October 2025 arXiv papers — page 200

ChunJun Cao, Gong Cheng, Tianci Zhou

A bottleneck for analyzing the interplay between magic and entanglement is the computation of these quantities in highly entangled quantum many-body magic states. Efficient extraction of entanglement can also inform our understanding of dynamical quantum processes such as measurement-induced phase transition and approximate unitary designs. We develop an eff

Joakim Argillander, Daniel Spegel-Lexne, Martin Clason, Pedro R. Dieguez

Certifying random number generators is challenging, especially in security-critical fields like cryptography. Here, we demonstrate a measurement-device-independent quantum random number generator (MDI-QRNG) using high-dimensional photonic path states. Our setup extends the standard qubit beam-splitter QRNG to a three-output version with tunable fiber-optic i

Christopher Kang, Yuan Su

The efficient implementation of matrix arithmetic operations underpins the speedups of many quantum algorithms. We develop a suite of methods to perform matrix arithmetics -- with the result encoded in the off-diagonal blocks of a Hamiltonian -- using Hamiltonian evolutions of input operators. We show how to maintain this $\textit{Hamiltonian block encoding}

A. Lola Danhaive, Sandro Tacchella, William McClymont, Brant Robertson

Understanding how galaxies assemble their mass during the first billion years of cosmic time is a central goal of extragalactic astrophysics, yet joint constraints on their sizes and kinematics remain scarce. We present one of the first statistical studies of the $\mathrm{H}\alpha$ size-mass relation at high redshift with a sample of 213 galaxies at spectros

Arianna Garcia Caffaro, Ian Moult, Chase Shimmin

Jet substructure provides one of the most exciting new approaches for searching for physics in and beyond the Standard Model at the Large Hadron Collider. Modern jet substructure searches are often performed with Neural Network (NN) taggers which study the jets' radiation distributions in great detail, far beyond what is theoretically described by parton sho

Kai Bartnick, Detlev Koester, Rolf-Peter Kudritzki, Konstantin Springmann

We present the equation of state for two classes of new ultralight particles, a scalar field coupling to electrons and a light $\mathbb{Z}_\mathcal{N}$ QCD axion field coupling to nucleons. Both are potential candidates for dark matter. Using the scalar modified equations of state, we calculate models for white dwarf stars and compare their radii and masses

Federico Marinacci, Marco Baldi, Giuliano Iorio, M. Celeste Artale

(Edited) We introduce a flexible framework for building gravitational wave (GW) event catalogs in hydrodynamic simulations of galaxy formation. Our framework couples the state-of-the-art binary population synthesis code SEVN with Arepo-GW -- a module fully integrated into the moving-mesh code Arepo -- to assign merger events of binary compact objects to stel

Sean D. Johnson, Nishant Mishra, Sowgat Muzahid, Gwen C. Rudie

In dwarf galaxy models, outflows expel metal-enriched interstellar medium (ISM) into the circumgalactic medium (CGM) to reproduce their observed low metallicities, but measurements of dwarf CGM properties are scarce. We present a study of the CGM of an isolated dwarf at $z=0.5723$ with a stellar mass of $\approx5\times10^7\rm\,M_{\odot}$ and star-formation r

Vijay Balasubramanian, William KL Chan, Chitraang Murdia

We demonstrate that the Euclidean two-point function of an appropriately chosen probe operator can detect the microstate of an asymptotically AdS black hole. This detection, which requires a tuned, state-dependent choice of probe, is the result of a new gravitational saddle, which dominates over the usual saddles. The gravitational result can be explicitly r

Noah E. Wolfe, Matthew Mould, Jack Heinzel, Salvatore Vitale

From catalogs of gravitational-wave transients, the population-level properties of their sources and the formation channels of merging compact binaries can be constrained. However, astrophysical conclusions can be biased by misspecification or misestimation of the population likelihood. Despite detection thresholds on the false-alarm rate (FAR) or signal-to-

Yue Chen, Xingyu Chen, Yuxuan Xue, Anpei Chen

We present Human3R, a unified, feed-forward framework for online 4D human-scene reconstruction, in the world frame, from casually captured monocular videos. Unlike previous approaches that rely on multi-stage pipelines, iterative contact-aware refinement between humans and scenes, and heavy dependencies, e.g., human detection, depth estimation, and SLAM pre-

Deheng Zhang, Yuqian Fu, Runyi Yang, Yang Miao

Most existing benchmarks for understanding egocentric vision focus primarily on daytime scenarios, overlooking the low-light conditions that are inevitable in real-world applications. To investigate this gap, we present EgoNight, the first comprehensive benchmark for nighttime egocentric vision, with visual question answering (VQA) as the core task. A key fe

Jiaru Zou, Soumya Roy, Vinay Kumar Verma, Ziyi Wang

Process Reward Models (PRMs) have recently emerged as a powerful framework for enhancing the reasoning capabilities of large reasoning models (LRMs), particularly in the context of test-time scaling (TTS). However, their potential for supervising LRMs on tabular reasoning domains remains underexplored. Through detailed empirical analyses, we identify that ex

Mert Kiray, Alican Karaomer, Benjamin Busam

We present DropD-SLAM, a real-time monocular SLAM system that achieves RGB-D-level accuracy without relying on depth sensors. The system replaces active depth input with three pretrained vision modules: a monocular metric depth estimator, a learned keypoint detector, and an instance segmentation network. Dynamic objects are suppressed using dilated instance

Yi Xin, Qi Qin, Siqi Luo, Kaiwen Zhu

We introduce Lumina-DiMOO, an open-source foundational model for seamless multi-modal generation and understanding. Lumina-DiMOO sets itself apart from prior unified models by utilizing a fully discrete diffusion modeling to handle inputs and outputs across various modalities. This innovative approach allows Lumina-DiMOO to achieve higher sampling efficiency

Ayush Shrivastava, Connelly Barnes, Xuaner Zhang, Lingzhi Zhang

Current text-to-image diffusion models excel at generating diverse, high-quality images, yet they struggle to incorporate fine-grained camera metadata such as precise aperture settings. In this work, we introduce a novel text-to-image diffusion framework that leverages camera metadata, or EXIF data, which is often embedded in image files, with an emphasis on

Mingkang Zhu, Xi Chen, Bei Yu, Hengshuang Zhao

Large language model (LLM) agents increasingly rely on external tools such as search engines to solve complex, multi-step problems, and reinforcement learning (RL) has become a key paradigm for training them. However, the trajectories of search agents are structurally heterogeneous, where variations in the number, placement, and outcomes of search calls lead

Albert Catalan-Tatjer, Niccolò Ajroldi, Jonas Geiping

While post-training quantization is widely adopted for efficient deployment of large language models, the mechanisms underlying quantization robustness remain unclear. We conduct a comprehensive analysis of quantization degradation across open-source language model training trajectories up to 32B parameters and 15T training tokens to accurately assess the re

Dmytro Gavinsky, Dar Gilboa, Siddhartha Jain, Dmitri Maslov

The no-cloning theorem can be used as a basis for quantum money constructions which guarantee unconditionally unforgeable currency. Existing schemes, however, either (i) require long-term quantum memory and quantum communication between the user and the bank in order to verify the validity of a bill or (ii) fail to protect user privacy due to the uniqueness

Andreas Anastasiou, Ivor Cribben

Financial fraud has been growing exponentially in recent years. The rise of cryptocurrencies as an investment asset has simultaneously seen a parallel growth in cryptocurrency scams. To detect possible cryptocurrency fraud, and in particular market manipulation, previous research focused on the detection of changes in the network of trades; however, market m

Francesca Fiori, Andrea Riebler, Sara Martino

Italy reports some of the lowest levels of mortality in the developed world. Recent evidence, however, suggests that even in low mortality countries improvements may be slowing and regional inequalities widening. This study contributes new empirical evidence to the debate by analysing mortality data by single year of age for males and females across 107 prov

Jiahao Wang, Zhenpei Yang, Yijing Bai, Yingwei Li

Recent advances in generative models have sparked exciting new possibilities in the field of autonomous vehicles. Specifically, video generation models are now being explored as controllable virtual testing environments. Simultaneously, end-to-end (E2E) driving models have emerged as a streamlined alternative to conventional modular autonomous driving system

Jiraphon Yenphraphai, Ashkan Mirzaei, Jianqi Chen, Jiaxu Zou

Video-conditioned 4D shape generation aims to recover time-varying 3D geometry and view-consistent appearance directly from an input video. In this work, we introduce a native video-to-4D shape generation framework that synthesizes a single dynamic 3D representation end-to-end from the video. Our framework introduces three key components based on large-scale

Zefu Lin, Rongxu Cui, Chen Hanning, Xiangyu Wang

Recent advances in control robot methods, from end-to-end vision-language-action frameworks to modular systems with predefined primitives, have advanced robots' ability to follow natural language instructions. Nonetheless, many approaches still struggle to scale to diverse environments, as they often rely on large annotated datasets and offer limited interpr

Joeri De Ro

Given two von Neumann algebras $A$ and $B$, the $W^*$-algebraic Eilenberg-Watts theorem, due to M. Rieffel, asserts that there is a canonical equivalence $\operatorname{Corr}(A,B)\simeq \operatorname{Fun}(\operatorname{Rep}(B), \operatorname{Rep}(A))$ of categories, where $\operatorname{Corr}(A,B)$ denotes the category of all $A$-$B$-correspondences, $\opera

Othman Younus, Behnaz Majlesein, Richard Nacke, Isaac N. O. Osahon

The demand for energy-efficient high-speed wireless communication, coupled with the rapid rise of IoT devices, requires systems that integrate power harvesting with optical data reception to eliminate the need for charging or battery replacements. Recent advances have explored the use of solar cells as optical receivers for high-speed data detection alongsid

Christopher Mitcheltree, Hao Hao Tan, Joshua D. Reiss

Modulations are a critical part of sound design and music production, enabling the creation of complex and evolving audio. Modern synthesizers provide envelopes, low frequency oscillators (LFOs), and more parameter automation tools that allow users to modulate the output with ease. However, determining the modulation signals used to create a sound is difficu

Seungeun Rho, Aaron Trinh, Danfei Xu, Sehoon Ha

Scaling unsupervised skill discovery algorithms to high-DoF agents remains challenging. As dimensionality increases, the exploration space grows exponentially, while the manifold of meaningful skills remains limited. Therefore, semantic meaningfulness becomes essential to effectively guide exploration in high-dimensional spaces. In this work, we present Refe

Mingxuan Wang, Satoshi Nakamura

Machine Speech Chain, simulating the human perception-production loop, proves effective in jointly improving ASR and TTS. We propose TokenChain, a fully discrete speech chain coupling semantic-token ASR with a two-stage TTS: an autoregressive text-to-semantic model co-trained with ASR and a masked-generative semantic-to-acoustic model for synthesis only. End

Weijian Li, Hong-Yu Chen, Nabeel Rehemtulla, Ved G. Shah

Current time series foundation model (TSFM) training corpora largely omit data with certain complexities like irregular temporal sampling. Astronomical time series of stellar fluxes (light curves) are available in immense quantities and exhibit irregular sampling, multiple variates, and heteroskedasticity. We introduce StarEmbed, the first public benchmark f

Chengyang Zhao, Uksang Yoo, Arkadeep Narayan Chaudhury, Giljoo Nam

Hair care is an essential daily activity, yet it remains inaccessible to individuals with limited mobility and challenging for autonomous robot systems due to the fine-grained physical structure and complex dynamics of hair. In this work, we present DYMO-Hair, a model-based robot hair care system. We introduce a novel dynamics learning paradigm that is suite

Wentao Deng, Jiahuan Pei, Zhiwei Xu, Zhaochun Ren

A multi-agent system (MAS) enhances its capacity to solve complex natural language processing (NLP) tasks through collaboration among multiple agents, where consensus-seeking serves as a fundamental mechanism. However, existing consensus-seeking approaches typically rely on voting mechanisms to judge consensus, overlooking contradictions in system-internal b

Nikos A Mitsiou, Ioannis Krikidis, George K Karagiannidis

Multiple-input multiple-output (MIMO) is critical for 6G communication, offering improved spectral efficiency and reliability. However, conventional fully digital designs face significant challenges due to high hardware complexity and power consumption. Low-bit MIMO architectures, such as those employing b-bit quantized phase shifters, provide a cost-effecti

Harold Blum, Yuchen Liu, Chenyang Xu, Ziquan Zhuang

We define the relative stability threshold of a family of Fano varieties over a DVR and show that it is computed by a divisorial valuation. In the case when the special fiber is K-unstable, but the generic fiber is K-semistable, we use the divisorial valuation computing the threshold to replace the special fiber by a new one with a strictly larger stability

Jaemarie Solyst, Chloe Fong, Faisal Nurdin, Rotem Landesman

As AI increasingly saturates our daily lives, it is crucial that youth develop skills to critically use and assess AI systems and envision better alternatives. We apply theories from culturally responsive computing to design and study a learning experience meant to support Black Muslim teen girls in developing critical literacy with generative AI (GenAI). We

Tommaso Faleo, Christopher L. Morrison, Roméo Beignon, Francesco Graffitti

Photonic quantum technologies rely on the efficient generation and interference of indistinguishable photons. Exceptional achievements in this respect have been obtained by domain engineering of quasi-phase-matched parametric down-conversion sources, demonstrating high two-photon interference visibility using only moderate bandpass spectral filtering. Here,

Yen-Ju Lu, Yashesh Gaur, Wei Zhou, Benjamin Muller

Auto-regressive speech-text models pre-trained on interleaved text tokens and discretized speech tokens demonstrate strong speech understanding and generation, yet remain substantially less compute-efficient than text LLMs, partly due to the much longer sequences of speech tokens relative to text. This modality imbalance disproportionately allocates pre-trai

Imre Bartos, Marek Kowalski

The origin of ultra-high-energy cosmic rays remains one of the central open questions in astroparticle physics. Recent measurements reveal anisotropies in arrival directions, a rigidity-dependent composition dominated by intermediate-mass nuclei, and striking hemispheric differences in the energy spectra. Here we show that \emph{rare transients in nearby gal

Tomohiro Yamada

The van der Laan-Padovan sequence $P_n ~ (n=0, 1, \ldots)$ is defined by $P_0=1, P_1=P_2=0$, and $P_{n+3}=P_{n+1}+P_n$ for $n=0, 1, \ldots$. We determine all pairs $(P_m, P_n)$ satisfying $P_m^b=2^{g_1} 3^{g_2} 5^{g_3} 7^{g_4} P_n^a$ for some integers $g_1, g_2, g_3, g_4$, $a$, and $b$. More generally, for a linear recurrence sequence $u_n$ satisfying the do

Jose J. Gil, Ignacio San Jose, Monica Canabal-Carbia, Juan Campos

Mueller polarimetry is a powerful technique with broad applications in astronomy, remote sensing, advanced material analysis, and biomedical imaging. However, instrumental constraints frequently restrict the measurement to an incomplete Mueller matrix limited to its upper-left 3x3 submatrix. Simply padding the missing entries with zeros to form a 4x4 matrix

Mariya Mamajiwala, Cesare Corrado, Chris Lanyon, Steven A. Niederer

Atrial fibrillation (AF) is a common cardiac arrhythmia characterised by disordered electrical activity in the atria. The standard treatment is catheter ablation, which is invasive and irreversible. Recent advances in computational electrophysiology offer the potential for patient-specific models, often referred to as digital twins, that can be used to guide

Robin Kokot, Wessel Poelman

This work investigates how a multilingual transformer model represents morphosyntactic properties of questions. We introduce the Question Type and Complexity (QTC) dataset with sentences across seven languages, annotated with type information and complexity metrics including dependency length, tree depth, and lexical density. Our evaluation extends probing m

Chenxiao Yang, Cai Zhou, David Wipf, Zhiyuan Li

Diffusion language models have recently emerged as a competitive alternative to autoregressive language models. Beyond next-token generation, they are more efficient and flexible by enabling parallel and any-order token generation. However, despite empirical successes, their computational power and fundamental limitations remain poorly understood. In this pa

Audrey Cheng, Shu Liu, Melissa Pan, Zhifei Li

Artificial Intelligence (AI) is starting to transform the research process as we know it by automating the discovery of new solutions. Given a task, the typical AI-driven approach is (i) to generate a set of diverse solutions, and then (ii) to verify these solutions and select one that solves the problem. Crucially, this approach assumes the existence of a r

Jakir Hasan, Shubhashis Roy Dipta

Real-time speech assistants are becoming increasingly popular for ensuring improved accessibility to information. Bengali, being a low-resource language with a high regional dialectal diversity, has seen limited progress in developing such systems. Existing systems are not optimized for real-time use and focus only on standard Bengali. In this work, we prese

Griffin Pitts, Aum Pandya, Darsh Rank, Tirth Bhatt

A significant portion of student programming submissions in CS1 learning environments are uncompilable, limiting their use in student modeling and downstream knowledge tracing. Traditional modeling pipelines often exclude these cases, discarding observations of student learning. This study investigates automated program repair as a strategy to recover uncomp

Chunyu Miao, Henry Peng Zou, Yangning Li, Yankai Chen

Large language models (LLMs) show the promise in supporting scientific research implementation, yet their ability to generate correct and executable code remains limited. Existing works largely adopt one-shot settings, ignoring the iterative and feedback-driven nature of realistic workflows of scientific research development. To address this gap, we present

Fernando Granha Jeronimo, Nikhil Shagrithaya

We present a general framework for derandomizing random linear codes with respect to a broad class of properties, known as local properties, which encompass several standard notions such as distance, list-decoding, list-recovery, and perfect hashing. Our approach extends the classical Alon-Edmonds-Luby (AEL) construction through a modified formalism of local

Will Donovan, Wahei Hara, Michał Kapustka, Marco Rampazzo

The local simple $9$-fold flop of Grassmannian type is a birational transformation between total spaces of vector bundles on the Grassmannians $\mathrm{Gr}(2, 5)$ and $\mathrm{Gr}(3, 5)$. We produce four different derived equivalences which commute with the pushforward functors for the flopping contractions. These equivalences are realized by identifying fou

Shreyas Iyer, Peixue Wu, Paula Belzig, Graeme Smith

Any reasonable measure of distinguishability of quantum states must satisfy a data processing inequality, that is, it must not increase under the action of a quantum channel. We can ask about the proportion of information lost or preserved and this leads us to study contraction and expansion coefficients respectively, which can be combined into a single \emp

Jinwen Xu, Qin Lu, Georgios B. Giannakis

Uncertainty quantification (UQ) over graphs arises in a number of safety-critical applications in network science. The Gaussian process (GP), as a classical Bayesian framework for UQ, has been developed to handle graph-structured data by devising topology-aware kernel functions. However, such GP-based approaches are limited not only by the prohibitive comput

Jordan Seneca, Suzanne Bintanja, Frank M. Selten

In climate science, the tuning of climate models is a computationally intensive problem due to the combination of the high-dimensionality of the system state and long integration times. Supermodelling is a technique which has shown the potential for reducing climate model biases by dynamically coupling multiple models together, and training their coupling on

Emre Adabag, Marcus Greiff, John Subosits, Thomas Lew

Differentiable model predictive control (MPC) offers a powerful framework for combining learning and control. However, its adoption has been limited by the inherently sequential nature of traditional optimization algorithms, which are challenging to parallelize on modern computing hardware like GPUs. In this work, we tackle this bottleneck by introducing a G

Bjørnar Gullikstad Hem

We apply poset cocalculus, a functor calculus framework for functors out of a poset, to study the problem of decomposing multipersistence modules into simpler components. We both prove new results in this topic and offer a new perspective on already established results. In particular, we show that a pointwise finite-dimensional bipersistence module is middle

Alan R. Pearse, Howard Bondell

This paper demonstrates that, under a particular convention, the convex functions that characterise the phi divergences also generate Archimedean copulas in at least two dimensions. As a special case, we develop the family of Archimedean copulas associated with the important family of power divergences, which we call the power-divergence copulas. The propert

Junyin Zhang, Shuhang Zheng, Jiachen Cai, Connor Denney

Decades of progress in radiofrequency (RF) transistors and receiver frontends have profoundly impacted wireless communications, remote sensing, navigation, and instrumentation. Growing demands for data throughput in 6G networks, timing precision in positioning systems, and resolution in atmospheric sensing and automotive radar have pushed receiver frontends

Dingyu Yao, Chenxu Yang, Zhengyang Tong, Zheng Lin

The Key-Value (KV) cache introduces substantial memory overhead during large language model (LLM) inference. Although existing vector quantization (VQ) methods reduce KV cache usage and provide flexible representational capacity across bit-widths, they suffer severe performance degradation at ultra-low bit-widths due to key cache outliers that hinder effecti

Nathan X. Kodama, Michael Hinczewski

We establish a fundamental connection between score-based diffusion models and non-equilibrium thermodynamics by deriving performance limits based on entropy rates. Our main theoretical contribution is a lower bound on the negative log-likelihood of the data that relates model performance to entropy rates of diffusion processes. We numerically validate this

Shuixin Li, Jiecheng Chen, Qingtang Jiang, Lin Li

To analyze signals with rapid frequency variations or transient components, the time-reassigned synchrosqueezing transform (TSST) and its variants have been recently proposed. Unlike the traditional synchrosqueezing transform, TSST squeezes the time-frequency (TF) coefficients along the group delay (GD) trajectories rather than the instantaneous frequency tr

Yuqian Huo, Daniel Leeds, Jason Ludmir, Nicholas S. DiBrita

Quantum computing, which has the power to accelerate many computing applications, is currently a technology under development. As a result, the existing noisy intermediate-scale quantum (NISQ) computers suffer from different hardware noise effects, which cause errors in the output of quantum programs. These errors cause a high degree of variability in the pe

Jan Peřina, Karol Bartkiewicz, Grzegorz Chimczak, Anna Kowalewska-Kudlaszyk

We investigate the hierarchy of quantum correlations in a quadratic bosonic parity-time-symmetric system (PTSS) featuring distinct dissipation and amplification channels. The hierarchy includes global nonclassicality, entanglement, asymmetric quantum steering, and Bell nonlocality. We elucidate the interplay between the system physical nonlinearity -- which

Naveenkumar G Venkataswamy, Yu Liu, Soumyabrata Dey, Stephanie Schuckers

Smartphone-based iris recognition in the visible spectrum (VIS) remains difficult due to illumination variability, pigmentation differences, and the absence of standardized capture controls. This work presents a compact end-to-end pipeline that enforces ISO/IEC 29794-6 quality compliance at acquisition and demonstrates that accurate VIS iris recognition is f

Jacob Lustig-Yaeger, Kristin S. Sotzen, Kevin B. Stevenson, Shang-Min Tsai

Theoretical studies have suggested using planetary infrared excess (PIE) to detect and characterize the thermal emission of transiting and non-transiting exoplanets, however the PIE technique requires empirical validation. Here we apply the PIE technique to a combination of JWST NIRSpec G395H transit and eclipse measurements of WASP-17b, a hot Jupiter orbiti

Siyu Cheng, Keyu Zeng, Yi Liu, Christopher Candelora

Kagome metals have developed into a vibrant playground for materials physics, where geometric frustration, electronic correlations and band topology come together to create a variety of exotic phenomena. Recently synthesized CsCr$_3$Sb$_5$ has provided a rare opportunity to explore unconventional superconductivity in a strongly correlated kagome system with

Baria Khan, Samuel A. Daza Rodriguez, Els Peeters, Alexander G. G. M. Tielens

Infrared emission from polycyclic aromatic hydrocarbons (PAHs) play a major role in determining the charge balance of their host environments that include photo-dissociation regions (PDRs) in galaxies, planetary nebulae, and rims of molecular clouds. We aim to investigate the distribution and sizes of charged PAHs across the key zones of the Orion Bar PDR. W

Rushik Desai, Junyeong Ahn, Alejandro Strachan, Arun Mannodi-Kanakkithodi

Among emerging energy materials, halide and chalcogenide perovskites have garnered significant attention over the last decade owing to the abundance of their constituent species, low manufacturing costs, and their highly tunable composition-structure-property space. Navigating the vast perovskite compositional landscape is possible using density functional t

Kurt Butler, Guanchao Feng, Petar Djuric

Feature attributions are post-training analysis methods that assess how various input features of a machine learning model contribute to an output prediction. Their interpretation is straightforward when features act independently, but it becomes less clear when the predictive model involves interactions, such as multiplicative relationships or joint feature

Sha Azyzy, Drew Jamieson, Eiichiro Komatsu, Toshiki Kurita

Parity-odd four-point correlation functions, or trispectra, of cosmic matter density fields provide a unique probe of fundamental symmetries in cosmology. Trispectra of primordial matter density fluctuations produced in the early universe are modified by the subsequent nonlinear structure formation. In this paper, we compute the nonlinear evolution of the pa

Shuang Cheng, Yihan Bian, Dawei Liu, Linfeng Zhang

We propose SDAR, a Synergistic Diffusion-Autoregression paradigm that unifies the training efficiency of autoregressive models with the parallel inference capability of diffusion. Instead of costly end-to-end diffusion training, SDAR performs a lightweight paradigm conversion that transforms a well-trained autoregressive (AR) model into a blockwise diffusion

Javier Diaz, Ignacio Pagonabarraga, Carles Calero

Propulsion of colloidal particles due to momentum transfer from localized surface reactions is investigated by solving the exact unsteady Stokes equation. We model the effect of surface reactions as either a {\it force dipole} acting on the fluid or a {\it pair force} acting on both the colloid and the fluid. Our analysis reveals that after a single reaction

Christopher Kolberg, Jules Kreuer, Jonas Huurdeman, Sofiane Ouaari

Revealing novel insights from the relationship between molecular measurements and pathology remains a very impactful application of machine learning in biomedicine. Data in this domain typically contain only a few observations but thousands of potentially noisy features, posing challenges for conventional tabular machine learning approaches. While prior-data

Jonathan W. Bober, Oleksiy Klurman, Besfort Shala

We prove distributional results for mixed character sums \begin{equation*} \sum_{n\le x }\chi(n)e(n\theta), \end{equation*} for fixed $\theta\in [0,1]$ and random character $\chi \pmod q$, as well as for a fixed character $\chi$ and randomly sampled $\theta\in [0,1].$ We present various applications of our results. For example, we construct Littlewood polyno

Blake Romrell, Abigail Austin, Braden Meyers, Ryan Anderson

Marine robotics simulators play a fundamental role in the development of marine robotic systems. With increased focus on the marine robotics field in recent years, there has been significant interest in developing higher fidelitysimulation of marine sensors, physics, and visual rendering capabilities to support autonomous marine robot development and validat

Qian Xu, Hengyun Zhou, Dolev Bluvstein, Madelyn Cain

High-rate quantum LDPC (qLDPC) codes reduce memory overhead by densely packing many logical qubits into a single block of physical qubits. Here we extend this concept to high-rate computation by constructing \emph{batched} fault-tolerant operations that apply the same logical gate across many code blocks in parallel. By leveraging shared physical resources t

Ksenija Lace, Marite Kirikova

Post-merger integration states unique challenges for professionals responsible for information system integration aimed on alignment and combination diverse system architectures of merging organizations. Although the theoretical and practical guidance exists for post-merger integration on the business level, there is a significant gap in training for informa

Yuna Watanabe, Natasha Yamane, Aarti Sathyanarayana, Varun Mishra

Wearable physiological monitors are ubiquitous, and photoplethysmography (PPG) is the standard low-cost sensor for measuring cardiac activity. Metrics such as inter-beat interval (IBI) and pulse-rate variability (PRV) -- core markers of stress, anxiety, and other mental-health outcomes -- are routinely extracted from PPG, yet preprocessing remains non-standa

Cristian F. Jiménez-Varón, Marina I. Knight

Financial spillovers in interconnected systems, such as global banking networks, require tools that capture temporal and frequency dynamics, while incorporating the underlying network topology. While current network time series models are developed in the time-domain, frequency-domain approaches, which reveal how cross-nodal dependencies vary across differen

Guillaume Rivière

The science of Human-Computer Interaction (HCI) is populated by isolated empirical findings, often tied to specific technologies, designs, and tasks. This situation probably lies in observing the wrong object of study, that is to say, observing interfaces rather than interaction. This paper proposes an experimental methodology, powered by a research methodol

Jae Jun Kim

We present a method in which the dimension-five and -six terms in a sequential gauge can coexist with that in a non-sequential gauge under a tetrahedron rotational symmetry. We start with the contents in the model proposed in a sequential gauge, meeting the conditions for the contents to be described in the rotational symmetry. Given that the breaking of the

Jian Zheng, Sahand Kiani, Mario Sznaier, Constantino Lagoa

This paper presents a data-driven receding horizon control framework for discrete-time linear systems that guarantees robust performance in the presence of bounded disturbances. Unlike the majority of existing data-driven predictive control methods, which rely on Willem's fundamental lemma, the proposed method enforces set-membership constraints for data-dri

Shubhodip Mondal, Tasos Moulinos, Lucy Yang

We introduce and develop the notion of "unipotent spectra." This is defined to be the stabilization of To\"en's category of affine stacks, and is related to recent work of Mondal--Reinecke. Unipotent spectra give rise to unipotent stable homotopy groups and unipotent homology, which are new invariants for schemes valued in unipotent group schemes. As applica

Aju Ani Justus, Chris Baber

A critical challenge in modelling Heterogeneous-Agent Teams is training agents to collaborate with teammates whose policies are inaccessible or non-stationary, such as humans. Traditional approaches rely on expensive human-in-the-loop data, which limits scalability. We propose using Large Language Models (LLMs) as policy-agnostic human proxies to generate sy

Dakota K. Keblbeck, Eric Mayotte, Uwe Greife, Kyle G. Leach

We present a comprehensive Monte Carlo simulation of muon-induced backgrounds for the Colorado Underground Research Institute (CURIE), a shallow-underground facility with $\approx 415$~m.w.e. overburden. Using coupled \textsc{mute} and \textsc{geant4} frameworks, we characterize the production and transport of muon-induced secondaries through site-specific r

Hwanwoo Kim, Dongkyu Derek Cho, Eric Laber

Temporal difference (TD) learning is a cornerstone of reinforcement learning. In the average-reward setting, standard TD($\lambda$) is highly sensitive to the choice of step-size and thus requires careful tuning to maintain numerical stability. We introduce average-reward implicit TD($\lambda$), which employs an implicit fixed point update to provide data-ad

Thiago de Souza Ferreira, Daniel Jonathan, Antonio Z. Khoury, Daniel S. Tasca

Fraunhofer diffraction plays a vital role in experimental physics not only because it accurately describes the behaviour of light in the usual propagation limit, but also because it links the diffracted light with the scattering object through one of the most important mathematical transformations in physics: the Fourier transform. Acting as a probe in mater

Giacomo De Palma, Marco Fanizza, Connor Mowry, Ryan O'Donnell

We study hypothesis testing (aka state certification) in the non-identically distributed setting. A recent work (Garg et al. 2023) considered the classical case, in which one is given (independent) samples from $T$ unknown probability distributions $p_1, \dots, p_T$ on $[d] = \{1, 2, \dots, d\}$, and one wishes to accept/reject the hypothesis that their aver

Jaehwan Jeong, Tuan-Anh Vu, Radha Lahoti, Jiawen Wang

Robotic pollination offers a promising alternative to manual labor and bumblebee-assisted methods in controlled agriculture, where wind-driven pollination is absent and regulatory restrictions limit the use of commercial pollinators. In this work, we present and validate a vision-guided robotic framework that uses data from an end-effector mounted RGB-D sens

Aditya Prakash, David Forsyth, Saurabh Gupta

We tackle the problem of forecasting bimanual 3D hand motion & articulation from a single image in everyday settings. To address the lack of 3D hand annotations in diverse settings, we design an annotation pipeline consisting of a diffusion model to lift 2D hand keypoint sequences to 4D hand motion. For the forecasting model, we adopt a diffusion loss to acc

Mijie Pang, Jianbing Jin, Arjo Segers, Hai Xiang Lin

Atmospheric chemistry encapsulates the emission of various pollutants, the complex chemistry reactions, and the meteorology dominant transport, which form a dynamic system that governs air quality. While deep learning (DL) models have shown promise in capturing intricate patterns for forecasting individual atmospheric component - such as PM2.5 and ozone - th

Zanyi Wang, Dengyang Jiang, Liuzhuozheng Li, Sizhe Dang

Referring Video Object Segmentation (RVOS) requires segmenting specific objects in a video guided by a natural language description. The core challenge of RVOS is to anchor abstract linguistic concepts onto a specific set of pixels and continuously segment them through the complex dynamics of a video. Faced with this difficulty, prior work has often decompos

Rushiv Arora

Multi-task reinforcement learning often relies on task metadata -- such as brief natural-language descriptions -- to guide behavior across diverse objectives. We present Lexical Policy Networks (LEXPOL), a language-conditioned mixture-of-policies architecture for multi-task RL. LEXPOL encodes task metadata with a text encoder and uses a learned gating module

Jieyun Wang, Anna L. Smith

Latent space models assume that network ties are more likely between nodes that are closer together in an underlying latent space. Euclidean space is a popular choice for the underlying geometry, but hyperbolic geometry can mimic more realistic patterns of ties in complex networks. To identify the underlying geometry, past research has applied non-Euclidean

Weihao Zeng, Keqing He, Chuqiao Kuang, Xiaoguang Li

Test-time compute can be scaled both sequentially and in parallel. Sequential scaling involves lengthening the generation process, while parallel scaling involves verifying and selecting among multiple candidate outputs. Combining these two strategies has led to the most powerful AI systems, such as Grok 4 Heavy and GPT-5 Pro. In certain contexts (e.g., solv

Soufiane Atouani, Olivier Marchal, Julyan Arbel

We investigate the problem of characterizing the optimal variance proxy for sub-Gaussian random variables,whose moment-generating function exhibits bounded growth at infinity. We apply a general characterization method to discrete random variables with equally spaced atoms. We thoroughly study 3-mass distributions, thereby generalizing the well-studied Berno

Jiawei Mao, Yuhan Wang, Lifeng Chen, Can Zhao

Recent advances in generative medical models are constrained by modality-specific scenarios that hinder the integration of complementary evidence from imaging, pathology, and clinical notes. This fragmentation limits their evolution into foundation models that can learn and reason across the full spectrum of biomedical data. We propose MeDiM, the first medic

Binita Maity, Shrutimoy Das, Anirban Dasgupta

In this paper, we present a local search-based algorithm for individually fair clustering in the presence of outliers. We consider the individual fairness definition proposed in Jung et al., which requires that each of the $n$ points in the dataset must have one of the $k$ centers within its $n/k$ nearest neighbors. However, if the dataset is known to contai

V. I. Lapushkin

In this paper, we study different properties of the motion equations of interacting fields. In the second section, we prove that "Wightman's" fields (we use only a subset of Wightman's axioms) are unitarily equivalent to some operators on the vector space ${\cal F}$ (with one mathematical assumption). In the third section, we introduce $L^{\infty}$ and $DL$

Xiao Liang, Lu Shen, Peihan Zhang, Soofiyan Atar

Chronic wounds, such as diabetic, pressure, and venous ulcers, affect over 6.5 million patients in the United States alone and generate an annual cost exceeding \$25 billion. Despite this burden, chronic wound care remains a routine yet manual process performed exclusively by trained clinicians due to its critical safety demands. We envision a future in whic

Haoxin Wang, Xiaolong Tu, Hongyu Ke, Huirong Chai

Large Language Models (LLMs) are increasingly integrated into everyday applications, but their prevalent cloud-based deployment raises growing concerns around data privacy and long-term sustainability. Running LLMs locally on mobile and edge devices (on-device LLMs) offers the promise of enhanced privacy, reliability, and reduced communication costs. However

Moumita Kamal, Douglas A. Talbert

In real-world applications, computational constraints often require transforming large models into smaller, more efficient versions through model compression. While these techniques aim to reduce size and computational cost without sacrificing performance, their evaluations have traditionally focused on the trade-off between size and accuracy, overlooking th