March 2026 arXiv papers — page 7

Annika Betken, Giorgio Micali, Johannes Schmidt-Hieber

Deep learning is widely deployed for time series learning tasks such as classification and forecasting. Despite the empirical successes, only little theory has been developed so far in the time series context. In this work, we prove that if the network inputs are generated from short-range dependent linear processes, the outputs of fully convolutional neural

Luca Schaefer, Barbara Drossel

We compare the spectrum and the localisation properties of the eigenmodes of the Laplacian and the adjacency matrix of 2D random geometric graphs, using numerical diagonalization of these matrices for different system sizes and connectivities. For sufficiently large ensembles of systems, we evaluate the spectrum, the probability distribution of the participa

Fedor Pakovich

Let $X$ and $Y$ be rational functions of degree at least two with complex coefficients such that $\mathbb{C}(X,Y)=\mathbb{C}(z)$. We study the problem of determining when the field extension $[\mathbb{C}(z):\mathbb{C}(X)\cap\mathbb{C}(Y)]$ is finite and attains the minimal possible degree ${\rm deg X}\cdot{\rm deg Y}$. We give a complete characterization in

Adrian Martínez, Ananya Gupta, Hanka Goralija, Mario Rico

Although Deep Reinforcement Learning has proven highly effective for complex decision-making problems, it demands significant computational resources and careful parameter adjustment in order to develop successful strategies. Evolution strategies offer a more straightforward, derivative-free approach that is less computationally costly and simpler to deploy.

Nico Oberländer, David Bani-Harouni, Tobias Zellner, Nassir Navab

Acute poly-substance intoxication requires rapid, life-saving decisions under substantial uncertainty, as clinicians must rely on incomplete ingestion details and nonspecific symptoms. Effective diagnostic reasoning in this chaotic environment requires fusing unstructured, non-medical narratives (e.g. paramedic scene descriptions and unreliable patient self-

Ellen Henke, Edoardo Salati

Saturated fusion systems are categories modeling properties of conjugacy of p-subgroups in finite groups. It was shown by Chermak that they correspond nicely to group-like structures called localities. In this paper we start to explore how concepts and results from a program of Meierfrankenfeld, Stellmacher and Stroth, aiming to reprove and generalize parts

David M. Evans

Suppose $R$ is a commutative ring and $G$ is a group acting on a set $W$. We consider the $RG$-module $RW$ in the case where $G$ is the automorphism group of an $\omega$-categorical structure $M$ and $W$ is, for example, $M^n$ (for $n \in \mathbb{N}$). We develop methods which may provide information about two questions in the case where $R$ is a field $F$:

Masafumi Hayashi, Kazuyasu Sugiyama

A body moving in a wall-bounded flow often experiences a hydrodynamic lift force normal to the wall, which plays an important role in many fluid systems. In this study, we develop a framework for diagnosing steady inertial lift from the internal structure of the flow field. Based on the generalised reciprocal theorem for finite-Reynolds-number flows, the lif

V. Arzt, P. Beremlijski, H. Gfrerer, J. V. Outrata

The paper deals with the 3D Stokes problem with Navier-Tresca stick-slip boundary conditions. A weak formulation of this problem leads to a variational inequality of the second kind, coupled with an equality constraint. This problem is then approximated using the mixed finite element method, yielding a generalized equation, to the numerical solution of which

Daniel Pérez-Cruz, Grigori E. Astrakharchik, Pietro Massignan

We study the superfluid response of a dilute bosonic fluid in the presence of two-dimensional composite potentials (such as triangular, Kagom\'e and quasiperiodic potentials, or superlattices), which may be obtained for example by superposing multiple laser beams. We first find a sufficient condition for the external potential to yield a fully isotropic supe

Md Sohel Mondal, Shashank Shekhar, Siddhartha Santra

The practical utility of a quantum network depends on its ability to establish entanglement between arbitrary node pairs with quality sufficient to execute entanglement enabled tasks. This capability can be assessed globally, through aggregate performance over all node pairs, as well as locally, at the level of individual nodes. Since entanglement-based conn

Maja Gwozdz

We address Steinerberger's Wasserstein transport problem on the cube $Q=[0,1]^d$. For every $d\ge2$, we consider a dyadic digital sequence $(x_n)\subset Q$ and prove that every prefix $\{x_1,\dots,x_N\}$ admits an exact equal-mass transport partition at the optimal scale. More precisely, for every $N\in\mathbb{N}$, there exist pairwise disjoint Borel sets $A

B. Tegomo Chiogo, M. Tagliavini, D. Wong, C. Schulz

The low-energy spin- and charge-sensitive thermodynamic properties of a broad range of strongly correlated 4f-electron systems follow Kondo scaling, with a characteristic Kondo temperature, $T_K$. While the theory is known for thermodynamic properties and high-energy spectroscopies of Kondo materials, the surface sensitivity of electron spectroscopy limits t

Jane Moore, Michael Hart, John McAllister

Compiling quantum circuits is a major bottleneck in quantum computing, and given the scale required in a few years, is likely to become infeasibly long. Techniques to reduce compilation time for quantum circuits are sorely needed. Furthermore, resources to test acceleration techniques are similarly lacking due to the limited scale of circuits in benchmark su

Claus Metzner, Achim Schilling, Andreas Maier, Thomas Kinfe

Reservoirs, typically implemented as recurrent neural networks with fixed random connection weights, can be combined with a simple trained readout layer to perform a wide range of computational tasks. However, increasing the magnitude of reservoir connection weights to exploit nonlinear dynamics can cause the network to develop strong spontaneous activity th

Alexey Kurnosenko

To construct a curve with a monotonic curvature (spiral), and given tangents and curvatures at the ends, the author proposed the following method. From given boundary conditions, the values of two inverse invariants are determined. Then, on some base spiral (initially, a logarithmic spiral was chosen), an arc with the same invariant values is sought for. A l

William Ford

This paper studies the uniqueness of solutions to the dual optimal transport problem, both qualitatively and quantitatively (bounds on the diameter of the set of optimisers). On the qualitative side, we prove that when one marginal measure's support is rectifiably connected (path-connected by rectifiable paths), the optimal dual potentials are unique up to a

Gehui Xu, Kaiwen Chen, Zhong-Ping Jiang, Thomas Parisini

An insider is a team member who covertly deviates from the team's optimal collaborative strategy to pursue a private objective while still appearing cooperative. Such an insider may initially behave cooperatively but later switch to selfish or malicious actions, thereby degrading collective performance, threatening mission success, and compromising operation

Yijia Chen

The proliferation of diverse, high-leverage trading instruments in modern financial markets presents a complex, "noisy" environment, leading to a critical question: which trading strategies are evolutionarily viable? To investigate this, we construct a large-scale agent-based model, "MAS-Utopia," comprising 10,000 agents with five distinct archetypes. This s

Erhard Aichinger, Mario Kapl, Bernardo Rossi

We provide polynomial completeness results for finite algebras in congruence permutable varieties. In 2001, Idziak and S{\l}omczy{\'n}ska introduced the completeness concept of being \emph{polynomially rich}: a finite algebra is polynomially rich if every function preserving congruences and the Tame Congruence Theory labelling of prime quotients in the congr

Aksel Bergfeldt

We define functions of the sub-Laplacian $\Delta$ on the Heisenberg group $\mathbb H^d$ as Fourier multipliers. In this setting, we show that the solution $u$ of the free fractional Schr\"odinger equation $i\partial_tu + (-\Delta)^\nu u = 0, u|_{t=0} = u_0$, for any $\nu > 0$, satisfies the Hardy space estimate that $$ \|u(t,\cdot)\|_{H^p(\mathbb H^d)} \leq

Janik Pinter, Maximilian Beichter, Ralf Mikut, Veit Hagenmeyer

Optimal scheduling of batteries has significant potential to reduce electricity costs and to enhance grid resilience. However, effective battery scheduling must account for both physical constraints as well as uncertainties in consumption and generation of renewable energy sources. Instead of optimizing fixed battery power setpoints, we propose an approach t

T. L. Killestein

The naming of astronomical objects has represented among the most significant challenges in the record-keeping of the field since the very beginning. Long and unwieldy coordinate names, uninformative and ambiguous internal names, and the sheer volume of aliases accumulated for some of the most studied objects conspire to complicate our study of the celestial

A. Takacs, J. Dubail, P. Calabrese

We study the dynamics of a lattice hard-core boson gas released from a domain wall initial state in the presence of two weak links (defects). When the two defects are separated by a finite distance, the resulting density profile exhibits clear deviations from the standard Euler-scale hydrodynamic description of the gas, due to genuine quantum interference ef

Vincent Cohen-Addad, Marina Drygala, Nathan Klein, Ola Svensson

The Weighted Tree Augmentation Problem (WTAP) is a fundamental network design problem where the goal is to find a minimum-cost set of additional edges (links) to make an input tree 2-edge-connected. While a 2-approximation is standard and the integrality gap of the classic Cut LP relaxation is known to be at least 1.5, achieving approximation factors signifi

Rachel Cannon, Aidan S. Arnold, Paul F. Griffin, Erling Riis

Laser-cooled atoms are increasingly being used to realise practical quantum devices, motivating the development of compact and robust atom sources. Grating magneto-optical traps (gMOTs) simplify the cold-atom source architecture but are typically vapour-loaded and provide limited atomic flux. Here we explore the loading of gMOTs from cold-atom beams. We nume

Xinkui Zhao, Hengxuan Lou, Yifan Zhang, Junjie Dai

Hybrid search, which jointly optimizes vector similarity and structured predicate filtering, has become a fundamental building block for modern AI-driven systems. While recent predicate-aware ANN indices improve filtering efficiency on CPUs, their performance is increasingly constrained by limited memory bandwidth and parallelism. Although GPUs offer massive

Xuan Zheng, Yan-Ting Xie, Shou-Jun Xu

Let $X\subseteq\{0,1\}^n$ be a set of binary strings of length $n$. The daisy cube $Q_n(X)$ is the subgraph of the hypercube $Q_n$ induced by the union of the intervals $I(x,0^n)$ for $x\in X$. As a subclass of partial cubes, it generalizes Fibonacci cubes and Lucas cubes. For a graph $G$ and a vertex $u\in V(G)$, we consider the cube polynomial $C_G(x)$, th

Alice Hedenlund, Trygve Poppe Oldervoll

An orientation theory for flow categories without bubbling is determined by a functor of $\infty$-categories $\mu \colon \mathcal{C} \to U/O$. For any such functor, we construct a stable $\infty$-category $\mathcal{F}low^{\mu}$ of $\mu$-structured flow categories and bimodules. We also construct the expected functors between such $\infty$-categories, giving

Lingfeng Lyu, Xiao Guo, Zongqi Liu

This paper studies transfer learning for ridge-regularized robust linear regression in the moderate-dimensional regime, where the number of predictors is of the same order as the sample size and the regression coefficients are not assumed to be sparse. We propose Trans-RR, which combines a robust ridge estimator from a source study with a robust ridge correc

Venelin P. Pavlov, Bogomila S. Nikolova, Peter A. Ivanov

We propose an adiabatic Ramsey interferometry technique for detecting weak nonlinearities with trapped ions. The method relies on using the quantum Rabi model as a probe, which is sensitive to nonlinear symmetry-breaking perturbations. We show that the couplings which arise either from anharmonic terms of the trapping potential or due to higher order terms i

Owen Lynch, David Jaz Myers, Eigil Fjeldgren Rischel, Sam Staton

One of the characteristic features of categorical systems theory is that the behavior of systems can be characterized by certain morphisms into them. In other words, behaviors form a representable covariant functor to Set. And more generally, in the compositional setting, behaviors form a representable double functor to Span. Clock systems are convenient bec

Afonso S. Bandeira, Daniil Dmitriev, Kevin Lucca, Petar Nizić-Nikolac

Randomstrasse101 is a blog dedicated to Open Problems in Mathematics, with a focus on Probability Theory, Computation, Combinatorics, Statistics, and related topics. This manuscript serves as a stable record of the Open Problems posted in 2025, with the goal of easing academic referencing. The blog can currently be accessed at randomstrasse101.math.ethz.ch

Jagadish Kashinath Kamble, Jayanta Mukhopadhyay, Debaditya Roy, Partha Pratim Das

Preserving intangible cultural dances rooted in centuries of tradition and governed by strict structural and symbolic rules presents unique challenges in the digital era. Among these, Bharatanatyam, a classical Indian dance form, stands out for its emphasis on codified adavus and precise key postures. Accurately generating these postures is crucial not only

Alberto Bressan, Giacomo Vecchiato, Ludmil Zikatanov

The paper analyzes a mollification algorithm, for the numerical computation of optimal irrigation patterns. This provides a regularization of the standard irrigation cost functional, in a Lagrangian framework. Lower semicontinuity and Gamma-convergence results are proved. The technique is then applied to some numerical optimization problems, related to the o

Yacoub Hendi, Daniel Persson, Magdalena Larfors

We study polynomial group convolutional neural networks (PGCNNs) for an arbitrary finite group $G$. In particular, we introduce a new mathematical framework for PGCNNs using the language of graded group algebras. This framework yields two natural parametrizations of the architecture, based on Hadamard and Kronecker products, related by a linear map. We compu

Maria Rosaria Formica, Eugene Ostrovsky, Leonid Sirota

We study the tail behaviour of measurable functions under generalized Riesz-type operators in the framework of Grand Lebesgue Spaces. By exploiting the connection between the growth of $L^p$ norms and the Young--Fenchel transform, we derive explicit tail estimates from suitable $L^p$ bounds. We also present model examples and apply the abstract result to the

Shiyu Wang, Delang Liang, Zhi Zheng, Mingyang Qin

Mid-infrared (MIR) emitters are critical components in advanced photonic systems, driving progress in fields such as chemical sensing, environmental monitoring, medical diagnostics, thermal imaging and free-space communications. Conventional MIR emitters based on III-V heterostructures rely on complex epitaxial growth on rigid lattice-matched substrates and

Luca Cattelani, Vittorio Fortino

Multi-omic datasets offer opportunities for improved biomarker discovery in cancer research, but their high dimensionality and limited sample sizes make identifying compact and effective biomarker panels challenging. Feature selection in large-scale omics can be efficiently addressed by combining machine learning with genetic algorithms, which naturally supp

Shahnaz Farhat, Denis Périce, Sören Petrat

We consider the ground state energy of the Bose--Hubbard model on a graph with large and homogeneous coordination number. In the limit of infinite coordination number, we prove convergence of the ground state energy to the minimizer of a mean-field energy functional. This functional is obtained by averaging the hopping term over the large number of connected

Jonas Ohnemus, Alexandre Didier, Ahmed Aboudonia, Andrea Carron

We consider safety-critical multi-agent systems with distributed control architectures and potentially varying network topologies. While learning-based distributed control enables scalability and high performance, a lack of formal safety guarantees in the face of unforeseen disturbances and unsafe network topology changes may lead to system failure. To addre

Robinson Ferrer, Damla Turgut, Zhongzhou Chen, Shashank Sonkar

Large Language Models (LLMs) show promise for automated grading, but their outputs can be unreliable. Rather than improving grading accuracy directly, we address a complementary problem: \textit{predicting when an LLM grader is likely to be correct}. This enables selective automation where high-confidence predictions are processed automatically while uncerta

Carl-Fredrik Nyberg-Brodda

We give a short proof of the following result due to Howie: if $A(\Gamma)$ is a right-angled Artin group embedding into some one-relator group, then $\Gamma$ is a finite forest. The proof only uses elementary Bass--Serre theory and classical properties of one-relator groups.

Qiyao Wang, Hongbo Wang, Longze Chen, Zhihao Yang

Scientific idea generation (SIG) is critical to AI-driven autonomous research, yet existing approaches are often constrained by a static retrieval-then-generation paradigm, leading to homogeneous and insufficiently divergent ideas. In this work, we propose FlowPIE, a tightly coupled retrieval-generation framework that treats literature exploration and idea g

Mizanur Rahaman, Mateusz Wasilewski

We study the structure of separable elements in bipartite C$^{\ast}$-algebras, focusing on the existence and size of a separable neighbourhood around the identity element. While this phenomenon is well understood in the finite-dimensional setting, its extension to general C$^{\ast}$-algebras presents additional challenges. We show that the problem of determi

Jakob Kellermann

Motivated by the success of score-based generative models, a number of diffusion-based algorithms have recently been proposed for the problem of sampling from a probability measure whose unnormalized density can be accessed. Among them, Grenioux et al. introduced SLIPS, a sampling algorithm based on Stochastic Localization. While SLIPS exhibits strong empiri

Martin Výboh, Gabriela Grmanová

Accurate event-based modeling of electric vehicle (EV) charging is essential for grid reliability and smart-charging design. While traditional statistical methods capture marginal distributions, they often fail to model the complex, non-linear dependencies between charging variables, specifically arrival times, durations, and energy demand. This paper addres

Hartmut Führ, Narjes Rashidi

The $n$-dimensional affine Weyl-Heisenberg group is a Lie group typically parameterized as $G_{aWH} = \mathbb{T} \times \mathbb{R}^n \times \widehat{\mathbb{R}^n} \times \mathrm{GL}(n, \mathbb{R})$, generated by all translation, dilation, and modulation operators acting on $L^2(G)$. It was introduced by Torr\'esani and his coauthors as a common framework to

Linda Zeng, Steven Y. Feng, Michael C. Frank

Multilingualism is incredibly common around the world, leading to many important theoretical and practical questions about how children learn multiple languages at once. For example, does multilingual acquisition lead to delays in learning? Are there better and worse ways to structure multilingual input? Many correlational studies address these questions, bu

Hannah Mertens, Tim Quatmann, Joost-Pieter Katoen

This paper develops an assume-guarantee (AG) framework for the compositional verification of probabilistic automata (PAs) with uncertain transition probabilities. We study parametric probabilistic automata (pPAs), where probabilities are given by polynomial functions over a finite set of real-valued parameters and robust probabilistic automata (rPAs)-a gener

P. Chigansky, F. Klebaner, M. Mrksa, S. Sagitov

To model amplification Polymerase Chain Reaction (PCR) techniques targeting DNA sequences of several types, we introduce a multitype PCR branching process as a generalized version of the Michaelis-Menten-based branching process model introduced in Jagers-Klebaner, 2003. We establish two limit theorems extending the results of Chigansky-Jagers-Klebaner, 2018

Petr Kovař, Yifan Zhang

We investigate the minimum number of cliques of orders $3$, $4$, and $5$ needed to cover the edges of $K_{33}$ with zero excess. General covering results yield the lower bound 57. The main result of the paper is that no decomposition of $K_{33}$ into $57$ blocks from $\{K_3,K_4,K_5\}$ exists. Our approach is algorithmic and relies on a layered exact-search p

M. H. Müser, B. N. J. Persson

The temperature increase in the contact regions between solids in sliding contact can easily reach several hundred Kelvin and thereby dramatically affect friction and wear. The classical theories by Jaeger, Archard, and Greenwood, commonly used to estimate flash temperature, ignore the multiscale nature of real surfaces and instead approximate the frictional

Haris Zafeiropoulos, Daniel Rios Garza

Flux sampling is an analysis that, based on a distribution, picks randomly an efficient number of points from the solution space of a metabolic model. Unlike most constraint-based analyses, flux sampling does not require an objective function to optimize, allowing for the exploration of the whole spectrum of the phenotypes a species can exhibit. However, sam

Jack Hughes, Ben Collier, Daniel R. Thomas

Existential risk scenarios relating to Generative Artificial Intelligence often involve advanced systems or agentic models breaking loose and using hacking tools to gain control over critical infrastructure. In this paper, we argue that the real threats posed by generative AI for cybercrime are rather different. We apply innovation theory and evolutionary ec

Daniel Platt

We apply verified numerics to the Nirenberg problem, proving that a genuine solution exists near two given computer-generated approximate solutions. This proves existence of a solution for a particular prescribed curvature that was previously predicted, but not proved, to exist. We are also able to determine the symmetry groups of the genuine solutions exact

Zhijie Tang, Albert Nieto-Morales, Arit Kumar Bishwas

Efficiently planning container loads onto trains is a computationally challenging combinatorial optimization problem, central to logistics and supply chain management. A primary source of this complexity arises from the need to model and reduce rehandle operations-unproductive crane moves required to access blocked containers. Conventional mathematical formu

Nigar Hashimzade, Haoran Sun

Industrial policy has returned to the centre of economic governance, particularly in the high-tech sectors where positive network externalities in demand make market dominance self-reinforcing. This paper studies the welfare effects of an industrial policy targeting a sector with network externalities in a two-country model with strategic trade and R&D inves

Tobias Bystrich, Lukas Hamm, Maria Hassan, Lea Fischbach

Due to the scarcity of labeled dialectal speech, audio dialect classification is a challenging task for most languages, including Swiss German. In this work, we explore the ability of large language models (LLMs) as agents in understanding the dialects and whether they can show comparable performance to models such as HuBERT in dialect classification. In add

Eunku Park

Computing the Voronoi diagram of mixed geometric objects in $R^3$ is challenging due to the high cost of exact geometric predicates via Cylindrical Algebraic Decomposition (CAD). We propose an efficient exact verification framework that characterizes the parameter space connectivity by computing certified topological transition sets. We analyze the fundament

Jingbin You, Zehao Li, Hao Jiang, Xinzhu Ma

3D Gaussian Splatting (3DGS) has emerged as a real-time, differentiable representation for neural scene understanding. However, existing 3DGS-based methods struggle to represent hierarchical 3D semantic structures and capture whole-part relationships in complex scenes. Moreover, dense pairwise comparisons and inconsistent hierarchical labels from 2D priors h

Siranush Karapetyan, Achim Zeileis, Moritz Flick, Bernd Saugel

Background: In clinical research, the Bland-Altman analysis is commonly used to assess agreement of metric measurements made by two or more techniques, devices or methods. The approach can also deal with repeated measurements per subject or observational unit. However, a strong and implicit assumption is that agreement of methods is homogeneous across subjec

Wilfried Buchmuller, Alexander Westphal

Jackiw-Teitelboim (JT) gravity in two-dimensional de Sitter space is an intriguing toy model for a quantum mechanical description of an inflationary phase of the universe, including initial conditions. Starting from exact solutions of the Wheeler-DeWitt equation, we study a conditional density matrix of the system. We find that the ground state is a mixed st

Xiao Liu, Xiaowei Fu, Fuxiang Huang, Lei Zhang

Network traffic classification using self-supervised pre-training models based on Masked Autoencoders (MAE) has demonstrated a huge potential. However, existing methods are confined to isolated byte-level reconstruction of individual flows, lacking adequate perception of the multi-granularity contextual relationship in traffic. To address this limitation, we

Folkert de Ronde, Stephan Wong, Sebastian Feld

Quantum computing is expected to become a foundational technology for solving problems that exceed the capabilities of classical systems. As quantum algorithms and hardware technologies continue to advance, the need for scalable architectures becomes increasingly clear. Distributed quantum computing offers a promising path forward by interconnecting multiple

Sowmya Vajrala, Aakash Parmar, Prasanna R, Sravanth Kodavanti

Generative Artificial Intelligence (GenAI) features such as image editing, object removal, and prompt-guided image transformation are increasingly integrated into mobile applications. However, deploying Large Vision Models (LVMs) for such tasks on resource-constrained devices remains challenging due to their high memory and compute requirements. While Low-Ra

Robert J. S Airey, Paul Chote, Klaas Wiersema, Ioannis Apergis

We present a coordinated multi-instrument photometric and polarimetric study of the defunct geosynchronous satellite, Gorizont-6. This observation campaign combined wide-field multi-colour observations with simultaneous multi-site photometry and linear polarimetry. Our results demonstrate that the combined simultaneous colour and polarimetric measurements ai

Ancheng Hou, Ruijia Liu, Xiang Yin

This paper studies offline, zero-shot planning under Signal Temporal Logic (STL) specifications. We assume access only to an offline dataset of state-action-state transitions collected by a task-agnostic behavior policy, with no analytical dynamics model, no further environment interaction, and no task-specific retraining. The objective is to synthesize a co

E. Javier Olucha, Valentin Preda, Amritam Das, Roland Tóth

The Linear Parameter-Varying (LPV) framework enables the construction of surrogate models of complex nonlinear and high-dimensional systems, facilitating efficient stability and performance analysis together with controller design. Despite significant advances in data-driven LPV modelling, existing approaches do not quantify the uncertainty of the obtained L

Jinyu Zhao, Guangdi Zhou, Shu Cai, Shuaihang Sun

The discovery of superconductivity with a transition temperature (Tc) exceeding 40 K in La3Ni2O7 and (La,Pr)3Ni2O7 thin films at ambient pressure provides a viable platform for the experiments that can only be conducted under ambient-pressure conditions, and for the theoretical investigations aimed at understanding the commonalities and peculiarities of the

Michel Denuit, José Miguel Flores-Contró, Christian Y. Robert

This paper studies proportional risk sharing at claim occurrence time in community-based insurance. Each participant is modeled by an individual Cram\'er-Lundberg surplus process, and, whenever a claim is reported within the pool, its cost is redistributed according to a fixed allocation matrix. We compare the infinite-time ruin probability of each participa

L. Ghiringhelli, A. Zambon, G. Tiana

We investigate the parameter space of transformer models trained on protein sequence data using a statistical mechanics framework, sampling the loss landscape at varying temperatures by Langevin dynamics to characterize the low-loss manifold and understand the mechanisms underlying the superior performance of transformers in protein structure prediction. We

Franziska Jahnke, Jonas van der Schaaf

We study the model theory of deeply ramified fields of positive characteristic. Generalizing the perfect case treated in work by Jahnke and Kartas on the model theory of perfectoid fields, we obtain Ax-Kochen/Ershov principles for certain deeply ramified fields of positive characteristic and fixed degree of imperfection. Our results apply in particular to al

Muhammad Akashi, Noam Soker

We conduct three-dimensional hydrodynamical simulations of core-collapse supernova (CCSN) explosion driven by jets in the framework of the jittering jets explosion mechanism (JJEM), and obtain a pair of opposite circum-jet rings similar to those observed in some CCSN remnants (CCSNRs). We launch two pairs of jets along the same axis, the first of two opposit

Liang Xu, Tao Liu, Zhiyun Lin

This paper investigates the robust output regulation problem for an uncertain linear minimum-phase plant with cooperative parallel operation of multiple actuators. Building on the internal model approach, we first propose a dynamic output feedback control law to solve the robust output regulation problem with a single actuator. Then, we construct a distribut

Yunqi Wang, Xinghuo Yu, Mahdi Jalili

Distribution-level studies increasingly require feeder models that are both electrically usable and structurally representative of practical service areas. However, detailed utility feeder data are rarely accessible, while benchmark systems often fail to capture the geographic organization of real urban and suburban networks. This paper presents GeoDistNet,

Steven Y. Feng, Alvin W. M. Tan, Michael C. Frank

Modern language models (LMs) must be trained on many orders of magnitude more words of training data than human children receive before they begin to produce useful behavior. Assessing the nature and origins of this "data gap" requires benchmarking LMs on human-scale datasets to understand how linguistic knowledge emerges from children's natural training dat

Peter Holy, Emma Palmer, Jonathan Schilhan

We develop a general theory for class-sized symmetric systems as a natural extension of symmetric systems with respect to class forcing. In particular, adapting the usual notions of pretameness and tameness for class forcing, we present sufficient conditions for the preservation of the axioms of G\"odel-Bernays set theory (without the axiom of choice), and f

Qing He, Xiaowei Fu, Lei Zhang

Encrypted traffic classification is a critical task for network security. While deep learning has advanced this field, the occlusion of payload semantics by encryption severely challenges standard modeling approaches. Most existing frameworks rely on static and homogeneous pipelines that apply uniform parameter sharing and static fusion strategies across all

Archish S, Mihir Agarwal, Ankit Garg, Neeraj Kayal

Recent work (Weller et al., 2025) introduced a naturalistic dataset called LIMIT and showed empirically that a wide range of popular single-vector embedding models suffer substantial drops in retrieval quality, raising concerns about the reliability of single-vector embeddings for retrieval. Although (Weller et al., 2025) proposed limited dimensionality as t

Alain Vázquez, Maria Inés Torres

Conversational systems should generate diverse language forms to interact fluently and accurately with users. In this context, Natural Language Generation (NLG) engines convert Meaning Representations (MRs) into sentences, directly influencing user perception. These MRs usually encode the communicative function (e.g., inform, request, confirm) via DAs and en

Hailay Kidu Teklehaymanot, Gebrearegawi Gebremariam, Wolfgang Nejdl

Despite rapid advances in large language models (LLMs), their linguistic abilities in low-resource and morphologically rich languages are still not well understood due to limited annotated resources and the absence of standardized evaluation frameworks. This paper presents LLM Probe, a lexicon-based assessment framework designed to systematically evaluate th

Yi-Huang Shen, Guangjun Zhu

Let $G$ be a finite connected simple graph, and let $\mathcal{J}_{K_m,G}$ denote its generalized binomial edge ideal. By investigating the colon ideals of $\mathcal{J}_{K_m,G}$, we derive a formula for the local $\mathrm{v}$-number of $\mathcal{J}_{K_m,G}$ with respect to the empty cut set. Furthermore, we classify graphs for which this generalized binomial

Akira Dohi, Asuka Ito, Shota Kisaka

The pulsar magnetosphere is a potential source of continuous gravitational waves due to the rapid charge-discharge process in short timescale, varying the electric-field energy density. We estimate the strain of the continuous gravitational waves, considering relativistic effects and different gap regions. We find that the strain from the polar cap is too sm

Chenxu Wang, Ryota Usui, Hiroaki Nakamura, Hideki Kawaguchi

Electron Bernstein Waves (EBWs) are electrostatic waves than can propagate in overdense plasmas without density cutoff, making them suitable for high density plasma heating. Since EBWs cannot be directly launched from vacuum, mode conversion processes such as O-X-B conversion are required. In this study, the O-X mode conversion process is investigated using

Shuyue Li, Miguel López-Benítez, Eng Gee Lim, Fei Ma

The reliable operation of Unmanned Underwater Vehicle (UUV) clusters is highly dependent on continuous acoustic communication. However, this communication method is highly susceptible to intermittent interruptions. When communication outages occur, standard state estimators such as the Unscented Kalman Filter (UKF) will be forced to make open-loop prediction

Dongzhe Fan, Chuanhao Ji, Zimu Wang, Tong Chen

Graph-based retrieval-augmented generation (GraphRAG) has recently emerged as a powerful paradigm for knowledge-intensive question answering, especially for tasks that require structured evidence organization and multi-hop reasoning. However, existing GraphRAG systems are typically built in a one-size-fits-all manner, relying on a fixed retrieval framework a

M. V. Pruzhinskaya, M. V. Kornilov, A. V. Dodin, A. Baluta

Modern wide-field time-domain surveys produce alert streams whose scientific potential is often concentrated in rare and unusual events. Efficient discovery therefore requires automated pipelines to be combined with rapid expert validation and follow-up. We present the first-year performance of the anomaly-detection (AD) pipeline operating within the Fink br

Gernot Akemann, Georg Angermann, Mario Kieburg, Adrian Padellaro

Explicit expressions are proven for derivatives of the ratio of a determinant or Pfaffian determinant and a Vandermonde determinant. Such ratios appear for example in general group integrals of Harish-Chandra--Itzykson--Zuber type and in expectation values of products of characteristic polynomials in random matrix theory. In the latter case we start from kno

Huaijin Ma, Te Liu, Jiachen Sheng, Xiaochang Pan

Existing acoustic topological textures are predominantly constructed within velocity fields, where the corresponding physical observables typically exhibit harmonic temporal oscillations. In contrast, stationary topological acoustic textures are highly desirable for characterizing topological phenomena and advancing potential applications of topological quas

Jasmine Moreira

Adoption of AI-assisted development in 2025 exposed a tool-agnostic failure pattern: experienced developers using frontier models were measurably slower while believing they were faster, and 10.3% of applications in one production showcase leaked data through misconfigured access. These failures share a structural cause, the verification gap: absent external

Francesco Moretti, Giulia Bianchi, Andrea Gallo

Nighttime images captured under hazy conditions suffer from severe quality degradation, including low visibility, color distortion, and reduced contrast, caused by the combined effects of atmospheric scattering, absorption by suspended particles, and non-uniform illumination from artificial light sources. While existing nighttime dehazing methods have achiev

Kangkang Sun, Jianhua Li, Xiuzhen Chen, Jianyong Zheng

Sustaining high inter-satellite link (ISL) throughput under intermittent solar harvesting is a fundamental challenge for LEO mega-constellations. Existing works impose static power ceilings that ignore real-time battery state and comprehensive onboard power budgets, causing eclipse-period energy crises. Learning-based approaches capture battery dynamics but

Eugene, Neige

How do galaxies form and evolve? This is one of the most puzzling questions in astronomy. Galaxy assembly takes place throughout the entire history of the Universe, but our understanding of it is hampered by the unfortunate fact that we can only observe galaxies at a single moment in time. Here, we use archival data of decades-long monitoring of the Milky Wa

Eugene Vasiliev, Anja Feldmeier-Krause, Mattia C. Sormani

We present a method for constructing dynamical models of stellar systems described by distribution functions and constrained by discrete-kinematic data. We implement various improvements compared to earlier applications of this approach, demonstrating with several examples that it can deliver meaningful constraints on the mass distribution even in situations

Julia Wagner, Tim Schlippe

In recent years, AI systems in the medical domain have advanced significantly. However, despite outperforming humans, they are rarely used in practice since it is often not clear how they make their decisions. Optimal explanation and visualization of the decision process are often lacking. Therefore, we conducted a comparative user-centric analysis of the la

Luoxin Chen, Yichi Zhou, Huishuai Zhang

Large language models (LLMs) have recently demonstrated impressive performance on complex, multi-step reasoning tasks, especially when post-trained with outcome-rewarded reinforcement learning Guo et al. 2025. However, it has been observed that outcome rewards often overlook flawed intermediate steps, leading to unreliable reasoning steps even when final ans

Zhenhai Sun, David Feldstein-Bofill, Ksenia Shagalov, Amalie T. J. Paulsen

Superconducting transmon qubits based on hybrid superconductor-semiconductor Josephson junctions (gatemons) offer gate tunability, but their relaxation times remain well below those of state-of-the-art transmons, and the origin of this discrepancy is not fully understood. Here, we co-fabricate gatemons and SIS-junction transmons with nominally identical circ

Gabriel Loiseau, Damien Sileo, Damien Riquet, Maxime Meyer

Accurate privacy evaluation of textual data remains a critical challenge in privacy-preserving natural language processing. Recent work has shown that large language models (LLMs) can serve as reliable privacy evaluators, achieving strong agreement with human judgments; however, their computational cost and impracticality for processing sensitive data at sca

Dan Oprisa, Peter Toth

We present Metriplector, a neural architecture primitive in which the input configures an abstract physical system -- fields, sources, and operators -- and the dynamics of that system is the computation. Multiple fields evolve via coupled metriplectic dynamics, and the stress-energy tensor T^{\mu\nu}, derived from Noether's theorem, provides the readout. The