November 2025 arXiv papers — page 118

Jackson Saunders, Camelia Prodan

We describe two 3D-printing approaches for realizing tight-binding models in acoustic metamaterials using H-shaped resonators: a modular system with tunable interconnections and an integrated one-piece design for reducing dissipation. The platform supports both positive and negative coupling through geometric control, enabling accurate acoustic analogs of to

Elena Petri, Koen J. A. Scheres, Erik Steur, W. P. M. H.

Neuromorphic engineering aims at designing computing and control systems inspired by the neurons and the brain. For the control community, neuromorphic control is an emerging topic that focuses on designing event-based spiking controllers in the form of spiking neural networks (SNNs). At present, systematic methods for designing and analyzing such controller

Mohammed Al-Kobaisi, Dmitry Ammosov, Yalchin Efendiev, Wing Tat Leung

In this paper, we propose a multicontinuum homogenization approach for nonlinear problems involving dynamically evolving multiscale media. The main idea of the proposed approach is that one of the fine-scale variables defines continua. It allows us to formulate macroscopic variables and derive new macroscopic models for nonlinear problems, where coefficients

Pierre Talbot

Constraint programming is a general and exact method based on constraint propagation and backtracking search. We provide a function decomposing a constraint network into a ternary constraint network (TCN) with a reduced number of operators. TCNs are not new and have been used since the inception of constraint programming, notably in constraint logic programm

Vadim V. Bobylev

The most complete sample of radio stars and masers with trigonometric parallaxes measured by the VLBI method, common with the Gaia EDR3 and Gaia DR3 catalogs, has been compiled using literature data. The sample contains 151 stars. An analysis of the differences in parallaxes and proper motions of Gaia-VLBI stars has been performed. A new estimate of the syst

Bernard T. Agyeman, Zhe Li, Ilias Mitrai, Prodromos Daoutidis

This work introduces an end-to-end graph-based agent for accelerating the computational efficiency of Benders Decomposition. The agent's policy is parameterized by a graph neural network which takes as input a bipartite graph representation of the master problem and proposes a candidate solution. The agent is trained using a two-stage approach that combines

Dhiraj K. Singh

Conventional photoelastic methods are largely limited to two-dimensional stress visualization, leaving a gap in techniques that can capture three-dimensional force interactions with high sensitivity at low stress levels, a capability that is critical for biomechanics and dynamic force analysis. This study develops and demonstrates a cubic photoelastic model

Namu Park, Giridhar Kaushik Ramachandran, Kevin Lybarger, Fei Xia

Large language models (LLMs) have shown considerable promise in clinical natural language processing, yet few domain-specific datasets exist to rigorously evaluate their performance on radiology tasks. In this work, we introduce an annotated corpus of 6,393 radiology reports from 586 patients, each labeled for follow-up imaging status, to support the develop

H. R. Paz

Predictive models for student dropout, while often accurate, frequently rely on opportunistic feature sets and suffer from undocumented data leakage, limiting their explanatory power and institutional usefulness. This paper introduces a leakage-aware data layer for student trajectory analytics, which serves as the methodological foundation for the CAPIRE fra

Jiong Tao, Yong-Liang Yang, Bailin Deng

Planar quadrilateral (PQ) mesh generation is a key process in computer-aided design, particularly for architectural applications where the goal is to discretize a freeform surface using planar quad faces. The conjugate direction field (CDF) defined on the freeform surface plays a significant role in generating a PQ mesh, as it largely determines the PQ mesh

Muzammal Shafique, Nasir Rahim, Jamil Ahmad, Mohammad Siadat

Segmentation of medical images constitutes an essential component of medical image analysis, providing the foundation for precise diagnosis and efficient therapeutic interventions in clinical practices. Despite substantial progress, most segmentation models do not explicitly encode boundary information; as a result, making boundary preservation a persistent

Ole König, Daniela Huppenkothen, Douglas Finkbeiner, Christian Kirsch

The dynamic range of imaging detectors flown on-board X-ray observatories often only covers a limited flux range of extrasolar X-ray sources. The analysis of bright X-ray sources is complicated by so-called pile-up, which results from high incident photon flux. This nonlinear effect distorts the measured spectrum, resulting in biases in the inferred physical

Ningyan Fang, Victor Botez, Fereshteh Rajabi, Martin Houde

We derive relativistic Maxwell-Bloch equations for potential applications in astronomical environments, where various radiative processes are known to occur, including the maser action and Dicke's superradiance. We show that for both phenomena a radiating system's response is preserved at different relative velocities between the system's rest frame and the

Seungha Um, Tulika Kakati, Lilia M Iakoucheva, Yile Chen

The PTPN11 gene encodes the Src homology 2 domain-containing protein tyrosine phosphatase (SHP2), a key regulator of cell growth, differentiation, and apoptosis through its modulation of various signaling pathways, including the RAS/MAPK signaling pathway. Missense variants in PTPN11 disrupt SHP2's proper catalytic activity and the regulation of signaling pa

Kevin V. Bigting, Shubhadeep Nag, Yaxin An

Coarse-graining (CG) reduces molecular details to extend the time and length scales of molecular dynamics simulations to microseconds and micrometers. However, the CG approaches have long been limited by the difficulty of constructing both accurate and transferable models efficiently, considering the large diversity of chemical structures of materials. Among

Cindy H. Shi, Mia C. Cano, Jason R. Casar, Parivash Moradifar

Upconverting nanoparticles (UCNPs) are promising optical biomechanical force sensors due to their near infrared excitation, low toxicity, photostability, and linear colorimetric sensitivity to micronewtons of force. Recently, a composite force sensor based on UCNPs embedded in a polystyrene microbead enabled the first real time measurement of feeding forces

Taimur Khan, Ramoza Ahsan, Mohib Hameed

Story understanding and analysis have long been challenging areas within Natural Language Understanding. Automated narrative analysis requires deep computational semantic representations along with syntactic processing. Moreover, the large volume of narrative data demands automated semantic analysis and computational learning rather than manual analytical ap

Andrew Krapivin, Benjamin Przybocki, Nicolás Sanhueza-Matamala, Bernardo Subercaseaux

We study the problem of partitioning the edges of a $d$-uniform hypergraph $H$ into a family $F$ of complete $d$-partite hypergraphs ($d$-cliques). We show that there is a partition $F$ in which every vertex $v \in V(H)$ belongs to at most $(\frac{1}{d!} + o_d(1))n^{d-1}/\lg n$ members of $F$. This settles the central question of a line of research initiated

Vahid Hemmati, Yonas Ayalew, Ahmad Mohammadi, Reza Ahmari

In this paper, we propose a conflict-free multi- agent flight scheduling that ensures robust separation in con- strained airspace for Urban Air Mobility (UAM) operations application. First, we introduce Pairwise Conflict Avoidance (PCA) based on delayed departures, leveraging kinematic principles to maintain safe distances. Next, we expand PCA to multi-agent

Nilesh Choudhury, Sandeep, Neesha Yadav, Mayank Shukla

Two-dimensional (2D) van der Waals (vdW) magnetic materials have emerged as key materials for next-generation magneto-electric and spintronic devices, where understanding the relationship between layer number, lattice dynamics, and magnetic interactions is very important. In this work, we report the observation of the layer breathing mode (LBM) in few-layer

Wei-Jia Chen, Min-Yen Tsai, Cheng-Yi Lee, Chia-Mu Yu

The rapid proliferation of pretrained models and open repositories has made model merging a convenient yet risky practice, allowing free-riders to combine fine-tuned models into a new multi-capability model without authorization. Such unauthorized model merging not only violates intellectual property rights but also undermines model ownership and accountabil

Ali Mashhadireza, Ali Sadighi

This paper proposes a robust control strategy that integrates Iterative Learning Control (ILC) with a simple lateral neural network to enhance the trajectory tracking performance of a linear Lorentz force actuator under friction and model uncertainties. The ILC compensates for nonlinear friction effects, while the neural network estimates the nonlinear ILC e

Junyang He, Judy Fox, Alireza Jafari, Ying-Jung Chen

Recent advances in time series research facilitate the development of foundation models. While many state-of-the-art time series foundation models have been introduced, few studies examine their effectiveness in specific downstream applications in physical science. This work investigates the role of integrating domain knowledge into time series models for hy

Denis V. Saklakov

This paper introduces a cognitive Retrieval-Augmented Generator (RAG) architecture that transcends transformer context-length limitations through phase-coded memory and morphological-semantic resonance. Instead of token embeddings, the system encodes meaning as complex wave patterns with amplitude-phase structure. A three-tier design is presented: a Morpholo

Ryan Singh, Austin Hamilton, Amanda White, Michael Wise

Ensuring worker safety remains a critical challenge in modern manufacturing environments. Industry 5.0 reorients the prevailing manufacturing paradigm toward more human-centric operations. Using a design science research methodology, we identify three essential requirements for next-generation safety training systems: high accuracy, low latency, and low cost

Afonso Rodrigues

I introduce a novel approach to structural modelling and estimation of continuous demand systems, utilising consideration sets to analyse differentiated products markets with very large choice sets and purchases over multiple goods, multiple units, and across product categories. I apply it to study intra-store competition in the Portuguese supermarket indust

K. A. I. N Jayarathne, R. M. N. M. Rathnayaka, D. P. S. S. Peiris

Deep-sea exploration poses significant challenges, including disorientation, communication loss, and navigational failures in dynamic underwater environments. This paper presents an Autonomous Underwater Cognitive System (AUCS) that integrates Simultaneous Localization and Mapping (SLAM) with a Soar-based cognitive architecture to enable adaptive navigation

Olaoluwa A. Adegboye, Kufre M. Udofia, Akaninyene Obot

This research presents a simple strategy for designing an exponentially tapered, triple-notched ultrawideband antenna. The antenna's microstrip line feed and radiating patch are matched using an exponential tapered transformer. This method inserts antenna notch elements, by cutting two inverted C-shaped slots in the radiating patch; frequency rejection can b

Lucas Fenaux, Christopher Srinivasa, Florian Kerschbaum

Transparency and security are both central to Responsible AI, but they may conflict in adversarial settings. We investigate the strategic effect of transparency for agents through the lens of transferable adversarial example attacks. In transferable adversarial example attacks, attackers maliciously perturb their inputs using surrogate models to fool a defen

Patrick Gerard, Aiden Chang, Svitlana Volkova

When large language models (LLMs) are aligned to a specific online community, do they exhibit generalizable behavioral patterns that mirror that community's attitudes and responses to new uncertainty, or are they simply recalling patterns from training data? We introduce a framework to test epistemic stance transfer: targeted deletion of event knowledge, val

Shubham Jaiswal, M Krithika, P Vanchinathan

In this article we establish certain variants of the Inverse Cluster Size problem. We introduce the notion of primitive extensions and establish the Primitive variant of the problem. Precisely, we prove the existence of primitive extensions over number fields of any given degree and cluster size less than the degree. We also introduce the notions of Strong a

Shuangyu Xie, Kaiyuan Chen, Wenjing Chen, Chengyuan Qian

When uncertainty is high, self-driving vehicles may halt for safety and benefit from the access to remote human operators who can provide high-level guidance. This paradigm, known as {shared autonomy}, enables autonomous vehicle and remote human operators to jointly formulate appropriate responses. To address critical decision timing with variable latency du

Milena Piotrowska, Francesco Giacosa

The positronium, as the lightest purely leptonic bound state, provides an ideal testing ground for a quantum field--theoretical (QFT) description of composite systems. While its electromagnetic annihilation is well understood as the dominant decay channel, the weak interaction sector of positronium is strongly suppressed. In this work, we extend the composit

Mina Sarem, Nuhamin Eshetu Deresse, Els Verstrynge, Stijn François

In this paper, we extend a micromechanics-based phase-field framework for fatigue fracture to incorporate cyclic plasticity with ratcheting. This mechanism is particularly relevant for low-cycle fatigue, where the accumulation of inelastic strains plays a critical role in the progression to final failure. An energetic formulation is proposed in which the rat

Fatemeh Elhambakhsh, Gaurav Ameta, Aditi Roy, Hyunwoong Ko

Machining process planning (MP) is inherently complex due to structural and geometrical dependencies among part features and machining operations. A key challenge lies in capturing dynamic interdependencies that evolve with distinct part geometries as operations are performed. Machine learning has been applied to address challenges in MP, such as operation s

Adaobi Amanna, Ishana Shinde

The integration of Generative Artificial Intelligence (GenAI) in healthcare is impeded by significant security challenges unaddressed by traditional frameworks, precisely the data-in-use gap where sensitive patient data and proprietary AI models are exposed during active processing. To address this, the paper proposes the Confidential Zero-Trust Framework (C

Vahid Hemmati, Ahmad Mohammadi, Abdul-Rauf Nuhu, Reza Ahmari

Adversarial robustness remains a critical challenge in deploying neural network classifiers, particularly in real-time systems where ground-truth labels are unavailable during inference. This paper investigates \textit{Volatility in Certainty} (VC), a recently proposed, label-free metric that quantifies irregularities in model confidence by measuring the dis

Dmitry Gayfulin, Erez Nesharim

In 1947 M.Hall proved that every real number is the sum of an integer and two real numbers whose partial quotients are at most $4$. Later, Cusick proved that every real number is the sum of an integer and two real numbers whose partial quotients are at least $2$. In a recent paper, the authors proved that every real number is the sum of two real numbers whos

Wenhao Zhou, Hao Zheng, Rong Zhao

Large Vision-Language Models (LVLMs) typically align visual features from an encoder with a pre-trained Large Language Model (LLM). However, this makes the visual perception module a bottleneck, which constrains the overall capabilities of LVLMs. Conventional evaluation benchmarks, while rich in visual semantics, often contain unavoidable local shortcuts tha

Mihir Gupte, Ramesh S

Autoformalization, the process of translating informal statements into formal logic, has gained renewed interest with the emergence of powerful Large Language Models (LLMs). While LLMs show promise in generating structured outputs from natural language (NL), such as Gherkin Scenarios from NL feature requirements, there's currently no formal method to verify

Wenwen Si, Sooyong Jang, Insup Lee, Osbert Bastani

While large language models (LLMs) have recently made tremendous progress towards solving challenging AI problems, they have done so at increasingly steep computational and API costs. We propose a novel strategy where we combine multiple LLM models with varying cost/accuracy tradeoffs in an agentic manner, where models and tools are run in sequence as determ

Viviana Gubitosi, Hipolito Treffinger

In this paper we revisit the notion of strict laura algebras through the lens of $\tau$-tilting theory to define the family of algebras determined by $\tau$-slices. We show that the representation dimension of every algebra determined by $\tau$-slices satisfying mild conditions is at most three.

Behnaz Bahmei, Siamak Arzanpour, Elina Birmingham

Speech quality and intelligibility are significantly degraded in noisy environments. This paper presents a novel transformer-based learning framework to address the single-channel noise suppression problem for real-time applications. Although existing deep learning networks have shown remarkable improvements in handling stationary noise, their performance of

Zhongping Dong, Pengyang Yu, Shuangjian Li, Liming Chen

Accurate single-object tracking and short-term motion forecasting remain challenging under occlusion, scale variation, and temporal drift, which disrupt the temporal coherence required for real-time perception. We introduce \textbf{SOTFormer}, a minimal constant-memory temporal transformer that unifies object detection, tracking, and short-horizon trajectory

Salman Sayeed, Bijoy Ahmed Saiem, Al-Amin Sany, Sadia Sharmin

CollaClassroom is an AI-enhanced platform that embeds large language models (LLMs) into both individual and group study panels to support real-time collaboration. We evaluate CollaClassroom with Bangladeshi university students (N = 12) through a small-group study session and a pre-post survey. Participants have substantial prior experience with collaborative

R. M. Aguirre

It is believed at present that the chiral transition changes from a smooth crossover to a first-order transition at low temperatures and high densities. Such regime is commonly analyzed using effective models since first principle calculations, as in lattice arrangements, are not feasible. This transition is assumed to be discontinuous, with unstable or meta

Hong-Jun Yoon, Faisal Ashraf, Thomas A. Ruggles, Debjani Singh

Information extraction from regulatory documents using large language models presents critical trade-offs between performance and computational resources. We evaluated seven open-weight models (0.6B-70B parameters) on hydropower licensing documentation to provide empirical deployment guidance. Our analysis identified a pronounced 14B parameter threshold wher

A. R. Pina, Shams El-Adawy, H. J. Lewandowski, Benjamin M. Zwickl

Continued growth of the quantum information science and engineering (QISE) industry has resulted in stakeholders spanning education, industry, and government seeking to better understand the workforce needs. This report presents a framework for the categorization of roles in the QISE industry based on 42 interviews of QISE professionals across 23 companies,

Ari Blondal, Hamed Hatami, Pooya Hatami, Chavdar Lalov

Dimension theory is a branch of topology concerned with defining and analyzing dimensions of geometric and topological spaces in purely topological terms. In this work, we adapt the classical notion of topological dimension (Lebesgue covering) to binary concept classes. The topological space naturally associated with a concept class is its space of realizabl

Steven Giacalone, Andrew W. Howard, Gregory J. Gilbert, Judah Van Zandt

Giant planets and brown dwarfs are thought to form via a combination of pathways, including bottom-up mechanisms in which gas is accreted onto a solid core and top-down mechanisms in which gas collapses directly into a gravitationally-bound object. One can distinguish the prevalence of these mechanisms using host star metallicities. Bottom-up formation thriv

Zhongde An, Jinhong You, Jiyanglin Li, Yiming Tang

Time series forecasting is essential in a wide range of real world applications. Recently, frequency-domain methods have attracted increasing interest for their ability to capture global dependencies. However, when applied to non-stationary time series, these methods encounter the $\textit{spectral entanglement}$ and the computational burden of complex-value

Zhuoya Cao, Abraham Loeb, Morgan MacLeod

The Oort Cloud's dynamical evolution is significantly influenced by both the galactic tide and stellar flybys. This study investigates the particular case of HD7977's close encounter 2.47 Myr ago, which likely repopulated the Inner Oort Cloud and potentially triggered a significant comet shower on Earth. Our results demonstrate that the shower's intensity st

Alex Kasman, Rob Milson, Michael Gekhtman

It is well known that for any wave function $\psi(x,z)$ of the KP hierarchy, there is another wave function called its ''adjoint'' such that the path integral of their product with respect to $z$ around any sufficiently large closed path is zero. For the wave functions in the adelic Grassmannian ${\rm Gr}^{\rm ad}$, the bispectral involution which exchanges

Thomas Gehrmann, Peter Meinzinger

The production of hadronic final states in electron-positron or electron-hadron collisions is induced predominantly by quasi-real photons that were emitted off incoming leptons. In these processes, the photon either enters directly or through its resolved parton content, which is at present only loosely constrained by experimental data. We perform a detailed

Yonatan Tussa, Andy Heredia, Nirupam Roy

Many promising applications of multimodal wearables require continuous sensing and heavy computation, yet users reject such devices due to privacy concerns. This paper shares our experiences building an ear-mounted voice-and-vision wearable that performs local AI inference using a paired smartphone as a trusted personal edge. We describe the hardware-softwar

Bertram Højer

Language models (LMs) are said to be exhibiting reasoning, but what does this entail? We assess definitions of reasoning and how key papers in the field of natural language processing (NLP) use the notion and argue that the definitions provided are not consistent with how LMs are trained, process information, and generate new tokens. To illustrate this incom

Sebastian Miles, A. Mert Bozkurt, Dániel Varjas, Michael Wimmer

We theoretically explore the electronic structure of holes in cylindrical Germanium/Silicon core/shell nanowires using a perturbation theory approach. The approach yields a set of interpretable and transferable effective low-energy models for the lowest few sub-bands up to fifth order for experimentally relevant growth directions. In particular, we are able

Marisol Traforetti, Mariam Abdelaziz, Daniele Bertacca, Raul Jimenez

We present a complete computation of the scalar power spectrum in the \emph{inflation without inflaton} (IWI) framework, where the inflationary expansion is driven solely by a de~Sitter (dS) background and scalar fluctuations arise as second-order effects sourced by tensor perturbations. By explicitly deriving and numerically integrating the full second-orde

Du Pei, David H. Wu

We investigate the physics of the E-string theory and its compactifications as well as their applications to four-dimensional topology. In particular, we compute the partition function of the topologically twisted theory on $M_4\times T^2$, where $M_4$ is a four-manifold. In a range of examples, we verify that this partition function, as a $q$-series, 1) has

J. K. M. Viuho, A. A. Djupvik, A. N. Sørensen, D. Kumar

Ground-based astronomy suffers from strong atmospheric line- and thermal continuum emission, at the near infrared (NIR, 0.7-1.1$\mu$m), and short-wave infrared (SWIR, 1.1-2.5$\mu$m) wavelengths. The thermal continuum emission increases exponentially towards the red sensitivity cutoff of the state-of-the-art 2.5$\mu$m cutoff SWIR detectors. Given availability

Anna R. Gallazzi, Stefano Zibetti, Arjen van der Wel, Angelos Nersesian

With a sample of 552 galaxies at z~0.7 from the LEGA-C survey, we investigate how current star formation influences light-weighted mean stellar ages and metallicities, and their median trends with stellar mass or velocity dispersion. The bimodality in the global age-mass relation stems from the different age distributions in the quiescent (Q) and star-formin

Shashank Dattathri, Frank C. van den Bosch, Uddipan Banik, Martin Weinberg

Dynamical friction governs the orbital decay of massive perturbers within galaxies and dark matter halos, yet its standard Chandrasekhar formulation fails in systems with cores of (roughly) constant density, where inspiral can halt or even reverse, phenomena known respectively as core stalling and dynamical buoyancy. Although these effects have been observed

Maria Demidik, Cenk Tüysüz, Michele Grossi, Karl Jansen

Quantum computers can efficiently sample from probability distributions that are believed to be classically intractable, providing a foundation for quantum generative modeling. However, practical training of such models remains challenging, as gradient evaluation via the parameter-shift rule scales linearly with the number of parameters and requires repeated

T. Giang Nguyen, Nicolas B. Cowan, Gunnar Montseny Gens, Charles-Edouard Boukare

Extreme instellation on lava planets causes the rocky surface to melt and vaporize. Because the rock vapour composition is intrinsically tied to the mantle, atmospheric characterization of lava planets can hold valuable insight into the interior processes of rocky planets. To help interpret current data and strategize for future observations, we develop the

Andrea Banfi, Jeffrey R. Forshaw, Jack Holguin

We show that one-jettiness ($\tau_1$) in colour-singlet plus jet production suffers from super-leading logarithms starting at order $\alpha_{\mathrm s}^4 \ln(1/\tau_1)^6$ relative to the Born level. This is one logarithm more dominant than any previously identified super-leading logarithms. The extra logarithm is not associated with additional poles, and is

Jan-Niklas Herre, Takuya Okugawa, Ammon Fischer, Christoph Karrasch

We study the interplay of magnetic order and superconductivity in the square-lattice Hubbard model under periodic driving with circularly polarized light. Formulating diagrammatic techniques based on the random-phase approximation in terms of Floquet Green's functions, allows us to analyze fluctuation-driven unconventional pairing for weak-to-moderate intera

Josef Leutgeb, Jonas Mager, Anton Rebhan

We use the Wentzel-Kramers-Brillouin (WKB) approximation to uncover divergences and instances of non-commuting limits in a large class of holographic soft-wall models. We show that the infinite sum over single resonance contributions for a variety of observables involving the Chern-Simons term, such as the vector-vector-axial (VVA) correlator or the hadronic

Beatrice Giudici, Michael Gabler, Hans-Thomas Janka

We present long-term three-dimensional (3D) simulations of Type-IIP supernovae (SNe) for 13 non-rotating, single-star, red-supergiant (RSG) progenitors with zero-age-main-sequence masses between 12.5 M$_{\odot}$ and 27.3 M$_{\odot}$. The explosions were modelled with a parametric treatment of neutrino heating to obtain defined energies, ${}^{56}$Ni yields, a

Grégoire Pierra, Alberto Colombo, Simone Mastrogiovanni

The release of the fourth Gravitational Wave Transient Catalog (GWTC-4.0) by the LIGO-Virgo-KAGRA collaboration includes more than 200 compact binary coalescence (CBC) candidates that can be used to probe the cosmic expansion. The population of merging binary black holes has been used so far to provide a constraint on the Hubble constant and dark matter frac

Miguel Correia, Tushar Gopalka, Giulia Isabella, Anna M. Wolz

We establish the analytic structure of the S-matrix in the complex-frequency plane for classical wave scattering on a Schwarzschild background in four space-time dimensions. Our argument relies on the analytic continuation of the gravitational potential, with the singularity behind the horizon playing a crucial role. We find that in the lower half-plane the

Guangxuan Xiao, Junxian Guo, Kasra Mazaheri, Song Han

Mixture of Block Attention (MoBA) (Lu et al., 2025) is a promising building block for efficiently processing long contexts in LLMs by enabling queries to sparsely attend to a small subset of key-value blocks, drastically reducing computational cost. However, the design principles governing MoBA's performance are poorly understood, and it lacks an efficient G

Max Hallgren, Robert Koirala, Zilu Ma

We establish new estimates for the size and structure of the nodal set $\{u=0\}$ and the singular set $\{u=|\nabla u|=0\}$ of solutions $u$ to parabolic inequalities with parabolic Lipschitz coefficients. In particular, we show that almost all of the nodal and singular sets are covered by regular parabolic Lipschitz graphs with estimates, and that both sets

Héber H. Arcolezi

We present \textsf{ModularSubsetSelection} (MSS), a new algorithm for locally differentially private (LDP) frequency estimation. Given a universe of size $k$ and $n$ users, our $\varepsilon$-LDP mechanism encodes each input via a Residue Number System (RNS) over $\ell$ pairwise-coprime moduli $m_0, \ldots, m_{\ell-1}$, and reports a randomly chosen index $j

Allen Emmanuel Binny, Anushri Dixit

Uncertainty-aware prediction is essential for safe motion planning, especially when using learned models to forecast the behavior of surrounding agents. Conformal prediction is a statistical tool often used to produce uncertainty-aware prediction regions for machine learning models. Most existing frameworks utilizing conformal prediction-based uncertainty pr

Robert J. Petrella

This work explores the bounds of the variance of unilaterally truncated Gaussian distributions (UTGDs) and scaled chi distributions (UTSCDs) with fixed means. For any arbitrary Gaussian distribution function, $f(x;\mu,\sigma)$, with a fixed, finite mean $M$ on the truncated domain $x \ge a$, where $a \in \mathbb{R}$, it is proven that the variance is bounded

Albert Tan, Mohsen Bayati, James Nordlund, Roman Istomin

We study randomized experiments in bipartite systems where only a subset of treatment-side units are eligible for assignment while all units continue to interact, generating interference. We formalize eligibility-constrained bipartite experiments and define estimands aligned with full deployment: the Primary Total Treatment Effect (PTTE) on eligible units an

Sylvia Yuan, Ruoxi Shi, Xinyue Wei, Xiaoshuai Zhang

Modeling 3D articulated objects with realistic geometry, textures, and kinematics is essential for a wide range of applications. However, existing optimization-based reconstruction methods often require dense multi-view inputs and expensive per-instance optimization, limiting their scalability. Recent feedforward approaches offer faster alternatives but freq

Afra Feyza Akyürek, Advait Gosai, Chen Bo Calvin Zhang, Vipul Gupta

Frontier model progress is often measured by academic benchmarks, which offer a limited view of performance in real-world professional contexts. Existing evaluations often fail to assess open-ended, economically consequential tasks in high-stakes domains like Legal and Finance, where practical returns are paramount. To address this, we introduce Professional

Reginald Wilcox, David Phillips, Matthew Steinecker, Erik Eisenach

We demonstrate $4\pi$-steradian vector magnetic field sensing using an ensemble of nitrogen-vacancy (NV) centers in a single-crystal diamond coupled to a microwave (MW) cavity. The MW cavity enhances the spin-photon coupling which enables efficient, high-contrast spin-state readout via MW interrogation and removes the need for bulky optical collection compon

Angelo Rodio, Giovanni Neglia, Zheng Chen, Erik G. Larsson

In semi-decentralized federated learning, devices primarily rely on device-to-device communication but occasionally interact with a central server. Periodically, a sampled subset of devices uploads their local models to the server, which computes an aggregate model. The server can then either (i) share this aggregate model only with the sampled clients (samp

Alex Kasman, Robert Milson

Sequences are often conveniently encoded in the form of a generating function depending on a formal variable. This note presents two observations that allow one to draw conclusions about the generated sequence from the generating function. The first constructively produces "recursion relations" for the sequence from differential operators in the formal varia

MiroMind Team, Song Bai, Lidong Bing, Carson Chen

We present MiroThinker v1.0, an open-source research agent designed to advance tool-augmented reasoning and information-seeking capabilities. Unlike previous agents that only scale up model size or context length, MiroThinker explores interaction scaling at the model level, systematically training the model to handle deeper and more frequent agent-environmen

Faizuddin Ahmed, İzzet Sakallı, Ahmad Al-Badawi

In a recent study [1], authors introduced a new class of exact space-times in Einstein's gravity, which are Kerr black holes immersed in an external uniform magnetic field that is oriented along the rotational axis. Motivated by this work, we investigate a Kerr-like black hole solution with a cloud of strings surrounded by a uniform magnetic field. For the z

Asraful Haque, Daniel T. Yimam, Jawad Chowdhury, Ralph Bulanadi

Autonomous laboratories typically rely on data-driven decision-making, occasionally with human-in-the-loop oversight to inject domain expertise. Fully leveraging AI agents, however, requires tightly coupled, collaborative workflows spanning hypothesis generation, experimental planning, execution, and interpretation. To address this, we develop and deploy a h

Michael Z. Zgurovsky, Pavlo O. Kasyanov, Liliia S. Paliichuk

This note presents an analytical framework for decision-making in drone swarm systems operating under uncertainty, based on the integration of Partially Observable Markov Decision Processes (POMDP) with Deep Deterministic Policy Gradient (DDPG) reinforcement learning. The proposed approach enables adaptive control and cooperative behavior of unmanned aerial

Dhairya Shah, Yuan Liu, Samuel Brzezicki

This study constructed a multi-scale theoretical framework to resolve the electric field singularity at the Triple Contact Point in electrowetting. Utilizing conformal transformation and complex analysis, we established the structure for both the global potential and local field solutions, complementing the analysis with numerical methods. Our primary findin

Xin An, Robbe Brants, Michal P. Heller, Yi Yin

Contemporary understanding of thermalization in quantum field theory stems largely from understanding properties of transient excitations of equilibria. These nonhydrodynamic excitations are known to structurally differ between weakly- and strongly-coupled quantum field theories with no known results at intermediate values of the interaction strength. We dem

Ziyang Zheng, Malte Schneider, Luca Amendola

We investigate how the cosmological Euler equation can be tested in the presence of viscous dark matter, violations of the equivalence principle (EP), and modifications of gravity, while relying on minimal theoretical assumptions. Extending the previous analysis, we generalize the observable $E_P$, which quantifies EP violation, to $\tilde{E}_P$, discuss the

Claudio Altafini

In machine learning, a self-attention dynamics is a continuous-time multiagent-like model of the attention mechanisms of transformers. In this paper we show that such dynamics is related to a multiagent version of the Oja flow, a dynamical system that computes the principal eigenvector of a matrix corresponding for transformers to the value matrix. We classi

Dawei Zhu, Rui Meng, Jiefeng Chen, Sujian Li

Comprehending long visual documents, where information is distributed across extensive pages of text and visual elements, is a critical but challenging task for modern Vision-Language Models (VLMs). Existing approaches falter on a fundamental challenge: evidence localization. They struggle to retrieve relevant pages and overlook fine-grained details within v

Dena Mujtaba, Brian Hu, Anthony Hoogs, Arslan Basharat

The deployment of decision-making AI agents presents a critical challenge in maintaining alignment with human values or guidelines while operating in complex, dynamic environments. Agents trained solely to achieve their objectives may adopt harmful behavior, exposing a key trade-off between maximizing the reward function and maintaining alignment. For pre-tr

J. Antonio Dantas Macedo, Hugo Fernandes, J. Eduardo Ferreira Ribeiro

Agile methods are characterised by iterative and incremental processes with a strong focus on flexibility and accommodating changing requirements based on either technical, regulatory, or stakeholder feedback. However, integrating Agile methods into safety-critical system development in the aerospace industry presents substantial challenges due to its strict

Yves Matanga, Yanxia Sun, Zenghui Wang

Multi-swarm particle optimisation algorithms are gaining popularity due to their ability to locate multiple optimum points concurrently. In this family of algorithms, clustering-based multi-swarm algorithms are among the most effective techniques that join the closest particles together to form independent niche swarms that exploit potential promising region

Shreya Meel, Sennur Ulukus

Verifying user attributes to provide fine-grained access control to databases is fundamental to attribute-based authentication. Either a single (central) authority verifies all the attributes, or multiple independent authorities verify the attributes distributedly. In the central setup, the authority verifies all user attributes, and the user downloads only

Latham Boyle, Wei-Ning Deng

K\"ahler-Dirac (KD) spinors have generated excitement in the lattice gauge theory community, as a way to (i) deal with the ``fermion doubling" problems that plague ordinary (Dirac, Majorana, or Weyl) spinors when discretized on a lattice, and (ii) help explain the structure of the standard model. But if one naively quantizes this theory in Lorentzian signatu

Luca Salasnich, Cesare Vianello

In these notes, we elucidate some subtle aspects of coherent-state path integrals, focusing on their application to the equilibrium thermodynamics of quantum many-particle systems. These subtleties emerge when evaluating path integrals in the continuum, either in imaginary time or in Matsubara-frequency space. Our central message is that, when handled with d

Thiago Marcilon, Murillo Inácio da Costa Silva

Given a graph $G=(V,E)$, and a function $f:V(G) \rightarrow \mathbb{N}$, an $f$-reversible process on $G$ is a dynamical system such that, given an initial vertex labeling $c_0 : V(G) \rightarrow \{0,1\}$, every vertex $v$ changes its label if and only if it has at least $f(v)$ neighbors with the opposite label. The updates occur synchronously in discrete ti

Danielle A. Braje, Matthew L. Markham, Jennifer M. Schloss, Michael A. Slocum

This report synthesizes the outcomes of a two-day workshop held in Washington, D.C. in May, 2025 that convened researchers, industry representatives, and government stakeholders to examine the current state and future directions of quantum diamond technologies. The workshop's goals were to assess the most promising use cases, to identify the key technical an

Irmak Sağlam, Mahdi Nazeri, Alessandro Abate, Sadegh Soudjani

We address the synthesis of control policies for unknown discrete-time stochastic dynamical systems to satisfy temporal logic objectives. We present a data-driven, abstraction-based control framework that integrates online learning with novel incremental game-solving. Under appropriate continuity assumptions, our method abstracts the system dynamics into a f

Banafsheh Akbari, Ethan Han, Sasha Lin, Benjamin Vakil

Considering a finite group $G$, for any element $x\in G$, the solvabilizer of $x$ in $G$ is defined as $Sol_G(x)=\{y \in G : \langle x, y \rangle \text{ is solvable}\}$. In this paper, we introduce $Solv(G)$ as the number of distinct solvabilizers of elements in $G$. A group is called $n$-solvabilizer if $|Solv(G)|=n$. We compute $|Solv(G)|$ for various clas