March 2026 arXiv papers — page 126

Di Yu, Sixiong You, Chaoying Pei

Coordinating large populations of autonomous agents, such as UAV swarms or satellite constellations, poses significant computational challenges for traditional multi-agent control methods. This paper introduces a new optimization framework for large-population control, termed occupation-measure mean-field control (OM-MFC). The framework models the evolution

Alejandro Paredes La Torre

Multimodal generative models have shown remarkable progress in single-modality video and audio synthesis, yet truly joint audio-video generation remains an open challenge. In this paper, I explore four key contributions to advance this field. First, I release two high-quality, paired audio-video datasets. The datasets consisting on 13 hours of video-game cli

Shin'ya Yamaguchi, Daiki Chijiwa, Tamao Sakao, Taku Hasegawa

Large vision-language models (LVLMs) employ multi-modal in-context learning (MM-ICL) to adapt to new tasks by leveraging demonstration examples. While increasing the number of demonstrations boosts performance, they incur significant inference latency due to the quadratic computational cost of Transformer attention with respect to the context length. To addr

Nicholas Milson, Arina Tashchilina, Kathleen Tamura, Douglas Florizone

Higher-dimensional Hilbert spaces in quantum simulation, as in all quantum science, expand the range of accessible phenomena. In this work, we experimentally realize a synthetic monopole using an ultracold spin-1 ensemble, where the monopole charge is quantified by the topologically invariant first Chern number and sources a synthetic magnetic field quantifi

Zhe-Qi Yang, Xiao-Yu Bi, Zhi-Rong Zhong

Manipulating the cavity-magnon polariton blockade is significant for achieving precise, on-demand control of individual photons (magnons) and has particular applications in realizing multifunctional quantum technologies, quantum information processing, and hybrid quantum networks. In this paper, we theoretically propose a scheme to realize an $n$-cavity-magn

Hui Wang, Tianzhu Hu, Mingming Li, Xi Zhou

Recommender systems are inherently dynamic feedback loops where prolonged local interactions accumulate into macroscopic structural degradation such as information cocoons. Existing representation learning paradigms are universally constrained by the assumption of a single flat space, forcing topologically grounded user associations and semantically driven h

Mikito Koga, Masashige Matsumoto

We present a new method of acoustically driven resonance that probes octupole degrees of freedom as well as a quadrupole usually hidden by the magnetic properties of a crystal field quartet. A characteristic of the quadrupole is reflected in the anisotropic resonance transition rate, which depends on the propagation direction of a surface acoustic wave under

Chang Nie, Tianchen Deng, Guangming Wang, Zhe Liu

While recent Vision-Language-Action (VLA) models have begun to incorporate audio, they typically treat sound as static pre-execution prompts or focus exclusively on human speech. This leaves a significant gap in real-time, sound-centric manipulation where fleeting environmental acoustics provide critical state verification during task execution. Consequently

Linghui Qiu, Jialin Zhang, Hongwei Yu

We have investigated the geometric phase acquired by a uniformly accelerated Unruh-DeWitt detector coupled to vacuum fluctuations of a massless conformal scalar field in anti-de Sitter (AdS) spacetime. Using the open-quantum-system formalism, we calculate the phase under three boundary conditions (Dirichlet, transparent, and Neumann) imposed on the field at

Lihui Liu, Carl Yang

Entity Matching (EM) is an important task that determines the logical relationship between two entities, such as Same, Different, or Undecidable. Traditional EM approaches rely heavily on supervised learning, which requires large amounts of high-quality labeled data. This labeling process is both time-consuming and costly, limiting practical applicability. A

Jiahe Fan, Xiao Ma, Sergey Vityazev, George Giakos

Driving scene parsing is critical for autonomous vehicles to operate reliably in complex real-world traffic environments. To reduce the reliance on costly pixel-level annotations, synthetic datasets with automatically generated labels have become a popular alternative. However, models trained on synthetic data often perform poorly when applied to real-world

Wei Liang, Chu-Wen Xiao, Guo-Mei Gan, Shi-Qi Zhou

Inspired by the invariant mass distributions for the decays $D^{0} \rightarrow K_S^{0} \pi^{0} \eta$ and $D^{0} \rightarrow K_S^{0} \pi^{0} \pi^{0}$ reported by the BESIII Collaboration, we investigate these processes with an unified final state interaction formalism by incorporating both the $S$-wave pseudoscalar meson-pseudoscalar meson interactions within

Anh Tuan Duong, Tuan Anh Dao

Let $V$ be a locally finite, connected and weighted graph. We study non-existence results of non-trivial, non-negative solutions of the system $$ \begin{cases} u_{t t}-\Delta u \geq h_1|v|^p & \text { in } V \times(0, \infty), v_{t t}-\Delta v \geq h_2|u|^q & \text { in } V \times(0, \infty), u=u_0;\;v=v_0 & \text { in } V \times\{0\}, u_t=u_1;\;v_t=v_1 & \t

Ankit Ghimire, Saydul Akbar Murad, Nick Rahimi

Bitcoin transaction networks are large scale socio- technical systems in which activities are represented through multi-hop interaction patterns. Graph Neural Networks(GNNs) have become a widely adopted tool for analyzing such systems, supporting tasks such as entity detection and transaction classification. Large-scale datasets like Elliptic have allowed fo

Xiao-Bai Li, Yifeng Chen, Chihyung Wen, Peixu Guo

Extensive studies have investigated the transition mechanism of boundary layers initiated by a single primary instability. In a real-world scenario, however, multiple primary instabilities of different physical nature would coexist and generate more complicated stages of mode--mode interactions. For this scenario, conventional secondary stability analysis ma

Athena Taymourtash, S. Mazdak Abulnaga, Esra Abaci Turk, P. Ellen Grant

Establishing dense volumetric correspondences across anatomical shapes is essential for group-level analysis but remains challenging for implicit neural representations. Most existing implicit registration methods rely on supervision near the zero-level set and thus capture only surface correspondences, leaving interior deformations under-constrained. We int

Takashi Obase, Takanori Kodama, Takao Kawasaki, Sam Sherriff-Tadano

It has been hypothesized that the Earth may have experienced snowball events in the past, during which its surface became completely covered with ice. Previous studies used general circulation models to investigate the onset and climate of such snowball events. Using the MIROC4m coupled atmosphere--ocean climate model, this study examined the changes in the

Dong Han

In this paper, we construct rotating frames for curves, including plane curves, space curves and curves on surfaces. Hence, the behaviour of an arbitrary moving point on a curve can be seen as the composite of linear motion and rotation. Conversely, it can also be proved that a curve can be determined by the two motions of a moving point on it, namely, linea

Zhao-Ling Zhang, Xu Gao, Wei-Min Song, Chang-Zheng Yuan

Located at the BEPCII $e^{+}e^{-}$ collider, the BESIII experiment provides a robust platform for investigating (anti)hyperon-nucleon ($YN$) and antineutron-nucleon ($\bar{n}N$) interactions. This is made possible by the high production cross-sections of $J/\psi$ and $\psi(3686)$ resonances and their substantial decay branches into these baryons. Although pr

Huan Wu, Yi-Feng Zhao, Antia S. Botana

We use first-principles calculations to investigate the crystal and electronic structure of the hybrid bilayer-trilayer Ruddlesden-Popper (RP) nickelate La$_7$Ni$_5$O$_{17}$ under hydrostatic pressure and biaxial compressive strain. By analyzing the irreducible representations of the dynamically unstable phonon modes in the high-symmetry $P4/mmm$ structure,

Peng Lin, Haopeng Yang

In this paper, scheduling problems of aircraft minimizing the total delays on a same runway and on dual runways are studied. In contrast to the algorithms based on mixed-integer optimization models in existing works, where the optimality and the real-time performance are usually unable to be dealt with at the same time, our work focuses on the interaction me

Ri Chi Ng, Aditi Kumaresan, Yujia Hu, Roy Ka-Wei Lee

Hate speech detection relies heavily on linguistic resources, which are primarily available in high-resource languages such as English and Chinese, creating barriers for researchers and platforms developing tools for low-resource languages in Southeast Asia, where diverse socio-linguistic contexts complicate online hate moderation. To address this, we introd

Xiaoyu He, Jiaxi Nie, Logan Post, Jacques Verstraëte

For a 3-uniform hypergraph (3-graph) $F$, let $r(F,n)$ be the smallest $N$ such that any $N$-vertex $F$-free 3-graph has an independent set of size $n$. We construct a $3$-graph $H_2$ with six vertices and five edges such that $r(H_2,n)=n^{\Theta(\log n)}$, and a more general family of $3$-graphs $F$ for which $r(F,n)=n^{\log^{\Theta(1)}(n)}$. These are the

Hisayuki Yokomizo, Taiki Miyanishi, Yan Gang, Shuhei Kurita

Vision-Language Models (VLMs) are increasingly applied to robotic perception and manipulation, yet their ability to infer physical properties required for manipulation remains limited. In particular, estimating the mass of real-world objects is essential for determining appropriate grasp force and ensuring safe interaction. However, current VLMs lack reliabl

Yuanhe Zhang, Xinyue Wang, Zhican Chen, Weiliu Wang

Given limited and costly computational infrastructure, resource efficiency is a key requirement for large language models (LLMs). Efficient LLMs increase service capacity for providers and reduce latency and API costs for users. Recent resource consumption threats induce excessive generation, degrading model efficiency and harming both service availability a

Vincenzo Buono, Peyman Sheikholharam Mashhadi, Mahmoud Rahat, Prayag Tiwari

Attribution methods in explainable AI rely on upsampling techniques that were designed for natural images, not saliency maps. Standard bilinear and bicubic interpolation systematically corrupts attribution signals through aliasing, ringing, and boundary bleeding, producing spurious high-importance regions that misrepresent model reasoning. We identify that t

Diego Mardien, Frank Liu

Physicians spend nearly half their workday on EHR tasks and administrative work, contributing to burnout and reducing time for direct patient care. We present MDwAIstScheduler, a low-cost, belt-worn voice assistant that allows hands-free calendar management during patient encounters. Hidden beneath a lab coat, the device avoids the eye-contact disruptions ca

Arth Singh

Safety alignment in diffusion language models (dLLMs) relies on a single load-bearing assumption: that committed tokens are permanent. We show that violating this assumption, by re-masking committed refusal tokens and injecting a short affirmative prefix, achieves 74-82% ASR on HarmBench across all three publicly available safety-tuned dLLMs, rising to 92-98

Qing-Mei Yang, Da-Qing Zhang

This paper introduces a novel variational Bayesian method that integrates Tucker decomposition for efficient high-dimensional inverse problem solving. The method reduces computational complexity by transforming variational inference from a high-dimensional space to a lower-dimensional core tensor space via Tucker decomposition. A key innovation is the introd

Yanru Wu, Weiduo Yuan, Ang Qi, Vitor Guizilini

Reinforcement Learning (RL) has shown great potential in refining robotic manipulation policies, yet its efficacy remains strongly bottlenecked by the difficulty of designing generalizable reward functions. In this paper, we propose a framework for online policy refinement by adapting foundation VLMs into online reward generators. We develop a robust, scalab

Yuna Lim, Jonggul Lee, Eunok Jung

Broad mobility restrictions can help control infectious disease spread, but their socioeconomic costs and the variation in transmission risks by mobility purpose, age group, and spatial connectivity highlight the need for targeted approaches. In this study, we developed an age-structured SEIR metapopulation model for COVID-19 across Seoul's 25 districts, int

Yifan Li, Seunghyun Yoon, Viet Dac Lai, Franck Dernoncourt

Vision Transformers (ViTs) based vision foundation models (VFMs) have achieved remarkable performance across diverse vision tasks, but suffer from quadratic complexity that limits scalability to long sequences. Existing linear attention approaches for ViTs are typically trained from scratch, requiring substantial computational resources, while linearization-

Hiroyuki Hanada, Satoshi Akahane, Noriaki Hashimoto, Shion Takeno

In practical machine learning, the environments encountered during the model development and deployment phases often differ, especially when a model is used by many users in diverse settings. Learning models that maintain reliable performance across plausible deployment environments is known as distributionally robust (DR) learning. In this work, we study th

Yu Miyazaki, Atsuhiro Tomita, Akihide Hayashi, So Takamoto

Universal machine-learning interatomic potentials (uMLIPs) enable reactive molecular simulations with near-DFT accuracy, yet applying them efficiently to large, realistic condensed-phase systems remains computationally demanding. Here we present PFP/MM, a hybrid approach that combines a uMLIP, PreFerred Potential (PFP), with molecular mechanics (MM) to enabl

Yu Li, Rui Miao, Zhengling Qi, Tian Lan

The dominant paradigm for improving mathematical reasoning in language models relies on Reinforcement Learning with verifiable rewards. Yet existing methods treat each problem instance in isolation without leveraging the reusable strategies that emerge and accumulate during training. To this end, we introduce ARISE (Agent Reasoning via Intrinsic Skill Evolut

Mahsa Tahghigh, Hassan Salmani

Always-on hardware Trojans pose a serious challenge to integrated circuit trust, as they remain active during normal operation and are difficult to detect in post-deployment settings without trusted golden references. This paper presents a reference-free detection framework based on cross-scale persistence analysis of electromagnetic (EM) side-channels, targ

Guanghui Zhao, Zhe Wang, Yu Dong, Guan Li

Scientific visualization pipelines encode domain-specific procedural knowledge with strict execution dependencies, making their construction sensitive to missing stages, incorrect operator usage, or improper ordering. Thus, generating executable scientific visualization pipelines from natural-language descriptions remains challenging for large language model

Masayuki Ohzeki

We present a theoretical framework that reinterprets Population Annealing (PA) through the lens of the discrete-time Schr\"odinger Bridge (SB) problem. We demonstrate that the heuristic reweighting step in PA is derived by analytically solving the Schr\"odinger system without iterative computation via instantaneous projection. In addition, we identify the th

Huamin Chen, Xunzhuo Liu, Yuhan Liu, Junchen Jiang

Sizing a GPU fleet for LLM inference is harder than it looks. The obvious questions -- how many GPUs, which type, where to split a two-pool fleet -- have no closed-form answers. They depend on the full token-length distribution, the routing policy, and queueing dynamics that turn ugly under heavy-tailed workloads. Existing tools optimize per-engine configura

Jeong Hee Kim, Jia Dong, Marissa Morales, Loza Tadesse

Raman spectroscopy is a promising tool for microbial identification, yet its implementation in microbiology and clinical workflow is still restricted due to the accompanying additional preparation required to focus on microbial signals. Here, we demonstrate Raman-based bacterial identification directly from unopened, inverted agar plates, the same conditions

Jinghan A Zeng

We consider the problem of packing edge-disjoint Steiner forests in a graph. The input consists of a multi-graph $G=(V,E)$ and a collection of $t$ vertex subsets $S = \{S_1,S_2,\ldots,S_t\}$. A Steiner forest for $S$, also called an $S$-forest, is a forest of $G$ in which each $S_i$ is connected. In the case where $t=1$, this is the Steiner Tree packing prob

Ding Wei

Large language models (LLMs) have made remarkable progress in generating fluent text, but they still face a critical challenge of contextual misalignment in long-term and dynamic dialogue. When human users omit premises, simplify references, or shift context abruptly during interactions with LLMs, the models may fail to capture their actual intentions, produ

Ikkyu Aihara, Ryu Takeda, Masahiro Shirasaka, Daichi Kominami

Acoustic animals (e.g., insects and frogs) aggregate and produce sounds for mating. Well-organized chorus structures like call alternation and call synchrony indicate the importance of the precise control of call timing by individual males. However, the stable monitoring of multiple acoustic features in natural environments, especially the variation in call

Eduardo Nebot, Julie Stephany Berrio Perez

Autonomous driving is undergoing a shift from modular rule based pipelines toward end to end (E2E) learning systems. This paper examines this transition by tracing the evolution from classical sense perceive plan control architectures to large driving models (LDMs) capable of mapping raw sensor input directly to driving actions. We analyze recent development

Zdena Dobesova, Tai Dinh, Pavel Novak

Urban areas are intricate systems shaped by socioeconomic, environmental, and infrastructural factors, with land use patterns serving as aspects of urban morphology. This paper proposes a novel methodology leveraging frequent item set mining and unsupervised learning techniques to identify similar cities based on co-occurring land use patterns. The Copernicu

Qian Feng, Pengfei Li, Rongshan Gao, Jiale Xu

Remote photoplethysmography (rPPG) based on traditional frame-based cameras often struggles with motion artifacts and limited temporal resolution. To address these limitations, we introduce EMPD (Event-based Multimodal Physiological Dataset), the first benchmark dataset specifically designed for non-contact physiological sensing via event cameras. The datase

Julio Cesar Siqueira Rocha, Rodrigo Alves Dias

Qudits, the multi-level generalization of qubits, provide a natural extension of the binary paradigm in quantum computation and offer new opportunities to enhance algorithmic performance. Beyond their direct applicability to the simulation of multi-level quantum systems, higher-dimensional ancillae can improve sampling efficiency in quantum algorithms by ena

Abhishek Menon, Michael Straus, George Tomaras, Liam Jeanette

Monolithic three-dimensional (3D) Paul traps combine the high-precision microfabrication of two-dimensional (2D) chip traps with the deep trapping potentials and low heating rates characteristic of macroscopic 3D Paul traps, which are typically machined by traditional means and mechanically assembled. However, achieving low motional heating rates and optical

Asheesh S. Momi, Isabella R. Graf, Michael C. Abbott, Benjamin B. Machta

After entering the ear, sound waves propagate as surface waves along the cochlea's basilar membrane. In recent work, we showed numerically that the system supports two types of modes: localized resonant modes, which underpin the modern understanding of cochlear mechanics, and a novel class of spatially extended modes. Here, we develop an analytic framework t

Keon Ho Kim, Boyce E. Griffith

The immersed peridynamics (IPD) method is a fluid-structure interaction (FSI) model to simulate fluid-driven material damage and failure of an immersed structure, in which a peridynamic (PD) constitutive correspondence model is employed within a classical immersed boundary (IB)-type framework to describe stresses, forces, and structural deformations of a str

Jungwoo Shim, Dae Won Kim, Sun Wook Kim, Soo Young Kim

Small language models (sLLMs) are increasingly deployed on-device, where imperfect user prompts--typos, unclear intent, or missing context--can trigger factual errors and hallucinations. Existing automatic prompt optimization (APO) methods were designed for large cloud LLMs and rely on search that often produces long, structured instructions; when executed u

Dongik Shin

Generalization remains a core challenge in embodied AI, as robots must adapt to diverse environments. While OpenVLA represents the State-of-the-Art (SOTA) in Vision-Language-Action models by leveraging large-scale pre-training, its zero-shot performance can be limited when encountering completely new environments. This paper proposes a parameter-efficient fi

Xiaozhou Ye, Feng Jiang, Zihan Wang, Xiulai Wang

Human Activity Recognition using wearable inertial sensors is foundational to healthcare monitoring, fitness analytics, and context-aware computing, yet its deployment is hindered by cross-user variability arising from heterogeneous physiological traits, motor habits, and sensor placements. Existing domain generalization approaches either neglect temporal de

Junwen Qiu, Leilei Mei, Junyu Zhang

The global Lipschitz smoothness condition underlies most convergence and complexity analyses via two key consequences: the descent lemma and the gradient Lipschitz continuity. How to study the performance of optimization algorithms in the absence of Lipschitz smoothness remains an active area. The relative smoothness framework from Bauschke-Bolte-Teboulle (2

Yiqun T. Chen, Moran Guo, Shengy Li

Modern studies increasingly leverage outcomes predicted by machine learning and artificial intelligence (AI/ML) models, and recent work, such as prediction-powered inference (PPI), has developed valid downstream statistical inference procedures. However, classical power and sample size formulas do not readily account for these predictions. In this work, we t

Hyun-Bin Kim, Byeong-Il Ham, Kyung-Soo Kim

This study proposes a non-contact photo-reflector-based joint torque sensor for precise joint-level torque control and safe physical interaction. Current-sensor-based torque estimation in many collaborative robots suffers from poor low-torque accuracy due to gearbox stiction/friction and current-torque nonlinearity, especially near static conditions. The pro

Xiu-Fen Wang, Hai-Jiang Tian, Yin-Long Yang, Long Zeng

In the paper, we have carried out research on the $D\to\pi$ decay process. We employ two different currents to study the $D\to\pi$ transition form factors (TFFs) by using the light-cone sum rule within the framework of chiral current approach. Firstly, we follow the right-handed and left-handed currents for the correlators to present the expression of the ve

Stathis Chadjiconstantinidis, Apostolos Bozikas

This paper introduces and studies a new uncertainty measure, the cumulative residual interval entropy (CRIE). Defined as the cumulative residual entropy of a doubly truncated (interval) continuous random variable, this measure has several applications when data fall between two points. The CRIE generalizes the cumulative residual entropy proposed by Rao et a

Kamil Bradler

I introduce a method to generate families of CSS codes with interesting code parameters. The object of study is Coxeter groups, both finite and infinite (reducible or not), and a geometrically motivated partial order of Coxeter group elements named after Bruhat. The Bruhat order is known to provide a link to algebraic topology -- it doubles as a face poset c

Fei Shang, Tomasz Woźniak

We propose a structural vector autoregressive model with a new and flexible specification of the volatility process which we call Sparse Heterogeneous Markov-Switching Heteroskedasticity. In this model, the conditional variance of each structural shock changes in time according to its own Markov process. Additionally, it features a sparse representation of M

Richard G. Forbes

This technical note provides a high-level overview of the present state of basic field electron emission (FE) theory, as suitable for use in the context of technological applications of FE theory. At present there is much theoretical confusion in FE literature, and a partial breakdown of the peer review system. Even in sensitive technological contexts, many

Yi Shen, Rihui Lan, Hua Wang

We propose first-order pressure-correction scheme for the incompressible Navier-Stokes equations, incorporating the recently developed the Dynamically Regularized Lagrange Multiplier (DRLM) methods. The resulting algorithms are fully decoupled and require solving only Poisson-type equations at each time step. Moreover, it exhibits unconditional energy stabil

F. J. Beron-Vera, G. Bonner, M. J. Olascoaga, S. Dong

We explore hurricane and ocean reanalysis data to understand how rapid intensification (RI) of tropical cyclones is impacted by the upper ocean density structure, with an emphasis on barrier layer (BL) thickness and thermocline depth in the eastern Caribbean Sea and adjacent western tropical North Atlantic. This analysis leverages transition path theory (TPT

Daniel Dantchev, Joseph Rudnick

We present results and compare the behavior of two fluctuation-induced forces pertinent for their corresponding ensembles: the critical Casimir force in the grand canonical (fixed external field $h$) one and the critical Helmholtz force in the canonical (fixed average value of the order parameter $m$) one. We do so by deriving exact results for their behavio

Steven Kordonowy, Bibhas Adhikari, Hannes Leipold

We develop a perfectly distributable quantum-classical streaming algorithm that processes signed edges to efficiently estimate the counts of triangles of diverse signed configurations in the single pass edge stream. Our approach introduces a quantum sketch register for processing the signed edge stream, together with measurement operators for query-pair call

Al Jaber Mahmud, Xuan Wang

We study rigid-body motion planning through multiple sequential narrow openings, which requires long-horizon geometric reasoning because the configuration used to traverse an early opening constrains the set of reachable configurations for subsequent ones. To achieve this, we propose a geometry-aligned large language model (LLM) fine-tuning framework that ge

Hiroto Yokoyama, Masanori Honda, Rinya Miyakawa, Yuki Shinohara

Simple shear flow can induce flow-aligned chain formation of particles suspended in viscoelastic fluids. Although this phenomenon has been reported for decades, direct {\it in situ} measurements of the alignment dynamics and particle trajectories during chain formation remain limited. Here, we develop an {\it in situ} observation platform based on parallel r

Christian Balfagon

We establish the first observational bounds on causal nonlocal extensions of gravity characterized by retarded Stieltjes-type kernels with positive spectral density rho(mu) >= 0, using two complementary gravitational-wave channels. From a Bayesian ringdown analysis of 17 binary black hole events in the LIGO-Virgo GWTC-3 catalogue, we set an observational cei

Dylan Albrecht, Sarah Thompson, N. Tobias Jacobson, Ryan Jock

The exchange interaction is a foundational building block for the operation of spin-based quantum processors. Extracting the exchange interaction coefficient $J(\mathbf{V})$, as a function of gate electrode voltages, is important for understanding disorder, faithfully simulating device performance, and operating spin qubits with high fidelity. Typical cohere

Jecia Z. Y. Mao, Francis X. Creighton, Russell H. Taylor, Manish Sahu

We introduce a speech-guided embodied agent framework for video-guided skull base surgery that dynamically executes perception and image-guidance tasks in response to surgeon queries. The proposed system integrates natural language interaction with real-time visual perception directly on live intraoperative video streams, thereby enabling surgeons to request

Arth Singh

What exactly do efficient sequence models gain over simple temporal averaging? We use exponential moving average (EMA) traces, the simplest recurrent context (no gating, no content-based retrieval), as a controlled probe to map the boundary between what fixed-coefficient accumulation can and cannot represent. EMA traces encode temporal structure: a Hebbian a

Neea Palojärvi, Sebastian Tudzi

In this article, we obtain effective estimates for the error term $\Delta_{k}(x)$ for all integers $k \geq2$, and completely explicit estimates for integers $k \in [3,9]$. The explicit results improve the powers of $x$ appearing in the known explicit bounds for $\Delta_{k}(x)$, and the effective bounds provide a method to derive such bounds for all integers

Tal Cohen, Helge Glöckner, Gil Goffer, Waltraud Lederle

We study ergodic invariant random subgroups that give full measure to the subset of compact subgroups. We show that in real Lie groups, compactly generated $p$-adic Lie groups, locally compact hyperbolic groups and infinitely ended groups they are always contained in a compact normal subgroup. In general $p$-adic Lie groups, we show they are contained in the

Jake Van Clief, David McDermott

Current approaches to AI agent orchestration typically involve building multi-agent frameworks that manage context passing, memory, error handling, and step coordination through code. These frameworks work well for complex, concurrent systems. But for sequential workflows where a human reviews output at each step, they introduce engineering overhead that the

Petar Radanliev, Omar Santos, Carsten Maple, Kay Atefi

Artificial Intelligence (AI) systems are increasingly dependent on complex, multi-layered software supply chains that introduce challenges for reproducibility, transparency, and security assurance. This study presents an Artificial Intelligence Bill of Materials (AIBOM) schema extending the CycloneDX standard to capture AI-specific provenance, model lineage,

Hiroki Fukui

Multi-agent orchestration -- in which a hidden coordinator manages specialized worker agents -- is becoming the default architecture for enterprise AI deployment, yet the safety implications of orchestrator invisibility have never been empirically tested. We conducted a preregistered 3x2 experiment (365 runs, 5 agents per run) crossing three organizational s

Aleksandar Anžel, Zewen Yang, Georges Hattab

While the polar system may lack the universal familiarity of its Cartesian counterpart, it remains indispensable for certain tasks. Summary polar diagrams, such as Taylor and mutual information diagrams, address tasks like discovering relationships, visualizing data similarity, and quantifying correspondence. Although these diagrams are invaluable tools for

Tianyu Wu, Lin Zhu

Motivation: Generative models for protein backbone design have to simultaneously ensure geometric validity, sampling efficiency, and scalability to long sequences. However, most existing approaches rely on iterative refinement, quadratic attention mechanisms, or post-hoc geometry correction, leading to a persistent trade-off between computational efficiency

Einar Aurbakken, Håkon Emil Kristiansen, Simen Kvaal, Antoine Camper

We present a computational study of the laser-driven quantum dynamics of positronium (Ps), PsH, and PsCl at the time-dependent Hartree-Fock level of theory. To eliminate finite-basis effects and to properly capture continuum dynamics, we use a spherical polar pseudospectral representation. The multicomponent theory and its implementation are described in det

Hajar Assil, Abderrahim El Allati, Gian Luca Giorgi

We use a Quantum Extreme Learning Machine for characterizing and estimating parameters of quantum dynamics generated by a tunable collision model. The input to the learning protocol consists of quantum states produced by successive system environment interactions, while the reservoir is implemented as a disordered many body quantum system evolving under a fi

Maria Fernanda Oliveira Guimarães, Ulisses Rosa, Ian Trudel, João Victor Amorim Vieira

Vectorization is a compiler optimization that replaces multiple operations on scalar values with a single operation on vector values. Although common in traditional compilers such as rustc, clang, and gcc, vectorization is not common in the Verilog ecosystem. This happens because, even though Verilog supports vector notation, the language provides no semanti

Elisa Iahn Goettems, Ricardo J. S. Afonso, Diogo O. Soares-Pinto, Daniel Valente

We investigate the dissipative adaptation hypothesis in a quantum regime using a system-reservoir approach. This hypothesis proposes that self-organization arises from a system's ability to dissipate the work transiently absorbed from an external drive. We analyze the quantum dynamics of a driven open system described by a time-dependent spin-boson Hamil

Luca Cattaneo, Federico Ettori, Giovanni Cerri, Paolo Biscari

We present an efficient Monte Carlo algorithm for the simulation of the two-dimensional Random Field Ising Model (RFIM). The method combines the event-driven, rejection-free character of the Bortz Kalos-Lebowitz (BKL) algorithm with Glauber transition probabilities, introducing hierarchical probabilistic counters to perform spin selection in O(log N) operati

Joseph A. Dickinson, Mathew Chow, Eno Paenurk, Sharon Hammes-Schiffer

Proton transfer is central to many processes of chemical interest. The simulation of proton transfer dynamics requires the inclusion of nuclear quantum effects, such as zero-point energy, nuclear delocalization, and tunneling. Herein, we introduce methods within the nuclear-electronic orbital (NEO) framework, where specified nuclei are treated quantum mechan

D. Martín-Pérez, F. Rodríguez-Díaz, D. Gutiérrez-Avilés, A. Troncoso

Quantum transfer learning combines pretrained classical deep learning models with quantum circuits to reuse expressive feature representations while limiting the number of trainable parameters. In this work, we introduce a family of compact quantum transfer learning architectures that attach variational quantum classifiers to frozen convolutional backbones f

Marc Hunkemöller, Thomas G. Seidel, Julien Javaloyes, Svetlana V. Gurevich

We theoretically investigate how phase modulation alters the dynamics of temporal localized structures (TLSs) in vertically emitting Kerr micro-cavities under detuned optical injection operating in the normal dispersion regime. We show that the emergence of TLSs in general is governed by a synchronization between the imposed modulation and the intrinsic puls

Hai Nguyen, Hieu Dao, Hung Nguyen, Nam Vu

This study presents high-throughput, real-time multi-agent affective computing framework designed to enhance classroom learning through emotional state monitoring. As large classroom sizes and limited teacher student interaction increasingly challenge educators, there is a growing need for scalable, data-driven tools capable of capturing students' emotio

Shun Chen, Xiao Yan Chew, Jutta Kunz

We investigate dyonic nonlinearly scalarized black holes in Einstein-Maxwell-scalar theory. The domain of existence of scalarized dyonic black holes consists of three branches. The cold branch and the hot branch bifurcate at a minimal value of the charge, analogous to the purely electrically charged scalarized black holes. However, the presence of both charg

Hanna Zeller, Lukas Danner, Max Hofheinz, Ciprian Padurariu

We present a novel scheme to detect itinerant microwave radiation at the single photon level. Using existing Josephson-photonics devices, where two microwave cavities are coupled by a dc-voltage biased superconducting junction, we theoretically show how to implement a stroboscopically repeated, near-projective measurement of a photon impinging on one of the

Xiao-Jin Liu, Ralph P. Eatough, Zhichen Pan, Matthew Bailes

PSR J1922+3745 was recently identified as a radio pulsar toward the old open cluster NGC 6791, raising the prospect of the first pulsar associated with an open cluster. We report FAST follow-up observations that yield a phase-coherent timing solution, a precise position, a measurement of the spin-down rate and the pulsar's polarization properties. PSR J1

Rajesh Jana, Afsal S Shajahan, Boby Joseph, Brahmananda Chakraborty

The high-pressure behavior of two Mn-based honeycomb-structured magnetoelectric materials, Mn4Nb2O9 (MNO) and Mn4Ta2O9 (MTO), was investigated using Raman spectroscopy, synchrotron x-ray diffraction, and density functional theory (DFT) calculations. In MTO, the application of a small pressure of only 0.5 GPa induces an isostructural transition driven by loca

Alma Lago

Mechanistic interpretability has transformed the analysis of transformer circuits by decomposing model behavior into competing algorithms, identifying phase transitions during training, and deriving closed-form predictions for when and why strategies shift. However, this program has remained largely confined to sequence-prediction architectures, leaving embo

Christine Li, Lia Yeh

The AND gate is not reversible$\unicode{x2014}$on qubits. However, it is reversible on qutrits, making it a building block for efficient simulation of qubit computation using qutrits. We first observe that there are multiple two-qutrit Clifford+T unitaries that realize the AND gate with T-count 3, and its generalizations to $n$ qubits with T-count $3n-3$. Ou

Manjari Dutta, Arnab Mukherjee, Sunandan Gangopadhyay

In this paper, we employ the quantum regression theorem, a powerful tool in the study of open quantum systems, to analytically study the correlation functions of an Unruh-DeWitt detector, which is an uniformly accelerated two-level quantum system, absorbing charges from an external classical coherent pulse. The system can thus be viewed as a relativistic qua

Masahiro Kaminaga

We study Schrödinger operators on $\mathbb R^3$ with finitely many concentric spherical $δ$-shell interactions. The operators are defined by the quadratic form method and are described by continuity across each shell together with the usual jump condition for the radial derivative. Using a boundary integral approach based on the free Green kernel and single-

I. S. Kuijf, F. B. Baalbergen, L. Seldenthuis, E. P. L. van Nieuwenburg

Photon-number resolved detection with superconducting nanowire single-photon detectors (SNSPDs) attracts increasing interest, but lacks a systematic framework for interpreting and benchmarking this capability. In this work, we combine principal component analysis (PCA) with a new readout technique to explore the photon-number resolving capabilities of SNSPDs

Kyle Miller, Surhud More, Bhuvnesh Jain

Upcoming weak-lensing surveys will probe the matter distribution at a few percent level on nonlinear scales ($k>1\,{\rm h\,Mpc}^{-1}$) where baryonic feedback from galaxy formation modifies the clustering of matter. Using the IllustrisTNG hydrodynamical simulations, we quantify the mass and radial dependence of baryonic suppression of the matter power spectr

Huixin Tan, Yuquan Xie, Wenjiao Yan

We first show that every isoparametric hypersurface in $\mathbb{S}^{n}\times \mathbb{R}^{m}$ or $\mathbb{H}^{n}\times \mathbb{R}^{m}$ possesses a constant angle function with respect to the canonical product structure. Exploiting this rigidity, we achieve a complete classification of isoparametric and homogeneous hypersurfaces in these product spaces. Furthe

Xiaoyun Qiu, Haichao Liu, Yue Pan, Jun Ma

In mixed-traffic environments, autonomous vehicles (AVs) must interact with heterogeneous human-driven vehicles (HVs) whose intentions and driving styles vary across individuals and scenarios. Such variability introduces uncertainty into lane change interactions, where safety and efficiency critically depend on accurately anticipating surrounding drivers&#39

Marco Ratta, Gaelle Saint-Hilary, Mauro Gasparini, Pavel Mozgunov

Robust Mixture Prior (RMP) is a popular Bayesian dynamic borrowing method, which combines an informative historical distribution with a less informative component (referred as robustification component) in a mixture prior to enhance the efficiency of hybrid-control randomized trials. Current practice typically focuses solely on the selection of the prior wei