October 2025 arXiv papers — page 187

Mathias Boulanger, Ericmoore Jossou

In this work, we investigate the use of spatio-temporalImplicit Neural Representations (INRs) for dynamic X-ray computed tomography (XCT) reconstruction under interlaced acquisition schemes. The proposed approach combines ADMM-based optimization with INCODE, a conditioning framework incorporating prior knowledge, to enable efficient convergence. We evaluate

Ha Min Son, Huan Ren, Xin Liu, Zhe Zhao

Android is the largest mobile platform, yet automatically building applications remains a practical challenge. While Large Language Models (LLMs) show promise for code repair, their use for fixing Android build errors remains underexplored. To address this gap, we first introduce AndroidBuildBench, a benchmark of 1,019 build failures curated from the commit

Yunbo Li, Jiaping Gui, Zhihang Deng, Fanchao Meng

Federated learning (FL) enables collaborative model training across multiple parties without sharing raw data, with semi-asynchronous FL (SAFL) emerging as a balanced approach between synchronous and asynchronous FL. However, SAFL faces significant challenges in optimizing both gradient-based (e.g., FedSGD) and model-based (e.g., FedAvg) aggregation strategi

Jiajing Wang

Predicting long-term loan defaults is hard because borrower behavior often changes and data distributions shift over time. This paper presents HYDRA-EI, a hybrid ensemble incremental learning framework. It uses several stages of feature processing and combines multiple models. The framework builds relational, cross, and frequency-based features. It uses grap

Virginia K. Felkner, Allison Lim, Jonathan May

Measurement of social bias in language models is typically by token probability (TP) metrics, which are broadly applicable but have been criticized for their distance from real-world language model use cases and harms. In this work, we test natural language inference (NLI) as an alternative bias metric. In extensive experiments across seven LM families, we s

Jinwoong Kim, Sangjin Park

The increasing influence of unstructured external information, such as news articles, on stock prices has attracted growing attention in financial markets. Despite recent advances, most existing newsbased forecasting models represent all articles using sentiment scores or average embeddings that capture the general tone but fail to provide quantitative, cont

Marcus J. Hamilton, Robert S. Walker

Why do human populations remain vulnerable to collapse, even when they are large? Classical demographic theory predicts that volatility in growth should decline rapidly with size due to the averaging effects of the law of large numbers. As such, while small-scale societies may be demographically fragile, large-scale societies should be much more stable. Usin

Azélie Picot

We define a version of the surface cobordism category $\mathrm{Cob}_2^{\mathrm{SG}}(X)$ over a base space $X$ where surfaces are considered up to self homotopy equivalences instead of diffeomorphisms. We prove the induced functor $B\mathrm{Cob}_2^{\mathrm{SG}}(-): \mathcal{S} \rightarrow \mathcal{S}$ is not $1$-excisive. We show its first derivative $\partia

Samuel E. Fontaine, J. E. Sipe, Marco Liscidini, Milica Banic

We propose cascaded spontaneous four-wave mixing (SFWM) in microring resonators as a scalable and efficient approach for directly generating non-Gaussian states of light. Focusing on the well-understood "low-gain" regime, we demonstrate that triplet generation through cascaded SFWM can be achieved with high efficiency and favorable spectral characteristics u

Jason R. Casar, Claire A. McLellan, Cindy Shi, Ariel Stiber

The forces generated by action potentials in muscle cells shuttle blood, food and waste products throughout the luminal structures of the body. Although non-invasive electrophysiological techniques exist, most mechanosensors cannot access luminal structures non-invasively. Here we introduce non-toxic ingestible mechanosensors to enable the quantitative study

James Baker

Personalizing diffusion models allows users to generate new images that incorporate a given subject, allowing more control than a text prompt. These models often suffer somewhat when they end up just recreating the subject image and ignoring the text prompt. We observe that one popular method for personalization, IP-Adapter, automatically generates masks tha

Yibo Li, Fengming Dong, Huiqing Liu

For an integer k\ge2, a [2,k]-ST of a connected graph G is a spanning tree of G in which there are no vertices of degree between 2 and k. A [2,k]-ST is a natural extension of a homeomorphically irreducible spanning tree (HIST), which is a spanning tree without vertices of degree 2. In this paper, we give a neighborhood union condition for the existence of a

Yanjie Pan, Qingdong He, Lidong Wang, Bo Peng

Video virtual try-on aims to replace the clothing of a person in a video with a target garment. Current dual-branch architectures have achieved significant success in diffusion models based on the U-Net; however, adapting them to diffusion models built upon the Diffusion Transformer remains challenging. Initially, introducing latent space features from the g

Jiawen Chen, Jinwei Zhang, Dongshen Peng, Yutong Song

Spatial variable genes (SVGs) reveal critical information about tissue architecture, cellular interactions, and disease microenvironments. As spatial transcriptomics (ST) technologies proliferate, accurately identifying SVGs across diverse platforms, tissue types, and disease contexts has become both a major opportunity and a significant computational challe

Harshala Gammulle, Clinton Fookes, Sridha Sridharan, Simon Denman

Action segmentation is a challenging yet active research area that involves identifying when and where specific actions occur in continuous video streams. Most existing work has focused on single-stream approaches that model the spatio-temporal aspects of frame sequences. However, recent research has shifted toward two-stream methods that learn action-wise f

Tianyu Pan, Vincent Z. Yu, Viswanath Devanarayan, Lu Tian

Cross-validation is a standard tool for obtaining a honest assessment of the performance of a prediction model. The commonly used version repeatedly splits data, trains the prediction model on the training set, evaluates the model performance on the test set, and averages the model performance across different data splits. A well-known criticism is that such

Vorrapan Chandee, Yoonbok Lee, Xiannan Li

We obtain the $n$th centered moments of one level densities of a large orthogonal family of $L$-functions associated with holomorphic Hecke newforms of level $q$, averaged over $q\sim Q$. We verify the Katz-Sarnak conjecture for these statistics, in the range where the sum of the supports of the Fourier transforms of test functions lies in $(-4, 4)$. In so d

Zichen Wang, Haoyang Hong, Chuanhao Li, Haoxuan Li

In multi-armed bandits with network interference (MABNI), the action taken by one node can influence the rewards of others, creating complex interdependence. While existing research on MABNI largely concentrates on minimizing regret, it often overlooks the crucial concern that an excessive emphasis on the optimal arm can undermine the inference accuracy for

Xin Jie Chua, Jeraelyn Ming Li Tan, Jia Xuan Tan, Soon Chang Poh

This paper presents Ryt AI, an LLM-native agentic framework that powers Ryt Bank to enable customers to execute core financial transactions through natural language conversation. This represents the first global regulator-approved deployment worldwide where conversational AI functions as the primary banking interface, in contrast to prior assistants that hav

Shuoqi Sun, Danula Hettiachchi, Damiano Spina

The modern information environment (MIE) is increasingly complex, shaped by a wide range of techniques designed to satisfy users' information needs. Information seeking (IS) models are effective mechanisms for characterizing user-system interactions. However, conceptualizing a model that fully captures the MIE landscape poses a challenge. We argue: Does such

Valery Asiryan

In this paper we consider the even monic degree-8 cuboid polynomial $P_{a,u}(t)$ with coprime integers $a\neq u>0$. We prove irreducibility over $\mathbb{Z}$ by excluding all degree-8 splittings. First, any putative $4{+}4$ factorization is shown to force a specific Diophantine constraint that has no integer solutions, via a short $2$- and $3$-adic analysis.

Đorđe Klisura, Joseph Khoury, Ashish Kundu, Ram Krishnan

Access control is a cornerstone of secure computing, yet large language models often blur role boundaries by producing unrestricted responses. We study role-conditioned refusals, focusing on the LLM's ability to adhere to access control policies by answering when authorized and refusing when not. To evaluate this behavior, we created a novel dataset that ext

Nik Weaver

We propose axioms governing the interaction of constructive assertibility and meaningfulness predicates with a self-applicative truth predicate characterized by the T-scheme, and we prove the consistency of the resulting formal system.

A. O. Leonov, G. Gödecke, J. Grefe, S. Süllow

We revisit the phenomenological Dzyaloshinskii framework, a central theoretical approach for describing magnetization processes in bulk chiral magnets, and demonstrate how magnetocrystalline cubic anisotropy reshapes the phase diagrams of states and provides the key mechanism stabilizing low-temperature skyrmion phases. We show that, for magnetic field direc

Favour Yahdii Aghaebe, Dustin Foley, Eric Atwell, Stephen Clark

This study advocates for employing clustering techniques to classify vacation rental properties active during the Covid pandemic to identify inherent patterns and behaviours. The dataset, a collaboration between the ESRC funded Consumer Data Research Centre (CDRC) and AirDNA, encompasses data for over a million properties and hosts. Utilising K-means and K-m

Kinjal Parikh, Danny M. Kaufman, David I. W. Levin, Alec Jacobson

Editing and animating text appearance for graphic designs, commercials, etc. remain highly skilled tasks requiring detailed, hands on efforts from artists. Automating these manual workflows requires balancing the competing goals of maintaining legibility and aesthetics of text, while enabling creative expression. Variable fonts, recent parametric extensions

Shashank Gupta, Gregoire Phillips, Alan C. Bovik

Large Multimodal Models (LMMs) have recently enabled considerable advances in the realm of image and video quality assessment, but this progress has yet to be fully explored in the domain of 3D assets. We are interested in using these models to conduct No-Reference Point Cloud Quality Assessment (NR-PCQA), where the aim is to automatically evaluate the perce

Haruka Kiyohara, Yusuke Narita, Yuta Saito, Kei Tateno

In many real recommender systems, novel items are added frequently over time. The importance of sufficiently presenting novel actions has widely been acknowledged for improving long-term user engagement. A recent work builds on Off-Policy Learning (OPL), which trains a policy from only logged data, however, the existing methods can be unsafe in the presence

Gavin R. Putland

A "spatiotemporal dipole" wave source, as defined by D.A.B. Miller (1991), differs from an ordinary ("spatial") dipole source in that the inverted monopole is delayed relative to the uninverted monopole, the delay being equal to the propagation time from one monopole to the other. A "generalized" spatiotemporal dipole (GSTD), as defined here, is generalized

Nara Guliyeva, Eshta Bhardwaj, Christoph Becker

The influential Limits to Growth report introduced a system dynamics-based model to demonstrate global dynamics of the world's population, industry, natural resources, agriculture, and pollution between 1900-2100. In current times, the rapidly expanding trajectory of data center development, much of it linked to AI, uses increasing amounts of natural resourc

Lewis R. B. Picard, Manuel Endres

Acousto-optic deflectors (AODs) are widely used across physics, microscopy, neuroscience, and laser engineering, providing fast, precise, and non-mechanical control of light. While conventional AODs naturally support multiplexing in one and two dimensions, no analogous device has existed for three-dimensional control, leaving a critical gap in rapid focus tu

Yinglun Zhu, Jiancheng Zhang, Fuzhi Tang

Frontier AI models have achieved remarkable progress, yet recent studies suggest they struggle with compositional reasoning, often performing at or below random chance on established benchmarks. We revisit this problem and show that widely used evaluation metrics systematically underestimate model capability. To correct this artifact, we introduce a group ma

Shreshth Saini, Shashank Gupta, Alan C. Bovik

Classifier-free guidance (CFG) is the workhorse for steering large diffusion models toward text-conditioned targets, yet its native application to rectified flow (RF) based models provokes severe off-manifold drift, yielding visual artifacts, text misalignment, and brittle behaviour. We present Rectified-CFG++, an adaptive predictor-corrector guidance that c

Shiliang Zhang, Sabita Maharjan, Kai Strunz, Jan Christian Bryne

Geographic data is vital in understanding, analyzing, and contextualizing energy usage at the regional level within electricity systems. While geospatial visualizations of electricity infrastructure and distributions of production and consumption are available from governmental and third-party sources, these sources are often disparate, and compatible geogra

Abdul Quadir, Haider Hasan Jafri

We study extreme events of avalanche activities in finite-size two-dimensional self-organized critical (SOC) models, specifically the stochastic Manna model (SMM) and the Bak-Tang-Weisenfeld (BTW) sandpile model. Employing the approach of block maxima, the study numerically reveals that the distributions for extreme avalanche size and area follow the general

Yung-Hsuan Tseng, Hsiang-Yi Karen Yang, Ryan Farber, Mateusz Ruszkowski

Galaxies falling into galaxy clusters can leave imprints on both the corona of galaxies and the intracluster medium (ICM) of galaxy clusters. Throughout this infall process, the galaxy's atmosphere is subjected to ram pressure from a headwind, leading to the stripping morphology observed in its tail. The morphological evolution is affected by the propert

Kaustabh Barman, Fabian Piper, Sanjeet Raj Pandey, Axel Kuepper

User authentication is one of the most important aspects for secure communication between services and end-users over the Internet. Service providers leverage Single-Sign On (SSO) to make it easier for their users to authenticate themselves. However, standardized systems for SSO, such as OIDC, do not guarantee user privacy as identity providers can track use

Chalamalasetti Kranti, Sowmya Vajjala

Prior NLP work studying poetry has focused primarily on automatic poem generation and summarization. Many languages have well-studied traditions of poetic meter which enforce constraints on a poem in terms of syllable and phoneme patterns. Such advanced literary forms offer opportunities for probing deeper reasoning and language understanding in Large Langua

Alexander Lopez, Sébastien Fumeron, Malte Henkel, Trifce Sandev

Fractional quantum dynamics provides a natural framework to capture nonlocal temporal behavior and memory effects in quantum systems. In this work, we analyze the physical consequences of fractional-order quantum evolution using a Green's function formulation based on the Caputo fractional derivative. Explicit iterative expressions for the evolved state

Xiaomin Liu, Jing Zhang, Jie Li, Rongguo Yang

Magnomechanical systems with YIG spheres have been proven to be an ideal system for studying magnomechanically induced transparency, dynamical backaction, and rich nonlinear effects, such as the magnon-phonon cross-Kerr effect. Accurate characterization of the magnetostriction induced deformation displacement is important as it can be used for, e.g., estimat

Priyanka Mondal, Sonu Verma, Wenze Lan, Lukas Krelle

Moiré superlattices in twisted bilayers enable profound reconstructions of the electronic bandstructure, giving rise to correlated states with remarkable tunability. Extending this paradigm to van der Waals magnets, twisting creates spatially varying interlayer exchange interactions that stabilize emergent spin textures and the coexistence of ferromagnetic a

Yu-tin Huang, Sara Ricossa, Francesco Riva, Jie-Da Tsai

In this letter, we consider constraints on the low-energy spectrum of amplitudes with higher-spin exchange. Assuming unitarity, crossing symmetry, and super-convergent high energy behavior, reminiscent of the scattering of spin-1 and spin-2 massless helicity states, we demonstrate that the spectrum that maximizes the leading higher spin couplings of the seco

Kun Fang, Michael X. Cao

We study the problem of quantum channel discrimination between two channels with an adversary input party (a.k.a. a jammer). This setup interpolates between the best-case channel discrimination as studied by (Wang & Wilde, 2019) and the worst-case channel discrimination as studied by (Fang, Fawzi, & Fawzi, 2025), thereby generalizing both frameworks. To addr

Russell K. Standish

Information-based complexity (IBC) is a well-defined complexity measure of any object given a description in a language and a classifier that identifies those descriptions with the object. Of course, the exact numerical value will vary according to the descriptive language and classifier, but under certain universality conditions (eg the classifier identifie

Tom Benhamou, Ben-Zion Weltsch

We study saturation properties of $σ$-complete measures on $P_κ(λ)$, where $λ$ can be either regular or singular. In particular, we prove that in contrast to Galvin's theorem, the Galvin property of Benhamou-Garti-Poveda fails for normal fine ultrafilters on $P_κ(λ)$, answering a question of the first author and Goldberg. We then provide several applicat

Tímea Nóra Török, Roland Kövecs, Ferenc Braun, Zsigmond Pollner

Objective: This work aims to demonstrate a low-power, biomimetic auditory sensing concept for fully implantable cochlear implants. The approach draws inspiration from the frequency selectivity and temporal encoding of the cochlea, and uses neuromorphic spike generation to replace conventional signal processing blocks. The goal is to establish a compact, ener

Syed Mumtahin Mahmud, Mahdi Mohd Hossain Noki, Prothito Shovon Majumder, Abdul Mohaimen Al Radi

Motion blur remains one of the most common and visually disruptive degradations in real-world smartphone imaging, yet existing deblurring benchmarks are often limited in scale, resolution, or domain relevance. This gap is especially pronounced for smartphones, where rolling shutter, small sensors, and ISP processing produce blur statistics that differ from G

Atharva Kulkarni, Yuan Zhang, Joel Ruben Antony Moniz, Xiou Ge

Hallucinations pose a significant obstacle to the reliability and widespread adoption of language models, yet their accurate measurement remains a persistent challenge. While many task- and domain-specific metrics have been proposed to assess faithfulness and factuality concerns, the robustness and generalization of these metrics are still untested. In this

Francesco Gabriele, Carmine Ortix, Mario Cuoco, Filomena Forte

Spin-orbit coupling and quantum geometry are fundamental aspects in modern condensed matter physics, with their primary manifestations in momentum space being spin textures and Berry curvature. In this work, we investigate their interplay with high-harmonic generation (HHG) in two-dimensional non-centrosymmetric materials, with an emphasis on even-order harm

Enrique Alvarado, Prashant Gupta, Bala Krishnamoorthy

We define a new framework that unifies the filtration and mapper approaches from TDA, and present efficient algorithms to compute it. Termed the box filtration of a PCD, we grow boxes (hyperrectangles) that are not necessarily centered at each point (in place of balls centered at points). We grow the boxes non-uniformly and asymmetrically in different dimens

Zhangdie Yuan, Han-Chin Shing, Mitch Strong, Chaitanya Shivade

Accurate clinical coding is essential for healthcare documentation, billing, and decision-making. While prior work shows that off-the-shelf LLMs struggle with this task, evaluations based on exact match metrics often overlook errors where predicted codes are hierarchically close but incorrect. Our analysis reveals that such hierarchical misalignments account

Diego Fallas Padilla, Raphael Kaubruegger, Adrianna Gillman, Stephen Becker

Entanglement underpins the power of quantum technologies, yet it is fragile and typically destroyed by dissipation. Paradoxically, the same dissipation, when carefully engineered, can drive a system toward robust entangled steady states. However, this engineering task is nontrivial, as dissipative many-body systems are complex, particularly when they support

Kaoru Sano, Hayata Morisaki, Seiseki Akibue

We study ancilla-free approximation of single-qubit unitaries $U\in {\rm SU}(2)$ by gate sequences over Clifford+$G$, where $G\in\{T,V\}$ or their generalization. Let $p$ denote the characteristic factor of the gate set (e.g., $p=2$ for $G=T$ and $p=5$ for $G=V$). We prove three asymptotic bounds on the minimum $G$-count required to achieve approximation err

Chongyu Fan, Changsheng Wang, Yancheng Huang, Soumyadeep Pal

Machine unlearning for large language models (LLMs) aims to remove undesired data, knowledge, and behaviors (e.g., for safety, privacy, or copyright) while preserving useful model capabilities. Despite rapid progress over the past two years, research in LLM unlearning remains fragmented, with limited clarity on what constitutes effective unlearning and how i

Alexander Du, Emre Adabag, Gabriel Bravo-Palacios, Brian Plancher

While Model Predictive Control (MPC) delivers strong performance across robotics applications, solving the underlying (batches of) nonlinear trajectory optimization (TO) problems online remains computationally demanding. Existing GPU-accelerated approaches either parallelize single solves, handle large batches at sub-real-time rates, or sacrifice model gener

Abdelhakim Benechehab, Gabriel Singer, Corentin Léger, Youssef Attia El Hili

Generative models form the backbone of modern machine learning, underpinning state-of-the-art systems in text, vision, and multimodal applications. While Maximum Likelihood Estimation has traditionally served as the dominant training paradigm, recent work have highlighted its limitations, particularly in generalization and susceptibility to catastrophic forg

Hannah R. Lawrence, Shannon Wiltsey Stirman, Samuel Dorison, Taedong Yun

Generative artificial intelligence (Generative AI) is transforming healthcare. With this evolution comes optimism regarding the impact it will have on mental health, as well as concern regarding the risks that come with generative AI operating in the mental health domain. Much of the investment in, and academic and public discourse about, AI-powered solution

Ewin Tang, John Wright, Mark Zhandry

We give a natural problem over input quantum oracles $U$ which cannot be solved with exponentially many black-box queries to $U$ and $U^\dagger$, but which can be solved with constant many queries to $U$ and $U^*$, or $U$ and $U^{\mathrm{T}}$. We also demonstrate a quantum commitment scheme that is secure against adversaries that query only $U$ and $U^\dagge

Saeideh Bakhshi, Phuong Mai Nguyen, Robert Schiller, Tiantian Xu

Recommendation systems have traditionally relied on short-term engagement signals, such as clicks and likes, to personalize content. However, these signals are often noisy, sparse, and insufficient for capturing long-term user satisfaction and retention. We introduce Retentive Relevance, a novel content-level survey-based feedback measure that directly asses

Muhammad Usman, Yugyung Lee

Dynamic trust evaluation in large, rapidly evolving graphs demands models that capture changing relationships, express calibrated confidence, and resist adversarial manipulation. DGTEN (Deep Gaussian-Based Trust Evaluation Network) introduces a unified graph-based framework that does all three by combining uncertainty-aware message passing, expressive tempor

Sahin Buyukdagli

Within the framework of a functional integral formalism incorporating ionic charge and hard-core (HC) interactions on an equal footing, we formulate a unified theory of equilibrium thermodynamics and ion association in charged solutions. Via comparison with recent Monte-Carlo (MC) simulation results (J. Forsman et al., PCCP 26, 19921 (2024)), it is shown tha

Kenneth L. Baker, Masakazu Teragaito

A knot is called an L-space knot if it admits a positive Dehn surgery yielding an L-space. In the SnapPy census, there are exactly 9 asymmetric L-space knots. Among them, the knot t12533 is the only known example of braid index 4. We generalize this knot, and give the first infinite family of asymmetric hyperbolic L-space knots of braid index 4.

Ankan Ganguly, Bhaswar B. Bhattacharya

In a multiplex network, a set of nodes is connected by different types of interactions, each represented as a separate layer within the network. Multiplexes have emerged as a key instrument for modeling large-scale complex systems, due to the widespread coexistence of diverse interactions in social, industrial, and biological domains. This motivates the deve

George M. Bergman

Let $<X>$ be the free monoid on a generating set $X$, and suppose one adjoins to $<X>$ universal 2-sided inverses to a finite set $S$ of its elements. We note an elementary algorithm which yields a normal form for elements of the resulting monoid $M$. We then show that if $S$ is allowed to be infinite, a similar normal form exists, though it cannot necessari

Peter Kosec, Laura Brenneman, Erin Kara, Teruaki Enoto

X-ray binary accretion disk winds can carry away a significant fraction of the matter transferred from the companion and hence strongly affect the accretion flow and the long-term evolution of the binary. However, accurate mass outflow rate measurements are challenging due to uncertainties in our understanding of the 3D wind structure. Most studies employ ab

Amine Barrak

Sequential multi-agent systems built with large language models (LLMs) can automate complex software tasks, but they are hard to trust because errors quietly pass from one stage to the next. We study a traceable and accountable pipeline, meaning a system with clear roles, structured handoffs, and saved records that let us trace who did what at each step and

Isabel Papadimitriou, Jacob Prince

Large language models (LLMs) work by manipulating the geometry of input embedding vectors over multiple layers. Here, we ask: how are the input vocabulary representations of language models structured, and how and when does this structure evolve over training? To answer this question, we use representational similarity analysis, running a suite of experiment

Sathwik Bharadwaj, Makoto Schreiber, Jungho Mun, Sam Ruttiman

In plasmonics, nonlocal effects arise when the material response to optical excitations is strongly dependent on the spatial correlations of the excitation. It is well known that a classical free electron gas system supports local Drude volume plasmon waves. Whereas a compressible quantum electron gas system sustains hydrodynamic volume plasmons with nonloca

Jingyang You, Hanna Kurniawati, Lashika Medagoda

The aging and increasing complexity of infrastructures make efficient inspection planning more critical in ensuring safety. Thanks to sampling-based motion planning, many inspection planners are fast. However, they often require huge memory. This is particularly true when the structure under inspection is large and complex, consisting of many struts and pill

Nate Laffan, Ashley Hom, Andrea Nadine Castillo, Elizabeth Gitelman

The Slow Space Editor is a 2D tool for creating 3D spaces. It was built as part of a research-through-design project that investigates how Virtual and Mixed Reality (XR) environments might be used for reflection and attention restoration. In this phase, we seek to radically simplify the creation of virtual environments, thereby broadening the potential group

Victor Victor, Tania Krisanty, Matthew McGinity, Stefan Gumhold

As the markets for unmanned aerial vehicles (UAVs) and mixed reality (MR) headsets continue to grow, recent research has increasingly explored their integration, which enables more intuitive, immersive, and situationally aware control systems. We present IGUANA, an MR-based immersive guidance, navigation, and control system for consumer UAVs. IGUANA introduc

Jiajin Xie, Yong Wang, Eduardo García-Portugués

Compositional data, representing proportions constrained to the simplex, arise in diverse fields such as geosciences, ecology, genomics, and microbiome research. Existing nonparametric density estimation methods often rely on transformations, which may induce substantial bias near the simplex boundary. We propose a nonparametric mixture-based framework for d

Sizhe Ma, Katherine A. Flanigan, Mario Bergés, James D. Brooks

Indirect structural health monitoring (iSHM) for broken rail detection using onboard sensors presents a cost-effective paradigm for railway track assessment, yet reliably detecting small, transient anomalies (2-10 cm) remains a significant challenge due to complex vehicle dynamics, signal noise, and the scarcity of labeled data limiting supervised approaches

Andrzej Łuczak, Hanna Podsędkowska, Rafał Wieczorek

The paper presents variational formulae for entropy-like functionals, including Segal and R\'enyi entropies, for normal states on semifinite von Neumann algebras. The considered functionals are of the form $\tau(f(h))$ where $\tau$ is a normal faithful semifinite trace on this algebra, $h$ is a positive selfadjoint operator from $L^1(\M,\tau)$, and $f$ is an

Yubo Bai, Tapti Palit

Rust is a memory-safe programming language that significantly improves software security. Existing codebases written in unsafe memory languages, such as C, must first be transpiled to Rust to take advantage of Rust's improved safety guarantees. RustAssure presents a system that uses Large Language Models (LLMs) to automatically transpile existing C codebases

Dmitry Teytelman

This volume contains contributions submitted to the 12th Low-Level RF Workshop which will be held in Newport News, Virginia, USA on October 12-16, 2025. This workshop continues the series of successful international workshops held in Newport News, USA (2001), Geneva, Switzerland (2005), Knoxville, USA (2007), Tsukuba, Japan (2009), Hamburg, Germany (2011), T

Thomas Fel, Binxu Wang, Michael A. Lepori, Matthew Kowal

DINOv2 is routinely deployed to recognize objects, scenes, and actions; yet the nature of what it perceives remains unknown. As a working baseline, we adopt the Linear Representation Hypothesis (LRH) and operationalize it using SAEs, producing a 32,000-unit dictionary that serves as the interpretability backbone of our study, which unfolds in three parts. In

Kaiyuan Ji, Bartosz Regula

The ultimate limits of quantum state discrimination are often thought to be captured by asymptotic bounds that restrict the achievable error probabilities, notably the quantum Chernoff and Hoeffding bounds. Here we study hypothesis testing protocols that are permitted a probability of producing an inconclusive discrimination outcome, and investigate their pe

Pouya Shiri, Ramin Sharifi, Amirali Baniasadi

The basic computational unit in Capsule Network (CapsNet) is a capsule (vs. neurons in Convolutional Neural Networks (CNNs)). A capsule is a set of neurons, which form a vector. CapsNet is used for supervised classification of data and has achieved state-of-the-art accuracy on MNIST digit recognition dataset, outperforming conventional CNNs in detecting over

Rachel M. Sun, Andrew Y. Chen, Yiming Ji, Eric M. Stewart

Magnetic remote actuation of soft materials is attractive for applications such as transforming materials and medical robots. However, due to manufacturing limitations, microscale magnetoactive devices are scarce -- light-based additive manufacturing methods, despite achieving microscale resolution, struggle with particle-induced light scattering. Moreover,

Likai Yang, Chunzhen Li, Jiacheng Xie, Hong X. Tang

Thin-film lithium niobate (TFLN) has played a pivotal role in the advancement of integrated photonics, by supporting a diverse range of applications including nonlinear optics, electro-optics, and piezo-optomechanics. The effective realization and enhancement of these interactions rely heavily on the implementation of high quality photonic microresonators. T

Nicolas Resch, S. Venkitesh

List recovery is a fundamental task for error-correcting codes, vastly generalizing unique decoding from worst-case errors and list decoding. Briefly, one is given ''soft information'' in the form of input lists S_1,...,S_n of bounded size, and one argues that there are not too many codewords that agree a lot with this soft information. This general problem

Mathilde Van Cuyck, Matthieu Bethermin, Guilaine Lagache, Alexandre Beelen

Context: Rotational CO transitions, while acting as a foreground for [C II] line-intensity mapping (LIM) experiments, trace the physical conditions of cold gas in galaxies at lower redshifts. Studying these transitions is also crucial for improving component-separation methods as LIM sensitivity increases. Aims: Galaxy-evolution models have so far predicted

William T. Reach, Mukremin Kilic, Carey M. Lisse, John H. Debes

White dwarf stars with high abundances of heavy elements in their atmospheres and infrared excesses are believed to be accreting planetary material. GD 362 is one of the most heavily polluted white dwarfs and has an exceptionally strong mid-infrared excess, reprocessing 2.4% of the star's light into the mid-infrared. We present a high signal-to-noise, medium

Shitij Govil, Jack P. Rodgers, Yuan-Tang Chou, Siqi Miao

Charged particle track reconstruction is a foundational task in collider experiments and the main computational bottleneck in particle reconstruction. Graph neural networks (GNNs) have shown strong performance for this problem, but costly graph construction, irregular computations, and random memory access patterns substantially limit their throughput. The r

Bohan Lin, Kuo Yang, Zelin Tan, Yingchuan Lai

Multi-agent systems (MAS) built on large language models promise improved problem-solving through collaboration, yet they often fail to consistently outperform strong single-agent baselines due to error propagation at inter-agent message handoffs.In this work, we conduct a systematic empirical analysis of such failures and introduce an edge-level error taxon

Sebastian Braun, Hannes Gamper, Dimitra Emmanouilidou

Modern generative and multimodal models increasingly rely on compact latent representations that trade and balance semantic richness with high-fidelity reconstruction. We introduce SALAD-VAE, a continuous and highly compact semantic Audio Variational Autoencoder, which operates in the frequency domain and achieves state-of-the-art compression with very low l

Chihiro Taguchi, Richard Sproat

We present a system that uses LLMs as a tool in the development of Constructed Languages -- ConLangs, which we call IASC (Interactive Agentic System for ConLangs). The system is modular in that it creates each of the components -- phonology, morphology and syntax, lexicon, orthography, and grammatical handbook, using module-specific sets of prompts. The appr

Rupam Patir, Keyan Guo, Haipeng Cai, Hongxin Hu

The code generation capabilities of Large Language Models (LLMs) have transformed the field of software development. However, this advancement also presents significant security challenges, as LLM-generated code often contains vulnerabilities. One direction of research strengthens LLMs by injecting or refining security knowledge through curated datasets, mod

Juan Viguera Diez, Mathias Schreiner, Simon Olsson

Understanding molecular structure, dynamics, and reactivity requires bridging processes that occur across widely separated time scales. Conventional molecular dynamics simulations provide atomistic resolution, but their femtosecond time steps limit access to the slow conformational changes and relaxation processes that govern chemical function. Here, we intr

Hussain Gohar

We introduce a generalized mass-horizon relation applicable to cosmological horizons. This formulation provides a unified framework for deriving a broad class of Bekenstein entropy extensions motivated by statistical mechanics, quantum gravity, and phenomenological considerations, through the application of the Clausius relation together with the Hawking tem

Jacob Chmura, Shenyang Huang, Tran Gia Bao Ngo, Ali Parviz

Well-designed open-source software drives progress in Machine Learning (ML) research. While static graph ML enjoys mature frameworks like PyTorch Geometric and DGL, ML for temporal graphs (TG), networks that evolve over time, lacks comparable infrastructure. Existing TG libraries are often tailored to specific architectures, hindering support for diverse mod

Sashank Makanaboyina

Accurate detection and segmentation of brain tumors in magnetic resonance imaging (MRI) are critical for effective diagnosis and treatment planning. Despite advances in convolutional neural networks (CNNs) such as U-Net, existing models often struggle with generalization, boundary precision, and limited data diversity. To address these challenges, we propose

Dhruv C. Desai, Lauren A. Tan, Jin-Jian Zhou, Shiyu Peng

Topological effects arising from the Berry curvature lead to intriguing transport signatures in quantum materials. Two such phenomena are the chiral anomaly and nonlinear Hall effect (NLHE). A unified description of these transport regimes requires a quantitative treatment of both band topology and electron scattering. Here, we show accurate predictions of t

Thomas Debris-Alazard, Philippe Gaborit, Romaric Neveu, Olivier Ruatta

Introduced in 2003 and 2005, Alekhnovich and Regev' schemes were the first public-key encryptions whose security is only based on the average hardness of decoding random linear codes and LWE, without other security assumptions. Such security guarantees made them very popular, being at the origin of the now standardized HQC or Kyber. We present an adaptation

Sihan Chen, Luca V. Delacretaz

Bosonization describes Fermi surface dynamics in terms of a collective field that lives on a part of phase space. While sensible semiclassically, the challenge of treating such a field quantum mechanically has prevented bosonization from providing as powerful a nonperturbative tool as in one dimension. We show that general Fermi surfaces can be exactly descr

Yuyan Bao, Tiark Rompf

Programming benefits from a clear separation between pure, mathematical computation and impure, effectful interaction with the world. Existing approaches to enforce this separation include monads, type-and-effect systems, and capability systems. All share a tension between precision and usability, and each one has non-obvious strengths and weaknesses. This p

Johanna Einsiedler, Katharina Ledebur, Peter Klimek, Laust Hvas Mortensen

Chronic diseases frequently co-occur in patterns that are unlikely to arise by chance, a phenomenon known as multimorbidity. This growing challenge for patients and healthcare systems is amplified by demographic aging and the rising burden of chronic conditions. However, our understanding of how individuals transition from a disease-free-state to accumulatin

Zhengsen Xu, Yimin Zhu, Zack Dewis, Mabel Heffring

Although change detection using MODIS time series is critical for environmental monitoring, it is a highly challenging task due to key MODIS difficulties, e.g., mixed pixels, spatial-spectral-temporal information coupling effect, and background class heterogeneity. This paper presents a novel knowledge-aware Mamba (KAMamba) for enhanced MODIS change detectio

Zhongqi Yue, Weishi Wang, Yundaichuan Zhan, Juncheng Li

Large Language Models (LLMs) are powerful reasoners in natural language, but their actions are typically confined to outputting vocabulary tokens. As a result, interactions with external environments -- such as symbolic operators or simulators -- must be expressed through text in predefined formats, parsed, and routed to external interfaces. This overloads t