April 2026 arXiv papers — page 149

Mingyang Li, Haofan Xu, Haowen Sun, Xinzhe Chen

Many everyday robot manipulation skills are affordance-dependent, with success determined by whether the robot contacts the functional object region required by the subsequent action. Current simulation data generators obtain contacts from generic grasp estimators or per-object manual contact annotations, but generic estimators rank stable grasps without tas

Zhaoyu Xing, Xiufan Yu

Heterogeneous network data with rich nodal information become increasingly prevalent across multidisciplinary research, yet accurately modeling complex nodal heterogeneity and simultaneously selecting influential nodal attributes remains an open challenge. This problem is central to many applications in economics and sociology, when both nodal heterogeneity

Ankit Agrawal, Jithin Garapati, Bohan Zhang

Software engineering practices for validating autonomous cyber-physical systems (e.g., Uncrewed Aerial Vehicles) remain fragmented across scenario design, simulation execution, and telemetry analysis, limiting traceability between requirements, tests, and evidence. This fragmentation reduces reproducibility, slows debugging and iteration, and hinders systema

Jiangyou Zhu, He Chen

Accurate material recognition is a fundamental capability for intelligent perception systems to interact safely and effectively with the physical world. For instance, distinguishing visually similar objects like glass and plastic cups is critical for safety but challenging for vision-based methods due to specular reflections, transparency, and visual decepti

Ambrish Pandey, Jörg Schumacher, Matteo Parsani, Katepalli R. Sreenivasan

We present three-dimensional direct numerical simulations of turbulent Rayleigh-B\'enard convection in a closed rectangular box whose width $L_y$ and length $L_x$ are 0.8 and 2.4 times the height $H$, respectively. The Rayleigh number $Ra$ varies from $10^5$ to $10^{10}$, and the Prandtl number is unity. The advantages of the present configuration are: (a) A

Carlos Segarra, Pedro Henrique Penna, Enrique Saurez, Íñigo Goiri

Serverless providers strive for high resource utilization by optimizing deployment density: how many applications can be deployed per host server. However, achieving high deployment density without compromising application performance or isolation remains an open challenge. High density can be achieved by sharing components across applications, yet applicati

Guillaume Astruc, Eduard Trulls, Jan Hosang, Loic Landrieu

The growing availability of co-located geospatial data spanning aerial imagery, street-level views, elevation models, text, and geographic coordinates offers a unique opportunity for multimodal representation learning. We introduce UNIGEOCLIP, a massively multimodal contrastive framework to jointly align five complementary geospatial modalities in a single u

Romeu Rossi Junior, José Augusto Miranda Nacif, Leonardo Antônio Mendes Souza, Marcus Henrique Soares Mendes

This work investigates the performance of a Hybrid Quantum Genetic Algorithm (HQGA) compared to a classical Genetic Algorithm (GA) for solving the portfolio optimization problem. Our results indicate that the HQGA converges faster to the optimal solution than its classical counterpart, while also maintaining a higher level of population diversity throughout

Zhen Liu, Qinghou Zeng

Addressing a question posed by Erd\H{o}s and Hajnal, Chen and Ma proved that, for all $n \ge 600$, the complete bipartite graph $K_{n,n+1}$ is the unique graph on $2n+1$ vertices with at least $n^2+n$ edges that contains no two vertices of equal degree joined by a path of length three. In this paper, we extend this result and show that, for all $n \ge 11$, $

Rajesh Ganguli, Raha Moraffah

Large Language Models (LLMs) have been shown to generate harmful content. However, the underlying causes of such behavior remain under explored. We propose a causal mediation analysis-based approach to identify the causal factors responsible for harmful generation. Our method performs a multi-granular analysis across model layers, modules (MLP and attention

Joe Stacey, Hadas Orgad, Kentaro Inui, Benjamin Heinzerling

Recent work has shown that the hidden states of large language models contain signals useful for uncertainty estimation, motivating a growing interest in efficient probe-based approaches. Yet it remains unclear how robust existing methods are, with prior work reporting conflicting conclusions under substantially different evaluation settings. We address this

Seong-Hoon Jang, Di Zhang, Xue Jia, Hung Ba Tran

Hydrogen is a promising energy carrier, yet its practical deployment is limited by the lack of storage materials that simultaneously achieve high storage capacity ($w$) and practical equilibrium pressure at room temperature ($P_{\rm eq,RT}$). Interstitial metal hydrides offer fast kinetics and favorable thermodynamics (high $P_{\rm eq,RT}$) but suffer from i

Lara D'Agata, Carlos Agulló-Domingo, Óscar Vera-López, Kaustubh Shivdikar

Fully homomorphic encryption (FHE) has recently attracted significant attention as both a cryptographic primitive and a systems challenge. Given the latest advances in accelerated computing, FHE presents a promising opportunity for progress, with applications ranging from machine learning to information security. We target the most computationally intensive

Andrea Gaspari, Rémi Avriller, Florian Marquardt, Fabio Pistolesi

The increasing resource demands of artificial neural networks have prompted the exploration of novel platforms better suited for machine learning. In this context, phase oscillators represent a promising candidate due to their intrinsic nonlinearity and their ability to exhibit collective synchronization when coupled together. In the present work, we investi

Iori Takaki, Ahmet Cetinkaya, Hideaki Ishii

This paper studies cyber attacks against informativity-based analysis in data-driven control. Focusing on strong observability, we consider an adversary who post-processes finite time-series data by an invertible linear transformation acting on the data matrices. We show that such transformations are capable of embedding malicious states into the invariant s

Victor Maus, Vinicius Pozzobon Borin

Exact hierarchical agglomerative clustering (HAC) of large spatial datasets is limited in practice by the $\mathcal{O}(n^2)$ time and memory required for the full pairwise distance matrix. We present GSHAC (Geographically Sparse Hierarchical Agglomerative Clustering), a system that makes exact HAC feasible at scales of millions of geographic features on a co

Riccardo Rosati, Edoardo Colucci, Massimiliano Bolognini, Adriano Mancini

The rapid adoption of Large Language Models (LLMs) in interactive systems has enabled the creation of dynamic, open-ended Role-Playing Agents (RPAs). However, evaluating these agents remains a significant challenge, as standard NLP metrics fail to capture the nuances of role adherence, logical consistency, and long-term narrative stability. This paper introd

Oskar Schweizer, Virginia Gali, Adam Y. Chaou, Gal Lemut

Bulk-boundary correspondence guarantees the presence of robust, anomalous states on the boundary of topological matter. The edges of a two-dimensional Chern insulator harbor one-dimensional chiral states, which have a conductance $n\, e^2/h$, where $n$ is an integer that is solely determined by the bulk. In this work we show that step edges on the surface of

David Wong, Zeynep Isik, Bin Wang, Marouane Tliba

We introduce GazeVaLM, a public eye-tracking dataset for studying clinical perception during chest radiograph authenticity assessment. The dataset comprises 960 gaze recordings from 16 expert radiologists interpreting 30 real and 30 synthetic chest X-rays (generated by diffusion based generative AI) under two conditions: diagnostic assessment and real-fake c

Yiqian Chen, Li Li, Peng Wang

This study investigates the optical appearance of rotating scalarized Kerr-Newman black holes in the Einstein-Maxwell-scalar theory with exponential coupling. By analyzing equatorial null geodesics, these black holes are classified into six types according to the number and properties of their light rings. Combining slow-rotation analysis with full numerical

José Cáceres, Delia Garijo, Alberto Márquez, Rodrigo I. Silveira

The Borsuk problem asks for the smallest number of subsets with strictly smaller diameters into which any bounded set in the $d$-dimensional space can be decomposed. It is a classical problem in combinatorial geometry that has been subject of much attention over the years, and research on variants of the problem continues nowadays in a plethora of directions

A. G. M. Pietrow, H. Balthasar, P. Váradi Nagy, R. Kamlah

The Fraunhofer G-band around 4304 {\AA} is widely used as a photospheric diagnostic and is generally not expected to show signatures of chromospheric or coronal structures. However, recent amateur observations have suggested the presence of off-limb prominence emission in this spectral region. We investigate the origin of the prominence emission in the G-ban

Jean-Pierre Gazeau, Kaoutar El Bachiri, Zakaria Bouameur, Yassine Hassouni

Every $n$-level density matrix can be diagonalized as $ρ=U\,\mathrm{diag}(p_1,\ldots,p_n)U^\dagger$, splitting it into an eigenvalue vector $(p_1,\ldots,p_n)$ and a unitary eigenbasis $U$. We introduce a new coordinate system on the eigenvalue vector alone: the \emph{gap coordinates} $r_a:=p_a-p_{a+1}\ge0$, $a=1,\ldots,n-1$. We show that $(r_1,\ldots,r_{n-1}

Xingshi Yu, Ipsita Chakraborty, Isaac Johnson, Savvas I. Raptis

We report an electrically actuated, low-loss non-volatile optical switch based on the phase-change material (PCM) Sb2Se3 integrated on a silicon nitride (Si3N4) platform. The device is fabricated using an 8-inch wafer-scale process flow, demonstrating the feasibility of scalable manufacturing for photonic integrated circuits (PICs). By employing transparent

Junxiao Yang, Haoran Liu, Jinzhe Tu, Jiale Cheng

Large language models (LLMs) often demonstrate strong safety performance in high-resource languages, yet exhibit severe vulnerabilities when queried in low-resource languages. We attribute this gap to a mismatch between language-agnostic semantic understanding ability and language-dominant safety alignment biased toward high-resource languages. Consistent wi

Andrea Coccaro, Carlo Schiavi, Alessandro Zaio

Collider experiments are equipped with trigger systems that rapidly inspect the physics content emerging from collisions to decide whether the resulting products are worth saving for later analysis. One crucial aspect for analyzing the final states originating from the collisions is to process the information produced by charged particles in the innermost de

Juan Martínez-Sykora, Paola Testa, Deborah Baker, Bart De Pontieu

The inverse First Ionization Potential (FIP) effect is rarely observed in the solar atmosphere, and this anomaly poses a challenging problem in understanding physical processes driving this chemical fractionation. In this work, we investigate various scenarios where the inverse FIP effect could occur using simplified 1D multi-fluid MHD models. The model trea

Wenxin Wang, Yingzhi Tian, Jing Wang

An edge subset \( S \subseteq E(G) \) is called a 3-restricted edge-cut if $G-S$ is disconnected and each component of \( G - S \) contains at least three vertices. The 3-restricted edge-connectivity of a graph \( G \), denoted by \( \lambda_3(G) \), is defined as the minimum cardinality among all 3-restricted edge-cuts if there are at least one; otherwise,

Roman Ovsiannikov, Kurt Jacobs, Andrii G. Sotnikov, Matthew E. Trusheim

In our recent study [arXiv:2601.03407] we showed that a hybrid non-degenerate parametric amplifier could be realized for a microwave mode and an ensemble of NV-centers (or other spins) by parametrically driving the spin ensemble. The parametric driving was sinusoidal at the sum of the spin and cavities frequencies. Here we consider whether the performance of

Sophia A. Drobchik, Sergey A. Khaibrakhmanov

We compiled a sample of $1155$ protoplanetary disks, combining data from ten surveys of star-forming regions. Based on the sample, we constructed a power-law approximation of the disk mass distribution: $dN/dM \propto M^{-\beta}$, $\beta = 1.36 \pm 0.14$. We used the sample for a statistical analysis of the gravitational stability of protoplanetary disks. To

Maurizio Carbone, Lorenzo Piro, Robin A. Heinonen, Luca Biferale

Locating and intercepting a moving target from possibly delayed, intermittent sensory signals is a paradigmatic problem in decision-making under uncertainty, and a fundamental challenge for, e.g., animals seeking prey or mates and autonomous robotic systems. Odor signals are intermittent, strongly mixed by turbulent-like transport, and typically lag behind t

Han Li, Yifan Yao, Letian Zhu, Rili Feng

Code agents are advancing rapidly, but debugging them is becoming increasingly difficult. As frameworks orchestrate parallel tool calls and multi-stage workflows over complex tasks, making the agent's state transitions and error propagation hard to observe. In these runs, an early misstep can trap the agent in unproductive loops or even cascade into fundamen

Kangyi Lu, Lan Wei, Zongcai Tan, Dandan Zhang

Microscale manipulation has advanced substantially in controlled locomotion and targeted transport, yet many biomedical applications require precise and adaptive interaction with biological micro-objects. At these scales, manipulation is realized through three main classes of platforms: embodied microrobots that physically interact as mobile agents, field-me

Maxim Bolshim, Alexander Kugaevskikh

Modern automatic differentiation frameworks (JAX, PyTorch) return the Hessian of the loss function as a monolithic tensor, without exposing the internal structure of inter-layer interactions. This paper presents an analytical formalism that explicitly decomposes the full Hessian into blocks indexed by the DAG of an arbitrary architecture. The canonical decom

Zeinab Mcheik

Let \((M^n,g)\) be a smooth closed Riemannian manifold of dimension \(n \ge 5\) with positive Yamabe invariant and semi-positive \(Q\)-curvature. We establish a precompactness result in the \(C^{\alpha}\)-H\"older topologie on the space of Riemannian metrics, for some \(\alpha>0\), for the set of metrics \(\tilde{g}\) conformal to \(g\), with volume equal to

Wenhao Li, Xueying Jiang, Gongjie Zhang, Xiaoqin Zhang

4D point cloud videos capture rich spatial and temporal dynamics of scenes which possess unique values in various 4D understanding tasks. However, most existing methods work in the spatiotemporal domain where the underlying geometric characteristics of 4D point cloud videos are hard to capture, leading to degraded representation learning and understanding of

Mokshagna Sai Teja Karanam, Tushar Kataria, Shireen Elhabian

Statistical shape modeling (SSM) is central to population level analysis of anatomical variability, yet most existing approaches rely on densely annotated segmentations and fixed latent representations. These requirements limit scalability and reduce flexibility when modeling complex anatomical variation. We introduce MorphoFlow, a sparse supervised generati

Vishnupriya K., Harikrishnan K. J., Amit Kumar Pal

Disorder is ubiquitous in quantum devices including quantum probes designed and fabricated for quantum parameter estimation and sensing. We investigate the robustness of a quantum probe against the presence of glassy disorder. We define a disorder marker quantifying the effect of the disorder by expanding the quantum Fisher information in terms of different

Mohammadreza Mahmoudian, Simon A. Rogers, Parisa Mirbod

Viscous resuspension, the process by which sedimented particles are re-entrained into a fluid under flow, is central to numerous natural and industrial systems, including environmental contaminant transport, riverbed erosion, and biogeochemical cycling. Despite its ubiquity and importance, predicting when and how resuspension occurs remains challenging, part

Colin Cooper, Alan Frieze

We study the existence of directed Hamilton cycles in random digraphs with $m$ edges where we condition on minimum in- and out-degree $\d \ge k+1$, where $k \ge 1$. Denote such a random graph by $D_{n,m}^{(\delta\geq k+1)}$. Let $m=cn$ and $c\ge c_k$, where $c_k$ is a sufficiently large constant. We prove that w.h.p. $D_{n,m}^{(\delta\geq k+1)}$ contains $k$

Guandong Li

Flow Matching models achieve state-of-the-art image generation quality but incur substantial inference cost due to iterative denoising through large Transformer networks. We observe that different layer groups within a Transformer exhibit markedly heterogeneous velocity dynamics: shallow layers are highly stable and amenable to aggressive caching, while deep

Alejandro Penacho Riveiros, Matthieu Barreau, Nicola Bastianello

Industrial control applications require detecting system anomalies as accurately and quickly as possible to enable prompt maintenance. In this context, it is common to consider several possible plant models, each linked to a different anomaly. The log-likelihood ratio method can then be used to identify the most accurate model and thereby classify which anom

Benny Lau, Wenlong Wu, Bo Yuan, Julian Nickel

We report the growth and characterization of single crystals of the disorted-kagome lattice compound CrPdAs. Spin-glass behaviour with $T_{SG} \sim 60\ {\rm K}$ is observed in all crystals tested. Some growths show in addition a magnetic impurity phase with $T_c$ around 200 K, but annealing produces single-phase crystals without the ferromagnetic impurity ph

Alejandro Penacho Riveiros, Nicola Bastianello, Matthieu Barreau

In this paper we address the problem of detecting differences or anomalies in a dynamical system, based on historical data of nominal operations. This problem encompasses quality control, where newly manufactured systems are tested against desired nominal operations, and the detection of changes in the dynamics due to degradation or repairs. We propose a mod

Yuqian Wu, Wei Chen, Zhengjun Huang, Junle Chen

Existing conversational memory systems rely on complex hierarchical summarization or reinforcement learning to manage long-term dialogue history, yet remain vulnerable to context dilution as conversations grow. In this work, we offer a different perspective: the primary bottleneck may lie not in memory architecture, but in the \textit{Signal Sparsity Effect}

Haicheng Wang, Yuan Liu, Yikun Liu, Zhemeng Yu

Multimodal Large Language Models (MLLMs) have recently demonstrated remarkable capabilities in cross-modal understanding and generation. However, the rapid growth of visual token sequences--especially in long-video and streaming scenarios--poses a major challenge to their scalability and real-world deployment. Thus, we introduce POINTS-Long, a native dual-mo

Haozhe Wang, Cong Wei, Weiming Ren, Jiaming Liu

Most reward models for visual generation reduce rich human judgments to a single unexplained score, discarding the reasoning that underlies preference. We show that teaching reward models to produce explicit, multi-dimensional critiques before scoring transforms them from passive evaluators into active optimization tools, improving generators in two compleme

Samrendra Roy, Souvik Chakraborty, Rizwan-uddin, Syed Bahauddin Alam

Neural operators have emerged as powerful surrogates for partial differential equation (PDE) solvers, yet they are typically trained as monolithic models for individual PDEs, require energy-intensive GPU hardware, and must be retrained from scratch when new physics emerge. We introduce the Spiking Compositional Neural Operator (SCNO), a modular architecture

Luca Magri, Andrea Nóvoa, Elise Özalp

This chapter offers a principled approach to the prediction of chaotic systems from data. First, we introduce some concepts from dynamical systems' theory and chaos theory. Second, we introduce machine learning approaches for time-forecasting chaotic dynamics, such as echo state networks and long-short-term memory networks, whilst keeping a dynamical systems

Charafeddine Mouzouni

We introduce Context Kubernetes, an architecture for orchestrating enterprise knowledge in agentic AI systems, with a prototype implementation and eight experiments. The core observation is that delivering the right knowledge, to the right agent, with the right permissions, at the right freshness -- across an entire organization -- is structurally analogous

Han Xu, Zhiyong Qin, Di Shang, Jiahong Zhang

Multimodal Large Language Models (MLLMs) have achieved remarkable progress but incur substantial computational overhead and energy consumption during inference, limiting deployment in resource-constrained environments. Spiking Neural Networks (SNNs), with their sparse event-driven computation, offer inherent energy efficiency advantages on neuromorphic hardw

Sergey Vasilyev, Roderik Krebbers, Dmitrii Konnov, Mathieu Walsh

We report a broadband long-wave infrared Fourier transform spectrometer with sensitivity exceeding that of previously reported direct-detection implementations. The system combines dual-comb spectroscopy with electro-optic sampling, multi-channel parallel near-infrared detection using InGaAs photodiodes, and real-time GPU-based computational corrections of m

Alexis Kirke

Quantum algorithms with a proven theoretical speedup over classical computation are rare. Among the most prominent is the Harrow-Hassidim-Lloyd (HHL) algorithm for solving sparse linear systems. Here, HHL is applied to encode melodic preference: the system matrix encodes Narmour implication-realisation and Krumhansl-Kessler tonal stability, so its solution v

Ryan O'Connor, Chun Huang, Alexander Y. Chen

The NASA Neutron star Interior Composition Explorer (\emph{NICER}) mission measures the X-ray pulse profiles of select millisecond pulsars and uses sophisticated pulse profile modeling (PPM) techniques to constrain their masses ($M$) and radii ($R$), in order to probe the state of matter in their interiors. One of the most studied pulsars, PSR J0030+0451, ha

Monika Rani, Subhashish Banerjee, Nikhil Swami, Supriyo Dutta

We investigate quantum state transfer on a class of bipartite graphs, namely the butterfly graphs, within the framework of discrete-time quantum walks. These graphs facilitate the construction of scalable quantum networks that enable communication between a sender and a receiver via perfect state transfer. Our analysis demonstrates that state transfer occurs

Silvio Meira

This paper proposes a Unified Field Theory of Phygital Space, positing that contemporary reality is not a dichotomy of "online" and "offline," but a unified ontological manifold of irreducible but coupled dimensions. We formalize Phygital Space as a sheaf over a topological site composed of the Physical (U), Networked Digital (D), and Networked Social (S) di

Xiaoting Wei, Lele Kang, Xuelian Pan, Jiannan Yang

The rapid growth of open-source large language models (LLMs) has created a complex ecosystem of model inheritance and reuse. However, existing research has focused mainly on descriptive analyses of lineage evolution, with limited attention to identifying which models play a disruptive role in shaping subsequent development. Using metadata from 2,556,240 mode

Cor Steging, Tadeusz Zbiegień

Bail decisions present a fundamental challenge for data-driven decision support systems. When bail is denied, the counterfactual outcome of whether the defendant would have appeared in court remains unobserved. As a result, historical bail data embed structural label indeterminacy: future decisions are influenced by past decisions whose outcomes are only par

Jinpeng Ye, Chongxi Wang, Wenqing Li, Bin Yuan

Matrix extensions have emerged as an essential feature in modern CPUs to address the surging demands of AI workloads. However, existing designs often incur substantial hardware and software design overhead. Tight coupling with the CPU pipeline complicates integration across diverse CPUs, while fine-grained synchronous instructions hinder the development of h

Xinyi Sui, Mankei Tsang

The prequel to this work [Ng et al., Phys. Rev. A 93, 042121 (2016)] proposes the method of spectral photon counting to enhance noise spectroscopy with an optical interferometer. While the predicted enhancement over homodyne detection is promising, the results there are derived by taking an asymptotic limit of infinite observation time; their validity for a

Alexander Sakhnovich, Lev Sakhnovich

First, we consider generalized wave and scattering operators and derive modifications of commutation relations (between scattering operators and unperturbed operators) when the corresponding deviation factors behave as $\exp\{i t {\mathcal C}_{\pm}\}$ for $t\to \pm \infty$. Then, we construct so called secondary generalized scattering operators for the relat

Jiashu Yao, Heyan Huang, Zeming Liu, Yuhang Guo

To overcome the sparse reward challenge in reinforcement learning (RL) for agents based on large language models (LLMs), we propose Mutual Information Self-Evaluation (MISE), an RL paradigm that utilizes hindsight generative self-evaluation as dense reward signals while simultaneously calibrating them against the environmental feedbacks. Empirically, MISE en

Yuqing Yang, Tengxiao Liu, Wang Bill Zhu, Taiwei Shi

As LLM-based assistants become persistent and personalized, they must extract and retain useful information from past conversations as memory. However, the types of information worth remembering vary considerably across tasks. We formalize the \textit{heterogeneous memory extraction} task and introduce \textbf{BEHEMOTH}, a benchmark that repurposes 18 existi

Benjamin Maltbie, Shivam Raval

Large language models exhibit sycophantic tendencies, but whether this behavior varies systematically with perceived user demographics is underexplored. Inspired by intersectionality (overlapping identities produce compounded effects), we probe whether frontier models conditionally exhibit sycophancy. Across 768 multi-turn conversations spanning 128 personas

Simachew Abebe Mengiste, Demian Battaglia

Dynamic functional connectivity (dFC) is ubiquitously observed in the brain, but why functional networks should remain dynamic even at rest is unclear. We asked whether temporal reconfiguration becomes advantageous when keeping a functional link active is costly. Modeling resting-state dFC as a temporal communication network, we show that empirical dFC outpe

Thomas A. Balsamo, Faustino G. Ibarlucea, Marcelo A. Romero

We present a theoretical investigation of resonant charge transfer in low-energy Li$^+$ ions backscattered from a MoS$_2$ surface, focusing on the influence of excited projectile states. Using a time-dependent Anderson model in the infinite-$U$ limit, we evaluate the individual contributions from the Li $2s$, $2p_x$, $2p_y$, and $2p_z$ orbitals to the final

Han Huang, Ningning Tao, Hongyu Wang, Teng Liu

The Atlantic surface currents associated with the Atlantic Meridional Overturning Circulation (AMOC) play a central role in regulating Earth's climate, yet their large scale dynamical response to climate variability remains poorly understood. Here we identify a previously unrecognized basin scale phase of Atlantic surface circulation, termed the Atlantic Con

Trinh Van Chien, Bui Trong Duc, Mohammadali Mohammadi, Hien Quoc Ngo

Cell-free massive multiple-input multiple-output is a potential candidate for future networks with pervasive connectivity by utilizing coherent joint transmission and distributed antenna arrays. This paper studies the exploitation of full-duplex communication for a distributed antenna array. Specifically, we derive a closed-form expression for the uplink and

Wyatt Kirkby, Lauriane Chomaz, Thomas Gasenzer

False-vacuum decay between two morphologically distinct supersolid phases via bubble nucleation is studied in a uniform dipolar gas confined to the plane. Starting from a metastable honeycomb state, the formation of stripe phase domains is simulated numerically by means of a stochastic projected extended Gross-Pitaevskii equation. The speed of bubble growth

Laurent Freidel, Atul Sharma

The $S$-algebra originally arose as a chiral algebra of asymptotic symmetries of Yang-Mills theory. We show that in the self-dual sector of Yang-Mills, the $S$-algebra gets upgraded to an infinite-dimensional algebra of $1$-form symmetries in the bulk. The associated 2-form currents encode the integrability and hierarchies of self-dual Yang-Mills. As an appl

Peijie Wang, Ming-Liang Zhang, Jun Cao, Chao Deng

Multimodal Large Language Models (MLLMs) have achieved remarkable progress but continue to struggle with geometric reasoning, primarily due to the perception bottleneck regarding fine-grained visual elements. While formal languages have aided plane geometry understanding, solid geometry which requires spatial understanding remains largely unexplored. In this

Vinooth Kulkarni, Jaehyun Lee, Adam Hutchings, Anas Albahri

Dynamic quantum circuits with mid-circuit measurement and classical feedforward are essential for near-term algorithms such as error mitigation, adaptive phase estimation, and Variational Quantum Eigensolvers (VQE), yet transpiling these programs across frameworks remains challenging due to inconsistent support for control flow and measurement semantics. We

Marcus Kubsch, Natasha G. Holmes, Antti Lehtinen

The Physics Lab Inventory of Critical Thinking (PLIC) measures three components of students' critical thinking in physics labs: evaluating data, evaluating methods, and proposing next steps. Prior work has analyzed these components in isolation or as a composite score. In this study, we apply latent profile analysis (LPA) to the three PLIC scales using a lar

Helmut Abels, Hanifah Mumtaz

We perform a rigorous examination of the sharp interface limit of a coupled Navier-Stokes and mass-conserving Allen-Cahn system in a two-dimensional, bounded, and smooth domain as the parameter $\varepsilon > 0$, representing the thickness of the diffuse interface, tends to zero. We prove the convergence of solutions from the mass-conserving Navier-Stokes/Al

Kazuki Ikeda, Yaron Oz

A spectrally isolated quartet can admit a local two-qubit description at each point in parameter space and still acquire a loop holonomy outside the local subgroup $\mathrm{U}(2)\otimes\mathrm{U}(2)$. We study this question in three localized topological settings, a BHZ ribbon, a spinful SSH chain, and a BBH corner quartet. On a fixed quartet, changing only

Emine Yegin, Doruk Pehlivanoğlu, T. Serkan Kasırga

The surface of ultra-thin materials plays a crucial role in determining the properties. This is particularly important in two-dimensional (2D) materials where the surface-bulk distinction is no longer present. While mechanical cleaning of two-dimensional materials to remove interfacial and surface contaminants is used to achieve better sample quality, low th

Shuiyuan Wang, Zhixian Zhao, Hongfei Xue, Chengyou Wang

Evaluating the emotional intelligence (EI) of audio language models (ALMs) is critical. However, existing benchmarks mostly rely on synthesized speech, are limited to single-turn interactions, and depend heavily on open-ended scoring. This paper proposes HumDial-EIBench, a comprehensive benchmark for evaluating ALMs' EI. Using real-recorded human dialogues f

Muhammad Ali Jamshed, Rohit Singh, Malik Muhammad Saad, Aryan Kaushik

Non-Terrestrial Networks (NTN) have emerged as a key enabler to fully realize the vision of integrated, intelligent, and ubiquitous connectivity in 6G systems. However, several operational challenges, including severe Doppler effects, interference, and latency, hinder the seamless integration of NTN and Terrestrial Networks (TN). In this context, Integrated

Felix del Teso, Carlos Fuertes-Moran, Julio D. Rossi

We introduce a game-theoretical framework for the doubly nonlinear parabolic equation \[ |\partial_t u|^{p-2} \partial_t u - \Delta_p u = 0. \] where $\Delta_p u = \nabla \cdot ( |\nabla u |^{p-2} \nabla u)$ with $p>2$ is the standard $p-$Laplacian. A key feature to our approach is a new asymptotic mean value formula (AMVF) for the $p-$Laplacian that is robu

Marco A. R. Ferreira

We develop a novel reference prior for Gaussian hierarchical models with intrinsic conditional autoregressive (ICAR) random effects. This is particularly important in the context of objective Bayes variable selection with sample size $n$ and $k$ regressors. In this context, a previously published reference prior requires the computation of spectral decomposi

Stefano Bianchettin, Giulio Rossolini, Giorgio Buttazzo

Nowadays, pretrained models are increasingly used as general-purpose backbones and adapted at test-time to downstream environments where target data are scarce and unlabeled. While this paradigm has proven effective for improving clean accuracy on the target domain, adversarial robustness has received far less attention, especially when the original pretrain

Shuitsu Koyama, Yuiga Wada, Daichi Yashima, Komei Sugiura

Automatic evaluation using multimodal large language models (MLLMs), commonly referred to as MLLM-as-a-Judge, has been widely used to measure model performance. If such MLLM-as-a-Judge methods were biased, they could distort model comparisons and benchmark-driven scientific progress. However, it remains unclear to what extent MLLM-as-a-Judge methods favor or

Ruixuan Zhao, Guitao Yang, Nicola Bastianello, Boli Chen

This paper proposes a novel Distributed Unknown Input Observer (DUIO) framework for state estimation in large-scale systems subject to local unknown inputs. We consider systems where outputs are measured by a network of spatially distributed sensors and inputs are introduced through multiple dispersed channels. In this framework, each local node utilizes onl

Yi Wang, Bingxian Mu, Shahab Shokouhi, May-Win Thein

This paper introduces Bidirectional Tight Informed Trees (BTIT*), an asymptotically optimal kinodynamic sampling-based motion planning algorithm that integrates an anytime bidirectional heuristic search (Bi-HS) and ensures the \emph{meet-in-the-middle} property (MMP) and optimality (MM-optimality). BTIT* is the first anytime MEET-style algorithm to utilize t

Sergio Sánchez Cruz

The production of top quark pairs is one of the most relevant production modes at the LHC, and allow for precise measurement of the properties of this particle. Top quarks are also produced through rarer mechanisms, including the production of multiple top quarks or the associated production of top quarks with electroweak gauge bosons. Although these process

Krishna Jaganathan, Patricio Vela

Multimodal perception systems for robotics and embodied AI often assume reliable RGB-D sensing, but in practice, depth is frequently missing, noisy, or corrupted. We thus present GeomPrompt, a lightweight cross-modal adaptation module that synthesizes a task-driven geometric prompt from RGB alone for the fourth channel of a frozen RGB-D semantic segmentation

Xiang Meng, Andrés Gómez, Rahul Mazumder

We study computational aspects of a key problem in robust statistics -- the penalized least trimmed squares (LTS) regression problem, a robust estimator that mitigates the influence of outliers in data by capping residuals with large magnitudes. Although statistically attractive, penalized LTS is NP-hard, and existing mixed-integer optimization (MIO) formula

Satoshi Okamoto, Adriana Moreo, Naoto Nagaosa, Stuart S. P. Parkin

Berry curvature is ubiquitous in condensed matter physics and materials science. Its main consequence is the intrinsic anomalous Hall effect (AHE) in magnetic materials and plays a pivotal role in spintronic applications and quantum technologies. Here, we present a theoretical study of the intrinsic AHE in tetragonal FeTe, a semimetallic van der Waals antife

Olga Chetverina

Standard subword tokenization methods fragment numbers inconsistently, causing large language models (LLMs) to lose positional and decimal structure - a primary driver of errors in arithmetic and scientific reasoning. We introduce Triadic Suffix Tokenization (TST), a deterministic scheme that partitions digits into three-digit triads and annotates each triad

Ioannis Gavras, George C. Alexandropoulos

This paper investigates the impact of practical features of the emerging antenna array technology of Dynamic Metasurface Antennas (DMAs) when used for wideband sensing. By adopting a realistic DMA response model capturing frequency selective magnetic polarizability, finite resonant frequency tuning, and waveguide phase and leakage effects, we first present a

Seongyu Kim, Seungwoo Lee, Hyeonggon Ryu, Joon Son Chung

We address the problem of tactile localization, where the goal is to identify image regions that share the same material properties as a tactile input. Existing visuo-tactile methods rely on global alignment and thus fail to capture the fine-grained local correspondences required for this task. The challenge is amplified by existing datasets, which predomina

Arunava Majumder, Hendrik Poulsen Nautrup, Hans J. Briegel

Measurement-based quantum computation (MBQC) is a framework for quantum information processing in which a computational task is carried out through one-qubit measurements on a highly entangled resource state. Due to the indeterminacy of the outcomes of a quantum measurement, the random outcomes of these operations, if not corrected, yield a variational quant

Christopher D. Long

We study the finite mutually exclusive outcome version of risk-constrained Kelly optimization with explicit state prices. The market has outcome probabilities $p_i>0$, state prices $q_i>0$, terminal wealths $W_i=c+x_i/q_i$, and a drawdown-surrogate constraint \[ \sum_{i=1}^n p_i W_i^{-\lambda}\le 1,\qquad \lambda>0. \] For constant relative risk aversion uti

Songlong Xing, Weijie Wang, Zhengyu Zhao, Jindong Gu

Despite their impressive zero-shot abilities, vision-language models such as CLIP have been shown to be susceptible to adversarial attacks. To enhance its adversarial robustness, recent studies finetune the pretrained vision encoder of CLIP with adversarial examples on a proxy dataset such as ImageNet by aligning adversarial images with correct class labels.

N. Heidari, A. A. Araujo Filho, Iarley P. Lobo

In this work, we study the motion of massive test particles and the gravitational--wave emission associated with periodic trajectories around a magnetically charged black hole immersed in a \textit{Hernquist} dark matter halo. We begin by analyzing the effective potential and the conditions for stable motion, with particular attention to the marginally bound

Chen Hu, Yintao Tai, Antonio Vergari, Frank Keller

Pixel-based language models are gaining momentum as alternatives to traditional token-based approaches, promising to circumvent tokenization challenges. However, the inherent perceptual diversity across languages poses a significant hurdle for multilingual generalization in pixel space. This paper introduces MIXAR, the first generative pixel-based language m

Marco Artiano, Hendrik Ranocha, Saurav Samantaray

We consider the compressible Euler system with anelastic scaling, modeling isentropic flows under the influence of gravity. In the zero-Mach-number limit, the solution of the compressible Euler system converges to a variable density anelastic incompressible limit system. In this work, we present the design and analysis of a class of higher-order linearly imp

Siyuan Xu, Tianshi Wang, Fengling Li, Lei Zhu

Vision-Language-Action models (VLAs) have demonstrated strong potential for embodied AI, yet their deployment on resource-limited robots remains challenging due to high memory and computational demands. While Post-Training Quantization (PTQ) provides an efficient solution, directly applying PTQ to VLAs often results in severe performance degradation during s

A. Tishelman-Charny, A. Buzzi, F. Capocasa, G. D'Amen

Low Gain Avalanche Diodes are prime candidates for high-resolution timing applications in High Energy Physics, Nuclear science, and several other fields. Operating these devices in high-radiation environments presents various hazards, including the risk of their permanent degradation or destruction caused by effects such as Single Event Burnout. Studies usin

Birgit Nierula, Karam Tomotaki-Dawoud, Daniel Johannes Meyer, Iryna Ignatieva

We present the early-stage design and implementation of a multimodal, real-time communication analysis system intended as a foundational interaction layer for adaptive VR training. The system integrates five parallel processing streams: (1) verbal and prosodic speech analysis, (2) skeletal gesture recognition from multi-view RGB cameras, (3) multimodal affec