October 2025 arXiv papers — page 116

Kuang-Da Wang, Zhao Wang, Yotaro Shimose, Wei-Yao Wang

Witnessed by the recent advancements on leveraging LLM for coding and multimodal understanding, we present WebGen-V, a new benchmark and framework for instruction-to-HTML generation that enhances both data quality and evaluation granularity. WebGen-V contributes three key innovations: (1) an unbounded and extensible agentic crawling framework that continuous

Genglin Wang, Liekang Zeng, Bufang Yang, Kaiwei Liu

Large Language Models (LLMs) are becoming key components in various mobile operating systems, driving smart applications like interactive chatbots and personal assistants. While bringing enhanced intelligence to mobile ends, their deployment suffers from a set of performance challenges, especially the generation quality degradation and prolonged latency. Pri

Zhuo Chen, Xinjian Xu, Shihui Ying, Tieyong Zeng

Bilevel optimization (BLO) offers a principled framework for hierarchical decision-making and has been widely applied in machine learning tasks such as hyperparameter optimization and meta-learning. While existing BLO methods are mostly developed in Euclidean spaces, many real-world problems involve structural constraints. In this paper, we propose a Riemann

Fei Wang, Li Shen, Liang Ding, Chao Xue

Large Language Models excel at natural language processing tasks, but their massive size leads to high computational and storage demands. Recent works have sought to reduce their model size through layer-wise structured pruning. However, they tend to ignore retaining the capabilities in the pruned part. In this work, we re-examine structured pruning paradigm

Ting Qiao, Xing Liu, Wenke Huang, Jianbin Li

Large web-scale datasets have driven the rapid advancement of pre-trained language models (PLMs), but unauthorized data usage has raised serious copyright concerns. Existing dataset ownership verification (DOV) methods typically assume that watermarks remain stable during inference; however, this assumption often fails under natural noise and adversary-craft

Wen Wu, Sheng Zhong

The limit functions generated by quasi-linear functions or sequences (including the sum of the Rudin-Shapiro sequence as an example) are continuous but almost everywhere non-differentiable functions. Their graphs are fractal curves. In 2017 and 2020, Chen, L\"u, Wen and the first author studied the box dimension of the graphs of the limit functions. In this

Yiheng Wang

Intelligent reflecting surface (IRS) assisted unmanned aerial vehicle (UAV) systems provide a new paradigm for reconfigurable and flexible wireless communications. To enable more energy efficient and spectrum efficient IRS assisted UAV wireless communications, this paper introduces a novel IRS-assisted UAV enabled spectrum sharing system with orthogonal freq

Minglei Shi, Haolin Wang, Wenzhao Zheng, Ziyang Yuan

Recent progress in diffusion-based visual generation has largely relied on latent diffusion models with variational autoencoders (VAEs). While effective for high-fidelity synthesis, this VAE+diffusion paradigm suffers from limited training efficiency, slow inference, and poor transferability to broader vision tasks. These issues stem from a key limitation of

Yijia Sun, Shanshan Huang, Zhiyuan Guan, Qiang Luo

Industrial-scale recommender systems rely on a cascade pipeline in which the retrieval stage must return a high-recall candidate set from billions of items under tight latency. Existing solutions either (i) suffer from limited expressiveness in capturing fine-grained user-item interactions, as seen in decoupled dual-tower architectures that rely on separate

Saurabh Deochake, Ryan Murphy, Jeremiah Gearheart

Static, long-lived credentials for workload authentication create untenable security risks that violate Zero-Trust principles. This paper presents a multi-cloud framework using Workload Identity Federation (WIF) and OpenID Connect (OIDC) for secretless authentication. Our approach uses cryptographically-verified, ephemeral tokens, allowing workloads to authe

Guojie Hu, Qingqing Wu, Lipeng Zhu, Kui Xu

Movable antenna (MA) is an emerging technology which can reconfigure wireless channels via adaptive antenna position adjustments at transceivers, thereby bringing additional spatial degrees of freedom for improving system performance. In this paper, from a security perspective, we exploit the MAenabled legitimate jammer (MAJ) to subvert suspicious multiuser

Yiming Lin, Shang Wang, Junkai Zhou, Qiufeng Wang

Single Positive Multi-Label Learning (SPMLL) addresses the challenging scenario where each training sample is annotated with only one positive label despite potentially belonging to multiple categories, making it difficult to capture complex label relationships and hierarchical structures. While existing methods implicitly model label relationships through d

Shiying Chen, Guangji Chen, Long Shi, Qingqing Wu

Integrated sensing and communication (ISAC) is viewed as a key enabler for future wireless networks by sharing the hardware and wireless resources between the functionalities of sensing and communication (S&C). Due to the shared wireless resources for both S&C, it is challenging to achieve a critical trade-off between these two integrated functionalities. To

Danil Parkhomenko, Pavel Ovchinnikov, Konstantin Soldatov, Vitalii Kapitan

In this paper we present a neural network-based method for the automatic detection of phase transitions and classification of hidden percolation patterns in a (1+1)-dimensional replication process. The proposed network model is based on the combination of CNN, TCN and GRU networks, which are trained directly on raw configurations without any manual feature e

Zhexian Li, Felipe de Barros, Ketan Savla

We consider constrained bilinear optimal control of second-order linear evolution partial differential equations (PDEs) with a reaction term on the half line, where control arises as a time-dependent reaction coefficient and constraints are imposed on the state and control variables. These PDEs represent a wide range of physical phenomena in fluid flow, heat

Guojie Hu, Qingqing Wu, Ming-Min Zhao, Wen Chen

In this paper, we investigate the movable antennas (MAs)-enabled multiple-input-single-output (MISO) systems, where the base station (BS) equipped with multiple MAs serves multiple single-antenna user. The delay-sensitive scenario is considered, where users refrain from periodically sending training signals to the BS for channel estimations to avoid addition

Luke P. Filippini, Adrianne L. Jenner, Elliot J. Carr

The diffusive transport of particles in anisotropic media is a fundamental phenomenon in computational, medical and biological disciplines. While deterministic models (partial differential equations) of such processes are well established, their inability to capture inherent randomness, and the assumption of a large number of particles, hinders their applica

Somphong Jitman

For coprime nonzero integers $a$ and $b$, a positive integer $\ell$ is said to be {\em good} with respect to $a$ and $b$ if there exists a positive integer $k$ such that $\ell |(a^{k}+b^{k})$. Since the early 1990s, such classical good integers have been studied intensively for their number theoretic structures and for applications, notably in coding theory.

Duc-Phuong Doan-Ngo, Thanh-Dang Diep, Thanh Nguyen-Duc, Thanh-Sach LE

Recognizability, a key perceptual factor in human face processing, strongly affects the performance of face recognition (FR) systems in both verification and identification tasks. Effectively using recognizability to enhance feature representation remains challenging. In deep FR, the loss function plays a crucial role in shaping how features are embedded. Ho

Visca Tri Winarty, Sena Safarina

Since the COVID-19 pandemic, the number of investors in the Indonesia Stock Exchange has steadily increased, emphasizing the importance of portfolio optimization in balancing risk and return. The classical mean-variance optimization model, while widely applied, depends on historical return and risk estimates that are uncertain and may result in suboptimal po

Chun Chet Ng, Zhen Hao Chu, Jia Yu Lim, Yin Yin Boon

Despite accounting for 96.1% of all businesses in Malaysia, access to financing remains one of the most persistent challenges faced by Micro, Small, and Medium Enterprises (MSMEs). Newly established businesses are often excluded from formal credit markets as traditional underwriting approaches rely heavily on credit bureau data. This study investigates the p

Zhenning Cai, Yixiao Sun, Geshuo Wang

We propose a general strategy to discretize the Dyson series without applying direct numerical quadrature to high-dimensional integrals, and extend this framework to open quantum systems. The resulting discretization can also be interpreted as a Strang splitting combined with a Taylor expansion. Based on this formulation, we develop a numerically exact itera

Lunchen Xie, Zehua He, Qingjiang Shi

Personalized Federated Learning (PFL) has emerged as a critical research frontier addressing data heterogeneity issue across distributed clients. Novel model architectures and collaboration mechanisms are engineered to accommodate statistical disparities while producing client-specific models. Parameter decoupling represents a promising paradigm for maintain

Xiaoxu Xu, Xuexun Liu, Jinlong Li, Yitian Yuan

3D weakly supervised semantic segmentation (3D WSSS) aims to achieve semantic segmentation by leveraging sparse or low-cost annotated data, significantly reducing reliance on dense point-wise annotations. Previous works mainly employ class activation maps or pre-trained vision-language models to address this challenge. However, the low quality of pseudo-labe

Jason Tsay, Zidane Wright, Gaodan Fang, Kiran Kate

Agentic systems interact with external systems by calling tools such as Python functions, REST API endpoints, or command line tools such as kubectl in Kubernetes. These tool calls often fail for various syntactic and semantic reasons. Some less obvious semantic errors can only be identified and resolved after analyzing the tool's response. To repair these er

Xianyang Qi, Yuan Tian, Zhaoyu Hu, Zhirui Kuai

Industrial recommender systems critically depend on high-quality ranking models. However, traditional pipelines still rely on manual feature engineering and scenario-specific architectures, which hinder cross-scenario transfer and large-scale deployment. To address these challenges, we propose \textbf{MTmixAtt}, a unified Mixture-of-Experts (MoE) architectur

Saeid Bayat, Jerry Zuo, Jing Sun

Offshore renewable energy systems offer promising solutions for sustainable power generation, yet most existing platforms harvest either wind or wave energy in isolation. This study presents a hybrid floating offshore platform that integrates a wind turbine with three oscillating surge wave energy converters (WECs) into a hexagonal semi-submersible structure

Jingao Xu, Shuoyoucheng Ma, Xin Song, Rong Jiang

Large Language Models (LLMs) as interactive agents show significant promise in Knowledge Graph Question Answering (KGQA) but often struggle with the semantic gap between natural language queries and structured knowledge graph (KG) representations. This leads to suboptimal planning and inefficient exploration on KG, while training-free approaches often underu

Nishad Kulkarni, Krithika Iyer, Austin Tapp, Abhijeet Parida

Magnetic Resonance Imaging (MRI) is the primary imaging modality used in the diagnosis, assessment, and treatment planning for brain pathologies. However, most automated MRI analysis tools, such as segmentation and registration pipelines, are optimized for healthy anatomies and often fail when confronted with large lesions such as tumors. To overcome this, i

Qingkai Kong, Avigyan Chatterjee, Chengping Chai, Alex Dzubay

Reliable automatic phase picking is important for many seismic applications. With the development of machine learning approaches, many algorithms are proposed, evaluated and applied to different areas. Many of these algorithms are single station based, while recent proposed methods start to combine surrounding stations into consideration in the problem of ph

Fan Liu, Jindong Han, Tengfei Lyu, Weijia Zhang

Foundation models (FMs), such as GPT-4 and AlphaFold, are reshaping the landscape of scientific research. Beyond accelerating tasks such as hypothesis generation, experimental design, and result interpretation, they prompt a more fundamental question: Are FMs merely enhancing existing scientific methodologies, or are they redefining the way science is conduc

Md Tariqul Hossain, Abir Malakar, Mohammad Yeasin, William O'Connell

Improving the efficacy of nasal sprays by enhancing targeted drug delivery to intra-airway tissue sites prone to infection onset is hypothesized to be achievable through an optimization of key device and formulation parameters, such as the sprayed droplet sizes, spray cone angle, and formulation density. This study focuses on the nasopharynx, a primary locus

Heyao Zhu, Yimeng Zhao, Zirui Zhang, Huansheng Yi

With the intensiffcation of population aging and increasing burden of chronic diseases, the demand for vital signs monitoring is becoming increasingly urgent. A key challenge facing current non-contact detection technologies using millimeter wave (mmWave) radar is the low efffciency of multi-channel signal fusion in array radar systems based on equal weighti

Bo Ling, Yi Gu

We study the optimal recovery problem for isotropic functions defined by second-order differential operators using both function and gradient values. We derive the upper bound for n-th optimal error with an explicit constant, which is independent of the specific form of the differential operators. Furthermore, for self-adjoint operators, we obtain asymptotic

Gnanasekaran Shanmugasundaram, Jitraj Saha

This paper investigates a class of chemotaxis systems modeling lethal interactions in a smooth, bounded domain $\Omega \subset \mathbb{R}^n$ with homogeneous Neumann boundary conditions. We examine two distinct cases: (i) a fully parabolic system where both equations exhibit parabolic dynamics, and (ii) a parabolic-elliptic system featuring a parabolic first

Runhao Han, Tao Hu, Jia Yang, Saifei Dai

We propose a direct experimental extraction technique for trapped charges and quantitative energy band diagrams in the FeFETs with metal-insulator-ferroelectric-insulator-semiconductor (MIFIS) structure, derived from the physical relationship between Vth and gate-side interlayer (G.IL) thickness. By decoupling trapped charges and ferroelectric polarization,

Xiangyi Peng, Lisen Ding, Wenlin Qiu

This work proposes an efficient space-time two-grid compact difference (ST-TGCD) scheme for solving the two-dimensional (2D) viscous Burgers' equation subject to initial and periodic boundary conditions. The proposed approach combines a compact finite difference discretization with a two-grid strategy to achieve high computational efficiency without sacrific

Liner Xiang, Jiayi Wang, Hengrui Cai

Contextual bandits, which leverage the baseline features of sequentially arriving individuals to optimize cumulative rewards while balancing exploration and exploitation, are critical for online decision-making. Existing approaches typically assume no interference, where each individual's action affects only their own reward. Yet, such an assumption can be v

Atsushi Senda, Yuki Takatsu, Ryokan Ikebe, Hiroshi Suginaka

Triage tools in routine emergency care are largely static, failing to exploit simple behavioral cues clinicians notice in real time. Here, we developed a Bayesian, sequentially updating framework that integrates incoming cues to produce calibrated, time-consistent risk. Using a prospective single-center cohort of ambulance arrivals in Japan (February-August

Jingrui Yu, Jun Liu, Kefei Ren, Joydeep Biswas

Efficient and accurate camera pose estimation forms the foundational requirement for dense reconstruction in autonomous navigation, robotic perception, and virtual simulation systems. This paper addresses the challenge via cuSfM, a CUDA-accelerated offline Structure-from-Motion system that leverages GPU parallelization to efficiently employ computationally i

Mucheng Ren, Yucheng Yan, He Chen, Danqing Hu

Medical texts, particularly electronic medical records (EMRs), are a cornerstone of modern healthcare, capturing critical information about patient care, diagnoses, and treatments. These texts hold immense potential for advancing clinical decision-making and healthcare analytics. However, their unstructured nature, domain-specific language, and variability a

Karthik Gajulapalli, Zeyong Li, Ilya Volkovich

In this work we study oblivious complexity classes. These classes capture the power of interactive proofs where the prover(s) are only given the input size rather than the actual input. In particular, we study the connections between the symmetric polynomial time $\mathsf{S_2P}$ and its oblivious counterpart $\mathsf{O_2P}$. Among our results, we construct a

Mucheng Ren, He Chen, Yuchen Yan, Danqing Hu

Automated International Classification of Diseases (ICD) coding assigns standardized diagnosis and procedure codes to clinical records, playing a critical role in healthcare systems. However, existing methods face challenges such as semantic gaps between clinical text and ICD codes, poor performance on rare and long-tail codes, and limited interpretability.

Xueqing Sun, Renzhen Wang, Quanziang Wang, Yichen Wu

Pseudo-labeling is a commonly used paradigm in semi-supervised learning, yet its application to semi-supervised regression (SSR) remains relatively under-explored. Unlike classification, where pseudo-labels are discrete and confidence-based filtering is effective, SSR involves continuous outputs with heteroscedastic noise, making it challenging to assess pse

Emam Hossain, Muhammad Hasan Ferdous, Devon Dunmire, Aneesh Subramanian

Causal modeling offers a principled foundation for uncovering stable, invariant relationships in time-series data, thereby improving robustness and generalization under distribution shifts. Yet its potential is underutilized in spatiotemporal Earth observation, where models often depend on purely correlational features that fail to transfer across heterogene

Davi Antônio da Silva Santos, Bruno César Ribas

Maratona Linux is the development environment used since 2016 on the ``Maratona de Programa\c{c}\~ao'', ICPC's South American regional contest. It consists of Debian packages that modify a standard Ubuntu installation in order to make it suitable for the competition, installing IDEs, documentation, compilers, debuggers, interpreters, and enforcing network re

Zhiyuan Fan, Yifeng Liu, Qingyue Zhao, Angela Yuan

Empirical scaling laws prescribe how to allocate parameters, data, and compute, while maximal-update parameterization ($\mu$P) enables learning-rate transfer across widths by equalizing early-time update magnitudes. However, in modern scale-invariant architectures, training quickly enters an optimizer-governed steady state where normalization layers create b

Jitesh Jain, Shubham Maheshwari, Ning Yu, Wen-mei Hwu

Riding on the success of LLMs with retrieval-augmented generation (RAG), there has been a growing interest in augmenting agent systems with external memory databases. However, the existing systems focus on storing text information in their memory, ignoring the importance of multimodal signals. Motivated by the multimodal nature of human memory, we present AU

Muhy Eddin Za'ter, Bri-Mathias Hodge, Kyri Baker

Solving the nonlinear AC optimal power flow (AC OPF) problem remains a major computational bottleneck for real-time grid operations. In this paper, we propose a residual learning paradigm that uses fast DC optimal power flow (DC OPF) solutions as a baseline, and learns only the nonlinear corrections required to provide the full AC-OPF solution. The method ut

Yangyang Li

Large language models are highly sensitive to prompt wording. However, popular automatic prompt search methods, including InstructZero, often degrade under distribution shift and adversarial evaluation because they optimize expected performance under a single evaluation distribution. Consequently, prompts that work in one setting frequently fail to transfer.

Chenwei Tang, Lin Long, Xinyu Liu, Jingyu Xing

Most commodity software lacks accessible Application Programming Interfaces (APIs), requiring autonomous agents to interact solely through pixel-based Graphical User Interfaces (GUIs). In this API-free setting, large language model (LLM)-based agents face severe efficiency bottlenecks: limited to local visual experiences, they make myopic decisions and rely

Muhy Eddin Za'ter, Bri-Mathias Hodge

Accurate voltage estimation in distribution networks is critical for real-time monitoring and increasing the reliability of the grid. As DER penetration and distribution level voltage variability increase, robust distribution system state estimation (DSSE) has become more essential to maintain safe and efficient operations. Traditional DSSE techniques, howev

Guiyao Tie, Zenghui Yuan, Zeli Zhao, Chaoran Hu

Self-correction of large language models (LLMs) emerges as a critical component for enhancing their reasoning performance. Although various self-correction methods have been proposed, a comprehensive evaluation of these methods remains largely unexplored, and the question of whether LLMs can truly correct themselves is a matter of significant interest and co

Xi Wang, Xianyao Ling, Kun Li, Gang Yin

In the current era of big data, extracting deep insights from massive, heterogeneous, and complexly associated multi-dimensional data has become a significant challenge. Large Language Models (LLMs) perform well in natural language understanding and generation, but still suffer from "hallucination" issues when processing structured knowledge and are difficul

Amir Ali Farzin, Yuen-Man Pun, Philipp Braun, Tyler Summers

We consider the minimisation problem of submodular functions and investigate the application of a zeroth-order method to this problem. The method is based on exploiting a Gaussian smoothing random oracle to estimate the smoothed function gradient. We prove the convergence of the algorithm to a global $\epsilon$-approximate solution in the offline case and sh

Ricardo Alonzo Fernández Salguero

Why have left-wing movements historically integrated participatory art forms (such as murals and protest songs) into their praxis, while right-wing movements have prioritized strategic communication and, more recently, the digital culture of memes? This article introduces the concept of aesthetic asymmetry to explain this divergence in political action. We a

Johnathan Cai, Ryan Diehl, William Gasarch, Ian Kim

The Prime Number Theorem states that the number of primes in $\{1,\ldots,x\}$, denoted $\pi(x)$, is approximately $\frac{x}{\ln(x)}$. In this paper, we investigate the distribution of primes for domains other than $\N$. First we look at $A_d=\{ x \colon x\equiv 1 \pmod d\}$. We give a heuristic argument to form a conjecture on the number of {\it congruence m

Dingya Feng, Dingyuan Xue

Accurate forecasting of avian disease outbreaks is critical for wildlife conservation and public health. This study presents a Transformer-based framework for predicting the disease risk at the terminal locations of migratory bird trajectories. We integrate multi-source datasets, including GPS tracking data from Movebank, outbreak records from the World Orga

Sensen Gao, Shanshan Zhao, Xu Jiang, Lunhao Duan

Document understanding is critical for applications from financial analysis to scientific discovery. Current approaches, whether OCR-based pipelines feeding Large Language Models (LLMs) or native Multimodal LLMs (MLLMs), face key limitations: the former loses structural detail, while the latter struggles with context modeling. Retrieval-Augmented Generation

Hui Chen, Dong Yang

Under a mild condition, the perfect derived category and the finite-dimensional derived category of a graded gentle one-cycle algebra are described as twisted root categories of certain infinite quivers of type $\mathbb{A}_\infty^\infty$. As a consequence, it is shown that the triangle structure of such derived categories is uniquely determined by the underl

Yingrui Zhuang, Lin Cheng, Ning Qi, Mads R. Almassalkhi

Scenario reduction (SR) alleviates the computational complexity of scenario-based stochastic optimization with conditional value-at-risk (SBSO-CVaR) by identifying representative scenarios to depict the underlying uncertainty and tail risks. Existing distribution-driven SR methods emphasize statistical similarity but often exclude extreme scenarios, leading

Mostafaali Ayubirad, Zeng Qiu, Hao Wang, Chris Weinkauf

In this paper, a predictive constraint-aware control scheme is formulated within the Command Governor (CG) framework for localized hydration management of a proton exchange membrane (PEM) fuel cell system. First, a comprehensive nonlinear dynamic model of the fuel cell system is presented which includes a pseudo 2-dimensional (P2D) model of the stack, reacta

Ugur G. Abdulla, Denis Brazke

This paper establishes a Wiener criterion at $\infty$ to characterise the unique solvability of the Dirichlet problem for degenerate elliptic equations with power-like weights in arbitrary open sets. In the measure-theoretical context, the criterion determines whether the $\A$-harmonic measure of $\infty$ is null or positive. From the topological point of vi

Ziqing Zhu

The accelerating digitalization and decentralization of modern power systems expose critical communication infrastructures to escalating cyber risks, particularly under emerging quantum computing threats. This paper presents an integrated techno-economic framework to evaluate the feasibility of Quantum Key Distribution (QKD) for secure power-system communica

Hsuan-Lin Liao, Van Tien Nguyen

We construct a quenching solution to the parabolic MEMS model \[ u_t = \Delta u - \frac{1}{u^2} \quad \text{in } \mathcal{B} \times (0,T), \quad u|_{\partial \mathcal{B}} = 1, \] where $\mathcal{B}$ is the unit disc in $\mathbb{R}^2$, and $T > 0$ denotes the quenching time. The constructed solution quenches only at the origin and admits the final profile \[

Seon-Geun Jeong, Mai Dinh Cong, Minh-Duong Nguyen, Xuan Tung Nguyen

Quantum annealing (QA) is a practical model of adiabatic quantum computation, already realized on hardware and considered promising for combinatorial optimization. However, its performance is critically dependent on the annealing schedule due to hardware decoherence and noise. Designing schedules that account for such limitations remains a significant challe

Lina Berrayana, Ahmed Heakl, Muhammad Abdullah Sohail, Thomas Hofmann

Current autoregressive language models (ARMs) achieve high accuracy but require long token sequences, making them costly. Discrete diffusion language models (DDLMs) enable parallel and flexible generation within a fixed number of steps and have recently emerged for their strong performance in complex reasoning and long-term planning tasks. We present a study

Shengyu Feng, Yun He, Shuang Ma, Beibin Li

Reinforcement learning (RL) has recently proven effective at scaling chain-of-thought (CoT) reasoning in large language models for tasks with verifiable answers. However, extending RL-based thought training to more general non-verifiable tasks-where supervision is provided only through pairwise human preferences-remains challenging. Existing approaches typic

Tadesse K Bahiru, Natnael Tilahun Sinshaw, Teshager Hailemariam Moges, Dheeraj Kumar Singh

Gender classification systems often inherit and amplify demographic imbalances in their training data. We first audit five widely used gender classification datasets, revealing that all suffer from significant intersectional underrepresentation. To measure the downstream impact of these flaws, we train identical MobileNetV2 classifiers on the two most balanc

Zhuo Li, Xian'an Jin, Qi Yan

We introduce a new group action on set systems, constructed as a semidirect product of a permutation group and a group generated by twist and loop complementation operations on a single element. This action extends the ribbon group framework of Abrams and Ellis-Monaghan from ribbon graphs to set systems, facilitating a systematic investigation of self-twuali

Dharunish Yugeswardeenoo

By 2050, the global demand for assistive care is expected to reach 3.5 billion people, far outpacing the availability of human caregivers. Existing robotic solutions remain expensive and require technical expertise, limiting accessibility. This work introduces a low-cost robotic arm for assistive tasks such as feeding, cleaning spills, and fetching medicine.

Aysan Aghazadeh, Adriana Kovashka

Text-to-image models are appealing for customizing visual advertisements and targeting specific populations. We investigate this potential by examining the demographic bias within ads for different ad topics, and the disparate level of persuasiveness (judged by models) of ads that are identical except for gender/race of the people portrayed. We also experime

Ziqing Zhu

The proliferation of distributed energy resources (DERs) and demand-side flexibility has made virtual power plants (VPPs) central to modern grid operation. Yet their end-to-end business pipeline, covering bidding, dispatch, metering, settlement, and archival, forms a tightly coupled cyber-physical-economic system where secure and timely communication is crit

Yee Lam Elim Thompson, Jonathan Fergus, Jonathan Chung, Jana G. Delfino

Objective: To quantify the impact of workflow parameters on time-savings in report turnaround time (TAT) due to an AI-triage device that prioritized pulmonary embolism (PE) in chest CT pulmonary angiography (CTPA) exams. Methods: This retrospective study analyzed 11252 adult CTPA exams conducted for suspected PE at a single tertiary academic medical center.

L. A. Longchar, M. Manikandan, R. Mahendiran

We report microwave-assisted synthesis and physical properties of La-doped SrCoO3-{\delta}. The Sr0.8La0.2CoO3-{\delta} synthesized via microwave heating (MWH) exhibits superior physical properties compared to a nominally identical composition obtained by conventional heating (CH) in an electrical furnace. The MWH sample exhibits an enhanced ferromagnetic Cu

Yusmantoro Yusmantoro, Freddy Permana Zen, Muhammad Lawrence Pattersons

The mass-radius relation, moment of inertia, and tidal love numbers of anisotropic neutron stars (NSs) have been investigated in $f(R,T)$ gravity by imposing two equations of state (EoS). We use the simplest form $f(R,T)=R+2\beta T$ model and adopt the anisotropy approach called Horvat model. To examine the viability of our calculations, we utilize the const

Xinrui Fang, Anran Xu, Chi-Lan Yang, Ya-Fang Lin

Critical reading is a primary way through which researchers develop their critical thinking skills. While exchanging thoughts and opinions with peers can strengthen critical reading, junior researchers often lack access to peers who can offer diverse perspectives. To address this gap, we designed an in-situ thought exchange interface informed by peer feedbac

Amitesh Badkul, Lei Xie

Reliable, informative, and individual uncertainty quantification (UQ) remains missing in current ML community. This hinders the effective application of AI/ML to risk-sensitive domains. Most methods either fail to provide coverage on new data, inflate intervals so broadly that they are not actionable, or assign uncertainties that do not track actual error, e

Tiansheng Hu, Tongyan Hu, Liuyang Bai, Yilun Zhao

Recent LLMs have demonstrated promising ability in solving finance related problems. However, applying LLMs in real-world finance application remains challenging due to its high risk and high stakes property. This paper introduces FinTrust, a comprehensive benchmark specifically designed for evaluating the trustworthiness of LLMs in finance applications. Our

Yuatyong Chaichana, Pittawat Taveekitworachai, Warit Sirichotedumrong, Potsawee Manakul

Large Audio-Language Models (LALMs) are often constrained by short audio context windows, even when their text backbones support long contexts, limiting long-form audio understanding. Prior work has introduced context-extension methods (e.g. YaRN) on unimodal LLMs, yet their application to LALMs remains unexplored. First, building on RoPE-based context exten

Zachary Nason

We prove that for all noetherian rings, the level of any homologically bounded complex $M$ with respect to the collection of projective or injective modules is bounded above by the projective dimension of $\bigoplus H(M)$ plus one or the injective dimemsion of $\bigoplus H(M)$ plus one, respectively. In addition, we also prove that if $\mathsf{C}$ is the col

Sina Kazemdehbashi, Yanchao Liu, Boris S. Mordukhovich

Natural and human-made disasters can cause severe devastation and claim thousands of lives worldwide. Therefore, developing efficient methods for disaster response and management is a critical task for relief teams. One of the most essential components of effective response is the rapid collection of information about affected areas, damages, and victims. Mo

Andreas Dechant

We derive an inequality relating the finite-frequency linear response and fluctuations of an observable in a physical system. The relation holds for arbitrary observables and perturbations in general Markovian dynamics, including over- and underdamped Langevin systems and jump processes, both in and out of equilibrium. As a consequence, we obtain a universal

Xiaohan Zhao, Hongyu Xiang, Shengze Ye, Song Li

This paper presents LongCat-Audio-Codec, an audio tokenizer and detokenizer solution designed for industrial grade end-to-end speech large language models. By leveraging a decoupled model architecture and a multistage training strategy, LongCat-Audio-Codec exhibits robust semantic modeling capabilities, flexible acoustic feature extraction capabilities, and

Mrunal Sarvaiya, Guanrui Li, Giuseppe Loianno

Aerial transportation robots using suspended cables have emerged as versatile platforms for disaster response and rescue operations. To maximize the capabilities of these systems, robots need to aggressively fly through tightly constrained environments, such as dense forests and structurally unsafe buildings, while minimizing flight time and avoiding obstacl

Xin Cao, Nan Yu

We propose utilizing fast and slow thinking to enhance the capabilities of large language model-based agents on complex tasks such as program repair. In particular, we design an adaptive program repair method based on issue description response, called SIADAFIX. The proposed method utilizes slow thinking bug fix agent to complete complex program repair tasks

Ho Truong Nam Hai, Makoto Arita, Kaveh Edalati

The photocatalytic water-splitting process is thermodynamically challenging and requires catalysts with suitable band structures, as well as the presence of supporting cocatalysts. By considering the unique charge carrier mobility in perovskites, this study introduces three new ABO3-type high-entropy perovskites (Ba1/2Sr1/2)(Ti1/3Zr1/3Hf1/3)O3, (Ba1/2Sr1/2)(

E. S. Ma, Z. Song

We study the dynamic response of magnetic domain walls in low-lying excited states of an Ising chain to an oscillating transverse field. Based on the exact instantaneous eigenstates, we find that when the frequency of the external field is in off-resonant regions, the domain wall exhibits Bloch oscillation, maintaining the magnetic order. However, the magnet

Gabriel Nixon Raj

We study sequential decision-making under distribution drift. We propose entropy-regularized trust-decay, which injects stress-aware exponential tilting into both belief updates and mirror-descent decisions. On the simplex, a Fenchel-dual equivalence shows that belief tilt and decision tilt coincide. We formalize robustness via fragility (worst-case excess r

Kevin Christiansen Marsim, Minho Oh, Byeongho Yu, Seungjae Lee

Autonomous navigation for legged robots in complex and dynamic environments relies on robust simultaneous localization and mapping (SLAM) systems to accurately map surroundings and localize the robot, ensuring safe and efficient operation. While prior sensor fusion-based SLAM approaches have integrated various sensor modalities to improve their robustness, t

Patricia Medina, Rasika Karkare

This work extends our previous study on enhancing 3D LiDAR point-cloud classification with product coefficients \cite{medina2025integratingproductcoefficientsimproved}, measure-theoretic descriptors that complement the original spatial Lidar features. Here, we show that combining product coefficients with an autoencoder representation and a KNN classifier de

Han Ouyang, Ayush Singhal, Jesse Hamilton, Saeed Amal

The stacking ensemble combining RF, LightGBM, and DNN performed well on internal test sets, exhibiting an NPV greater than 99.9% even with substantial class imbalance. While performance was lower on the external eICU cohort compared to the internal test sets, sensitivity remained robust. Therefore, the stacking ensemble may serve as a rule-out screening opti

Emily Alsentzer, Marie-Laure Charpignon, Bill Chen, Niharika D'Souza

The 6th Annual Conference on Health, Inference, and Learning (CHIL 2025), hosted by the Association for Health Learning and Inference (AHLI), was held in person on June 25-27, 2025, at the University of California, Berkeley, in Berkeley, California, USA. As part of this year's program, we hosted Research Roundtables to catalyze collaborative, small-group dia

Xuansheng Wu, Xiaoman Pan, Wenlin Yao, Jianshu Chen

Reinforcement learning with verifiable rewards (RLVR) can elicit strong reasoning in large language models (LLMs), while their performance after RLVR varies dramatically across different base models. This raises a fundamental question: what microscopic property of pre-trained models leads to this variation? To investigate, we formalize reasoning as chains of

Zhimin Qiu, Feng Liu, Yuxiao Wang, Chenrui Hu

This paper addresses the problem of traffic prediction in distributed backend systems and proposes a graph neural network based modeling approach to overcome the limitations of traditional models in capturing complex dependencies and dynamic features. The system is abstracted as a graph with nodes and edges, where node features represent traffic and resource

Andrew Li, R. Ravi, Karan Singh, Zihong Yi

Motivated by the problem of selling large, proprietary data, we consider an information pricing problem proposed by Bergemann et al. that involves a decision-making buyer and a monopolistic seller. The seller has access to the underlying state of the world that determines the utility of the various actions the buyer may take. Since the buyer gains greater ut

Jian Wang, Ruoyu P. T. Wang

For fractional wave equations with low H\"older regularity damping, we establish quantitative energy decay rates for their solutions when the geometric control condition holds. The energy decay rates depend explicitly on the H\"older regularity of the damping. In particular, we show damping functions with lower H\"older regularities that below a certain thre

Sarah Houdaigoui, Ken-ichi Kawarabayashi

As part of their graph minor project, Robertson and Seymour showed in 1990 that the class of graphs that can be embedded in a given surface can be characterized by a finite set of minimal excluded minors. However, their proof, because existential, does not provide any information on these excluded minors. Seymour proved in 1993 the first and, until now, only

Yongchan Kwon, Shang Zhu, Federico Bianchi, Kaitlyn Zhou

The ability of large language models (LLMs) to follow user instructions is central to their reliability, safety, and usefulness. While prior studies assess instruction adherence in the model's main responses, we argue that it is also critical for large reasoning models (LRMs) to follow user instructions throughout their reasoning process. Reasoning instructi

Chenrui Hu, Ziyu Cheng, Di Wu, Yuxiao Wang

This paper focuses on intelligent routing in microservice systems and proposes an end-to-end optimization framework based on graph neural networks. The goal is to improve routing decision efficiency and overall system performance under complex topologies. The method models invocation relationships among microservices as a graph. In this graph, service nodes