April 2026 arXiv papers — page 4

Anthony Britto, Carlos Oliveira, Max Kleinebrahm

Despite many decades of research, economically grounded models that analyse energy consumption and energy-efficiency adoption within a unified framework remain underdeveloped. This article addresses this gap by proposing a model of consumption, investment, and energy-efficiency adoption under uncertainty. It develops new definitions of the rebound and backfi

Andrea Bonito, Diane Guignard

We address the problem of numerically approximating the velocity and pressure governed by the Stokes system when the boundary conditions are only partially known and thus do not uniquely determine the velocity-pressure couple. We propose an algorithm that takes advantage of available linear measurements of the velocity and pressure to construct a numerical a

Taslim Jamal Arif, Kuldeep Singh

Text-to-SQL (T2SQL) evaluation in production environments poses fundamental challenges that existing benchmarks do not address. Current evaluation methodologies whether rule-based SQL matching or schema-dependent semantic parsers assume access to ground-truth queries and structured database schema, constraints that are rarely satisfied in real-world deployme

Raphael C. Kim, Brian Gilbert, Ramin Zabih, Michele Santacatterina

Time-to-event outcomes are commonly used as primary endpoints in randomized clinical trials. Despite this, relatively little work incorporates baseline covariate information while also accounting for stratified randomization, a common form of randomization. Moreover, leveraging efficiency gains using these approaches typically requires pre-specifying a subse

Jing Yu, Junchi Zhang

We prove a transfer theorem for hereditary classes of $(r+1)$-uniform hypergraphs. Let $\mathcal H$ be such a class, and for $H\in\mathcal H$ write $\Delta(H)$ and $d(H)$ for the maximum degree and average degree of $H$, respectively. We show that, for every nearly logarithmic function $f$ in the sense defined below, a maximum-degree lower bound for the inde

Rahul Ramachandran, Nidhi Jha, Muthukumaran Ramasubramanian

We present Collaborative Agent Reasoning Engineering (CARE), a disciplined methodology for engineering Large Language Model (LLM) agents in scientific domains. Unlike ad-hoc trial-and-error approaches, CARE specifies behavior, grounding, tool orchestration, and verification through reusable artifacts and systematic, stage-gated phases. The methodology employ

Dmitriy Yavorskiy, David Hagenmuller, Noureddine Charrouj, Yurii Ivonyak

Deep strong light-matter coupling represents an extreme non-perturbative regime of quantum electrodynamics, in which the interaction strength exceeds the bare frequencies of the uncoupled systems. The ground state features strong quantum correlations between photons and matter excitations, and new cavity-driven phase transitions are expected to occur. Whethe

Carlos J. Barrios H., Frédéric Le Mouël, Oscar Carrillo

We discuss a Quantum-Enhanced Computing Continuum, a heterogeneous, hybrid architecture that integrates quantum processing units (QPUs) within an Edge-Cloud-HPC fabric. Promote sustainability by shifting from performance to "energy-aware integration.' The architecture has three layers: a Physical Layer with shared fiber-optic infrastructure, a Control and Or

Saleemullah Memon, Ali Krayani, Pamela Zontone, Lucio Marcenaro

Vehicular sensing-based intelligence has made substantial progress in transportation systems, leading to higher levels of safety and sustainability for smart cities and autonomous systems. This paper proposes a new approach to learn an interactive generalized dynamic Bayesian network (I-GDBN) model aiming to predict future LiDAR sensor blockages from time-se

Jialu Shen, Han Lyu, Suyang Zhong, Hanzheng Li

Spectra are a prevalent yet highly information-dense form of scientific imagery, presenting substantial challenges to multimodal large language models (MLLMs) due to their unstructured and domain-specific characteristics. Here we introduce SpecVQA, a professional scientific-image benchmark for evaluating multimodal models on scientific spectral understanding

Eduard Buss, Till Aust, Heiko Hamann

Purpose: Fast detection of plant stress is key to plant phenotyping, precision agriculture, and automated crop management. In particular, efficient irrigation management requires early identification of water stress to optimize resource use while maintaining crop performance. Direct physiological sensing offers the potential to detect stress responses before

Marc Dymetman

Exponential families encompass the distributions central to modern machine learning -- softmax, Gaussians, and Boltzmann distributions -- and underlie the theory of variational inference, entropy-regularized reinforcement learning, and RLHF. We isolate a simple identity for exponential families that expresses the KL difference $\mathrm{KL}(q \| p_{\lambda_2}

Claudio Bonanno, Claudio Bonati, Massimo D'Elia

In this chapter we provide a pedagogical introduction to the main theoretical aspects related to topology and $\theta$-dependence in Quantum Chromo-Dynamics (QCD), and to their phenomenological relevance in the Standard Model ($\eta^\prime$ physics, neutron electric dipole moment) and beyond (strong CP problem and the axion solution). We then provide an over

Emília Garcia-Casademont, Ramon Ferrer-i-Cancho

The syntactic structure of a sentence can be represented as a tree where edges indicate syntactic dependencies between words. When that structure is a star, it has been demonstrated that the head should be placed in the middle of the linear arrangement according to the principle of syntactic dependency distance minimization. However, hubs of stars tend to be

Kai Zhang

For a graph \(G\), let $avm(G)$ denote the average size of its maximal matchings. This parameter was introduced by Engbers and Erey in the study of extremal problems for maximal matchings, and they asked for extensions from trees and unicyclic graphs to \(k\)-cyclic graphs. In this paper, we solve the first non-unicyclic case by determining the minimum value

Shun Takagi, Seng Pei Liew

We study $d$-dimensional unbiased mean estimation in the single-message shuffle model, where each user sends a single privatized message and the analyzer only observes the shuffled multiset of reports. While minimax-optimal mechanisms are well understood in the local differential privacy setting, the corresponding notion of optimality after shuffling has rem

Garvin Kruthof

When researchers iteratively refine ideas with large language models, do the models preserve fidelity to the original objective? We introduce DriftBench, a benchmark for evaluating constraint adherence in multi-turn LLM-assisted scientific ideation. Across 2,146 scored benchmark runs spanning seven models from five providers (including two open-weight), four

Jeanne Monnier, Thomas George, Frédéric Guyard, Christèle Tarnec

Fairness in machine learning remains challenging due to its ethical complexity, the absence of a universal definition, and the need for context-specific bias metrics. Existing methods still struggle with intersectionality, multiclass settings, and limited flexibility and generality. To address these gaps, we introduce MIFair, a unified framework for bias ass

Haihan Duan, Tengfei Ma, Yuyang Qin, Runhao Zeng

In the era of big data, large-scale machine learning models have revolutionized various fields, driving significant advancements. However, large-scale model training demands high financial and computational resources, which are only affordable by a few technological giants and well-funded institutions. In this case, common users like mobile users, the real c

Chuanlian Xiao, Joachim Maier

We report on a new type of rectifier which is in full contact equilibrium and thus, if down-sized to the nanoscale, shows no drift even if exposed to elevated temperatures and/or extreme waiting times. This is in contrast to existing diodes which rely on frozen doping profiles and are hence non-equilibrium devices. Our rectifiers are related to Schottky diod

Smit Jivani, Sarvam Maheshwari, Sunita Sarawagi

Large language models (LLMs) have revolutionized Text-to-SQL generation, allowing users to query structured data using natural language with growing ease. Yet, real-world deployment remains challenging, especially in complex or unseen schemas, due to inconsistent accuracy and the risk of generating invalid SQL. We introduce Template Constrained Decoding (TeC

Klaus Mosegaard, Andrew Curtis

In a recent preprint (Mosegaard and Curtis, 2024, arXiv:2411.13570v2) we analyzed the consequences of ignoring the well-known inconsistency of classical conditional probability densities. We explained how this inconsistency, together with acausality in hierarchical methods, invalidate a variety of commonly applied Bayesian methods when applied to problems in

Jiaying Ying, Heming Du, Kaihao Zhang, Sean M. Tweedy

Single-image human mesh recovery provides a compact 3D, person-centric representation that supports analysis, animation, AR and VR, rehabilitation, and human-computer interaction. However, prevailing systems impose an intact-limb prior and degrade on people with limb loss, because fixed-topology models cannot represent residual limbs. In this work, we presen

Zhiqiang Kou, Junxiang Wu, Wenke Huang, Wenwen He

Federated Multi-Label Learning is a distributed paradigm where multiple clients possess heterogeneous multi-label data and perform collaborative learning under privacy constraints without sharing raw data. However, modeling label correlations under heterogeneous distributions remains challenging. Due to client-specific label spaces and varying co-occurrence

Ganesh Bagler, Gopal Krishna Tewari, Aditya Raj Yadav, Akshat Singh

Cooking is a cultural expression of human creativity that transcends geography and time through the orchestration of ingredients and techniques, much like languages do through words and syntax. Yet, beneath the apparent diversity of culinary traditions, whether recipes obey statistical laws comparable to those of other symbolic systems remains unknown. Here

Shuo Jiang, Jianxi Luo

Design Structure Matrix (DSM) modularization, the task of partitioning system elements into cohesive modules, is a fundamental combinatorial challenge in engineering design. Traditional methods treat modularization as a pure graph optimization, without access to the engineering context embedded in the system. Building on prior work on LLM-based combinatorial

Andrey Voronin, Roman Vlasov, Vladimir Gorgadze, Andrey Seoev

Constant Product Market Makers use fees that are typically fixed proportions of trade size. When these fees are automatically reinvested into the pool, as in Uniswap~V2 and some designs of Uniswap V4, the final state after a trade can depend on how the trade is split into smaller transactions. This path dependence complicates the risk assessment for liquidit

Linjie Lyu, Ayush Tewari, Jianchun Chen, Thomas Leimkühler

3D Gaussian Splatting has emerged as a powerful scene representation for real-time novel-view synthesis. However, its standard adaptive density control relies on screen-space positional gradients, which do not distinguish between geometric misplacement and frequency aliasing, often leading to either over-blurred high-frequency textures or inefficient over-de

Chien-Hua Chen

In this paper, we prove that if the Frobenius traces agree at all but finitely many places, then two $l$-adic Galois representations, associated to rank-$2$ non-CM Drinfeld modules of generic characteristic, are isomorphic. As a generalization, we show that the "Frobenius trace equality at all but finitely many places forces isomorphism" between two Galois r

Ignat Melnikov, Roman Vlasov, Vladimir Gorgadze, Andrey Seoev

Decentralized Finance (DeFi) is a rapidly evolving segment of blockchain technology that enables a transformative approach to financial services through Web3 applications. By leveraging smart contracts, DeFi allows developers to build flexible and innovative financial instruments. Among the most prominent DeFi primitives by liquidity are decentralized exchan

Daniel Azenkot, Michael Fire, Eran Ben Elia

Accurate forecasting of bus ridership (passengers numbers) is crucial for efficient management and optimization of public transport systems. Traditional forecasting models often fail to capture the unique and localized dynamics of different urban areas by treating the entire city as a single, homogeneous region. This paper introduces a novel framework that e

Shalini Sarode, Brian Moser, Joachim Folz, Federico Raue

The Forward-Forward (FF) algorithm presents a compelling, bio-inspired alternative to backpropagation. However, while efficient in training, it has a computationally prohibitive inference process that requires a separate forward pass for every class that is evaluated. In this work, we introduce the Hyperspherical Forward-Forward (HFF), a novel reformulation

Ninisha Manaswini, Danté M. Hewitt, Laura G. Spitler, Jason W. T. Hessels

Fast radio bursts (FRBs), thought to originate from magnetars, exhibit diverse polarization properties that constrain their emission physics and local magneto-ionic environments. The polarization position angle (PPA) is particularly sensitive to magnetic-field geometry in the emitting region and propagation effects in the magnetosphere and beyond. In hyper-a

Jing Zhang, Wentao Jiang, Tao Huang, Zhiwei Wang

Ultrasound interpretation requires both precise lesion localization and holistic clinical reasoning, yet existing methods typically excel at only one of these capabilities: specialized detectors offer strong localization but limited reasoning, whereas multimodal large language models (MLLMs) provide flexible reasoning but weak grounding in specialized medica

Y. -X. Xiao, J. -Z. Han, Z. Zheng, Z. -H. Zhang

Quantum control with restricted state access is central to near-term quantum devices, where full wave-function information is unavailable. We study this problem through multiqubit disentanglement scheduling from partial observations, where a controller receives only two-qubit reduced density matrices and selects which qubit pair to disentangle at each step.

Thierry Valet, Nicolas Vukadinovic

For three-dimensional (3D) magnetic objects with linear size $L$ exceeding a few exchange lengths, the micromagnetic state exhibits pronounced informational sparsity: low-dimensional, high-gradient regions (e.g., domain walls) coexist with near-uniformly magnetized volumetric domains. Because standard micromagnetic simulation methods discretize the magnetiza

Zeynep Okray, Nils Otto, Anna A. Cook, Clifford Talbot

Associating multiple sensory cues with a single experience or object is a fundamental process that improves object recognition and memory performance. However, neural mechanisms that bind sensory features during learning and augment memory expression are unknown. Here we demonstrate multisensory appetitive and aversive memory in Drosophila. Combining colours

Jiahua Zhao, Umair bin Waheed, Jing Sun, Yang Cui

The demand for high-resolution subsurface imaging and continuous Earth monitoring has driven rapid growth in active and passive seismic data from dense geophone deployments, distributed acoustic sensing (DAS) arrays, and large-scale 2D and 3D surveys. This expansion makes complex noise suppression increasingly challenging, especially when signal fidelity mus

Sebastian Pokutta

We consider smooth convex minimization over compact convex sets, i.e., $\min_{x \in C} f(x)$ with the (vanilla) Frank-Wolfe algorithm. Well-known lower bounds establish a worst-case $\Omega(1/t)$ primal-gap barrier in the general smooth convex case, and faster convergence usually requires favorable function properties such as H\"older error bounds or strong

Caleb A. Mickelson

In the study of open quantum systems, one commonly describes the evolution of a system of interest through reduced dynamics, obtained by treating the environment indirectly rather than as a part of the full model. This thesis presents an expository account of an alternative, dilation-based viewpoint in the finite-dimensional setting, where a family of reduce

K. N. Trivedi, M. Carminati, Èlia Solé Cardona, T. Bonaccorsi

We demonstrate a technique for spatially resolved temperature measurement utilizing Rydberg Doppler broadening thermometry. This method employs two focused laser beams arranged perpendicularly to excite laser-cooled atoms from the ground state to a Rydberg state via two photon absorption process. Temperature is obtained through the Doppler broadening of the

Habtom Kahsay Gidey, Alexander Lenz, Alois Knoll

Integrating multimodal foundation models into enterprise ecosystems presents a fundamental software architecture challenge. Architects must balance competing quality attributes: the high latency and non-determinism of vision language action (VLA) models versus the strict determinism and real-time performance required by enterprise control loops. In this stud

Paul K. H. Yeung, Takayuki Saito

Despite the diversity and energy dependence of $\gamma$-ray pulse morphologies in Crab, Vela and Dragonfly, the phaseograms of these three canonical young pulsars can be organised within a single four-beam geometric template. Using \textit{Fermi} Large Area Telescope data, we fit the 60~MeV--3~GeV phaseograms with a mechanism-agnostic, geometry-first paramet

M. F. P. ten Eikelder, A. Brunk

Diffuse-interface (phase-field) models are widely used to describe multiphase mixtures and their interfacial dynamics. In multiphase settings, however, the constitutive closure should remain meaningful across different representations of the same mixture. Existing N-phase phase-field constructions commonly enforce reduction only when a phase is absent (restr

Jingcheng Deng, Zihao Wei, Liang Pang, Junhong Wu

Latent reasoning offers a more efficient alternative to explicit reasoning by compressing intermediate reasoning into continuous representations and substantially shortening reasoning chains. However, existing latent reasoning methods mainly focus on supervised learning, and reinforcement learning in latent space remains highly unstable. We study this proble

Jarod Govers, Sanja Šćepanović, Daniele Quercia

A key task in AI practice is to assess potential impacts to prevent harm. Current AI tools assisting AI impact assessment have not been designed or evaluated for collaborative team brainstorming, and they do not capture the range of views in diverse teams. We studied how AI can support team brainstorming during AI impact assessment and made three contributio

Stefan Dittmaier

Electroweak radiative corrections form a crucial ingredient in modern precision calculations for particle processes at high-energy colliders such as the Large Hadron Collider. The salient features of electroweak corrections as well as currently used techniques and concepts for their calculation are reviewed. Recent progress in this enterprise is illustrated

Juan M. Torres-Rincon, Glòria Montaña

This review provides an overview of thermal effects on hadron properties, focusing on the theoretical frameworks used to describe in-medium modifications of masses, decay widths, and spectral functions. We examine the application of finite-temperature quantum field theory -- specifically the imaginary-time formalism (ITF) -- to analyze both light- and heavy-

Dario Bocchi, Giulio Biroli, Chiara Cammarota, Federico Ricci-Tersenghi

The Baik-Ben Arous-Peche (BBP) transition sets fundamental limits for detecting low-rank structure in noisy high-dimensional data and underlies a wide range of spectral methods in many fields from physics to statistics and data sciences. In standard settings, this transition is continuous, implying that signal recovery emerges gradually above a sharp thresho

Henry Schoeller, Robin Chemnitz, Péter Koltai, Maximilian Engel

We investigate the lifetime of dynamical regimes under the impact of noise motivated by low-dimensional models of the atmosphere. One may expect that the inclusion of noise tends to make the system leave prescribed regions of the state space faster. However, for relevant systems with complexities ranging from phenomenological toy models to reduced models of

Ludovico Battista, Juan Souto

We associate a musical instrument, a "hyperbolic marimba", to every pair $(X,\Gamma)$ where $X$ is a hyperbolic surface and $\Gamma\subset X$ a simple multicurve labeled with musical keys. It works as follows: take a geodesic and every time it hits $\Gamma$, play the corresponding note. In this paper we investigate to which extent the so-produced melodies ch

Julien Portier

We show that every minimally generically globally rigid graph in $\mathbb R^d$ which contains a subgraph isomorphic to $K_{d+2}$ is itself isomorphic to $K_{d+2}$, confirming a conjecture by Garamv{\"o}lgyi, Jackson, and Jord{\'a}n. The proof is entirely generated by ChatGPT 5.5.

S. Ramaiya, M. J. Jarvis, M. Vincenzi, M. Sullivan

Type Ia supernova (SN Ia) standardisation parameters exhibit evidence for systematic variation across the host galaxy star-formation rate - stellar mass (SFR$-M_\star$) plane, motivating the incorporation of galaxy SFR information in cosmological inference. SFRs are commonly estimated via spectral energy distribution (SED) fitting with far-infrared (FIR) mea

Nghia Bui, Lijing Wang

Fine-tuning pretrained models has become a standard approach to adapting pretrained knowledge to improve the accuracy on new sparse, imbalance datasets. However, issues arise when optimization falls into a collapsed state, where the model gets stuck, leading to degraded performance and unstable training. One possible reason for this is the cancellation of gr

Ugo Dal Lago, Guido Fiorillo, Paolo Pistone

The problem of determining whether a probabilistic program terminates almost surely (i.e.~with probability one) is undecidable, and actually $\Pi^0_2$-complete. For this reason, a growing literature has explored classes of programs for which this and related problems can be shown (semi-)decidable. In this work we consider the termination problem for the lang

Milan Shah, Sheng Di, Michela Becchi

In recent years, novel AI accelerators have emerged as promising alternatives to GPUs for AI model training and inference. One such accelerator, the Cerebras CS-3, has demonstrated strong performance on machine learning as well as scientific applications, such as molecular dynamics and seismic simulations. While the benefits of Cerebras systems for dense wor

Greg Friedman, Anibal M. Medina-Mardones, Dev Sinha

Let $M$ be a smooth manifold without boundary and let $\mathcal{T}$ be a countable collection of manifolds with corners, each equipped with a smooth map to $M$. We show that the singular simplicial set $\mathrm{Sing}(M)$ of $M$ deformation retracts onto the simplicial subset $\mathrm{Sing}^{\mathcal{T}}\!(M)$ of smooth singular simplices that are transverse

Tijn de Vos, Leo Wennmann, Malte Baumecker, Yannic Maus

In this paper, we consider the Santa Claus problem in the CONGEST model. This NP-hard problem can be modeled as a bipartite graph of children and gifts where an edge indicates that a child desires a gift. Notably, each gift can have a different value. The goal is to assign the gifts to the children such that the least happy child is as happy as possible. Eve

Teiji Kunihiro

We first note the peculiar property of the pion as the pseudoscalar particle, which play the essential role in realizing the basic properties of the nuclear matter such as the density/energy saturations. Then, we introduce the notion of chirality using the Dirac equation, and show how chiralities are mixed in the massive Dirac field with an emphasis on the s

Pourya Zamanvaziri, Amirhossein Sadr, Aida Pakniyat, Dara Rahmati

Multivariate time series forecasting plays a pivotal role in numerous real-world applications, including financial analysis, energy management, and traffic planning. While Transformer-based architectures have gained popularity for this task, recent studies reveal that simpler MLP-based models can achieve competitive or superior performance with significantly

Francesco Di Filippo, David Kubiznak, Aravindhan Srinivasan

We study the null junction conditions in (re-summed) quasi-topological gravity theories, showing that no null thin shells exist within the realms of standard distributional theory for the pure gravity regular black hole solutions we have analyzed. This implies that the usual derivation of the mass inflation instability, which makes use of null thin shells, i

Andrei Seoev, Dmitry Belousov, Anastasiia Smirnova, Ksenia Kurinova

Maximal Extractable Value (MEV) represents billions of dollars in extracted value that fundamentally shapes blockchain network dynamics and participant incentives. While research has focused on MEV extraction and mitigation, we lack systematic methods to attribute MEV opportunities to their on-chain origins. This paper formalizes the MEV opportunity attribut

Miroslav Bulíček, Tomáš Los, Jakub Woźnicki

Viscoelastic rate-type fluid models constitute a fundamental framework for the mathematical description of complex materials exhibiting coupled elastic and viscous effects, with a wide range of applications in engineering, biomaterials, and medicine. In realistic regimes, thermal effects are essential and lead to strongly coupled systems in which heat conduc

Hanane Nour Moussa, Yifei Li, Zhuoyang Li, Yankai Yang

Despite recent progress in language models and agents for scientific data-driven discovery, further advancing their capabilities is held back by the absence of verifiable environments representing real-world scientific tasks. To fill this gap, we introduce D3-Gym, the first automatically constructed dataset with verifiable environments for scientific Data-Dr

Fengxian Ji, Jingpu Yang, Zirui Song, Yuanxi Wang

Despite the rapid progress of large vision-language models (LVLMs), fine-grained, state-conditioned GUI interaction remains challenging. Current evaluations offer limited coverage, imprecise target-state definitions, and an overreliance on final-task success, obscuring where and why agents fail. To address this gap, we introduce \textbf{FineState-Bench}, a b

Qianzhong Ou

We study the Liouville equation $\triangle u+e^{2u} =0$ in a Riemannian surface $(M, g)$ with nonnegative $Ricci$ curvature. Under some asymptotic lower bound assumptions, we classify all the solutions to this equation, meanwhile we obtain the rigidity results for the ambient manifold. Note that our assumptions are optimal in some sense and differ from the c

Johannes Pfau, Panagiotis Vrettis

Since the dawn of Trading Card Games, the genre has grown into a multi-billion-dollar industry engaging millions of analog and digital players worldwide. Popular TCGs rely on regular updates, balance adjustments, and rotating constraints to sustain engagement. Yet, as metagames stabilize, predictable strategies dominate and viable card options diminish, ofte

Stefan Güttel, Lauri Nyman

We derive a sharp upper bound on the residuals produced by the flexible GMRES (FGMRES) method. The bound shows that FGMRES exhibits two phases of convergence depending on the residual tolerance of the inner preconditioner. For small tolerances, the convergence of FGMRES is practically geometric with a constant rate throughout, while for looser tolerances the

Motoi Endo, Syuhei Iguro, Satoshi Mishima

We study semileptonic sum rules for $b \to c \tau \overline{\nu}$ transitions involving orbitally excited charm hadrons. Starting from the amplitude-level relation implied by the heavy quark symmetry, we construct sum rules relating these decays. We then examine deviations from the small-velocity limit. Our numerical analysis shows that the deviations genera

Guang Yang, Xing Hu, Xiang Chen, Xin Xia

Multimodal large language models (MLLMs) are increasingly used to translate visual artifacts into code, from UI mockups into HTML to scientific plots into Python scripts. A circuit diagram can be viewed as a visual domain-specific language for hardware: it encodes timing, topology, and bit level semantics that are invisible to casual inspection yet safety cr

Shiqi Xu, Moritz Burmester, Katharina Prasse, Isaac Bravo

The pervasive growth of digital content, specifically short videos on social media platforms, has significantly altered how topics are discussed and understood in public discourse. In this work, we advance automated visual theme detection by assessing zero-shot and clustering capabilities on social media data. (1) We evaluated the capabilities of notable VLM

Davide Bilò, Shiri Chechik, Keerti Choudhary, Sarel Cohen

An important tool in the design of fault-tolerant graph data structures are $(L,f)$-replacement path coverings (RPCs). An RPC is a family $\mathcal{G}$ of subgraphs of a given graph $G$ such that, for every set $F$ of at most $f$ edges, there is a subfamily $\mathcal{G}_F \,{\subseteq}\, \mathcal{G}$ with the following properties. (1) No subgraph in $\mathca

Benjamin Sorkin, Ned S. Wingreen

Living systems routinely consume energy to achieve motility, often using intricate biomolecular machinery. In this work, we show that active droplets can sustain indefinite self-propulsion of a spherical colloid in an otherwise homogeneous, isotropic, and autonomous environment. Our proposed minimal mechanism consists of phase-separating proteins, enzymes pa

Giovanni Buraglio, Wolfgang Dvorak, Stefan Woltran

Assumption-Based Argumentation (ABA) is a well-established formalism for modelling and reasoning over debates, with a wide range of applications. However, the high computational complexity of core reasoning tasks in ABA poses a significant challenge for its applicability. This issue is further aggravated when ABA frameworks (ABAFs) are instantiated into grap

Marco Pizzocaro, Clara Zyskind, Anne Amy-Klein, Erik Benkler

Optical clocks have achieved remarkable estimated fractional frequency uncertainties reaching the $10^{-18}$ level and below, enabling applications in fundamental physics, general relativity, and geodesy. However, the challenge of verifying the international consistency of optical clocks remains critical as efforts intensify toward redefining the SI second b

João Pedro Gandarela, Thiago Rios, Stefan Menzel, André Freitas

Designing mechanical linkages involves combinatorial topology selection and continuous parameter fitting. We show that language models can systematically improve linkage designs through symbolic representations. Language model agents explore discrete topologies while numerical optimisers fit continuous parameters. A symbolic lifting operator translates simul

Sebastian Zimper, Nataša Djurdjevac Conrad, Federico Cornalba, Ana Djurdjevac

Clustering is a fundamental collective phenomenon in agent-based models (ABMs) of opinion dynamics. To study clustering in systems with co-evolving social and opinion variables, we derive stochastic partial differential equation (SPDE) models that describe the evolution of clusters on a reduced state space. We consider two settings: one in which opinions do

Dingbao Shao, Song Wu, Shenyi Wang, Ye Wang

Due to the scarcity of large-scale in-the-wild triplet data and the improper use of masks, the performance of video virtual try-on models remains limited. In this paper, we first introduce **TripVVT-10K**, the largest and most diverse in-the-wild triplet dataset to date, providing explicit video-level cross-garment supervision that existing video datasets la

Mert Mermerci, Emile Pascoe, Fredrik Edström, Hedvig Kjellström

We present a museum installation in a 180{\deg} dome theater, which gives the museum visitor the experience of conducting a symphony orchestra. We have pre-recorded a short music piece performed by a professional orchestra. This recording is played back in the dome with the visitor standing in the conductor's position. The visitor's gestures are captured wit

Junan Hu, Jian Liu, Jingxiang Lai, Jiarui Hu

Graphical User Interface (GUI) agents have emerged as a promising paradigm for intelligent systems that perceive and interact with graphical interfaces visually. Yet supervised fine-tuning alone cannot handle long-horizon credit assignment, distribution shifts, and safe exploration in irreversible environments, making Reinforcement Learning (RL) a central me

José María Pérez-Poyatos

In this work, we extend the standard pre-inflationary misalignment mechanism for axion-like particles (ALPs) by introducing a pre-oscillatory phase with constant equation of state $\omega_\phi\in[-1,1]$, generated by a tracking potential. During the radiation-dominated era, the potential undergoes a rapid transition to the conventional cosine potential. The

Kenneth J. K. Ong

As Vision-Language Models (VLMs) become increasingly integrated into decision-making systems, it is essential to understand how visual inputs influence their behavior. This paper investigates the effects of visual priming on VLMs' cooperative behavior using the Iterated Prisoner's Dilemma (IPD) as a test scenario. We examine whether exposure to images depict

Wentao Hou, Yimin Bai, Zelei Luo, Jiadong Hong

Joint image compression and wireless transmission remain relatively underexplored compared to generic image restoration, despite its importance in practical communication systems. We formulate this problem under an equivalent linear model, and propose Diffusion-OAMP, a training-free reconstruction framework that embeds a pre-trained diffusion model into the

Joss Armstrong

A selector that allocates work across opaque components must evaluate them without being told how they performed. We study the least communication that suffices. Each parent maintains an allocation vector over its children and updates it from outcomes by proportional redistribution. Each child reads the sign of its own allocation change, one bit per round it

Jules Flin

We investigate the unique stationary measure of a positive recurrent reflecting Brownian motion in the upper half-plane, where the direction of reflection is constant on each half-axis. The Laplace transform of the stationary distribution is characterized by a functional equation, whose resolution is reduced to solving a discontinuous Riemann boundary value

Vladimir Matveev, Yuri Nikolayevsky

A Killing tensor field on a Riemannian space corresponds to an integral of the geodesic flow polynomial in momenta. A Killing tensor field is called decomposable if it is a polynomial in Killing vector fields. In this paper, we first prove that the study of Killing tensor fields on symmetric spaces can be reduced to the case of compact irreducible ones. Then

Charlotte Rohleder, Raul Sîmpetru, Annika Wünsch, Alessandro Del Vecchio

Simultaneous multi-directional force measurement across all five digits is essential for studying hand coordination, compensatory forces, and myoelectric control, yet existing systems trade off digit coverage, force dimensionality, and anatomical adaptability. Reliable full-hand acquisition remains challenging because multi-axis calibration, hand-size adjust

Oliver Wege, Kaan Atak, Marek Behr, Norbert Hosters

Adaptive mesh refinement (AMR) is indispensable for efficient finite element analyses. However, its performance depends not only on the refinement itself but also on strategy to mark elements for refinement and the way it is tuned. This work compares classical marking methods (maximum, D\"orfler bulk-chasing, quantile) with non-classical, statistically based

Jan McGarry-Furriol

We prove the existence of many non-trivial characteristic classes of smooth oriented bundles with fibre a product $ S^{n}\times S^{n} $ of odd-dimensional spheres. We do so by proving injectivity of the map from the ring of rational characteristic classes of oriented fibrations with fibre $ S^{n}\times S^{n} $; the latter is proven by Berglund--Zeman to be i

Alexis Kafantaris

In this paper an attractor FCM is created, tested, and analyzed. This FCM is neither a hebbian based nor agentic, nor a hybrid; it rather is a gradient descent based, physics constrained, Jacobian version of an FCM. Moreover, this model has several quirks; it uses residual memory, back propagation through time, and a fixed point anchor that is recursively im

Xian-Peng Zhang, Yan-Qing Feng, Wanxiang Feng, Yugui Yao

Quantum decoherence-the loss of quantum coherence due to interactions with an environment-plays a central role in quantum transport, and controlling this ubiquitous yet inevitable phenomenon is essential for practical quantum technologies. Despite its importance, the microscopic mechanisms of decoherence in infinite-size topological insulators remain poorly

Fangzhi Li, Cunhua Pan, Hong Ren, Dongming Wang

Millimeter-wave (mmWave) communication depends on highly directional beamforming, while fast mobility, blockage, and rapid geometry changes in vehicle-to-everything (V2X) scenarios make beam tracking challenging. In cooperative multi-base-station (BS) systems, conventional hierarchical methods usually separate BS selection and beam selection, which may cause

Mark C. Ballandies, Michael T. C. Chiu, Claudio J. Tessone

Large-scale IoT weather sensing networks require incentive mechanisms to sustain participation, yet determining how much value individual data contributions bring to the network remains an open problem. Existing approaches address data quality but not data valuation; in operational meteorology, adjoint-based methods derive value from the forecast model itsel

Runjia Zhang, Akash nag Oruganti, Huy Q. Nguyen, Ivan Derkach

In recent years, continuous-variable quantum key distribution (CV-QKD) has become a promising paradigm for enabling secure communication among multiple end users sharing the same telecommunication backbone. CV-QKD with reverse reconciliation naturally enables scalability from conventional point-to-point links to quantum access networks based on passive quant

Djamel Bouchaffra, Faycal Ykhlef, Mustapha Lebbah, Hanane Azzag

Collective intelligence emerges across biological, physical, and artificial systems without central coordination, yet a unifying principle governing such behaviour remains elusive. The Free Energy Principle explains how individual agents adapt through variational inference, while game theory formalises strategic interactions. Here we introduce the Game-Theor

T. Vozár, L. Čechová, J. Buday, M. Füleky

The colonisation of extraterrestrial planets requires sustainable food production independent of Earth-based supplies. Due to the high costs and complicated logistics of food transport, in-situ cultivation will be essential. Growing plants directly in regolith offers a practical approach to achieve sustainable long-term human habitation beyond Earth. In this

Isaac David, Marco Guarnieri, Arthur Gervais

Agentic security systems increasingly combine LLM planners with tools that can discover, validate, and report vulnerabilities. This creates an asymmetric control problem: the system should retain strong offensive capability inside an authorized engagement, while the same capabilities must be denied outside scope. Existing guardrails provide useful policy con

Callum Bell, David Sloan

Singular theories, characterised by the presence of degeneracies in their Lagrangian or Hamiltonian descriptions, require the systematic implementation of constraints in order to obtain well-defined dynamics. While the symplectic framework provides the standard geometrical setting for conservative mechanical systems, those theories which exhibit dissipative

Fabian Achammer, Stefan Hetzl, Renate A. Schmidt

Second-order quantifier elimination is the problem of finding, given a formula with second-order quantifiers, a logically equivalent first-order formula. While such formulas are not computable in general, there are practical algorithms and subclasses with applications throughout computational logic. One of the most prominent algorithms for second-order quant

Bohan Hou, Gen Li, Jindou Jia, Tuo An

World models, which are predictive representations of how environments evolve under actions, have become a central component of robot learning. They support policy learning, planning, simulation, evaluation, data generation, and have advanced rapidly with the rise of foundation models and large-scale video generation. However, the literature remains fragment