April 2026 arXiv papers — page 10

Zhenjie Ren, Xiaoli Wei, Xiang Yu, Xun Yu Zhou

This paper is a continuation work of Ren et al. (2026) aiming to further devise q-learning algorithms for mean-field control (MFC) with controlled common noise. Based on the relaxed control formulation, we first establish the martingale condition of the value function and the Iq-function by evaluating along the conditional state distributions generated by al

Yang Bai, Kun-Feng Lyu, Yue Zhao

We propose a novel mechanism for electroweak baryogenesis in which collapsing domain walls formed by an axion-like field replace the bubble walls in a strong first-order electroweak phase transition. The axion-like particle coupling to the Higgs mass term allows domain walls to separate regions with distinct electroweak phases, while the electroweak crossove

Chunhui Wei

We establish isomorphism ranges for the comparison maps between algebraic and topological K-groups, extending classical Quillen-Lichtenbaum conjecture to separated complex schemes of finite type after refinement. Additionally, we generalizes the conjecture through the lens of noncommutative geometry.

Zhenjie Ren, Xiaoli Wei, Xiang Yu, Xun Yu Zhou

This paper investigates the continuous-time counterpart of the Q-function for entropy-regularized mean-field control (MFC) with controlled common noise, coined as q-function by Jia and Zhou (2023) in the single agent's model. We first show that, under discretely sampled actions, the value function in the exploratory formulation converges to the one in the re

Linbo Shao, Zichen Xi, Zengyu Cen, Joseph G. Thomas

Exceptional points (EP) featuring enhanced responsivity and rich dynamics have attracted extensive attentions in device developments and sensing applications. However, it remains debated whether employing EP systems is beneficial in practical sensing applications. Here, we demonstrate that a nonlinear EP in our microwave-frequency acoustic-wave oscillator im

Saroj K. Mishra, Y. Sasaki, S. Isogami, I. Suzuki

Achieving ultrahigh recording densities with low power consumption is a central challenge for next generation heat assisted magnetic recording (HAMR), as conventional L10 FePt media require intense laser heating due to their high coercivity (Hc) and high Curie temperature (700 K). Here, we address this issue using FePt/FeRh bilayers, where the antiferromagne

Syed Mhamudul Hasan, Mohd. Farhan Israk Soumik, Abdur R. Shahid

Clickbait is characterized by disproportionately high emotional intensity relative to informational content, often reinforced by specific structural patterns. However, current research considers clickbait as a static textual phenomenon characterized by linguistic patterns and structural cues. Additionally, existing detection systems primarily rely on surface

Zhipeng Zhang

Statistical inference in observational science typically relies on a fundamental assumption: as sample size increases and uncertainties decrease, the inferred results should converge to the true physical quantities. This assumption underpins the notion that big data lead to more reliable conclusions. In Galactic archaeology, stellar ages inferred from spectr

Yang You, Won Kyung Do, Aiden Swann, Rika Antonova

Simulating optical tactile sensors presents significant challenges due to their high deformability and intricate optical properties. To address these issues and enable a physically accurate simulation, we propose DOT-Sim: Differentiable Optical Tactile Simulation. Unlike prior simulators that rely on simplified models of deformable sensors, DOT-Sim accuratel

Lijin Yang, Jianing Huang, Zhongzhan Huang, Shu Liu

Recent advances in vision language action (VLA) models have shown remarkable potential for autonomous driving by directly mapping multimodal inputs to control signals. However, previous VLA-based methods have not explicitly exploited the critic capability of VLAs to refine driving decisions, even though such capability has been well demonstrated in other LLM

Syed Mhamudul Hasan, Mohd. Farhan Israk Soumik, Abdur R. Shahid

The rapid proliferation of harmful and emotionally damaging content on social media platforms has intensified concerns regarding societal harm. While content moderation efforts primarily focus on detecting and removing harmful posts, less attention has been given to mitigating harm through stylistic text transformation while preserving semantic meaning. In t

Peifu Liu, Tingfa Xu, Jie Wang, Huan Chen

Hyperspectral image classification demands spatially coherent predictions and precise boundary delineation. Yet prevailing superpixel-based methods face an inherent contradiction: clustering aggregates similar pixels into regions, but the subsequent classifier operates pixel-wise, undermining regional consistency. Consequently, existing approaches do not gua

Rong Lu

We present TADI (Tool-Augmented Drilling Intelligence), an agentic AI system that transforms drilling operational data into evidence-based analytical intelligence. Applied to the Equinor Volve Field dataset, TADI integrates 1,759 daily drilling reports, selected WITSML real-time objects, 15,634 production records, formation tops, and perforations into a dual

Shang Liu

Understanding quantum phases and phase transitions in the presence of symmetries is a central objective of quantum many-body physics. A powerful modern paradigm for investigating this problem is topological holography, which relates symmetries in $k$ dimensions to "bulk" topological orders in $(k+1)$ dimensions. While conceptually profound, most existing bul

Shisheng Li

Buchbinder and Feldman recently gave a deterministic $(1-1/e-\varepsilon)$-approximation for maximizing a non-negative monotone submodular function subject to a matroid constraint, with query complexity $\widetilde{O}_\varepsilon(nr)$. Their algorithm uses an integer parameter $\ell$, which Buchbinder and Feldman fix to $\ell = 1 + \lceil 1/\varepsilon \rcei

Yingrui Wu, Youkang Kong, Mingyang Zhao, Weize Quan

Synthesizing realistic 3D indoor scenes remains challenging due to data scarcity and the difficulty of simultaneously enforcing global architectural constraints and local semantic consistency. Existing approaches often overlook structural boundaries or rely on fully connected relation graphs that introduce redundant generation errors. Inspired by human desig

Yanzhen Xiong

Let $\mathcal{R}$ be an association scheme with nontrivial relations $A_1,\ldots,A_d$. We call $\mathcal{R}$ amorphic if every possible fusion of its nontrivial relations gives rise to a fusion scheme. We define the fusing-relations $3$-hypergraph of $\mathcal{R}$ to be the $3$-uniform hypergraph on the vertex set $\{A_1,\ldots,A_d\}$ such that $\{ A_i, A_j,

Jean Martins, Leonid Mokrushin, Marin Orlic

Intent-based networking promises to revolutionize telecommunications network management by enabling operators to specify high-level goals rather than low-level configurations. The TM Forum Intent Ontology (tio) provides a standardized vocabulary for expressing network intents, yet lacks formal validation mechanisms to ensure intent correctness before its adm

Yuan Sun

As large language model (LLM) agents are deployed in high-stakes environments, the question of how safely to delegate subtasks to specialized sub-agents becomes critical. Existing work addresses multi-agent architecture selection at design time or provides broad empirical guidelines, but neither provides a runtime mechanism that dynamically adjusts the safet

Jialu Liu, Yue Cui, Shan Yu

Accurate multiclass segmentation of the Circle of Willis (CoW) is essential for neurovascular disease management but remains challenging due to complex vascular topology and variable morphology. Existing deep learning methods often suffer from vascular discontinuities and inter-class misclassification, while current topological loss functions incur prohibiti

Ta-Yang Wang, Rajgopal Kannan, Viktor Prasanna

Heterogeneous graphs are widely used to model multi-relational systems, but missing node attributes remain a major bottleneck for downstream learning. In this paper, we identify and formalize type-dependent information asymmetry: the phenomenon that different node types provide substantially different levels of useful signal for attribute completion. Motivat

Brendan Gould, Chih-Yuan Chiu, Antoine P. Leeman, Kyriakos G. Vamvoudakis

We study the set of solutions to a parameterized, strongly convex optimization problem whose cost depends on uncertain, bounded parameters. We compute a certified outer approximation of the corresponding set of optimizers, using convergence properties of the projected gradient descent (PGD) algorithm for convex programs. Concretely, by treating the cost para

Angshul Majumdar

We study the decision version of tensor spectral norm from the viewpoint of real algebraic complexity. For a rationally specified tensor, the tensor spectral threshold problem asks whether its spectral norm exceeds a prescribed rational threshold. Since the feasible domain is compact, attainment itself is trivial; the meaningful question is the threshold dec

Louth Bin Rawshan, Zhuoyu Wang, Brian Y. Lim

Explainable AI (XAI) aims to improve user understanding and decisions when using AI models. However, despite innovations in XAI, recent user evaluations reveal that this goal remains elusive. Understanding human cognition can help explain why users struggle to effectively use AI explanations. Focusing on reasoning on structured (tabular) data, we examined va

Yabo Luo, Xiaoyun Wang, Cunrong Li

Gait recognition has emerged as a compelling biometric modality for surveillance and security applications, offering inherent advantages such as non-intrusiveness, resistance to disguise, and long-range identification capability. However, prevailing approaches struggle to comprehensively capture and exploit the rich biometric cues embedded in human locomotio

Zihao Li, Jiaru Zou, Feihao Fang, Xuying Ning

Agentic large language model systems have demonstrated strong capabilities. However, their reliance on language as the universal interface fundamentally limits their applicability to many real-world problems, especially in scientific domains where domain-specific foundation models have been developed to address specialized tasks beyond natural language. In t

Weifeng Zhang, Jipeng Tan, Mengye Yang, Yong Min

Health messages on social media are typically constructed through combinations of source cues, appeals, frames, and evidence, which jointly shape communication and persuasive effects. However, prior research has largely focused on single elements or simple pairwise interactions, offering insufficient insight into how multiple elements operate together in rea

Emerson Q. Fernando, Julius Ceazar G. Tolentino, Maria Anna D. Cruz, Jordan L. Salenga

The increasing dependency among Filipino college students on artificial intelligence (AI) poses concerns about the potential decline of fundamental academic competencies. This study examines the extent of AI dependency and its perceived effects on students' critical thinking, writing skills, learning independence, research skills, and academic engagement. Us

Vimal Singh, Akash Choudhary

Viscoelasticity governs the locomotion strategies of deformable microorganisms, rendering it a fundamental mechanical property of microbial motility and an integral component in the design of envisioned microbots. Recent studies have shown that it can enable effective propulsion through non-reciprocal body deformations, even under time-reversible actuation.

Junhao Liu, Zhen Wang, Lanpeng Ni, Yujie Lu

High-brightness femtosecond-to-attosecond pulses are indispensable for probing electron dynamics on their fundamental temporal scales. X-ray free-electron lasers (XFELs) at high repetition rates will facilitate high-statistics measurements and time-resolved studies that were previously inaccessible. Although energy recovery linacs (ERLs) are well suited for

Vanessa B. Sibug, Emerson Q. Fernando, Almer B. Gamboa, Roque Francis B. Dianelo

This study examines the factors influencing pre-service teachers' behavioral intention to use AI-enabled educational tools during their practicum, using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) as the theoretical framework. The model includes the core UTAUT2 constructs such as performance expectancy, effort expectancy, hedonic motiva

Keito Inoshita, Xiaokang Zhou, Akira Kawai, Katsutoshi Yada

Human annotators frequently disagree on emotion labels, yet most evaluations of Large Language Model (LLM) emotion annotation collapse these judgments into a single gold standard, discarding the distributional information that disagreement encodes. We ask whether LLMs capture the structure of this disagreement, not just majority labels, by comparing emotion

Wentao Chen, Jingtang Chen, Mingjian Fu, Tiantian Li

Deep reinforcement learning (DRL) finds extensive application in autonomous drone navigation within complex, high-risk environments. However, its practical deployment faces a safety-exploration dilemma: soft penalty mechanisms encourage risky trial-and-error, while most constraint-based methods suffer degraded performance under sensor noise and intent uncert

Almer B. Gamboa, Erika M. Pineda, Rhiziel P. Manalese, Aileen P. De Leon

This study examines the development, influence, and collaboration patterns in AI-supported social presence research within online learning environments. Utilizing 59 open-access empirical studies from Scopus, the study applies citation analysis, co-authorship mapping, institutional analysis, and keyword clustering using Python-based bibliometric tools. Findi

Marwa Ennaceur

This paper studies Rota-Baxter operators on the matrix $C^*$-algebra $M_n(\mathbb{C})$, motivated by the discrete Toeplitz algebra (whose role is purely heuristic; see Remark~\ref{rem:toeplitz_scope}). We provide a structural classification of such operators compatible with the $C^*$-norm, analyze their induced Lie brackets, and apply them to deform system m

Yinan Wang, Poyen Chen, Teruyasu Mizoguchi

Sliding ferroelectricity in bilayer hexagonal boron nitride (h-BN) offers compelling prospects for next-generation non-volatile memory, yet the atomistic dynamics of electric-field-driven polarization switching remain poorly understood. Here, we present a fully data-driven, coupled atomistic framework that integrates a fine-tuned MACE machine learning potent

Harm Derksen, Alexandra Pevzner

We consider the modular action of the symmetric group $S_n$ on $R = k[x_1,\ldots,x_n]$ when $\mathrm{char}(k) = p \leq n$. We show that the image of the transfer map $R\to R^{S_n}$ is an elimination ideal $J\cap R^{S_n}$, where $J\subset R^{S_n}[t]$ is generated by $p$ polynomials with generic coefficients. The structure of this elimination ideal depends onl

Ziyao Xu, Cong Wang, Houfeng Wang

Compositional generalization tests are often used to estimate the compositionality of LLMs. However, such tests have the following limitations: (1) they only focus on the output results without considering LLMs' understanding of sample compositionality, resulting in explainability defects; (2) they rely on dataset partition to form the test set with combinat

Zhaoxing Wu, Haoyang Wu, Thulile Mathenjwa, Elphas Okango

This article introduces novel methodologies for estimating contextual exposure to HIV population viral load using GPS data. We propose a comprehensive analytical framework comprising (i) local (grid-cell level) estimation of HIV population viral load, (ii) derivation of individual activity spaces from GPS trajectories, and (iii) quantification of contextual

Xingsi Pu, Lang Wang

In this paper, we study the geometry of bounded domains with piecewise smooth boundary. Specifically, we obtain the relationship between the squeezing function corresponding to polydisk and Levi flatness on bounded generic convex domains. As an application, we prove that a two dimensional bounded generic convex domain with piecewise $C^2$-smooth boundary tha

Siu On Chan, Tommaso d'Orsi, Jeff Xu

Under what condition is a random constraint satisfaction problem hard to refute by the sum-of-squares (SoS) algorithm? A sufficient condition is t-wise uniformity, that is, each constraint has a t-wise uniform distribution of satisfying assignments, as shown by the lower bounds of Kothari, Mori, O'Donnell, and Witmer (STOC 2017). This condition is also neces

Naeem Rehmat, Muhammad Saad Saeed, Ijaz Ul Haq, Khalid Malik

Web filtering systems rely on accurate web content classification to block cyber threats, prevent data exfiltration, and ensure compliance. However, classification is increasingly difficult due to the dynamic and rapidly evolving nature of the modern web. Embedding-based zero-shot approaches map content and category descriptions into a shared semantic space,

Yu Fang, Tao Feng, Xiaomiao Wang

A skew Bollob\'{a}s system $\mathcal{P}=\{(A_i,B_i):1\leq i\leq m\}$ is a collection of pairs of disjoint subsets of $[n]$ such that $A_i\cap B_j\ne\emptyset$ for any $1\leq i<j\leq m$. Denote by $S_1(a, b)$ or $S_2(a, b)$ the maximum size of $\bigcup_{i=1}^m A_i$ or $\bigcup_{i=1}^m B_i$, respectively, over all possible skew Bollob\'{a}s systems $\mathcal{P

Fernando Nogueira, Nabson Silva, Tayana Conte

Context: Documenting Architectural Design Decisions (ADDs) is a critical factor in the software lifecycle, essential for efficient system maintenance, developer onboarding, and preventing knowledge vaporization. Although various templates for Architectural Decision Records (ADRs) have been proposed, there is a lack of empirical evidence comparing them. Goal:

Minhang Guo, Feng Yuan, Suoqing Ji, Bocheng Zhu

This is the fourth paper of our series investigating the effects of active galactic nucleus (AGN) feedback in the evolution of an elliptical galaxy using the {\it MACER} framework. While previous works considered only AGN radiation and wind, we now add jet feedback. The values of the jet parameters are taken from small-scale general relativity MHD simulation

Shiva Kant Tiwari, Tijs Karman, Valentin Walther

Recent experiments show that optically generated Rydberg excitons in cuprous oxide can neutralize charged impurities, strongly reducing stray electric fields and effectively purifying the crystal. Here, we develop a multichannel theory of Rydberg exciton-impurity scattering that resolves the competing roles of capture, elastic scattering, and inelastic trans

Edoardo Maggioni, Diego Garlaschelli, Rossana Mastrandrea, Luca Maria Aiello

Fostering coordinated pro-environmental behaviors at scale is a key challenge for climate mitigation. Individual actions only generate meaningful impact when they diffuse widely and become socially coordinated, yet monitoring such processes remains difficult with traditional survey-based tools alone. In this study, we examine whether large-scale online clima

Youkang Kong, Yang Liu, Yue Dong, Xin Tong

3D shapes from scanning, reconstruction, or AI-generated content often lack simple quad mesh layouts -- critical for efficient editing and modeling. Existing quad-remeshing techniques typically produce complex layouts with irregular loops, leading to tedious manual cleanup and extensive algorithm tuning. We introduce SQuadGen, a diffusion-based generative fr

Xiao-Kan Guo

We study the semiclassical Ehrenfest trajectories in open quantum systems. We first derive in explicit form the Fokker-Planck equation that governs the time evolution of the mixing measure for a Gaussian mixture. Then, we embed the generalized Ehrenfest theorem recently obtained for open quantum systems into this phase-space picture to study the time evoluti

H. W. Yin, B. D. Zhu, H. Peng, T. Wang

To accommodate classical communication systems with progressively increasing transmission rates, quantum access networks (QAN) have undergone systematic and protocol-level optimizations in recent years, where quantum passive optical network (QPON) architectures are gaining significant attention due to their simple structure. It is challenging for the previou

Tengya Zhang, Feng Gao, Lin Qi, Junyu Dong

Hyperspectral image super-resolution is essential for enhancing the spatial fidelity of HSI data, yet existing deep learning methods often struggle with substantial spectral redundancy and the limited non-linear modeling capacity of standard feed-forward networks (FFNs). To address these challenges, we propose Spectral Dynamic Attention Network (SDANet), a f

Yue Song

EigenDecomposition (ED) is at the heart of many computer vision algorithms and applications. One crucial bottleneck limiting its usage is the expensive computation cost, particularly for a mini-batch of matrices in deep neural networks. Our previous work proposed a dedicated QR-based ED algorithm for batched small matrices (dim${<}32$). This short paper targ

Pratim Ugale, Ilya Tyagin, Karunya Shirali, Kien X. Nguyen

This work introduces Q3SAT-GPT, a generative model for discovering quantum circuits for the Max-E3-SAT problem. Our method learns from high-performing QAOA-style ans\"atze to directly generate candidate circuits. To create high-quality supervision, we also introduce Mosaic Adaptive QAOA (MosaicADAPT-QAOA), an adaptive strategy for constructing low-depth QAOA

Chuanzheng Gong, Feng Gao, Junyan Lin, Junyu Dong

Hyperspectral image (HSI) and SAR/LiDAR data offer complementary spectral and structural information for land-cover classification. However, their effective fusion remains challenging due to two major limitations: The spectral redundancy in high-dimensional HSI and the heterogeneous characteristics between multi-source data. To this end, we propose Represent

Chenyang Wu, Lina Lei, Fan Li, Chun-Le Guo

Recent advances in Diffusion Transformer (DiT)-based video generation technologies have shown impressive results for video object removal. However, these methods still suffer from substantial inference latency. For instance, although MiniMax Remover achieves state-of-the-art visual quality, it operates at only around 10FPS, primarily due to dense computation

Md Hasan Saju, Akramul Azim

Security Operations Centers (SOCs) face mounting operational challenges. These challenges come from increasing threat volumes, heterogeneous SIEM platforms, and time-consuming manual triage workflows. We present an end-to-end threat management framework that integrates ensemble-based detection, syntax-constrained query generation, and retrieval-augmented res

Esha Bhatia, Paolo Salucci, Tiziano Schiavone, Sandeep Haridasu

In this work, we aim to advance the Universal Rotation Curve (URC) paradigm by leveraging new data and extending its observational domain. Building on previous studies that established the URC using optical rotation curves reaching the galaxy optical radius $R_{opt}$, we exploit the SPARC sample's extended HI rotation curves to construct the URC out to $2R_{

Jun Yeon Won, Xin Jin, Shiqing Ma, Zhiqiang Lin

Large Language Models (LLMs) have achieved remarkable progress in recent years, driving their adoption across a wide range of domains, including computer security. In reverse engineering, LLMs are increasingly applied to critical tasks such as function and variable name recovery and type inference. However, despite the rapid growth of research in this area,

Jan Ole Ernst, Dmitri Michelangelo Saberi, Derek Christ, Thomas Zimmermann

The primary goal of Design Verification (DV) is to ensure that a proposed chip design implementation (either in code, or physical form) exactly matches its specification and is free of functional errors in order to avoid costly re-designs. Achieving this often demands extensive manual interpretation, translating the specification document into a formal, test

Xiaoyu Xin, Longxu Zhang, Jinlong Lei, Yiguang Hong

Revealing the interaction topology underlying strategic behavior is fundamental to prediction, intervention, and policy design in networked systems. Yet the interaction matrix is often unobservable, and passive observation of repeated actions fails to provide sufficient excitation for reliable recovery. This paper studies topology recovery in repeated linear

E. M. Bursch, J. K. Park, N. C. Logan, F. Mao

This paper presents an updated n=1 error field penetration threshold scaling, which increases fit quality compared to previous error field scaling laws, is produced from an expanded database, and exhibits reduced uncertainty in projections to future conventional tokamaks. It improves confidence in tokamak engineering tolerances, which are a significant drive

Quanlong Yang, Yapeng Dou, Dongyang Wang, Yihua Zhong

Label-free identification and real-time tracking of biochemical substances became critical for molecular diagnostics and chemical analysis, yet conventional resonant terahertz metasurface sensing relies on a single resonance, limiting spectral selectivity and dynamic capability. Here, we suggest multiresonant membrane metasurfaces and implement them for simu

Miki Kimura-Ida

Cross-national comparison of research funding projects is increasingly important for science policy and strategic planning, but language differences remain a major obstacle. In particular, KAKENHI project descriptions are written primarily in Japanese, whereas projects from major overseas funding agencies, such as NSF, NIH, and UKRI, are documented in Englis

Shu-Xiang Li, Ren-You Zhang, Xiao-Feng Wang, Pan-Feng Li

For the two-loop next-to-leading-order electroweak (NLO EW) corrections to $gg \rightarrow ZH$, the light-fermion contributions can be classified into eight distinct topologies. Using the canonical differential-equations method, we perform an analytic computation of the master integrals (MIs) associated with the four planar topologies. Canonical bases are co

Hira Saleem, Flora Salim, Cormac Purcell

Operational weather prediction has long relied on physics-based numerical weather prediction (NWP), whose accuracy comes at the cost of substantial compute and complex simulation workflows. Recent transformer-based forecasters offer efficient data-driven alternatives, however transformers are physics-agnostic models. Additionally, standard transformer encode

Germano Hartmann Brill, Pablo Enrique Jurado Silvestrin, Sebastian Gonçalves

Epidemics have shaped human history, often with devastating consequences, motivating the development of mathematical models to understand and control their dynamics. Among the many aspects of epidemic behavior, the conditions that lead to epidemic extinction stand out as a central-if not the fundamental-question in epidemic modeling. In this work, we study e

Pedro-Aarón Hernández-Ávalos, Luciano García-Bañuelos

The advent of Large Language Models (LLMs) has significantly transformed tasks across Software Engineering. In the context of Business Process Management, LLMs are now being explored as tools to derive process models directly from textual descriptions. Existing approaches range from chatbot-driven systems that assist with iterative, text-based modeling to fu

Asiri Thennakoon, Prena Chaudhary, Sankha Subhra Bakshi, Tommy Park

Collective motions in strongly interacting magnets involve many spins and are often described in terms of integer-spin excitations. However, in certain cases, the collective motion can behave as if these integer excitations break apart into smaller, particle-like entities with unusual properties. Such fractionalized excitations in quantum magnets are commonl

Aaryan Shah, Andrew Hines, Alexia Downs, Denis Bajet

Clinical AI systems require not just point-in-time evaluation but continuous governance: the ongoing practice of monitoring, evaluating, iterating, and re-evaluating performance throughout deployment. We present an end-to-end framework of governance that integrates rubric validation, live deployment feedback, technical performance monitoring, and cost tracki

Raviteja Anantha, Nick Levato, Layne C. Price

Parameter-efficient fine-tuning (PEFT) methods face a tradeoff between adapter size and expressivity: ultra-low-parameter adapters are confined to fixed low-rank subspaces, capping performance even with extended training. We propose BoostLoRA, a gradient-boosting framework that overcomes this limit by iteratively training and merging minimal adapters on the

Tianyu Yang, Md. Noor-E-Alam

Causal inference is essential for data-driven decision-making, as it aims to uncover causal relationships from observational data. However, identifying causality remains challenging due to the potential for confounding and the distinction between correlation and causation. While recent advances in causal machine learning and matching algorithms have improved

Saber Zerhoudi, Michael Granitzer, Jelena Mitrovic

Retrieval-augmented generation (RAG) systems are frequently evaluated via fact-based metrics, yet standard implementations retrieve passages or static propositions. This unit mismatch between evaluation and retrieval objects hinders maintenance when corpora evolve and fails to capture superseded facts or source disagreements. We propose NuggetIndex, a retrie

Jing Ouyang, Chengyu Cui, Yunxiao Chen, Kean Ming Tan

Regression models with both high-dimensional responses and covariates have attracted growing attention. Standard multivariate regression models become inadequate when the response variables depend not only on observed covariates but also on latent variables that capture key unobserved characteristics. To draw statistical inferences on covariate effects while

Huiwen Wang, Wei Jiang

Twisted bilayer graphene aligned with hexagonal boron nitride (TBG/hBN) hosts rich topological and correlated quantum phases, such as (fractional) Chern insulators, whose character is dictated by the topology of the moir\'{e} flat band. This topology is highly sensitive to several material parameters in the continuum model, yet a systematic understanding of

Daniil Serebrennikov

We establish two consequences of the Kawamata--Morrison--Totaro cone conjecture, and prove them unconditionally in all dimensions. First, for a K-trivial variety, the natural action of its automorphism group on the set of ample divisor classes of fixed volume has only finitely many orbits. Second, the number of (isomorphism classes of) minimal models for a g

Peter Clark, Yong Guan, Zhonghao Liao

Android residential proxy applications represent a growing class of potentially-unwanted programs (PUPs) that covertly route third-party traffic through end-user devices, enabling ad fraud, credential abuse, and evasion of geolocation controls by sophisticated threat actors. Attributing an unknown APK to a specific proxy network remains challenging due to co

Minhang Guo, Suoqing Ji, Feng Yuan, Bocheng Zhu

Winds and jets are symbiotic when the accretion rate is low, according to black hole accretion theory. Both components are potentially important for active galactic nucleus (AGN) feedback, but previous works typically include only jets with free parameters. We perform hydrodynamical simulations of an isolated elliptical galaxy with both jets and winds includ

Jianpeng Chen, Wangzhi Zhan, Dongqi Fu, Junkai Zhang

Metamaterial discovery seeks microstructured materials whose geometry induces targeted mechanical behavior. Existing inverse-design methods can efficiently generate candidates, but they typically require explicit numerical property targets and are less suitable for early-stage exploration, where researchers often begin with incomplete constraints and qualita

H. W. Yin, X. J. Liao, Y. H. Xu, P. Huang

Continuous-variable quantum key distribution (CVQKD) using passive state preparation (PSP) offers low-cost, high-rate secure communication. However, the existing PSP-CVQKD scheme with a transmitted local oscillator has high photon leakage noise and poor stability, making it unsuitable for high-loss transmission. In this work, for the first time, we propose a

Zuo Wang, Linlin Zhong

Thermal plasma properties play a critical role in plasma simulations and plasma-related applications. However, their strong nonlinear dependence on temperature, pressure, and gas composition makes accurate and efficient evaluation challenging. In this work, an operator learning-based model, termed DeepPropNet, is proposed for fast prediction of thermodynamic

Gyoung S. Na, Chanyoung Park

Deriving governing equations from empirical observations is a longstanding challenge in science. Although artificial intelligence (AI) has demonstrated substantial capabilities in function approximation, the discovery of explainable and extrapolatable equations remains a fundamental limitation of modern AI, posing a central bottleneck for AI-driven scientifi

Wei Cheng, Yongchang Cao, Chen Shen, Binhua Li

Large Language Models (LLMs) are increasingly used for code editing, yet the prevalent full-code generation paradigm suffers from severe efficiency bottlenecks, posing challenges for interactive coding assistants that demand low latency and cost. Despite the predominant focus on scaling model capabilities, the edit format itself has been largely overlooked i

Ming-Hong Yao, Di Wang, Jian Cui, Jin-Yan Chen

Learning rate scheduling has evolved from the single global fixed rate of early SGD to sophisticated layer-wise adaptive strategies. We systematize this evolution into five generations: (Gen1) global fixed learning rates, (Gen2) global scheduling, (Gen3) parameter-level adaptation, (Gen4) layer-level differentiation, and (Gen5) joint layer-time scheduling. W

Sobhan Subhra Mishra, Thomas CaiWei Tan, Faxian Xiu, Ranjan Singh

Quantum geometry encoded in the momentum space structure of electronic wavefunctions, governs charge dynamics through Berry curvature, enabling unconventional transport and optical responses. In topological semimetals, this geometry is sampled over Fermi pockets, suggesting electrical control by Fermi surface tuning, yet such control has remained largely lim

Xiang Gao, Shuai Hang

In classroom teaching, student behavior can reflect their learning state and classroom participation, which is of great significance for teaching quality analysis. To address the problems of dense student targets, numerous small objects, frequent occlusions, and imbalanced class distribution in real classroom scenes, this paper proposes an improved student c

Cameron MacMahon

In this paper we provide a geometric condition satisfied by certain closed subsets of the Riemann sphere which implies that their hyperbolic convex hulls in $\mathbb{H}^3$ have infinite volume. As a corollary, we characterize continua in the Riemann sphere whose hyperbolic convex hulls have infinite volume, answering a question of Danny Calegari. Furthermore

Christina Angharad Hodges

Existing research gives conditions for when the outer automorphism group of a graph product of primary cyclic groups $W_\Gamma$ is finite, virtually abelian, or large. We seek to prove a set of conditions for when this outer automorphism group is virtually cyclic. To this end, we study the finite index subgroup $\text{Out}^0(W_\Gamma)$, which is generated by

Stephen W. Bianchi, Lisa R. Goldberg

Counterintuitively, the S&P 500 Index rose between January 1, 2022, and December 29, 2023, while exchange-traded funds (ETFs) seeking to deliver 2x and 3x daily returns of the index delivered substantially negative returns. Roughly two-thirds of the difference between the returns of the index and the levered ETFs can be attributed to compounding and volatili

Seth Taylor, Raymond J. Spiteri, Stéphane Gaudreault

We construct and analyze a thermodynamic extension of the recently proposed information geometric regularization of Cao and Sch\"afer. The construction extends their shock-mitigating Hessian metric geometry using the Shannon entropy to constrain the regularized motion based on a thermodynamic length. Reformulating the equations in terms of mass and specific

Ayed Al Sayem, Shiekh Zia Uddin, Ting-Chen Hu, Alaric Tate

We demonstrate stable operation of a thin-film lithium tantalate (TFLT) modulator at very high operating temperatures. We show that the electro-optic modulation and bandwidth of the TFLT modulators are not affected by high-temperature operation, and both waveguide and resonant modulators are DC-bias stable even at 120{\deg}C. At higher temperatures, we even

Varin Sikand, Andrew Nemec

Quantum secret sharing schemes are a family of quantum cryptographic protocols which provide secure quantum encodings, mapping one secret to multiple shares of information such that the original secret cannot be accessed without an authorized set of shares present for decoding. In this work, we describe a protocol that enables sender-anonymity during the sec

Mehmet Iscan

Large language model (LLM)-based coding agents increasingly rely on external memory to reuse prior debugging experience, repair traces, and repository-local operational knowledge. However, retrieved memory is useful only when the current failure is genuinely compatible with a previous one; superficial similarity in stack traces, terminal errors, paths, or co

Marco Pollanen

Pretrial risk assessment tools are used on over one million U.S. defendants each year, yet their use for predicting rare violent re-offense faces a basic statistical barrier. We derive a universal precision bound -- the Likelihood Ratio Wall -- showing that when violent re-arrest rates are low (2-5%), achieving even a 50% hit rate among people labeled "high

Yurii Halychanskyi, Jianfeng Steven Guo, Volodymyr Kindratenko

Accent conversion has rapidly progressed alongside growing interest in improving global cross-cultural communication. This survey presents an overview of the evolution of accent conversion methodologies, analyzing how the field has developed in response to fundamental challenges related to data alignment, representation disentanglement, and resource scarcity

Minghao Gu, Weizhi Lin, Qiang Huang

Nonstationary Gaussian processes (GPs) are essential for modeling complex, locally heterogeneous spatial data. A common modeling approach is the spatial deformation method that warps the domain to recover isotropy. However, this static method does not account for changes in spatial correlation induced by covariates, limiting its ability to predict nonstation

Nazar Kozak

Audio-based stuttering systems to date have been trained for detection -- what disfluency is present now -- leaving prediction, the capability needed for closed-loop intervention, unstudied at deployable scale. We train a 616K-parameter CNN on SEP-28k (Apple, 20,131 three-second clips) to predict whether the next contiguous clip contains any disfluency. (1)

Tianrui Dai

This work establishes a Lipschitz stability result for identifying unknown polygonal inclusions along with their unknown constant conductivity values, given boundary measurements encoded in the Dirichlet-to-Neumann map.

Matthew Christian Agustin

Large language model (LLM) reading assistants are increasingly used in settings that require interpretation rather than simple retrieval. In these contexts, the central risk is not only error or unsafe output, but interpretive displacement: the transfer of meaning-making work from reader to system. This paper examines that problem through the concept of epis

Dahlia Shehata, Ming Li

As AI transitions toward multi-agent systems (MAS) to solve complex workflows, research paradigms operate on the axiomatic assumption that agent collaboration mirrors the "Wisdom of the Crowd". We challenge this assumption by formalizing the Consensus Paradox: a phenomenon where agentic swarms prioritize internal architectural agreement over external logical

Enrique Barba Roque, Luís Cruz

AI efficiency has recently taken the spotlight in both academy and industry due to massive model scales, high energy demands, and environmental costs. While reporting Floating Point Operations (FLOPs) is a traditional approach for assessing computational costs, the relationship between FLOPs and execution time is not straightforward, as layers with the same