April 2026 arXiv papers — page 13

A. V. Eskin, A. P. Martynenko, F. A. Martynenko, D. K. Pometko

The variational method is used to study the energy levels of muonic helium $(\mu^{-} \, e^{-} \, He)$ with an electron in the ground state and a muon in an excited state with principal and orbital quantum numbers $n \sim l+1 \sim 14$. The variational wave functions are chosen in the Gaussian form. The matrix elements of the Hamiltonian in the nonrelativistic

Joel A. Tropp

Random matrices now play a role in many parts of computational mathematics. To advance these applications, it is desirable to have tools that are flexible, easy to use, and powerful. Over the last 25 years, researchers have developed a remarkable family of results, called matrix concentration inequalities, that meet the criteria. This paper offers an invitat

Yassine Ibork, Nhat Ha Nguyen, Myounggyu Won, Lokesh Das

We present a priority-aware intelligent lane change advisory system based on multi-agent federated reinforcement learning, namely PALCAS, for autonomous vehicles (AVs). While existing lane-change approaches typically focus on single-agent systems or centralized multi-agent systems, we introduce a federated reinforcement learning-based multi-agent lane change

Eduardo Ferreira da Silva, Mayki dos Santos Oliveira, Joel Machado Pires, Denis Dantas Boaventura

Recommender systems increasingly incorporate textual reviews to enrich user and item representations. However, most review-aware models remain optimized for rating prediction rather than ranking quality. This misalignment limits their effectiveness in top-N recommendation scenarios, where discriminative ranking is essential. To address this gap, we propose a

Simon Daley-Yates, Moira M. Jardine, Luke Bouma

We investigate the survival of dust trapped in magnetically confined cool gas clouds (or {\it prominences}) around rapidly rotating M-dwarfs exhibiting the ``scallop-shell'' light-curve morphology. Using a two-dimensional magnetohydrodynamic simulation, we extend previous coronal prominence models to include a passive tracer field to allow for a single injec

M. K. Khalidi Siam, Md. Tausif-Ul-Islam, Md. Reshad Romim Khan, Mohammed Ali Hossain

Neuron pruning is widely used to reduce the computational cost and parameter footprint of large language models, yet it remains unclear whether neurons in task-specific models contribute uniformly to task performance. In this work, we provide empirical evidence for the existence and importance of task-specific neurons through a systematic pruning study on la

Aqsa Fayyaz, Ulises Martin Diaz, Jianyu Dai, Homero Castaneda

Developing scalable and environmentally benign anticorrosion coatings is critical for protecting steel infrastructure in chloride-rich environments. Here, a nacre-inspired multilayer epoxy coating reinforced with four MXene systems is investigated. This architecture forms a dense lamellar network that increases diffusion tortuosity and introduces electroacti

Elizabeth Dinella

This paper explores the effectiveness of modular randomized testing for object oriented programs in Java. Modular testing involves testing individual components of a program in isolation. Often times, for effective test generation, a series of non-target setup calls must be included to obtain high coverage of the target component. In this work, we evaluate a

Georgia Harbor-Collins

Let $K$ be a finite $p$-adic field with uniformiser $\pi$. In this paper we study the image of the logarithm attached to a Lubin-Tate series $[\pi](X)$ on the maximal ideal of so-called $\pi$-regular extensions of $K$; for such an extension $L|K$ we compute a basis for the additive group $\log_{[\pi]}(\mathcal{F}(\mathfrak{m}_L))$ as an $O_K$-module, where $

Nischal Binod Gautam, Enrique P. Blair

Project-based learning is recognized as an effective approach for improving engagement and applied understanding in STEM education. In quantum engineering courses, however, the question is no longer only whether students benefit from projects but how those projects should culminate if the goal is authentic disciplinary preparation. This paper examines the ed

T. J. Davidge

The evolutionary state of the 198 day eclipsing binary BM Cas is examined using spectra that cover five orbital cycles. Radial velocities measured from SiII 6347 and SiII 6371 track the motion of the primary, and a mass function is found that is similar to that obtained by Popper(1977) from MgII 4481. Absorption from a circumsystem shell complicates efforts

David Békollè, Solange B. Difo, Hugues O. Défo, Edgar L. Tchoundja

We study properties of the following four classes of operators on the Fock space in $\mathbb C^n:$ 1) weakly localized operators; 2) sufficiently localized operators in the sense of Xia and Zheng; 3) sufficiently localized operators; 4) strongly localized operators. In this respect, we examine composition operators, Toeplitz operators with a measure symbol w

Igor Pak, Zachary Slonim

For a permutation $u\in S_n$, let $N\ast u\in S_{Nn}$ be the permutation with scaled Lehmer code. For given $u,v,w\in S_n$ and integer $N$, the stretched Schubert coefficients are defined as $f_{u,v,w}(N):=c_{N*u,N*v}^{N*w}$. Our main result is that the function $f_{u,v,w}(N)$ is eventually quasi-polynomial. This proves Kirillov's conjecture (2004), that the

Andrii Zadaianchuk, Leonardo Barcellona, Lennard Schuenemann, Christian Gumbsch

Accurately reconstructing complex full multi-object scenes from sparse observations remains a core challenge in computer vision and a key step toward scalable and reliable simulation for robotics. In this work, we introduce RecGen, a generative framework for probabilistic joint estimation of object and part shapes, as well as their pose under occlusion and p

Jakub Kosmydel, Paweł Gajewski, Arkadiusz Białek

Analyzing mutual gaze (MG) and joint attention (JA) is critical in developmental psychology but traditionally relies on labor-intensive manual coding. Automating this process in multi-camera laboratory settings is computationally challenging due to complex cross-camera relational dynamics. In this paper, we propose a highly efficient dual-stream Transformer

Melih Şahin, Ozgur B. Akan

Molecular communication suffers from severe inter-symbol interference, which makes constrained coding essential for reliable transmission. Run-length-limited ISI-mitigation codes are attractive because they select low-weight constrained codebooks, reducing ISI while allowing more molecules to be assigned to each transmitted 1-symbol under the usual molecular

Isaac Tettey Adjokatse, Samuel Senyo Koranteng, George Yamoah Afrifa, Theophilus Ansah-Narh

Soil contamination by heavy metals poses a persistent environmental and public health concern in rapidly urbanising regions of Ghana, particularly at unregulated waste disposal sites. This study applies an unsupervised machine learning framework to detect and characterise anomalous heavy metal contamination patterns in soils from twelve waste sites and resid

Bipasha Kundu, Cristian Linte

Accurate segmentation and localization of left atrial (LA) ablation scars from Late gadolinium enhancement (LGE)-MRI is essential for assessing the lesion completeness and guiding ablation therapy. Incomplete or discontinuous lesions can increase the recurrence rate of the therapy and inaccurate localization can misguide treatment planning. However, reliable

Tenley Hutchinson-Smith, Brooke D. Simmons, Karen L. Masters, Alison Coil

Understanding the role of galactic scale bars in disk galaxy evolution requires detailed measurements of bar properties across galaxies hosting bars at many redshifts. We present measurements of bar lengths and widths in a sample of 8230 disk galaxies from Hubble Space Telescope (HST) Legacy surveys. The highest-redshift barred galaxies in the sample have $z

Hooman Davoudiasl, Rachel Houtz, Seyda Ipek

Mesogenesis provides a path for generating the baryon asymmetry of the Universe, using only the CP violation furnished by the Standard Model in the decay of $B$ mesons. While this is an intriguing possibility, it is largely constrained by the data on $B$ meson branching fractions into baryons and missing energy carried into the dark sector. We point out that

Jean Liendo

The generalized composition graph is used by Cardoso and some researchers for factorization of the adjacency spectrum and Laplacian of a simple graph. Because the generalized composition graph is an example of a set-theoretic linear operad, this operation can be iterated at more than one level, where the complex language of partition refinement in the iterat

Adela Bara, Gabriela Dobrita, Simona-Vasilica Oprea

The purpose of our paper is to develop a unified multi-agent architecture that automates end-to-end machine learning (ML) pipeline generation from datasets and natural-language (NL) goals, improving efficiency, robustness and explainability. A five-agent system is proposed to handle profiling, intent parsing, microservice recommendation, Directed Acyclic Gra

Joshua Flight, Clément Gosselin

This paper discusses null-space wrench components in parallel manipulators. We examine the adaptation of the two most common characterizations of these components in grasp-like systems, namely, interaction forces and internal loads, to parallel manipulators with actuation redundancy. We identify critical oversights in the existing literature on the subject,

Gonzalo Marcelo Ramírez-Ávila, S. Leo Kingston, Marek Balcerzak, Jérôme Daquin

We discovered generalized structures, named astrocytes due to their shape, that constitute a defined region characterizing regular behavior within the parameter plane (PP) of dynamical systems (DSs). Morphologically, they are characterized by a branch and a soma with several vertices (arms) and sometimes with multiple periodicities. A bunch of infinite astro

Sergio Gaudio

The quantum noise fraction $β$ -- the share of a detector's noise power that is quantum in origin -- bounds the sensitivity gain from any quantum technique at $\mathcal{E}_{\max}=1/\sqrt{1-β}$. In the kHz--GHz band the readout is a bosonic mode and $β=1/(2\bar{n}_{\rm th}+1+ν)$, so quantum noise dominates only below a thermal frontier $k_BT\ln 3=\hbarω$

Mingqian Zheng, Malia Morgan, Liwei Jiang, Carolyn Rose

Current LLM safety alignment techniques improve model robustness against adversarial attacks, but overlook whether and how LLMs can recover helpfulness when benign users clarify their intent. We introduce CarryOnBench, the first interactive benchmark that measures whether LLMs can revise their interpretation of user intent and recover utility, while remainin

Shuxing Yang, Fujia Chen, Rui Zhao, Junyao Wu

Scientific research has long been human-led, driving new knowledge and transformative technologies through the continual revision of questions, methods and claims as evidence accumulates. Although large language model (LLM)-based agents are beginning to move beyond assisting predefined research workflows, none has yet demonstrated end-to-end autonomous disco

CMS Collaboration

The inclusive cross section for top quark pair ($\mathrm{t\bar{t}}$) production in lead-lead (PbPb) collisions is reported for the first time at a center-of-mass energy per nucleon pair of 5.36 TeV. The analysis uses data corresponding to an integrated luminosity of 1.58 nb$^{-1}$ collected by the CMS experiment at the CERN LHC in 2023. The $\mathrm{t\bar{t}

R. A. García, S. Mathur, G. T. Hookway, D. Godoy-Rivera

Solar analogs, stars that closely match the fundamental properties of the Sun, provide key benchmarks for testing stellar structure and evolution across different ages and activity levels. Their detailed characterization helps place the Sun in context within the broader population of solar-like stars. This study presents the characterization of six seismic s

Ahan Gupta, Zhihao Wang, Neel Dani, Masahiro Tanaka

Large-language-models (LLMs) demonstrate enormous utility in long-context tasks which require processing prompts that consist of tens to hundreds of thousands of tokens. However, existing LLM training libraries do not provide easy to use abstractions to optimize for long-context training, instead focusing on optimizations for models with large parameter coun

Sanjaye Ramgoolam, Michał Studziński

The walled Brauer algebras $B_N(m,n)$ govern Schur--Weyl duality for unitary groups $U(N)$ acting on mixed tensor spaces $V_N^{\otimes m}\otimes \overline{V}_N^{\otimes n}$ and play an important role in applications ranging from AdS/CFT to quantum information theory. In the stable regime $N\ge m+n$ the algebra is semisimple and its representation theory is w

V. M. Kalari, W. D. Vacca

A high-resolution ($\sim$45000), high signal-to-noise ($>$100) K-band spectral atlas of massive stars is presented. It includes 81 stars consisting of known optical standards, spanning spectral and luminosity subclasses from O2 to O9, and supergiant luminosity and spectral subclasses from O2-B1. The telluric-corrected reduced spectra are publicly available,

Yibin Luo, Shiwei Gao, Huichuan Zheng, Youyou Lu

Fine-tuning Large Language Models (LLMs) on consumer-grade GPUs is highly cost-effective, yet constrained by limited GPU memory and slow PCIe interconnects. Pipeline parallelism combined with CPU offloading mitigates these hardware bottlenecks by reducing communication overhead. However, existing PP schedules suffer from an inherent limitation termed the wei

Reshma Prasad, Michele Polese, Tommaso Melodia

Radio Access Network (RAN) configuration has traditionally required significant manual effort due to indirect causal dependencies between observable Key Performance Indicators (KPIs), and context-dependent characteristics, where the optimal configurations vary with network conditions. Although recent data-driven approaches improve parameter tuning, they rema

Naibin Gu, Chenxu Yang, Qingyi Si, Chuanyu Qin

RLVR and OPD have become standard paradigms for post-training. We provide a unified analysis of these two paradigms in consolidating multiple expert capabilities into a single model, identifying capability loss in different ways: mixed RLVR suffers from inter-capability divergence cost, while the pipeline of first training experts and then performing OPD, th

Emma Casey, David Roberts, David Sim, Ian Beaver

We present a framework for migrating production Large Language Model (LLM) based systems when the underlying model reaches end-of-life or requires replacement. The key contribution is a Bayesian statistical approach that calibrates automated evaluation metrics against human judgments, enabling confident model comparison even with limited manual evaluation da

Indranil Biswas, Ambar N. Sengupta

Take a compact Sasakian threefold $M$ and consider the associated irreducible $\text{SL}(r,{\mathbb C})$-character variety ${\mathcal R} := \text{Hom}(\pi_1(M, x_0), \text{SL}(r, {\mathbb C}))^{ir}/ \text{SL}(r, {\mathbb C})$ of $M$, where $\text{Hom}(\pi_1(M, x_0), \text{SL}(r, {\mathbb C}))^{ir}$ is the space of irreducible homomorphisms. We first construc

Tarush Tiwari, Sudhir K. Sahu, Guilhem Ribeill, Michael Senatore

Entangling operations are at the heart of all approaches to quantum information processing. Parametric gates, in particular, offer a versatile solution to strongly couple off-resonant superconducting qubits with suppressed parasitic crosstalk to spectator qubits due to frequency-selective activation. In this work, we demonstrate a parametric iSWAP gate betwe

Chris T. Woodward

We develop a calculus for counting pseudoholomorphic disks with boundary in tropical Lagrangians contained in almost toric manifolds, using our previous work with Venugopalan. The results are mostly in dimension four under monotonicity assumptions although in principle the same technique works in any dimension and without monotonicity. The calculus is given

Mateo Díaz, Benjamin Grimmer, Ian McPherson

Optimization on Hadamard manifolds -- the natural Riemannian setting for globally geodesically convex problems -- relies on exponential maps to retract tangent vectors and parallel transport to connect tangent spaces across the manifold. These primitives are often computationally expensive, leading software packages to rely on approximations: first-order ret

Boris Shigida, Boris Hanin, Andrey Gromov

The Normalized Transformer, or nGPT (arXiv:2410.01131) achieves impressive training speedups and does not require weight decay or learning rate warmup. However, despite having hyperparameters that explicitly scale with model size, we observe that nGPT does not exhibit learning rate transfer across model dimension and token horizon. To rectify this, we combin

Ya. V. Turkin, N. G. Pugach, F. M. Maksimov, A. S. Pakhomov

Superconductors can transport spin without Joule dissipation, yet their coherent coupling to short-wavelength magnons in insulating magnets remains largely unexplored. Here we demonstrate experimentally and theoretically that a conventional diffusive superconductor can enable the conversion of the uniform ferromagnetic-resonance (FMR) mode into perpendicular

Han Fu, Andreas Ermedahl, Sigrid Eldh, Kristian Wiklund

Due to hardware-software co-development in embedded systems, continuous integration (CI) builds frequently fail because of complex cross-compilation, board configurations, and toolchain constraints. Although CI build logs contain valuable diagnostic information, they are short-lived and difficult to reuse due to heterogeneous runners, toolchains, and log for

Ewan McCulloch, J. Alexander Jacoby, Sarang Gopalakrishnan

We develop a framework to describe how quantum coherences between distinct charge sectors evolve under generic charge-conserving dynamics. Our framework captures the nonperturbative interactions between quantum coherences and hydrodynamic large deviations -- i.e., rare ``voids'' of low charge entropy. Conditional on surviving, the quantum coherence and its s

Yinbin Ma, Daniela Tuninetti

Coded caching is a technique that leverages locally cached contents at the end users to reduce the network's peak-time communication load. Coded caching has been shown to achieve significant performance gains with a centralized placement orchestrated by the server and is thus considered a promising technique to boost performance in future networks by effecti

Bidyut Dey, Andrea Nava, Domenico Giuliano

We study dissipative phase transitions in a system of two coupled fully-connected quantum Ising models interacting with an environment. The dynamics is governed by a Lindblad master equation combining coherent unitary evolution and incoherent dissipative processes, where the unitary part is described within a self-consistent mean-field framework effectively

Jaclyn B. Champagne, Helmut Dannerbauer, Jose Manuel Perez-Martinez, Caitlin M. Casey

Measuring the properties of cold molecular gas available for intense star formation in galaxy protoclusters at $z>2$ is a crucial step in understanding large scale structure formation. We present ALMA observations of CO(3$-$2) in five dusty star-forming galaxies within $\sim0.5-4$ cMpc of the core of the Spiderweb protocluster at $z=2.16$ to measure the mole

Huan Song, Shijun Cheng, Huanhuan Tang, Wei Ouyang

Effective suppression of surface-related multiples is essential to prevent imaging artifacts and erroneous structural interpretations. While conventional approaches rely on accurate priors or subsurface model knowledge, and supervised learning methods require labeled data that are impractical to obtain for real seismic data. To overcome these limitations, a

Ethan D'Arcy, Arnaud Delfante, Stefan Fredenhagen

We provide a holographic bulk realization of Carrollian free-field structures arising in three-dimensional asymptotically flat (higher-spin) gravity. We construct a class of boundary conditions that generalizes the diagonal gauge of Anti-de Sitter to flat spacetimes. We show that the associated asymptotic symmetries decompose into genuine physical transforma

Arash Arabi Ardehali, Daniel J. Resnick

We find the perturbative potential on the 3d $\mathcal{N}\!=\!2$ Coulomb branch arising from a chiral 4d $\mathcal{N}\!=\!1$ gauge theory on $\mathbb{R}^3 \times S^1$, zeta-regularizing the D-term couplings generated by the Kaluza-Klein modes. This fills a significant gap in the literature on circle-compactified SUSY gauge theories. Unlike earlier indirect a

John-Michael Eberhard, Amy E. Reines

We present a detailed study of radio-detected dwarf galaxies (with stellar masses less than 3 billion solar masses) to characterize extreme star formation and search for (variable) radio AGNs. Our sample comes from Reines et al. (2020) (arXiv:1909.04670), who used the Karl G. Jansky Very Large Array (VLA) with 0.25 arcsecond resolution to observe 111 dwarf g

Aditya A. Ramesh, Alex Lewandowski, Jürgen Schmidhuber

Continual learning agents with finite capacity must balance acquiring new knowledge with retaining the old. This requires controlled forgetting of knowledge that is no longer needed, freeing up capacity to learn. Weight decay, viewed as a mechanism for forgetting, can serve this role by gradually discarding information stored in the weights. However, a fixed

Abhay Jindal, Igor Klep, Scott McCullough

We prove a Positivstellensatz for operator-valued noncommutative polynomials that are positive on matrix convex sets. Specifically, let $p$ be an operator-valued polynomial in $B(H)\otimes C<x>$ of degree at most $2d+1$, where $H$ is separable and infinite-dimensional. Let $L(x)=I+\sum_{j=1}^{g} A_j x_j$ be a monic linear operator pencil, and let $D_L=\{X: L

S. Konstantinou-Rizos, T. Kouloukas

We study a generalisation of the set-theoretic Yang-Baxter equation and investigate the connection between its solutions and matrix refactorisation problems. We refer to such solutions as scalene Yang-Baxter maps. Moreover, we construct scalene Yang-Baxter maps associated with integrable equations of KdV and NLS type.

Christian Coester, Yichen Huang

Metric embeddings into structured spaces, particularly hierarchically well-separated trees (HSTs), are a fundamental tool in the design of online algorithms. In the classical online embedding setting, points arrive sequentially and must be embedded irrevocably upon arrival, resulting in strong distortion lower bounds of $\Omega(\min(n, \log n\log \Delta))$,

Arianna Dolfi, Facundo A. Gómez, Rebekka Bieri, Francesca Fragkoudi

Lopsidedness is common in disk galaxies, yet its origin and evolution remain unclear. Previous studies typically examined stellar and gas asymmetries separately, but a combined analysis offers a stronger probe of the mechanisms driving lopsidedness, recent galaxy evolution, and environment. We analyze the density and kinematics of stellar and atomic hydrogen

Noah S. J. Rogers, Evan D. Skillman, Danielle A. Berg, Karla Z. Arellano-Córdova

Large surveys of galaxies in the local and high-redshift Universe have, traditionally, relied on the intensity of rest-optical emission lines from metal ions in the Interstellar Medium (ISM) to indirectly estimate the O/H abundance in the gas. However, these optical strong line diagnostics are also sensitive to the electron gas temperature ($T_e$), resulting

Mario Collura, Benjamin Béri, Emanuele Tirrito

Nonlocal magic quantifies the irreducible nonstabilizerness of a bipartite quantum state after optimizing over local basis changes. We study nonlocal magic for pure fermionic Gaussian states, and derive a simple closed-form entanglement spectrum bound in terms of the singular values of the subsystem-restricted covariance matrix. We benchmark our result again

Violet Concepcion, Kyle Ritchie

Recent proposals suggest that a notion of generalized complexity, analogous to generalized entropy, may be necessary for understanding the dynamics of holographic complexity in settings where quantum effects are non-negligible, such as evaporating black holes. Beginning with a notion of generalized complexity, we introduce a complexity-based quantum expansio

Peilun Han, Zijian Liang, Yifei Wang, Bowen Yang

Topological entanglement entropy (TEE) is a key diagnostic of long-range entanglement in two-dimensional gapped phases of matter, but it can suffer from spurious contributions that overestimate the total quantum dimension of the underlying topological order. In this work, we identify the microscopic origin of spurious TEE and introduce a concave partition fo

Akshunna S. Dogra

Nonlinear models and optimization methods have successfully tackled a rapidly growing set of problems in recent years. Indeed, a relatively small toolbox of such models and methods can provide sufficient performance across a large landscape of tasks: deep learning alone has made significant recent contributions in scientific modelling, natural language proce

Zhengkang Zhang

Neural network field theory (NNFT) represents fields as neural networks and samples field configurations by drawing network parameters from a probability distribution. We identify a previously unexplored architectural freedom in NNFT, parameterized by $\alpha$, that leaves the infinite-width theory invariant but dramatically affects finite-width errors in th

Tom Oeffner, Ludwig Bordfeldt, Andreas Feuerpfeil, Lukas Elter

We investigate the resilience of spinon quasiparticles in the $J_1$-$J_2$ zig-zag spin chain ($J_2>0$) from the viewpoint of momentum-space entanglement. For small $J_2$, we show that deconfined spinons survive well past the liquid-dimer transition before eventually collapsing towards the Majumdar-Ghosh point. In the highly frustrated zig-zag regime ($J_2 \g

Itamar Cohen, Dina Meylakh, Michael Smolkin, Israel Warszawiak

In this work, we revisit black hole Love numbers from two complementary perspectives. First, we develop a manifestly gauge-invariant framework that directly integrates out the short-distance degrees of freedom of a static black hole in arbitrary spacetime dimensions. This approach yields the effective point-particle action and its associated Love numbers wit

P. S. Koliogiannis, N. Paar

The equation of state of the outer crust of neutron stars is sensitive to nuclear mass predictions and provides a direct connection to the properties of nuclei throughout the nuclide map, including those beyond experimental reach. We quantify the impact of contemporary nuclear mass models on the composition and thermodynamic properties of the outer crust and

Samuel L Pugh, Eric Yang, Alexander Muir Sutherland, Alessandra Breschi

As Large Language Model (LLM) agents transition from single-session tools to persistent systems managing longitudinal healthcare journeys, their memory architectures face a critical challenge: reconciling two imperfect sources of truth. The patient's evolving self-report is current but prone to recall bias, while the Electronic Health Record (EHR) is medical

Gongbo Zhang, Wen Wang, Ye Tian, Li Yuan

Diffusion large language models (dLLMs) offer parallel decoding and bidirectional context, but state-of-the-art dLLMs require billions of parameters for competitive performance. While existing distillation methods for dLLMs reduce inference steps within a single architecture, none address cross-architecture knowledge transfer, in which the teacher and studen

Ashis Tamang, Nishal Rai, Karl Landsteiner, Eugenio Megias

We consider a holographic Einstein-Maxwell model in five dimensions with pure gauge and mixed gauge-gravitational Chern-Simons terms to study anomaly-induced transport in the presence of explicit symmetry breaking. We include the full backreaction of the scalar field and gauge fields on the metric and compute the anomalous transport coefficients using Kubo f

Jean Tapie, Philipp del Hougne

Sensing the direction of arrival and polarization of impinging signals is a key prerequisite for beamforming and interference mitigation in modern wireless communication systems. Dynamic metasurface antennas (DMAs) can multiplex direction- and polarization-dependent field information onto a single detector by sequentially switching between programmable confi

Jared Benson, C. E. Sturner, A. R. Huffman, Sanghyeok Park

Orbital energy splittings are important quantum dot parameters for the operation of hole spin qubits. They are known to depend on the lateral confinement of the quantum dots. However, when changing top, plunger gate voltages, which are the typical control parameter for qubit applications, such energy splitting changes are typically negligible, both as measur

Arkopal Dutt, Anirban Chowdhury, Kristan Temme, Hari Krovi

We introduce a quantum algorithm for simulating the dynamics of electrical circuits consisting of resistors, inductors and capacitors (aka RLC circuits) along with power sources. Given oracle access to the connectivity of the circuit and values of the electrical elements, our algorithm prepares a quantum state that encodes voltages and current values either

Alexandre Benoit, Nicolas Brisebarre, Bruno Salvy

We consider series expansions in bases of classical orthogonal polynomials. When such a series solves a linear differential equation with polynomial coefficients, its coefficients satisfy a linear recurrence equation. We interpret this equation as the numerator of a fraction of linear recurrence operators. This interpretation lets us give a simple and unifie

CMS Collaboration

Measurements of Higgs boson pair (HH) production in the four bottom quark (4b) final state are presented using proton-proton (pp) collision data at $\sqrt{s}$ = 13.6 TeV collected by the CMS experiment at the CERN LHC, corresponding to an integrated luminosity of 62 fb$^{-1}$. Events in which the Higgs boson decays, H$\mathrm{t\bar{t}}$, are separately recon

Alexander Raistrick, Karhan Kayan, Jack Nugent, David Yan

We introduce ProcFunc, a library for Blender-based procedural 3D generation in Python. ProcFunc provides a library of easy-to-use Python functions, which streamline creating, combining, analyzing, and executing procedural generation code. ProcFunc makes it easy to create large-scale diverse training data, by combinatorial compositions of semantic components.

Shayan Hundrieser, Insung Kong, Johannes Schmidt-Hieber

We introduce Hyper Input Convex Neural Networks (HyCNNs), a novel neural network architecture designed for learning convex functions. HyCNNs combine the principles of Maxout networks with input convex neural networks (ICNNs) to create a neural network that is always convex in the input, theoretically capable of leveraging depth, and performs reliable when tr

Zylan Benjert, Júlia Komjáthy, Johannes Lengler, John Lapinskas

It is a fundamental question in epidemiology to estimate, model and predict the growth rate of a pandemic. Analogously, analysing the diffusion of innovation, (fake) news, memes, and rumours is of key importance in the social sciences. The resulting epidemic growth curves can be classified according to their growth rates. These have been found to range from

Zefeng Wang, Shijun Cheng, Weijian Mao, Wei Ouyang

Implicit full waveform inversion (IFWI) introduces implicit neural representations to parameterize the subsurface velocity model as a continuous function of spatial coordinates, which alleviates the dependence on the initial model and improves inversion flexibility. However, IFWI still requires a large number of iterative updates for each new exploration are

Xuebin Zhao, Andrew Curtis, Klaus Mosegaard

Geoscientists often solve inverse problems to estimate values of parameters of interest given relevant data sets. Bayesian inference solves these problems by combining probability distributions that describe uncertainties in both observations and unknown parameters, and we require that the solution provides unbiased uncertainty estimates in order to inform r

Ming Li, Xingzhong Liu

We establish that $C^\infty$ three-dimensional flows with positive topological entropy admit only finitely many ergodic measures of maximal entropy, even when singularities (zero-velocity points) are present. Furthermore, every ergodic measure of maximal entropy is rapid mixing for such flows within a $C^\infty$ open and dense subset. To prove this, we devel

Catherine Liu, Tao Long, Asya Vaisberg, Chau Vu

Creativity support tools (CSTs) aim to elevate the quality of artists' creative processes and artifacts. Yet most current CST evaluations overlook temporal and social aspects of tool use. To address this gap, we present a longitudinal, group-based CST evaluation through a three-week deployment of ArtKrit, a computational drawing tool that supports discipline

Wanyue Zhang, Wenxiang Wu, Wang Xu, Jiaxin Luo

Vision-language models (VLMs) have shown strong performance on static visual understanding, yet they still struggle with dynamic spatial reasoning that requires imagining how scenes evolve under egocentric motion. Recent efforts address this limitation either by scaling spatial supervision with synthetic data or by coupling VLMs with world models at inferenc

Albert Alcalde, Giovanni Fantuzzi

We introduce SILAS, a data-driven framework for discovering polynomial ordinary differential equations (ODEs) with provably bounded trajectories. Boundedness is certified by compact absorbing sets defined via polynomial Lyapunov functions. We jointly identify the ODE vector field and the Lyapunov function using a well-posed nonconvex optimization problem bui

Junan Lin, Paul J. Goulart, Luca Furieri

The Alternating Direction Method of Multipliers (ADMM) is a widely used method for structured convex optimization, and its practical performance depends strongly on the choice of penalty and relaxation parameters. Motivated by settings such as Model Predictive Control (MPC), where one repeatedly solves related optimization problems with fixed structure and c

Sungjun Cho

Hybrid-capture novel view synthesis combines images at substantially different camera distances (e.g., aerial drone and ground-level views). Standard 3D Gaussian Splatting (3DGS), trained for 30K iterations with one rendered view per optimizer step, under-fits the minority regime by 1-3 dB on five hybrid-capture benchmarks. We isolate the lever that closes t

Garrett Parzych, Joshua J. Daymude

We introduce the problem of adaptive self-organization in which the nodes of an anonymous, synchronous dynamic network must distributively change the collective distribution of their responses (or &#34;colors&#34;) as a function of time-varying environmental signals, even when these signals are only perceived locally and the network topology changes adversar

Shihan Dou, Yujiong Shen, Chenhao Huang, Junjie Ye

Today's AI assistants such as OpenClaw are designed to handle context effectively, making context learning an increasingly important capability for models. As these systems move beyond professional settings into everyday life, the nature of the contexts they must handle also shifts. Real-life contexts are often messy, fragmented, and deeply tied to personal

Haocheng Zhang, Benjamin de Jonge, Manel Errando, Xiaocan Li

Identifying the physical mechanism driving blazar flares remains a central challenge in high-energy astrophysics. We show that the energy dependence of the standard deviation of the polarization angle variability ($\sigma_\text{PA}$) provides a powerful and robust discriminator of blazar flaring mechanisms. Using particle-in-cell-integrated polarized radiati

Michael T. M. Emmerich

This paper studies exact fixed-cardinality Solow--Polasky diversity subset selection on ordered finite $\ell_1$ point sets, with monotone biobjective Pareto fronts and their higher-dimensional staircase analogues as central applications. Solow--Polasky diversity was introduced in biodiversity conservation, whereas the same inverse-matrix expression appears i

Carles Falcó, Samuel W. S. Johnson, Mohit P. Dalwadi, Philip K. Maini

Cell invasion and spatial pattern formation are two distinct manifestations of cellular self-organisation in development, regeneration, and disease. Here, we develop and analyse a unified theoretical framework that links these two seemingly different behaviours within a single mechanistic model for adhesion-mediated self-organisation in growing cell populati

Francesco Orabona

In Orabona and P\'al [2016], we introduced the shifted KT potentials, to remove the $\ln \ln T$ factor in the parameter-free learning with expert bound. In this short technical note, I show that this is equivalent to changing the prior in the Krichevsky--Trofimov algorithm. Then, I show how to use the same idea to remove the $\ln \ln T$ factor in the data-in

Samantha Gilbert-Janizek, Jacob Lustig-Yaeger, Joshua Krissansen-Totton

NASA's Habitable Worlds Observatory (HWO) will search for biosignatures on Earth-like exoplanets using reflected light spectroscopy. A critical instrument design parameter is resolving power, which must balance biosignature detectability against exposure time and detector noise constraints. We assess the resolving power needed to detect and characterize key

Yeheng Chen, Chaoxiang Xie, Yuling Shi, Wenhao Zeng

LLMs have achieved strong results on both function-level code synthesis and repository-level code modification, yet a capability that falls between these two extremes -- compositional code creation, i.e., building a complete, internally structured class from a specification -- remains underserved. Current evaluations are either confined to isolated functions

Steve Hanneke, Alkis Kalavasis, Shay Moran, Grigoris Velegkas

Learning curves are a fundamental primitive in supervised learning, describing how an algorithm's performance improves with more data and providing a quantitative measure of its generalization ability. Formally, a learning curve plots the decay of an algorithm's error for a fixed underlying distribution as a function of the number of training samples. Prior

David Miloschewsky, Supartha Podder, Dorian Rudolph

We study the power of quantum witnesses under perfect completeness. We construct a classical oracle relative to which a language lies in $\mathsf{QMA}_1$ but not in $\mathsf{QCMA}$ when the $\mathsf{QCMA}$ verifier is only allowed polynomially many adaptive rounds and exponentially many parallel queries per round. Additionally, we derandomize the permutation

David Novikov, Eilon Vaknin, Narek Tumanyan, Mark Sheinin

The task of capturing and rendering 3D dynamic scenes from 2D images has become increasingly popular in recent years. However, most conventional cameras are bandwidth-limited to 30-60 FPS, restricting these methods to static or slowly evolving scenes. While overcoming bandwidth limitations is difficult for general scenes, recent years have seen a flurry of c

Evangelia Kopadi, Dimitris Kalles

Can Neural Assemblies -- groups of neurons that fire together and strengthen through co-activation -- learn the direction of causal influence between variables? While established as a computationally general substrate for classification, parsing, and planning, neural assemblies have not yet been shown to internalize causal directionality. We demonstrate that

Maribel Loaiza, Miguel Antonio Morales-Ramos, María del Rosario Ramírez-Mora, Josué Ramírez-Ortega

In the context of studying $C^*$-algebras generated by Toeplitz operators acting on the poly-Bergman space $\mathcal{A}^2_{n}(\Pi)$ of the upper half-plane $\Pi$, we introduce a system of all-but-one orthogonal projections in generic position. We show that the $C^*$-algebra generated by these orthoprojections is closely related to the $C^*$-algebra generated

Zijie Wu, Chaohui Yu, Fan Wang, Xiang Bai

Recent advances in 4D content generation have attracted increasing attention, yet creating high-quality animated 3D models remains challenging due to the complexity of modeling spatio-temporal distributions and the scarcity of 4D training data. We present AnimateAnyMesh++, a feed-forward framework for text-driven animation of arbitrary 3D meshes with substan

Marco Parentin, Bjarne Bergh, Nilanjana Datta, Mark M. Wilde

Superactivation of quantum capacity is the phenomenon whereby two quantum channels, each with zero quantum capacity, can exhibit a strictly positive capacity when used in tandem. In this work, we explore superactivation in the previously unexplored non-asymptotic regime of finitely many channel uses. We give a definition of finite-blocklength superactivation

D. Gonzalez, G. Niz, A. Aviles, C. Garcia-Quintero

The Dark Energy Spectroscopic Instrument (DESI) provides an unprecedented opportunity to test deviations from general relativity (GR) that introduce a new physical scale within its redshift range. Using the connection between a Yukawa-like potential and the Hu-Sawicki $f(R)$ model, we place strong constraints on the range of a hypothetical fifth force mediat