April 2026 arXiv papers — page 89

Pablo Soberón

We give a new simple geometric proof that any seven points in the plane have four Tverberg partitions into three sets. This is the only confirmed non-trivial case of Sierksma's conjecture. Earlier proofs, by Stephan Hell, relied on topological arguments.

Kangan Qian, ChuChu Xie, Yang Zhong, Jingrui Pang

Vision-Language-Action (VLA) models drive next-generation autonomous systems, but training them requires scalable, high-quality annotations from complex environments. Current cloud pipelines rely on generic vision-language models (VLMs) that lack geometric reasoning and domain semantics due to their 2D image-text pretraining. To address this mismatch, we pro

Lino Reggiani, Eleonora Alfinito, Federico Intini

Although quantum transport at the nanoscale has received widespread attention since Landauer's pioneering work in 1957, we remark, that a general theory that sheds light on the difference between classical and quantum relativistic physical models is still lacking. By considering a classical 3D gas of non-interacting quasi.particles, the article presents a un

Sacha Huriot, Ihab Tabbara, Hussein Sibai

Safety filters have been shown to be effective tools to ensure the safety of control systems with unsafe nominal policies. To address scalability challenges in traditional synthesis methods, learning-based approaches have been proposed for designing safety filters for systems with high-dimensional state and control spaces. However, the inevitable errors in t

Abdeladhim Tahimi

This paper is a step-by-step, self-contained guide to the complete training cycle of a Physics-Informed Neural Network (PINN) -- a topic that existing tutorials and guides typically delegate to automatic differentiation libraries without exposing the underlying algebra. Using a first-order initial value problem with a known analytical solution as a running e

K. R. Goodearl, E. S. Letzter

An associative division algebra D is said to be _affine_ over a central subfield k if D is finitely generated as a k-algebra. In 1956 Amitsur famously proved that, when k is uncountable, D cannot be k-affine unless D is algebraic over k. In this paper we consider affineness -- and nonaffineness -- for certain naturally occurring classes of division algebras

Soumyadip Paul, Sourav Banerjee, Debanjan Bhowmik, Neel Kanth Kundu

Data detection in large-scale multiple-input multiple-output (MIMO) systems with higher-order quadrature amplitude modulation (QAM) remains a challenging problem due to the exponential complexity of the classical maximum likelihood (ML) detector. This challenge is further amplified by Gray-coded modulation, which introduces nonlinear symbol-to-bit mappings a

Harish Santhanalakshmi Ganesan

Persistent memory is the bottleneck separating stateless chatbots from long-running agentic systems. Retrieval-augmented generation (RAG) over flat vector stores fragments facts into chunks, loses cross-session identity, and has no first-class notion of supersession or contradiction. Recent bitemporal knowledge-graph systems (Graphiti, Memento, Hydra DB) add

Sarwan Ali, Taslim Murad

Biological classification with interpretability remains a challenging task. For this, we introduce a novel encoding framework, Multi-Scale Reversible Chaos Game Representation (MS-RCGR), that transforms biological sequences into multi-resolution geometric representations with guaranteed reversibility. Unlike traditional sequence encoding methods, MS-RCGR emp

Hao Vo, Khoa Vo, Thinh Phan, Ngo Xuan Cuong

Camera-only 3D object detection has emerged as a cost-effective and scalable alternative to LiDAR for autonomous driving, yet existing methods primarily prioritize overall performance while overlooking the severe long-tail imbalance inherent in real-world datasets. In practice, many rare but safety-critical categories such as children, strollers, or emergenc

Ravi Ranjan, Shubh Narayan Singh, Surbhi Kumari, Shidra Jamil

The prime-coprime graph $\Theta(G)$ of a finite group $G$ is the simple graph with vertex set $G$, where two distinct elements are adjacent whenever the greatest common divisor of their orders is either $1$ or a prime. We characterize all finite groups $G$ for which $\Theta(G)$ is a split graph. We establish a general lower bound for the independence number

Jacob Morrison, Sanjay Adhikesaven, Akshita Bhagia, Matei Zaharia

Extending a fully post-trained language model with new domain capabilities is fundamentally limited by monolithic training paradigms: retraining from scratch is expensive and scales poorly, while continued training often degrades existing capabilities. We present BAR (Branch-Adapt-Route), which trains independent domain experts, each through its own mid-trai

Vivek Shrivastav, Mani K Chettri, Britan Singh, Hemam D. Singh

Recent Magnetospheric Multiscale (MMS) observations report approximate equality between electric and magnetic field energy spectral densities, $\varepsilon_{0} P[\delta E]/2 \approx P[\delta B]/(2\mu_{0})$, at sub-electron scales in reconnection-driven magnetotail turbulence, interpreted as relaxation toward thermodynamic equilibrium. We derive the electric-

Enshu Liu, Xuefei Ning, Yu Wang, Zinan Lin

Discrete diffusion language models (dLLMs) have recently emerged as a promising alternative to traditional autoregressive approaches, offering the flexibility to generate tokens in arbitrary orders and the potential of parallel decoding. However, existing heuristic sampling strategies remain inefficient: they choose only a small part of tokens to sample at e

Beatrice Caon, Mattia Corti, Francesca Bonizzoni, Paola F. Antonietti

Alzheimer's disease is the most common neurodegenerative disorder. Its pathological development is connected with the misfolding and accumulation of two toxic proteins: amyloid-beta and tau proteins. Mathematical models provide a valuable quantitative tool for monitoring disease progression. In this work, we proposed and compare a novel framework where the s

Jonas Sievers, Mardavij Roozbehani

Baseline estimation is critical to Demand Response (DR) settlement in electricity markets, yet existing machine learning methods remain limited in predictive performance, while methodologies from causal inference and counterfactual prediction are still underutilized in this domain. We introduce a Generalized Synthetic Control Method that builds on the classi

Tianshi Cao, Jiawei Ren, Yuxuan Zhang, Jaewoo Seo

Closed-loop simulation is a core component of autonomous vehicle (AV) development, enabling scalable testing, training, and safety validation before real-world deployment. Neural scene reconstruction converts driving logs into interactive 3D environments for simulation, but it does not produce complete 3D object assets required for agent manipulation and lar

Chupei Tang, Junxiao Kong, Moyu Tang, Di Wang

Motivation: Peptide-protein interactions (PepPIs) are central to cellular regulation and peptide therapeutics, but experimental characterization remains too slow for large-scale screening. Existing methods usually emphasize either interaction prediction or peptide generation, leaving candidate prioritization, residue-level interpretation, and target-conditio

Michi Hostettler, Xavier Buffat, Tobias Persson, Tatiana Pieloni

The TRAIN code, developed in 1995 as a post-processor for second-order transport maps from MAD, has been used extensively at the LEP and the LHC to study self-consistent closed orbits, tunes and chromaticities of bunch trains under the presence of beam-beam long-range (BBLR) and PACMAN effects.. This paper presents a modern re-implementation of the TRAIN con

Yidi Yuan

Semantic Tube Prediction (STP) leverages representation geometric to regularize LLM hidden-state trajectories toward locally linear geodesics during fine-tuning, thereby greatly improving data efficiency. The original STP recipe samples random token sub-spans, which is compatible with the base large language model (LLM) training architecture. Inspired by STP

Tao Zhang, Kaixian Qu, Zhibin Li, Jiajun Wu

Large language models are increasingly used as planners for robotic systems, yet how safely they plan remains an open question. To evaluate safe planning systematically, we introduce DESPITE, a benchmark of 12,279 tasks spanning physical and normative dangers with fully deterministic validation. Across 23 models, even near-perfect planning ability does not e

Eric C. P. Breard, Claudia Elijas Parra, Mattia de' Michieli Vitturi

Excess pore pressure in granular--fluid mixtures can transiently suppress frictional contacts and dramatically enhance flow mobility, yet its evolution is commonly modeled using constant effective diffusivities. Here we show that the apparent diffusivity is not intrinsic but emerges from the coupling between pore-pressure diffusion and granular compaction. S

Hyundae Lee, Matias Ruiz, Sanghyeon Yu

We present a rigorous spectral analysis of plasmonic resonances in the nonlocal regime of spatially dispersive media. We adopt the quasi-static approximation of the hydrodynamic Drude model, which provides an analytically tractable setting to account for nonlocal effects. By reformulating the governing equations as a boundary integral system, we obtain an an

Sijie Mai, Shiqin Han

Multimodal affective computing aims to predict humans' sentiment, emotion, intention, and opinion using language, acoustic, and visual modalities. However, current models often learn spurious correlations that harm generalization under distribution shifts or noisy modalities. To address this, we propose a causal modality-invariant representation (CmIR) learn

Kecheng Zhang, Zongxin Yang, Mingfei Han, Haihong Hao

Visual agents operating in the wild must respond to queries precisely when sufficient evidence first appears in a video stream, a critical capability that is overlooked by conventional video LLMs evaluated in offline settings. The shift to an online, streaming paradigm introduces significant challenges: a lack of decision transparency, the difficulty of alig

Tattwamasi Amrutam, Yongle Jiang

We introduce the notion of confined subalgebras in the context of the group von Neumann algebra. We also define Uniformly Recurrent States -- an operator-algebraic analog of Uniformly Recurrent Subgroups. Using this framework, we show that a countable discrete group is $C^*$-simple if and only if it admits no non-trivial amenable confined subalgebras. This g

Benno Lossin

We study $\infty$-categories in the synthetic simplicial type theory developed by Riehl and Shulman. In particular, we define cocartesian fibrations and prove their closure properties using a novel equivalence between LARI adjunctions and initial sections. We formalize our work using the experimental proof assistant rzk and upstream our work to the formaliza

Edison S. Carrera, Grégoire Misguich

Rydberg atom arrays are powerful platforms for studying quantum many-body systems. We consider the Rydberg-Ising Hamiltonian on periodic chains and numerically study ensembles of states generated by random global pulse sequences subject to hardware constraints and fixed evolution times. We compare the statistical properties of such states with those of Haar-

R. Schwengelbeck, M. Pandini, R. Daraban, J. Schachenmayer

We analyze vibrational dynamics in a toy model for polaritonic chemistry under collective electronic strong coupling. In a Holstein-Tavis-Cummings model, incoherently excited by a photon, we show that disorder leads to non-Gaussian states of vibrational modes on short time scales at the single-molecule level. Using exact matrix product state simulations, we

Yutian Pang, Daniel Delahaye, John-Paul Clarke

Terminal airspace congestion remains a major bottleneck in the global air traffic network. Although the Aircraft Sequencing and Scheduling Problem (ASSP) has been widely studied, many methods rely on simplified node-link abstractions that ignore the practical flight path, producing schedules that can be hard to execute under real airspace geometric constrain

Manuel Klädtke, Feiran Zhao, Florian Dörfler, Moritz Schulze Darup

This paper proposes an explainability concept for direct data-driven linear quadratic regulation (LQR) with quadratic regularization. Our perspective follows the parametric effect of regularization, an analysis approach that translates regularization costs from auxiliary variables to system quantities, enabling intuitive interpretations. The framework furthe

Clayton Fields, Casey Kennington

Vision-language modeling is rapidly increasing in popularity with an ever expanding list of available models. In most cases, these vision-language models have parameters in the tens of billions, which is necessary for some needs, but in many cases smaller models are necessary (e.g., on edge devices or independent robotic platforms). Unfortunately, there is l

Florentin Coeurdoux, Grégoire Ferré, Jean-Philippe Bouchaud

Empirical studies of trained models often report a transient regime in which signal is detectable in a finite gradient descent time window before overfitting dominates. We provide an analytically tractable random-matrix model that reproduces this phenomenon for gradient flow in a linear teacher--student setting. In this framework, learning occurs when an iso

Jiyao Wang, Song Yan, Xiao Yang, Qihang He

Silent automation failures, where a system fails to detect a hazard without warning, pose a critical safety challenge for partially automated vehicles. While research has mostly focused on takeover requests, how to support a driver in silent failure remains underexplored. We conducted a multi-modal driving simulator study with 48 participants to investigate

Nicholas R. Allgood

We study the preservation and stability of synchronization subspaces in tensor products of finite-dimensional Hilbert spaces. Given self-adjoint operators $T_A$ and $T_B$ on local subsystems, the synchronization subspace is defined as the kernel of the difference operator $K = T_A \otimes I - I \otimes T_B$. We establish two main results: First for $\epsilon

Chenzhi Xu, Jiawei Yan, Ye Chen, Winfried Decking

Attosecond pulses from X-ray free-electron laser (XFEL) have opened new opportunities for probing ultrafast electronic dynamics on the Angstrom--attosecond spatiotemporal scale. Most attosecond XFEL concepts rely on generating an ultrashort high-current spike through either external laser modulation or accelerator-based beam manipulation. Despite their diffe

Tomasz Szołdra, Matheus S. Palmero, Peter Schmelcher

Observables of out-of-equilibrium quantum many-body systems display complex temporal behavior that encodes the underlying physical mechanisms but typically resists straightforward interpretations. We introduce recurrence analysis - a nonlinear time-series analysis framework long established for classical dynamical systems - to investigate correlated quantum

Chongxiao Li, Pengwei Jin, Di Huang, Guangrun Sun

Performance, power, and area (PPA) optimization is a fundamental task in RTL design, requiring a precise understanding of circuit functionality and the relationship between circuit structures and PPA metrics. Recent studies attempt to automate this process using LLMs, but neither feedback-based nor knowledge-based methods are efficient enough, as they either

Florian Kittler, Sheethal Bhat, Andreas Maier

Zero-shot vision-language models (VLMs) have shown promise for chest radiograph classification, but their performance is often limited by confounding label co-occurrence, long-tail class imbalance, and transfer instability under domain shift. We propose ProtoCLIP, a refinement strategy for CLIP-style VLMs that improves zero-shot discrimination through target

Alexandre Carvunis, Gael Finauri, Paolo Gambino, Martin Jung

We present the first global phenomenological fit of inclusive $\bar B\to X_c\ell\bar\nu$ observables to all available experimental data allowing for generic dimension-six New Physics interactions in the Weak Effective Theory. The fit includes both New Physics Wilson coefficients and non-perturbative QCD parameters. New Physics contributions are calculated in

Sergey A. Denisov, Giorgio Young

We study the wave operators for a Jacobi matrix whose spectral measure satisfies the Szeg\"o condition. We prove existence and completeness of wave operators under a mild additional assumption on the Verblunsky coefficients of the associated measure on the unit circle.

Alejandro Cholaquidis, Emilien Joly, Leonardo Moreno

Conformal prediction provides finite-sample, distribution-free coverage under exchangeability, but standard constructions may lack robustness in the presence of outliers or heavy tails. We propose a robust conformal method based on a non-conformity score defined as the half-mass radius around a point, equivalently the distance to its $(\lfloor n/2\rfloor+1)$

Jan Nowosielski, Marcin Jastrzębski, Wojciech Wasilewski, Mateusz Mazelanik

Rydberg atom-based sensors have emerged as highly sensitive tools for terahertz (THz) metrology, yet most current imaging techniques discard crucial phase information. In this Letter, we present a coherent THz-to-optical conversion scheme in warm Rb vapor that enables complex-amplitude field imaging. By manipulating the phase-matching conditions via an adjus

Shen Sun, Wei Tang, Weiwei He, Igor Polozov

Modelling fracture behavior of the shape memory alloy (SMA) that interacts with martensitic transformation and the associated elastocaloric effect (eCE) still remains challenging. Herein, a thermo-mechanically coupled phase-filed fracture model considering elastocaloric effect of SMA is proposed to simulate the cracking process coupled with the non-isotherma

Hanfeng Zhai, Hongtao Qiao, Hassan Mansour, Christopher Laughman

We present a scalable, data-driven simulation framework for large-scale heating, ventilation, and air conditioning (HVAC) systems that couples physics-informed neural ordinary differential equations (PINODEs) with differential-algebraic equation (DAE) solvers. At the component level, we learn heat-exchanger dynamics using an implicit PINODE formulation that

Tommaso Giacometti, Paola Surcinelli, Mariachiara Stellato, Nico Curti

Background: The Rorschach inkblots are ambiguous stimuli developed to evoke subjective interpretations in humans, while modern artificial intelligence (AI) models are trained to recognize well established patterns and classes. The comparison of these two opposite systems arises a simple and provocative question: what happens when we ask an AI model to interp

Otto Overkamp, Ismaele Vanni

Let $\mathcal{O}_K$ be a complete discrete valuation ring with field of fractions $K$ and algebraically closed residue field $k.$ Let $G$ be a smooth connected commutative algebraic group over $K$ which does not contain a copy of $\mathbf{G}_{\mathrm{a}}.$ For each $d$ prime to $p:=\mathrm{char}\, k,$ let $K(d)$ be the unique extension of $K$ of degree $d.$

Junzhe Xiao, Zekun Niu, Lyu Li, Minghui Shi

In this paper, the quasi-constant modulus (QCM) property is analyzed and leveraged in the design of nonlinearity-tolerant four-dimensional (4D) modulation formats. Accordingly, we propose a family of QCM-based quadrature amplitude modulation (QCM-QAM) constellations with high spectral efficiencies (SEs) of 9, 11, and 13 bit/4D-sym, respectively. The quasi-co

Anisha Sen, S. P. Rajaguru, Ruizhu Chen, Junwei Zhao

Using time-distance helioseismology applied to 14-years of SDO/HMI observations spanning solar cycle 24 and rising phase of cycle 25, we present evidence that meridional flows in the lower half of the near-surface shear layer (NSSL), modulated by active-region magnetic fields, play a central role in the episodic global transport of magnetic flux. In particul

Armin Nurkanović, Anton Pozharskiy, Moritz Diehl

Model predictive control (MPC) of hybrid dynamical systems is challenging because the associated optimization problem is nonsmooth and the resulting feedback law is discontinuous. This paper develops real-time MPC algorithms for nonlinear hybrid systems modeled as dynamical complementarity systems. The resulting optimal control problems are formulated as mat

Simon D. Lindner, Elisabeth L. Zeilinger, Amelie Fuchs, Simone Lubowitzki

Treatment of cancer involves heterogeneous, complex care pathways. The relationship between these longitudinal trajectories, baseline mental health, and prognostic outcomes remains poorly understood. We introduce an interpretable time-analysis framework leveraging these temporal dynamics, analyzing care patterns spanning up to 37 years for >8,000 patients. U

Howard Hui, Chi Nguyen, Ryan Wills, Katrina Bossert

We present measurements of near-infrared (NIR) terrestrial airglow produced by helium and oxygen in the exosphere as observed by SPHEREx. Using eight months of survey data obtained from a 680 km low-Earth orbit, emission from HeI $\lambda$10830, OI $\lambda$8446, and OI $\lambda$11287 is mapped with both global spatial and multi-season temporal coverage. The

Yakoub Bazi, Mohamad M. Al Rahhal, Mansour Zuair, Faroun Mohamed

Change visual question answering (Change VQA) addresses the problem of answering natural-language questions about semantic changes between bi-temporal remote sensing (RS) images. Although vision-language models (VLMs) have recently been studied for temporal RS image understanding, Change VQA remains underexplored in the context of modern multimodal models. I

Alejandro Lopez-Bezanilla

Alterelectricity is a compensated ferroic state in which quadrupolar electronic order reshapes low-energy electronic structure without producing a net polarization. Here we show that a moir\'e superlattice can turn such order into a controllable phase. Within a Bloch-periodic two-orbital theory, the slowly varying interlayer registry is coarse-grained into a

Raman Kumar, Sebastian Will

Fiber-integrated micro-optical elements promise a scalable approach to photon collection and beam shaping for quantum information processing. Here, we demonstrate single-step fabrication of micro-spherical, micro-spiral, and micro-axicon structures directly on the core of single-mode optical fibers using focused ion beam (FIB) machining with nanometer-scale

Luis Eduardo Ramos-Solís, Sayan Choudhury, Freddy Jackson Poveda-Cuevas, Eduardo Ibarra-García-Padilla

In recent years, the dynamics of interacting quantum systems far from equilibrium have attracted significant research interest. Driven by rapid progress in quantum simulators, various non-equilibrium phenomena have now been realized experimentally. In this work, we use the time-evolving block decimation (TEBD) method to investigate the dynamics of an anisotr

Sara Ghasvarianjahromi, Joshua Barr, Yauhen Yakimenka, Jörg Kliewer

This paper introduces and analyzes a search and retrieval model for RAG-like systems under {token} erasures. We provide an information-theoretic analysis of remote document retrieval when query representations are only partially preserved. The query is represented using term-frequency-based features, and semantically adaptive redundancy is assigned according

Raghvendra Kumar, Devankar Raj, Sriparna Saha

India's linguistic landscape, spanning 22 scheduled languages and hundreds of marginalized dialects, has driven rapid growth in NLP datasets, benchmarks, and pretrained models. However, no dedicated survey consolidates resources developed specifically for Indian languages. Existing reviews either focus on a few high-resource languages or subsume Indian langu

Zijian Li, Chenyu Xu, Xin Hua, Yongqiang Du

Integrated photonics is widely regarded as a key enabler for scalable quantum key distribution (QKD), offering compactness, stability, and compatibility with semiconductor fabrication. Despite rapid advances in chip-based QKD, the implementation security of integrated photonic components remains insufficiently understood. Here we present the first systematic

Margaret Campbell-Brown

We report on an eight year survey of simultaneous optical and radar meteor detections with the goal of isolating the fraction of meteors missed by specular radars. A total of 10,503 Electron Multiplied Charge Couple Device (EMCCD) meteors with peak brightness above +7 were simultaneously detected by the Canadian Meteor Orbit Radar (CMOR) and used to estimate

Michal Valko, Rémi Munos, Branislav Kveton, Tomáš Kocák

Smooth functions on graphs have wide applications in manifold and semi-supervised learning. In this paper, we study a bandit problem where the payoffs of arms are smooth on a graph. This framework is suitable for solving online learning problems that involve graphs, such as content-based recommendation. In this problem, each item we can recommend is a node a

Jiyao Liu, Jianghan Shen, Sida Song, Tianbin Li

Recent advances in deep research systems enable large language models to retrieve, synthesize, and reason over large-scale external knowledge. In medicine, developing clinical guidelines critically depends on such deep evidence integration. However, existing benchmarks fail to evaluate this capability in realistic workflows requiring multi-step evidence inte

Serin Lee, Lin Yuan, Elijah Begin, Dali Yang

Methane (CH4) and nitrous oxide (N2O) are potent greenhouse gases that represent substantial chemical energy. Conversion of these abundant waste gases to high-value chemicals typically requires high temperatures up to 1000 C, producing substantial CO2 emissions and limited selectivity toward desirable multi-carbon products. Here we demonstrate a plasmonic ph

Guillermo Ballesteros, Jesús Gambín Egea, Alejandro Pérez Rodríguez

We compare the correlation functions of inflationary perturbations computed either with quantum or classical dynamics. Even if they are enforced to agree at a specific time during inflation, classical and quantum correlations will differ at the end of inflation, provided that interactions are relevant. The difference between the results of the classical and

Gustavo Castillo, Nicolás Mujica

We investigate the kinetics of particle aggregation within the framework of the Smoluchowski coagulation equation, extending it to account for electrostatic interactions among charged clusters. Using a stochastic Monte Carlo implementation, we examine how different charge distributions and net system charge affect cluster growth dynamics. Electrostatic inter

Zhenhua Dang, Lei Zhang, Long Wang, Guowei He

Data-driven discovery of governing equations has advanced significantly in recent years; however, existing methods often struggle in multiscale systems where dynamically significant terms may have small coefficients. Therefore, we propose Balance-Guided SINDy (BG-SINDy) inspired by the principle of dominant balance, which reformulates $\ell_0$-constrained sp

Junsong Pu, Yichen Li, Zhuangbin Chen

Graph-based code indexing can improve context retrieval for LLM-based code agents by preserving call chains and dependency relationships that keyword search and similarity retrieval often miss. ABCoder is an open-source framework that parses codebases into a function-level code index called UniAST. Its existing parsers combine lightweight AST parsers for syn

Riccardo Cipolloni, Federico Ricci-Tersenghi, Francesco Zamponi

Activated transitions have rates that are often exponentially small in system size. Extracting the associated activation barriers is challenging in practice, especially in the deeply metastable regimes and in the presence of disorder. Here, we use transition path sampling to evaluate transition probabilities between ferromagnetic states in the Ising model on

Boyu Yao, Yury Dvorkin

Power system expansion depends on the equipment required to connect, convert, regulate, and condition electricity, yet grid-supporting equipment (GSE) is rarely modeled as an explicit constraint. We develop a framework integrating dynamic stock-flow modeling, bill-of-materials accounting, multi-regional supply-use analysis, and expansion optimization to quan

Asad Husein, Kimmo Rasilainen, Juha-Pekka Mäkelä, Veikko Hovinen

The evolution of large aperture antennas and arrays at the sub-THz band (100-300 GHz) results in traditional far-field (FF) gain measurements to require large distances due to the high frequency nature making them impractical in many laboratory environments. In the presented work, absolute antenna gain measurements are performed in localized distance cluster

Pablo Ochoa, Ariel Salort

In this article, we study certain transcendental function spaces arising in potential theory within the framework of Orlicz spaces. Specifically, we generalize Bessel and Lizorkin-Triebel spaces to the nonstandard setting of Orlicz spaces. We recover classical results from potential theory, such as the fact that Bessel-Orlicz spaces of integer order coincide

Liangzhu Leon Wang, Huiheng Liu, Honghao Fu, Zhipeng Deng

Quantum computing is a new approach to computation that utilizes superposition, entanglement, interference, and tunneling to solve problems too complex for classical computers. This paper discusses the basic concepts and development of quantum computing, exploring its potential applications in the built environment and urban microclimate research. In buildin

Susanne C. Brenner, Li-yeng Sung, Jai Tushar

We design and analyze virtual element methods for a quad-curl problem on general polygonal domains that are based on the Hodge decomposition of divergence-free vector fields. Numerical results that corroborate the theoretical analysis are also presented.

Dominik Neuenfeld, Christopher Tellinger

We study the effect of boundary deformations on the von Neumann entropy of subregions in two-dimensional boundary conformal field theories (BCFTs) at zero and finite temperature. The deformations considered are infinitesimal global conformal transformations that move the boundary and can equivalently be viewed as the leading-order effect of certain BCFT pert

Chad Coleman, W. Russell Neuman, Manan Shah, Ali Dasdan

We present Six Llamas, a comparative study examining whether large language models fine-tuned on distinct religious corpora encode systematically different patterns of ethical reasoning. Six variants of Meta-Llama-3.1-8B are constructed: one unmodified control and five LoRA-adapted models trained exclusively on the sacred and theological texts of Christianit

Tim S. Lyon, Eugenio Orlandelli

We introduce cut-free nested sequent systems for a broad class of quantified modal logics (QMLs). The QMLs we consider are semantically defined using relational models that assign both an inner and outer domain to each world. This rich model structure enables the specification of various QMLs by enforcing different frame conditions, including increasing, dec

Othmane Aboussaad, Adam Miraoui, Boumediene Hamzi, Houman Owhadi

Selecting an appropriate kernel is a central challenge in kernel-based spectral methods. In \emph{Kernelized Diffusion Maps} (KDM), the kernel determines the accuracy of the RKHS estimator of a diffusion-type operator and hence the quality and stability of the recovered eigenfunctions. We introduce two complementary approaches to adaptive kernel selection fo

Oleksiy Dovgoshey, Olga Rovenska

Let $G=(V,E)$ be a finite connected graph with vertex set $V$ and edge set $E$, and let $U(G)$ be the set of all ultrametric spaces $(V,d_l)$ generated by vertex labelings $l\colon V \to \mathbb R^+$. We prove that the inequality $$ |D(V)| \le |E| + 1 $$ holds for all $(V,d_l) \in U(G)$, where $D(V)$ is the distance set of $(V,d_l)$. The necessary and suffic

Takato Yasuno

Daily infrastructure management in preparation for disasters is critical for urban resilience. When bridges remain resilient against disaster-induced external forces, access to hospitals, shops, and residences via metapaths can be sustained, maintaining essential urban functions. However, prioritizing bridge maintenance under limited budgets requires quantif

Yixuan Wang, Yue Huang, Hong Qian, Yunzhao Wei

Creativity has become a core competence in the era of LLMs and human-AI collaboration, underpinning innovation in real-world problem solving. Crucially, the systematic improvement of creativity necessitates scientifically valid assessment instruments. Psychometric research recognizes context-based assessment as an effective way to measure creative thinking.

Yu-Cheng Qiu, Yongchao Zhang

The axion-like particles $a$ can be produced in the Sun via the process of $p + D \to {}^3{\rm He} +a$, with mass up to 5.5 MeV. The photons in the subsequent decay $a \to \gamma\gamma$ can deviate significantly from the Sun, or even from roughly the opposite direction of the Sun. The nontrivial angular and spectral distributions of such photons enable us ne

Bowen Cai, Weiheng Bai, Hangyun Tang, Youshui Lu

Smart contracts extended blockchain functionality beyond simple transactions, powering complex applications like decentralized finance (DeFi). However, this complexity introduces serious security challenges, including price manipulation and inflation attacks. Despite the development of various security tools, the rapid rise in financially motivated exploits

Yilei Jiang, Jinyuan Hu, Qianyin Xiao, Yaozhi Zheng

Game development sits at the intersection of creative design and intricate software engineering, demanding the joint orchestration of game engines, real-time loops, and tightly coupled state across many files. While Large Language Models (LLMs) and code agents now solve isolated programming tasks with ease, they consistently stumble when asked to produce a f

Boan Zhang, Wen Li, Guanhua Yu, Xiyang Liu

Diffusion models have achieved outstanding performance in unsupervised industrial anomaly detection (uIAD) by learning a manifold of normal data under the common assumption that off-manifold anomalies are harder to generate, resulting in larger reconstruction errors in data space or lower probability densities in the tractable latent space. However, their it

Miroslav Kosanic, Marija Ilic

AI data center loads create query-driven power transients on millisecond timescales. Such loads can violate the timescale separation assumptions underlying internal inverter control of grid-following resources collocated with data centers as supplementary generation. This paper develops a singular perturbation-based modeling and control for stabilizing fast

Alex Jäger, Gerhard Kramer

A non-iterative phase noise compensation method based on the sum-product algorithm (SPA) is applied to the outputs of intersymbol interference (ISI) channels. The outputs are modeled as independent Gaussian random variables, and the receiver applies mismatched processing with von Mises statistics. The performance is compared with that of linear minimum-mean-

Esteban Rodríguez-Betancourt, Edgar Casasola-Murillo

In self-supervised learning, self-distilled methods have shown impressive performance, learning representations useful for downstream tasks and even displaying emergent properties. However, state-of-the-art methods usually rely on ensembles of complex mechanisms, with many design choices that are empirically motivated and not well understood. In this work, w

Yang Liu, Chenhui Chu

Prompt sensitivity, which refers to how strongly the output of a large language model (LLM) depends on the exact wording of its input prompt, raises concerns among users about the LLM's stability and reliability. In this work, we consider LLMs as multivariate functions and perform a first-order Taylor expansion, thereby analyzing the relationship between mea

Mei Dong, Linbo Wang, Lin Liu, Oliver Dukes

We discuss the regression-by-composition framework of Farewell, Daniel, Stensrud and Huitfeldt, highlighting a key consequence of its sequential construction: order dependence. Reordering the flows may change the implied conditional distribution, the interpretation of model parameters, and the associated estimation problem, with consequences for model specif

Samuel D. Escribano, Yael Kriheli, Samuel Goldstein, Daniel Dahan

We propose a broadband Purcell-protection scheme based on a single shared filter integrated directly into the feedline, enabling simultaneous protection of multiple qubits in a compact architecture with minimal hardware overhead. The filter consists of two open-ended stubs connected by an in-line transmission line, forming a $\Pi$ geometry, and operates via

Rupert L. Frank, Long Meng, Phan Thành Nam, Heinz Siedentop

We study the spectral properties of minimizers of the M\"uller functional for atoms and molecules with $N$ electrons and total nuclear charge $Z$. We prove that under some suitable assumptions on $Z$ and $N$, the $k$-th eigenvalue of a M\"uller minimizer $\gamma_*$ behaves as $A_* k^{-8/3}$ when $k\to \infty$, with a constant $A_*>0$ determined explicitly by

Daniel Russo

Software engineering faces a fundamental challenge: multi-agent AI systems fail in ways that defy explanation by traditional theories. While individual agents perform correctly, their interactions degrade entire ecosystems, revealing a gap in our understanding of software evolution. This paper argues that AI-native software ecosystems must be studied as comp

Yang Zhang, Ling-Fang Lin, Thomas A. Maier, Elbio Dagotto

The discovery of superconductivity with Tc ~ 80 K in the nickelate Ruddlesden-Popper bilayer La3Ni2O7 at high pressure has opened a new platform for unconventional superconductivity, followed by the subsequent observation of superconductivity in trilayer La4Ni3O10, also at high pressure. Remarkably, ambient-pressure superconductivity was also observed recent

Andrew Cochran, Harshvardhan Gupta, Vishal Jain, Maysamreza Chamanzar

We introduce a novel electro-optomechanic neural sensor for realizing ultra-compact neural recording probes that can detect and relay electrophysiology signals from within neural tissue. This technology addresses outstanding challenges faced by existing neural recording technologies, including the resolution trade-off with signal-to-noise-ratio (SNR) due to

Markus B. Fröb, Leonardo Sangaletti

We show how one can use the convexity of non-commutative $L^p$ norms to bound the relative entropy between a faithful state on a von Neumann algebra and an arbitrary excitation thereof. Our results hold for general von Neumann algebras, including the local algebras of type III that are ubiquitous in quantum field theory, and do not require knowledge of the r

Justin Bauer, Thomas Walshe, Derek Pham, Harit Vishwakarma

Fine-tuning Large Language Models (LLMs) typically relies on large quantities of high-quality annotated data, or questions with well-defined ground truth answers in the case of Reinforcement Learning with Verifiable Rewards (RLVR). While previous work has explored the benefits to model reasoning capabilities by scaling both data and compute used for RLVR, th

Alexandra Volokhova, Alex Hernandez-Garcia

Artificial intelligence (AI) technologies are increasingly used in modern weapons systems. Notably, these systems have recently been involved in mass killings and destruction at scale. Furthermore, there is currently a strong interest and competition among powerful players to accelerate the proliferation of weapons with automated or AI-based components, a ph

Jonathan Oel, Lucas Porth, Peter Schneider, Elena Silvestre-Rosello

Traditional galaxy-galaxy lensing is a well-established method of probing the statistical properties of the Universe's matter and galaxy distribution. However, this measure does not carry all the statistical information, provided the matter and galaxy distribution contain non-Gaussian features. In order to study these non-Gaussianities, it is necessary to co

Chao Yuan, Yujian Zhao, Haoxuan Xu, Guanglin Niu

In text-to-image person retrieval tasks, the diversity of natural language expressions and the implicitness of visual semantics often lead to the problem of Expression Drift, where semantically equivalent texts exhibit significant feature discrepancies in the embedding space due to phrasing variations, thereby degrading the robustness of image-text alignment

Hongwei Zheng, Weiqi Wu, Zhengjia Wang, Guanyu Jiang

Conversational agents, such as ChatGPT and Doubao, have become essential daily assistants for billions of users. To further enhance engagement, these systems are evolving from passive responders to proactive companions. However, existing efforts focus on activation within ongoing dialogues, while overlooking a key real-world bottleneck. In the conversation i