October 2025 arXiv papers — page 89

Fabien Malbet, J. Amiaux, F. Ardellier-Desages, E. Doumayrou

Astrometry, one of the oldest branches of astronomy, has been revolutionized by missions like Hipparcos and especially Gaia, which mapped billions of stars with extraordinary precision. However, challenges such as detecting Earth-like exoplanets in nearby habitable zones and probing the influence of dark matter in galactic environments require sub-microarcse

Runnan Qi, Yanan Ni, Lumin Jiang, Zongyuan Li

This paper proposes Memory-Augmented State Machine Prompting (MASMP), a novel framework for LLM agents in real-time strategy games. Addressing key challenges like hallucinations and fragmented decision-making in existing approaches, MASMP integrates state machine prompting with memory mechanisms to unify structured actions with long-term tactical coherence.

Yong Zhang, Nishanth Sastry

Undoubtedly, the Internet has become one of the most important conduits to information for the general public. Nonetheless, Internet access can be and has been limited systematically or blocked completely during political events in numerous countries and regions by various censorship mechanisms. Depending on where the core filtering component is situated, ce

Florian Hörsch, Csaba Király, Mirabel Mendoza-Cadena, Gyula Pap

For a graph (undirected, directed, or mixed), a cycle-factor is a collection of vertex-disjoint cycles covering the entire vertex set. Cycle-factors subject to parity constraints arise naturally in the study of structural graph theory and algorithmic complexity. In this work, we study four variants of the problem of finding a cycle-factor subject to parity c

Giovanni Bologni, Martin Bo Møller, Richard Heusdens, Richard C. Hendriks

Conventional acoustic beamformers typically assume short-time stationarity and process frequency bins independently, ignoring inter-frequency correlations. This is suboptimal for almost-periodic noise sources such as engines, fans, and musical instruments: these signals are better modeled as (almost) cyclostationary (ACS) processes with statistically correla

Ryuichiro Kitano, Ian Low, Ryutaro Matsudo, Shohei Okawa

We explore the discovery potential of heavy neutral leptons (HNLs), motivated by models addressing the origin of neutrino masses, at the proposed high-energy same-sign muon collider known as $μ$TRISTAN. The study focuses on two complementary HNL-mediated signatures: (i) the lepton-flavor-violating (LFV) channel $μ^+μ^+ \to W^+τ^+\barν_μ$ and (ii) the lepton-

Hicham Hamoudi, Sara Iyad Ahmad, Atef Zekri, Kamal Toumi

Building on our prior work, where our team transcended self assembled molecular monolayers (SAMs) research from a 2D configuration to 3D structured materials and successfully introduced the molecular self assembled 3D printer to fabricate technomolecular materials hybrid carbon metal nanosheets that mimic biological self assembly through cooperative organic

Jian Lu, Xiaohuang Huang

This paper investigates the approximation properties of shallow neural networks with activation functions that are powers of exponential functions. It focuses on the dependence of the approximation rate on the dimension and the smoothness of the function being approximated within the Barron function space. We examine the approximation rates of ReLU$^{k}$ act

Silvère Ségaud, Charlie Budd, Matthew Elliot, Graeme Stasiuk

Quantification of protoporphyrin IX (PpIX) fluorescence in human brain tumours has the potential to significantly improve patient outcomes in neuro-oncology, but represents a formidable imaging challenge. Protoporphyrin is a biological molecule which interacts with the tissue micro-environment to form two photochemical states in glioma. Each exhibits markedl

Camille Moisset, Paul Billant, Junho Park, Stéphane Mathis

This paper investigates the non-linear dynamics of horizontal shear instability in an incompressible, stratified and rotating fluid in the non-traditional $f$-plane, i.e. with the full Coriolis acceleration, using direct numerical simulations. The study is restricted to two-dimensional horizontal perturbations. It is therefore independent of the vertical (tr

Moreno Joel, Jimenez Marilyn, Okerstrom Daniel, Bradley Michael P.

In this article, we present a detailed characterisation of a multicusp-assisted inductively coupled RF plasma source for plasma immersion ion implantation (PIII). Using laser-induced fluorescence (LIF) and RF-compensated Langmuir probe diagnostics, we measured ion temperature T i and drift velocity v z in argon plasmas near an immersed electrode. The multicu

Hailiang Li, Li-Yong Zhou, Xiaoping Zhang

Planetary migration is a crucial stage in the early solar system, explaining many observational phenomena and providing constraints on details related to the solar system's origins. This paper aims to investigate the acceleration during planetary migration in detail using numerical simulations, delving deeper into the early solar system's preserved informati

Giorgio Piras, Qi Zhao, Fabio Brau, Maura Pintor

Adversarial pruning methods have emerged as a powerful tool for compressing neural networks while preserving robustness against adversarial attacks. These methods typically follow a three-step pipeline: (i) pretrain a robust model, (ii) select a binary mask for weight pruning, and (iii) finetune the pruned model. To select the binary mask, these methods mini

Chuan Fan, Qian Huang, Kailiang Wu

Quadrature-based moment methods (QBMM) provide tractable closures for multiscale kinetic equations, with diverse applications across aerosols, sprays, and particulate flows, etc. However, for the derived hyperbolic moment-closure systems, seeking numerical schemes preserving moment realizability is essential yet challenging due to strong nonlinear coupling a

Clément L. Canonne, Abigail Gentle, Vikrant Singhal

We initiate the study of distribution testing under \emph{user-level} local differential privacy, where each of $n$ users contributes $m$ samples from the unknown underlying distribution. This setting, albeit very natural, is significantly more challenging that the usual locally private setting, as for the same parameter $\varepsilon$ the privacy guarantee m

Yuequn Li, Alessandro Palmieri

In this paper, we consider a semilinear system of damped wave equations coupled through power nonlinearities of derivative-type. In particular, we consider a classical damped wave equation, i.e., with constant coefficients, and a wave equation with a time-dependent coefficient for the damping term. For this time-dependent coefficient we analyze two cases: th

Yuqing Luo, Yixiao Li, Jiang Liu, Jun Fu

Image complexity assessment (ICA) is a challenging task in perceptual evaluation due to the subjective nature of human perception and the inherent semantic diversity in real-world images. Existing ICA methods predominantly rely on hand-crafted or shallow convolutional neural network-based features of a single visual modality, which are insufficient to fully

Qionglei Chen, Zhen Li, Changxing Miao

In this paper, we investigate the quantitative stability for the 2D Couette flow on the infinite channel $\mathbb{R}\times [-1,1]$ with non-slip boundary condition. Compared to the case $\mathbb{T}\times [-1,1]$, we establish the stability in the context of long wave associated with the frequency range $0\leq |k|<1$ by developing the resolvent estimate argum

Ke-Ji Chen, Fan Wu

Phase imprinting enables the dynamic generation of superflow in bosonic atoms, effectively overcoming traditional limitations such as vortex number constraints and heating effects. However, the mechanisms underlying superflow formation remain insufficiently understood. In this work, we reveal these mechanisms by studying the time evolution of the transferred

Monorama Swain, Bubai Maji, Jagabandhu Mishra, Markus Schedl

In this work, we address the challenge of building fair English ASR systems for second-language speakers. Our analysis of widely used ASR models, Whisper and Seamless-M4T, reveals large fluctuations in word error rate (WER) across 26 accent groups, indicating significant fairness gaps. To mitigate this, we propose fairness-prompted finetuning with lightweigh

Wanchen Li, Kahina Chalabi, Sabbah Maxime, Thomas Bousquet

This paper presents a novel framework for real-time human action recognition in industrial contexts, using standard 2D cameras. We introduce a complete pipeline for robust and real-time estimation of human joint kinematics, input to a temporally smoothed Transformer-based network, for action recognition. We rely on a new dataset including 11 subjects perform

Karol Pietrak, Piotr Łapka, Małgorzata Kujawińska

In this paper, the results of numerical experiments verifying a novel setup for laser beam profiling are presented. The experimental setup is based on infrared thermography and includes laser beam illuminating a thin metal plate. The method allows to determine four parameters of the short high-power laser pulse, namely the Super-Gaussian profile coefficient,

Mingxin Li, Haibo Hu, Jinghuai Deng, Yuchen Xi

Validation of autonomous driving systems requires a trade-off between test fidelity, cost, and scalability. While miniaturized hardware-in-the-loop (HIL) platforms have emerged as a promising solution, a systematic framework supporting rigorous quantitative analysis is generally lacking, limiting their value as scientific evaluation tools. To address this ch

Gangda Deng, Yuxin Yang, Ömer Faruk Akgül, Hanqing Zeng

Graph Neural Networks (GNNs) have become essential tools for learning on relational data, yet the performance of a single GNN is often limited by the heterogeneity present in real-world graphs. Recent advances in Mixture-of-Experts (MoE) frameworks demonstrate that assembling multiple, explicitly diverse GNNs with distinct generalization patterns can signifi

Chang Liu, Matteo Ippoliti, Wen Wei Ho

We report a phase transition in the projected ensemble - the collection of post-measurement wavefunctions of a local subsystem obtained by measuring its complement. The transition emerges in systems undergoing random permutation dynamics, a type of quantum time evolution wherein computational basis states are shuffled without creating superpositions. It sepa

Donghyeon Ko, Yeguk Jin, Kyubyung Chae, Byungwook Lee

We present $\textbf{Korean SimpleQA (KoSimpleQA)}$, a benchmark for evaluating factuality in large language models (LLMs) with a focus on Korean cultural knowledge. KoSimpleQA is designed to be challenging yet easy to grade, consisting of 1,000 short, fact-seeking questions with unambiguous answers. We conduct a comprehensive evaluation across a diverse set

Zhaoyi Du, Jiewen Wei, Ding Ding, Martina Rimmele

Metal halide perovskites (MHPs) have had a profound impact on numerous emerging optoelectronic technologies, achieving performance metrics that rival or exceed incumbent materials. This impact is underpinned by the exceptional properties of MHPs, including tuneable band gaps, high absorption coefficients, long carrier diffusion lengths and combined with unco

Qionglei Chen, Zhen Li, Changxing Miao

We investigate the stability of the 2-D Navier-Stokes equations in the infinite channel $\mathbb{R}\times [-1,1]$ with the Navier-slip boundary condition. We show that if the initial perturbations $\omega^{in}$ around the Couette flow satisfy $\|\omega^{in}\|_{H^3_{x,y}\cap L^1_x H^3_y}\leq c\nu^{\frac13}$, the solution admits enhanced dissipation at $x$-fre

Quim Motger, Xavier Franch, Vincenzo Gervasi, Jordi Marco

Large Language Models (LLMs) are increasingly used to recommend mobile applications through natural language prompts, offering a flexible alternative to keyword-based app store search. Yet, the reasoning behind these recommendations remains opaque, raising questions about their consistency, explainability, and alignment with traditional App Store Optimizatio

Zhen Zhang, Bingsheng He

Unsupervised Graph Domain Adaptation has become a promising paradigm for transferring knowledge from a fully labeled source graph to an unlabeled target graph. Existing graph domain adaptation models primarily focus on the closed-set setting, where the source and target domains share the same label spaces. However, this assumption might not be practical in t

Duoxun Tang, Xi Xiao, Guangwu Hu, Kangkang Sun

The vulnerability of deep neural networks (DNNs) has been preliminarily verified. Existing black-box adversarial attacks usually require multi-round interaction with the model and consume numerous queries, which is impractical in the real-world and hard to scale to recently emerged Video-LLMs. Moreover, no attack in the video domain directly leverages featur

Yuxuan Liu, Chiya Zhang, Yifeng Yuan, Chunlong He

The advancement of next-generation Wi-Fi technology heavily relies on sensing capabilities, which play a pivotal role in enabling sophisticated applications. In response to the growing demand for large-scale deployments, contemporary Wi-Fi sensing systems strive to achieve high-precision perception while maintaining minimal bandwidth consumption and antenna

Qi Chen, Hao Li, Shunlin Shen, Zhifei Zhang

In this paper, we establish the inviscid damping and enhanced dissipation estimates for the linearized Navier-Stokes system around the symmetric flow in a finite channel with the non-slip boundary condition. As an immediate consequence, we prove the asymptotic stability of the symmetric flow in the high Reynolds number regime. Namely, if the initial velocity

Sunwoo Kim, Hyunjin Hwang, Kijung Shin

The performance of a deep learning model on a specific task and dataset depends heavily on its neural architecture, motivating considerable efforts to rapidly and accurately identify architectures suited to the target task and dataset. To achieve this, researchers use machine learning models-typically neural architecture encoders-to predict the performance o

Firas Gabetni, Giuseppe Curci, Andrea Pilzer, Subhankar Roy

Uncertainty quantification (UQ) is essential for deploying deep neural networks in safety-critical settings. Although methods like Deep Ensembles achieve strong UQ performance, their high computational and memory costs hinder scalability to large models. We introduce Hydra Ensembles, an efficient transformer-based ensemble that prunes attention heads to crea

Jiajun Hong, Jianan Wei, Wenguan Wang

Human-Object Interaction Detection (HOI-DET) aims to localize human-object pairs and identify their interactive relationships. To aggregate contextual cues, existing methods typically propagate information across all detected entities via self-attention mechanisms, or establish message passing between humans and objects with bipartite graphs. However, they p

Enrique Macías-Virgós, Ángel Méndez-Vázquez, David Mosquera-Lois

We introduce two new algebraic invariants, the (co)homological distances between continuous maps, which provide computable lower bounds for the homotopic distance and strictly refine the classical cup-length estimates. We then define the simplicial cohomological distance between simplicial maps and prove a convergence theorem showing that, after sufficiently

Lan-Lan Cai, Meng-Yun Lai, De-Cheng Zou, Lina Zhang

Regular Hayward black holes provide a useful setting for investigating scalarization in theories with nonminimally coupled matter sectors. Within the framework of Einstein-nonlinear electromagnetic-scalar gravity, we identify the tachyonic threshold that signals the bifurcation from the bald Hayward background and then obtain scalarized charged black holes f

Yi-Lun Wu, Bo-Kai Ruan, Chiang Tseng, Hong-Han Shuai

Direct preference optimization (DPO) methods have shown strong potential in aligning text-to-image diffusion models with human preferences by training on paired comparisons. These methods improve training stability by avoiding the REINFORCE algorithm but still struggle with challenges such as accurately estimating image probabilities due to the non-linear na

Dariusz Kalociński, Tomasz Steifer

Understanding when learning is possible is a fundamental task in the theory of machine learning. However, many characterizations known from the literature deal with abstract learning as a mathematical object and ignore the crucial question: when can learning be implemented as a computer program? We address this question for universal online learning, a gener

Wei Dai, Su Gao, Víctor Hugo Yañez

A topological group $G$ is said to have the L\'evy property if it admits a dense subgroup which is decomposed as the union of an increasing sequence of compact subgroups $\mathcal{G}=\{G_i:i\in\mathbb{N}\}$ of $G$ which exhibits concentration of measure in the sense of Gromov and Milman. We say that $G$ has the strong L\'evy property whenever the sequence $\

Izhar Oppenheim, Inga Valentiner-Branth

Coboundary expansion with non-Abelian coefficients is a strong version of high-dimensional expansion for simplicial complexes. One motivation for studying this notion is that it was recently shown to have deep connections to problems in theoretical computer science. However, very few examples of families of simplicial complexes with this type of expansion ar

P. G. Grinevich, I. A. Taimanov

For PT-symmetric periodic Schrodinger operator, which is a small perturbation of the zero potential, we calculate the spectrum and the divisor of zeroes of the Bloch function in the leading order of the perturbation theory. In particular, we show that the analogs of lacunae of the Bloch spectrum are ellipses, and their focal points coincide with the branch p

Muhammad Hanif, Reiji Terunuma, Takumi Sumino, Kelvin Cheng

This article addresses collaborative 3D map reconstruction using multiple drones. Achieving high-quality reconstruction requires capturing images of keypoints within the target scene from diverse viewing angles, and coverage control offers an effective framework to meet this requirement. Meanwhile, recent advances in real-time 3D reconstruction algorithms ma

Jainee Patel, Chintan Bhatt, Himani Trivedi, Thanh Thi Nguyen

The rapid spread of misinformation on online platforms undermines trust among individuals and hinders informed decision making. This paper shows an explainable and computationally efficient pipeline to detect misinformation using transformer-based pretrained language models (PLMs). We optimize both RoBERTa and DistilBERT using a two-step strategy: first, we

Yudong Li, Hao Li, Xianxu Hou, Linlin Shen

Compared to the prosperity of pre-training models in natural image understanding, the research on large-scale pre-training models for facial knowledge learning is still limited. Current approaches mainly rely on manually assembled and annotated face datasets for training, but labeling such datasets is labor-intensive and the trained models have limited scala

Vipul Rathore, Malik Hammad Faisal, Parag Singla, Mausam

Distantly Supervised Relation Extraction (DSRE) remains a long-standing challenge in NLP, where models must learn from noisy bag-level annotations while making sentence-level predictions. While existing state-of-the-art (SoTA) DSRE models rely on task-specific training, their integration with in-context learning (ICL) using large language models (LLMs) remai

Emily L. Hunt, Tristan Cantat-Gaudin, Friedrich Anders, Sagar Malhotra

Open clusters are among the most useful and widespread tracers of Galactic structure. The completeness of the Galactic open cluster census, however, remains poorly understood. For the first time ever, we establish the selection function of an entire open cluster census, publishing our results as an open-source Python package for use by the community. Our wor

Mandip Goswami

Room impulse responses are a core resource for dereverberation, robust speech recognition, source localization, and room acoustics estimation. We present RIR-Mega, a large collection of simulated RIRs described by a compact, machine friendly metadata schema and distributed with simple tools for validation and reuse. The dataset ships with a Hugging Face Data

Peng Tang, Xiaobin Hu, Tingcheng Li, Yang Nan

Multi-class unsupervised anomaly detection (MUAD) has garnered growing research interest, as it seeks to develop a unified model for anomaly detection across multiple classes, i.e., eliminating the need to train separate models for distinct objects and thereby saving substantial computational resources. Under the MUAD setting, while advanced Transformer-base

Kaiyuan Tan, Yingying Shen, Haiyang Sun, Bing Wang

Realistic view extrapolation is critical for closed-loop simulation in autonomous driving, yet it remains a significant challenge for current Novel View Synthesis (NVS) methods, which often produce distorted and inconsistent images beyond the original trajectory. This report presents our winning solution which ctook first place in the RealADSim Workshop NVS

Jongmin Lee, Ernest K. Ryu

The classical policy gradient method is the theoretical and conceptual foundation of modern policy-based reinforcement learning (RL) algorithms. Most rigorous analyses of such methods, particularly those establishing convergence guarantees, assume a discount factor $\gamma < 1$. In contrast, however, a recent line of work on policy-based RL for large languag

Lara Ahrens, Wilhelm Haverkamp, Nils Strodthoff

Domain-adapted open-weight large language models (LLMs) offer promising healthcare applications, from queryable knowledge bases to multimodal assistants, with the crucial advantage of local deployment for privacy preservation. However, optimal adaptation strategies, evaluation methodologies, and performance relative to general-purpose LLMs remain poorly char

Wenhui Huang, Changhe Chen, Han Qi, Chen Lv

Integrating visual-language instructions into visuomotor policies is gaining momentum in robot learning for enhancing open-world generalization. Despite promising advances, existing approaches face two challenges: limited language steerability when no generated reasoning is used as a condition, or significant inference latency when reasoning is incorporated.

Wangye Jiang, Haoming Yang, Xinyu Lu, Mingyuan Wang

As wireless communication systems evolve, automatic modulation recognition (AMR) plays a key role in improving spectrum efficiency, especially in cognitive radio systems. Traditional AMR methods face challenges in complex, noisy environments, particularly in low signal-to-noise ratio (SNR) conditions. This paper introduces MCANet (Multimodal Collaborative At

In-Jee Jeong, Deokwoo Lim

We study incompressible Euler equations in $\mathbb{R}^d$ with $d \ge 4$ under bi-rotational symmetry without swirl, which reduces the Euler equations to a scalar vorticity advection in the first quadrant. We show that patch type initial vorticities exhibit infinite growth of the support diameter.

An-Kang Gao, Chenyue Xie, Xi-Yun Lu

Whilst surface-stress integration remains the standard approach for fluid force evaluation, control-volume integral methods provide deeper physical insights through functional relationships between the flow field and the resultant force. In this work, by introducing a second-order tensor weight function into the Navier-Stokes equations, we develop a novel we

Yepeng Liu, Xuandong Zhao, Dawn Song, Gregory W. Wornell

Despite progress in watermarking algorithms for large language models (LLMs), real-world deployment remains limited. We argue that this gap stems from misaligned incentives among LLM providers, platforms, and end users, which manifest as three key barriers: competitive risk, detection-tool governance, and attribution issues. We revisit three classes of water

Gargi Roy, Dalia Chakrabarty

We introduce parametrisation of that property of the available training dataset, that necessitates an inhomogeneous correlation structure for the function that is learnt as a model of the relationship between the pair of variables, observations of which comprise the considered training data. We refer to a parametrisation of this property of a given training

Ahmed Yousef Mohamed, Byoung Gun Han, Hyeonseo Jang, Jun Oh Jeon

The Cu-deficient CuxInSe2 (x larger than 0.3) phase can be stabilized as a thin film. A uniform Cu-deficient composition with a chalcopyrite structure was obtained by the precision engineering of a two-step synthesis process involving electron-beam evaporation and Se vapor deposition. Detailed structural and chemical analyses were performed employing various

Xavier Fernández-Real, Hui Yu

For the Alt-Caffarelli problem, we study free boundary regularity of energy minimizers. In six dimensions, we show that free boundaries are analytic for generic boundary data. In general, we improve previous generic Hausdorff dimensions of the singular sets. To achieve this, we analyze positive solutions to the linearized equation around homogeneous minimize

Guangxiang Su

In this note, we consider the case where the condition ``constant near infinity" in the definition of $\Lambda^2$-enlargeable manifolds is replaced by the condition ``locally constant near infinity" and prove that a $\Lambda^2$-enlargeable manifold in this modified sense still cannot carry a complete Riemannian metric of positive scalar curvature. As a conse

Zhong Li, Qi Huang, Yuxuan Zhu, Lincen Yang

We introduce Time-Conditioned Contraction Matching (TCCM), a novel method for semi-supervised anomaly detection in tabular data. TCCM is inspired by flow matching, a recent generative modeling framework that learns velocity fields between probability distributions and has shown strong performance compared to diffusion models and generative adversarial networ

Yunkun Wang, Yue Zhang, Guochang Li, Chen Zhi

Large Language Models (LLMs) frequently generate buggy code with complex logic errors that are challenging to diagnose. While existing LLM-based self-repair approaches conduct intensive static semantic analysis or reply on superficial execution logs, they miss the in-depth runtime behaviors that often expose bug root causes-lacking the interactive dynamic an

Zhengdong Hu, Chong Han, Wolfgang Gerstacker, Robert Schober

Millimeter-wave (mmWave) and Terahertz (THz)-band communications hold great promise in meeting the growing data-rate demands of next-generation wireless networks, offering abundant bandwidth. To mitigate the severe path loss inherent to these high frequencies and reduce hardware costs, ultra-massive multiple-input multiple-output (UM-MIMO) systems with hybri

Seong-Hoon Jang

Interpretable scientific machine learning often trades predictive performance for structural transparency. When physical targets arise from hierarchical and nonlinear descriptor entanglement, weakly interacting white-box models underfit, whereas highly expressive black-box models obscure physical insight. Here I introduce GoodRegressor, a hierarchical depth-

Passimzouwé Dagou, Pagdame Tiebekabe, Kokou Tcharie

In this paper, we focus on Narayana numbers which can be written as a products of four repdigits in base $g$, where $g$ is an integer with $g\geq2$. We prove that for $g$ between $2$ and $12$, there are finitely many of these numbers. Moreover we have fully determined them.

Gyeonghoon Park, Jaehan Kim, Jinu Choi, Jinwoo Kim

Host-based cryptomining malware, commonly known as cryptojackers, have gained notoriety for their stealth and the significant financial losses they cause in Linux-based cloud environments. Existing solutions often struggle with scalability due to high monitoring overhead, low detection accuracy against obfuscated behavior, and lack of integrated remediation.

Fei Wang, Xuanxi Cai, Teng Xiao, Changhua Bao

Floquet engineering has emerged as a powerful approach for dynamically tailoring the electronic structures of quantum materials through time-periodic light fields generated by ultrafast laser pulses. The light fields can transiently dress Bloch electrons, creating novel electronic states inaccessible in equilibrium. While such temporal modulation provides dy

Chen Chen, ZeYang Hu, Fengjiao Chen, Liya Ma

Multimodal Large Languages models have been progressing from uni-modal understanding toward unifying visual, audio and language modalities, collectively termed omni models. However, the correlation between uni-modal and omni-modal remains unclear, which requires comprehensive evaluation to drive omni model's intelligence evolution. In this work, we introduce

Afrozah Nadeem, Mark Dras, Usman Naseem

Large language models often display undesirable behaviors embedded in their internal representations, undermining fairness, inconsistency drift, amplification of harmful content, and the propagation of unwanted patterns during extended dialogue and conversations. Although training-time or data-centric methods attempt to reduce these effects, they are computa

Taeseong Yoon, Heeyoung Kim

Uncertainty quantification (UQ) is crucial for deploying machine learning models in high-stakes applications, where overconfident predictions can lead to serious consequences. An effective UQ method must balance computational efficiency with the ability to generalize across diverse scenarios. Evidential deep learning (EDL) achieves efficiency by modeling unc

Jinlin Li, Yuran Wang, Yifei Yuan, Xiao Zhou

Large Vision-Language Models (LVLMs) have recently achieved impressive results in multimodal tasks such as image captioning and visual question answering. However, they remain prone to object hallucination -- generating descriptions of nonexistent or misidentified objects. Prior work has partially mitigated this via auxiliary training objectives or external

Thorgal Hinault, Tony Yue YU

We establish a comparison result relating non-archimedean cylinder counts and logarithmic cylinder counts in a smooth affine log Calabi-Yau variety. Using the decomposition theorem and the gluing formula from log Gromov-Witten theory, we can express logarithmic cylinder counts in terms of wall type invariants. As a corollary, we show that in the surface case

Aaron Bell, Amit Aides, Amr Helmy, Arbaaz Muslim

Geospatial data offers immense potential for understanding our planet. However, the sheer volume and diversity of this data along with its varied resolutions, timescales, and sparsity pose significant challenges for thorough analysis and interpretation. This paper introduces Earth AI, a family of geospatial AI models and agentic reasoning that enables signif

Yinuo Ma, He Gao

Gamma-ray bursts (GRBs) are the most energetic phenomena in the Universe, characterized by prompt gamma-ray emission followed by multiwavelength afterglows. X-ray flares, observed during the afterglow phase, are generally believed to originate from the prolonged activity of the central engine, though direct evidence has been scarce. In this study, we present

Chengshu Li, Mengdi Xu, Arpit Bahety, Hang Yin

Imitation learning from large-scale, diverse human demonstrations has been shown to be effective for training robots, but collecting such data is costly and time-consuming. This challenge intensifies for multi-step bimanual mobile manipulation, where humans must teleoperate both the mobile base and two high-DoF arms. Prior X-Gen works have developed automate

Wang Zixian

We present Anchored Direct Preference Optimization (ADPO), a policy alignment method derived from first principles of KL-regularized reinforcement learning. Unlike standard approaches that treat the reference policy merely as a regularizer, we show that the optimal policy in reinforcement learning from human feedback inherently operates in a differential coo

Zheng Zhang, Jiarui He, Yuchen Cai, Deheng Ye

As large language model (LLM) agents increasingly automate complex web tasks, they boost productivity while simultaneously introducing new security risks. However, relevant studies on web agent attacks remain limited. Existing red-teaming approaches mainly rely on manually crafted attack strategies or static models trained offline. Such methods fail to captu

Nathan J. Carlson, J. Richard Bond, Dongwoo T. Chung, Patrick Horlaville

We present the $\mathtt{WebSky}$ $\mathrm{[CII]}$ line-intensity mock maps and forecast the capabilities of upcoming wide-field submillimeter-wave surveys of cosmological $\mathrm{[CII]}$ emission from the epoch of reionization (EoR). Using the $\mathtt{Peak~Patch}$ algorithm to generate light-cone dark matter (DM) halo catalogues and the $\mathtt{WebSky}$ f

Shadmaan Hye, Matthew P. LeGendre, Katherine E. Isaacs

In applications where efficiency is critical, developers may examine their compiled binaries, seeking to understand how the compiler transformed their source code and what performance implications that transformation may have. This analysis is challenging due to the vast number of disassembled binary instructions and the many-to-many mappings between them an

Zijian Li, Minghao Fu, Junxian Huang, Yifan Shen

Modeling hierarchical latent dynamics behind time series data is critical for capturing temporal dependencies across multiple levels of abstraction in real-world tasks. However, existing temporal causal representation learning methods fail to capture such dynamics, as they fail to recover the joint distribution of hierarchical latent variables from \textit{s

Hillol Kumar Barman, Pathik Das, Syed Yunus Ali

Interesting theoretical problems of target search or threshold crossing, formally known as {\it first passage}, often arise in both diffusive transport problems as well as problems of chemical reaction kinetics. We study three systems following different chemical kinetics, and are special as they {\it toggle between two states}: (i) a population dynamics of

Haowei Lou, Hye-Young Paik, Wen Hu, Lina Yao

Controlling speaking style in text-to-speech (TTS) systems has become a growing focus in both academia and industry. While many existing approaches rely on reference audio to guide style generation, such methods are often impractical due to privacy concerns and limited accessibility. More recently, large language models (LLMs) have been used to control speak

Hy Cao, Abhishek Saha, Lisa V. Poulikakos

In fluid flow imaging, intensity gradients are a good measure of spatial variations in scalar properties, which play an important role in controlling transport processes. However, current flow imaging techniques exhibit system-limited spatial resolutions, thus inhibiting the ability to accurately detect intensity gradients. To address this challenge, we pres

Hongkun Gao, Tianjun Li, Lina Wu, Wenxing Zhang

We propose the generic high-quality axion models with anomalous $U(1)_X$ gauge symmetry and vector-like particles. We briefly review the gauge anomaly cancellations via the Green-Schwarz mechanism, study the breaking of the $U(1)_X$ gauge symmetry, as well as derive the Nambu-Goldstone boson, Peccei-Quinn (PQ) axion, and axion decay constant in general. The

Rafi Khan, Bo Zhao

Seattle has long been a center for refugee resettlement in the United States, creating multicultural neighborhoods with complex social and spatial dynamics. This study analyzes the refugee experience in Seattle by examining patterns of housing cost burden, language isolation, religious infrastructure, and crime incidence. These spatial findings are combined

Vincent Guillet, Léo Vacher, Jonathan Aumont, François Boulanger

Thermal dust is the major polarized foreground hindering the detection of primordial cosmic microwave background (CMB) B-modes. Its signal exhibits complex behavior in frequency space, arising from the combined variation in our Galaxy of the orientation of magnetic fields and the spectral properties of dust grains aligned with magnetic field lines. In this w

Liangyu Chen, James Burgess, Jeffrey J Nirschl, Orr Zohar

Imaging technologies are fundamental to biomedical research and modern medicine, requiring analysis of high-resolution images across various modalities. While multimodal large language models (MLLMs) show promise for biomedical image analysis, most are designed for low-resolution images from general-purpose datasets, risking critical information loss. We inv

Lehan Wang, Yi Qin, Honglong Yang, Xiaomeng Li

Incentivizing the reasoning ability of Multimodal Large Language Models (MLLMs) is essential for medical applications to transparently analyze medical scans and provide reliable diagnosis. However, existing medical MLLMs rely solely on internal knowledge during reasoning, leading to hallucinated reasoning and factual inaccuracies when encountering cases beyo

Yuma Shida, Yuji Ito

Distributionally robust optimization (DRO) is an effective framework for controlling real-world systems with various uncertainties, typically modeled using distributional uncertainty balls. However, DRO problems often involve infinitely many inequality constraints, rendering exact solutions computationally expensive. In this study, we propose a discrete DRO

Jake Hassan, Rosalba Perna, Matteo Cantiello, Philip Armitage

Observations by JWST have confirmed the presence of supermassive black holes (BHs) at redshifts $z\gtrsim10$, lending support to scenarios in which BHs experience rapid growth through intense gas accretion. Here we investigate the growth of a BH embedded at the center of a quasi-star, a theoretically predicted object formed via direct collapse. In a quasi-st

Ankur Lahiry, Ayush Pokharel, Banooqa Banday, Seth Ockerman

Large-scale GPU traces play a critical role in identifying performance bottlenecks within heterogeneous High-Performance Computing (HPC) architectures. However, the sheer volume and complexity of a single trace of data make performance analysis both computationally expensive and time-consuming. To address this challenge, we present an end-to-end parallel per

Hao Qin, Thang Duong, Ming F. Li, Chicheng Zhang

In millimeter wave (mmWave) communications, beam alignment and tracking are crucial to combat the significant path loss. As scanning the entire directional space is inefficient, designing an efficient and robust method to identify the optimal beam directions is essential. Since traditional bandit algorithms require a long time horizon to converge under large

Arnab Dey Sarkar, Bard Ermentrout

In the classic view of cortical rhythms, the interaction between excitatory pyramidal neurons (E) and inhibitory parvalbumin neurons (I) has been shown to be sufficient to generate gamma and beta band rhythms. However, it is now clear that there are multiple inhibitory interneuron subtypes and that they play important roles in the generation of these rhythms

Lei Li, Xiao Zhou, Yingying Zhang, Xian Wu

Medical question answering (QA) requires extensive access to domain-specific knowledge. A promising direction is to enhance large language models (LLMs) with external knowledge retrieved from medical corpora or parametric knowledge stored in model parameters. Existing approaches typically fall into two categories: Retrieval-Augmented Generation (RAG), which

Aayushi Dangol, Smriti Kotiyal, Robert Wolfe, Alex J. Bowers

As generative AI (genAI) rapidly enters classrooms, accompanied by district-level policy rollouts and industry-led teacher trainings, it is important to rethink the canonical ``adopt and train'' playbook. Decades of educational technology research show that tools promising personalization and access often deepen inequities due to uneven resources, training,

Shouvik Sadhukhan, C. S. Narayanamurthy

The present study extends the analysis of turbulence-affected beam statistics through a manifold-based statistical framework that unifies probabilistic modeling with geometric interpretation. The spatial intensity distributions, distorted by dynamic turbulence, are represented using Gaussian Mixture Models (GMMs), whose probability landscapes are refined via

Jiayan Yang, Leping Li, Huadong Chen, Yi Bi

Combining the high-quality observations from the {\it Solar Dynamics Observatory} (SDO), the Global Oscillation Network Group (GONG), and the Chinese H$\alpha$ Solar Explorer (CHASE), we report a solar coronal jet triggered by the sympathetic eruption of two filaments on 2024 January 11. Initially, the western segment of an active region filament erupted. Th

Hala Abdelkader, Mohamed Abdelrazek, Priya Rani, Rajesh Vasa

Ensuring robustness in ML-enabled software systems requires addressing critical challenges, such as silent failures, out-of-distribution (OOD) data, and adversarial attacks. Traditional software engineering practices, which rely on predefined logic, are insufficient for ML components that depend on data and probabilistic decision-making. To address these cha