April 2026 arXiv papers — page 139

Terra Blevins, Stephen Mayhew, Marek Šuppa, Hila Gonen

While multilingual language models promise to bring the benefits of LLMs to speakers of many languages, gold-standard evaluation benchmarks in most languages to interrogate these assumptions remain scarce. The Universal NER project, now entering its fourth year, is dedicated to building gold-standard multilingual Named Entity Recognition (NER) benchmark data

Danielle S. Fox, Brenda L. Robles, Elizabeth DiPietro Brovey, Christian D. Schunn

While recent research has explored AI tools' ability to classify the quality of mathematical tasks (arXiv:2603.03512), little is known about their capacity to increase the quality of existing tasks. This study investigated whether AI tools could successfully upgrade low-cognitive-demand mathematics tasks. Eleven tools were tested, including six broadly avail

A. A. Mazanik, F. S. Bergeret

We investigate the Josephson coupling between two superconducting electrodes connected by a ferromagnetic racetrack hosting a Bloch-like domain wall (DW). We show that the interplay between superconductivity and the DW leads to highly non-trivial spatial distributions of the supercurrent, including the formation of current loops and a strong sensitivity to t

Martina Hentschel

Mesoscopic devices, with system sizes in the range of several to several dozens wavelengths, represent paradigmatic model systems for the observation of quantum chaotic behaviour based on semiclassical concepts. Those electronic and photonic billiard cavities are small enough for interference effects not to be ignored. Nonetheless, the classical ray or parti

Qendresa Selimi, Christiana Charalambous, Taban Baghfalaki, John Booth

This study investigates the relationship between longitudinal serum creatinine measurements and the risk of adverse kidney outcomes in paediatric patients with auto-immune disorders at Great Ormond Street Hospital for Children NHS Foundation Trust, London. To jointly analyse repeated biomarker measurements and time-to-event outcomes, we employed a joint mode

Kunkun Wang, Lei Xiao, Stefano Longhi, Peng Xue

Decoherence and dissipation, arising from unavoidable interactions with the environment, can exert a dual influence on transport in physical systems, suppressing coherent propagation while inducing diffusion and mitigating localization in disordered systems. Non-Hermitian physics reveals a qualitatively different scenario, in which structured dissipation can

Slava Pimenov

The notion of a Frobenius manifold appears in relation to various topics in algebraic and analytic geometry, such and quantum cohomology, deformation of meromorphic connections, unfolding of singularities and others. In the local setting the structure of a Frobenius manifold admits two other equivalent descriptions, either as an algebra over a cyclic operad

Gustavo de Carvalho Bertoli

While Federated Learning (FL) mitigates direct data exposure, the resulting trained models remain susceptible to membership inference attacks (MIAs). This paper presents an empirical evaluation of Differential Privacy (DP) as a defense mechanism against MIAs in FL, leveraging the environment of the 2025 NIST Genomics Privacy-Preserving Federated Learning (PP

Xingyu Lin, Yilin Wen, Du Su, Jinchang Hou

Group Relative Policy Optimization (GRPO) has significantly advanced the reasoning ability of large language models (LLMs), particularly in their mathemat ical reasoning performance. However, GRPO and related entropy regularization methods still struggle with token-level sparse-rewards, which is an inherent chal lenge in chain-of-thought (CoT) reasoning. The

Yuan Fang, Yang Liu, Dmitry V. Averyanov, Ivan S. Sokolov

2D ferromagnets offer valuable insights into the fundamentals of magnetism and stimulate the progress of ultracompact spintronics. The demand for seamless integration of the materials with the Si technology, particularly helpful to their applications in nanoelectronics, draws attention to 2D magnetic silicides. MnSi is a prominent silicide hosting magnetic p

Kiran Voderhobli Holla

In recent years, Sound AI is being increasingly used to predict machine failures. By attaching a microphone to the machine of interest, one can get real time data on machine behavior from the field. Traditionally, Convolutional Neural Net (CNN) architectures have been used to analyze spectrogram images generated from the sounds captured and predict if the ma

W. S. Oliveira, Julián Faúndez, Welles Morgado

We investigate quantum percolation in a honeycomb lattice with site dilution and random spin-orbit coupling. Using exact diagonalization combined with finite-size scaling analysis, we study the metal-insulator transition, extracting the quantum percolation threshold $p_q$, and the correlation-length exponent, $\nu$. In the absence of spin-orbit coupling, we

Diane Mittaine, Andrea Francesco Battaglia, Laura Rodríguez-García, Nils Janitzek

Generally, large solar flares accelerate electrons to high energies more efficiently than microflares. However, some microflares, known as hard microflares (HMFs), also produce high-energy electrons, as indicated by their flat hard X-ray (HXR) spectra. These events are typically associated with footpoints located in or at the edge of sunspots. The mechanisms

Stefan Duling, Joachim Saur, Darrell Strobel, Philippa Molyneux

While photoionization rates of Ganymede's O$_2$ dominated atmosphere are well constrained, the contribution of electron-impact ionization is rather uncertain. Previous quantitative estimates have relied on assumptions about densities and energy distributions of precipitating electrons, or on rare spacecraft measurements that cannot be unambiguously mapped to

Marti Ramis, Markos Paradinas, Jose M. Caicedo, Claudio Cazorla

Single-crystalline perovskite oxide membranes provide a powerful platform to access physical properties that are inaccessible in bulk crystals and substrate-clamped thin films. Within this context, the deliberate fabrication of tailored corrugations provides a reliable mean to impose local curvature enabling deterministic modulation of functional properties.

Andrés Rubiano, Armando Reyes

We determine sufficient criteria for the differential smoothness of ambiskew polynomial rings defined and studied by D. A. Jordan in several papers \cite{FishJordan2019, Jordan1993b, Jordan2000, JordanWells2013}.

Malik Amir, Sourangshu Ghosh

Classical Fisher-information asymptotics describe the covariance of regular efficient estimators through the local quadratic approximation of the log-likelihood, and thus capture first-order geometry only. In curved models, including mixtures, curved exponential families, latent-variable models, and manifold-constrained parameter spaces, finite-sample behavi

J. M. Agüero Trejo, Cristian S. Calude, O. C. Stoica

We propose a method to test whether a photonic 3D QRNG works according to the underlying theory, thereby generating highly incomputable/unpredictable sequences of random digits. The test relies on undoing the unitary evolution realized by the 3D QRNG. The test verifies the unitarity, the magnitude of the noise, and other potential errors, such as photon loss

P. Brown, L. McFadden, D. McCormack, M. Adams

Here we present a survey aimed at detecting hydroacoustic signals from fireballs using the six hydrophone stations operated as part of the Comprehensive Test Ban Treaty Organisation (CTBTO) International Monitoring System. We identified 30 fireballs where propagation paths to stations exist. These included high energy fireballs (E $\geq$ 5 kT), those which o

E. A. Karashtin, I. Yu. Pashen'kin, A. V. Gorbatova, E. D. Lebedeva

We study THz emission from ferromagnet / nonmagnetic material (FM/NM) spintronic nanostructures in which the $Ni_xCu_{1-x}$ alloy with different $x$ is used as an FM, an NM, or both layers. The stoichiometric composition of the NiCu alloys standing at two positions (we denote it as [FM] or [PM]) is chosen so that it is ferromagnetic at room temperature in th

Shreya Gupta, Prottay Kumar Adhikary, Bhavyaa Dave, Salam Michael Singh

Clinical case formulation organizes patient symptoms and psychosocial factors into causal models, often using the 5P framework. However, constructing such graphs from therapy transcripts is time consuming and varies across clinicians. We present InsightFlow, an LLM based approach that automatically generates 5P aligned causal graphs from patient-therapist di

Mia-Katrin Kvalsund, James Stovold

Throughout the literature on Neural Cellular Automata (NCAs), it is often taken for granted that the systems learn attractors. This is shown through evolving the system for many timesteps and noting visual similarity to the goal state. There remain many questions after such an analysis. Namely, what kind of attractors do we have? Is their behavior ordered or

Adam T. Müller, Tobias Rögelein, Nicolaj C. Stache

The deployment of deep neural networks in safety-critical systems necessitates reliable and efficient uncertainty quantification (UQ). A practical and widespread strategy for UQ is repurposing stochastic regularizers as scalable approximate Bayesian inference methods, such as Monte Carlo Dropout (MCD) and MC-DropBlock (MCDB). However, this paradigm remains u

Jacopo Tosca, Cristiano Ciuti, Claudio Conti, Marcello Calvanese Strinati

Ising machines are physical platforms designed to minimize the energy of classical Ising Hamiltonians, yet accessing specific excited states remains an open challenge of both fundamental and practical relevance. In this letter we show that a network of Kerr parametric oscillators (KPOs) naturally implements an Ising selector machine. By tuning the frequency

Zhenyu Ma, Yuyang Song, Chunyi Yang, Jingyi Zhu

LLM-based autonomous agents perform well on general reasoning tasks but still struggle to reliably use task structure, key constraints, and prior experience in complex real-world settings. We propose a case-based learning framework that converts experience from past tasks into reusable knowledge assets, allowing agents to transfer prior case experience to ne

M. C. Araújo, J. G. Lima, J. Furtado, T. Mariz

In this work, we investigate the scattering of spin-$3/2$ fermionic particles mediated by a Yukawa-like coupling in the context of the massive Rarita-Schwinger model. The interaction is introduced by replacing $m \to m_{\psi} + g\phi$ in the free spin-$3/2$ Lagrangian. The analysis is performed at both zero and finite temperatures. In the latter case, therma

Filippo Mantovani, Fabio Banchelli, Pablo Vizcaino, Roger Ferrer

This paper presents EPAC, a RISC-V-based accelerator chip developed within the European Processor Initiative (EPI) as part of a multi-year, multi-partner effort to build a European HPC processor ecosystem. EPAC is implemented in GlobalFoundries 22FDX (GF22FDX) technology, covers an area of 27 sq mm with approximately 0.3 billion transistors, and integrates t

Antoine Gilles Lordet, Ariel Goobar, Jens Jasche, Stuart McAlpine

Within the volume-limited subsample at $z<0.06$ of the Zwicky Transient Facility (ZTF) DR2 sample, we confirm a statistically significant excess of Type Ia supernovae (SNe Ia) at $z \simeq 0.02$-$0.04$, previously reported but not explained by survey selection effects. Forward simulations assuming a uniform volumetric SN Ia rate and realistic ZTF detection e

Philipp G. Haselwarter, Alejandro Aguirre, Simon Oddershede Gregersen, Kwing Hei Li

Differential privacy is the standard method for privacy-preserving data analysis. The importance of having strong guarantees on the reliability of implementations of differentially private algorithms is widely recognized and has sparked fruitful research on formal methods. However, the design patterns and language features used in modern DP libraries as well

Aelson Sobral

We establish a Weiss-type almost-monotonicity formula for a broad class of variable-coefficient energy functionals, assuming only minimal regularity of the coefficients. As an application, we classify blow-up limits for the Alt--Phillips problem with variable coefficients under significantly weaker regularity hypotheses than those imposed in Ara\'ujo et al.

Mingkai Zhang, Davood Farhadi

Thin sheets can be assembled into tubular origami structures that combine deployability with pronounced anisotropic stiffness, enabling applications ranging from robotics to deployable systems. However, most existing tubular origami designs remain limited to degree-four vertex topologies and are characterized primarily in axial and radial loading modes, with

Xinyu Peng, Ziyang Zheng, Wenrui Dai, Duoduo Xue

Task-adapted compressed sensing magnetic resonance imaging (CS-MRI) is emerging to address the specific demands of downstream clinical tasks with significantly fewer k-space measurements than required by Nyquist sampling. However, existing task-adapted CS-MRI methods suffer from the uncertainty problem for medical diagnosis and cannot achieve adaptive sampli

Ignacio García-Mata, Diego A. Wisniacki

One of the fundamental manifestations of classical chaos is exponential sensitivity to initial conditions that is, two trajectories starting from nearly identical initial states diverge exponentially over time. This behavior is quantified by the Lyapunov exponents. Due to the unitary nature of quantum mechanics, such exponential divergence is elusive in quan

LHCb collaboration, R. Aaij, M. Abdelfatah, A. S. W. Abdelmotteleb

A precision measurement of the $W$-boson production cross-section is performed using the $W \to \mu\nu$ decay channel, based on a sample of proton-proton collision data collected by the LHCb experiment at $\sqrt{s}$ = 13 TeV and corresponding to an integrated luminosity of 5.1 $fb^{-1}$. The cross-section is measured for muons with transverse momentum betwee

Sedighe Sajadian, Man Ho Chan

Light curves from binary systems containing white dwarfs with neutron star or stellar-mass black hole companions (WD+NS and WD+BH) with edge-on orbital planes potentially show self-lensing/eclipsing signals. Here, we evaluate the properties and detectability of these signals in the NASA's Transiting Exoplanet Survey Satellite (TESS), and the Nancy Grace Roma

Henrique Gieg, Ramon Jaeger, Maximiliano Ujevic, Tim Dietrich

We present a set of numerical-relativity binary neutron star merger simulations incorporating muons and muonic reactions for two baseline baryonic equations-of-state. In order to investigate the possible impact of muons and muonic weak reactions, we treat neutrinos with a gray (energy-independent) truncated moments scheme and an implicit-explicit time integr

I. Mäntynen, E. Kankare, S. Mattila, A. Efstathiou

Core-collapse supernovae (CCSNe) in luminous infrared galaxies (LIRGs) can have extreme line-of-sight host galaxy dust extinctions, which leads to a large fraction of the events remaining undetected by optical and infrared surveys. This population of undetected CCSNe is important to constrain in order to determine the cosmic CCSN rates. Our aim is to confirm

Ceyhun Kirimli, Elcim Elgun, Yagmur Tugtag

Accurate inference from quartz crystal microbalance (QCM) measurements in liquids is often limited by reducing resonance behavior to two scalar endpoints (frequency and dissipation shifts, $\Delta f$ and $\Delta D$) or by relying on single-equation analytical models (Kanazawa-model). We propose an AI-ready, impedance-resolved workflow that preserves full res

Juliana G. F. Souza, Conghui Li, Cong Ling

We construct multilevel lattice codes from multiquadratic number fields for the compound block-fading wiretap channel. More precisely, we specialize Construction $\pi_A$ over the ring of integers $\mathcal{O}_K$ and exploit rational primes that split completely in $K$ to obtain a Chinese Remainder Theorem (CRT) decomposition into small residue alphabets, not

Tithiparna Das, Soumik Mukhopadhyay

Controlling magnetic phases in two-dimensional systems, where charge transport is highly sensitive to real-space spin inhomogeneities, is central to understanding emergent magnetic states in reduced dimensions. In this context, thickness-dependent magnetotransport provides access to irreversible magnetic processes that are not captured by reversible transpor

Shufang Lin, Muyang Chen, Xiabing Zhou, Rongrong Zhang

Understanding human intent in complex multi-turn interactions remains a fundamental challenge in human-computer interaction and behavioral analysis. While existing intent recognition datasets focus mainly on single utterances or simple dialogues, real-world scenarios often involve sophisticated strategic interactions where participants must maintain complex

Rhyan Barrett, Sophia Wesely, Julia Westermayr

Transferable excited-state dynamics offer a route to efficient screening of photophysical behavior across molecular systems, but conventional nonadiabatic simulations remain prohibitively expensive. Here we introduce X-MACE, a transferable machine-learning potential for excited-state dynamics that predicts multiple potential energy surfaces, forces and oscil

Hiromichi Takagi

In our paper [Tak6], we constructed eight families of quasi-smooth prime $\mathbb{Q}$-Fano threefolds, anticanonically embedded in codimension four, using weighted projectivizations of the $14$-dimensional affine variety $\Pi_{\mathbb{A}}^{14}$or its cone. Let $\widehat{f}\colon\widehat{X}\to X$ be the unique divisorial extraction at one specified singularit

Mathieu Da Silva

For $F \in \mathbb{Z}[s,t]$ a binary quadratic form which is irreducible over $\mathbb{Q}$, and $L$ an abelian number field with class number $1$, we obtain the order of magnitude for the number of values $F(s,t)$ which are a norm from $L$. Our result relies on the fundamental lemma of sieve theory and on geometry of numbers.

Daniel Albert

A longest common extension (LCE) query on a string computes the length of the longest common suffix or prefix at two given positions. A dynamic LCE algorithm maintains a data structure that allows efficient LCE queries on a string that can change via character insertions and deletions. A dynamic parallel constant-time algorithm is presented that can maintain

Yutaro Nagae, Andreas P. Schnyder, Satoshi Ikegaya

We theoretically investigate charge transport in a junction between a conventional superconductor and a V$_2$O-based altermagnet exhibiting distinctive spin-split quasi-one-dimensional Fermi surfaces. The altermagnet is described by a microscopically motivated six-orbital model that incorporates sublattice degrees of freedom associated with both V and O site

Huayan Kou, Yuwen Gu, Yi Lian, Rui Zhang

Sparse penalized quantile regression provides an effective framework for variable selection and robust estimation in high-dimensional data analysis. When ex planatory variables are organized into groups, achieving sparsity both within and between groups is essential. However, existing quantile regression methods often fail to meet this dual objective. To add

Abolfazl Mohammadi-Seif, Ricardo Baeza-Yates

Deep learning models often achieve expert-level accuracy in medical image classification but suffer from a critical flaw: semantic incoherence. These high-confidence mistakes that are semantically incoherent (e.g., classifying a malignant tumor as benign) fundamentally differ from acceptable errors which stem from visual ambiguity. Unlike safe, fine-grained

Apostolos Giannopoulos, Antonios Hmadi

We study the Banach-Mazur distance between random normed spaces generated by centrally symmetric random polytopes associated with isotropic log-concave measures in $\mathbb{R}^n$. We show that, in a wide range of parameters, if $x_1,\dots,x_m$ and $y_1,\dots,y_m$ are independent samples from an isotropic log-concave probability measure on $\mathbb{R}^n$, the

Gregory Berkolaiko, Sven Gnutzmann

Quantum graphs are a paradigmatic model for quantum chaos as well as for spectral theory. We give a concise didactical introduction to quantum graphs, or Schr\"odinger Hamiltonians on metric graphs, with a focus on results related to quantum chaos, periodic orbit theory and spectral theory. We summarise related seminal results, and give an overview over a fe

Igor Kavrakov, Yaswanth Sai Jetti, Ahmet Oguzhan Yuksel, Fehmi Cirak

We present an approach for synthesising observational data with elastodynamic finite element models by extending the statistical finite element method (statFEM) framework. The proposed formulation adopts a Bayesian filtering approach to account for uncertainties in the data, the finite element model, and the discrepancies between the model and the physical s

Jagadeesh Rachapudi, Ritali Vatsi, Praful Hambarde, Amit Shukla

Recent advances in deep learning underscore the need for systems that can not only acquire new knowledge through Continual Learning (CL) but also remove outdated, sensitive, or private information through Machine Unlearning (MU). However, while CL methods are well-developed, MU techniques remain in early stages, creating a critical gap for unified frameworks

Yangyang Luan, Muhammad Ahsan Razaq, Xiaoqun Wu, Claudio Altafini

This paper investigates DeGroot-Friedkin (DF) dynamics over signed influence networks with interdependent topics. We propose a multi-topic signed framework that combines repelling interpersonal interactions with cross-issue self-appraisal, examining how antagonism and topic interdependence shape the evolution of agent-level social power. When the logic matri

Valentine Nyirahafashimana, Sharifah Kartini Said Husain, Umair Abdul Halim, Ahmed Jellal

Orthogonal geometric constructions are the basis of many many quantum error-correcting codes (QEC), but strict orthogonality constraints limit design flexibility and resource efficiency. We introduce a quasi-orthogonal geometric framework for stabilizer codes that relaxes these constraints while preserving the symplectic commutation structure on the binary s

Riku Rantanen, Mikael Huppunen, Erkki Thuneberg, Vladimir Eltsov

Observing the structure of quantized vortices can provide evidence for the pairing nature of a superfluid or superconductor and pinpoint its order parameter. Spin-triplet superfluid $^3$He supports a variety of vortices, calculated and identified so far in bulk fluid. We show numerically that the vortex core in $^3$He is strongly altered near a surface, resu

Robert G. West, Kostas Kanellopulos, Lukas Hrachowina, Magnus Borgström

We present a quantitative framework for on-axis optical transduction of vertical InP nanowire resonators, correlating laser position to signal amplitude, calibration, and frequency stability. Photothermal resonance detuning is used to reconstruct the local beam intensity profile and to calibrate the photodetector signal using the thermomechanical noise. A no

Zhen Zhao, Ruwen Wang, Hua Zhang, Tong Liu

Chromium-based materials with complex lattice geometries provide an important platform for investigating correlated electronic and magnetic states. However, Cr-based compounds with unusual crystal geometries are still rarely reported. Here, we report a new Cr-based compound, Cs$_4$Cr$_7$Te$_{10}$, featuring interwoven Cr and Te sublattices that can be viewed

William Balderrama

We revisit the Kahn-Priddy theorem from the perspective of modern equivariant homotopy theory. This allows for a short proof that may be applied in other settings with sufficiently robust analogues of multiplicative norms and the Adams isomorphism. We illustrate this by establishing new Kahn-Priddy theorems in $L_n$ and $L_n^f$-local homotopy theory, motivic

Bijie Yang, Chengyuan Liu, Lu Tian, Yuping Qian

This work proposes a statistically enhanced framework to address the instability and limited predictive capability of conventional Galerkin-Proper Orthogonal Decomposition (Galerkin-POD) models. The method reformulates the correction of the Galerkin-projected ODE system as a statistical inverse problem, in which the coefficients are inferred through Bayesian

Yuki Arai, Kosuke Nakayama, Takemi Kato, Tomonori Nakamura

The interplay between real-space topology such as magnetic skyrmions and momentum-space topology characterized by topological surface states (TSSs) is predicted to realize novel phenomena and functionalities, yet materials hosting both topologies are scarce. Skyrmion-hosting helimagnet family EuGa$_2$Al$_2$ and EuAl$_4$ has been a prime candidate for such a

Antonio Avilés, Eugene Bilokopytov

We consider the category $\mathbf{AOVS}$ of Archimedean ordered vector spaces with linear maps which preserve all existing suprema, and its full subcategories $\mathbf{DAOVS}$, $\mathbf{DVL}$ and $\mathbf{UVL}$, consisting of directed spaces, Dedekind complete vector lattices and universally complete vector lattices, respectively. We deduce from some results

Y. Arai, K. Nakayama, A. Honma, S. Souma

The Dzyaloshinskii-Moriya (DM) interaction has been considered essential for skyrmion formation, however, the discovery of skyrmion lattices (SkLs) in nominally centrosymmetric materials where the DM interaction is forbidden, such as Eu(Ga$_{1-x}$Al$_x$)$_4$, has challenged this established view. Recent structural investigations of Eu(Ga$_{1-x}$Al$_x$)$_4$ h

Torsten Mandel, Jonathan Bader, Hanyoung Yoo, Stephan Kraft

Large enterprises often operate extensive Continuous Integration (CI) pipelines on large, heterogeneous compute clusters, where conservative, statically defined resource requirements are used to ensure build reliability. This practice leads to substantial system memory over-allocation, reduced cluster utilization, and increased operational costs. In this pap

G. Leidi, A. Holas, K. Vitovsky, F. Rizzuti

We present PHLEGETHON, a fully compressible, Eulerian magnetohydrodynamic (MHD) code designed for multidimensional simulations in stellar astrophysics. The code uses a time-explicit, second-order, finite-volume method optimized to model a wide range of dynamical processes in stars, from very low-Mach-number turbulent convection in the cores of massive stars

Ozan Kaya, Nikoletta Athanassopoulou, George G. Malliaras, Marco Vinicio Alban-Paccha

Objective: This study aimed to assess how wearable physiological signals, alone and combined with salivary cortisol, distinguish physical and psychological stress and their recovery states. Methods: Six healthy adults completed three laboratory sessions on separate days: rest, physical stress (high-intensity cycling), or psychological stress (modified Trier

Konnor Walter, Jagadeesh Sure, Adrien Couet, Emmanuelle A. Marquis

The response of Ni-Cr alloys to exposure to molten chloride and fluoride salts is typically characterized by Cr dealloying with the formation of a Cr-depleted bi-continuous porous subsurface layer. The exact mechanism behind the loss of Cr over distances unattainable by lattice diffusion alone is still debated. To address this question, two different surface

Jintao Xue, Xiao Li, Nianmin Zhang

In advanced manufacturing systems, humans and robots collaborate to conduct the production process. Effective task planning and allocation (TPA) is crucial for achieving high production efficiency, yet it remains challenging in complex and dynamic manufacturing environments. The dynamic nature of humans and robots, particularly the need to consider spatial i

Haoyang Jiang, Zekun Wang, Mingyang Yi, Xiuyu Li

The Diffusion Probabilistic Model (DPM) achieves remarkable performance in image generation, while its increasing parameter size and computational overhead hinder its deployment in practical applications. To improve this, the existing literature focuses on obtaining a smaller model with a fixed architecture through model compression. However, in practice, DP

Jintao Xue, Xiao Li, Nianmin Zhang

Human-robot collaborative manufacturing, a core aspect of Industry 5.0, emphasizes ergonomics to enhance worker well-being. This paper addresses the dynamic human-robot task planning and allocation (HRTPA) problem, which involves determining when to perform tasks and who should execute them to maximize efficiency while ensuring workers' physical fatigue rema

Chuang Peng, Wei Zhang, Renshuai Tao, Xinhao Zhang

Text-based web agents offer computational efficiency for autonomous web navigation, yet developing robust agents remains challenging due to the noisy and heterogeneous nature of real-world HTML. Standard Supervised Fine-Tuning (SFT) approaches fail in two critical dimensions: they lack discrimination capabilities to reject plausible but incorrect elements in

Zikai Song, Junqing Yu, Yi-Ping Phoebe Chen, Wei Yang

Motion reasoning serves as the cornerstone of multi-object tracking (MOT), as it enables consistent association of targets across frames. However, existing motion estimation approaches face two major limitations: (1) instability caused by noisy or probabilistic predictions, and (2) vulnerability under occlusion, where trajectories often fragment once visual

Ashmi Banerjee, Adithi Satish, Wolfgang Wörndl, Yashar Deldjoo

Traditional conversational travel recommender systems primarily optimize for user relevance and convenience, often reinforcing popular, overcrowded destinations and carbon-intensive travel choices. To address this, we present TRACE (Tourism Recommendation with Agentic Counterfactual Explanations), a multi-agent, LLM-based framework that promotes sustainable

Carlos Magno O. Pereira, Edilberto O Silva

We investigate the optical response of a conduction electron in a helically twisted mesoscopic medium containing a screw dislocation and a uniform torsional background, in the presence of an axial magnetic field and an Aharonov--Bohm flux. We show that the coupling between longitudinal motion and the geometric background produces an effective in-plane confin

Khouloud Saadi, Di Wang

Reasoning distillation aims to transfer multi-step reasoning capabilities from large language models to smaller, more efficient ones. While recent methods have shown promising gains, they typically rely on static teacher-student hierarchies and frame distillation as trajectory imitation. This is misaligned with the structure of reasoning, where intermediate

Rui Wang, Yi Zheng, Dongxin Wang, Haiping Huang

Existing topic modeling methods, from LDA to recent neural and LLM-based approaches, which focus mainly on statistical coherence, often produce redundant or off-target topics that miss the user's underlying intent. We introduce Human-centric Topic Modeling, \emph{Human-TM}), a novel task formulation that integrates a human-provided goal directly into the top

Mickaël De Backer, Johan Verbeeck, Vivian Lanius, Marc Vandemeulebroecke

There is a growing recognition of the importance to involve patients in every stage of drug development. This shift acknowledges that patients' perspectives, experiences, and preferences are essential for ensuring that treatments meet real-world needs. In this context, a new body of statistical literature has emerged, focusing not only on the simultaneous co

Ismail Nassar, Dan Cogan, Ido Schwartz

Quantum states of light are central resources for quantum communication, networking, and photonic information processing. In many quantum emitters, coherent internal dynamics arising from intrinsic or field-induced level splittings imprint a deterministic, time-dependent phase on the emitted light. When emission times are stochastic and detector timing resol

Faizuddin Ahmed, Sardor Murodov, Bekzod Rahmatov

In this work, we investigate the thermodynamics and motion of neutral test particles around a regular black hole immersed in a perfect fluid dark matter environment. We begin by examining the horizon structure and key thermodynamic properties, with particular emphasis on quantities such as the Hawking temperature and the specific heat capacity. These aspects

Lars-Phillip Spiegel, Jonas Haldimann, Jesse Heyninck, Gabriele Kern-Isberner

In nonmonotonic reasoning from conditional belief bases, an inference operator satisfying syntax splitting postulates allows for taking only the relevant parts of a belief base into account, provided that the belief base splits into subbases based on disjoint signatures. Because such disjointness is rare in practice, safe conditional syntax splitting has bee

Kaiqi Hu, Linda Xiao, Shiyue Xu, Ziyi Tang

Vision-language models(VLMs) are increasingly applied to visual stock price forecasting, yet existing benchmarks inadequately evaluate their understanding of stock price in candlestick charts. First, prior studies fail to isolate VLMs' comprehension of visual inputs genuinely improves predictive performance and whether VLMs truly comprehend candlestick patte

Muhammad Junaid

In this study, we investigate the dynamical evolution of a massive binary black hole (MBHB) in the Leo I dwarf spheroidal galaxy model and examine how dark matter along with stellar matter's gravitational interactions influence its long-term behavior. Using high-resolution direct N-body simulations, we follow the orbital evolution of the binary within a real

Francesco Maria Mancinelli, Matteo Torzoni, Domenico Maisto, Francesco Donnarumma

Active Inference is an emerging framework providing a quantitative account of behavioral processes in neuroscience and a principled approach to decision-making under uncertainty. Its application to agency problems is natural, offering an autopoietic interpretation of action while addressing classical challenges such as the exploration-exploitation trade-off.

Baoyun Wang, Zhuoren Li, Ran Yu, Yu Che

End-to-end diffusion planning has shown strong potential for autonomous driving, but the physical feasibility of generated trajectories remains insufficiently addressed. In particular, generated trajectories may exhibit local geometric irregularities, violate trajectory-level kinematic constraints, or deviate from the drivable area, indicating that the commo

Anasuya Chattopadhyay, Daniel Reti, Hans D. Schotten

Cloud networks increasingly rely on machine learning based Network Intrusion Detection Systems to defend against evolving cyber threats. However, real-world deployments are challenged by limited labeled data, non-stationary traffic, and adaptive adversaries. While semi-supervised learning can alleviate label scarcity, most existing approaches implicitly assu

Georgios Pantazis, Michelle S. Chong

We study finite horizon reachable set estimation for unknown discrete-time dynamical systems using only sampled state trajectories. Rather than treating scenario optimization as a black-box tool, we show how it can be tailored to reachable set estimation, where one must learn a family of sets based on whole trajectories, while preserving probabilistic guaran

Daniel Rutschmann

In 1972, Fredman proposes the problem of sorting under partial information: preprocess a directed acyclic graph $G$ with vertex set $X$ so that you can sort $X$ in $O(\log e(G))$ time, where $e(G)$ is the number of sorted orders compatible with $G$. Cardinal, Fiorini, Joret, Jungers and Munro [STOC'10] show that you can preprocess $G$ in $O(n^{2.5})$ time an

Jinlong Liu, Wanggui He, Peng Zhang, Mushui Liu

Reinforcement learning (RL) can improve the prompt following capability of text-to-image (T2I) models, yet obtaining high-quality reward signals remains challenging: CLIP Score is too coarse-grained, while VLM-based reward models (e.g., RewardDance) require costly human-annotated preference data and additional fine-tuning. We propose PromptEcho, a reward con

Alkid Baci, Luke Friedrichs, Caglar Demir, N'Dah Jean Kouagou

Knowledge graph embedding (KGE) models perform well on link prediction but struggle with unseen entities, relations, and especially literals, limiting their use in dynamic, heterogeneous graphs. In contrast, pretrained large language models (LLMs) generalize effectively through prompting. We reformulate link prediction as a prompt learning problem and introd

Miao Liu, Fangda Wei, Jing Wang, Xinyuan Qian

Existing deepfake detection research has primarily focused on scenarios where the manipulated subject is actively speaking, i.e., generating fabricated content by altering the speaker's appearance or voice. However, in realistic interaction settings, attackers often alternate between falsifying speaking and listening states to mislead their targets, thereby

Xiafei Chang, for the NA62 Collaboration

The ultra-rare decay $K^+\to\pi^+\nu\bar\nu$ is a golden mode in flavor physics. The Standard Model prediction for its branching ratio is below $10^{-10}$. This decay mode is highly sensitive to new physics models at mass scales up to $\mathcal{O}(100\,\mathrm{TeV})$. The NA62 experiment at CERN SPS is designed to measure this decay mode. A preliminary resul

Fan Zhang, Shiming Fan, Hua Wang

Despite the recent success of large language models (LLMs) in time-series forecasting, most existing methods still adopt a Deep Synchronous Fusion strategy, where dense interactions between textual and temporal features are enforced at every layer of the network. This design overlooks the inherent granularity mismatch between modalities and leads to what we

Tsai-Ning Wang, Herman Teun den Dekker, Lin-Lin Chen, Neil Zeghidour

Automated respiratory audio analysis promises scalable, non-invasive disease screening, yet progress is limited by scarce labeled data and costly expert annotation. Zero-shot inference eliminates task-specific supervision, but existing methods apply uniform computation to every input regardless of difficulty. We introduce TRIAGE, a tiered zero-shot framework

G. Singh, T. Rook, J. Rivera-Dean, C. Figueira de Morisson Faria

We demonstrate that quantum light statistics can be used to control strong-field ionization at the tunneling step. Using a bichromatic linearly polarized field composed of a strong coherent driver and a weak bright squeezed vacuum (BSV), we show through simulation that photoelectron momentum distributions (PMDs) exhibit asymmetries that exceed those obtained

Julian Rasmus Bankwitz, Ravi Pradip, Julius Römer, Frank Brückerhoff-Plückelmann

Controlling the statistical properties of light, namely the fluctuations in photon arrival, entropy and number, is essential for both classical and quantum photonics. While integrated systems provide tunable control over amplitude, phase, and wavelength, real-time modulation of photon statistics has remained a long-standing challenge. Herein, we introduce th

Alejandro García-Fernández, Boris Sedlak, José Antonio Parejo, Pantelis Frangoudis

Deploying applications across the computing continuum requires selecting infrastructure nodes from geographically distributed and heterogeneous environments while satisfying constraints (e.g., performance, location). This decision problem is an important facet of resource allocation. As infrastructures grow in scale and heterogeneity, the resulting decision

R. Altar Ciceksiz, Victor Falgas-Ravry, Sabrina Lato, Maryam Sharifzadeh

Set $[n]=\{1, 2, \ldots , n\}$. The hypergrid $[t]^n$ is the collection of functions $f: \ [n]\rightarrow [t]$. We equip it with the natural partial order by letting $f\leq g$ whenever $f(x)\leq g(x)$ holds for all $x\in [n]$. Given a poset $P$ which can be embedded as an induced subposet of $[t]^n$, the induced poset saturation function $\mathrm{sat}^{\star

Xinyue Li, Sixuan Li, Ying Xiao, Jie M. Zhang

Fairness is a critical requirement for human-related, high-stakes software systems, motivating extensive research on bias mitigation. Prior work has largely focused on tabular data settings using traditional Machine Learning (ML) methods. With the rapid rise of Large Language Models (LLMs), recent studies have begun to explore their use for bias mitigation i

Yu-Chen Wang, Jeremy O. Richardson

A path-integral hybrid Monte Carlo approach with enveloping bridging potentials (PIHMC-EBP) is proposed for calculating numerically exact tunneling splittings in molecular systems. The central idea is to construct an approximately barrierless bridging potential that smoothly connects symmetry-related regions of ring-polymer phase space, enabling direct sampl

Lea Zupan, Yu-Chen Wang, Jeremy O. Richardson

We extend our previous symmetrized path-integral molecular dynamics approach to calculate tunneling splittings of molecules in rotationally excited states. In this new formalism, the system is rigorously projected onto selected rotational manifolds and states of a chosen symmetry through an Eckart spring, which connects the two end beads of the ring polymer

Ram Band, Siegfried Beckus, Felix Pogorzelski, Lior Tenenbaum

We study spectral properties of Schr\"odinger operators associated with substitution dynamical systems in higher dimensions. Focusing on periodic approximations generated by iterating substitutions on initial configurations, we analyze how structural defects influence the limiting spectral behavior. In contrast to the one-dimensional setting, we show that su