March 2026 arXiv papers — page 58

Longfei Guo, Pengbo Li, Ting Gao, Yonghai Zhong

With the rapid advancement of AI technology, we have seen more and more concerns on data privacy, leading to some cutting-edge research on machine learning with encrypted computation. Fully Homomorphic Encryption (FHE) is a crucial technology for privacy-preserving computation, while it struggles with continuous non-polynomial functions, as it operates on di

Risa Shinoda, Kaede Shiohara, Nakamasa Inoue, Kuniaki Saito

Understanding animal species from multimodal data poses an emerging challenge at the intersection of computer vision and ecology. While recent biological models, such as BioCLIP, have demonstrated strong alignment between images and textual taxonomic information for species identification, the integration of the audio modality remains an open problem. We pro

Jia Wang, Qian Xu, Xuanwen Ding, Zhuangqi Li

In this paper, we introduce ProcureGym, an data-driven multi-agent simulation platform that models China's National Volume-Based drug Procurement (NVBP) as a Markov Game. Based on real-world data from 7 rounds of NVBP (covering 325 drugs and 2,267 firms), the platform establishes a high-fidelity simulation environment. Within this framework, we evaluate dive

Timothy Y. Chow

In a remarkable paper, Tatsuyuki Hikita settled a longstanding e-positivity conjecture of Stanley and Stembridge. Among many other things, he wrote down a certain formula ${\varphi}_k$, and proved that the ${\varphi}_k$ sum to one, thereby defining a probability distribution. Though Hikita's proof was simple, it remains surprising that the ${\varphi}_k$ sum

John Albert, Steven Levandosky

For a class of semilinear elliptic equations, we establish criteria that guarantee that the linearized operator associated with a solution satisfies certain spectral assumptions that are widely used in the analysis of the stability of solitary waves. The criteria only involve the symbol of the linear operator and positivity and symmetry of the solution, and

Li Ma, Hao Peng, Yiming Wang, Hongbin Luo

Large language models (LLMs) have demonstrated exceptional potential in complex reasoning,pioneering a new paradigm for autonomous agent decision making in dynamic settings. However, in Real-Time Strategy (RTS) scenarios, LLMs suffer from a critical speed-quality trade-off. Specifically expansive state spaces and time limits render inference delays prohibiti

Bingxue Zhao, Qi Zhang, Hui Huang

Modeling realistic pedestrian trajectories requires accounting for both social interactions and environmental context, yet most existing approaches largely emphasize social dynamics. We propose \textbf{EnvSocial-Diff}: a diffusion-based crowd simulation model informed by social physics and augmented with environmental conditioning and individual--group inter

Ernest Fokoué, Gregory Babbitt, Yuval Levental

In Part I of this series, we established a rigorous mathematical isomorphism between ant colony decision-making and random forest learning, demonstrating that variance reduction through decorrelation is a universal principle shared by biological and computational ensembles. Here we turn to the complementary mechanism: bias reduction through adaptive weightin

Forest Agostinelli

DeepXube is a free and open-source Python package and command-line tool that seeks to automate the solution of pathfinding problems by using machine learning to learn heuristic functions that guide heuristic search algorithms tailored to deep neural networks (DNNs). DeepXube is comprised of the latest advances in deep reinforcement learning, heuristic search

Tsutomu T. Takeuchi, Satoshi Kuriki, Keisuke Yano

Galaxy surveys provide finite catalogs of objects observed within bounded volumes, yet clustering statistics are often interpreted using theoretical frameworks developed for infinite point processes. In this work, we formulate key statistical quantities directly for finite point processes and examine the structural consequences of finite-number and finite-wi

Chunchao Fan, Qizhong Lin

We prove that for all $k \ge 3$ and any integers $\Delta, n$ with $n \ge 2^\Delta,$ there exists a $k$-graph on $n$ vertices with maximum degree at most $\Delta$ such that $r(H)\geq\tw_{k-1}(c_k \Delta) \cdot n$ for some constant $c_k > 0$, where $\tw_k$ denotes the tower function. This makes the first progress toward a problem proposed by Conlon, Fox, and S

Ken Ding

Large language models trained with reinforcement learning (RL) for mathematical reasoning face a fundamental challenge: on problems the model cannot solve at all - "cliff" prompts - the RL gradient vanishes entirely, preventing any learning signal from reaching these failure modes. We introduce Hybrid Distillation Policy Optimization (HDPO), which augments s

Boyuan Li, Shuoyao Wang, Suzhi Bi, Liping Qian

Semantic communication has emerged as a promising paradigm for improving transmission efficiency and task-level reliability, yet most existing reliability-enhancement approaches rely on retransmission strategies driven by semantic fidelity checking that require additional check codewords solely for retransmission triggering, thereby incurring substantial com

Yuang Geng, Junkai Zhou, Kang Yang, Pan He

In this paper, we address the challenging problem of single-scene, fully unsupervised video anomaly detection (VAD), where raw videos containing both normal and abnormal events are used directly for training and testing without any labels. This differs sharply from prior work that either requires extensive labeling (fully or weakly supervised) or depends on

Mahamendige Jayama Lalani Mendis, Ian M. Wanless

Paratopism is a well known action of the wreath product $\mathcal{S}_n\wr\mathcal{S}_3$ on Latin squares of order $n$. A paratopism that maps a Latin square to itself is an autoparatopism of that Latin square. Let $\mathrm{Par}(n)$ denote the set of paratopisms that are an autoparatopism of at least one Latin square of order $n$. We prove a number of general

Yuichiro Iwashita, Ahtisham Fazeel Abbasi, Koichi Kise, Andreas Dengel

Background: Single-cell RNA sequencing (scRNA-seq) enables gene expression profiling at cellular resolution but is inherently affected by sparsity caused by dropout events, where expressed genes are recorded as zeros due to technical limitations. These artifacts distort gene expression distributions and compromise downstream analyses. Numerous imputation met

Nobuhito Kasahara, Shota Yamanaka, Homei Miyashita

Typical success-rate prediction models for tapping exclude targets near screen edges. However, design constraints often force such placements, and in scrollable user interfaces, any element can move close to the screen edges. In this work, we model how target-edge distance affects touch pointing accuracy. We propose the Skewed Dual Normal Distribution Model,

Yuxi Wei, Wei Huang, Qirui Chen, Lu Hou

Spatial understanding is fundamental for embodied agents, yet most spatial VLMs and benchmarks remain offline-evaluating post-hoc QA over pre-recorded inputs and overlooking two crucial deployment-critical requirements: long-horizon streaming inference and active perception when the current view is insufficient. To address this gap, we introduce S3-Bench, a

Xiaoming Zhai

Generative AI (GenAI) has rapidly entered education, yet its user experience is often explained through adoption-oriented constructs such as usefulness, ease of use, and engagement. We argue that these constructs are no longer sufficient because systems such as ChatGPT do not merely support learning tasks but also participate in knowledge construction. Exist

Kunal Khatri, Vineet Mehta

Our work addresses the problem of predicting the highest priority functional group present in an organic molecule. Functional Groups are groups of bound atoms that determine the physical and chemical properties of organic molecules. In the presence of multiple functional groups, the dominant functional group determines the compound's properties. Fourier-tran

Fangzhou Yu, Yiqi Su, Ray Lee, Shenfeng Cheng

Neural ODEs are increasingly used as continuous-time models for scientific and sensor data, but unconstrained neural ODEs can drift and violate domain invariants (e.g., conservation laws), yielding physically implausible solutions. In turn, this can compound error in long-horizon prediction and surrogate simulation. Existing solutions typically aim to enforc

Yunrui Yu, Hang Su, Jun Zhu

This work investigates the critical role of activation function curvature -- quantified by the maximum second derivative $\max|\sigma''|$ -- in adversarial robustness. Using the Recursive Curvature-Tunable Activation Family (RCT-AF), which enables precise control over curvature through parameters $\alpha$ and $\beta$, we systematically analyze this relations

Yike Xie, Weidong Mei, Dong Wang, Yingqi Wen

Analog beamforming holds great potential for future terahertz (THz) communications due to its ability to generate high-gain directional beams with low-cost phase shifters. However, conventional analog beamforming may suffer substantial performance degradation in wideband systems due to the beam squint effect. Instead of relying on high-cost true-time delayer

Qiang Niu, Wen Jiang, Jie Fei, Ruoyu Xiong

High-order interpolation on the Grassmann manifold $\Gr(n, p)$ is often hindered by the computational overhead and derivative instability of SVD-based geometric mappings. To solve the challenges, we propose a stabilized framework that combines Maximum-Volume (MV) local coordinates with Arnoldi-orthogonalized polynomial bases. First, manifold data are mapped

Dylan J. Restrepo, Nicholas J. Restrepo, Frank Y. Huo, Neil F. Johnson

The adoption of generative AI across commercial and legal professions offers dramatic efficiency gains -- yet for law in particular, it introduces a perilous failure mode in which the AI fabricates fictitious case law, statutes, and judicial holdings that appear entirely authentic. Attorneys who unknowingly file such fabrications face professional sanctions,

Mohammad Ratul Mahjabin, Raiyan Abdul Baten

General intellectual humility (GIH) -- the recognition that one's beliefs may be fallible and revisable -- is associated with improved reasoning, learning, and social discourse, yet is widely regarded as a stable trait resistant to intervention. We test whether GIH can be elevated through a conversational intervention that combines staged cognitive scaffoldi

Pablo Lara-Martínez, Bibiana Obregón-Quintana, Larry S. Liebovitch, Peter T. Coleman

Traditional methods for assessing national peace levels typically rely on socio-economic indicators or conflict incidence, often overlooking the nuanced signals embedded in public discourse. This study presents a novel computational framework to quantify peace levels by analyzing the structural and stylistic features of news text, rather than solely its cont

Salah A Faroughi, Farinaz Mostajeran, Amirhossein Arzani, Shirko Faroughi

Symbolic discovery of governing equations is a long-standing goal in scientific machine learning, yet a fundamental trade-off persists between interpretability and scalable learning. Classical symbolic regression methods yield explicit analytic expressions but rely on combinatorial search, whereas neural networks scale efficiently with data and dimensionalit

Yanjing Yang, Chenxing Zhong, Ke Han, Zeru Cheng

Large Language Model (LLM)-based agents increasingly rely on APIs to operate complex web applications, but rapid evolution often leads to incomplete or inconsistent API documentation. Existing work falls into two categories: (1) static, white-box approaches based on source code or formal specifications, and (2) dynamic, black-box approaches that infer APIs f

Daxue Hao, Hao Huang, Geng Li, Yu Wu

Understanding the coupling between structural phase transitions and thermal transport is essential for designing functional materials with tunable properties. Here, we investigate this interplay in CaSnF$_6$ by combining first-principles calculations with a machine-learned neuroevolution potential that enables large-scale molecular dynamics simulations acros

Dawei Chen, Hannah Larson

The tautological rings of strata of differentials are known to be generated by divisor classes. In this paper, we give lower bounds on the degrees of relations among them, depending on the genus $g$ and the number of simple zeros. For strata with more than $4g/3$ simple zeros, our results show that there are no relations in degrees less than $\lfloor g/3 \rf

Blessy Antony, Amartya Dutta, Sneha Aggarwal, Vasu Gatne

The lack of high-quality ground truth datasets to train machine learning (ML) models impedes the potential of artificial intelligence (AI) for science research. Scientific information extraction (SIE) from the literature using LLMs is emerging as a powerful approach to automate the creation of these datasets. However, existing LLM-based approaches and benchm

Praveen Kumar Myakala, Manan Agrawal, Rahul Manche

LLMs are increasingly used as long-running conversational agents, yet every major benchmark evaluating their memory treats user information as static facts to be stored and retrieved. That's the wrong model. People change their minds, and over extended interactions, phenomena like opinion drift, over-alignment, and confirmation bias start to matter a lot. Be

Jorge R. Colon-Berrios, Derek Luzano, Daniel Chen, Jeffrey A. Nanzer

Active incoherent millimeter-wave (AIM) imaging is a recently developed technique that has been shown to generate fast millimeter-wave imaging using sparse apertures and Fourier domain sampling. In these systems, spatial frequency sampling is determined by cross-correlation between antenna pairs, making array geometry an important aspect that dictates the fi

Luigi Calligaris, Claudio Puglia, Gianluca Lamanna

We introduce the FLASH haloscope experiment and present its electronic read-out system, currently under development. FLASH searches for Dark Matter (DM) particles and High-Frequency Gravitational Waves (HFGWs) using two cryogenic resonant cavities to scan the radio frequency spectrum between 117 and 360 MHz, looking for signals as weak as 10-22 W. The signal

Kyeonghun Kim, Jaehyeok Bae, Youngung Han, Joo Young Bae

Deep learning and generative models are advancing rapidly, with synthetic data increasingly being integrated into training pipelines for downstream analysis tasks. However, in medical imaging, their adoption remains constrained by the scarcity of reliable annotated datasets. To address this limitation, we propose 3D-LLDM, a label-guided 3D latent diffusion m

Aiko Yakeno, Hiroyuki Okuizumi, Kento Inokuma, Yoshiyuki Watanabe

This study experimentally investigates the aerodynamic drag reduction capabilities of distributed micro-roughness (DMR) coatings on a streamlined model, utilising the 1-m magnetic suspension and balance system (MSBS) at Tohoku University. Previous direct numerical simulations (DNS) indicated that DMR can mitigate turbulent-energy growth by suppressing Tollmi

Yuhao Chen, Yi Xu, Xinyun Ding, Xiang Fang

With the growing demand for intelligent in-vehicle experiences, vehicle-based agents are evolving from simple assistants to long-term companions. This evolution requires agents to continuously model multi-user preferences and make reliable decisions in the face of inter-user preference conflicts and changing habits over time. However, existing benchmarks are

Shigeru Takata, Soma Sakata, Aoto Takahashi, Masanari Hattori

The thermal relaxation problem between two parallel plates with the same temperature is investigated, aiming to study the behavior of the free energy of the dense gas described by the Enskog equation. Two types of Enskog equation have been used: one is the Enskog equation with the original Enskog factor, while the other is that with a modified Enskog factor

Han Zheng, Yining Ma, Brandon Araki, Jingkai Chen

Lifelong Multi-Agent Path Finding (MAPF) is critical for modern warehouse automation, which requires multiple robots to continuously navigate conflict-free paths to optimize the overall system throughput. However, the complexity of warehouse environments and the long-term dynamics of lifelong MAPF often demand costly adaptations to classical search-based sol

Mingjie Zhu, Yejian Lyu, Ziming Yu, Chong Han

Terahertz (THz) wireless communication has emerged as a promising solution for future data center interconnects; however, accurate channel characterization and system-level performance evaluation in complex indoor environments remain challenging. In this work, a measurement-calibrated AI-assisted digital twin (DT) framework is developed for THz wireless data

Wanbin Li, Zihan Wang, Yuejing Huang, Bingfeng Wei

The optimization of the dynamic aperture (DA) of a storage ring is typically a non-convex problem with multiple local optima. Recent studies showed that reducing the variation of resonance driving terms (RDTs) along the longitudinal position improves DA very effectively, as the reduction in the longitudinal variation of lower-order RDTs suppresses higher-ord

Sattwik Ghosal, Xuran Meng, Yi Li

There remain theoretical gaps in deep neural network estimators for the nonparametric Cox proportional hazards model. In particular, it is unclear how gradient-based optimization error propagates to population risk under partial likelihood, how pointwise bias can be controlled to permit valid inference, and how ensemble-based uncertainty quantification behav

Yang-Yang Yan, Wei-Jie Sheng

This paper is concerned with the speeds of propagation for the monostable Lotka-Volterra competition-diffusion system in general unbounded domains of $\mathbb{R}^N$. We first establish various definitions of spreading speeds at large time in the situation where one species is an invader and the other is a resident. Then, we study fundamental properties of th

Youseung Cho, Minsuk Yang

We study smooth solutions to the three-dimensional stationary Navier--Stokes equations and establish new Liouville-type theorems under refined decay assumptions. Building on the work of Cho et al., we introduce a refinement to previously known integrability criteria and analyze the associated averaged quantities. Our main result shows that if the $L^p$ growt

Aleksei Kulikov, Martin Dam Larsen

We study the eigenvalues of the localization operator $S_{A, B} = P_A\mathcal{F}^{-1}P_B\mathcal{F} P_A$, where $\mathcal{F}$ is the Fourier transform and $A = cA_0, B = B_0$ for some fixed sets $A_0, B_0\subset \mathbb{R}^d$ and a large parameter $c > 0$. For the counting function of the eigenvalues $|\{n: \varepsilon < \lambda_n(A,B)\le 1-\varepsilon\}|$ w

Emi Zeger, Mert Pilanci

Deep neural networks (DNNs), particularly those using Rectified Linear Unit (ReLU) activation functions, have achieved remarkable success across diverse machine learning tasks, including image recognition, audio processing, and language modeling. Despite this success, the non-convex nature of DNN loss functions complicates optimization and limits theoretical

Boxuan Ma, Shinichi Konomi

Generative AI (GenAI) can generate working code with minimal effort, creating a tension in introductory programming: students need timely help, yet direct solutions invite copying and can short-circuit reasoning. To address this, we propose example-based scaffolding, where GenAI provides scaffold examples that match a target task's underlying reasoning patte

Gunjan Mishra, Yash Mishra

Financial systems have a growing reliance on computer-based and distributed systems, making FinTech systems vulnerable to advanced and quickly emerging cyber-criminal threats. Traditional security systems and fixed machine learning systems cannot identify more intricate fraud schemes whilst also addressing real-time performance and trust demands. This paper

Ryoya Koyama, Zhiyao Wang, Devi Karolita, Jialong Li

Modern automated accessibility testing tools for mobile applications have significantly improved the detection of interface violations, yet their impact on remediation remains limited. A key reason is that existing tools typically produce low-level, technical outputs that are difficult for non-specialist stakeholders, such as product managers and designers,

Taro Asuke

We study characteristic classes for deformations of foliations. Those classes include known classes such as the Godbillon--Vey class and the Fuks--Lodder--Kotschick class. We introduce a certain differential graded algebra (DGA for short) which recovers the Bott vanishing and some formulae by Heitsch. Some basic properties and structures of the cohomology of

Sizhong Sun

This paper studies firms&#39; optimal response to a trade liberalization shock in terms of export and product innovation both theoretically and empirically. We find that trade liberalization, namely China&#39;s WTO accession, reduces iceberg trade cost by around 13%, thus promoting export participation. Subsequently, it affects firms&#39; product innovation

Ze-Wen Li, Shu-Jun Rong, Ya-Ru Wang

Flavor oscillations in curved space-time provide a novel channel to explore the unknown parameters of neutrinos. In this work, the gravity-modulated CP violations (CPVs) in neutrino oscillations were investigated under the Reissner-Nordstrom, Hayward, and Simpson-Visser metric. The interplay among the CPV, the properties of neutrinos, and the space-time is i

Ting Yu Tsai, An Yu, Lucy Lee, Felix X. -F. Ye

Animal models, particularly rats, play a critical role in seizure research for studying epileptogenesis and treatment response. However, progress is limited by the lack of datasets with precise temporal annotations and standardized evaluation protocols. Existing animal behavior datasets often have limited accessibility, coarse labeling, and insufficient temp

Naiming Liu, Richard Baraniuk, Shashank Sonkar

Knowledge tracing models mastery over interconnected concepts, often organized by prerequisites. We analyze hierarchical prerequisite propagation through a circuit-complexity lens to clarify what is provable about transformer-style computation on deep concept hierarchies. Using recent results that log-precision transformers lie in logspace-uniform $\mathsf{T

Ao Ding, Hongzong Li, Zi Liang, Zhanpeng Shi

Large language models (LLMs) are increasingly deployed on edge devices under strict computation and quantization constraints, yet their security implications remain unclear. We study query-based knowledge extraction from quantized edge-deployed LLMs under realistic query budgets and show that, although quantization introduces noise, it does not remove the un

Joshua Rozner, Cory Shain

Linguistic representation learning in deep neural language models (LMs) has been studied for decades, for both practical and theoretical reasons. However, finding representations in LMs remains an unsolved problem, in part due to a dilemma between enforcing implausible constraints on representations (e.g., linearity; Arora et al. 2024) and trivializing the n

Calum Buchanan, Peter Dankelmann, Isabel Harris, Paul Horn

A graph $G$ is $D$-distinguishable if there is a labeling of its vertices with $D$ labels such that the only automorphism of $G$ which preserves the labeling is the identity. The distinguishing number of $G$ is the minimum value $D$ for which $G$ is $D$-distinguishable. The fixing number of $G$ is the minimum cardinality of a subset of the vertices of $G$ wh

Shuozhi Yuan, Jinqing Wang, Zihao Liu, Miaomiao Yuan

Knowledge distillation is typically realized by transferring a teacher model's knowledge into a student's parameters through supervised or reinforcement-based optimization. While effective, such approaches require repeated parameter updates and large-scale training data, limiting their applicability in resource-constrained environments. In this work, we prop

Zhongli Dong, Young Choon Lee, Albert Y. Zomaya

Blockchain technology is often discussed as if it emerged from nowhere, yet its architectural DNA traces directly to the decentralized computing principles James~N. Gray articulated in 1986. This paper maps the conceptual lineage from Gray's requestor/server model to modern blockchain architectures, showing how his emphasis on modularity, autonomy, data inte

Guy F. de Teramond, Stanley J. Brodsky, Hans Gunter Dosch

We show that the infinite tower of hard exclusive amplitudes in holographic light-front QCD leads to a spectral generator $G(α,λ)$ which encodes the full Regge spectrum. The construction assumes a Poisson distribution of Fock-state components, where $λ$ represents the average parton multiplicity above the valence configuration. The resulting generator yields

Valentina Di Marco, Nir Guttman, Matthew T. Miles, Andrew Zic

A careful characterisation of the noise processes in pulsar timing data is a prerequisite for pulsar timing array experiments. While single-pulsar noise analyses are crucial for both gravitational-wave searches and astrophysical studies, they are often computationally intensive and rely on running and comparing multiple fixed noise models. We present tPTABil

Elaheh Sanoubari, Alicia Pan, Keith Rebello, Neil Fernandes

Social robots are increasingly used in education, but most applications cast them as tutors offering explanation-based instruction. We explore an alternative: Robot-Mediated Applied Drama (RMAD), in which robots function as life-like puppets in interactive dramatic experiences designed to support reflection and social-emotional learning. This paper presents

Yin Min Goh, Chao Li, Yunchan Hwang, Helaman Flores

Visible photonic integrated circuits underpin applications ranging from AR/VR to quantum control, yet lack a high-resolution, nondestructive diagnostic comparable to the optical frequency-domain reflectometry used in infrared silicon photonics. Here we adapt spectral-domain optical coherence tomography to measure guided-mode back-reflections in visible PICs.

Gustave Bainier, Antoine Chaillet, Rodolphe Sepulchre, Alessio Franci

The fading-memory (FM) property captures the progressive loss of influence of past inputs on a system&#39;s current output and has originally been formalized by Boyd and Chua in an operator-theoretic framework. Despite its importance for systems approximation, reservoir computing, and recurrent neural networks, its connection with state-space notions of nonl

Philip Goyal

The mathematical rules used to handle systems of identical quantum particles bring into question whether the elementary constituents of matter, such as electrons, have the fundamental characteristics of persistence and reidentifiability that are usually attributed to classical particles. However, despite considerable philosophical debate, the metaphysical pr

Ibrahim Bilau, Stacie Smith, Abdurrahman Baru, Marwan Shagar

Virtual reality (VR) has emerged as a promising tool for assessing instrumental activities of daily living (IADLs) in older adults. However, the ecological validity of these simulations is often compromised by simplified or low-fidelity environmental design that fails to elicit a genuine sense of presence. This paper documents a reproducible Reality-to-VR pi

Soonho Kwon, Dong Whi Yoo, Younah Kang

This speculative video piece showcases participants interacting with a career counseling AI agent, unaware that the responses were actually derived from the fortunetelling of a mudang (a Korean traditional shaman). Our work captures this deception and documents participants' reactions, showcasing shifts in their initial perceptions of the agent's advice foll

Daisuke Niizumi, Daiki Takeuchi, Masahiro Yasuda, Binh Thien Nguyen

Since the introduction of Masked Autoencoders, various improvements to masking techniques have been explored. In this paper, we rethink masking strategies for audio representation learning using masked prediction-based self-supervised learning (SSL) on general audio spectrograms. While recent informed masking techniques have attracted attention, we observe t

Paolo Soffitta, Niccolò Bucciantini, Josephine Wong, Denis Gonzalez Caniulef

The Crab pulsar experienced two relatively small glitches separated by only 20 days in September and October 2025. IXPE observed the source twice, with delay times since the glitch epoch ranging between 35 and 75 days, depending on the observation. We carried out a multi-method analysis to investigate whether there is evidence for significant changes in the

S. I. Krasheninnikov, R. D. Smirnov

The results of numerical simulations of the Hasegawa-Wakatani equation demonstrate that, similarly to decaying turbulence in 2D fluids, at a small electron adiabaticity parameter, the resistive-drift-wave (RDW) turbulence is dominated by the vortices. Occasionally, vortices with different signs become coupled and propagate ballistically as a dipole over a la

Reshabh K Sharma, Shraddha Barke, Benjamin Zorn

AI agents are increasingly embedded in real software systems, where they execute multi-step workflows through multi-turn dialogue, tool invocations, and intermediate decisions. These long execution histories, called agentic traces, make validation difficult. Outcome-only benchmarks can miss critical procedural failures, such as incorrect workflow routing, un

Akshay Rangamani, Altay Unal

Neural Collapse is a phenomenon that helps identify sparse and low rank structures in deep classifiers. Recent work has extended the definition of neural collapse to regression problems, albeit only measuring the phenomenon at the last layer. In this paper, we establish that Neural Regression Collapse (NRC) also occurs below the last layer across different t

Francisco Riberi, Gerardo Paz-Silva, Lorenza Viola

Dephasing noise is a ubiquitous source of decoherence in current atomic sensors. We address the problem of entanglement-assisted frequency estimation subject to classical dephasing noise with full spatial correlations (collective) and arbitrary temporal correlations. Our contributions are threefold. (i) We derive rigorous, state-independent bounds on the ach

Bon Choe, Minhee Kang, Heejin Ahn

In this paper, we present DROP, high-Density Relocation-free sequential OPerations in automated valet parking. DROP addresses the challenges in high-density parking & vehicle retrieval without relocations. Each challenge is handled by jointly providing area-efficient layouts and relocation-free parking & exit sequences, considering accessibility with relocat

Merlin Stein

Today's AI agents are built on large language models (LLMs) equipped with tools to access and modify external environments, such as corporate file systems, API-accessible platforms and websites. AI agents offer the promise of automating computer-based tasks across the economy. However, developers, researchers and governments lack an understanding of how AI a

Shenghan Zheng, Qifan Zhang

AI agent protocols -- including MCP, A2A, ANP, and ACP -- enable autonomous agents to discover capabilities, delegate tasks, and compose services across trust boundaries. Despite massive deployment (MCP alone has 97M+ monthly SDK downloads), no systematic security framework for these protocols exists. We present three contributions. First, the Agent Protocol

Nickson Golooba, Woldegebriel Assefa Woldegerima

Physics-informed neural networks (PINNs) are increasingly used in mathematical epidemiology to bridge the gap between noisy clinical data and compartmental models, such as the susceptible-exposed-infected-removed (SEIR) model. However, training these hybrid networks is often unstable due to competing optimization objectives. As established in recent literatu

Ivanna M. Boras Vazquez, Jacob Ewaniuk, Nir Rotenberg

We introduce the architecture and timing algorithm to realize a time-bin-encoded quantum photonic neural network (QPNN): a reconfigurable nonlinear photonic circuit inspired by the brain and trained to process quantum information. Unlike the typical spatially-encoded QPNN, time-encoded networks require the same number of photonic elements (e.g. phase shifter

Margaret Cychosz, Adriana Weisleder

Children in many parts of the world hear relatively little speech directed to them, yet still reach major language development milestones. What differs about the speech input that infants learn from when directed input is rare? Using longform, infant-centered audio recordings taken in rural Bolivia and the urban U.S., we examined temporal patterns of infants

Valerio La Gatta, Nathan Subrahmanian, Kaitlyn Wang, Larry Birnbaum

The use of reinforcement learning to dynamically adapt and evade detection is now well-documented in several cybersecurity settings including Covert Social Influence Operations (CSIOs), in which bots try to spread disinformation. While AI bot detectors have improved greatly, they are largely limited to detecting static bots that do not adapt dynamically. We

K. V. Lezhnin, V. Ospina-Bohórquez, J. Griff-McMahon, K. Bhutwala

Laser-driven proton acceleration provides a powerful route for generating ultrashort, high-charge proton beams. Many applications, including secondary neutron sources and inertial fusion, benefit from tight proton beam focusing. Concave targets offer a robust solution, yet the scaling of proton focusing with laser and target parameters remains poorly underst

Faezeh Ardali, Mwembezi A. Nyelele, Gerald M. Knapp

The open shop scheduling problem (OSSP) arises in many industrial and service settings but remains computationally challenging as the number of jobs and machines increases. While exact methods quickly become intractable, classical dispatching rules and metaheuristics may require substantial tuning to maintain solution quality at large scales. This study deve

Panagiota Konstantinou, Georgios Stathakis

This study examines the relationship between smart port city infrastructure, tourists or crew cultural sensitivity and digital engagement among international sailing tourists in the Mediterranean and particularly in Greece. It is based on an interdisciplinary literature synthesis and primary data from a survey conducted with a total of 203 respondents over t

Olga E. Sorokoletova, Francesco Giarrusso, Vincenzo Suriani, Daniele Nardi

Responsible AI initiatives place great emphasis on the safety of Large Language Model (LLM)-based systems. In particular, it has become standard practice to subject these models to an alignment procedure aimed at preventing harmful outputs. However, once aligned, a model is not guaranteed to maintain this alignment throughout its lifecycle. Moreover, the lik

Yi-Xiang Hu

Mixed-Integer Linear Programming (MILP) decision engines routinely output nominally optimal plans for high-stakes industrial systems. Yet deployment rarely matches solve-time assumptions: small perturbations in costs, demands, or resource availability can invalidate feasibility or trigger discontinuous shifts to qualitatively different solutions. We argue th

Tao Wang, Lipeng Zhu, Jiayong Li, Feng Gao

Defect grading of power transmission equipment (DGPTE) is crucial to the stability of electric energy transmission. Although existing machine learning methods exhibit strong capabilities in defect detection, they are plagued by difficulties in integrating expert experience and facing class imbalance in more refined defect grading field. To address this issue

Uğur Aydın, Tamer Başar

In this work, we study the contraction conditions of iterative algorithms for stationary and finite-horizon discrete-time regularized mean-field games (MFGs) with multiple populations, where each population only interacts with the state distributions of the other populations. Due to the high dimensionality caused by the interaction of different populations,

Tymoteusz Zapała, Julia Farganus, Dominik Galus, Mikołaj Czachorowski

Facial editing is an important task with applications in entertainment, virtual reality, and digital avatars. Most existing approaches rely on generative models in the 2D image domain, while in 3D the task is typically performed through labor-intensive manual editing. We propose FaceParts, a framework for unsupervised segmentation and editing of Gaussian Spl

Ido Sobol, Kihyuk Sohn, Yoav Blum, Egor Zakharov

We often aim to generate images that are both photorealistic and 3D-consistent, adhering to precise geometry, material, and viewpoint controls. Typically, this is achieved by fine-tuning an image generator, pre-trained on billions of real images, using renders of synthetic 3D assets, where annotations for control signals are available. While this approach ca

Jônatas H. dos Santos, Julio C. S. Reis, Philipe Melo, João F. H. Olivetti

We introduce WhaVax, a new expert-annotated dataset of vaccine-related WhatsApp messages collected from large Brazilian public groups spanning multiple pandemic years. The dataset was constructed through a rigorous, carefully designed pipeline that integrates keyword-based data collection, semantic deduplication to remove near-duplicate content, and a multi-

Mateo Rodríguez, José Campos-Martínez, Marta I. Hernández

Previous research based on electronic structure calculations and molecular dynamics (MD) simulations have demonstrated that graphdiyne (GDY) is a very suitable two-dimensional membrane for the separation of small molecules in a gas mixture of different species. However, quantum effects may play a role in the dynamics of these permeation processes when light

Sungsu Kang, Jinho Rhee, Joodeok Kim, Sam Oaks-Leaf

Atomic structures of nanomaterials are inherently dynamic, continuously reshaped through interactions with chemical species and external stimuli. Such dynamics are further amplified as the size and dimensionality of nanomaterials are reduced. Despite advances in analytical methods, it remains challenging to capture structural dynamics of nanomaterials in rea

Gerasimos Kouniatalis, Theodoros Papanikolaou, Spyros Basilakos, Emmanuel N. Saridakis

We study primordial black hole (PBH) formation in a minimally coupled $f(T)$ teleparallel cosmology that generates a transient departure from standard radiation domination. The model is constructed so that modified-gravity effects are negligible at early and late times, but become dynamically relevant over a finite epoch, during which an effective torsion co

Luca Boccioli, Lorenzo Roberti, Chris L Fryer, Samar Safi-Harb

The merger between the carbon (C) and oxygen (O) shells hours to days before the collapse of a massive star significantly changes its nucleosynthesis, which is reflected in the elemental ratios observed in supernova remnants (SNRs). We present a nucleosynthesis study of $^{44}$Ti production in core-collapse supernovae (CCSNe), highlighting large silicon (Si)

Daniele Agostinelli, Thomas Agostinelli, Andrea Generosi, Maura Mengoni

Appearance-based gaze estimation frequently relies on deep Convolutional Neural Networks (CNNs). These models are accurate, but computationally expensive and act as &#34;black boxes&#34;, offering little interpretability. Geometric methods based on facial landmarks are a lightweight alternative, but their performance limits and generalization capabilities re

C. O&#39;Toole, J. M. Vos, E. N. Nasedkin, J. S. Pineda

Variability monitoring provides an unparalleled insight into the atmospheric processes of brown dwarfs and directly imaged exo-planets. Inhomogeneous clouds, aurorae and magnetic spots have all been postulated as potential drivers of variability. While objects at the L/T transition have had their variability studied extensively, the variability of early L-dw

Dominik R. G. Schleicher, Matías Liempi, Mirek Giersz, Marcelo C. Vergara

The James Webb Space Telescope (JWST) has detected, through gravitational lensing, several young massive star clusters (YMCs), which are considered as relevant building blocks of high redshift galaxies. In this work, we show how a significant fraction of these YMCs could act as relevant birth places for intermediate-mass black holes. We first consider the fo

Joseph P. Devlin, Georg H. Hoffstaetter, Desmond P. Barber

Spin-polarized beams are important for some nuclear and high-energy physics experiments, such as those planned for the future Electron-Ion Collider (EIC). However, maintaining polarization during the acceleration of a charged-particle beam is difficult because the periodic nature of circular accelerators leads to spin-orbit resonances where the spin-precessi

Ashley Micuda, Daniel Newsted, Nastaran Shakourifar, Sachin Pandey

Clinical imaging is routinely used for cochlear implant surgical planning yet lacks the resolution and contrast necessary to visualize the fine intracochlear structures critical for individualized intervention. To address this limitation, an ensemble deep learning model was developed to automatically segment cochlear micro-anatomy from standard clinical scan