October 2025 arXiv papers — page 96

Persefoni Konteli, Nikolaos Makris, Evgenia Niovi Sassalou, Stylianos A. Kazazis

We demonstrate a centrally controlled dynamic switched-QKD network, with integrated PUF-based dynamic authentication for each QKD link. The performance of the dynamic switched-QKD network with real-time PUF-based authentication is analyzed.

Michael Arnold Bruna

We investigate the Schur-complement curvature of D_N-equivariant folded exponential families on the simplex. Our main structural results are: (i) the curvature kappa_Schur(theta) is convex in the log-parameter theta = ln(q); (ii) it admits a unique stationary point at the golden ratio value q* = phi^-2 (in particular for N = 12); and (iii) it obeys a quadrat

Andrew Hall, Duncan Wilson, Stuart Barber, Sarah R Brown

The main objective of dose finding trials is to find an optimal dose amongst a candidate set for further research. The trial design in oncology proceeds in stages with a decision as to how to treat the next group of patients made at every stage until a final sample size is reached or the trial stopped early. This work applies a Bayesian decision-theoretic ap

J. Pratt, M. Schneider, A. Perloff

An optimal control problem described by the Hamilton-Jacobi-Bellman equation can be developed into a problem that can be solved by general computational fluid dynamics packages. We describe how this formulation would allow a classical problem in optimal control, Zermelo's problem, to be treated as a multi-fluid problem. This approach has the advantage of all

Ivan Vujmilovic, Sara Collins, Luka Leskovec, Sasa Prelovsek

We present the first lattice QCD determination of the electromagnetic form factors of the exotic tetraquark $T_{bb} \ (bb \bar u \bar d)$ with quantum numbers $I( J^P ) = 0( 1^+ )$. The extracted form factors encode information about its internal structure, including the charge distribution and the magnetic dipole moments, determined separately for the light

Nisrine Rair, Alban Goupil, Valeriu Vrabie, Emmanuel Chochoy

Language models are often evaluated with scalar metrics like accuracy, but such measures fail to capture how models internally represent ambiguity, especially when human annotators disagree. We propose a topological perspective to analyze how fine-tuned models encode ambiguity and more generally instances. Applied to RoBERTa-Large on the MD-Offense dataset,

Jiale Zhang, Hui Tian, Stefano Bellotti, Tianqi Cang

Detecting coherent radio bursts from nearby M dwarfs provides opportunities for exploring their magnetic activity and interaction with orbiting exoplanets. However, it remains uncertain if the emission is related to flare-like activity similar to the Sun or magnetospheric process akin to magnetized planets. Using observations (1.0 - 1.5 GHz) taken by the Fiv

Dezhong Tong, Andrew Choi, Jiaqi Wang, Weicheng Huang

Flexible slender structures such as rods, ribbons, plates, and shells exhibit extreme nonlinear responses bending, twisting, buckling, wrinkling, and self contact, that defy conventional simulation frameworks. Discrete Differential Geometry (DDG) has emerged as a geometry first, structure preserving paradigm for modeling such behaviors. Unlike finite element

Yichen Liu, Yan Lin, Shengnan Guo, Zeyu Zhou

Vehicle GPS trajectories record how vehicles move over time, storing valuable travel semantics, including movement patterns and travel purposes. Learning travel semantics effectively and efficiently is crucial for real-world applications of trajectory data, which is hindered by two major challenges. First, travel purposes are tied to the functions of the roa

Neil Lutz

This paper develops multihead finite-state compression, a generalization of finite-state compression, complementary to the multihead finite-state dimensions of Huang, Li, Lutz, and Lutz (2025). In this model, an infinite sequence of symbols is compressed by a compressor that produces outputs according to finite-state rules, based on the symbols read by a con

Jiayi Huang, Sangwoo Park, Nicola Paoletti, Osvaldo Simeone

Edge intelligence enables low-latency inference via compact on-device models, but assuring reliability remains challenging. We study edge-cloud cascades that must preserve conditional coverage: whenever the edge returns a prediction set, it should contain the true label with a user-specified probability, as if produced by the cloud model. We formalize condit

Udrea Păun

We give an extension of the $G$ method, with results, the extension and results being partly suggested by the finite Markov chains and specially by the finite-time consensus problem for the DeGroot model and that for the DeGroot model on distributed systems. For the (homogeneous and nonhomogeneous) DeGroot model, using the $G$ method, a result for reaching a

Xiaobo Zheng, Pan Tang, Defu Lin, Shaoming He

Swarm trajectory optimization problems are a well-recognized class of multi-agent optimal control problems with strong nonlinearity. However, the heuristic nature of needing to set the final time for agents beforehand and the time-consuming limitation of the significant number of iterations prohibit the application of existing methods to large-scale swarm of

Olivier Parisot, Mahmoud Jaziri

The growing negative impact of the visibility of satellites in the night sky is influencing the practice of astronomy and astrophotograph, both at the amateur and professional levels. The presence of these satellites has the effect of introducing streaks into the images captured during astronomical observation, requiring the application of additional post pr

Jorge Vicente-Puig, Judit Chamorro-Servent, Ernesto Zacur, Inés Llorente-Lipe

Cardiac arrhythmias are a major cause of morbidity and mortality increasing the risk of stroke, heart failure, and sudden cardiac death. Imageless electrocardiographic imaging (ECGI) provides a non invasive alternative to electrical mapping from body surface potentials, but conventional ECGI is confined to epicardial reconstructions and can miss arrhythmias

Mahdi Javidnasab, Sajjad Hosseinzade

This review systematically analyzes patent disclosures regarding plasmonic structures, devices, and integrated applications, highlighting the technology's capability to confine and manipulate electromagnetic energy at the nanoscale. Core materials rely on highly conductive noble metals (Gold, Silver), alongside innovative alternatives like Transparent Conduc

Rirong Yuan

We use certain Morse functions to construct conformal metrics with negative sectional curvature on locally conformally flat manifolds with boundary. Moreover, without conformally flatness assumption, we also construct conformal metric of positive Einstein tensor.

Yumeng Wang, Jirui Qi, Catherine Chen, Panagiotis Eustratiadis

Large Language Models (LLMs) have emerged as promising zero-shot rankers, but their performance is highly sensitive to prompt formulation. In particular, role-play prompts, where the model is assigned a functional role or identity, often give more robust and accurate relevance rankings. However, the mechanisms and diversity of role-play effects remain undere

D. Jakubíková-Studenovská, R. Pöschel, S. Radeleczki

Quasiorders $\varrho\subseteq A^{2}$ have the property that an operation $f:A^{n}\to A$ preserves $\varrho$ if and only if each (unary) translation obtained from $f$ is an endomorphism of $\rho$. Generalized quasiorders $\rho\subseteq A^{m} $ are generalizations of (binary) quasiorders sharing the same property. We show how new generalized quasiorders can be

Yichen Yu, Qiaoran Wang

Today's young people are facing increasing psychological stress due to various social issues. Traditional stress management tools often rely on static scripts or passive content, which are ineffective in alleviating stress. NieNie addresses this gap by combining rhythm biofeedback with real-time psychological guidance through a large language model (LLM), of

Balint Rago

Let $H$ be an additively written monoid and let $\mathcal{P}_{0}(H)$ denote the reduced power monoid of $H$, that is, the monoid consisting of all subsets of $H$ containing $0$ with set addition as operation. Following work of Tringali, Wen and Yan, we give a full description of the automorphism group of $\mathcal{P}_{0}(G)$, where $G$ is a finite abelian gr

Niannian Wu, Rongpeng Li, Zongyu Yang, Yong Xiao

Traditional PID controllers have limited adaptability for plasma shape control, and task-specific reinforcement learning (RL) methods suffer from limited generalization and the need for repetitive retraining. To overcome these challenges, this paper proposes a novel framework for developing a versatile, zero-shot control policy from a large-scale offline dat

Xu He, Xiaolin Meng, Youdong Zhang, Lingfei Mo

This perspective analyzes the intricate interplay among neuroscience, Brain-Inspired Intelligence (BII), and Brain-Inspired Navigation (BIN), revealing a current lack of cooperative relationship between Brain-Computer Interfaces (BCIs) and BIN fields. We advocate for the integration of neuromorphic-empowered BCI into BIN, thereby bolstering the unmanned syst

Yovin Yahathugoda, Davide Prezzi, Patricia A. Gutierrez, Piyalitt Ittichaiwong

Active Surveillance (AS) is a treatment option for managing low and intermediate-risk prostate cancer (PCa), aiming to avoid overtreatment while monitoring disease progression through serial MRI and clinical follow-up. Accurate prostate segmentation is an important preliminary step for automating this process, enabling automated detection and diagnosis of PC

Eivind Xu Djurhuus, Gereon Quick

We show that an often used example of a cohomology algebra with non-vanishing triple Massey product is intrinsically A_3-formal and therefore, in fact, cannot be realized as the cohomology of a differential graded algebra with non-vanishing triple Massey product. We prove this result by computing the graded Hochschild cohomology group which contains the pote

Wei Huang, Andi Han, Yujin Song, Yilan Chen

The capacity of deep learning models is often large enough to both learn the underlying statistical signal and overfit to noise in the training set. This noise memorization can be harmful especially for data with a low signal-to-noise ratio (SNR), leading to poor generalization. Inspired by prior observations that label noise provides implicit regularization

Sidney Bender, Ole Delzer, Jan Herrmann, Heike Antje Marxfeld

Deep learning models remain vulnerable to spurious correlations, leading to so-called Clever Hans predictors that undermine robustness even in large-scale foundation and self-supervised models. Group distributional robustness methods, such as Deep Feature Reweighting (DFR) rely on explicit group labels to upweight underrepresented subgroups, but face key lim

Alexander Kappes

The IceCube neutrino observatory has been successfully operating in its full configuration for almost 15 years and is characterized by a remarkably high stability and uptime. During this time, it has made many groundbreaking observations, such as the first detection of a high-energy diffuse cosmic neutrino flux or, more recently, the identification of the AG

Francesco Balassone, Víctor Mayoral-Vilches, Stefan Rass, Martin Pinzger

We empirically evaluate whether AI systems are more effective at attacking or defending in cybersecurity. Using CAI (Cybersecurity AI)'s parallel execution framework, we deployed autonomous agents in 23 Attack/Defense CTF battlegrounds. Statistical analysis reveals defensive agents achieve 54.3% unconstrained patching success versus 28.3% offensive initial a

Canran Xiao, Chuangxin Zhao, Zong Ke, Fei Shen

Long-tail imbalance is endemic to multi-label learning: a few head labels dominate the gradient signal, while the many rare labels that matter in practice are silently ignored. We tackle this problem by casting the task as a cooperative potential game. In our Curiosity-Driven Game-Theoretic Multi-Label Learning (CD-GTMLL) framework, the label space is split

Yongshun Zhang, Zhongyi Fan, Yonghang Zhang, Zhangzikang Li

In recent years, large-scale generative models for visual content (\textit{e.g.,} images, videos, and 3D objects/scenes) have made remarkable progress. However, training large-scale video generation models remains particularly challenging and resource-intensive due to cross-modal text-video alignment, the long sequences involved, and the complex spatiotempor

Tzu-Wei Tsai, Hsiang-Yi Karen Yang

Active galactic nucleus (AGNs) feedback is widely accepted as the key mechanism to suppress cooling flows in galaxy clusters. However, the dependence of heating efficiency on jet properties is not fully understood. In this work, we present three-dimensional hydrodynamic simulations of a Perseus-like cluster, including both single-jet and self-regulated model

Hangcheng Cao, Baixiang Huang, Longzhi Yuan, Haonan An

A driver's health state serves as a determinant factor in driving behavioral regulation. Subtle deviations from normalcy can lead to operational anomalies, posing risks to public transportation safety. While prior efforts have developed detection mechanisms for functionally-driven temporary anomalies such as drowsiness and distraction, limited research has a

Tiancheng Hu, Joachim Baumann, Lorenzo Lupo, Nigel Collier

Large language model (LLM) simulations of human behavior have the potential to revolutionize the social and behavioral sciences, if and only if they faithfully reflect real human behaviors. Current evaluations of simulation fidelity are fragmented, based on bespoke tasks and metrics, creating a patchwork of incomparable results. To address this, we introduce

Hoang Pham, The-Anh Ta, Tom Jacobs, Rebekka Burkholz

Sparse neural networks promise efficiency, yet training them effectively remains a fundamental challenge. Despite advances in pruning methods that create sparse architectures, understanding why some sparse structures are better trainable than others with the same level of sparsity remains poorly understood. Aiming to develop a systematic approach to this fun

Jun Wu, Stéphane Guerrier, Si Gou, Yogeshvar N. Kalia

Testing the equivalence of multiple quantiles between two populations is important in many scientific applications, such as clinical trials, where conventional mean-based methods may be inadequate. This is particularly relevant in bridging studies that compare drug responses across different experimental conditions or patient populations. These studies often

M. J. Luo

This paper proposes an intrinsic or background-independent quantum framework based on entangled state rather than absolute quantum state, it describes a quantum relative state between the under-study quantum system and the quantum measuring apparatus as a quantum reference system, without relying on any external absolute parameter. The paper focuses on a sim

Kosta Pavlović, Lazar Stanarević, Petar Nedić, Elena Nešović Slavko Kovačević

Prevailing practice in learning-based audio watermarking is to pursue robustness by expanding the set of simulated distortions during training. However, such surrogates are narrow and prone to overfitting. This paper presents AWARE (Audio Watermarking with Adversarial Resistance to Edits), an alternative approach that avoids reliance on attack-simulation sta

Inês Rainho, Jamie Bamber, Davide Guerra, Miquel Miravet-Tenés

We perform general relativistic magnetohydrodynamic (GRMHD) simulations of binary neutron star (BNS) mergers with four distinct magnetic field topologies: (i) a dipole pulsar-like configuration, (ii) a mixed linear superposition of poloidal and toroidal components inside the star, and (iii-iv) two topologies featuring a smooth transition from a confined mixe

Christopher Schwanke

Given an Archimedean vector lattice $E$, we present one elementary property of $E$ which is equivalent to the entire traditional list of axioms which makes $E$ a $\Phi$-algebra. We call a vector lattice with this property ``square closed". More generally, we then introduce the notion of a pseudo square closed vector lattice and prove that an Archimedean vect

Shiyu Ni, Keping Bi, Jiafeng Guo, Minghao Tang

Honesty alignment-the ability of large language models (LLMs) to recognize their knowledge boundaries and express calibrated confidence-is essential for trustworthy deployment. Existing methods either rely on training-free confidence estimation (e.g., token probabilities, self-consistency) or training-based calibration with correctness annotations. While eff

Li Shan, Xi Shen

Parallel physical information neural networks (P-PINNs) have been widely used to solve systems with multiple coupled physical fields, such as the coupled Stokes-Darcy equations with Beavers-Joseph-Saffman (BJS) interface conditions. However, excessively high or low physical constants in partial differential equations (PDE) often lead to ill conditioned loss

Karol Pietrak, Radosław Muszyński, Adam Marek, Piotr Łapka

Results are presented for the numerical verification of a method devised to identify an unknown spatio-temporal distribution of heat flux that occurs at the surface of thin aluminum plate, as a result of pulsed, high-power laser beam excitation. The presented identification of boundary heat flux function is a part of newly-proposed laser beam profiling metho

Lachlan Ewen MacDonald, Hancheng Min, Leandro Palma, Salma Tarmoun

Classical optimisation theory guarantees monotonic objective decrease for gradient descent (GD) when employed in a small step size, or ``stable", regime. In contrast, gradient descent on neural networks is frequently performed in a large step size regime called the ``edge of stability", in which the objective decreases non-monotonically with an observed impl

Jaeyeon Won, Willow Ahrens, Joel S. Emer, Saman Amarasinghe

Programming high-performance sparse GPU kernels is notoriously difficult, requiring both substantial effort and deep expertise. Sparse compilers aim to simplify this process, but existing systems fall short in two key ways. First, they are primarily designed for CPUs and rarely produce high-performance GPU code. Second, when computations involve both sparse

Jingshu Liu, Raheel Qader, Gaëtan Caillaut, Mariam Nakhlé

While BERT is an effective method for learning monolingual sentence embeddings for semantic similarity and embedding based transfer learning BERT based cross-lingual sentence embeddings have yet to be explored. We systematically investigate methods for learning multilingual sentence embeddings by combining the best methods for learning monolingual and cross-

El Mahdi Chayti, Martin Jaggi

Stochastic difference-of-convex (DC) optimization is prevalent in numerous machine learning applications, yet its convergence properties under small batch sizes remain poorly understood. Existing methods typically require large batches or strong noise assumptions, which limit their practical use. In this work, we show that momentum enables convergence under

Li-Hsiang Shen

This letter proposes a novel six-dimensional movable metasurface (6DMM)-assisted downlink non-orthogonal multiple access (NOMA) system, in which a conventional base station (BS) equipped with fixed antennas serves multiple users with the assistance of a reconfigurable intelligent surface (RIS) with six-dimensional spatial configurability. In contrast to trad

Yuanli Wu, Long Zhang, Yue Du, Bin Li

We propose a rubric-guided, pseudo-labeled, and prompt-driven zero-shot video summarization framework that bridges large language models with structured semantic reasoning. A small subset of human annotations is converted into high-confidence pseudo labels and organized into dataset-adaptive rubrics defining clear evaluation dimensions such as thematic relev

Yuzhou Fang, Dexuan Zhang, Dezhi Wang, Xuefeng Zhang

TianQin is a proposed space-based mission for gravitational wave detection, employing a constellation of three drag-free satellites in high Earth orbits to form a laser interferometric observatory. A critical technical challenge is mitigating tilt-to-length (TTL) coupling noise, which is expected to be the third dominant noise source after laser frequency an

Zihan Liu, Shun Zheng, Xumeng Wen, Yang Wang

Long-form chain-of-thought reasoning has become a cornerstone of advanced reasoning in large language models. While recent verification-refinement frameworks have enabled proprietary models to solve Olympiad-level problems, their effectiveness hinges on strong, reliable verification and correction capabilities, which remain fragile in open-weight, smaller-sc

Delio Mugnolo

We introduce a semigroup framework for Laplacians on directed hypergraphs, extending the classical heat flow models on graphs and establishing hypergraphs as prototypical models for non-Markovian diffusion. We apply spectral surgery methods to derive eigenvalue bounds, thus describing large-time behaviour of the heat flow. Unlike on standard graphs, heat flo

Giacomo Camposampiero, Michael Hersche, Roger Wattenhofer, Abu Sebastian

We introduce I-RAVEN-X, a symbolic benchmark designed to evaluate generalization and robustness in analogical and mathematical reasoning for Large Language Models (LLMs) and Large Reasoning Models (LRMs). I-RAVEN-X extends I-RAVEN by increasing operand complexity, attribute range, and introducing perceptual uncertainty. Compared to LLMs, empirical results sh

G. W. F. Drake, Aaron T. Bondy, Oliver P. Hallett, Benjamin C. Najem

High precision variational calculations in Hylleraas coordinates are presented for all singlet and triplet $P$-states of helium up to principal quantum number $n = 35$ with a uniform accuracy of 1 part in $10^{22}$ for the nonrelativistic energy. Mass polarization, relativistic and quantum electrodynamic effects are included to achieve a final accuracy of $\

Jacob Neumann

We reformulate recent advances in directed type theory--a type theory where the types have the structure of synthetic (higher) categories--as a logical calculus with multiple context 'zones', following the example of Pfenning and Davies. This allows us to have two kinds of variables--'neutral' and 'polar'--with different functoriality requirements. We focus

Kamil Rychlewicz

We extend the theorem of Hausel and the author from arXiv:2212.11836 that relates equivariant cohomology rings and algebras of functions on zero schemes. This paper combines three separate results. We prove that for a reductive group G acting on a smooth projective variety one can see the equivariant cohomology ring as the ring of functions on the zero schem

David Angulo-Garcia, Alessandro Torcini

Recurrent neural networks with balanced excitation and inhibition exhibit irregular asynchronous dynamics, which is fundamental for cortical computations. Classical balance mechanisms require strong external inputs to sustain finite firing rates, raising concerns about their biological plausibility. Here, we investigate an alternative mechanism based on shor

Yihong Tang, Kehai Chen, Liang Yue, Jinxin Fan

With the rise of large language models (LLMs), LLM agents capable of autonomous reasoning, planning, and executing complex tasks have become a frontier in artificial intelligence. However, how to translate the research on general agents into productivity that drives industry transformations remains a significant challenge. To address this, this paper systema

Huan-Chen Shi, Er-Liang Cui, Dan Zhou

The optimization of neural wave functions in variational Monte Carlo crucially relies on a robust convergence criterion. While the energy variance is theoretically a definitive measure, its practical application as a primary convergence criterion has been underexplored. In this work, we develop a lightweight, general-purpose solver that utilizes the energy v

Yongxin He, Shan Zhang, Yixuan Cao, Lei Ma

Detecting AI-involved text is essential for combating misinformation, plagiarism, and academic misconduct. However, AI text generation includes diverse collaborative processes (AI-written text edited by humans, human-written text edited by AI, and AI-generated text refined by other AI), where various or even new LLMs could be involved. Texts generated throug

Antonio Pasqua

In this work, we study a Dark Energy (DE) energy density model which depends on the Hubble parameter squared $H^2$ and on its first, second and third time derivatives $\dot{H}$, $\ddot{H}$ and $\dddot{H}$. Considering a scale factor $a$ with a power-law dependence on the time (with $n$ indicating the power-law index), we obtain some important cosmological qu

Maxim Bolshim, Alexander Kugaevskikh

We introduce a methodology for analyzing neural networks through the lens of layer-wise Hessian matrices. The local Hessian of each functional block (layer) is defined as the matrix of second derivatives of a scalar function with respect to the parameters of that layer. This concept provides a formal tool for characterizing the local geometry of the paramete

Marko Kostic

In this research article, we consider the uniqueness sequences for multidimensional vector-valued Laplace transform. We establish the fundamental relationships between uniqueness sequences for one-dimensional Laplace transform and uniqueness sequences for multidimensional Laplace transform. We also provide several illustrative examples, open problems and use

Muhammad Umer Ramzan, Ali Zia, Abdelwahed Khamis, Noman Ali

Salient object detection (SOD) aims to segment visually prominent regions in images and serves as a foundational task for various computer vision applications. We posit that SOD can now reach near-supervised accuracy without a single pixel-level label, but only when reliable pseudo-masks are available. We revisit the prototype-based line of work and make two

Zheyue Tan, Zhiyuan Li, Tao Yuan, Dong Zhou

Mixture-of-Experts (MoE) architectures have emerged as a promising approach to scale Large Language Models (LLMs). MoE boosts the efficiency by activating a subset of experts per token. Recent works show that fine-grained experts substantially enriches the combinatorial flexibility of active experts and enhances model expressiveness. However, such a design i

Chenxu Dang, Haiyan Liu, Jason Bao, Pei An

Semantic occupancy has emerged as a powerful representation in world models for its ability to capture rich spatial semantics. However, most existing occupancy world models rely on static and fixed embeddings or grids, which inherently limit the flexibility of perception. Moreover, their ``in-place classification" over grids exhibits a potential misalignment

Aurélien Bellet, Edwige Cyffers, Davide Frey, Romaric Gaudel

Decentralized Learning (DL) enables users to collaboratively train models without sharing raw data by iteratively averaging local updates with neighbors in a network graph. This setting is increasingly popular for its scalability and its ability to keep data local under user control. Strong privacy guarantees in DL are typically achieved through Differential

Feng Zhou, Wenkai Guo, Pu Cao, Zhicheng Zhang

Sparse-view 3D Gaussian Splatting (3DGS) often overfits to the training views, leading to artifacts like blurring in novel view rendering. Prior work addresses it either by enhancing the initialization (\emph{i.e.}, the point cloud from Structure-from-Motion (SfM)) or by adding training-time constraints (regularization) to the 3DGS optimization. Yet our cont

Guillaume Rongier, Luk Peeters

High costs and uncertainties make subsurface decision-making challenging, as acquiring new data is rarely scalable. Embedding geological knowledge directly into predictive models offers a valuable alternative. A joint approach enables just that: process-based models that mimic geological processes can help train generative models that make predictions more e

U. Özdem

We investigate the electromagnetic properties of the axial-vector molecular states $D^* K$, $DK^*$, $B^* K$, and $BK^*$, which are used to model the charmed states $D_{s1}^{+}(2460)$, $D_{s1}^{+}(2536)$, and their bottom partners with quantum numbers $J^P = 1^+$. To our knowledge, this presents the first comprehensive calculation of the magnetic and quadrupo

Fo Hu, Can Wang, Qinxu Zheng, Xusheng Yang

Significant inter-individual variability limits the generalization of EEG-based emotion recognition under cross-domain settings. We address two core challenges in multi-source adaptation: (1) dynamically modeling distributional heterogeneity across sources and quantifying their relevance to a target to reduce negative transfer; and (2) achieving fine-grained

Davide Salvi, Hendrik Vincent Koops, Elio Quinton

The proliferation of highly realistic singing voice deepfakes presents a significant challenge to protecting artist likeness and content authenticity. Automatic singer identification in vocal deepfakes is a promising avenue for artists and rights holders to defend against unauthorized use of their voice, but remains an open research problem. Based on the pre

Milena Crnogorčević

The dark matter track at ICRC~2025 showed a field in transition. Direct detection has entered the \emph{neutrino-floor era}, with XENONnT and PandaX-4T now limited by Solar neutrinos. Indirect searches have become truly \emph{multimessenger}, combining $\gamma$-rays, neutrinos, cosmic rays, and radio data under unified likelihoods and shared systematics. Non

Paula Cordero-Encinar, Andrew B. Duncan

Recent advances such as self-consistency and test-time reinforcement learning (TTRL) improve the reliability of large language models (LLMs) without additional supervision, yet their underlying mechanisms and statistical guarantees remain poorly understood. We present a unified framework for certifiable inference in LLMs, showing that majority voting provide

Peter W. Evans

This paper develops an agent-centric account of measurement that treats the preferred-basis problem is fundamentally perspectival. On this view, the system--apparatus--environment decomposition and the observables that are apt to become classically robust are determined by the physical constitution and epistemic constraints of an embodied class of agents. De

Sung-Soo Byun, Christophe Charlier, Philippe Moreillon, Nick Simm

We study the last passage time in geometric last passage percolation (LPP). As the system size increases, we derive precise large deviation probabilities -- up to and including the constant terms -- for both the lower and upper tails. A key step in proving these results is to establish a duality formula that reformulates the LPP problem in terms of the large

Jing Liu

Transformers exhibit compositional reasoning on sequences not observed during training, a capability often attributed to in-context learning (ICL) and skill composition. We investigate this phenomenon using the Random Hierarchy Model (RHM), a probabilistic context-free grammar that generates sequences through recursive rule application. Models are trained on

Yongbin Shao, Xinyi Zhao, Long Ma, Ming Xin

Arm-locking frequency stabilization is a key technique for suppressing laser frequency noise in space-based gravitational-wave detectors. The robustness of the arm-locking control loop is crucial for maintaining laser frequency stability, which directly impacts the accuracy of gravitational-wave measurements. In this work, a parametric stability analysis fra

Dan Zheng, Jing Feng, Juan Liu

Current research in Electrocardiogram (ECG) biometrics mainly emphasizes resting-state conditions, leaving the performance decline in rest-exercise scenarios largely unresolved. This paper introduces CrossStateECG, a robust ECG-based authentication model explicitly tailored for cross-state (rest-exercise) conditions. The proposed model creatively combines mu

Fathima Jesbin, Ananthanarayanan Chockalingam

The performance of Zak-OTFS modulation is critically dependent on the choice of the delay-Doppler (DD) domain pulse shaping filter. The design of pulses for $L^2(\mathbb{R})$ is constrained by the Balian-Low Theorem, which imposes an inescapable trade-off between time-frequency localization and orthogonality for spectrally efficient systems. In Zak-OTFS, thi

Alberto De Marchi

Optimization problems with convex quadratic cost and polyhedral constraints are ubiquitous in signal processing, automatic control and decision-making. We consider here an enlarged problem class that allows to encode logical conditions and cardinality constraints, among others. In particular, we cover also situations where parts of the constraints are noncon

Meryem Bouaziz, Samir El Masaoudi, Aymen Mahmoudi, Eva Desgue

Van der Waals (vdW) heterostructures, which combine bi-dimensional materials of different properties, enable a range of quantum phenomena. Here, we present a comparative study between the electronic properties of mono- and bi-layer of platinum diselenide (PtSe2) grown on hexagonal boron nitride (h-BN) and graphene substrates using molecular beam epitaxy (MBE

Cor Steging, Tadeusz Zbiegień

Machine learning is increasingly used in the legal domain, where it typically operates retrospectively by treating past case outcomes as ground truth. However, legal outcomes are often shaped by human interventions that are not captured in most machine learning approaches. A final decision may result from a settlement, an appeal, or other procedural actions.

He Du, Bowen Li, Aijun Yang, Siyang He

Reliable verifiable data has become a key driver of capability gains in modern language models, enabling stable reinforcement learning with verifiable rewards and effective distillation that transfers competence across math, coding, and agentic tasks. Yet constructing generalizable synthetic verifiable data remains difficult due to hallucination-prone genera

Francesco Troisi, Simone Latini, Heiko Appel, Martin Lüders

Light-matter coupled Hamiltonians are central to cavity materials engineering and polaritonic chemistry, but are challenging to simulate with classical hardware due to the scaling of the Hilbert space with the number of quantum photon modes and matter complexity. Leveraging the fact that quantum computers naturally represent photonic modes efficiently, we pr

Muhammad Farmal Khan, Mousumi Akter

Idioms remain a persistent challenge in natural language processing due to their figurative and culturally grounded meanings, which distinguish them from literal expressions. Although recent advances in large language models (LLMs) have improved idiom handling across several languages, limited attention has been given to low resource languages such as Urdu.

Jiawei Zhou, Ruicheng Zhu, Mengshi Chen, Jianwei Wang

Agentic systems have recently emerged as a promising tool to automate literature-based ideation. However, current systems often remain black-box, with limited transparency or control for researchers. Our work introduces TrustResearcher, a multi-agent demo system for knowledge-grounded and transparent ideation. Specifically, TrustResearcher integrates meticul

N. Lagarde, R. -M. Ouazzani, J. Malzac, M. Clavel

The Commission Femmes et Astronomie of the French Astronomical Society, has conducted a statistical study aimed at mapping the current presence of women in French professional astronomy and establishing a baseline for tracking its evolution over time. This study follows an initial survey carried out in 2021, which covered eight astronomy and astrophysics ins

Ayrat Abdullin, Denis Anikiev, Umair Bin Waheed

Deep neural networks like PhaseNet show high accuracy in detecting microseismic events, but their black-box nature is a concern in critical applications. We apply Explainable Artificial Intelligence (XAI) techniques, such as Gradient-weighted Class Activation Mapping (Grad-CAM) and Shapley Additive Explanations (SHAP), to interpret the PhaseNet model's decis

Li Sun, Zhenhao Huang, Ming Zhang, Philip S. Yu

Message Passing Neural Networks (MPNNs) is the building block of graph foundation models, but fundamentally suffer from oversmoothing and oversquashing. There has recently been a surge of interest in fixing both issues. Existing efforts primarily adopt global approaches, which may be beneficial in some regions but detrimental in others, ultimately leading to

Filip Bečanović, Vincent Bonnet, Kosta Jovanović, Samer Mohammed

Muscle force sharing is typically resolved by minimizing a specific objective function to approximate neural control strategies. An inverse optimal control approach was applied to identify the "best" objective function, among a positive linear combination of basis objective functions, associated with the gait of two post-stroke males, one high-functioning (s

Young-Pil Choi, Jinwook Jung

We develop a unified relative entropy framework for macroscopic limits of kinetic equations with Riesz-type interactions and Fokker-Planck relaxation. Our analysis covers three prototypical singular regimes: the diffusive limit leading to a drift-diffusion equation, the high-field limit yielding the aggregation equation in the repulsive regime, and the stron

Hongwen Zhang, Bowen Ai, Zekun Gong, Tianyi Sui

Nanoporous membranes, leveraging their high-throughput characteristics, have been widely applied in fields such as molecular separation and energy conversion. Due to interpore interactions, besides the applied voltage and solution environment, the ion transport properties in porous membranes are influenced by the pore number and spacing. Here, to understand

Bruno Predojević

We study two related quantities which generalize the concept of upper Banach density of a set to two measurable subsets of the plane. The first of them allows us to generalize a classic result on sufficiently large distances realized in a set of positive upper density, to distances between points of two sets satisfying an appropriate density condition. The s

Florent Foucaud, Harmender Gahlawat, Fionn Mc Inerney, Prafullkumar Tale

The VC-dimension is a well-studied and fundamental complexity measure of a set system (or hypergraph) that is central to many areas of machine learning. We establish several new results on the complexity of computing the VC-dimension. In particular, given a hypergraph $\mathcal{H}=(\mathcal{V},\mathcal{E})$, we prove that the naive $2^{\mathcal{O}(|\mathcal{

Johan Schubert, Farzad Kamrani, Tove Gustavi

We develop an active inference route-planning method for the autonomous control of intelligent agents. The aim is to reconnoiter a geographical area to maintain a common operational picture. To achieve this, we construct an evidence map that reflects our current understanding of the situation, incorporating both positive and "negative" sensor observations of

Souradeep Pal

We investigate the measurability of effective inspiral spin in the detectable compact binary mergers using gravitational-wave observations. Measurements from the latest gravitational-wave transient catalog do not rule out the existence of binary systems with non-zero effective spins. However, we observe an apparent correlation between the inferred effective

Mirko Mizzoni, Pieter van Goor, Barbara Bazzana, Antonio Franchi

We study switching among multiple square selections of output functions (melds) drawn from a deck of candidate outputs for nonlinear systems that are static feedback linearizable via outputs. Fixing an operating point, each meld induces a distinct feedback-linearizing coordinate chart defined on a common neighborhood. Switching between melds therefore produc

Martin de Borbon, Dmitri Panov

We give necessary and sufficient conditions for the existence of polyhedral K\"ahler metrics on $\mathbb{CP}^n$ whose singular set is a hyperplane arrangement and whose cone angles are in $(0, 2\pi)$. These conditions take the form of linear and quadratic constraints on the cone angles and are entirely determined by the intersection poset of the arrangement.

Juulia-Gabrielle Moreau, Argo Jõeleht, Anna Losiak, Meng-Hua Zhu

Sedimentary rocks often form the upper layers or the entire target rocks in impact events. Thermodynamic properties of sedimentary rocks related to porosity and water saturation affect the process of impact crater formation. The heterogeneous distribution of sedimentary facies can complicate the development and distribution of shock effects, especially in nu