomplete training data is challenging: ambient-space generative modeling is computationally expensive and statistically fragile when the matrix dimension is large but the sample size is limited. We propose CoreFlow, a geometry-preserving low-rank flow model that learns shared row/colum

Dominik Winecki, Arnab Nandi

Video data is increasingly used alongside conventional data for interactive data exploration, necessitating interfaces for exploring and presenting mixed-modality data. However, integrating video into visualizations remains difficult due to its distinct paradigms and inherent performance challenges. We identify three classes of video data visualization - syn

Harold P. Boas

The topic is the history of the concepts of equivalence relation, Cauchy sequence, and metric space. The thesis is that disused definitions of these notions could profitably be revived.

Xinming Tu, Tianze Wang, Yingzhou, Lu

As benchmarks grow in complexity, many apparent agent failures are not failures of the agent at all - they are failures of the benchmark itself: broken specifications, implicit assumptions, and rigid evaluation scripts that penalize valid alternative approaches. We propose employing frontier LLMs as systematic auditors of evaluation infrastructure, and reali

NVIDIA, :, Amala Sanjay Deshmukh, Kateryna Chumachenko

We introduce Nemotron 3 Nano Omni, the latest model in the Nemotron multimodal series and the first to natively support audio inputs alongside text, images, and video. Nemotron 3 Nano Omni delivers consistent accuracy improvements over its predecessor, Nemotron Nano V2 VL, across all modalities, enabled by advances in architecture, training data and recipes.

Ming Li, Jie Wu, Justin Cui, Xiaojie Li

While preference optimization is crucial for improving visual generative models, how to effectively scale this paradigm remains largely unexplored. Current open-source preference datasets contain conflicting preference patterns, where winners excel in some dimensions but underperform in others. Naively optimizing on such noisy datasets fails to learn prefere

Xinxin Liu, Ming Li, Zonglin Lyu, Yuzhang Shang

Human visual preferences are inherently multi-dimensional, encompassing aesthetics, detail fidelity, and semantic alignment. However, existing datasets provide only single, holistic annotations, resulting in severe label noise: images that excel in some dimensions but are deficient in others are simply marked as winner or loser. We theoretically demonstrate

Biaoyan Hu, Mingyuan Hu, Franz Demmel, Andrey A. Podlesnyak

Crystal-field symmetry restricts the ground-state Kramers doublet of ErBr$_3$ to one of two classes. We show that the compressed octahedral environment selects the class with $\langle ψ_\pm | J^{\pm} | ψ_\mp \rangle = 0$, suppressing the lowest-order $J^{\pm}$-mediated exchange. Thermodynamic measurements reveal two zero-field anomalies at 0.375 and 0.200~K.

Lukas Schulthess, Andreas Rätz, Michele Magno, Philipp Mayer

Energy harvesting promises maintenance-free operation of wireless sensor nodes but introduces strong dependencies on stochastic and deployment-specific environmental conditions. In particular, solar-powered systems are highly sensitive to variations in irradiance and spectral composition, which complicates system-level design, parameter tuning, and reliable

J. D. Baker, C. A. Bertulani, R. V. Lobato

We present a physics-informed Bayesian neural-network framework to infer neutron-star equations of state from theoretical priors and to propagate the associated uncertainties to stellar observables. Trained on a large and representative ensemble of hadronic EoSs, the model learns $P(\epsilon)$ via stochastic variational inference, incorporating soft constrai

Omar Faruque, Sahara Ali, Xue Zheng, Jianwu Wang

The widespread availability of complex time series data in various domains such as environmental science, epidemiology, and economics demands robust causal discovery methods that can identify intricate contemporaneous and lagged relationships in non-stationary, nonlinear, and noisy settings. Existing constraint-based methods often rely heavily on conditional

Dallin Fisher, Qi-Jun Hong

We present a validation of the asdf method, an information-theoretic framework for computing thermodynamic entropy from molecular configurations. The method reformulates entropy estimation as the Shannon entropy of a residual mapping distribution defined between two decorrelated microstates. We demonstrate analytically that for the closed-form Hamiltonians w

Cheng-Han Lee, Maniratnam Mandal, Neil Birkbeck, Yilin Wang

With the rise of mobile video consumption on diverse handheld display resolutions and orientation modes, altering videos to aspect ratios poses challenges. Static cropping and border padding often compromises visual quality, while warping may distort a video's intended meaning. Here we advocate for a more effective approach: cropping significant regions with

Janina Vohdin, Christof Holzer

We present a theoretical and numerical study of the correlation between electrons and the fermionic $^{13}$C and $^{19}$F nuclei. We use the random-phase approximation (RPA) as a valuable tool in obtaining these correlation energies. A special connection between the RPA and second-order perturbation theory for the inter-fermionic interaction is outlined. Sub

Eugenio Bianchi, Chaosong Chen, Mauricio Gamonal

We study the analytic properties and three equivalent representations of the Toller matrices $T^{(\pm)}$ which appear in the causal formulation of spinfoam transition amplitudes for 4d Lorentzian quantum gravity. These are polynomially bounded functions on the Lorentz group which satisfy the relation $T^{(+)}+T^{(-)}=D$, where the Wigner matrix $D$ provides

Joshua Ritchie

The purpose of this work is to discuss how matter fields are coupled to gravity within the framework of General Relativity. Our particular focus here is on the coupling of scalar field models. In a first step, we suggest a new method for coupling scalar fields to the Einstein equations \emph{without} the use of a variational principle or Lagrangian. We show

John T. Baldwin, Constantin C. Brîncuş

We provided in \cite{BaldwinBrincusI} extensions of first order logic by modified inferential definitions of the classical $\omega$-rule in $1$ or $2$ sorts. These logics are categorical in the inferential sense. Arithmetic has a unique countable model in each case, e.g. first order PA is categorical in our first logic. The 2-sorted case interprets $L_{\omeg

Diyi Cheng, Tim Kodalle, Anika T. Promi, Ansuman Halder

Sol-gel synthesis is a wet-chemical processing route for the fabrication of functional materials offering control over composition, morphology, and microstructure at relatively low processing temperatures compared to conventional solid-state synthesis methods. While the sol-gel process initiates with intermixed molecular precursors, the transformation pathwa

Orhan Demirci, Sezer Aptourachman, Aydın Kaya

Word embeddings are fundamental to natural language processing, yet traditional approaches represent each word with a single vector, creating representational bottlenecks for polysemous words and limiting semantic expressiveness. While multi-anchor representations have shown promise by representing words as combinations of multiple vectors, they have been li

Gia Quoc Bao Tran, Thach Ngoc Dinh, Zhenhua Wang

We provide a systematic interval observer design method for detectable linear time-invariant (LTI) systems, where a part of the state is observable from the measured output. An observability-based invertible LTI transformation decomposes the state into two parts. The first part is decoupled from the other and observable from the output, while the second is a

Vabuk Pahari, Balakrishnan Chandrasekaran, Johnnatan Messias, Krishna P. Gummadi

A decentralized autonomous organization (DAO) is a governing entity that empowers its stakeholders (i.e., users who hold one or more of its tokens) to manage blockchain-based protocols (i.e., smart contracts) collaboratively. The governance of a DAO is explicitly encoded in the DAO's governance contract, which defines how stakeholders participate in governan

Chandler Squires, Pradeep Ravikumar

Techniques for concept extraction, such as sparse autoencoders and transcoders, aim to extract high-level symbolic concepts from low-level nonsymbolic representations. When these extracted concepts are used for downstream tasks such as model steering and unlearning, it is essential to understand their guarantees, or lack thereof. In this work, we present a u

Jing Chen, Abhijay Deevi, Onat Gungor, Tajana Rosing

The Controller Area Network (CAN) is a safety-critical in-vehicle communication protocol that lacks built-in security mechanisms, making intrusion detection essential. Existing approaches predominantly formulate CAN intrusion detection as a classification task, mapping complex traffic patterns to attack labels. However, this formulation abstracts away the te

Zhongzheng Zhang, Maxwell Ruyle, Andrew Kappes, Tyler Ruble

Intelligent Transportation Systems (ITS) increasingly rely on vision-based perception and learning-based control, necessitating experimental platforms that support realistic hardware-in-the-loop validation. Small-scale platforms for autonomous racing offer a practical path to hardware validation, but often suffer from limited modularity, high integration com

Mohammed Ali El Adlouni, Aurian Quelennec, Pierre Chouteau, Geoffroy Peeters

General audio foundation models have recently achieved remarkable progress, enabling strong performance across diverse tasks. However, state-of-the-art models remain extremely large, often with hundreds of millions of parameters, leading to high inference costs and limited deployability on edge devices. Knowledge distillation is a proven strategy for model c

Chris Stevens, Juan A. Valiente Kroon

We provide a formulation of the initial boundary value problem for Friedrich's extended conformal Einstein field equations in which boundary data is prescribed on a timelike hypersurface located at a finite position in the spacetime. Our construction relies on a gauge based on the properties of conformal geodesics and requires the the boundary is ruled by ti

Jiaming Qiu, Roch Guerin

Delivering hard delay guarantees over packet networks is increasingly important to applications ranging from automotive systems, avionics, industrial control, etc. Traffic control and schedulers play an essential role in enforcing such guarantees. In this paper, we focus on ``simple'' static priority and FIFO schedulers, and explore how reprofiling flows ent

Yunsu Kim, Kaden Uhlig, Joern Wuebker

Agent benchmarks remain largely English-centric, while their multilingual versions are often built with machine translation (MT) and limited post-editing. We argue that, for agentic tasks, this minimal workflow can easily break benchmark validity through query-answer misalignment or culturally off-target context. We propose a refined workflow for adapting En

Richard Fitzpatrick

The controlled ramp down of the toroidal plasma current in the ITER tokamak is simulated using a simple model that employs cylindrical geometry. The magnetohydrodynamical (MHD) stability of the plasma throughout the whole current ramp is also calculated. The only potentially unstable MHD mode is the m=2/n=1 classical tearing mode. The envisioned 60 second ra

Florentin Smarandache

In this paper, for the first time, we extend the Over/Under/Off Set/Logic/Probability used in uncertain theories (such as: fuzzy, neutrosophic and extensions) to the Over/Under/Off Mass that could be used in Information Fusion. The approach is exemplified in three scenarios: (1) wildfire evacuation and resource allocation with satellite, IoT, and social medi

Ö. Adebali, A. G. M. Pietrow

By using the Doppler images of {\lambda} And, we aim to investigate whether surface temperature information can be reversed to create its activity parameters, by feeding a toy model with solar spectra, based on the surface images. At the same time, we examine whether spot contributions alone are sufficient to explain the observed activity modulation of the R

Muhammad Arslan, Ishtiaq Ahmed, Muhammad Jamil Aslam

We present a global analysis of the current $B$-meson flavor anomalies and extend it to the baryonic sector through the decay $\Lambda_b^0 \to \Lambda_c^+(\to \Lambda^0 \pi^+) \tau^-(\to \pi^- \nu_\tau)\bar{\nu}_\tau$. The lepton flavor universality ratios $R_{\tau/(\mu,e)}(D^{(*)})$, measured by BaBar, Belle, and LHCb, exhibit a combined $3.8\sigma$ deviati

Yifei Wei, Linqing Zhong, Yi Liu, Yuxiang Lu

Vision-Language-Action (VLA) models are a promising paradigm for generalist robotic manipulation by grounding high-level semantic instructions into executable physical actions. However, prevailing approaches typically adopt a monolithic generation paradigm, directly mapping visual-linguistic features to high-frequency motor commands in a flat, non-hierarchic

Bin Han, Muxia Sun, H. Vincent Poor, Hans D. Schotten

Injecting artificial noise (AN) along the tangent space of a curved constellation makes each transmitted symbol induce a Gaussian observation with a symbol-dependent rank-one covariance, so the matched maximum-likelihood (ML) decoder differs from the Euclidean nearest-neighbor decoder by a single rank-one correction per candidate. We develop a baseband-demap

Alexander Altland, Tobias Micklitz, Devasheesh Sharma, Maksimilian Usoltcev

The two-dimensional hyperbolic plane, $\mathbb{H}^2$, is an unusual system in that dimensionality changes with scale: locally two-dimensional and planar at short distances, but effectively infinite-dimensional at large scales, it provides an interesting paradigm for the study of (quantum) phase transitions, notably the disorder-driven Anderson transition. Ge

Fang Wan, Guangyi Huang, Tianyu Wu, Zishang Zhang

We introduce asRoBallet, to the best of our knowledge, the first end-to-end reinforcement learning (RL) locomotion policy deployed on a humanoid ballbot hardware platform. Historically, ballbots have served as a canonical benchmark for underactuated and nonholonomic control, which are characterized by a reality gap in complex friction models for wheel-ball-f

Mohamed El Morsalani, Mohammed Barkatou

We analyze the inverse problem of recovering geometric information from the return map induced by a round-trip between a convex core C and an admissible domain. This process defines a discrete dynamical system on the boundary of C governed by a thickness function d. We prove that the return map determines the gradient structure of d, including its critical p

Raluca M. Balan, Jinxin Wang

In this article, we introduce a time-independent version of the L\'evy colored noise considered in Balan (2015) and Balan and Jim\'enez (2026). We study the existence of the solution of a linear stochastic partial differential equation with this type of noise, and we identify some necessary conditions which guarantee that the solution has finite $p$-th order

Joseph Lemaitre, Justin Lessler

Forecasting infectious disease incidence can provide important information to guide public health planning, yet is difficult because epidemic dynamics are complex. Current mechanistic and statistical approaches often struggle to capture multimodal uncertainty or emergent trends. Influpaint adapts denoising diffusion probabilistic models to epidemic forecasti

Arielle Sanford, Andrew T. Kamen, Frederic T. Chong, Andy J. Goldschmidt

We introduce HAML (Hamiltonian Adaptation via Meta-Learning), a framework for fast online adaptation of effective Hamiltonian models of superconducting quantum processors. HAML proceeds in two phases. A supervised training phase uses an ensemble of simulated devices to learn an offline map from control inputs and device parameters to effective Hamiltonian co

Matthew Marsh, Benoît Chachuat, Antonio del Rio Chanona

Machine Learning is becoming more prevalent in science and engineering, but many approaches do not provide meaningful uncertainty estimates and predictions may also violate known physical knowledge. We propose a Bayesian framework to embed linear relationships across inputs and outputs into the learning process, whilst characterizing full predictive uncertai

Alessandro Veneziani, Annalisa Quaini, Marco Tezzele, Omer San

The combination of data and models, enhanced by AI methodologies, leads to the paradigm called Digital Twins. This concept is expected to bring unprecedented support to personalized medicine. The combination of mathematical and numerical models with diagnostic devices that provide patient-specific knowledge in a bidirectional framework can be a formidable de