October 2025 arXiv papers — page 147

Ouri Karni, Chirag Vaswani, Thibault Chervy

Recent advances in optical simulation and computational techniques have renewed interest in high-finesse optical cavities for applications such as enhancing light-matter interactions, engineering complex photonic band structures, and storing quantum information. However, the extended interaction times enabled by these cavities often come at the cost of slow

Brett Levac, Jon Tamir, Marcelo Pereyra, Julian Tachella

Diffusion models (DMs) have rapidly emerged as a powerful framework for image generation and restoration. However, existing DMs are primarily trained in a supervised manner by using a large corpus of clean images. This reliance on clean data poses fundamental challenges in many real-world scenarios, where acquiring noise-free data is hard or infeasible, and

Bo Qiang, Chengyue Gong, Xinshi Chen, Yuxuan Zhang

Lightweight inference is critical for biomolecular structure prediction and downstream tasks, enabling efficient real-world deployment and inference-time scaling for large-scale applications. While AF3 and its variants (e.g., Protenix, Chai-1) have advanced structure prediction results, they suffer from critical limitations: high inference latency and cubic

Aditya Gupta, Kirandeep Kaur, Vinayak Gupta, Chirag Shah

In natural language processing, current methods for understanding Transformers are successful at identifying intermediate predictions during a model's inference. However, these approaches function as limited diagnostic checkpoints, lacking a mathematical framework for mechanistically modeling how each layer facilitates transitions between these evolving stat

Bowei Guo, Shengkun Tang, Cong Zeng, Zhiqiang Shen

Diffusion models are renowned for their generative capabilities, yet their pretraining processes exhibit distinct phases of learning speed that have been entirely overlooked in prior post-training acceleration efforts in the community. In this study, we introduce a novel framework called MosaicDiff that aligns diffusion pretraining dynamics with post-trainin

P. Kabáth, M. Skarka, A. Hatzes, E. Guenther

The upcoming space missions that will characterize exoplanets, such as PLATO and Ariel, will collect huge amounts of data that will need to be complemented with ground-based observations. The aim of the PLATOSpec project is to perform science with an echelle spectrograph capable of measuring precise radial velocities. The main focus of the spectrograph will

Shehzaib Irfan, Nabeel Ahmad, Alexander Vinel, Daniel F. Silva

Extreme value theory (EVT) is well suited to model extreme events, such as floods, heatwaves, or mechanical failures, which is required for reliability assessment of systems across multiple domains for risk management and loss prevention. The block maxima (BM) method, a particular approach within EVT, starts by dividing the historical observations into block

Allison Powell, Paramahansa Pramanik

Prostate cancer (PCa) remains a significant global health concern among men, particularly due to the lethality of its more aggressive variants. Despite therapeutic advancements that have enhanced survival for many patients, high grade PCa continues to contribute substantially to cancer related mortality. Emerging evidence points to the MYB proto-oncogene as

A. K. M. Bahalul Haque, Bharat Bhushan, Gaurav Dhiman

The emergence of smart cities and sustainable development has become a globally accepted form of urbanization. The epitome of smart city development has become possible due to the latest innovative integration of information and communication technology. Citizens of smart cities can enjoy the benefits of a smart living environment, ubiquitous connectivity, s

Xuan Luo, Weizhi Wang, Xifeng Yan

Decoder-only transformers have become the standard architecture for large language models (LLMs) due to their strong performance. Recent studies suggest that, in pre-trained LLMs, early, middle, and late layers may serve distinct roles: Early layers focus on understanding the input context, middle layers handle task-specific processing, and late layers conve

Andreu Ferré Moragues, Andreas Koutsogiannis

We give examples of sequences defined by smooth functions of intermediate growth, and we study the Furstenberg systems that model their statistical behavior. In particular, we show that the systems are Bernoulli. We do so by studying exponential sums that reflect the strong equidistribution properties of said sequences. As a by-product of our approach, we al

Gabrielle Kaili-May Liu, Bryan Li, Arman Cohan, William Gantt Walden

Real-world use cases often present RAG systems with complex queries for which relevant information is missing from the corpus or is incomplete. In these settings, RAG systems must be able to reject unanswerable, out-of-scope queries and identify failures of retrieval and multi-hop reasoning. Despite this, existing RAG benchmarks rarely reflect realistic task

Arip Asadulaev, Semyon Semenov, Abduragim Shtanchaev, Eric Moulines

Modern continuous-time generative models typically induce \emph{V-shaped} flows: each sample travels independently along a nearly straight trajectory from the prior to the data. Although effective, this independent movement overlooks the hierarchical structures that exist in real-world data. To address this, we introduce \emph{Y-shaped generative flows}, a f

Mauro Jose Pacchiotti, Luciana Ballejos, Mariel Ale

In recent years, natural language generative models have shown outstanding performance in text generation tasks. However, when facing specific tasks or particular requirements, they may exhibit poor performance or require adjustments that demand large amounts of additional data. This work introduces PAGE (Prompt Augmentation for text Generation Enhancement),

Sam Yu-Te Lee, Jingya Chen, Albert Calzaretto, Richard Lee

Enterprise chatbots show promise in supporting knowledge workers in information synthesis tasks by retrieving context from large, heterogeneous databases before generating answers. However, when the retrieved context misaligns with user intentions, the chatbot often produces "irrelevantly right" responses that provide little value. In this work, we introduce

Quentin Fruytier, Akshay Malhotra, Shahab Hamidi-Rad, Aditya Sant

Learning disentangled representations, where distinct factors of variation are captured by independent latent variables, is a central goal in machine learning. The dominant approach has been the Variational Autoencoder (VAE) framework, which uses a Kullback-Leibler (KL) divergence penalty to encourage the latent space to match a factorized Gaussian prior. In

Priyanka Dey, Daniele Rosa, Wenqing Zheng, Daniel Barcklow

Personalization in LLMs often relies on costly human feedback or interaction logs, limiting scalability and neglecting deeper user attributes. To reduce the reliance on human annotations, we introduce GRAVITY (Generative Response with Aligned Values, Interests, and Traits of You), a framework for generating synthetic, profile-grounded preference data that ca

Hikari Iwasaki

Gale duality is an involution of point configurations in projective spaces. Goppa duality extends this concept to a duality between linear series on a Gorenstein curve passing through prescribed points. We generalize this classical result to surfaces, establishing a duality for linear series on surfaces realizing prescribed points as a complete intersection

Nashat Sawai, Jorge Diaz Cruz, Andy Benwell, Sonya Hoobler

The LCLS began operations in 2009, utilizing SLAC's normal-conducting (NC) LINAC, which features control equipment dating back to the 1960s and 1980s. The Linac Electronics Modernization Plan (LEMP) aims to replace the legacy control equipment with a system based on the open-source Marble carrier board and Zest+ digitizer board, both of which are used in the

Tejmani Behera, Boris Louis, Lukas Paesen, Roel Vanden Brande

The stability and performance of halide perovskite photovoltaic devices are critically limited by progressive defect generation and associated local non-radiative losses during operation. Self-healing of defects provides a promising pathway to prolong device functionality, yet the underlying microscopic mechanisms remain poorly understood, particularly the r

Cristian Ivanescu, Hunter Labrecque

We establish the preservation of the way-below relation with respect to the tensor product.

Ethan T. Ritz, Guru Khalsa, Hsin-Yu Ko, Ju-an Zhang

We present a technique for partitioning the total energy from a semi-local density functional theory calculation into contributions from individual electronic states in a localized Wannier basis. We use our technique to reveal the key role played by the $s$ and $p$ orbitals of the apical oxygen atoms in a curious elastic anomaly exhibited by ferroelectric Pb

Yupei Li, Philipp Borchert, Gerasimos Lampouras

Large Language Models (LLMs) excel at both informal and formal (e.g. Lean 4) mathematical reasoning but still struggle with autoformalisation, the task of transforming informal into formal mathematical statements. Yet, the performance of current Math LLMs is constrained by the scarcity of large-scale corpora, particularly those containing pairs of informal a

Alexandre Galashov, Matt Jones, Rosemary Ke, Yuan Cao

Within the landscape of inference-time scaling methods for foundation models, a width-based approach to scaling -- which involves the insertion of <pause> tokens in the input stream to delay model responses -- offers a unique advantage by increasing model expressivity while remaining highly parallelizable at both training and inference. The existing literatu

Leandro Arosio, Håkan Samuelsson Kalm, Erlend F. Wold

We show that if $X$ is a totally real $d$-dimensional manifold attached to a polynomially convex compact set $K$ in $\mathbb{C}^n$, $d<n$, then there are arbitrarily small perturbations $X'$ of $X$ such that $K\cup X'$ is polynomially convex. The perturbations are induced by diffeomorphisms of $\mathbb{C}^n$ fixing $K$, which are $\bar\partial$-flat on $K\cu

Tomaso Poggio

We show that \emph{efficient Turing computability} at any fixed input/output precision implies the existence of \emph{compositionally sparse} (bounded-fan-in, polynomial-size) DAG representations and of corresponding neural approximants achieving the target precision. Concretely: if $f:[0,1]^d\to\R^m$ is computable in time polynomial in the bit-depths, then

Rebecca Lin, Michal Lukáč, Mackenzie Leake

While bodies change over time and trends vary, most store-bought clothing comes in fixed sizes and styles and fails to adapt to these changes. Alterations can enable small changes to otherwise static garments, but these changes often require sewing and are non-reversible. We propose a modular approach to garment design that considers resizing, restyling, and

Ryan Boukrouche, Rodrigo Caballero, Neil Lewis

Teegarden's Star b, a nearby terrestrial world receiving an Earth-like instellation, is a prime candidate for next-generation observatories targeting temperate exoplanets in their habitable zones. We employ a suite of three-dimensional global climate model (GCM) simulations to (1) map the inner boundary of the habitable zone of Teegarden's Star b and (2) cha

Valter Borges, Matheus Andrade Ribeiro de Moura Horácio, João Paulo dos Santos

In this article, we give a new proof of a result due to J. Kim, which states that the Ricci tensor of a gradient Ricci soliton with dimension $n \geq 4$ and harmonic Weyl tensor has at most three distinct eigenvalues. This result constitutes an essential step in the classification of such manifolds, originally established by J. Kim in dimension $4$ and subse

Yanying Lin, Shijie Peng, Chengzhi Lu, Chengzhong Xu

Serving Large Language Models (LLMs) in production faces significant challenges from highly variable request patterns and severe resource fragmentation in serverless clusters. Current systems rely on static pipeline configurations that struggle to adapt to dynamic workload conditions, leading to substantial inefficiencies. We present FlexPipe, a novel system

Arjun Devraj, Umesh Krishnaswamy, Ying Zhang, Karuna Grewal

Cloud providers have recently decentralized their wide-area network traffic engineering (TE) systems to contain the impact of TE controller failures. In the decentralized design, a controller fault only impacts its slice of the network, limiting the blast radius to a fraction of the network. However, we find that autonomous slice controllers can arrive at di

Dat Q. Tran, Minho Kim, Okhyun Nam, Vanya Darakchieva

Thermal transport in high-electron-mobility-transistor (HEMT) structures grown on 4H-SiC substrates by metalorganic-vapour-phase epitaxy (MOCVD) is systematically investigated. The thermal conductivity of the GaN channel and AlN buffer layers is measured by thermoreflectance (TTR). A pronounced thickness dependence of thermal conductivity as a result of phon

Eleftherios Tachtsis, Eliza Wajch

For an infinite set $X$, a closed under finite unions family $\mathcal{Z}$ with $[X]^{<\omega}\subseteq\mathcal{Z}\subseteq\mathcal{P}(X)$, and any $\mathcal{A}\subseteq\mathcal{P}(X)$, the topology $\tau_{\mathcal{A}}[\mathcal{Z}]=\{V\in\mathcal{A}: (\forall x\in V)(\exists z\in \mathcal{Z})(x\cap z=\emptyset \wedge \{y\in\mathcal{A}: x\subseteq y\subseteq

Pakwan Chanprakhon, Michael F. Reid, and Jon-Paul R. Wells

We report on the synthesis and spectroscopic characterization of Sm$^{3+}$-doped K$_2$YF$_5$ microparticles. The particles were synthesized via the hydrothermal technique, yielding a particle size of approximately 20 $\mu$m in length. Scanning electron microscopy (SEM) and X-ray diffraction (XRD) analyses confirmed their orthorhombic crystal structure. A com

Hiroshi Nonaka, Simon Ambrozak, Sofia R. Miskala-Dinc, Amedeo Ercole

In this work, we propose three efficient restart paradigms for model-free non-stationary reinforcement learning (RL). We identify two core issues with the restart design of Mao et al. (2022)'s RestartQ-UCB algorithm: (1) complete forgetting, where all the information learned about an environment is lost after a restart, and (2) scheduled restarts, in which r

N. Shypka, O. Hryhorchak, V. Pastukhov

We consider bound and scattering states of the one-dimensional dimer formed by two coupled non-identical atoms when one of them also interacts with the zero-range potential located at the origin. By calculating the dimer localized and scattering wave functions, we identify properties of the system without the two-body bound-state collapse. In general, we pre

Kavindi Madduma Hewage, Carlos Munoz, Mehmet Ozdogan, Catherine A. Brissette

Lyme disease, caused by the bacterium Borrelia burgdorferi, is a significant public health concern in North America, with approximately 500,000 cases reported annually in the United States. The dissemination of B. burgdorferi from the initial tick bite site to various tissues is facilitated by surface adhesins that bind to extracellular matrix (ECM) proteins

Kazi Tahsin Mahmood, M. Arif Hasan

Mechanical lattices support topological wave phenomena governed by geometric phases. We develop a compact Hilbert space description for one-dimensional elastic chains, expressing intra-cell motion as a normalized superposition of orthogonal eigenstates and tracking complex amplitudes as trajectories on a Bloch sphere. For diatomic lattices, this framework ma

Emiliano Ventura-Macias, Jose Martinez-Castro, Guillermo Haas, Jara Trujillo-Mulero

Bond-resolved STM (BRSTM) is a recent technique that combines the advantages of scanning tunneling microscopy (STM) with the outstanding intramolecular resolution provided by non-contact atomic force microscopy (ncAFM) using a CO-functionalized tips, offering unique insights into molecular interactions at surfaces. In this work, we present a novel and easily

Richard Csaky

This thesis delves into the world of non-invasive electrophysiological brain signals like electroencephalography (EEG) and magnetoencephalography (MEG), focusing on modelling and decoding such data. The research aims to investigate what happens in the brain when we perceive visual stimuli or engage in covert speech (inner speech) and enhance the decoding per

Lorena Calvo-Bartolomé, Valérie Aldana, Karla Cantarero, Alonso Madroñal de Mesa

Multilingual question answering (QA) systems must ensure factual consistency across languages, especially for objective queries such as What is jaundice?, while also accounting for cultural variation in subjective responses. We propose MIND, a user-in-the-loop fact-checking pipeline to detect factual and cultural discrepancies in multilingual QA knowledge ba

Rifat Ara Proma, Michael Correll, Ghulam Jilani Quadri, Paul Rosen

Line charts surface many features in time series data, from trends to periodicity to peaks and valleys. However, not every potentially important feature in the data may correspond to a visual feature which readers can detect or prioritize. In this study, we conducted a visual stenography task, where participants re-drew line charts to solicit information abo

Nayan Sanjay Bhatia, Pranay Kocheta, Russell Elliott, Harikrishna S. Kuttivelil

Indoor Wi-Fi positioning remains a challenging problem due to the high sensitivity of radio signals to environmental dynamics, channel propagation characteristics, and hardware heterogeneity. Conventional fingerprinting and model-based approaches typically require labor-intensive calibration and suffer rapid performance degradation when devices, channel or d

Yanan Du, Zeyang Sun, Yilan Zhang, Sai Xu

This paper proposes a secure indoor communication scheme based on simultaneous transmitting and reflecting intelligent reflecting surface (STAR-IRS). Specifically, a transmitter (Alice) sends confidential information to its intended user (Bob) indoors, while several eavesdroppers (Eves) lurk outside. To safeguard the transmission from eavesdropping, the STAR

Ji Xia, Yizi Zhang, Shuqi Wang, Genevera I. Allen

Characterizing interactions between brain areas is a fundamental goal of systems neuroscience. While such analyses are possible when areas are recorded simultaneously, it is rare to observe all combinations of areas of interest within a single animal or recording session. How can we leverage multi-animal datasets to better understand multi-area interactions?

Juno Nam, Bálint Máté, Artur P. Toshev, Manasa Kaniselvan

Diffusion-based samplers learn to sample complex, high-dimensional distributions using energies or log densities alone, without training data. Yet, they remain impractical for molecular sampling because they are often slower than molecular dynamics and miss thermodynamically relevant modes. Inspired by enhanced sampling, we encourage exploration by introduci

Ian U. Roederer, Vinicius M. Placco, Amanda I. Karakas, Elizabeth A. Den Hartog

We present an analysis of the heavy-element abundances of HD 196944, a carbon-enhanced metal-poor (CEMP) star enriched with elements produced by the slow neutron-capture process (s-process). We obtained a new high-resolution ultraviolet (UV) spectrum of this star, the UV-brightest known CEMP-s star, with the Space Telescope Imaging Spectrograph on board the

Yongbin Du, Jia-Rui Sun, Xiangdong Zhang

We study information paradox of four dimensional covariant black holes inspired by loop quantum gravity (LQG) with two well motivated solutions. We first prepare the spacetime in the Hartle-Hawking state, compute the radiation entropy and recover a linear growth at late time. When considering the mass loss and incorporating greybody factors, we show that for

Armel Zebaze, Rachel Bawden, Benoît Sagot

Large reasoning models (LRMs) have led to new possibilities in terms of problem-solving, through the devising of a natural language thought process prior to answering a query. While their capabilities are well known across mathematics and coding tasks, their impact on the task of machine translation (MT) remains underexplored. In this work, we explore the be

M. I. Balakaeva, A. Yu. Veretennikov

An approximation method for construction of optimal strategies in the bid \& ask limit order book in the high-frequency trading (HFT) is studied. The basis is the article by M. Avellaneda \& S. Stoikov 2008, in which certain seemingly serious gaps have been found; in the present paper they are carefully corrected. However, a bit surprisingly, our corrections

Gour Pati, Daniel Mechael, Frederik K. Fatemi, Renu Tripathi

We demonstrate millimeter wave (mmWave) imaging using Autler-Townes (AT) splitting-induced fluorescence in rubidium vapor. By employing counter-propagating probe and coupling beams, we create a plane of atoms in a dark state that is sensitive to incident mmWave and demonstrate the method with mmWaves resonant with the transition between two neighboring Rydbe

Silvia Gagliardini, Simone Dall'Osso, Dafne Guetta, Angela Zegarelli

Massive stars (>8 $M_{\odot}$) often undergo intense mass loss through winds or eruptive events in the final stages of their evolution, leading to the formation of a dense circumstellar medium (CSM). This material, expelled months to years before core collapse, shapes the pre-explosion environment and influences the early supernova (SN) emission. In particul

Deeksha Hareesha Kulal, Chidozie Princewill Arannonu, Afsah Anwar, Nidhi Rastogi

Phishing remains a critical cybersecurity threat, especially with the advent of large language models (LLMs) capable of generating highly convincing malicious content. Unlike earlier phishing attempts which are identifiable by grammatical errors, misspellings, incorrect phrasing, and inconsistent formatting, LLM generated emails are grammatically sound, cont

Alexandre G. Leclercq, Sébastien Bougleux, Noémie N. Moreau, Alexis Desmonts

Glioblastoma (GBM) is an aggressive primary brain tumor with a median survival of approximately 15 months. In clinical practice, the Stupp protocol serves as the standard first-line treatment. However, patients exhibit highly heterogeneous therapeutic responses which required at least two months before first visual impact can be observed, typically with MRI.

Liliana Apolinário, Chiara Le Roux, Korinna Zapp

The onset of jet quenching, i.e. the suppression of high transverse momentum particles and jets, is an important question in the context of understanding the onset of collective behaviour and small collision systems. We investigate a minimal scenario where a hard parton experiences a single soft re-scattering that leaves the kinematics unmodified, but the co

Gabriela Barenboim, Yeji Park, Liliana Velasco-Sevilla

Extensions of the Standard Model typically contain ``flaton fields" defined as fields with large vacuum expectation values and almost flat potentials where scalar self-coupling is small or vanishes at tree level. Such potentials have been used to drive a secondary inflationary epoch after a primary phase of inflation, in what are called thermal inflation mod

Rifat Ara Proma, Ghulam Jilani Quadri, Paul Rosen

Overplotted line charts can obscure trends in temporal data and hinder prediction. We conduct a user study comparing three alternatives-aggregated, trellis, and spiral line charts against standard line charts on tasks involving trend identification, making predictions, and decision-making. We found aggregated charts performed similarly to standard charts and

Fatihcan M. Atay, Haibo Ruan

We study systems of coupled units in a general network configuration with a coupling delay. We show that the destabilizing bifurcations from an equilibrium are governed by the extreme eigenvalues of the coupling matrix of the network. Based on the equivariant degree method and its computational packages, we perform a symmetry classification of destabilizing

Tyler Maunu

We propose a new general framework for recovering low-rank structure in optimal transport using Schatten-$p$ norm regularization. Our approach extends existing methods that promote sparse and interpretable transport maps or plans, while providing a unified and principled family of convex programs that encourage low-dimensional structure. The convexity of our

Louis González, Andrew Mugler

Cells frequently employ extracellular vesicles, or exosomes, to signal across long distances and coordinate collective actions. Exosomes diffuse slowly, can be actively degraded, and contain stochastic amounts of molecular cargo. These features raise the question of the efficacy of exosomes as a directional signal, but this question has not be systematically

M. P. Ross, E. A. Shaw, C. Gettings, S. K. Apple

We search for ultra-light vector dark matter interacting with a rotating torsion balance with a baryon minus lepton number composition dipole. Our search spans candidate masses in the ultra-low mass range from 1.3~$\times10^{-22}$ to 1.9~$\times10^{-18}$ eV. We set limits on the coupling strength to baryon minus lepton number for each dark matter candidate r

Anton Petrunin

This is a collection of open questions in geometry that I think of as \textit{puzzles}: they stick to my brain --- I see grips, but no spare hands. Puzzle-charm is the only criterion for inclusion here; importance is ignored.

Blessing Agyei Kyem, Neema Jakisa Owor, Andrews Danyo, Joshua Kofi Asamoah

Traffic safety analysis requires complex video understanding to capture fine-grained behavioral patterns and generate comprehensive descriptions for accident prevention. In this work, we present a unique dual-model framework that strategically utilizes the complementary strengths of VideoLLaMA and Qwen2.5-VL through task-specific optimization to address this

Andrea Visonà, Robert Morel, Hélène Joisten, Bernard Dieny

Magnetically driven microparticles provide a versatile platform for probing and manipulating biological systems, yet the physical framework governing their actuation in complex environments remains only partially explored. Within the field of cellular magneto-mechanical stimulation, vortex microdiscs have emerged as particularly promising candidates for deve

Patrick Haller, Mark Ibrahim, Polina Kirichenko, Levent Sagun

For Large Language Models (LLMs) to be reliable, they must learn robust knowledge that can be generally applied in diverse settings -- often unlike those seen during training. Yet, extensive research has shown that LLM performance can be brittle, with models exhibiting excessive sensitivity to trivial input variations. In this work, we explore whether this b

Y Wu, S. Portegies Zwart, H Dijkstra

Planetary obliquity (axial tilt) plays an important role in regulating the climate evolution and habitability of water-covered planets. Despite the suspicion of large obliquities in several exoplanetary systems, this phenomenon remains hard to observe directly. We aimed to study the effect of mass, obliquity, and rotation on the steady state climate of water

Rizal Fathony, Igor Melnyk, Owen Reinert, Nam H. Nguyen

User event modeling plays a central role in many machine learning applications, with use cases spanning e-commerce, social media, finance, cybersecurity, and other domains. User events can be broadly categorized into personal events, which involve individual actions, and relational events, which involve interactions between two users. These two types of even

O. Cherry, J. D. Carter, J. D. D. Martin

An atom chip has been fabricated for the study of interactions between $^{87}$Rb Rydberg atoms and a Au surface. The chip tightly confines cold atoms by generating high magnetic field gradients using microfabricated current-carrying wires. These trapped atoms may be excited to Rydberg states at well-defined atom-surface distances. For the purpose of Rydberg

Nicolò Forcillo, Alessio Porretta

We prove long-time contractivity estimates and exponential rates of convergence to equilibrium for solutions of hypoelliptic diffusion equations, which include the well-known Kolmogorov equation and similar kinetic Fokker-Planck equations in $\R^d$. Compared to the existing literature, our proof exploits a different approach, elementary and self-contained, b

Guillermo Palma, Gabriel Gomez

We propose a bulk viscous unified dark matter scenario based on a nonlinear extension of the full causal Israel-Stewart theory. This framework allows the viscous fluid to remain far from equilibrium, an essential feature for a physically consistent description of viscosity-driven accelerated expansion. We adopt the standard parametrization for the bulk visco

Chenliang Li, Junyu Leng, Jiaxiang Li, Youbang Sun

Robust reinforcement learning (Robust RL) seeks to handle epistemic uncertainty in environment dynamics, but existing approaches often rely on nested min--max optimization, which is computationally expensive and yields overly conservative policies. We propose \textbf{Adaptive Rank Representation (AdaRL)}, a bi-level optimization framework that improves robus

Rayed Suhail Ahmad, Rehan Ahmad, Quamar Niyaz

Wi-Fi networks are ubiquitous in both home and enterprise environments, serving as a primary medium for Internet access and forming the backbone of modern IoT ecosystems. However, their inherent vulnerabilities, combined with widespread adoption, create opportunities for malicious actors to gain unauthorized access or compromise sensitive data stored on conn

Sara Rosenthal, Maeda Hanafi, Yannis Katsis, Lucian Popa

Grounding conversations in existing passages, known as Retrieval-Augmented Generation (RAG), is an important aspect of Chat-Based Assistants powered by Large Language Models (LLMs) to ensure they are faithful and don't provide misinformation. Several benchmarks have been created to measure the performance of LLMs on this task. We present a longitudinal study

Miguel Cárdenas, Alejandro Nettle, Leandro Núñez

We write down the lagrangian for the snowplow model of the Z pinch system. Then, we develop the Euler-Lagrange equations to find out the corresponding equations of motion. Next, we set a criterion for quantitatively estimating the performance of Z pinch devices. Finally, we apply this criterion to a specific Z pinch system.

Maryam Aliakbarpour, Alireza Fallah, Swaha Roy, Ria Stevens

We study statistical estimation under local differential privacy (LDP) when users may hold heterogeneous privacy levels and accuracy must be guaranteed with high probability. Departing from the common in-expectation analyses, and for one-dimensional and multi-dimensional mean estimation problems, we develop finite sample upper bounds in $\ell_2$-norm that ho

Jesús A. De Loera, Jillian Eddy, Sawyer Jack Robertson, José Alejandro Samper

We study Forman--Ricci and effective resistance curvatures on the skeleta of convex polytopes. Our guiding questions are: how frequently do polytopal graphs exhibit everywhere positive curvature, and what structural constraints does positivity impose? For Forman--Ricci curvature we derive an exact identity for the average edge curvature in terms of flag $f$-

Lia Bronsard, Benoît Merlet, Marc Pegon

We investigate liquid drop models with screened Riesz-type interactions, focusing in particular on truncated Coulomb and Yukawa potentials in three dimensions. While in the classical Gamow model with Coulomb interaction the only minimizers are balls, as recently proved by Chodosh and Gianocca (2026), we show that this is no longer true if the interaction is

Kai Mei, Jiang Guo, Shuaichen Chang, Mingwen Dong

Large Language Models (LLMs) can serve as world models to enhance agent decision-making in digital environments by simulating future states and predicting action outcomes, potentially eliminating costly trial-and-error exploration. However, this capability is fundamentally limited by LLMs' tendency toward hallucination and their reliance on static training k

Haoran He

This paper proposes a machine learning-assisted channel estimation approach for massive MIMO systems, leveraging DNNs to outperform traditional LS and MMSE methods. In 5G and beyond, accurate channel estimation mitigates pilot contamination and high mobility issues that harm system reliability. The proposed DNN architecture includes multi-layer perceptrons w

Seiji Zenitani, Shunsuke Usami, Shuichi Matsukiyo

Numerical procedures for generating non-Maxwellian velocity distributions in particle simulations are presented. First, Monte Carlo methods for an $(r,q)$ distribution that generalizes flattop and kappa distributions are discussed. Then, two rejection methods for the regularized kappa distribution are presented, followed by a comparison in the $\kappa$ space

Bernhard Kliem, Georgios Chintzoglou, Tibor Török, Jie Zhang

We study the relationship between the speed of coronal mass ejections (CMEs) and the height profile of the ambient magnetic field, quantified by its decay index, n(h). Our sample is composed of 15 very fast CMEs (Vcme > 1500 km/s; all halo CMEs) and 22 halo CMEs below this speed limit from Solar Cycle 23. The very fast CMEs yield a high correlation of 0.81 b

Jack Laiho, Kenny Ratliff

We calculate Euclidean correlation functions through next-to-leading order in the low energy effective theory of gravity. We focus on correlation functions of curvature and volume operators, calculating these functions through one-loop order. We show that quantum fluctuations of the background spacetime must be taken into account in order to obtain gauge inv

Matthew Kowalski

We investigate the well- and ill-posedness theory for the Gabitov--Turitsyn equation, which models the long-time dynamics of pulses in dispersion-managed optical fibers. We identify two critical regularities, corresponding to two scaling pseudo-symmetries, that demarcate regimes of ill-posedness. First, we identify $s_m = \tfrac{d}{2} - \tfrac{2}{p},$ coinci

André L. G. Mandolesi

We give a more detailed description of the new system of Pl\"ucker-like equations from [4], discuss how it relates to the usual Pl\"ucker equations, and correct a mistake in that article.

A. E. Bernardini, W. de Paula, R. da Rocha

Heavy-light-flavor meson resonances with charm, in the $D^0$ and $D^*$ families, and charmonium-like states, in the $\eta_c$ and $\chi_{c1}$ families, are explored and discussed in the AdS/QCD model with four quark flavors. The differential configurational entropy is computed and analyzed for these four charmed meson families, also combining 4-flavor AdS/QCD

Jimin Lim, Arjun Damerla, Arthur Jiang, Nam Le

Large language models (LLMs) have shown to be increasingly capable of performing reasoning tasks, but their ability to make sequential decisions under uncertainty only using natural language remains underexplored. We introduce a novel benchmark in which LLMs interact with multi-armed bandit environments using purely textual feedback, "you earned a token", wi

Klaudia Dilcher, Piotr Bania, Diana Mendez-Avalos, Aleksandra Sierant

Precise, real-time monitoring of magnetic field evolution is important in applications including magnetic navigation and searches for physics beyond the standard model. One main field-monitoring technique, the spin-precession magnetometer (SPM), observes electron, nucleus, colour centre, or muon spins as they precess in response to their local magnetic field

Sicheng Zhou, Lei Wu, Cao Xiao, Parminder Bhatia

Self-supervised learning (SSL) has transformed vision encoder training in general domains but remains underutilized in medical imaging due to limited data and domain specific biases. We present MammoDINO, a novel SSL framework for mammography, pretrained on 1.4 million mammographic images. To capture clinically meaningful features, we introduce a breast tiss

Luca Ciotti

It is shown how the well-known formula for the gravitational energy of self-gravitating regular polytropes of finite mass can be obtained in an elementary way by using Gauss's divergence theorem and the Chandrasekhar virial tensor, without resorting to lengthy algebra, to specific properties of Lane-Emden functions, and to thermodynamics arguments, as is ins

David Fabre

This work considers the transition to unsteadiness in the wake of 2D slender bodies, and questions the relevance of the generally accepted scenario involving a region of absolute instability within the near wake. The case of a thin plate at zero incidence is first considered. Despite the absence of absolute instability region, global stability analysis revea

Manuel Ettengruber, Gonzalo Herrera

Cosmic ray collisions at high center of mass energy could enable graviton and black hole production as expected in theories of low-scale quantum gravity, such as extra-dimensions, many species, or some versions of string theory. Here we propose three novel phenomenological tests of these theories. We first consider the collision of cosmic rays with ambient p

Anastasiya Pechko, Piotr Borycki, Joanna Waczyńska, Daniel Barczyk

As the demand for immersive 3D content grows, the need for intuitive and efficient interaction methods becomes paramount. Current techniques for physically manipulating 3D content within Virtual Reality (VR) often face significant limitations, including reliance on engineering-intensive processes and simplified geometric representations, such as tetrahedral

Xiaohang Tang, Zhuowen Cheng, Satyabrat Kumar

The Transformer, a highly expressive architecture for sequence modeling, has recently been adapted to solve sequential decision-making, most notably through the Decision Transformer (DT), which learns policies by conditioning on desired returns. Yet, the adversarial robustness of reinforcement learning methods based on sequence modeling remains largely unexp

J. M. Nield, M. C. Baddock, G. F. S. Wiggs, J. Best

Ripples are the most fundamental and ubiquitous aeolian bedforms formed on sandy surfaces, but their small size and fast response times make them inherently difficult to measure. However, these attributes also make ripples excellent flow indicators, and they have been used extensively in planetary locations for this purpose. Here, we use terrestrial laser sc

Srikanth B. Iyengar

This article gives an overview of some recent results in commutative algebra that are inspired by the work of Wiles, Taylor and Wiles, Diamond, Lenstra and others on the modularity of elliptic curves.

A. C. D. Hoffmann, T. N. Bernard, M. Francisquez, G. W. Hammett

Designing economical magnetic confinement fusion power plants motivates computational tools that can estimate plasma behavior from engineering parameters without direct reliance on experimental measurement of the plasma profiles. In this work, we present full-$f$ global gyrokinetic (GK) turbulence simulations of edge and scrape-off layer turbulence in tokama

Alessandro Cornacchia, Vaastav Anand, Muhammad Bilal, Zafar Qazi

Agentic AI applications increasingly rely on multiple agents with distinct roles, specialized tools, and access to memory layers to solve complex tasks -- closely resembling service-oriented architectures. Yet, in the rapid evolving landscape of programming frameworks and new protocols, deploying and testing AI agents as distributed systems remains a dauntin

Poorbita Kundu, Nathan Wycoff

Active subspace analysis uses the leading eigenspace of the gradient's second moment to conduct supervised dimension reduction. In this article, we extend this methodology to real-valued functionals on Hilbert space. We define an operator which coincides with the active subspace matrix when applied to a Euclidean space. We show that many of the desirable pro

Renai Chen, Galen T. Craven

Understanding energy transport at the nanoscale is an open and fundamental challenge in the molecular sciences with direct implications for the design of new electronics, computing devices, and materials. While nanoscale energy transport under steady-state conditions has been studied extensively, there is much less known about energy transport under time-dep

Lael Edwards-Costa

The classical inner and outer billiards can be formulated in variational terms, with length and area as the respective generating functions. The other two combinations, ``inner with area'' and ``outer with length,'' are more recently described. Here, we consider the latter system in the special case of polygonal tables. We describe the behavior of orbits far