March 2026 arXiv papers — page 99

Christian Jendreiko, Karl Svozil

We connect partition logic with Generative Logic by translating finite partition logics into Prolog-based Simple Generative Logic Grammars. As a proof of concept, we use the five-atom V-logic L_{12} to generate a modular visual artifact, the \emph{Quantum Square}. The approach separates logical structure from its visual, textual, or sonic realization. This m

Amandine Brunetto

Generating audio that is acoustically consistent with a scene is essential for immersive virtual environments. Recent neural acoustic field methods enable spatially continuous sound rendering but remain scene-specific, requiring dense audio measurements and costly training for each environment. Few-shot approaches improve scalability across rooms but still r

Ricardo Burity, Thiago Fiel, Zaqueu Ramos, Aron Simis

Let $R$ be a standard graded polynomial ring over a field $k$. The paper focuses on homogeneous ideals $J \subset R$ of codimension $2$ generated by three forms of the same degree $d \geq 2$ that are almost Cohen--Macaulay, i.e., of homological dimension $2$. Based on the structure of the minimal graded free resolution of $J$ and numerical data encoded in ce

D. M. van Egmond, L. C. Ferreira, A. D. Pereira, G. Peruzzo

The inclusion of a mass-like term for the gluon in Yang-Mills theories quantized in the Landau gauge has proven to be an effective way of reproducing lattice results for gauge-fixed correlation functions within perturbative computations. Since those quantities are gauge dependent, it is natural to question how general this prescription is for describing the

Edward Lin, Sahil Modi, Siva Kumar Sastry Hari, Qijing Huang

As agentic AI systems become increasingly capable of generating and optimizing GPU kernels, progress is constrained by benchmarks that reward speedup over software baselines rather than proximity to hardware-efficient execution. We present SOL-ExecBench, a benchmark of 235 CUDA kernel optimization problems extracted from 124 production and emerging AI models

Yuegui Huang, Zhiyuan Fang, Weiqi Luo, Ruoyu Wu

Despite the computational efficiency of MoE models, the excessive memory footprint and I/O overhead inherent in multi-expert architectures pose formidable challenges for real-time inference on resource-constrained edge platforms. While existing static methods struggle with a rigid latency-accuracy trade-off, we observe that expert importance is highly skewed

Tuomas Orponen, Pablo Shmerkin

We prove sharp $\delta$-discretised versions of some variants of the Furstenberg set problem under weaker or different non-concentration assumptions compared to previous works.

Yicheng Zeng, Ruturaj S. Sambhus, Basit Muhammad Imran, Jeeseop Kim

This paper proposes a fully decentralized model predictive control (MPC) framework with control barrier function (CBF) constraints for safety-critical trajectory planning in multi-robot legged systems. The incorporation of CBF constraints introduces explicit inter-agent coupling, which prevents direct decomposition of the resulting optimal control problems.

Zhan Jin, Yu Luo, Yizhou Zhang, Ziyang Cui

Conventional pixel-wise loss functions fail to enforce topological constraints in coronary vessel segmentation, producing fragmented vascular trees despite high pixel-level accuracy. We present ARIADNE, a two-stage framework coupling preference-aligned perception with RL-based diagnostic reasoning for topologically coherent stenosis detection. The perception

Swagat Padhan, Lakshya Jain, Bhavya Minesh Shah, Omkar Patil

Robots collaborating with humans must convert natural language goals into actionable, physically grounded decisions. For example, executing a command such as "go two meters to the right of the fridge" requires grounding semantic references, spatial relations, and metric constraints within a 3D scene. While recent vision language models (VLMs) demonstrate str

Amartya Mukherjee, Maxwell Fitzsimmons, David C. Del Rey Fernández, Jun Liu

Uncertainty quantification for partial differential equations is traditionally grounded in discretization theory, where solution error is controlled via mesh/grid refinement. Physics-informed neural networks fundamentally depart from this paradigm: they approximate solutions by minimizing residual losses at collocation points, introducing new sources of erro

Francisco Marín Sola, Francesco Salerno

We investigate Brunn-Minkowski-type inequalities for the torsional rigidity $T_\gamma$ and the first eigenvalue $\lambda_\gamma$ associated with the Ornstein-Uhlenbeck operator. Counterexamples are provided showing that neither concavity nor convexity properties hold for $T_\gamma$ on general bounded convex sets. We also demonstrate that log-concavity and lo

Yuyang Liu

Combinatorial optimization problems arise in logistics, scheduling, and resource allocation, yet existing approaches face a fundamental trade-off among generality, performance, and usability. We present cuGenOpt, a GPU-accelerated general-purpose metaheuristic framework that addresses all three dimensions simultaneously. At the engine level, cuGenOpt adopts

Ugo Giuseppe Aglietti, Giancarlo Ferrera, Lorenzo Rossi

We present a comprehensive global analysis of Energy-Energy Correlation (EEC) data in electron-positron annihilation into hadrons, spanning a wide range of center-of-mass energies ($7.7\,\,\text{GeV}\!\leq\!\sqrt{s}\!\leq\! 91.2\,\,\text{GeV})$. In the back-to-back (two-jet) region, we resum to all orders the logarithmically-enhanced contributions up to next

Chris Elliott, Owen Gwilliam, Ingmar Saberi, Brian R. Williams

We propose a non-perturbative description of the moduli spaces encoding p-form generalized Maxwell theories in any dimension, using derived differential geometry. Our approach synthesizes the Batalin--Vilkovisky formalism with differential cohomology. Within this framework we formulate Dirac charge quantization and show how such charge-quantized moduli space

Xiaojian Lin, Yaomin Shen, Junyuan Ma, Yujie Sun

Monocular vertex-level human-scene contact prediction is a fundamental capability for interactive systems such as assistive monitoring, embodied AI, and rehabilitation analysis. In this work, we study this task jointly with single-image 3D human mesh reconstruction, using reconstructed body geometry as a scaffold for contact reasoning. Existing approaches ei

Reagan Mozer

Prediction-powered inference (PPI) is a rapidly growing framework for combining machine learning predictions with a small set of gold-standard labels to conduct valid statistical inference. In this article, I argue that the core estimators underlying PPI are equivalent to well-established estimators from the survey sampling literature dating back to the 1970

Kiril Hristov, Naotaka Kubo, Yi Pang

We study the perturbative large-$N$ expansion of the round three-sphere partition function in a class of M2-brane theories, including flavored SYM and ABJM theories as well as more general 3d theories admitting dual $(p,q)$ 5-brane web descriptions. Using the Fermi gas formalism and quantum curve techniques, we derive the Airy-function representation of the

Kwanyoung Lee, SeungJu Cha, Yebin Ahn, Hyunwoo Oh

Diffusion-based text-to-image (T2I) models have made remarkable progress in generating photorealistic and semantically rich images. However, when the target concepts lie in low-density regions of the training distribution, these models often produce semantically misaligned or structurally inconsistent results. This limitation arises from the long-tailed natu

Kwanyoung Lee, Hyunwoo Oh, SeungJu Cha, Sungho Koh

Generating rare compositional concepts in text-to-image synthesis remains a challenge for diffusion models, particularly for attributes that are uncommon in the training data. While recent approaches, such as R2F, address this challenge by utilizing LLM for prompt scheduling, they suffer from inherent variance due to the randomness of language models and sub

C. Andratschke, B. B. Brandt, E. Garnacho-Velasco, L. Pannullo

Spectral reconstruction is a well studied numerically ill-posed problem which arises due to the relation of the Euclidean correlator to the spectral function via an inhomogeneous Fredholm equation of the first kind. Several different methods are on the market to resolve this issue, each taking different approaches and assumptions. In this proceedings we focu

Jean Tapie, Bruno Sokal, André L. F. de Almeida, Philipp del Hougne

Dynamic metasurface antennas (DMAs) are an emerging hybrid-MIMO technology distinguished by an ultrathin form factor, low cost, and low power consumption. In real-world DMA prototypes, mutual coupling (MC) between meta-elements is generally non-negligible; some architectures even deliberately exploit strong MC to enhance wave-domain flexibility. In this pape

Ema Tsang-King-Sang, Josquin Errard, Simon Biquard, Pierre Chanial

We assess the impact of non-ideal, continuously rotating half-wave plates (HWPs) on cosmic microwave background (CMB) polarization measurements targeting large angular scale signal. Such hardware solutions are used in or planned for multiple modern CMB efforts, both ground-based, for instance, small aperture telescopes of Simons Observatory or satellite born

Chonghan Liu, Yimin Du, Qi An, Xin He

Large language models frequently exhibit suboptimal performance on low resource languages, primarily due to inefficient subword segmentation and systemic training data imbalances. In this paper, we propose Variable Entropy Policy Optimization (VEPO), which leverages Reinforcement Learning with Verifiable Rewards to incorporate deterministic structural constr

U. Ozdem

We systematically investigate the electromagnetic properties of exotic states whose internal structures remain uncertain and for which different models have been proposed. In this work, we focus on the magnetic dipole moments of hidden-charm pentaquark states using QCD light-cone sum rules with four distinct interpolating currents. The analysis accounts for

Kristján Orri Ragnarsson, Jacky Mallett

Industrial Control Systems (ICS), and many simple Internet of Things (IoT) devices, commonly communicate using unencrypted or unauthenticated protocols. For ICS this is an historical carryover since the introduction of these systems predated practical lightweight cryptography. As the processing power of small devices has grown exponentially at the same time

Skyler Seto, Pierre Ablin, Anastasiia Filippova, Jiayuan Ye

Language models achieve impressive performance on a variety of knowledge, language, and reasoning tasks due to the scale and diversity of pretraining data available. The standard training recipe is a two-stage paradigm: pretraining first on the full corpus of data followed by specialization on a subset of high quality, specialized data from the full corpus.

Subhashree Chatterjee, Rabindra Basnet, Rajeev Nepal, Ramesh C. Budhani

Understanding how optical excitation couples with polarization and interfacial electrostatics in van der Waals (vdW) ferroelectrics (FEs) is essential for the development of light-programmable nanoelectronic and optoelectronic devices. Here, we present direct nanoscale evidence of photoferroionic coupling in the vdW FE semiconductor CuInP2S6 (CIPS), where op

Lei Yang, Han Wan, Min Zhang, Ling Liang

In this paper, we study a nonconvex, nonsmooth, and non-Lipschitz generalized symmetric matrix factorization model that unifies a broad class of matrix factorization formulations arising in machine learning, image science, engineering, and related areas. We first establish two exactness properties. On the modeling side, we prove an exact penalty property sho

Ruilin Li, Heming Zou, Xiufeng Yan, Zheming Liang

Recent paradigms in Random Projection Layer (RPL)-based continual representation learning have demonstrated superior performance when building upon a pre-trained model (PTM). These methods insert a randomly initialized RPL after a PTM to enhance feature representation in the initial stage. Subsequently, a linear classification head is used for analytic updat

Bahar Taşkesen

Generative AI has transformed the economics of information production, making explanations, proofs, examples, and analyses available at very low cost. Yet the value of information still depends on whether downstream users can absorb and act on it. A signal conveys meaning only to a learner with the structural capacity to decode it: an explanation that clarif

Zikang Ding, Junchi Yao, Junhao Li, Yi Zhang

Large language models (LLMs) exhibit pronounced social biases. Output-level or data-optimization--based debiasing methods cannot fully resolve these biases, and many prior works have shown that biases are embedded in internal representations. We propose \underline{U}nified \underline{G}raph \underline{I}somorphism for \underline{D}ebiasing large language mod

Tomasz Wietrzykowski

Current transformer language models are trained with uniform computational budgets across all layers, implicitly assuming layer homogeneity. We challenge this assumption through empirical analysis of SmolLM2-135M, a 30-layer, 135M-parameter causal language model, using five diagnostic metrics: weight predictability (R2), ablation degradation, recovery speed,

Leonardo Chiani, Pietro Andreoni, Laurent Drouet, Tobias Schmidt

Direct air carbon capture and storage (DACCS) is a promising CO2 removal technology, but its deployment at scale remains speculative. Yet, its technological, economic, and policy-related uncertainties have often been overlooked in mitigation pathways. This paper conducts the first uncertainty quantification and global sensitivity analysis of DACCS on technol

Oliver Jeong, Michel Piat, Aritoki Suzuki

We present a high efficiency, moderate resolution on-chip superconducting filterbank spectrometer designed for line intensity mapping and broadband wave-like dark matter searches. Existing implementations used by the millimeter-wave community rely on resistive feedline termination for standing wave mitigation which caps the per-channel efficiency $η_\mathrm{

Mingxing Zhang, Nicola Rossberg, Simone Innocente, Katarzyna Komolibus

In recent years, machine learning models have been increasingly applied to spectroscopic datasets for chemical and biomedical analysis. For their successful adoption, particularly in clinical and safety-critical settings, professionals and researchers must be able to understand and trust the reasoning behind model predictions. However, the inherently high di

Sebastian Bahamonde, Jorge Gigante Valcarcel

We investigate the phenomenon of black hole superradiance in the presence of torsion within the framework of Poincar\'e gauge theory. In particular, in contrast to the classical approach of General Relativity, we show that the inclusion of torsion in the space-time geometry enables the energy extraction from rotating black holes by Dirac fermions via chiral

Qiang Li, XiangRui Zhang, Haining Wang

Binary vulnerability analysis is increasingly performed by LLM-based agents in an iterative, multi-pass manner, with the model as the core decision-maker. However, how such systems organize exploration over hundreds of reasoning steps remains poorly understood, due to limited context windows and implicit token-level behaviors. We present the first large-scal

Yiren Lu, Yi Du, Disheng Liu, Yunlai Zhou

Effective embodied exploration requires agents to accumulate and retain spatial knowledge over time. However, existing scene representations, such as discrete scene graphs or static view-based snapshots, lack \textit{post-hoc re-observability}. If an initial observation misses a target, the resulting memory omission is often irrecoverable. To bridge this gap

Mohammad Al Ridhawi, Mahtab Haj Ali, Hussein Al Osman

Stock markets exhibit regime-dependent behavior where prediction models optimized for stable conditions often fail during volatile periods. Existing approaches typically treat all market states uniformly or require manual regime labeling, which is expensive and quickly becomes stale as market dynamics evolve. This paper introduces an adaptive prediction fram

Miguel Ángel Berbel, Marco Castrillón López

This paper presents a Hamiltonian reduction procedure for field theories over affine principal bundles introducing a canonical identification to describe the reduced multisymplectic space without the introduction of a connection. The main goal is to provide a Hamiltonian analogue of the Lagrangian reduction theory developed in M. Castrill\'on L\'opez, P. M.

Victor Nikhil Antony, Zhili Gong, Yoonjae Kim, Chien-Ming Huang

We present M, an open-source, low-cost social robot platform designed to reduce platform friction that slows social robotics research by making robots easier to reproduce, modify, and deploy in real-world settings. M combines a modular mechanical design, multimodal sensing, and expressive yet mechanically simple actuation architecture with a ROS2-native soft

Yida Zhang, Zhiyong Gao, Shuaibing Yue, Jie Li

Recent advancements and widespread adoption of Large Language Models (LLMs) in both industry and academia have catalyzed significant demand for LLM serving. However, traditional cloud services incur high costs, while on-device inference alone faces challenges due to limited resources. Edge-cloud collaboration emerges as a key research direction to combine th

Muhammad Hamza Ali, Amritanshu Pandey

The growing use of inverter-based resources in modern power systems has made grid-following inverters a central topic in power-system modeling, control, and simulation. Despite their widespread deployment, introductory material that explains grid-following inverter operation from first principles and connects control design to time-domain simulation remains

Zhuofan Li, Hongkun Yang, Zhenyang Chen, Yangxuan Chen

Vision-Language-Action (VLA) models have recently enabled embodied agents to perform increasingly complex tasks by jointly reasoning over visual, linguistic, and motor modalities. However, we find that the prevailing notion of ``efficiency'' in current VLA research, characterized by parameters, FLOPs, or token decoding throughput, does not reflect actual per

E. Knudstrup, M. L. Marcussen, S. H. Albrecht, M. S. Lundkvist

Spin-orbit obliquity measurements of hot-Jupiter systems constrain giant planet migration and tidal evolution. In binary systems, combining stellar obliquities with the orbit-orbit angle ($\gamma$) between the planetary and stellar companion orbits provides further insight into the dynamical influence of stellar companions. Here we aim to determine the proje

Frederic Latremoliere

Almost commutative models provide a framework for Connes' work on the standard model of particle physics. These models are constructed as products of a the canonical spectral triple of a compact connected spin manifold with a finite dimensional spectral triple. Motivated by the fundamental question of the dependence of the spectra of Dirac operators under ch

Patrick Yubeaton, Siddharth Garg, Chinmay Hegde

Large language models (LLMs) have made rapid advancements in code generation for popular languages such as Python and C++. Many of these recent gains can be attributed to the use of ``agents'' that wrap domain-relevant tools alongside LLMs. Hardware design languages such as Verilog have also seen improved code generation in recent years, but the impact of ag

Haggai Landa

We describe an empirical approach to identify low-weight combinations of columns of the decoding matrices of a quantum circuit-level noise model, for which belief-propagation (BP) algorithms converge possibly very slowly. Focusing on the logical-idle syndrome cycle of the low-density parity check gross code, we identify criteria providing a characterization

John D. Gibbon, Dario Vincenzi

Contrary to accepted turbulence folklore, which holds that no mathematical relation exists between the Navier-Stokes equations (NSEs) and the multifractal model (MFM) of Parisi and Frisch, we develop a theory that reconciles the MFM with Leray's weak solutions of Navier-Stokes analysis. From a combination of Euler invariant scaling and the NSEs set in a thre

Bruna Mariana Braido da Silva Percinotti

We study the affine variety $L_{n}(\mathfrak{g})$ of Lie algebra representations, the collection of all homomorphisms from an arbitrary $n$-dimensional Lie algebra into a fixed real semi-simple Lie algebra $\mathfrak{g}$. Using techniques from real Geometric Invariant Theory, we equip this variety with a natural moment map and associated energy functional ar

Weilin Chen, Jiahao Rao, Wenhao Wang, Xinyang Li

The creation of high-fidelity, customizable 3D indoor scene textures remains a significant challenge. While text-driven methods offer flexibility, they lack the precision for fine-grained, instance-level control, and often produce textures with insufficient quality, artifacts, and baked-in shading. To overcome these limitations, we introduce CustomTex, a nov

Felice Fruncillo, Paolo Luchini, Flavio Giannetti

This work develops a spherical-multipole expansion of the surface terms of an acoustic-analogy formulation, for the prediction of tonal noise from rotating propellers. The acoustic field is expressed through spherical multipoles, which separate source integrals from the observer dependence. This decoupling leads to computational efficiency: once the multipol

Yingqing Chen, Christos G. Cassandras, Wei Xiao, Anni Li

This paper is motivated by controllers developed for autonomous vehicles which occasionally result into conditions where safety is no longer guaranteed. We develop an exact-time safety recovery framework for any control-affine nonlinear system when its state is outside a safe region using time-varying Control Barrier Functions (CBFs) with optimal barrier tra

Maksym Del, Markus Kängsepp, Marharyta Domnich, Ardi Tampuu

Uncertainty estimation is critical for deploying reasoning language models, yet remains poorly understood under extended chain-of-thought reasoning. We study parallel sampling as a fully black-box approach using verbalized confidence and self-consistency. Across three reasoning models and 17 tasks spanning mathematics, STEM, and humanities, we characterize h

Hala Hawashin, Deep Nath, Marco Alberto Javarone

In this work, we review quantum approaches to combinatorial optimization, with the aim of bridging theoretical developments and industrial relevance. We first survey the main families of quantum algorithms, including Quantum Annealing, the Quantum Approximate Optimization Algorithm (QAOA), Quantum Reinforcement Learning (QRL), and Quantum Generative Modeling

Alfredo P. Vega-Leal, Jose L. Mora

Analog multiplexing for sigma delta modulated Digital to Analog Converters has been recently proposed as a means of achieving robustness. This preprint analyses said scheme via simulations. The main limitation introduced by the proposed architecture comes from mismatch in the DACs gain, which can drastically impact performances. A new technique of dynamic el

Sandra Vadhin, Jeffrey D. Varner

We present BSTModelKit.jl, an open-source Julia package for constructing, solving, and analyzing Biochemical Systems Theory (BST) models of biochemical networks. The package implements S-system representations, a canonical power-law formalism for modeling metabolic and regulatory networks. BSTModelKit.jl provides a declarative model specification format, dyn

Nam Q. Le

We study the solvability and uniqueness for several degenerate Monge--Amp\`ere equations including the Monge--Amp\`ere eigenvalue problem in real Euclidean spaces that involve singular Borel measures. Our approach systematically analyzes the Monge--Amp\`ere energy from the variational point of view and appropriately exploits monotonicity arguments. Our main

Yunhui Cai, Joar Bagge, Per-Gunnar Martinsson

The paper describes a numerical method for solving acoustic multibody scattering problems in two and three dimensions. The idea is to compute a highly accurate approximation to the scattering operator for each body through a local computation, and then use these scattering matrices to form a global linear system. The resulting coefficient matrix is relativel

Jiachen Li, Weitao Yang

We derive analytical expressions for chemical potentials within the random phase approximation (RPA), equivalently the $GW$ energy functional evaluated using non interacting Green's functions ($G_s$). The chemical potential is obtained using two formally equivalent approaches: a direct derivative of the total energy with respect to particle number, and a fun

Ryan Alvarado, Michał Dymek, Przemysław Górka, Nijjwal Karak

Sobolev-type embeddings on metric measure spaces encode a subtle interaction between the analytic regularity of functions and the geometry of the underlying domain space. In this paper we develop an embedding theory for variable Haj{\l}asz-type smoothness spaces on metric measure spaces whose ``dimension'' is allowed to vary pointwise through a bounded expon

Jonathan Z. Lu, Alexander Poremba, Yihui Quek, Akshar Ramkumar

Post-quantum cryptography currently rests on a small number of hardness assumptions, posing significant risks should any one of them be compromised. This vulnerability motivates the search for new and cryptographically versatile assumptions that make a convincing case for quantum hardness. In this work, we argue that decoding random quantum stabilizer codes

Jorge Terol Calvo, Marco Taoso, Andrea Caputo, Michela Negro

We perform a search for an X-ray monochromatic line arising from dark matter (DM) decay in the halo of the Large Magellanic Cloud. An emission line can be expected from two well-motivated DM candidates: sterile neturinos and axion-like particles (ALPs). We analyze the eROSITA-DE DR1 datasets in the energy range between 1 and 9 keV. No evidence for a DM line

Cosmin Safta, Habib N. Najm

This report examines numerical aspects of constructing Karhunen-Lo\`{e}ve expansions (KLEs) for second-order stochastic processes. The KLE relies on the spectral decomposition of the covariance operator via the Fredholm integral equation of the second kind, which is then discretized on a computational grid, leading to an eigendecomposition task. We derive th

Jan Priessnitz, Anna Birk Hellenes, Riccardo Comin, Libor Šmejkal

Couplings between ferroelectric and magnetic orders offer promising routes toward low-dissipation electronics. However, such couplings are notably rare, largely due to the poor compatibility between insulating band structures and ferromagnetism. Here, we study a different strategy: we identify previously overlooked time-reversal-symmetric $p$- and $f$-wave s

Luca Santosuosso, Fei Teng, Sonja Wogrin

This paper addresses the energy dispatch of a virtual power plant comprising renewable generation, energy storage, and thermal units under uncertainty in renewable output, energy prices, and energy demand. The nonlinear dynamics and multiple sources of uncertainty render traditional stochastic model predictive control (MPC) computationally intractable as the

Victor P. Goncalves, Emmanuel Moulin, Igor Reis, Aion Viana

The production of very-high-energy (VHE, $E_{\gamma} \gtrsim 100$ GeV) gamma rays resulting from the scattering of high-energy cosmic-ray protons off axion-like particles (ALPs) populating the dark matter halo of the Milky Way is investigated. By employing the latest instrument response functions for current and future facilities, we demonstrate that ground-

Boudouma Adam, Zarka Philippe, Louis Corentin, Imai Masafumi

Measurements of Waves instrument onboard the Juno spacecraft suggest that narrowband kilometric radiation (nKOM; 20-141 kHz) and narrowband low-frequency radiation (nLF; 5-70 kHz) are generated within the plasma near the Io plasma torus (IPT) in low-latitude regions. While these emissions are thought to result from the conversion of the natural modes of the

Víctor Chaves-Santos, Lucas C. F. Ferreira

We analyze the incompressible Navier-Stokes equations on a class of non-compact Riemannian manifolds within the framework of Morrey spaces. Assuming bounded geometry together with negative Ricci and sectional curvature (e.g., hyperbolic spaces), we establish dispersive and smoothing estimates for the heat semigroups associated with the Beltrami, Bochner and

Sheng Liu, Panos Papadimitratos

FL has emerged as a transformative paradigm for ITS, notably camera-based Road Condition Classification (RCC). However, by enabling collaboration, FL-based RCC exposes the system to adversarial participants launching Targeted Label-Flipping Attacks (TLFAs). Malicious clients (vehicles) can relabel their local training data (e.g., from an actual uneven road t

Paul Borrill

Civilization maintains an elaborate infrastructure devoted to the maintenance of synchronized time. Governments mandate daylight saving time. Standards bodies insert leap seconds into Coordinated Universal Time. Engineers debate leap milliseconds and leap nanoseconds. The Global Positioning System applies relativistic corrections at the nanosecond level. All

Yuqiang Lin, Kehua Chen, Sam Lockyer, Arjun Yadav

Traffic Anomaly Understanding (TAU) is important for traffic safety in Intelligent Transportation Systems. Recent vision-language models (VLMs) have shown strong capabilities in video understanding. However, progress on TAU remains limited due to the lack of benchmarks and task-specific methodologies. To address this limitation, we introduce Roundabout-TAU,

Yilin Wang, Yuchun Fan, Jiaoyang Li, Ziming Zhu

Retrieval-augmented generation (RAG) systems have made significant progress in solving complex multi-hop question answering (QA) tasks in the English scenario. However, RAG systems inevitably face the application scenario of retrieving across multilingual corpora and queries, leaving several open challenges. The first one involves the absence of benchmarks t

Michael Crocoll, Christian Döding, Benjamin Dörich, Roland Maier

In this work, we propose a neural network-enhanced finite element strategy to compute the minimizer of the Ginzburg-Landau energy based on an unsupervised deep Ritz-type strategy. We treat the parameter $\kappa$ as a variable input parameter to obtain possible minimizers for a large range of $\kappa$-values. This allows for two possible strategies: 1) The ne

M. Paris, A. Mura, F. Zambon, A. Genova

Io's internal heat is generated by Jupiter-driven tidal dissipation and Laplace resonance. This energy partially melts the mantle, but the melt fraction, depth, and spatial distribution of dissipation remain poorly constrained. Tidal deformation is linked to the mantle's physical state via a parametric approach accounting for melting onset depth and latent h

Jeanne Gipouloux, Matteo Brunelli, Leticia Cugliandolo, Rosario Fazio

We introduce and characterize different models for an active quantum particle where activity arises from engineered dissipation-- specifically, from a suitably coupled nonequilibrium environment. These include a model of a particle moving on a lattice with coherent and dissipative hopping, as well as quantum generalizations of well-studied models of active b

Shravika Mittal, Erin Kasson, Layna Paraboschi, Eleanor Laufenberg

Generative AI (GenAI) is increasingly being integrated into the online ecosystem, including online health communities (OHCs), where people with diverse health conditions exchange social support. For example, in OHCs, support providers are beginning to share content generated, directly or indirectly, by popular GenAI-based tools. OHCs are governed by norms th

Carlos Hinojosa, Clemens Grange, Bernard Ghanem

Vision-language models (VLMs) are increasingly deployed in real-world and embodied settings where safety decisions depend on visual context. However, it remains unclear which visual evidence drives these judgments. We study whether multimodal safety behavior in VLMs can be steered by simple semantic cues. We introduce a semantic steering framework that appli

Qin Jiang, Chengjia Wang, Michael Lones, Dongdong Chen

Spectral Graph Neural Networks (Spectral GNNs) for node classification promise frequency-domain filtering on graphs, yet rest on flawed foundations. Recent work shows that graph Laplacian eigenvectors do not in general have the key properties of a true Fourier basis, but leaves the empirical success of Spectral GNNs unexplained. We identify two theoretical g

Yukai Lu, Connor M. Holland, Callum L. Welsh, Xing-Yan Chen

Models of interacting quantum spins are used in many areas of physics ranging from the study of magnetism and strongly correlated materials to quantum sensing. In this work, we study coherent many-body dynamics of interacting spin models realized using polar molecules trapped in rearrangeable optical tweezer arrays. Specifically, we encode quantum spins in l

Ximing Wang, Yunlong Xiao

Incorporating sample efficiency, by requiring the number of states consumed by broadcasting does not exceed that of a naive prepare-and-distribute strategy, gives rise to the no practical quantum broadcasting theorem. To navigate this limitation, we introduce approximate and probabilistic virtual broadcasting and derive analytic expressions for their optimal

Matteo Cerutti, Andrea Simonelli

We investigate the impact of phenomenological prescriptions in the Collins-Soper-Sterman (CSS) approach for global extractions of Transverse Momentum Distributions (TMDs). We show that fits to low-energy Drell-Yan data with different choices of $b_*$ prescription yield equally good agreement with data and similar TMDs at small partonic transverse momentum. I

Xihan Xiong, Minfeng Qi, Shiping Chen, Guangsheng Yu

Ethereum Inscriptions (Ethscriptions) repurpose Ethereum calldata into a persistent inscription channel by embedding \texttt{data:}~URI payloads. These transactions typically target externally owned accounts, allowing the payload to bypass EVM execution while remaining permanently replicated across full nodes. Although calldata was originally designed for co

David Morales-Zapien, Stefano Profumo

The nature of the so-called G objects orbiting the Galactic Center remains unresolved. These sources exhibit compact Br$\gamma$ emission, extreme infrared colors, and remarkable dynamical stability through close passages to the central supermassive black hole, challenging conventional interpretations as stars or unbound gas clouds. We investigate the hypothe

Madeline Bittner, Dina Demner-Fushman, Yasmeen Shabazz, Davis Bartels

Health literacy is a critical determinant of patient outcomes, yet current screening tools are not always feasible and differ considerably in the number of items, question format, and dimensions of health literacy they capture, making documentation in structured electronic health records difficult to achieve. Automated detection from unstructured clinical no

Zainab Farooq, Amar Pashov, Pieter Reumers, Stijn François

The evaluation of elastodynamic Green's functions across numerous source-receiver locations, frequencies, and material properties, particularly in the context of parametric studies or boundary element computations, is computationally demanding and memory intensive. This paper presents a reduced order modeling strategy based on the Greedy Tucker Approximation

James King, Bálint Kaszás, Gergely Buza, William Jussiau

We investigate the persistence and regularity of spectral submanifolds (SSMs) in high-dimensional parametric dynamical systems undergoing a Hopf bifurcation. By analyzing how resonances in the linearized spectrum near bifurcation points limit the existence and smoothness of SSMs, a phenomenon that has been mostly overlooked, we show that low-order Taylor coe

Sangwoo Shin, Kunzhao Ren, Xiaobin Xiong, Josiah P. Hanna

Recent work in reinforcement learning has shown that incorporating structural priors for articulated robots, such as link connectivity, into policy networks improves learning efficiency. However, dynamics properties, despite their fundamental role in determining how forces and motion propagate through the body, remain largely underexplored as an inductive bi

Ye Wang, Wei Lu, Zhihui You, Keyan Chen

Change detection in optical remote sensing imagery is susceptible to illumination fluctuations, seasonal changes, and variations in surface land-cover materials. Relying solely on RGB imagery often produces pseudo-changes and leads to semantic ambiguity in features. Incorporating near-infrared (NIR) information provides heterogeneous physical cues that are c

Moyang Li, Zihan Zhu, Marc Pollefeys, Daniel Barath

We present a robust, real-time RGB SLAM system that handles dynamic environments by leveraging differentiable Uncertainty-aware Bundle Adjustment. Traditional SLAM methods typically assume static scenes, leading to tracking failures in the presence of motion. Recent dynamic SLAM approaches attempt to address this challenge using predefined dynamic priors or

Timothy C. Andrews, Thomas M. Bendall

This paper outlines a conservative transport scheme for scalar tracers within a compatible finite element model for geophysical fluid equations. Instead of using the advective transport equation for a mixing ratio, a conservative transport equation is solved for the tracer density of the mixing ratio multiplied by the dry density. This ensures mass conservat

Duyi Pan, Tianao Lou, Xin Li, Haoze Song

Large Language Models (LLMs) exhibit hallucinations in knowledge-intensive tasks. Graph-based retrieval augmented generation (RAG) has emerged as a promising solution, yet existing approaches suffer from fundamental recall and precision limitations when operating over black-box knowledge graphs -- graphs whose schema and structure are unknown in advance. We

Fengxiaoxiao Li, Xiao Mao, Mingfeng Fan, Yifeng Zhang

Robotic systems often require a team of robots to collectively visit multiple targets while optimizing competing objectives, such as total travel cost and makespan. This setting can be formulated as the Multi-Objective Multiple Traveling Salesman Problem (MOMTSP). Although learning-based methods have shown strong performance on the single-agent TSP and multi

Léo Simpson, Katrin Baumgärtner, Johannes Köhler, Moritz Diehl

This paper presents uniform-in-time finite-sample bounds for regularized linear regression with vector-valued outputs and conditionally zero-mean subgaussian noise. By revisiting classical self-normalized martingale arguments, we obtain bounds that apply directly to multi-output regression, unlike most of the prior work. Compared to the state of the art, the

Sonja Cox, Matas Urbonas

We establish upper bounds for the weak and strong error resulting from a perturbation of the noise driving the stochastic Burgers equation, where we assume the noise to be additive and of trace class and the initial value to be sufficiently regular. More specifically, replacing the covariance operator of the driving noise $Q_1 \in \mathcal{L}_1(L^2)$ in the

Francesca Margari, Simone Bacchio, Alessandro De Santis, Antonio Evangelista

The $R$-ratio is a phenomenological observable of great relevance, both in itself and in applications such as the dispersive approach to the muon anomalous magnetic moment. It can be investigated from first-principles with controlled statistical and systematic errors in lattice QCD by introducing an arbitrary smearing kernel and employing spectral reconstruc

L. Poulain d'Andecy

The main purpose of this note is to provide an elementary discussion of some simple triangles of integer numbers in particular through their connections with representation theory of $sl_2$. The triangles under consideration are the Catalan triangle and the Motzkin triangle together with their generalisations that we introduce here. We advocate the point of

Julia Apportin, Christian Peltz, Pavel Polynkin, Misha Ivanov

We present a computationally efficient model that describes the interaction of intense, ultrashort infrared laser pulses with transparent materials in the strong ionization regime. The model is augmented with a detailed self-consistent description of the local response due to ionization and collisional plasma dynamics. It incorporates the direct solution of

Mohamed Badi, Chaouki Ben Issaid, Mehdi Bennis

Federated learning (FL) enables collaborative model training across distributed devices without sharing raw data, but applying FL to multi-modal settings introduces significant challenges. Clients typically possess heterogeneous modalities and model architectures, making it difficult to align feature spaces efficiently while preserving privacy and minimizing