November 2025 arXiv papers — page 154

Ariel Kamen, Yakov Kamen

This study introduces an ensemble framework for unstructured text categorization using large language models (LLMs). By integrating multiple models, the ensemble large language model (eLLM) framework addresses common weaknesses of individual systems, including inconsistency, hallucination, category inflation, and misclassification. The eLLM approach yields a

Dmitrii Vlasiuk

We develop a finite-horizon model in which liquid-asset returns exhibit Levy-stable scaling on a data-driven window [tau_UV, tau_IR] and aggregate into a finite-variance regime outside. The window and the tail index alpha are identified from the log-log slope of the central body and a two-segment fit of scale versus horizon. With an anchor horizon tau_0, we

Chanakya Ekbote, Vijay Lingam, Sujay Sanghavi, Jun Huan

Reinforcement Learning with Verifiable Rewards (RLVR) has become a standard recipe for post-training LLMs on reasoning tasks, with Group Relative Policy Optimization (GRPO) emerging as a leading approach. However, GRPO and its variants are inherently single-turn: they optimize from terminal rewards on isolated prompt-response pairs, leaving them poorly suite

Zewu Zheng, Yuanyuan Lin

The sim-to-real gap, where agents trained in a simulator face significant performance degradation during testing, is a fundamental challenge in reinforcement learning. Extansive works adopt the framework of distributionally robust RL, to learn a policy that acts robustly under worst case environment shift. Within this framework, our objective is to devise al

Yifan Wang, Muhammad Sakib Shahriar, Salma Soliman, Noah Vaillancourt

The exponential growth of edge artificial intelligence demands material-focused solutions to overcome energy consumption and latency limitations when processing real-time temporal data. Physical reservoir computing (PRC) offers an energy-efficient paradigm but faces challenges due to limited device scalability and reconfigurability. Additionally, reservoir a

Manu Sebastian, Sreenath Balakrishnan, Safvan Palathingal

The mechanical environment of a substrate plays a key role in influencing the behavior of adherent biological cells. Traditional tunable substrates have limitations as their mechanical properties cannot be dynamically altered in-situ during cell culture. We present an alternate approach by using compliant mechanisms that enable realization of tunable substra

Youssef Megahed, Inok Lee, Robin Ducharme, Aylin Erman

The proposed study aimed to develop a deep learning model capable of detecting ventriculomegaly on prenatal ultrasound images. Ventriculomegaly is a prenatal condition characterized by dilated cerebral ventricles of the fetal brain and is important to diagnose early, as it can be associated with an increased risk for fetal aneuploidies and/or underlying gene

Yujie Zhou, Rulong Wang, Yong Xiao, Yingyu Li

This paper investigates a key challenge faced by joint source-channel coding (JSCC) in digital semantic communication (SemCom): the incompatibility between existing JSCC schemes that yield continuous encoded representations and digital systems that employ discrete variable-length codewords. It further results in feasibility issues in achieving physical bit-l

Zhiyao Zhang, Zhuqing Liu, Xin Zhang, Wen-Yen Chen

As machine learning (ML) applications grow increasingly complex in recent years, modern ML frameworks often need to address multiple potentially conflicting objectives with coupled decision variables across different layers. This creates a compelling need for multi-objective bilevel learning (MOBL). So far, however, the field of MOBL remains in its infancy a

Kanglin Qu, Pan Gao, Qun Dai, Zhanzhi Ye

Due to the long-range modeling ability and linear complexity property, Mamba has attracted considerable attention in point cloud analysis. Despite some interesting progress, related work still suffers from imperfect point cloud serialization, insufficient high-level geometric perception, and overfitting of the selective state space model (S6) at the core of

Collin Hague, Artur Wolek

This paper considers the problem of searching for a point of interest (POI) moving along an urban road network with an uncrewed aerial vehicle (UAV). The UAV is modeled as a variable-speed Dubins vehicle with a line-of-sight sensor in an urban environment that may occlude the sensor's view of the POI. A search strategy is proposed that exploits a probabilist

Lu Gan, Xi Li

The development of high-performance, on-device keyword spotting (KWS) systems for ultra-low-power hardware is critically constrained by the scarcity of specialized, multi-command training datasets. Traditional data collection through human recording is costly, slow, and lacks scalability. This paper introduces SYNTTS-COMMANDS, a novel, multilingual voice com

Gong Jingyu, Tong Kunkun, Chen Zhuoran, Yuan Chuanhan

Human motion synthesis in 3D scenes relies heavily on scene comprehension, while current methods focus mainly on scene structure but ignore the semantic understanding. In this paper, we propose a human motion synthesis framework that take an unified Scene Semantic Occupancy (SSO) for scene representation, termed SSOMotion. We design a bi-directional tri-plan

Diya Mamoria, Harshit Jain, Aswani Kumar Cherukuri

This research proposes a decentralized and cryptographically secure framework to address the most acute issues of privacy, data security, and protection in the ecosystem of medical insurance claim processing. The scope of this study focuses on enabling the management of insurance claims in a transparent, privacy-protecting manner while maintaining the effici

Indranil Biswas, Jacques Hurtubise, Lisa C. Jeffrey, Sean Lawton

We show that a Frobenius reciprocity map on character varieties of surfaces is a Poisson embedding.

Jarett Dewbury, Chi-en Amy Tai, Alexander Wong

Current deep learning approaches for prostate cancer lesion segmentation achieve limited performance, with Dice scores of 0.32 or lower in large patient cohorts. To address this limitation, we investigate synthetic correlated diffusion imaging (CDI$^s$) as an enhancement to standard diffusion-based protocols. We conduct a comprehensive evaluation across six

Karel Walter Gomez Orellana, Berthyn Rodrigo Tiñini Chuquimia, Juan Carlos Paredes Condori, Rodrigo Apaza Huanca

The deployment of 5G millimeter-wave (mmWave) systems poses significant challenges in maintaining power amplifier linearity and efficiency under varying conditions, such as temperature-induced gain variations that degrade error vector magnitude (EVM). This paper presents a comparative study of three control strategies-PID, pure integral, and fuzzy-integral (

Fangu Chen

Baba and Granath generalize Elkies' theorem on infinitude of supersingular primes for elliptic curves to abelian surfaces with quaternionic multiplication of discriminant $6$, whose field of moduli is $\mathbb{Q}$ and which is a Jacobian in characteristic $2$ and $3$. We extend the field of moduli to any number field with a real embedding, and weaken the loc

Haida Feng, Hao Wei, Zewen Xu, Haolin Wang

Recently, large language models (LLMs) have been explored widely for 3D scene understanding. Among them, training-free approaches are gaining attention for their flexibility and generalization over training-based methods. However, they typically struggle with accuracy and efficiency in practical deployment. To address the problems, we propose Sparse3DPR, a n

Zhenchen Tang, Songlin Yang, Bo Peng, Zichuan Wang

The rapid progress of multi-modal large language models (MLLMs) has boosted the task of image quality assessment (IQA). However, a key challenge arises from the inherent mismatch between the discrete token outputs of MLLMs and the continuous nature of quality scores required by IQA tasks. This discrepancy significantly hinders the performance of MLLM-based I

Sagar Gupta, Thanh Vinh Nguyen, Thieu Long Phan, Vidul Attri

We present a hybrid multi-robot coordination framework that combines decentralized path planning with centralized conflict resolution. In our approach, each robot autonomously plans its path and shares this information with a centralized node. The centralized system detects potential conflicts and allows only one of the conflicting robots to proceed at a tim

Fabian Parsch, Hanrui Zhang

We construct a geodesic net in the plane with four boundary (unbalanced) vertices that has 25 balanced vertices and that is irreducible, i.e. it does not contain nontrivial subnets. This net is novel and remarkable for several reasons: (1) It increases the previously known maximum for balanced vertices of nets of this kind from 16 to 25. (2) It is, to our kn

Sara Kangaslahti, Danny Ebanks, Jean Kossaifi, Anqi Liu

This paper proposes a topic modeling method that scales linearly to billions of documents. We make three core contributions: i) we present a topic modeling method, Tensor Latent Dirichlet Allocation (TLDA), that has identifiable and recoverable parameter guarantees and sample complexity guarantees for large data; ii) we show that this method is computational

Zhongle Ren, Hui Ding, Kai Wang, Biao Hou

Although significant advances have been achieved in SAR land-cover classification, recent methods remain predominantly focused on supervised learning, which relies heavily on extensive labeled datasets. This dependency not only limits scalability and generalization but also restricts adaptability to diverse application scenarios. In this paper, a general-pur

Zeinab Elkhatib, Ali Sekmen, Kamrul Hasan

With the rapid advancements in machine learning, models have become increasingly capable of learning and making predictions in various industries. However, deploying these models in critical infrastructures presents a major challenge, as concerns about data privacy prevent unrestricted data sharing. Homomorphic encryption (HE) offers a solution by enabling c

Jean-Philippe Lignier

Real-time energy forecasting on edge devices represents a major challenge for smart grid optimization and intelligent buildings. We present LAD-BNet (Lag-Aware Dual-Branch Network), an innovative neural architecture optimized for edge inference with Google Coral TPU. Our hybrid approach combines a branch dedicated to explicit exploitation of temporal lags wi

Shaomeng Wang, He Wang, Xiaolu Wei, Longquan Dai

Diffusion models have achieved remarkable success in conditional image generation, yet their outputs often remain misaligned with human preferences. To address this, recent work has applied Direct Preference Optimization (DPO) to diffusion models, yielding significant improvements.~However, DPO-like methods exhibit two key limitations: 1) High computational

Panpan Chen, Nader Motee, Qiyu Sun

Nonlinear dynamical systems are widely encountered in various scientific and engineering fields. Despite significant advances in theoretical understanding, developing complete and integrated frameworks for analyzing and designing these systems remains challenging, which underscores the importance of efficient linearization methods. In this paper, we introduc

Tianyu Su, Brian J. Blankenau, Namhoon Kim, Kshitij Vijayvargia

Hydrogen embrittlement (HE) in austenitic stainless steels is advanced by hydrogen enhanced localized plasticity (HELP), typically accompanied by a transition from homogeneous to localized slip. Short-range order (SRO) in face-centered cubic (FCC) alloys is known to promote slip planarity, and recent studies suggest that H may amplify this localization behav

Wenhao Xu, Akshatha Arodi, Jian-Yun Nie, Arsene Fansi Tchango

Modern slavery affects millions of people worldwide, and regulatory frameworks such as Modern Slavery Acts now require companies to publish detailed disclosures. However, these statements are often vague and inconsistent, making manual review time-consuming and difficult to scale. While NLP offers a promising path forward, high-stakes compliance tasks requir

Utkarsh Prakash Srivastava, Kaushik Gupta, Kaushik Nath

We study multilabel classification of chest X-rays and present a simple, strong pipeline built on SE-ResNeXt101 $(32 \times 4d)$. The backbone is finetuned for 14 thoracic findings with a sigmoid head, trained using Multilabel Iterative Stratification (MIS) for robust cross-validation splits that preserve label co-occurrence. To address extreme class imbalan

Todd Morrill, Aahlad Puli, Murad Megjhani, Soojin Park

Deep mixture-of-experts models have attracted a lot of attention for survival analysis problems, particularly for their ability to cluster similar patients together. In practice, grouping often comes at the expense of key metrics such as calibration error and predictive accuracy. This is due to the restrictive inductive bias that mixture-of-experts imposes,

Siyu Xia, Zekun Xu, Jiajun Chai, Wentian Fan

Large Language Models (LLMs) based agents have demonstrated remarkable potential in autonomous task-solving across complex, open-ended environments. A promising approach for improving the reasoning capabilities of LLM agents is to better utilize prior experiences in guiding current decisions. However, LLMs acquire experience either through implicit memory vi

Renyong Guan, Yuxi Hu

This paper establishes the nonlinear time-asymptotic stability of shifted planar viscous shock waves for the three-dimensional relaxed compressible Navier-Stokes equations, in which a modified Maxwell-type model replaces the classical Newtonian constitutive relation. Under the assumptions of sufficiently small shock strength and initial perturbations, we pro

Runmin Cong, Anpeng Wang, Bin Wan, Cong Zhang

Cross-domain few-shot segmentation (CD-FSS) aims to tackle the dual challenge of recognizing novel classes and adapting to unseen domains with limited annotations. However, encoder features often entangle domain-relevant and category-relevant information, limiting both generalization and rapid adaptation to new domains. To address this issue, we propose a Di

Benjamin Davis, Hannah Stuart

Vision-based tactile sensors (VBTSs) are a promising technology for robots, providing them with dense signals that can be translated into a multi-faceted understanding of contact. However, existing VBTS tactile surfaces make use of silicone gels, which provide high sensitivity but easily deteriorate from loading and surface wear. We propose that polyurethane

Arthur R. C. McCray, Stephanie M. Ribet, Georgios Varnavides, Colin Ophus

Electron ptychography enables dose-efficient atomic-resolution imaging, but conventional reconstruction algorithms suffer from noise sensitivity, slow convergence, and extensive manual hyperparameter tuning for regularization, especially in three-dimensional multislice reconstructions. We introduce a deep generative prior (DGP) framework for electron ptychog

Hua Zhou, Bing Ma, Yufei Zhang, Yi Zhao

This paper comprehensively elaborates on the construction methodology, multi-dimensional evaluation system, and underlying design philosophy of CUFEInse v1.0. Adhering to the principles of "quantitative-oriented, expert-driven, and multi-validation," the benchmark establishes an evaluation framework covering 5 core dimensions, 54 sub-indicators, and 14,430 h

Binayak Kara, Ujjwal Sahua, Ciza Thomas, Jyoti Prakash Sahoo

Securing Dew-Enabled Edge-of-Things (EoT) networks against sophisticated intrusions is a critical challenge. This paper presents HybridGuard, a framework that integrates machine learning and deep learning to improve intrusion detection. HybridGuard addresses data imbalance through mutual information based feature selection, ensuring that the most relevant fe

Ramkhelavan Kanaujiya, Atanu K. Metya, Rajnish Kumar, Tarak K Patra

Gas hydrates are crystalline compounds formed when water molecules encapsulate guest gas molecules under high pressure and low temperatures. They have gained significant interest due to their potential as alternative energy resources and their applications in gas storage, transportation, and carbon sequestration. However, the fundamental mechanisms governing

Arun Soor

We construct a fully-faithful functor of $\infty$-categories from complexes of D-cap modules with Fr\'echet cohomology to quasi-coherent sheaves on an analytic stack. We prove various descent results for $\infty$-categories of D-cap modules in the analytic topology.

Camille Mau, Nicolas Privault

We derive rates of convergence for the mixing of operators under infinitely divisible measures in the framework of linear dynamics on Banach spaces. Our approach is based on the characterization of mixing in terms of codifference functionals and control measures, and extends previous results obtained in the Gaussian setting via the use of covariance operator

Rochana R. Obadage, Sarah M. Rajtmajer, Jian Wu

Sentiments about the reproducibility of cited papers in downstream literature offer community perspectives and have shown as a promising signal of the actual reproducibility of published findings. To train effective models to effectively predict reproducibility-oriented sentiments and further systematically study their correlation with reproducibility, we in

Mingjie Zhu, Yejian Lyu, Chong Han

Vehicle-to-everything (V2X) technology has emerged as a key enabler of intelligent transportation systems, while the Terahertz (THz) band offers abundant spectrum resources to support ultra-high-speed and low-latency V2X communications. This paper investigates the in-vehicle wireless channel in the 300~GHz band. First, channel measurement based on vector-net

Benjamin Richards, Pushpa Kumar Balan

The high accuracy of large-scale weather forecasting models like Aurora is often accompanied by a lack of transparency, as their internal representations remain largely opaque. This "black box" nature hinders their adoption in high-stakes operational settings. In this work, we probe the physical consistency of Aurora's encoder by investigating whether its la

Hanwen Huang

Score-based generative models have recently attracted significant attention for their ability to generate high-fidelity data by learning maps from simple Gaussian priors to complex data distributions. A natural generalization of this idea to transformations between arbitrary probability distributions leads to the Schr\"odinger Bridge (SB) problem. However, S

Cooper M. Selco, Christian Bengs, Chaitali Shah, Zhuorui Zhang

Elucidating the emergence of irreversible macroscopic laws from reversible quantum many-body dynamics is a question of broad importance across all quantum science. Many-body decoherence plays a key role in this transition, yet connecting microscopic dynamics to emergent macroscopic behavior remains challenging. Here, in a doubly disordered electron-nuclear s

Haolun Wu, Zhenkun Li, Lingyao Li

Multi-agent debate (MAD) has recently emerged as a promising framework for improving the reasoning performance of large language models (LLMs). Yet, whether LLM agents can genuinely engage in deliberative reasoning, beyond simple ensembling or majority voting, remains unclear. We address this question through a controlled study using the Knight--Knave--Spy l

Hao Luo, Saeed R. Khosravirad, Ahmed Alkhateeb

Massive MIMO systems can enhance spectral and energy efficiency, but they require accurate channel state information (CSI), which becomes costly as the number of antennas increases. While machine learning (ML) autoencoders show promise for CSI reconstruction and reducing feedback overhead, they introduce new challenges with standardization, interoperability,

George Tringas, Timm Wrase

We provide the first scale-separated AdS solutions from compactifications of the heterotic string. Our solutions have parametrically weak coupling, large volume, and the internal KK scale is parametrically smaller than the AdS length. These AdS$_3$ vacua preserve $\mathcal{N}=1$ supersymmetry and arise from compactifications on $G_2$ structure manifolds with

Likang Peng, Chao Su, Wenyuan Wu, Yuan Sun

Cross-modal hashing (CMH) facilitates efficient retrieval across different modalities (e.g., image and text) by encoding data into compact binary representations. While recent methods have achieved remarkable performance, they often rely heavily on fully annotated datasets, which are costly and labor-intensive to obtain. In real-world scenarios, particularly

Tiankuo Zhang, Paria Nourmohammadi, Sixtine Guerin, Benoit Montreuil

Less than truckload shipping plays a critical role in modern supply chains by consolidating freight from multiple shippers into shared vehicles. Despite its operational flexibility and potential sustainability benefits, the LTL sector faces persistent challenges, including high per unit costs and financial instability, as evidenced by recent industry bankrup

Ao Ding, Licheng Sun, Yongjie Hou, Huaqing Zhang

Multi-agent reinforcement learning (MARL) has demonstrated remarkable performance in multi-agent collaboration problems and has become a prominent topic in artificial intelligence research in recent years. However, traditional credit assignment schemes in MARL cannot reliably capture individual contributions in strongly coupled tasks while maintaining traini

Jeyoung Lee, Hochul Kang

Recent progress in diffusion-based generative models has enabled high-quality image synthesis conditioned on diverse modalities. Extending such models to brain signals could deepen our understanding of human perception and mental representations. However,electroencephalography (EEG) presents major challenges for image generation due to high noise, low spatia

Zhenghao Zhou, Yiyan Li, Xinjie Yu, Runlong Liu

Power system time series analytics is critical in understanding the system operation conditions and predicting the future trends. Despite the wide adoption of Artificial Intelligence (AI) tools, many AI-based time series analytical models suffer from task-specificity (i.e. one model for one task) and structural rigidity (i.e. the input-output format is fixed

Gina Sohn, Genghan Zhang, Konstantin Hossfeld, Jungwoo Kim

Dynamic behaviors are becoming prevalent in tensor applications, like machine learning, where many widely used models contain data-dependent tensor shapes and control flow. However, the limited expressiveness of prior programming abstractions for spatial dataflow accelerators (SDAs) forces these dynamic behaviors to be implemented statically and/or unoptimiz

Masashi Wakamatsu

Whether the time-dependent Aharonov-Bohm (AB) effect even exists or not has been the subject of long-standing debate. There are two factors complicating the problem. First, in the closed spacetime line integral of the vector potential that is thought to give the AB-phase shift, how to treat the time-varying vector potential is highly nontrivial. Second, the

Per-Gunnar Martinsson, Michael O'Neil

This survey describes a class of methods known as "fast direct solvers". These algorithms address the problem of solving a system of linear equations $\boldsymbol{Ax}=\boldsymbol{b}$ arising from the discretization of either an elliptic PDE or of an associated integral equation. The matrix $\boldsymbol{A}$ will be sparse when the PDE is discretized directly,

Shourya Batra, Pierce Tillman, Samarth Gaggar, Shashank Kesineni

As Large Language Models (LLMs) evolve into personal assistants with access to sensitive user data, they face a critical privacy challenge: while prior work has addressed output-level privacy, recent findings reveal that LLMs often leak private information through their internal reasoning processes, violating contextual privacy expectations. These leaky thou

Kesong Zheng, Zhi Song, Peizhou Li, Shuyi Yao

To address insufficient accuracy in multi-scale segmentation for agricultural habitat recognition, this study proposes a Dynamic Weighted Feature Fusion Network (DWFF-Net). Its encoder uses frozen DINOv3 to extract basic features and introduces a data-level adaptive dynamic weighting strategy based on relationships between image categories and feature maps.

Yanxian Tao, Lingyun Wan, Xiongzhi Zeng, Yingdi Jin

Accurate and efficient prediction of electronic wavefunctions is central to ab initio molecular dynamics (AIMD) and electronic structure theory. However, conventional ab initio methods require self-consistent optimization of electronic states at every nuclear configuration, leading to prohibitive computational costs, especially for large or strongly correlat

Yi Ni, Liwei Zhu, Shuai Li

Chimeric antigen receptor T-cell (CAR-T) therapy represents a paradigm shift in cancer treatment, yet development timelines of 8-12 years and clinical attrition rates exceeding 40-60% highlight critical inefficiencies in target selection, safety assessment, and molecular optimization. We present Bio AI Agent, a multi-agent artificial intelligence system powe

Zewei Guo, Zhen Jia, JinXiao Zhu, Wenhao Huang

Radio frequency (RF) fingerprinting exploits hardware imperfections for device identification, but distinguishing between same-model devices remains challenging due to their minimal hardware variations. Existing datasets for RF fingerprinting are constrained by small device scales and heterogeneous models, which hinder robust training and fair evaluation of

Somnath Maity, Ryusuke Hamazaki

Nonstabilizerness, or magic, constitutes a fundamental resource for quantum computation and a crucial ingredient for quantum advantage. Recent progress has substantially advanced the characterization of magic in many-body quantum systems, with stabilizer R\'enyi entropy (SRE) emerging as a computable and experimentally accessible measure. In this work, we in

Jiachen Li, Shihao Li, Dongmei Chen

Real time model based control of high dimensional nonlinear systems presents severe computational challenges. Conventional reduced order model control relies heavily on expert tuning or parameter adaptation and seldom offers mechanisms for online supervised reconstruction. We introduce AURORA, Autonomous Updating of ROM and Controller via Recursive Adaptatio

Dimitris Oikonomou, Matthew Buchholz, Yuen-Man Pun, Robert M. Gower

Schedule-Free SGD, proposed in [Defazio et al., 2024], achieves optimal convergence rates without requiring the training horizon in advance, by replacing learning rate schedules with a principled form of iterate averaging. However, the method still requires tuning a base learning rate whose optimal value depends on unknown problem constants. In this work, we

Yuri Faenza, Víctor Verdugo, José Verschae, Matías Villagra

The presence of symmetries is one of the central structural features that make some integer programs challenging for state-of-the-art solvers. In this work, we study the efficacy of Linear Programming (LP) hierarchies in the presence of symmetries. Our main theorem unveils a connection between the algebraic structure of these relaxations and the geometry of

R. E. Keeley, A. M. Nierenberg, D. Gilman, T. Treu

We present the full sample of measurements of the warm dust emission of 31 strongly-lensed, multiply imaged quasars, observed with JWST MIRI multiband imaging, which we use to constrain the particle properties of dark matter. The strongly lensed warm dust region of quasars is compact and statistically sensitive to a population of dark matter halos down to ma

Terry-Ann Suer, Edgar S. Steenstra, Simone Marchi, John A. Tarduno

Metal-rich asteroids and iron meteorites are considered core remnants of differentiated planetesimals and or products of oxygen-depleted accretion. Investigating the origins of iron-rich planetesimals could provide key insights into planet formation mechanisms. Using differentiation models, we evaluate the interior structure and composition of representative

Rushan Zhang, Golo Wimmer, Qi Tang

Magnetohydrodynamic (MHD) solvers used to study dynamic plasmas for magnetic confinement fusion typically rely on initial conditions that describe force balance, which are provided by an equilibrium solver based on the Grad-Shafranov (GS) equation. Transferring such equilibria from the GS discretization to the MHD discretization often introduces errors that

Shakibul Chowdhury

We present a compact and reproducible method for general relativistic polarization transport in the Kerr metric that achieves median electric vector position angle (EVPA) residuals of $\langle \Delta \mathrm{PA} \rangle \approx 0.09^\circ$, a 95th percentile of $0.31^\circ$, and a worst case $\Delta \mathrm{PA} \lesssim 0.32^\circ$ for spins up to $|a/M|=0.9

Myles Pasetsky, Jiawei Lin, Bradley Guo, Sarah Dean

High-altitude balloons (HABs) are common in scientific research due to their wide range of applications and low cost. Because of their nonlinear, underactuated dynamics and the partial observability of wind fields, prior work has largely relied on model-free reinforcement learning (RL) methods to design near-optimal control schemes for station-keeping. These

Xiaofan Tu, Tiantian Duan, Shuyi Miao, Hanwen Zhang

As mixing services are increasingly being exploited by malicious actors for illicit transactions, mixing address association has emerged as a critical research task. A range of approaches have been explored, with graph-based models standing out for their ability to capture structural patterns in transaction networks. However, these approaches face two main c

Chengli Li, Xingzhi Zhan

A graph $G$ is called an $[s,t]$-graph if any induced subgraph of $G$ of order $s$ has size at least $t.$ An edge $e$ in a graph $G$ of order $n$ is called pancyclic if for every integer $k$ with $3\le k\le n,$ $e$ lies in a $k$-cycle. We prove that every $2$-connected $[4, 2]$-graph of order at least seven contains a pancyclic edge. This strengthens an exis

Song Yan, Wei Zhai, Chenfeng Wang, Xinliang Bi

Diffusion models start generation from an isotropic Gaussian latent, yet changing only the random seed can lead to large differences in prompt faithfulness, composition, and visual quality. We study this seed sensitivity through the semantic map from initial noise to generated meaning. Although the sampling flow is locally invertible, the subsequent semantic

Aja Khanal, Ahmed Faid, Apurva Narayan

Deep learning vision systems are increasingly deployed in safety-critical domains such as healthcare, yet they remain vulnerable to small adversarial patches that can trigger misclassifications. Most existing defenses assume a single patch and fail when multiple localized disruptions occur, the type of scenario adversaries and real-world artifacts often expl

Hiroshi Yokota

Motivated by intracellular phase separation, we theoretically investigate how molecular orientation and multi-component nature affect phase behavior. We construct a minimal model for a ternary mixture composed of isotropic (I), anisotropic (A), and solvent (s) components by combining the Flory-Huggins and Maier-Saupe theories. We obtain two main results from

Viqar Husain, Irfan Javed

We show that the canonical formulation of the semiclassical Einstein equation, where the matter terms in the constraints are replaced by expectation values of the corresponding operators in quantum states, is inconsistent due to the non-closure of the resulting constraint algebra.

Shiva Upadhye, Richard Futrell

Contextual predictability shapes how we choose and encode words in production. The effects of a word's predictability given preceding or past context are generally well-understood in both production and comprehension, but studies of naturalistic production have also revealed a poorly-understood yet robust backward predictability effect of a word given on

E. Kirkinis, A. Levchenko

The values of liquid odd-viscosity coefficients remain largely unknown, with only a single experimental measurement reported to date [Nature Physics 15, 1188 (2019)]. In this work, inspired by the well-known consequences of dispersion surfaces in classical liquids from the work of Lighthill, we theoretically determine the shapes of constant-phase wave crests

Durgakant Pushp, Weizhe Chen, Zheng Chen, Chaomin Luo

Navigating complex real-world environments requires semantic understanding and adaptive decision-making. Traditional reactive methods without maps often fail in cluttered settings, map-based approaches demand heavy mapping effort, and learning-based solutions rely on large datasets with limited generalization. To address these challenges, we present Pareto-O

Shengqian Zhu, Chengrong Yu, Qiang Wang, Ying Song

Class incremental medical image segmentation (CIMIS) aims to preserve knowledge of previously learned classes while learning new ones without relying on old-class labels. However, existing methods 1) either adopt one-size-fits-all strategies that treat all spatial regions and feature channels equally, which may hinder the preservation of accurate old knowled

Yuezhe Yang, Yiyue Guo, Wenjie Cai, Qingqing Ruan

AI-assisted ultrasound video diagnosis presents new opportunities to enhance the efficiency and accuracy of medical imaging analysis. However, existing research remains limited in terms of dataset diversity, diagnostic performance, and clinical applicability. In this study, we propose \textbf{Auto-US}, an intelligent diagnosis agent that integrates ultrasoun

Masaya Hiraishi, Gabrielle A. Hunter-Smith, Gavin G. G. King, Alexandra A. Turrini

We characterise optical transitions of neodymium ions (Nd3+) in antiferromagnetic neodymium gallate (NdGaO3) with applied fields up to 3 T. The magnetic phase of this material has not previously been studied with the field along its magnetisation axis. The measured optical spectra indicate three magnetic phases -- antiferromagnetic, intermediate, and paramag

Samuel W. Yee, Shreyas Vissapragada

Recent discoveries have revealed a population of "popcorn planets" that have masses similar to that of Neptune but radii comparable to Jupiter, leading to exceptionally low bulk densities $\rho_p \lesssim 0.3\,\mathrm{g}\,\mathrm{cm}^{-3}$. Their anomalously-inflated radii, along with recent JWST atmospheric observations, suggest a source of internal heating

Akiko M. Nakamura, Keita Nomura, Sunao Hasegawa

Impact ejecta with velocities exceeding the escape velocity of planetary bodies become meteorites and dust particles in interplanetary space. We present a new method that allows simultaneous measurement of the size and velocity of the largest high-velocity ejecta. High-speed camera images revealed the time required for the ejecta to reach the secondary targe

Daniel Cher, Brian Wei, Srikumar Sastry, Nathan Jacobs

We introduce VectorSynth, a diffusion-based framework for pixel-accurate satellite image synthesis conditioned on polygonal geographic annotations with semantic attributes. Unlike prior text- or layout-conditioned models, VectorSynth learns dense cross-modal correspondences that align imagery and semantic vector geometry, enabling fine-grained, spatially gro

Yuezhe Yang, Qingqing Ruan, Wenjie Cai, Yudang Dong

Ultrasound imaging is a cornerstone of non-invasive clinical diagnostics, yet its limited field of view poses challenges for novel view synthesis. We present UltraGS, a real-time framework that adapts Gaussian Splatting to sensorless ultrasound imaging by integrating explicit radiance fields with lightweight, physics-inspired acoustic modeling. UltraGS emplo

Luan Lazzari, Kleinner Farias

Modeling is a central and demanding activity in software engineering that requires skills such as abstraction, consistency maintenance, and precise communication. These skills are difficult to master and even harder to teach effectively. Educators and students often struggle to understand and manage inconsistencies that arise during the modeling process. To

Jianan Ma, Jingyi Wang, Qi Xuan, Zhen Wang

It is known that deep neural networks may exhibit dangerous behaviors under various security threats (e.g., backdoor attacks, adversarial attacks and safety property violation) and there exists an ongoing arms race between attackers and defenders. In this work, we propose a complementary perspective to utilize recent progress on "neural network repair" to mi

Kaiyuan Zheng, Zihang Peng, Hanyu Liao, Yijun Huang

Integrated photonic sensors have attracted significant attention recently for their potential for high-density integration. However, they face challenges in sensing gases with high sensitivity due to weak light-gas interaction. Slow light, which dramatically intensifies light-matter interaction through spatial compression of optical energy, provides a promis

Fan Chang

We study Boolean functions on the $p$-biased hypercube $(\{0,1\}^n,\mu_p^n)$ through the lens of Fourier (spectral) entropy, i.e. the Shannon entropy of the squared $p$-biased Fourier coefficients. Motivated by recent restriction-based advances on upper bounds toward the Fourier-Entropy-Influence (FEI) conjecture, we prove a complementary, sharp lower bound

Donglai Xu, Hongzheng Yang, Yuzhi Zhao, Pingping Zhang

Reinforcement Learning with Verifiable Rewards (RLVR) for Multimodal Large Language Models (MLLMs) is highly dependent on high-quality labeled data, which is often scarce and prone to substantial annotation noise in real-world scenarios. Existing unsupervised RLVR methods, including pure entropy minimization, can overfit to incorrect labels and limit the cru

Steve Dai, Cunxi Yu, Kalyan Krishnamani, Brucek Khailany

While accelerated computing has transformed many domains of computing, its impact on logical reasoning, specifically Boolean satisfiability (SAT), remains limited. State-of-the-art SAT solvers rely heavily on inherently sequential conflict-driven search algorithms that offer powerful heuristics but limit the amount of parallelism that could otherwise enable

Joseph Mathews, Scott C. Schmidler

Statistical inference in evolutionary models with site-dependence is a long-standing challenge in phylogenetics and computational biology. We consider the problem of approximating marginal sequence likelihoods under dependent-site models of biological sequence evolution. We prove an upper bound on the mixing time for a Markov chain Monte Carlo algorithm that

Ander Aguirre, Hoi H. Nguyen, Jingheng Wang

In this paper, we investigate the number of real zeros of random Weyl polynomials of degree \(n \to \infty\) with general coefficient distributions. Motivated by the results of arXiv:1409.4128 and arXiv:1402.4628 as well as arXiv:1711.03316 and arXiv:1912.11901, we determine how the expected number of real zeros and their variance, over various natural inter

Stanislav Selitskiy

Large Artificial Neural Network (ANN) models have demonstrated success in various domains, including general text and image generation, drug discovery, and protein-RNA (ribonucleic acid) binding tasks. However, these models typically demand substantial computational resources, time, and data for effective training. Given that such extensive resources are oft

Shu Hong, Yongsheng Mei, Mahdi Imani, Tian Lan

Bayesian optimization (BO) is a powerful framework for optimizing expensive black-box objectives, yet extending it to graph-structured domains remains challenging due to the discrete and combinatorial nature of graphs. Existing approaches often rely on either full graph topology-impractical for large or partially observed graphs-or incremental exploration, w

Y. Kosuga, R. Matsui, P. H. Diamond

Recent experiments reported a correlation between power law core temperature spectra and $D_\alpha$ emission, suggesting that heat avalanches penetrate the SOL. This paper derives a threshold criterion for avalanche penetration using a reduced model. Avalanches with $(\nabla\tilde T)_{rms}>\nabla\tilde T_{crit}$ at the separatrix are predicted to penetrate,

Sandeep Routray, Hengkai Pan, Unnat Jain, Shikhar Bahl

Can we turn a video prediction model into a robot policy? Videos, including those of humans or teleoperated robots, capture rich physical interactions. However, most of them lack labeled actions, which limits their use in robot learning. We present Video Prediction for Robot Actions (ViPRA), a simple pretraining-finetuning framework that learns continuous ro