October 2025 arXiv papers — page 212

Siddhartha Jain, Vishnu Iyer, Rolando D. Somma, Ning Bao

We present a new primitive for quantum algorithms that implements a discrete Hermite transform efficiently, in time that depends logarithmically in both the dimension and the inverse of the allowable error. This transform, which maps basis states to states whose amplitudes are proportional to the Hermite functions, can be interpreted as the Gaussian analogue

Mingyang Li, Hongyi Liu

We study 4-dimensional Poincar\'e-Einstein manifolds whose conformal class contains a K\"ahler metric. Such Einstein metrics are non-K\"ahler and admit a Killing field extending to the conformal infinity, and the Einstein equation reduces to a Toda-type equation. When the Killing field integrates to an $\mathbb{S}^1$-action, we formulate a Dirichlet boundary

Usman Akram, Yiyue Chen, Haris Vikalo

Automatic modulation classification (AMC) is a core enabler of cognitive wireless systems, providing spectrum awareness and supporting adaptive communication at the network edge. However, training AMC models on centrally aggregated data incurs high communication overhead, raises privacy concerns, and often lacks robustness to real-world conditions. We propos

Eyal Cohen, Christophe Denis, Mohamed Hebiri

Set-valued classification is used in multiclass settings where confusion between classes can occur and lead to misleading predictions. However, its application may amplify discriminatory bias motivating the development of set-valued approaches under fairness constraints. In this paper, we address the problem of set-valued classification under demographic par

Yogal Prasad Ghimirey, Laxman Nagireddy, Cephise Cacho, Neil R. Wilson

We report the electronic structure of monolayer CrSBr exfoliated onto mica template-stripped gold substrates. Angle-resolved photoemission spectroscopy reveals charge transfer from the substrate, populating the conduction band of monolayer CrSBr, accompanied by a pronounced reduction in the quasiparticle band gap. Furthermore, we observe two separate conduct

Ardavan Rahimian

We study how far a diffusion process on a graph can deviate from a designed starting pattern when the pattern is generated via Laplacian regularisation. Under standard stability conditions for undirected, entrywise nonnegative graphs, we give a closed-form, instance-specific upper bound on the steady-state spread, measured as the relative change between the

Alec K. Peltekian, Halil Ertugrul Aktas, Gorkem Durak, Kevin Grudzinski

Mixture-of-Experts (MoE) architectures achieve scalable learning by routing inputs to specialized subnetworks through conditional computation. However, conventional MoE designs assume homogeneous expert capability and domain-agnostic routing-assumptions that are fundamentally misaligned with medical imaging, where anatomical structure and regional disease he

Huanhuan Yang, Guangfu Luo

Oxygen reduction catalysts frequently suffer from degradation under harsh operating conditions, and the limited understanding of the underlying mechanisms hampers the development of effective mitigation strategies. In this study, we integrate first-principles calculations with a time-dependent microkinetic model to investigate the deactivation pathways of si

Richard Cleve, Zhiqian Ding, Luke Schaeffer

The commutative depth model allows gates that commute with each other to be performed in parallel. We show how to compute Clifford operations in constant commutative depth more efficiently than was previously known. Bravyi, Maslov, and Nam [Phys. Rev. Lett. 129:230501, 2022] showed that every element of the Clifford group (on $n$ qubits) can be computed in c

Davood Rafiei, Morgan Lindsay Heisler, Weiwei Zhang, Mohammadreza Pourreza

Supervised Fine-Tuning (SFT) is an effective method for adapting Large Language Models (LLMs) on downstream tasks. However, variability in training data can hinder a model's ability to generalize across domains. This paper studies the problem of dataset alignment for Natural Language to SQL (NL2SQL or text to SQL), examining how well SFT training data matche

Sanjeev Khanna, Ashwin Padaki, Krish Singal, Erik Waingarten

We study the space complexity of estimating the diameter of a subset of points in an arbitrary metric space in the dynamic (turnstile) streaming model. The input is given as a stream of updates to a frequency vector $x \in \mathbb{Z}_{\geq 0}^n$, where the support of $x$ defines a multiset of points in a fixed metric space $M = ([n], \mathsf{d})$. The goal i

Daniele Macuglia, Giovanni Ciccotti, Benoît Roux

From the onset of fundamental statistical mechanical constructs formulated in the late 19th century, alchemical free-energy methods slowly emerged and transitioned to become operational tools of biomolecular simulation applicable to a wide range of problems including protein-ligand binding for drug discovery research. This article reconstructs how statistica

Giulio Weikmann, Gianmarco Perantoni, Lorenzo Bruzzone

Deep learning has become increasingly important in remote sensing image classification due to its ability to extract semantic information from complex data. Classification tasks often include predefined label hierarchies that represent the semantic relationships among classes. However, these hierarchies are frequently overlooked, and most approaches focus on

S. Rasoul Etesami

We consider the problem of the existence of an envy-free allocation up to any good (EFX) for linear valuations and establish new results by connecting this problem to a fixed point framework. Specifically, we first use randomized rounding to extend the discrete EFX constraints into a continuous space and show that an EFX allocation exists if and only if the

Maximilian Delto, Alexander Penin, Lorenzo Tancredi

We present a new class of evolution equations which govern the high-energy behavior of power-suppressed scattering amplitudes. The equations can be viewed as a renormalization group flow with respect to the relevant effective field theory cutoff. A distinct feature of the method is in the use of a multidimensional cutoff to separate the relevant scales in pr

Andreas Bathelt, Benjamin Deutschmann, Hyeon Seok Rou, Kuranage Roche Rayan Ranasinghe

In every imaging or sensing application, the physical hardware creates constraints that must be overcome or they limit system performance. Techniques that leverage additional degrees of freedom can effectively extend performance beyond the inherent physical capabilities of the hardware. An example includes synchronizing distributed sensors so as to synthesiz

Ngeyen Yinkfu, Sunday Nwovu, Jonathan Kayizzi, Angelique Uwamahoro

In Kigali, Rwanda, motorcycle taxis are a primary mode of transportation, often navigating unpredictably and disregarding traffic rules, posing significant challenges for autonomous driving systems. This study compares four object detection models--YOLOv5, Faster R-CNN, SSD, and RetinaNet--for motorbike detection using a custom dataset of 198 images collecte

Noh Zainal Abidin, Frederic Grondin, Pol Muller, Jean-François Sigrist

This research explores several critical factors affecting CFD-based prediction accuracy of submarine hydrodynamics and builds upon previous work on preliminary mesh and solver benchmarking. A scaled submarine model is analyzed numerically using the Reynolds-Averaged Navier-Stokes (RANS) turbulence model at a Reynold number (Re) of 3.6x106 with wall-resolved

Jie Yang, Kexin Zhang, Guibin Zhang, Philip S. Yu

Time Series Imputation (TSI), which aims to recover missing values in temporal data, remains a fundamental challenge due to the complex and often high-rate missingness in real-world scenarios. Existing models typically optimize the point-wise reconstruction loss, focusing on recovering numerical values (local information). However, we observe that under high

Noh Zainal Abidin, Frédéric Grondin, Pol Muller, Jean-François Sigrist

Submarines are vital for maritime defense, requiring optimized hydrodynamic performance to minimize resistance. Advancements in Computational Fluid Dynamics (CFD) enable accurate predictions of submarine hydrodynamics for optimal design. This study compared the meshing capabilities of OpenFOAM and commercial software as well as the performance of High-Perfor

Haotian Gao, Zheng Dong, Jiawei Yong, Shintaro Fukushima

Spatio-temporal forecasting is essential for real-world applications such as traffic management and urban computing. Although recent methods have shown improved accuracy, they often fail to account for dynamic deviations between current inputs and historical patterns. These deviations contain critical signals that can significantly affect model performance.

Jan Naumann, Jens Eisert, Philipp Schmoll

We introduce a general corner transfer matrix renormalization group algorithm tailored to projected entangled-pair states on the triangular lattice. By integrating automatic differentiation, our approach enables direct variational energy minimization on this lattice geometry. In contrast to conventional approaches that map the triangular lattice onto a squar

Raphael Mu

We develop a simple model of the scientific peer review process, in which authors of varying ability invest to produce papers of varying quality, and journals evaluate papers based on a noisy signal, choosing to accept or reject each paper. We find that the first-best outcome is the limiting case as the evaluation technology is perfected, even though author

Congpei An, Jiashu Ran, Hao-Ning Wu

This paper surveys hyperinterpolation, a quadrature-based approximation scheme. We cover classical results, provide examples on several domains, review recent progress on relaxed quadrature exactness, introduce methodological variants, and discuss applications to differential and integral equations.

Yongfan Li, Muming Hao, Noël Brunetière, Qiang Li

A comprehensive numerical solution is developed for the transient thermo-elasto-hydrodynamic (TEHD) characteristics of mechanical face seals. Transient lubrication features of the fluid film, transient thermal deformation features of the seal rings, dynamic behavior, and rough faces contacting are coupled. The finite volume method is utilized for the fluid f

Jonathan Colaço Carr, Qinyi Sun, Cameron Allen

Skills are essential for unlocking higher levels of problem solving. A common approach to discovering these skills is to learn ones that reliably reach different states, thus empowering the agent to control its environment. However, existing skill discovery algorithms often overlook the natural state variables present in many reinforcement learning problems,

Nick Janssen, Melanie Schaller, Bodo Rosenhahn

Understanding the robustness of deep learning models for multivariate long-term time series forecasting (M-LTSF) remains challenging, as evaluations typically rely on real-world datasets with unknown noise properties. We propose a simulation-based evaluation framework that generates parameterizable synthetic datasets, where each dataset instance corresponds

Keane Ong, Wei Dai, Carol Li, Dewei Feng

Using intelligent systems to perceive psychological and social behaviors, that is, the underlying affective, cognitive, and pathological states that are manifested through observable behaviors and social interactions, remains a challenge due to their complex, multifaceted, and personalized nature. Existing work tackling these dimensions through specialized d

Zheng Xiong, Kang Li, Zilin Wang, Matthew Jackson

Built upon language and vision foundation models with strong generalization ability and trained on large-scale robotic data, Vision-Language-Action (VLA) models have recently emerged as a promising approach to learning generalist robotic policies. However, a key drawback of existing VLAs is their extremely high inference costs. In this paper, we propose Hype

Ana Milinski, Annick Dejaegere, Roland Stote

Correlated motions of proteins underpin many physiological mechanisms, such as substrate binding, signal transduction, enzymatic activity and allostery. These motions arise from low frequency collective movements of biomolecules and have mostly been studied using molecular dynamics simulations. Here, we present the effects of two different empirical energy f

Pi Duan, Weiyao Ke, Guang-You Qin, Lei Wang

Jets selected with high internal charged-particle multiplicity exhibit markedly different substructure patterns compared to inclusive jet samples. Such correlations motivate a systematic study of jet observables as a function of the normalized multiplicity, $\nu = N_{\rm ch}/\langle N_{\rm ch}\rangle$. In this work, we develop a theoretical framework for the

Patricio Guzmán, Agustín Huerta, Hugo Parada

In this paper, we study the rapid stabilization of an unstable wave equation, in which an unknown disturbance is located at the boundary condition. We address two different boundary conditions: Dirichlet- Dirichlet and Dirichlet-Neumann. In both cases, we design a feedback law, located at the same place as the unknown disturbance, that forces the exponential

A. K. Shargh, C. D. Stiles, J. A. El-Awady

Ice-nucleating proteins (INPs) are a unique class of biological macromolecules that catalyze the freezing of supercooled water far more efficiently than homogeneous nucleation. Their remarkable efficiency has motivated applications across diverse sectors, including agricultural frost protection, food processing and packaging, biomedical cryopreservation, and

Punya Syon Pandey, Hai Son Le, Devansh Bhardwaj, Rada Mihalcea

Large language models (LLMs) are increasingly deployed in contexts where their failures can have direct sociopolitical consequences. Yet, existing safety benchmarks rarely test vulnerabilities in domains such as political manipulation, propaganda and disinformation generation, or surveillance and information control. We introduce SocialHarmBench, a dataset o

Shihan Fang, Wenxin Zheng

Modern processors increasingly rely on SIMD instruction sets, such as AVX and RVV, to significantly enhance parallelism and computational performance. However, production-ready compilers like LLVM and GCC often fail to fully exploit available vectorization opportunities due to disjoint vectorization passes and limited extensibility. Although recent attempts

Oliver E. Jensen, Christopher K. Revell

Multicellular tissues, such as the epithelium coating a developing embryo, often combine complex tissue shapes with heterogeneity in the spatial arrangement of individual cells. Discrete approximations, such as the cell vertex model, can accommodate these geometric features, but techniques for analysis of such models are underdeveloped. Here, we express diff

Pengfei Zhu, Hai Zhang, Stefano Sfarra, Elena Pivarčiová

Terahertz time-domain spectroscopy (THz-TDS) provides a non-invasive and label-free method for probing the internal structure and electromagnetic response of materials. Numerical simulation of THz-TDS can help understanding wave-matter interactions, guiding experimental design, and interpreting complex measurement data. However, existing simulation technique

Alina Ermilova, Dmitrii Kornilov, Sofia Samoilova, Ekaterina Laptenkova

Identifying disease interconnections through manual analysis of large-scale clinical data is labor-intensive, subjective, and prone to expert disagreement. While machine learning (ML) shows promise, three critical challenges remain: (1) selecting optimal methods from the vast ML landscape, (2) determining whether real-world clinical data (e.g., electronic he

Alibordi Muhammad

Particle physics classification often assumes flat geometry, ignoring the curved statistical structure of collision data. We present a geometric framework for Vector Boson Fusion Higgs classification that combines physics-inspired observables with product manifold neural networks. The method unifies Euclidean, hyperbolic, and spherical representations to cap

Ranjan Mishra, Julian I. Bibo, Quinten van Engelen, Henk Schaapman

In this study, we reproduced the work done in the paper "XRec: Large Language Models for Explainable Recommendation" by Ma et al. (2024). The original authors introduced XRec, a model-agnostic collaborative instruction-tuning framework that enables large language models (LLMs) to provide users with comprehensive explanations of generated recommendations. Our

Adi Banerjee, Anirudh Nair, Tarik Borogovac

Error attribution in Large Language Model (LLM) multi-agent systems presents a significant challenge in debugging and improving collaborative AI systems. Current approaches to pinpointing agent and step level failures in interaction traces - whether using all-at-once evaluation, step-by-step analysis, or binary search - fall short when analyzing complex patt

Yuxin Wen, Arman Zharmagambetov, Ivan Evtimov, Narine Kokhlikyan

Prompt injection poses a serious threat to the reliability and safety of LLM agents. Recent defenses against prompt injection, such as Instruction Hierarchy and SecAlign, have shown notable robustness against static attacks. However, to more thoroughly evaluate the robustness of these defenses, it is arguably necessary to employ strong attacks such as automa

Adam Schroeder, Russell Funk, Jingyi Guan, Taylor Okonek

Thresholding--the pruning of nodes or edges based on their properties or weights--is an essential preprocessing tool for extracting interpretable structure from complex network data, yet existing methods face several key limitations. Threshold selection often relies on heuristic methods or trial and error due to large parameter spaces and unclear optimizatio

Nathan Shankar, Pawel Ladosz, Hujun Yin

This paper presents a novel approach for enabling robust robotic perception in dark environments using infrared (IR) stream. IR stream is less susceptible to noise than RGB in low-light conditions. However, it is dominated by active emitter patterns that hinder high-level tasks such as object detection, tracking and localisation. To address this, a Deep Mult

Elian Morel

Private Information Retrieval (PIR) allows a client to retrieve an entry $\text{DB}[i]$ from a public database $\text{DB}$ held by one or more servers, without revealing the queried index $i$. Traditional PIR schemes achieve sublinear server computation only under strong assumptions, such as the presence of multiple non-colluding servers or the use of public

Stefano Almi, Maicol Caponi, Manuel Friedrich, Francesco Solombrino

This paper addresses the asymptotics of functionals with linear growth depending on the Riesz $s$-fractional gradient on piecewise constant functions. We consider a general class of varying energy densities and, as $s\to 1$, we characterize their local limiting functionals in the sense of $\Gamma$-convergence.

Zhuoran Bao, Daniel F. V. James

A qubit, or quantum bit, is conventionally defined as "a physical system for storing information that is capable of existing in either of two quantum states or in a superposition of both". In this paper, we examine the simple question of whether two distinct levels, each consisting of multiply degenerate sub-states, could serve as a practical quantum bit. We

E Daviaud

In this article, given a base-b self-similar set K, we study the random covering of K by horizontal or vertical rectangles, with respect to the Alfhors-regular measure on K, and the rectangular shrinking target problem on K.

Xiangyang Xu, Hongyang Gao

Low-energy molecular conformers generation (MCG) is a foundational yet challenging problem in drug discovery. Denoising-based methods include diffusion and flow-matching methods that learn mappings from a simple base distribution to the molecular conformer distribution. However, these approaches often suffer from error accumulation during sampling, especiall

Anton Alekseev, Matthias Christandl, Thomas C. Fraser

Horn's problem is concerned with characterizing the eigenvalues $(a,b,c)$ of Hermitian matrices $(A,B,C)$ satisfying the constraint $A+B=C$ and forming the edges of a triangle in the space of Hermitian matrices. It has deep connections to tensor product invariants, Littlewood-Richardson coefficients, geometric invariant theory and the intersection theory of

Demi Allen, Thomas Jordan, Benjamin Ward

Since the introduction of the shrinking target problem by Hill and Velani in 1995 there has been a surge of interest in the area. In this paper we consider the case where the target is a rectangle, rather than a ball, and the underlying space is a self-similar carpet. We calculate the exact Hausdorff dimension of the resulting shrinking target set. Interesti

Dusan Popov

In this paper we extend the applicability of Fox-Wright functions beyond mathematics, specifically in quantum physics. We focused our attention on a new application, on the connection between the Fox-Wright functions and the generalized coherent states formalism. We constructed the generalized coherent states in the Barut-Girardello manner, in which the Fox-

Danylo Radchenko, Qihang Sun

We give a construction of radial Fourier interpolation formulas in dimensions 3 and 4 using Maass--Poincar\'e type series. As a corollary we obtain explicit formulas for the basis functions of these interpolation formulas in terms of what we call real-variable Kloosterman sums, which were previously introduced by Stoller. We also improve the bounds on the co

Andrea Ercolino, Harim Jin, Norbert Langer, Avishay Gal-Yam

The observational properties of core-collapse supernovae (CC-SNe) are shaped by the envelopes of their progenitors. In massive binary systems, mass-transfer alters the pre-SN structures compared to single stars, leading to a diversity in SN explosions. Aims. We compute the distribution of CC-SN properties based on comprehensive detailed grids of single and b

Alexia Jolicoeur-Martineau

Hierarchical Reasoning Model (HRM) is a novel approach using two small neural networks recursing at different frequencies. This biologically inspired method beats Large Language models (LLMs) on hard puzzle tasks such as Sudoku, Maze, and ARC-AGI while trained with small models (27M parameters) on small data (around 1000 examples). HRM holds great promise fo

Double Chooz Collaboration, T. Abrahão, H. Almazan, J. C. dos Anjos

Neutrino emission from nuclear reactors provides real-time insights into reactor power and fuel evolution, with potential applications in monitoring and nuclear safeguards. Following reactor shutdown, a low-intensity flux of ``residual neutrinos'' persists due to the decay of long-lived fission isotopes in the partially burnt fuel remaining within the reacto

Seyed Soroush Karimi Madahi, Kenneth Bruninx, Bert Claessens, Chris Develder

In Europe, profit-seeking balance responsible parties can deviate in real time from their day-ahead nominations to assist transmission system operators in maintaining the supply-demand balance. Model predictive control (MPC) strategies to exploit these implicit balancing strategies capture arbitrage opportunities, but fail to accurately capture the price-for

Shubham Jain, Anurag Tripathi, Jayanta Chakraborty, Jitendra Kumar

Drum granulation is a size enlargement process where granular material is agitated with a liquid binder to form larger size granules. Discrete element modeling is increasingly being used to better understand and investigate the granulation process. However, unlike experiments the measurement of granule size within a DEM framework often necessitates an explic

Ryotaro Honma, Tan Van Vu

The thermodynamic uncertainty relation quantifies a trade-off between the relative fluctuations of trajectory currents and the thermodynamic cost, indicating that the current precision is fundamentally constrained by entropy production. In classical bipartite systems, it has been shown that information flow between subsystems can enhance the current precisio

Christoffer Söderberg

We study $2$-representation finite $\mathbb{K}$-algebras obtained from tensor products of tensor algebras of species. In earlier work we computed the higher preprojective algebra of said algebras to be given as Jacobian algebras of certain species with potential $(S, W)$, which are self-injective and finite dimensional. Truncating these Jacobian algebras yie

Ciem Cornelissen, Sander De Coninck, Axel Willekens, Sam Leroux

This paper presents an end-to-end, IoT-enabled robotic system for the non-destructive, real-time, and spatially-resolved mapping of grape yield and quality (Brix, Acidity) in vineyards. The system features a comprehensive analytical pipeline that integrates two key modules: a high-performance model for grape bunch detection and weight estimation, and a novel

Reza Ashtari, Sean Collins, Jared Splinter, Kevin B. Stevenson

LTT-9779 b is an ultra-hot Neptune (Rp ~ 4.7 Re, Mp ~ 29 Me) orbiting its Sun-like host star in just 19 hours, placing it deep within the "hot Neptune desert," where Neptunian planets are seldom found. We present new JWST NIRSpec G395H phase-curve observations that probe its atmospheric composition in unprecedented detail. At near-infrared wavelengths, which

Sam Earle, Zehua Jiang, Eugene Vinitsky, Julian Togelius

Procedural Content Generation via Reinforcement Learning (PCGRL) offers a method for training controllable level designer agents without the need for human datasets, using metrics that serve as proxies for level quality as rewards. Existing PCGRL research focuses on single generator agents, but are bottlenecked by the need to frequently recalculate heuristic

Zihan Zhao, Fengtao Zhou, Ronggang Li, Bing Chu

Intraoperative pathology is pivotal to precision surgery, yet its clinical impact is constrained by diagnostic complexity and the limited availability of high-quality frozen-section data. While computational pathology has made significant strides, the lack of large-scale, prospective validation has impeded its routine adoption in surgical workflows. Here, we

Siwei Han, Kaiwen Xiong, Jiaqi Liu, Xinyu Ye

As Large Language Model (LLM) agents increasingly gain self-evolutionary capabilities to adapt and refine their strategies through real-world interaction, their long-term reliability becomes a critical concern. We identify the Alignment Tipping Process (ATP), a critical post-deployment risk unique to self-evolving LLM agents. Unlike training-time failures, A

Elena Corbetta, Thomas Bocklitz

Optical microscopy is one of the most widely used techniques in research studies for life sciences and biomedicine. These applications require reliable experimental pipelines to extract valuable knowledge from the measured samples and must be supported by image quality assessment (IQA) to ensure correct processing and analysis of the image data. IQA methods

Fabio Di Cosmo, Vladislav G. Kupriyanov, Patrizia Vitale

We consider the problem of defining the field strength of abelian potentials when the spacetime is a Poisson manifold, within the groupoidal approach. The natural definition in terms of gauge invariant momenta is proved to be equivalent to covariant and invariant tensors of a local symplectic groupoid representing a symplectic realization of the Poisson mani

Katharina Egg, Alison M. W. Mitchell

Pulsar wind nebulae (PWNe) are prominent sources in the very-high energy (VHE) gamma-ray sky, constituting the most numerous identified source class in the H.E.S.S. Galactic Plane Survey (HGPS). They are comprised of energetic particles originating from the pulsar and expanding into the surrounding medium. As such, PWNe are of very high scientific interest a

Martial Guidez, Stefan Duffner, Yannick Alpou, Oscar Röth

Although deep neural networks and in particular Convolutional Neural Networks have demonstrated state-of-the-art performance in image classification with relatively high efficiency, they still exhibit high computational costs, often rendering them impractical for real-time and edge applications. Therefore, a multitude of compression techniques have been deve

Junqi Jiang, Francesco Leofante, Antonio Rago, Francesca Toni

Counterfactual explanations (CEs) provide recourse recommendations for individuals affected by algorithmic decisions. A key challenge is generating CEs that are robust against various perturbation types (e.g. input and model perturbations) while simultaneously satisfying other desirable properties. These include plausibility, ensuring CEs reside on the data

Cheyu Lin, John Martins, Katherine A. Flanigan, Ph. D

Cyber-physical systems (CPS) integrate sensing, computing, and control to improve infrastructure performance, focusing on economic goals like performance and safety. However, they often neglect potential human-centered (or ''social'') benefits. Cyber-physical-social infrastructure systems (CPSIS) aim to address this by aligning CPS with social objectives. Th

Junfeng Cai, Marco Lovera

A novel active fault-tolerant control (AFTC) scheme for a dual-system vertical takeoff and landing (VTOL) unmanned aerial vehicle (UAV) during transition flight is proposed in this paper. The AFTC scheme is composed of a baseline control law and an online control reallocation module. First, the structured $H_{\infty}$ baseline control law is able to guarante

Victor May, Diganta Misra, Yanqi Luo, Anjali Sridhar

AI coding assistants are rapidly becoming integral to modern software development. A key challenge in this space is the continual need to migrate and modernize codebases in response to evolving software ecosystems. Traditionally, such migrations have relied on rule-based systems and human intervention. With the advent of powerful large language models (LLMs)

Dongge Han, Camille Couturier, Daniel Madrigal Diaz, Xuchao Zhang

We introduce LEGOMem, a modular procedural memory framework for multi-agent large language model (LLM) systems in workflow automation. LEGOMem decomposes past task trajectories into reusable memory units and flexibly allocates them across orchestrators and task agents to support planning and execution. To explore the design space of memory in multi-agent sys

Hengxiang Zhang, Hyeong Kyu Choi, Sharon Li, Hongxin Wei

Reasoning distillation has emerged as a prevailing paradigm for transferring reasoning capabilities from large reasoning models to small language models. Yet, reasoning distillation risks data contamination: benchmark data may inadvertently be included in the distillation data, thereby inflating model performance metrics. In this work, we formally define the

Elisei Rykov, Kseniia Petrushina, Maksim Savkin, Valerii Olisov

Hallucination detection remains a fundamental challenge for the safe and reliable deployment of large language models (LLMs), especially in applications requiring factual accuracy. Existing hallucination benchmarks often operate at the sequence level and are limited to English, lacking the fine-grained, multilingual supervision needed for a comprehensive eva

Yuto Nishida, Masaru Isonuma, Yusuke Oda

When training large language models (LLMs), it is common practice to track downstream task performance throughout the training process and select the checkpoint with the highest validation score. However, downstream metrics often exhibit substantial fluctuations, making it difficult to identify the checkpoint that truly represents the best-performing model.

G. Eurin, A. Delbart, R. De Oliveira, A. Drozd

The gain measurements of several 1 mm-thick, 10$\times$10 cm$^2$ Thick Gaseous Electron Multipliers (ThGEMs), operated in pure argon at 3.3 bar and room temperature are presented. Electrostatic simulations, performed with the COMSOL MultiPhysics software, were employed to guide the design of the detectors, and the field configurations are discussed. A modifi

E. Aprile, J. Aalbers, K. Abe, M. Adrover

We report the measurement of the $^{214}$Bi beta-decay spectrum to the ground state of $^{214}$Po using the XENONnT detector. This decay is classified as first-forbidden non-unique, for which theoretical predictions require detailed nuclear structure modeling. A dedicated identification algorithm isolates a high-purity sample of ground-state beta-decays, exp

Yi Luo, Matthew S. Powell, Joseph A. M. Paddison, Brenden R. Ortiz

We report single-crystal neutron spectroscopy and bulk characterization on hydrothermally grown Nd2Sn2O7, revealing a dynamical moment fragmentation embedded within the all-in-all-out ordered state. The spectra show a nearly flat band with pinch-point-like momentum dependence, accompanied by dispersive branches that generate half-moon features across multipl

Cheyu Lin, Katherine A. Flanigan

Understanding the dynamic relationship between humans and the built environment is a key challenge in disciplines ranging from environmental psychology to reinforcement learning (RL). A central obstacle in modeling these interactions is the inability to capture human psychological states in a way that is both generalizable and privacy preserving. Traditional

Paolo Lazzaroni, Shubham Sharma, Mariana Rossi

We present a first-principles machine-learning computational framework to investigate anharmonic effects in polarization-orientation (PO) Raman spectra of molecular crystals, focusing on anthracene and naphthalene. By combining machine learning models for interatomic potentials and polarizability tensors, we enable efficient, large-scale simulations that cap

Yorgos Felekis, Theodoros Damoulas, Paris Giampouras

Causal Abstraction (CA) theory provides a principled framework for relating causal models that describe the same system at different levels of granularity while ensuring interventional consistency between them. Recent methods for learning CAs, however, assume fixed and well-specified exogenous distributions, leaving them vulnerable to environmental shifts an

Jihong Huang, Tommy Ohlsson, Sampsa Vihonen, Shun Zhou

In this work, we emphasize that it is necessary to take into account one-loop corrections of $2.0\%$ to the neutrino matter potential in the precision measurements of neutrino oscillation parameters and in the experimental searches for new physics beyond the Standard Model. With the numerical simulation of the DUNE experiment, we study how radiative correcti

Viktor Kozák, Jan Chudoba, Libor Přeučil

An accurate and up-to-date model of a photovoltaic (PV) power plant is essential for its optimal operation and maintenance. However, such a model may not be easily available. This work introduces a novel approach for PV power plant mapping based on aerial overview images. It enables the automation of the mapping process while removing the reliance on third-p

Shuo Sha, Anupam Bhakta, Zhenyuan Jiang, Kevin Qiu

Accurate online inertial parameter estimation is essential for adaptive robotic control, enabling real-time adjustment to payload changes, environmental interactions, and system wear. Traditional methods often struggle to track abrupt parameter shifts or incur high computational costs, limiting their effectiveness in dynamic environments and for computationa

Muquan Li, Hang Gou, Dongyang Zhang, Shuang Liang

The growing demand for efficient deep learning has positioned dataset distillation as a pivotal technique for compressing training dataset while preserving model performance. However, existing inner-loop optimization methods for dataset distillation typically rely on random truncation strategies, which lack flexibility and often yield suboptimal results. In

Guillaume Godin

Bond Centered FingerPrint (BCFP) are a complementary, bond-centric alternative to Extended-Connectivity Fingerprints (ECFP). We introduce a static BCFP that mirrors the bond-convolution used by directed message-passing GNNs like ChemProp, and evaluate it with a fast rapid Random Forest model on Brain-Blood Barrier Penetration (BBBP) classification task. Acro

William Boulanger, Jakub Curda, Emma Harvey, Yizhi Li

Free independence is an important tool for studying the structure of operator algebras. It is natural to ask from the model-theoretic standpoint whether free independence is captured well in first-order model theory via the notion of a definable set. We prove that pairs of freely independent elements do not form a definable set in the sense of continuous mod

Wentao Gao, Renata Borovica-Gajic, Sang Kil Cha, Tian Qiu

Fuzzing is a highly effective automated testing method for uncovering software vulnerabilities. Despite advances in fuzzing techniques, such as coverage-guided greybox fuzzing, many fuzzers struggle with coverage plateaus caused by fuzz blockers, limiting their ability to find deeper vulnerabilities. Human expertise can address these challenges, but analyzin

Idan Attias, Lev Reyzin, Nathan Srebro, Gal Vardi

Despite the theoretical significance and wide practical use of regular expressions, the computational complexity of learning them has been largely unexplored. We study the computational hardness of improperly learning regular expressions in the PAC model and with membership queries. We show that PAC learning is hard even under the uniform distribution on the

Pablo Hidalgo-Palencia, Cody Hutcheson, Joseph Kasel

We provide very mild sufficient conditions for space-time domains (non-necessarily cylindrical) which ensure that the continuous Dirichlet problem and the H\"older Dirichlet problem are well-posed, for any parabolic operator in divergence form with merely bounded coefficients. Concretely, we show that the parabolic measure exists, even for unbounded domains,

Christopher Bartley, Anton Ragni

Nearly half of the world's languages are endangered. Speech technologies such as Automatic Speech Recognition (ASR) are central to revival efforts, yet most languages remain unsupported because standard pipelines expect utterance-level supervised data. Speech data often exist for endangered languages but rarely match these formats. Manx Gaelic ($\sim$2,200 s

Boyang Wu, Miguel Onorato, Zaher Hani, Yulin Pan

In this work, we provide a validity condition for the normal form transformation to remove the non-resonant cubic terms in the $\beta$-FPUT system. We show that for a wave field with random phases, the normal form transformation is valid by dominant probability if $\beta \ll 1/N^{1+\epsilon}$, with $N$ the number of masses and $\epsilon$ an arbitrarily small

Kalle Kytölä

We formalize in Lean certain calculational proofs about infinite-dimensional Lie algebras. Specifically, we construct the Virasoro algebra as a central extension of the Witt algebra associated with a nontrivial 2-cocycle, and we construct representations of the Virasoro algebra by Sugawara constructions.

Han Chang Chiam, Franz König, Martin Posch

Hybrid randomized controlled trials (hybrid RCTs) integrate external control data, such as historical or concurrent data, with data from randomized trials. While numerous frequentist and Bayesian methods, such as the test-then-pool and Meta-Analytic-Predictive prior, have been developed to account for potential disagreement between the external control and r

Ryan M. McMullen, Michael A. Gallis, Ishan Srivastava, Andrew J. Nonaka

Recently, Liao and Qin [J. Fluid Mech. 1008, R2 (2025)] claimed that numerical noise in direct numerical simulation of turbulence using the deterministic Navier-Stokes equations is "approximately equivalent" to the physical noise arising from random molecular motion (thermal fluctuations). We show here that it this claim not supported by their results and th

Mohammad Tahsin Alam, Zafrin Jahan Nikita, Ying Yin Tsui, Md. Zahurul Islam

Plasmonic resonances enable strong electromagnetic field confinement and have been widely exploited in plasmonic nanolasers, particularly through surface plasmon polaritons and localized surface plasmons. However, their performance is often limited by bidirectional output coupling and multimode far-field emission, primarily due to higher-order diffraction ar

Antonio Max

The rapid progression of Artificial General Intelligence (AGI) research demands conceptual tools capable of distinguishing between systems developed for open, commercial integration and those destined for sovereign, securitized deployments. Without such distinctions, risk assessments and regulatory debates collapse AGI into legacy dual-use frameworks that ar

Rui Wang, Yunfeng Xiong, Zhengru Zhang

The chemotaxis PDE system with singular sensitivity was originally proposed by Short et al. (Math. Mod. Meth. Appl. Sci., 2008) as the continuum limit of a biased random walk model to account for the formation of crime hotspots and environmental feedback successfully. Recently, this idea has also been applied to epidemiology to model the impact of human soci