April 2026 arXiv papers — page 78

Mihailo Stojnic

In [97,99,100], an fl-RDT framework is introduced to characterize \emph{statistical computational gaps} (SCGs). Studying \emph{symmetric binary perceptrons} (SBPs), [100] obtained an \emph{algorithmic} threshold estimate $\alpha_a\approx \alpha_c^{(7)}\approx 1.6093$ at the 7th lifting level (for $\kappa=1$ margin), closely approaching $1.58$ local entropy (

Maurice Chiodo, Toni Erskine, Dennis Müller, James G. Wright

We analyse the 2025 Signalgate leak of sensitive US military information by the Trump administration, addressing why confidentiality was violated (messages leaked to the press) in spite of encryption (Signal), to deepen the socio-technical considerations when designing and deploying encryption. First, we use applied pi-calculus to formally model the boutique

Zewei Zhou, Ruining Yang, Xuewei, Qi

Vision-Language-Action (VLA) models offer a promising autonomous driving paradigm for leveraging world knowledge and reasoning capabilities, especially in long-tail scenarios. However, existing VLA models often struggle with the high latency in action generation using an autoregressive generation framework and exhibit limited robustness. In this paper, we pr

Yanmo Hu, Weifeng Zhu, Chenshu Wu, Shuowen Zhang

This paper considers a networked tracking architecture in 6G integrated sensing and communication (ISAC) systems, where multiple base stations (BSs) cooperatively transmit radio signals and process received echo signals to track multiple moving targets. Compared to the single-BS counterpart, networked tracking allows the moving targets to be associated with

Alexandre Ern, Brendan Keith, Dohyun Kim, Rami Masri

We introduce a family of proximal discontinuous Galerkin methods for variational inequalities, focusing on the obstacle problem as a didactic example. Each member of this family is born from applying a different well-known nonconforming finite element discretization to the Bregman proximal point method. We explicitly treat four examples: the symmetric interi

Ivan Tymoshenko, Luca Maraschi, Matteo Collina

Kubernetes offers two default paths for scaling Node\.js workloads, and both have structural limitations. The Horizontal Pod Autoscaler scales on CPU utilization, which does not directly measure event loop saturation: a Node.js pod can queue requests and miss latency SLOs while CPU reports moderate usage. KEDA extends HPA with richer triggers, including even

Ziemowit M. Wójcicki

We study the local Lipschitz one subsets of a finite dimensional space, that is, sets for which there exists a continuous function whose local Lipschitz derivative is the characteristic function of said set. We give a characterization of a local Lipschitz one set on the real line in terms of a certain measure-theoretic density condition, which we call quasi-

Carter Adams, Rafael Oliveira, Gabriel Almeida, Sofia Torres

Reinforcement fine-tuning with verifiable rewards (RLVR) has emerged as a powerful paradigm for equipping large vision-language models (LVLMs) with agentic capabilities such as tool use and multi-step reasoning. Despite striking empirical successes, most notably Visual Agentic Reinforcement Fine-Tuning (Visual-ARFT), the theoretical underpinnings of this par

Yacine Ali-Haïmoud

Inspiralling supermassive black-hole binaries (SMBHBs) are expected to be the main source of the nanohertz gravitational-wave background (GWB) targeted by pulsar timing arrays (PTAs). We provide a simple and general analytic expression for the probability distribution function (PDF) of the GWB characteristic strain squared $h_c^2$ in the limit of a large but

Cyprien Tamekue

We study null controllability for the parabolic equation on $\mathbb{S}^{2}$ endowed with its canonical almost-Riemannian structure. For a spherical crown $\omega=\{\alpha<x_3<\beta\}$, where $0\le \alpha<\beta\le1$, we prove the sharp minimal time formula $T_{\min}(\omega)=\ln(1/\sqrt{1-\alpha^{2}})$ for null controllability in $\omega$. We also prove that,

Cagri Eryilmaz

Large Language Models (LLMs) show promise for generating Register-Transfer Level (RTL) code from natural language specifications, but single-shot generation achieves only 60-65% functional correctness on standard benchmarks. Multi-agent approaches such as MAGE reach 95.9% on VerilogEval yet remain untested on harder industrial benchmarks such as NVIDIA's CVD

Segun Aroyehun, Stephan Lewandowsky, David Garcia

The pursuit of truth is central to democratic deliberation and governance, yet political discourse reflects varying epistemic orientations, ranging from evidence-based reasoning grounded in verifiable information to intuition-based reasoning rooted in beliefs and subjective interpretation. We introduce a scalable approach to measure epistemic orientation usi

Guillaume Gautier, Rémi Bardenet, Michal Valko

The standard Monte Carlo estimator $\widehat{I}_N^{\mathrm{MC}}$ of $\int fd\omega$ relies on independent samples from $\omega$ and has variance of order $1/N$. Replacing the samples with a determinantal point process (DPP), a repulsive distribution, makes the estimator consistent, with variance rates that depend on how the DPP is adapted to $f$ and $\omega$

Jing Jin, Hao Liu, Yan Bai, Yihang Lou

Multimodal large language models (MLLMs) have shown promising reasoning abilities, yet evaluating their performance in specialized domains remains challenging. STEM reasoning is a particularly valuable testbed because it provides highly verifiable feedback, but existing benchmarks often permit unimodal shortcuts due to modality redundancy and focus mainly on

Jean-Bastien Grill, Omar Darwiche Domingues, Pierre Ménard, Rémi Munos

We propose SmoothCruiser, a new planning algorithm for estimating the value function in entropy-regularized Markov decision processes and two-player games, given a generative model of the environment. SmoothCruiser makes use of the smoothness of the Bellman operator promoted by the regularization to achieve problem-independent sample complexity of order O~(1

Ariel Linden

Mixed-effects logistic regression is widely used for binary outcomes in hierarchical data, yet formal goodness-of-fit tests remain limited to random-intercept models and do not address sparse cluster settings. We extend a grouping-based Wald test to mixed-effects logistic models with random slopes. The procedure groups observations by predicted probabilities

Samuele Centorrino, Christopher F. Parmeter

Causal inference methods (instrumental variables, difference-in-differences, regression discontinuity, etc.) are primary tools used across many social science milieus. One area where their application has lagged however, is in the study of productivity and efficiency. A main reason for this is that the nature of the stochastic frontier model does not immedia

Anil Belli, Ugur Gul, William T. Ross, Aristomenis G. Siskakis

This paper explores a version of the classical Ces`aro integral operator for the Lebesgue space L2(0, 1) where we discuss its norm, adjoint, spectral properties, and invariant subspaces. An important tool will be semigroups of weighted composition operators on L2(0, 1).

Burak Ulaş

This study presents a multi-task machine learning framework for simultaneous morphology classification and physical parameter estimation of eclipsing binaries using photometric light curves. We train Random Forest and XGBoost ensemble models on 845 of 995 well-characterized systems comprising three morphological configurations by extracting 51 domain-specifi

Shuai Wang, Hongyi Zhu, Jia-Hong Huang, Yixian Shen

Understanding artworks requires multi-step reasoning over visual content and cultural, historical, and stylistic context. While recent multimodal large language models show promise in artwork explanation, they rely on implicit reasoning and internalized knowl- edge, limiting interpretability and explicit evidence grounding. We propose A-MAR, an Agent-based M

Xabier Gutiérrez, Lorenzo Laneve, Mikel Sanz

Quantum Signal Processing (QSP) and Quantum Singular Value Transformation (QSVT) provide an efficient framework for implementing polynomials of block-encoded matrices, and thus offer a systematic approach to quantum algorithm design. However, despite a number of recent advances, important limitations remain. In particular, QSP can only transform unitary matr

Z. M. McIntyre, Daniel Loss

The use of noise-robust qubit encodings provides a way of extending the lifetime of quantum information at the hardware level. In this work, we introduce the spin Kerr-cat encoding, which leverages a clock transition in the spectrum of quadrupolar nuclei (having spin length $I\geq 1$) to achieve a first-order suppression of noise leading to qubit dephasing.

Kai Heussen, Jawad Kazmi, Narges Mehran, Artjoms Obushevs

Reproducibility, traceability, and transparency in testing cyber-physical energy systems are crucial for scientific advancement and cross-laboratory collaboration. Current experimentation and test documentation practices lack formal semantics, making it difficult to reproduce experiments, share data, and apply, for example, the artificial intelligence-driven

Saransh Sharma, Pritika Ramu, Aparna Garimella, Koyel Mukherjee

Answering open-ended questions remains challenging for AI systems because it requires synthesis, judgment, and exploration beyond factual retrieval, and users often refine answers through multiple iterations rather than accepting a single response. Existing QA benchmarks do not explicitly support this refinement process. To address this gap, we introduce a n

Salvatore Greco, Jacek Karolczak, Roman Słowiński, Jerzy Stefanowski

Explainable artificial intelligence (XAI) has predominantly focused on generating model-centric explanations that approximate the behavior of black-box models. However, such explanations often overlook a fundamental aspect of interpretability: different users require different explanations depending on their goals, preferences, and cognitive constraints. Alt

Archisman Ghosh, Avimita Chatterjee, Swaroop Ghosh

Practical quantum advantage is expected to depend on fault-tolerant quantum computing, although the architectural overhead needed to support fault tolerance is still extremely high. Prior FTQC designs generally emphasize either fast logical-qubit accessibility at the cost of significant qubit overhead, or high logical-qubit density at the cost of added workl

Yunfan Lou, Xiaowei Chi, Xiaojie Zhang, Zezhong Qian

World models derived from large-scale video generative pre-training have emerged as a promising paradigm for generalist robot policy learning. However, standard approaches often focus on high-fidelity RGB video prediction, this can result in overfitting to irrelevant factors, such as dynamic backgrounds and illumination changes. These distractions reduce the

Anton Fehnker

We implement methods from the geometry of numbers to give explicit estimates for the number of integral ideals in a number field. We pay particular attention to minimising the effect of the degree $n$ of the number field on the error term and avoid terms on the order of $n^{n^2}$. We do this by studying fundamental domains for the action of multiplying with

Weitao Du

While standard flow-matching models transport noise to data uniformly, incorporating an explicit generation order - specifically, establishing coarse, low-frequency structure before fine detail - has proven highly effective for synthesizing natural images. Two recent works offer distinct paradigms for this. K-Flow imposes a hard frequency constraint by reint

Hunter L. Brown, Geoffrey Hollinger, Stefan Lee

Reinforcement learning-based control policies have been frequently demonstrated to be more effective than analytical techniques for many manipulation tasks. Commonly, these methods learn neural control policies that predict end-effector pose changes directly from observed state information. For tasks like inserting delicate connectors which induce force cons

Zhi Chen, Runze Hu, Le Zhang

Flow matching has recently emerged as a principled framework for learning continuous-time transport maps, enabling efficient ODE-based sampling without relying on stochastic diffusion processes. While generative modeling has shown promise for medical image segmentation, particularly in capturing uncertainty and complex anatomical variability, existing approa

Connor Auge, Michael Koss, Kriti K. Gupta, Claudio Ricci

We use high-resolution UV-to-optical imaging from the Hubble Space Telescope (HST) to construct spatially resolved spectral energy distributions (SEDs) for seven nearby ($z<0.07$) hard (14--195$\,$keV) X-ray-selected broad-line active galactic nuclei (AGN) with $L_{\rm bol}=10^{43.26}-10^{45.34}\,\rm{erg\,s^{-1}}$. The high spatial resolution of HST, which p

Pierre Perrault, Jennifer Healey, Zheng Wen, Michal Valko

We introduce a new budgeted framework for online influence maximization, considering the total cost of an advertising campaign instead of the common cardinality constraint on a chosen influencer set. Our approach better models the real-world setting where the cost of influencers varies and advertisers want to find the best value for their overall social adve

Alex Cuellar, Michael Hagenow, Julie Shah

Effective human-robot teaming is crucial for the practical deployment of robots in human workspaces. However, optimizing joint human-robot plans remains a challenge due to the difficulty of modeling individualized human capabilities and preferences. While prior research has leveraged the multi-cycle structure of domains like manufacturing to learn an individ

Gabriel Sánchez-Pérez

This thesis is framed within the field of Mathematical Relativity and is organized into six chapters. After an introduction to the topic in Chapter 1, Chapter 2 reviews and further develops the formalism of hypersurface data, which provides the unifying framework for the entire thesis. In Chapter 3 we study the characteristic Cauchy problem from a fully deta

Anastasios Fasoulakis, Ross C. Schofield, Rupert F. Oulton, Alex S. Clark

The concept of cavity funneling has emerged recently as a promising route towards creating indistinguishable photons from highly dephased emitters. So far, all suggested solutions are solely based on dielectric cavities that require extremely high quality factors that are difficult to reach at visible wavelengths. Here we suggest a hybrid funneling architect

Nico Baumgart, Markus Lange-Hegermann, Jan Henze

Efficient semantic access to industrial product data is a key enabler for factory automation and emerging LLM-based agent workflows, where both human engineers and autonomous agents must identify suitable components from highly structured catalogs. However, the vocabulary mismatch between natural-language queries and attribute-centric product descriptions li

Quang-Huy Nguyen, Thanh-Hai Nguyen, Khac-Manh Thai, Duc-Hoang Pham

Counterfactual explanations (CEs) provide an intuitive way to understand recommender systems by identifying minimal modifications to user-item interactions that alter recommendation outcomes. Existing CE methods for recommender systems, however, have been evaluated under heterogeneous protocols, using different datasets, recommenders, metrics, and even expla

Gastón Avetta, Jose Lobera, Juan José Zárate, Inés Samengo

Perceptual judgments of sequential stimuli are systematically biased by prior expectations and by the temporal structure of sensory input. In haptic discrimination tasks, these effects often manifest as time-order asymmetries, whereby the perceived difference between two stimuli depends on their presentation order. Here, we introduce a dynamical Bayesian mod

Cuiju Yu, Jose L. Lado

Altermagnets feature unconventional magnetism due to their momentum-dependent spin splitting purely driven by magnetic order, for which a variety of transition-metal-based d-wave altermagnets have been proposed. However, carbon-based altermagnets in graphene structures remain elusive, even though magnetism in graphene nanostructures has been widely demonstra

Eren Berk Kama, Murat Babek Salman, Isaac Skog, Emil Björnson

This paper presents a sensing management frame- work for integrated sensing and communications (ISAC) within cell-free massive multiple-input multiple-output (MIMO) systems to reduce pilot-based channel state information (CSI) acquisition overhead. Conventional communication systems rely on frequent channel estimation procedures that impose significant signa

Diletta Burini, Damian A. Knopoff

This paper develops a conceptual extension of the Kinetic Theory of Active Particles, building upon the framework introduced in [2]. Living systems cannot be adequately described within classical single-scale paradigms, even when refined. To overcome this limitation, we introduce a Multiscale Kinetic Theory of Active Particles (MS-KTAP), in which a sub-micro

Xudong Jian, Charikleia Stoura, Simon Scandella, Eleni Chatzi

Damage identification is a core task in structural health monitoring. In practice, however, its reliability is often compromised by confounding non-damage effects, such as variations in excitation and environmental conditions, which can induce changes comparable to or larger than those caused by structural damage. To address this challenge, this study propos

Robert Stanley, Avi Verma, Lillian Tsai, Konstantinos Kallas

AI agents promise to serve as general-purpose personal assistants for their users, which requires them to have access to private user data (e.g., personal and financial information). This poses a serious risk to security and privacy. Adversaries may attack the AI model (e.g., via prompt injection) to exfiltrate user data. Furthermore, sharing private data wi

Yiwen Qiu, Linjuan Wu, Yizhou Liu, Yuchen Yan

Large language models have achieved remarkable progress on complex reasoning tasks. However, they often implicitly fabricate information when inputs are incomplete, producing confident but unreliable conclusions -- a failure mode we term ungrounded reasoning. We argue that this issue arises not from insufficient reasoning capability, but from the lack of inf

Pierandrea Vergallo, Mats Vermeeren

We introduce the concept of Hamiltonian potential variables to map Hamiltonian operators into symplectic operators in a dual space. This generalises the classical trick of switching to a potential variable to obtain a Lagrangian density for the Korteweg-de Vries (KdV) equation. Building on this concept, we present the Lagrangian structure for bi-Hamiltonian

Shuyao Qi, Haoyuan Liu, Shizhen Zhao

Fine-grained, per-micro-batch load balancing is essential for efficient Mixture-of-Experts (MoE) training, yet every prior dynamic scheduling scheme pays for it with extra communication that is hard to hide. Especially on modern bulk-transfer backends such as DeepEP. We make a simple but consequential observation: on the NVIDIA Hopper architecture the NVLink

Aya Cherigui, Florent Guépin, Arnaud Legendre, Jean-François Couchot

Human mobility data are used in numerous applications, ranging from public health to urban planning. Human mobility is inherently sensitive, as it can contain information such as religious beliefs and political affiliations. Historically, it has been proposed to modify the information using techniques such as aggregation, obfuscation, or noise addition, to a

Mary C. Knowlton, Justin S. Spilker, Rachel Bezanson, Vincenzo R. D'Onofrio

We present the serendipitous discovery of an overdensity of submillimeter galaxies (SMGs) in the field of SDSSJ0909-0108, a massive z~0.7 post-starburst galaxy from the SQuIGGLE survey. ALMA observations at 870um and 2mm reveal six galaxies within a 35'' region with flux ratios consistent with emission from dust. Given the rarity of 870um sources and the sma

Haoyuan Li, Lu-Meng Liu, Jinhui Chen, Yu-Gang Ma

We study the impact of neutron-skin thickness on $J/\psi$ photoproduction in ultra-peripheral $^{208}\mathrm{Pb}+{}^{208}\mathrm{Pb}$ collisions. Within the Color Glass Condensate framework, we calculate coherent and incoherent cross sections and examine their dependence on the momentum transfer $|t|$ for different neutron-skin thicknesses. We find a clear i

Michael Mandl, Dénes Sexty, Daniel Unterhuber

We present the results of continuum-extrapolated lattice simulations of quantum chromodynamics (QCD) above the crossover temperature and for unprecedentedly high baryon densities at the physical point, employing the complex Langevin equation. In particular, we determine the QCD equation of state by computing the baryon density as well as the pressure as func

Yanhui Chen, Baoyao Yang, Siqi Liu, Jingchao Wang

SAM3 advances open-vocabulary semantic segmentation by introducing a prompt-driven mask generation paradigm. However, in multi-class open-vocabulary scenarios, masks generated independently from different category prompts lack a unified and inter-class comparable evidence scale, often resulting in overlapping coverage and unstable competition. Moreover, syno

Lin Yang, Pierre-Clément A. Simon, Emre Yildirim, José Trueba

The complexity and significance of multiscale phenomena in fusion energy systems make advanced modeling necessary for designing, optimizing, and safely deploying fusion plants. Tritium accountancy is one of those challenges for deuterium-tritium fusion systems. Its availability is constrained by its short half-life (12.33 years) and limited natural abundance

Mario Ziller

Starting from pseudometrics and preorders on sets of integers, we extend the focus to sets of finite sequences of integers, in particular sequences of consecutive integers. We outline existing concepts for deriving centred pseudometrics and preorders in a given pseudometric space and their application to $\mathbb{Z}$ and develop approaches to generalize the

Andrew Hong, Jason Potteiger, Luis E. Zapata

An earlier paper (Hong, Potteiger, and Zapata 2026) established that an unoptimized GPT 4.1 prompt predicts fan-reported experience ratings within one point 67% of the time from open-ended survey text. This paper tests the relative impact of prompt design and model selection on that performance. We compared four configurations on approximately 10,000 post-ga

Alex Lin, Lei Gao, Narsimlu Kemsaram, Sriram Subramanian

AcoustoBots are mobile acoustophoretic robots capable of delivering mid-air haptics, directional audio, and acoustic levitation, but existing implementations rely on scripted commands and lack an intuitive interface for real-time human control. This work presents a gesture-based visual learning framework for contactless human-swarm interaction with a multimo

Wen Cheng, Tuochao Chen, Karim Helwani, Sriram Srinivasan

Edge devices such as smartwatches and smart glasses cannot continuously run even the smallest 100M-1B parameter language models due to power and compute constraints, yet cloud inference introduces multi-second latencies that break the illusion of a responsive assistant. We introduce micro language models ($\mu$LMs): ultra-compact models (8M-30M parameters) t

Thinh Hoang, Zhengyi Wang, Leila Zerrouki, Daniel Delahaye

Air Traffic Flow Management (ATFM) traffic regulations are being increasingly used as rising demand meets persistent workforce shortages. This operational strain has amplified a critical phenomenon that we call \emph{regulation cascading}: the compounding, non-linear interactions that occur when multiple regulations influence one another in unpredictable way

Ling Zhang, Zeyue Zhang, Liu He, Bin Jiang

The intrinsic incommensurate charge density wave in metal-organic frameworks has remained elusive due to the lack of direct evidence linking atomic-scale structural modulation to macroscopic electronic properties. Using high-quality Pr3HHTP2 (HHTP = 2,3,6,7,10,11-hexahydroxytriphenylene) single crystals as a model system, we precisely resolve, for the first

Tongxin Li

Modern world models are becoming too complex to admit explicit dynamical descriptions. We study safety-critical contextual control, where a Planner must optimize a task objective using only feasibility samples from a black-box Simulator, conditioned on a context signal $\xi_t$. We develop a sample-based Penalized Predictive Control (PPC) framework grounded i

Josue Torres-Fonseca, Naihao Deng, Yinpei Dai, Shane Storks

Multimodal Large Language Models are increasingly adopted as autonomous agents in interactive environments, yet their ability to proactively address safety hazards remains insufficient. We introduce SafetyALFRED, built upon the embodied agent benchmark ALFRED, augmented with six categories of real-world kitchen hazards. While existing safety evaluations focu

Diana Jimenez Marti, Benny Rodriguez Saenz, Peter Hartmann, Evdokiya Kostadinova

Dusty plasmas, composed of electrons, ions, neutral particles, and charged dust grains, exhibit self-organization phenomena such as string-like structures observed in microgravity experiments. The formation of these structures is influenced by ion wakes generated by streaming ions under external electric fields, as well as by time-evolving plasma inhomogenei

Xiangyang Luo, Xiaozhe Xin, Tao Feng, Xu Guo

Synthesizing human--object interaction (HOI) videos has broad practical value in e-commerce, digital advertising, and virtual marketing. However, current diffusion models, despite their photorealistic rendering capability, still frequently fail on (i) the structural stability of sensitive regions such as hands and faces and (ii) physically plausible contact

Peng Cheng, Hector Parra De Freitas

We study asymmetric orbifolds of the $E_8\times E_8$ heterotic string from the perspective of worldsheet Dai-Freed anomalies. Focusing on cyclic symmetries $G = \mathbb{Z}_m$ that act chirally on the fermions and symmetrically on the bosons, we compute the corresponding spin-bordism invariants and derive the conditions for the vanishing of global anomalies f

Ilia Chuprina, Genko Genov, Christoph Findler, Johannes Lang

Nitrogen-vacancy (NV) centers in diamond are of great interest for nano- and macro-scale magnetic field sensing. Most sensing protocols rely on conventional optical readout, which is limited by photon shot noise. The recently developed photoelectrical (PE) readout of the NV center electron spin state promises to overcome these limitations. However, the noise

Anton Kolonin, Alexey Glushchenko, Evgeny Bochkov, Abhishek Saxena

Evaluating the reasoning capabilities of Large Language Models (LLMs) for complex, quantitative financial tasks is a critical and unsolved challenge. Standard benchmarks often fail to isolate an agent's core ability to parse queries and orchestrate computations. To address this, we introduce a novel evaluation methodology and benchmark designed to rigorously

Xuejiao Wang, Bohao Zhang, Changbo Wang, Gaoqi He

Dynamic Scene Graph Generation (DSGG) aims to structurally model objects and their dynamic interactions in video sequences for high-level semantic understanding. However, existing methods struggle with fine-grained relationship modeling, semantic representation utilization, and the ability to model tail relationships. To address these issues, this paper prop

Allen Weitsman

Graphs of solutions to the minimal surface equation over simply connected domains with boundary values 0 can have at most exponential growth.

Amanda Ehn, Florian Trybel, Talha Bin Masood, Leonid V. Pourovskii

Understanding the behavior of 4$f$ electrons in materials containing rare earth elements is one of the fundamental questions within condensed matter physics. In this work the electronic properties of isostructural CeCN$_5$ and TbCN$_5$, both recently synthesized at extreme pressure, are investigated using Density Functional Theory (DFT) calculations. We incl

Daniel Engel, Freek Verbeek, Pranav Kumar, Binoy Ravindran

The binary executable format is the standard method for distributing and executing software. Yet, it is also as opaque a representation of software as can be. If the binary format were augmented with metadata that provides security-relevant information, such as which data is intended by the compiler to be executable instructions, or how memory regions are ex

Francisco Rodríguez

Bastos, Geloso, and Bologna Pavlik (2026) argue that the US embargo explains less than one tenth of the difference in per capita income between Cuba and a counterfactual scenario in which the country did not follow socialist economic policies. We show that their results are driven by the use of an elasticity of income to trade openness that is neither repres

Nik Dennler, Elle Stark, Saimon Collaku, Lars Larson

Chemical sensing in real-world environments requires resolving rapidly fluctuating and spatially heterogeneous concentration fields. However, these dynamics are strongly distorted by widely used, low-cost metal-oxide (MOx) gas sensors, whose thermal and surface-kinetic response acts as a low-pass filter on the underlying concentration signal. Quantifying and

Nikita Guseynov, Zoë Holmes, Armando Angrisani

We introduce coherent-state propagation, a computational framework for simulating bosonic systems. We focus on bosonic circuits composed of displaced linear optics augmented by Kerr nonlinearities, a universal model of bosonic quantum computation that is also physically motivated by driven Bose-Hubbard dynamics. The method works in the Schr\"odinger picture

Inhyeok Choi, Hyuncheol Park

Edge-cloud hybrid inference offloads difficult inputs to a powerful remote model, but the uplink channel imposes hard per-request constraints on the number of bits that can be transmitted. We show that selecting transmitted content based solely on attention-based importance, the standard approach in collaborative inference, is inherently limited under hard b

Chun-Ting Lin, Diganta Dasgupta, Tinglu Yang, Cesare Malosso

Although electrolyte density measurements have been reported for over a century, employing them to obtain accurate partial molar volume (PMV) profiles as a function of salt concentration has remained elusive. Obtaining such curves requires precise density measurements combined with a proper treatment of the associated virial expansion. In this work, we obtai

Samuel Aeschbach, Rui Mata, Kaidi Lõo, Simon De Deyne

Free-association norms provide essential empirical data for investigating linguistic, semantic, and cultural phenomena in the cognitive sciences. Although large-scale norms exist for languages such as English, Dutch, Spanish, and Mandarin Chinese, no comparable resource has been available for German. To address this gap, we present free-association norms for

Wen Li, Hui Wang, Jinya Su, Cunjia Liu

Reliable pipeline inspection is critical to safe energy transportation, but is constrained by long distances, complex terrain, and risks to human inspectors. Unmanned aerial vehicles provide a flexible sensing platform, yet reliable autonomous inspection remains challenging. This paper presents an autonomous quadrotor near-proximity pipeline inspection frame

Ahmet Faruk Saz, Faramarz Fekri

This paper develops a principled foundation for goal-oriented semantic communication for logical decision-making. Consider a setting where autonomous agents engage in collaborative perception. In such settings, the volume of sensory data and limited bandwidth often make transmission of raw observations infeasible, requiring intelligent selection of task-rele

J. Bayron Orjuela-Quintana, Mauricio Reyes, Elena Giusarma, Marco Baldi

Accurate modeling of non-linear gravitational dynamics is essential for constraining extensions to the standard cosmological model using large-scale structure observations. While high-resolution $N$-body simulations provide the required fidelity, they are computationally prohibitive for the large ensembles needed to analyze Modified Gravity (MG) scenarios. W

Angshuman R. Goswami

The primary objective of this paper is to investigate the notions of geometric and sequential convexity within a graph-theoretic framework, with the aim of examining various structural properties and exploring the connection between these two branches of mathematics. A simple connected vertex-weighted graph $G(V,E)$ with a non-empty set of leaf vertices is s

Wen-Sheng Fang, Tobias Huber, Xin-Qiang Li, Eleftheria Malami

Using experimental information on branching ratios as well as direct and mixing-induced CP asymmetries, we perform a data-driven analysis of charmless non-leptonic $B \to PP$ decays, where $P$ is any of the light pseudoscalar mesons. Implementing flavour-$SU(3)$ breaking at the level of transition form factors, decay constants and phase space factors, we fin

Zeyu Fang, Shu Hong, Huu Trung Thieu, Nakjung Choi

Open Radio Access Network (O-RAN) enables network control through multi-vendor xApps operating both within and across layers, subnets, and domains, whose concurrent execution can trigger conflicts that are latent during the development phase. Existing conflict management approaches rely heavily on joint-execution data, which is often unavailable in practice.

Kadir Yilmaz, Adrian Kruse, Tristan Höfer, Daan de Geus

Transformers have become a common foundation across deep learning, yet 3D scene understanding still relies on specialized backbones with strong domain priors. This isolates the field from the broader Transformer ecosystem, limiting the transfer of research advances from other domains and the benefits of increasingly optimized software and hardware stacks. To

Kyungjin Shin, Nicolas Leconte, Jeil Jung, Hongki Min

Extending moir\'{e} graphene beyond twisted bilayers, helical trilayer graphene has shown topological bands and correlated states with reshaped moir\'{e} periodicity. Here we develop a theoretical framework for helical multilayer graphene to investigate its supermoir\'{e} relaxation and low-energy electronic structure. Using real-space lattice calculations,

B. R. McNamara, A. C. Fabian, H. R. Russell, P. E. J. Nulsen

We evaluate whether dissipation of turbulence injected into hot cluster atmospheres by jets and bubbles can offset radiative cooling flows. No trends are found between atmospheric velocity dispersion, $\sigma_v$, and either the ratio of kinetic to thermal energy or jet power over nearly four decades of jet power. Apparently, jets disperse their energy gently

Xue Xia, Chengkai Yao, Mingyu Tsoi, Xinjie Mao

Systematic ablations are essential to attribute performance gains in AI Virtual Cells, yet they are rarely performed because biological repositories are under-standardized and tightly coupled to domain-specific data and formats. While recent coding agents can translate ideas into implementations, they typically stop at producing code and lack a verifier that

Adrian Schmautz, Rico Zacher

We introduce and analyse a mathematical model describing the dynamics of particles generated by charge-exchange interactions. The model extends the well-established exchange-driven growth model, previously studied in several works, by allowing for particle densities defined on the entire integer lattice. Despite the many similarities between the two models,

Roger A. Horn, Shengxuan Luo, Hongwei Xu, Zai Yang

A notable difference between the ordinary and Hadamard products is that the Hadamard product of two singular positive semidefinite matrices can be nonsingular, and one of the factors can even be indefinite. We present an eigenvalue lower bound for a Hadamard product that depends on the rank, effective condition number, and diagonal entries of one factor, and

Qingkui Ma, Hehu Xie, Xiaobo Yin

Fractional PDEs involving the fractional Laplacian on bounded domains are challenging because of hypersingular nonlocal kernels, exterior Dirichlet constraints, reduced boundary regularity, and the high computational cost in high dimensions. To address these issues, we first adopt a spatially varying radius with directional distance-to-boundary information,

Riku Anttila, Sylvester Eriksson-Bique

It is a long-standing open question to determine whether the Sierpi\'nski carpet attains its conformal dimension or not. While this problem remains unresolved, we prove that Cartesian products $\mathbb{S}^k$, where $\mathbb{S}$ is the Sierpi\'nski carpet and $k \geq 2$, do not attain their conformal dimension. Our approach is based on the Sobolev spaces and

Guangjin Pan, Zhuojun Tian, Mehdi Bennis, Henk Wymeersch

Wireless agentic systems enable agents to autonomously perceive, reason, and act. However, existing works neglect the tight coupling between sensing and control in closed-loop integrated sensing and communication (ISAC) systems. In this paper, we propose an active inference (AIF)-driven wireless agentic system for closed-loop ISAC, which jointly optimizes co

Aleksandra Lekić, Vujo Drndarević

In this paper an algorithm for transient simulation of switching converters using prediction and correction to calculate duty ratio is proposed. It provides large signal simulation on the level of averaged currents and voltages in the circuit. Calculation of duty ratio using inductor current and capacitor voltage prediction and correction do not require thei

Diego Berti, Andrea Corli, Luisa Malaguti

We consider a reaction-diffusion equation in a one-dimensional space, where the diffusion coefficient changes sign from positive to negative and back to positive. The reaction term is bistable, with its interior zero located in the region where the diffusivity is negative. The model does not admit continuous wavefronts, i.e., continuous traveling waves that

Alessio Miranda, Ryoichi Ishihara, Salahuddin Nur

Color centers in diamond are a promising platform for quantum computing applications because of their optical and spin properties. However, diamond presents some technological challenges that limit its use in complex or large photonic circuits. To mitigate these limitations, it is technically effective to separate the smallest possible diamond photonic struc

Mircea Timpuriu, Mihaela-Claudia Cercel, Dumitru-Clementin Cercel

The importance of clear and correct text in legal documents cannot be understated, and, consequently, a grammatical error correction tool meant to assist a professional in the law must have the ability to understand the possible errors in the context of a legal environment, correcting them accordingly, and implicitly needs to be trained in the same environme

Jienan Lyu, Miao Yang, Jinchen Cai, Yiwen Hu

Fine-grained high-resolution remote sensing mapping typically relies on localized visual features, which restricts cross-domain generalizability and often leads to fragmented predictions of large-scale land covers. While global geospatial foundation models offer powerful, generalizable representations, directly fusing their high-dimensional implicit embeddin

Theodoros Anastasiadis, George Androulakis

In this article, we prove that the quantum $f$-divergence between two normal states on a semifinite von~Neumann algebra is equal to the classical $f$-divergence between two corresponding classical states, which are called Nussbaum-Szkoła distributions. This result has been proved by the second named author and T.C.~John for normal states on the von~Neumann a

Shibin Dai, Abba Ramadan, Natasha Sharma

In this paper, we theoretically and numerically study the minimizers for the Cahn-Hilliard energy with the Flory-Huggins potential under the strong anchoring condition, i.e., the Dirichlet boundary condition. We reveal bifurcation phenomena mediated by the boundary condition, the transition layer thickness, and the temperature of the system. Numerical simula

Jiale Liu, Victor S. Bursztyn, Lin Ai, Haoliang Wang

In open-ended domains, teams must reconcile diverse viewpoints to produce strong deliverables. Answer aggregation approaches commonly used in closed domains are ill-suited to this setting, as they tend to suppress minority perspectives rather than resolve underlying disagreements. We present TeamFusion, a multi-agent system designed to support teamwork in op

Matthias Diez, Johannes K. Krondorfer, Albert Hirtenfelder, Andreas W. Hauser

Among the possible types of magnetic dipole interactions in molecular systems, couplings between nuclear motion and the nuclear spin have probably received the least attention in molecular spectroscopy. Although very small in comparison to effects related to electron spin, this type of hyperfine interaction plays an important role in the NMR spectroscopy of