March 2026 arXiv papers — page 47

Mohammad A. Farmani, Hoshin V. Gupta, Ali Behrangi, Muhammad Jawad

Machine learning models can achieve high predictive accuracy in hydrological applications but often lack physical interpretability. The Mass-Conserving Perceptron (MCP) provides a physics-aware artificial intelligence (AI) framework that enforces conservation principles while allowing hydrological process relationships to be learned from data. In this study,

Zhihui Yao, Hengran Zhang, Keping Bi

Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) with external knowledge but remains vulnerable to low-authority sources that can propagate misinformation. We investigate whether LLMs can perceive information authority - a capability extending beyond semantic understanding. To address this, we introduce AuthorityBench, a comprehensi

Yunpeng Zhou

Most vision-language systems are static observers: they describe pixels, do not act, and cannot safely improve under shift. This passivity limits generalizable, physically grounded visual intelligence. Learning through action, not static description, is essential beyond curated data. We present Pixelis, a pixel-space agent that operates directly on images an

Yiru Wang, Anqing Jiang, Shuo Wang, Yuwen Heng

The integration of Vision-Language-Action (VLA) models into autonomous driving systems offers a unified framework for interpreting complex scenes and executing control commands. However, the necessity to incorporate historical multi-view frames for accurate temporal reasoning imposes a severe computational burden, primarily driven by the quadratic complexity

Rebecca Aschwanden, Nicolás Claro-Rodríguez, Ruizhe Zhao, Patricia Kallert

Beamsplitters represent fundamental components in both classical and quantum optical systems, enabling the distribution of light, as well as the generation of interference, superposition and entanglement. However, optical networks constructed from conventional bulk 2x2-beamsplitters encounter inherent scalability issues, as the number of required beamsplitte

Tzu-Yen Ma, Bo Zhang, Zichen Tang, Junpeng Ding

We present THEMIS, a novel multi-task benchmark designed to comprehensively evaluate multimodal large language models (MLLMs) on visual fraud reasoning within real-world academic scenarios. Compared to existing benchmarks, THEMIS introduces three major advances. (1) Real-World Scenarios and Complexity: Our benchmark comprises over 4,000 questions spanning se

Chengxu Yang, Jingling Yuan, Chuang Hu, Jiawei Jiang

Multimodal Large Language Models often suffer from object hallucination. While existing research utilizes attention enhancement and visual retracing, we find these works lack sufficient interpretability regarding attention drift in final model stages. In this paper, we investigate the layer wise evolution of visual features and discover that hallucination st

Hao Zhuang

For the de Rham mapping cone cochain complex induced by a smooth closed 2-form, we explicitly write down the associated mapping cone Thom form in the sense of Mathai-Quillen. Our construction uses the mapping cone covariant derivative, carrying the extra information brought by the 2-form. Our main tool is the Berezin integral. As the main result, we show tha

Paramahansa Pramanik

We develop a Euclidean path-integral control to characterize optimal firm behavior in an economy governed by Walrasian equilibrium, Pareto efficiency, and non-cooperative Markovian feedback Nash equilibrium. The approach recasts the problem as a Lagrangian stochastic control system with forward-looking dynamics, thereby avoiding the explicit construction of

Dexing Miao, Giovanni Ambrosi, Mattia Barbanera, Baasansuren Batsukh

The AMS-02 experiment plans to install a new silicon microstrip tracker layer (Layer-0) on top of the existing detector, increasing the cosmic-ray acceptance by a factor of 3. Layer-0 employs a design in which multiple silicon microstrip detectors (SSDs) are connected in series to form long detector ladders. We present a detailed performance study of the fli

Yang Yang, Yu-Xuan Bai, Yong-Zhuang Li, Yu Han

We study the influence of the loop quantum gravity (LQG) holonomy-correction parameter $\xi$ on black hole horizon structure, timelike geodesic motion, and gravitational wave emission in two rotating LQG-inspired black hole spacetimes, constructed via Newman-Janis algorithm from two distinct spherically symmetric seed metrics (type BH-I and BH-II). The physi

Haoran Pei, Yuguang Yang, Kexin Liu, Juan Zhang

Out-of-Distribution (OOD) generalization has become a primary metric for evaluating image analysis systems. Since deep learning models tend to capture domain-specific context, they often develop shortcut dependencies on these non-causal features, leading to inconsistent performance across different data sources. Current techniques, such as invariance learnin

Richard H. Bamler, Eric Chen, Ronan J. Conlon

We construct an example of an asymptotically conical (AC) non-K\"ahler expanding gradient Ricci soliton that has a K\"ahler tangent cone at infinity. This yields an example of a K\"ahler cone that can be desingularised by a smooth AC expanding gradient Ricci soliton but not by a smooth AC expanding gradient K\"ahler--Ricci soliton.

Zhong-Wang Niu, Shi-Sheng Zhang, Bing-Nan Lu

Nuclear lattice effective field theory (NLEFT) provides an efficient ab initio framework for computing low-lying states via imaginary-time projection. However, the extraction of unstable resonances, especially those with broad widths, remains a significant challenge. Traditional techniques such as the complex scaling method are often limited by sign problems

Dexing Miao, Zhiyu Xiang, Giovanni Ambrosi, Mattia Barbanera

A high-granularity telescope system with a large sensitive area and low material budget has been developed for high-energy heavy ion beam tests. The telescope consists of nine layers of silicon microstrip detectors (SSDs), whose performance was validated through a heavy ion beam test at the CERN SPS. A hybrid machine learning algorithm is proposed to address

Hani Abdelwahab, Ivan Kaygorodov, Roman Lubkov

The notion of $\delta$-Novikov algebras was introduced recently as a generalization of Novikov and bicommutative algebras. It looks like $\delta$-Novikov algebras have a richer structure than Novikov algebras. So, unlike Novikov algebras, they have a $2$-dimensional simple algebra for $\delta=-1.$ The present paper is dedicated to the study of $3$-dimensiona

Abhay Kumar, Santosh V. Vadawale, N. P. S. Mithun, Tanmoy Chattopadhyay

The scientific potential of X-ray polarimetry has long been recognized, but the challenges in measuring polarization have left it largely unexplored, particularly in the hard X-ray regime. While tremendous advancement has been made in soft X-ray polarimetery, the lack of sensitive hard X-ray polarimeters and polarisation measurements continues to limit our u

Jinda Lu, Junkang Wu, Jinghan Li, Kexin Huang

Extending Reinforcement Learning with Verifiable Rewards (RLVR) to multimodal large language models (MLLMs) faces a fundamental challenge: their responses inherently interleave perception-related tokens, which ground visual content, with reasoning-related tokens, which construct reasoning chains. These token types instantiate distinct yet interdependent capa

Artur Kawalec

We derive a simple expression to analytically continue the prime zeta function to the domain $\Re(s)>\frac{1}{2}$ assuming (RH) and taking into account a proper branch cut. We also verify the formula numerically and provide several plots.

Yunpeng Zhou

Sparse autoencoders (SAEs) improve interpretability in multimodal models, but it remains unclear whether SAE features form modular, composable units for reasoning-an assumption underlying many intervention-based steering methods. We test this modularity hypothesis and find it often fails: intervening on a task-selective feature set can modestly improve reaso

Nanxiang Jiang, Zhaoxin Fan, Baisen Wang, Daiheng Gao

Concept erasure serves as a vital safety mechanism for removing unwanted concepts from text-to-image (T2I) models. While extensively studied in U-Net and dual-stream architectures (e.g., Flux), this task remains under-explored in the recent emerging paradigm of single-stream diffusion transformers (e.g., Z-Image). In this new paradigm, text and image tokens

Nayan Adhikary, Anima Nagar

In this article we give several characterizations for various transitivity properties for linear operators. We define a general form of `Hypercyclicity Criterion' using a Furstenberg family $\mathcal{F}$ to characterize $\mathcal{F}$-transitive operators. In particular, we find an equivalent characterization for mixing operators. We study proximal and asympt

Junpeng Ma, Sashuai Zhou, Guanghao Li, Xin Gao

Video Large Language Models (VLMs) have achieved remarkable success in video understanding, but the significant computational cost from processing dense frames severely limits their practical application. Existing methods alleviate this by selecting keyframes, but their greedy decision-making, combined with a decoupled evaluation of relevance and diversity,

Nikolay Moshchevitin

We study properties of Diophantine exponents of lattices and so-called related "weak" uniform approximations introduced in recent papers by Oleg German, in the simplest two-dimensional case. In contrast to the multidimensional case, in the two-dimensional case we can use a powerful tool of continued fractions. We develop an analog of Jarn\'{\i}k's theory dea

Syed Rayhan Masud, SK Muktadir Hossain, Md. Ridoy Sarkar, Mohammad Sakib Mahmood

Agriculture is increasingly challenged by climate change, soil degradation, and resource depletion, and hence requires advanced data-driven crop classification and recommendation solutions. This work presents an explainable ensemble learning paradigm that fuses optimized feature pyramids, deep networks, self-attention mechanisms, and residual networks for bo

Nayan Adhikary, Anima Nagar

The dual concepts of `universality' and `hypercyclicity' are better understood and studied as `topological transitivity'. In this article we consider transitivity properties of skew products, essentially with non-compact fibers. We study the `Universality Conditions' and `Hypercyclicity Criterion' associated with the dynamical properties of transitivity, wea

Fumiyasu Makinoshima, Yuya Yamaguchi, Eigo Segawa, Koichiro Niinuma

Traffic digital twins, which inform policymakers of effective interventions based on large-scale, high-fidelity computational models calibrated to real-world traffic, hold promise for addressing societal challenges in our rapidly urbanizing world. However, conventional fine-grained traffic simulations are non-differentiable and typically rely on inefficient

Muntaka Ibnath, Mohammadreza Rezvani, Daniel Wong

Many system management runtimes (SMRs), such as resource management and power management techniques, rely on quality-of-service (QoS) metrics, such as tail latency or throughput, as feedback. These QoS metrics are generally neither observable with hardware performance counters nor directly observable within the OS kernel. This introduces complexity and overh

Zihao Qi, Christopher Earls, Yang Peng

Capturing the dynamics of quantum many-body systems under time-dependent driving protocols is a central challenge for numerical simulations. Existing methods such as tensor networks and time-dependent neural quantum states, however, must be re-run for every protocol. In this work, we introduce the Neural Operator Quantum State (NOQS) as a foundation model fo

Binwei Chen, Huachao Leng, Chi Yeung Mang, Tsz Wai Cheung

Hard coatings play a critical role in industry, with ceramic materials offering outstanding hardness and thermal stability for applications that demand superior mechanical performance. However, deploying artificial intelligence (AI) for surface roughness classification is often constrained by the need for large labeled datasets and costly high-resolution ima

Soumyo Kheto, Alexey Verkhovtsev, Bobby Antony, Andrey V. Solov'yov

Irradiation-driven fragmentation and chemical transformations of organometallic molecules play a central role in nanofabrication techniques based on the use of focused charged-particle beams. In this paper, the electron irradiation-induced fragmentation dynamics of W(CO)$_6$, a commonly used precursor for focused electron beam-induced deposition (FEBID), is

Nathaniel Gorski, Shusen Liu, Bei Wang

Recent agentic systems demonstrate that large language models can generate scientific visualizations from natural language. However, reliability remains a major limitation: systems may execute invalid operations, introduce subtle but consequential errors, or fail to request missing information when inputs are underspecified. These issues are amplified in rea

Yueru Yan, Siqi Wu

Personalization algorithms are ubiquitous in modern social computing systems, yet their effects on comment sections remain underexplored. In this work, we conducted an algorithmic auditing experiment to examine comment personalization on TikTok. We trained sock-puppet accounts to exhibit left-leaning or right-leaning preferences and successfully validated 17

Ellis R. Owen, Kinwah Wu, Yoshiyuki Inoue, Tatsuki Fujiwara

Cosmological filaments, galaxy clusters, and galaxies are magnetized reservoirs of cosmic rays (CRs). The exchange of CRs across these structures is usually modeled assuming that they remain charged and magnetically confined. At high energies, hadronic interactions can convert CR protons to neutrons. This physics is routinely included in air-shower and ultra

Jason Ho, Robin Kleiv, Siyuan Li, Stephan Narison

We present an analysis of the light tensor ($J^{PC}=2^{++}$) hybrid meson mass and coupling from QCD Laplace Sum Rules where the next-to-leading order (NLO) perturbative (PT) corrections and the contributions of the non-perturbative (NP) condensates up to dimension-six ($D=6$) are included. NLO leading-logarithms corrections due to the condensates which cont

Xuankai Zhang, Junjin Xiao, Shangwei Huang, Wei-shi Zheng

We present an approach for high-quality dynamic Gaussian Splatting from monocular videos. To this end, we in this work go one step further beyond previous methods to explicitly model continuous position and orientation deformation of dynamic Gaussians, using an SE(3) B-spline motion bases with a compact set of control points. To improve computational efficie

Behrad Samari, Henrik Sandberg, Karl H. Johansson, Abolfazl Lavaei

This paper develops a direct data-driven framework for constructing reduced-order models (ROMs) of discrete-time linear dynamical systems with unknown dynamics and process disturbances. The proposed scheme enables controller synthesis on the ROM and its refinement to the original system via an interface function designed using noisy data. To achieve this, th

Ron Litvak

System prompt configuration can make the difference between near-total phishing blindness and near-perfect detection in LLM email agents. We present PhishNChips, a study of 11 models under 10 prompt strategies, showing that prompt-model interaction is a first-order security variable: a single model's phishing bypass rate ranges from under 1% to 97% depending

Alexei Stepanov

In the present paper, we discuss for the first time the theoretical Kendall correlation coefficient for non-identical bivariate data. In the non-identical case, we first introduce a theoretical Kendall correlation coefficient $\tau_n$ and show that the expected value of the rank Kendall correlation coefficient $\tilde{\tau}_n$ is equal to $\tau_n$. We then p

Jing Tao, Taihang Lei, Banglei Guan, Ying Qu

Real-time monitoring of high-energy propellant combustion is difficult. Extreme high dynamic range (HDR), microsecond-scale particle motion, and heavy smoke often occur together. These conditions drive saturation, motion blur, and unstable particle extraction in conventional imaging. We present a closed-loop Event--SVE measurement system that couples a spati

Liyuan Zhu, Manjunath Narayana, Michal Stary, Will Hutchcroft

We present GaussFusion, a novel approach for improving 3D Gaussian splatting (3DGS) reconstructions in the wild through geometry-informed video generation. GaussFusion mitigates common 3DGS artifacts, including floaters, flickering, and blur caused by camera pose errors, incomplete coverage, and noisy geometry initialization. Unlike prior RGB-based approache

Miranda Muqing Miao, Lyle Ungar

Large language models (LLMs) tend to verbalize confidence scores that are largely detached from their actual accuracy, yet the geometric relationship governing this behavior remain poorly understood. In this work, we present a mechanistic interpretability analysis of verbalized confidence, using linear probes and contrastive activation addition (CAA) steerin

Filip Dobranić, Tina Munda, Oliver Pejić, Vojko Gorjanc

This study presents a computational analysis of the Slovene historical newspapers \textit{Slovenec} and \textit{Slovenski narod} from the sPeriodika corpus, combining topic modelling, large language model (LLM)-based aspect-level sentiment analysis, entity-graph visualisation, and qualitative discourse analysis to examine how collective identities, political

Yongqi Chen, Ming Zhu, Qingfeng Bian, Xiumei Yin

Photonic bound states in the continuum (BICs) provide a revolutionary paradigm for boosting light-matter interactions in integrated nanocavity systems. Nevertheless, precise manipulation of open cavity-emitter architectures still faces critical challenges, especially in realizing deterministic directional radiation and suppressing the perturbation of intrins

Guangyu Hu, Chen Chen, Xiaofeng Zhou, Jiaxi Zhang

Property Directed Reachability (PDR) is a powerful algorithm for formal verification of hardware and software systems, but its performance is highly sensitive to parameter configurations. Manual parameter tuning is time-consuming and requires domain expertise, while traditional automated parameter tuning frameworks are not well-suited for time-sensitive veri

Huyen Ngoc Tran, Dung Trung Tran, Hong Nguyen, Xuan Vu Phan

Precipitation forecasting remains a persistent challenge in tropical regions like Vietnam, where complex topography and convective instability often limit the accuracy of Numerical Weather Prediction (NWP) models. While data-driven post-processing is widely used to mitigate these biases, most existing frameworks rely on point-wise objective functions, which

Yun Zou

We investigate the dynamics of two identical spinless fermions on a one-dimensional lattice with open boundary conditions (OBC), subject to quasiperiodic long-range interactions. Using numerical exact diagonalization (ED), we study this non-integrable system as a continuous-time quantum walk and uncover a robust correlated dynamical regime. This regime, char

Young-Chae Son, Dae-Kwan Ko, Yoon-Ji Choi, Soo-Chul Lim

In recent human-robot collaboration environments, there is a growing focus on integrating diverse sensor data beyond visual information to enable safer and more intelligent task execution. Although thermal data can be crucial for enhancing robot safety and operational efficiency, its integration has been relatively overlooked in prior research. This paper pr

Wonjoon Lee, Sungmin Woo, Donghyeong Kim, Jungho Lee

Online reconstruction of dynamic scenes aims to learn from streaming multi-view inputs under low-latency constraints. The fast training and real-time rendering capabilities of 3D Gaussian Splatting have made on-the-fly reconstruction practically feasible, enabling online 4D reconstruction. However, existing online approaches, despite their efficiency and vis

Yicheng Zou, Dongsheng Zhu, Lin Zhu, Tong Zhu

We introduce Intern-S1-Pro, the first one-trillion-parameter scientific multimodal foundation model. Scaling to this unprecedented size, the model delivers a comprehensive enhancement across both general and scientific domains. Beyond stronger reasoning and image-text understanding capabilities, its intelligence is augmented with advanced agent capabilities.

Ryan Bennink, Olena Burkovska, Konstantin Pieper, Jorge Ramirez

This review is designed to introduce mathematicians and computational scientists to quantum computing (QC) through the lens of uncertainty quantification (UQ) by presenting a mathematically rigorous and accessible narrative for understanding how noise and intrinsic randomness shape quantum computational outcomes in the language of mathematics. By grounding q

Johnathan Tucker, Denis Liu, Aiden Swann, Allen Ren

Vision-Language-Action (VLA) models such as $\pi_0$ have demonstrated remarkable generalization across diverse fixed-base manipulators. However, transferring these foundation models to aerial platforms remains an open challenge due to the fundamental mismatch between the quasi-static dynamics of fixed-base arms and the underactuated, highly dynamic nature of

Jianbo Qi, Mengyao Li, Baogui Jiang, Yidan Chen

Satellite Earth observation has accumulated massive spatiotemporal archives essential for monitoring environmental change, yet these remain organized as discrete raster files, making them costly to store, transmit, and query. We present GeoNDC, a queryable neural data cube that encodes planetary-scale Earth observation data as a continuous spatiotemporal imp

Subhasish Das, Vivek Kumar Agrawal, Parijat Thakur, G. C. Dewangan

AstroSat/LAXPC and SXT observed the persistent neutron star low-mass X-ray binary 4U 1820-30 between 2016 and 2022. During these observations, the hardness-intensity diagram (HID) and color-color diagram (CCD) indicated that the source was in the banana state. We divided the CCD into 11 segments for spectral and timing analyses. For each segment in the CCD,

Veeraraju Elluru, Arth Singh, Roberto Aguero, Ajay Agarwal

Compressed vision-language models (VLMs) are widely used to reduce memory and compute costs, making them a suitable choice for real-world deployment. However, compressing these models raises concerns about whether internal computations and safety behaviors are preserved. In this work, we use causal circuit analysis and crosscoder-based feature comparisons to

Viacheslav V. Klimenko, Varsha P. Kulkarni, Monique C. Aller

We present results from a mini-survey of dust spectral features arising in galaxies at redshifts $0.5 < z < 1.2$ in our James Webb Space Telescope (JWST) mid-infrared spectra of physically-unrelated background quasars. We analyze the JWST Mid-infrared Instrument (MIRI) Medium-Resolution Spectrometer (MRS) spectra of five quasars presented in Klimenko, Kulkar

Steffen Lukas

Foundation models excel in stable environments, yet often fail where reliability matters most: medicine, finance, and policy. This Fidelity Paradox is not just a data problem; it is structural. In domains where rules change over time, extra model capacity amplifies noise rather than capturing signal. We introduce Epistemic Compression: the principle that rob

Zimo Yan, Zheng Xie, Chang Liu, Yiqin Lv

Positional encoding has become a standard component in graph learning, especially for graph Transformers and other models that must distinguish structurally similar nodes, yet its fundamental identifiability remains poorly understood. In this work, we study node localization under a hybrid positional encoding that combines anchor-distance profiles with quant

Peng Wei, Wesley Shu

Recent events surrounding the relationship between frontier AI suppliers and national-security customers have made a structural problem newly visible: once a privately governed model becomes embedded in military workflows, the supplier can influence not only technical performance but also the operational boundary conditions under which the system may be used

Donghui Yang, Jie Zhong

We study a class of degenerate parabolic equations with boundary point degeneracy in dimensions N>=2 and investigate the associated boundary observability problem by means of shape design. While one-dimensional degenerate models have been treated in the literature, the genuinely higher-dimensional case remains much more delicate because the degeneracy occurs

Jiahao Liu, Lin Li, Zhiyuan Li, Kaixi Hu

Sequential recommendation models, particularly those based on attention, achieve strong accuracy but incur quadratic complexity, making long user histories prohibitively expensive. Sub-quadratic operators such as Hyena provide efficient alternatives in language modeling, but their potential in recommendation remains underexplored. We argue that Hyena faces c

Xu Liu

Medical image restoration is essential for improving the usability of noisy, incomplete, and artifact-corrupted clinical scans, yet existing methods often rely on task-specific retraining and offer limited control over the trade-off between faithful reconstruction and prior-driven enhancement. This lack of controllability is especially problematic in clinica

Wenshuo Wang, Fan Zhang

Autoregressive neural PDE simulators predict the evolution of physical fields one step at a time from a finite history, but low-cost context-window selection for such simulators remains an unformalized problem. Existing approaches to context-window selection in time-series forecasting include exhaustive validation, direct low-cost search, and system-theoreti

Chenxu Yu, Wenqi Fang

As a representative continuous-depth neural network approach, stochastic differential equation (SDE)-based Bayesian neural networks (BNNs) have attracted considerable attention due to their solid theoretical foundations and strong potential for real-world applications. However, their reliance on numerical SDE solvers inevitably incurs a large number of funct

Zhi Li

Nonstabilizerness, or magic, is a necessary resource for quantum advantage beyond the classically simulatable Clifford framework. Recent works have begun to chart the structure of magic in many-body states, introducing the concepts of long-range magic -- nonstabilizerness that cannot be removed by finite-depth local unitary (FDU) circuits -- and the magic hi

Peng Wei, Wesley Shu

Knowledge distillation, model extraction, and behavior transfer have become central concerns in frontier AI. The main risk is not merely copying, but the possibility that useful capability can be transferred more cheaply than the governance structure that originally accompanied it. This paper presents a public, trade-secret-safe theoretical framework for red

Zhe Gao, Shiyu Shen, Taifeng Chai, Weinong Wang

Existing Multimodal Large Language Models (MLLMs) often suffer from hallucinations in long video understanding (LVU), primarily due to the imbalance between textual and visual tokens. Observing that MLLMs handle short visual inputs well, recent LVU works alleviate hallucinations by automatically parsing the vast visual data into manageable segments that can

Zhangyu Jin, Maksim Siniukov, Deuksin Kwon, Ashutosh Chaubey

Generating realistic 3D head motion for dyadic interactions is a significant challenge in virtual human synthesis. While recent methods achieve impressive results with speaking heads, they frequently suffer from the `Regression-to-the-Mean' problem in listener motions, collapsing into static faces, and lack the parameter space for complex nonverbal motions.

Hong Sun, Jinxin Zhong, Yimin Yao, Jun Zhou

Inspired by the recent observation of the orbital Seebeck effect in alpha-quartz, we identify an intrinsic amplification mechanism for thermally generated phonon angular momentum and phonon magnetic moment in chiral insulators. We propose a Heisenberg-type long-range coupling between phonon angular momenta, referred to here as L-L coupling, which opens a sel

Nima Anari, Alireza Haqi

We present the first polylogarithmic-round algorithm for sampling a random spanning tree in the (Broadcast) Congested Clique model. For any constant $c > 0$, our algorithm outputs a sample from a distribution whose total variation distance from the uniform spanning tree distribution is at most $O(n^{-c})$ in at most $c \cdot \log^{O(1)}(n)$ rounds. The expon

Wenjin Zhang, Yixin Wang, Yuqi Gu

Causal representation learning seeks to uncover causal relationships among high-level latent variables from low-level, entangled, and noisy observations. Existing approaches often either rely on deep neural networks, which lack interpretability and formal guarantees, or impose restrictive assumptions like linearity, continuous-only observations, and strong s

Jorge Meza-Domínguez, Tonatiuh Matos

We establish a rigorous geometric framework for quantum fields on a stochastic gravitational background. Starting from a master partition function that averages over metric fluctuations, we define a matter amplitude $\mathcal{K}$, whose logarithmic derivative yields a complex velocity field $\eta_{\mu} = \pi_{\mu} - i u_{\mu}$. This object, originating in Ne

Tony Mason

System prompt instructions that cooperate in English compete in Spanish, with the same semantic content, but opposite interaction topology. We present instruction-level ablation experiments across four languages and four models showing that this topology inversion is mediated by social register: the imperative mood carries different obligatory force across s

Chatchawan Panraksa, Detchat Samart, Songpon Sriwongsa

Let $\mathbb{F}_q$ denote a finite field of order $q$. A rational function $r(x)\in \mathbb{Q}(x)$ is said to be arithmetically exceptional if it induces a permutation on $\mathbb{P}^1(\mathbb{F}_p)$ for infinitely many primes $p$. Based on some computational results, Odaba\c{s} conjectured that for each $k\in \mathbb{N}$, the $k$-th Latt\`{e}s map attached

Khoi Trinh, Jay Rothenberger, Scott Seidenberger, Dimitrios Diochnos

Iterative prompt refinement is central to reproducing target images with text to image generative models. Previous studies have incorporated image similarity metrics (ISMs) as additional feedback to human users. Existing ISMs such as LPIPS and CLIP provide objective measures of image likeness but often fail to align with human judgments, particularly in cont

Shinya Kumashiro, Takanori Nagamine

Let $R$ be a domain and $B=R[x_1^{\pm1},\ldots,x_n^{\pm1}]$ the Laurent polynomial ring over $R$. In this paper we study pre-factorially closed (pfc) and quasi-factorially closed (qfc) $R$-subalgebras of $B$, which generalize the notion of factorially closed subalgebras. We first establish a localization criterion for the qfc property. Using this criterion,

Haihua Wei, Kanghui Qian, Zhongkai Li, Yeli Niu

For $\lambda\ge0$, the so-called $\lambda$-analytic functions are defined in terms of the (complex) Dunkl operators $D_{z}$ and $D_{\bar{z}}$. In the paper we introduce a Bloch type space on the disk ${\mathbb D}$ associated with $\lambda$-analytic functions, called the $\lambda$-Bloch space and denoted by ${\mathfrak{B}}_{\lambda}({\mathbb D})$. Various pro

Thong Nguyen, Cosimo Rulli, Franco Maria Nardini, Rossano Venturini

State-of-the-art Learned Sparse Retrieval (LSR) models, such as Splade, typically employ a Language Modeling (LM) head to project latent hidden states into a lexically-anchored logit matrix. This intermediate matrix is subsequently transformed into a sparse lexical representation through element-wise operations (ReLU, Log1P) and max-pooling over the sequence

Licheng Liu

We propose a Bayesian propensity score-augmented latent factor model for causal inference with time-series cross-sectional data. The framework explicitly models the treatment assignment mechanism by incorporating latent factor loadings, while the outcome model flexibly incorporates the propensity score, for example through stratification. Relative to existin

Shalima Binta Manir, Anamika Paul Rupa

Grokking the delayed transition from memorization to generalization in neural networks remains poorly understood, in part because prior empirical studies confound the roles of architecture, optimization, and regularization. We present a controlled study that systematically disentangles these factors on modular addition (mod 97), with matched and carefully tu

Thanh-Hai Le, Hoang-Hau Tran, Trong-Nghia Vu

This paper presents Few TensoRF, a 3D reconstruction framework that combines TensorRF's efficient tensor based representation with FreeNeRF's frequency driven few shot regularization. Using TensorRF to significantly accelerate rendering speed and introducing frequency and occlusion masks, the method improves stability and reconstruction quality under sparse

Zhiyi Liu, Lihua Feng, Tingzeng Wu

Let $V$ be an $n$-dimension real vector space with a direct sum decomposition $V = V_1 \oplus \cdots \oplus V_r$. Let $\mathcal{P} = \{(A_i, B_i) : i \in [m]\}$ be a skew Bollob\'as system of subspaces of $V$ such that each $i\in [m]$, $ A_i = \bigoplus_{k=1}^r (A_i \cap V_k)$ and $ B_i = \bigoplus_{k=1}^r (B_i \cap V_k)$. We prove that $$\sum_{i=1}^{m} \pro

Md. Rokon Mia, Rakib Hossain Sajib, Abdullah Al Noman, Abir Ahmed

Early detection of rice leaf diseases is critical, as rice is a staple crop supporting a substantial share of the world's population. Timely identification of these diseases enables more effective intervention and significantly reduces the risk of large-scale crop losses. However, traditional deep learning models primarily rely on cross entropy loss, which o

Md Faizul Ibne Amin, Yutaka Watanobe, Md. Mostafizer Rahman, Daniel M. Muepu

Programming is a core skill in CS and SE, yet identifying and resolving code errors remains challenging for practitioners. LLMs have shown remarkable capabilities in NL understanding, but how code-specialized LLMs behave when paired with DL sequence decoders, and which component of such a pipeline drives performance, remains insufficiently explored. This stu

Yike Wu, Necva Bolucu, Stephen Wan, Dadong Wang

Zero-shot referring expression comprehension (REC) aims to locate target objects in images given natural language queries without relying on task-specific training data, demanding strong visual understanding capabilities. Existing Vision-Language Models~(VLMs), such as CLIP, commonly address zero-shot REC by directly measuring feature similarities between te

Saima Aslam, Matthew Faust, Jonathan D. Hauenstein, Jordy Lopez Garcia

It is well established that a general pair of twisted cubic curves in complex projective space has ten common secant lines. As an initial investigation, we show that the monodromy group of the ten common secant lines over the complex numbers is the full symmetric group demonstrating that the common secant lines have no special structure over the complex numb

Yoshiharu Shinnaka, Ko Tsujimoto, Hideyo Kawakita, Hitomi Kobayashi

We present high-resolution optical spectroscopy of interstellar comet 3I/ATLAS (C/2025 N1) obtained with the High Dispersion Spectrograph mounted on the Subaru Telescope on UT 2026 January 7, when the comet was on its outbound trajectory at a heliocentric distance of $r_{\mathrm{h}} = 2.87$ au. The spectra cover the forbidden atomic oxygen lines, [O~I], at 5

Yeonjun In, Mehrab Tanjim, Jayakumar Subramanian, Sungchul Kim

Failure attribution is essential for diagnosing and improving multi-agent systems (MAS), yet existing benchmarks and methods largely assume a single deterministic root cause for each failure. In practice, MAS failures often admit multiple plausible attributions due to complex inter-agent dependencies and ambiguous execution trajectories. We revisit MAS failu

WenXi Wang, JunQi Zhang

Rapid transit of emergency vehicles is critical for saving lives and reducing property loss but often relies on surrounding ordinary vehicles to cooperatively adjust their driving behaviors. It is important to ensure rapid transit of emergency vehicles while minimizing the impact on ordinary vehicles. Centralized mathematical solver and reinforcement learnin

Rakib Hossain Sajib, Md. Rokon Mia, Prodip Kumar Sarker, Abdullah Al Noman

The Internet of Vehicles (IoV) has become an essential component of smart transportation systems, enabling seamless interaction among vehicles and infrastructure. In recent years, it has played a progressively significant role in enhancing mobility, safety, and transportation efficiency. However, this connectivity introduces severe security vulnerabilities,

Michael Hardy, Joshua Gilbert, Benjamin Domingue

The validity of assessments, from large-scale AI benchmarks to human classrooms, depends on the quality of individual items, yet modern evaluation instruments often contain thousands of items with minimal psychometric vetting. We introduce a new family of nonparametric scalability coefficients based on interitem isotonic regression for efficiently detecting

Xiao-Mei Tang, Jin-Yu Huo, Qi Huang, Rui Chen

The exploration of exotic hadrons provides a crucial testing ground for quantum chromodynamics in its non-perturbative regime. In this work, we perform a systematic study of the two-body strong decay properties of single-charm molecular pentaquarks in the $Y_c\bar{K}^{(*)}$ systems, where $Y_c = \Lambda_c$, $\Sigma_c$, $\Xi_c$, and $\Xi_c'$. Employing an eff

Federico Bongiorno

Drawing on the theory of Minimal Model Program singularities for foliations, we define relative canonical and log-canonical singularities for algebraic stacks with finite generic stabilisers. We show that if a point has log-canonical singularities, its stabiliser group is a finite extension of an algebraic torus, thus, \'etale locally, the good moduli space

Ziye Geng, Guang Yang, Yihang Chen, Changqing Luo

We propose IrisFP, a novel adversarial-example-based model fingerprinting framework that enhances both uniqueness and robustness by leveraging multi-boundary characteristics, multi-sample behaviors, and fingerprint discriminative power assessment to generate composite-sample fingerprints. Three key innovations make IrisFP outstanding: 1) It positions fingerp

Zeya Chen, Zach Pino, Ruth Schmidt

Data donation, an emerging user-centric data collection method for public sector research, faces a gap between participant willingness and actual donation. This suggests a design absence in practice: while promoted as "donor-centered" with technical and regulational advances, a design perspective on how data choices are presented and intervene on individual

Junoh Lee, Junmyeong Lee, Yeon-Ji Song, Inhwan Bae

The reconstruction of dynamic 3D scenes using 3D Gaussian Splatting has shown significant promise. A key challenge, however, remains in modeling realistic motion, as most methods fail to align the motion of Gaussians with real-world physical dynamics. This misalignment is particularly problematic for monocular video datasets, where failing to maintain cohere

Ray-Yuan Chung, Jaime Snyder, Zixuan Xu, Daeun Yoo

In pediatric chronic care, the triadic relationship among patients, caregivers, and healthcare providers introduces unique challenges for youth in managing their conditions. Diverging values, roles, and asymmetrical situational awareness across decision-maker groups often hinder collaboration and affect health outcomes, highlighting the need to support colla

Yusri Al-Sanaani, Rebecca Thornhill, Sreeraman Rajan

Accurate segmentation of the left atrial (LA) wall in 3D late gadolinium-enhanced MRI (LGE-MRI) is essential for wall thickness mapping and fibrosis quantification, yet it remains challenging due to the wall's thin geometry, complex anatomy, and low contrast. We propose C2W-Tune, a two-stage cavity-to-wall transfer framework that leverages a high-accuracy LA

Filippo Cenacchi

Predictive systems increasingly span heterogeneous modalities such as graphs, language, and tabular records, but sparsity and efficiency remain modality-specific (graph edge or neighborhood sparsification, Transformer head or layer pruning, and separate tabular feature-selection pipelines). This fragmentation makes results hard to compare, complicates deploy

Ebonye Smith, Sampada Deglurkar, Jingqi Li, Gechen Qu

Hamilton-Jacobi (HJ) reachability provides formal safety guarantees for nonlinear systems. However, it becomes computationally intractable in high-dimensional settings, motivating learning-based approximations that may introduce unsafe errors or overly optimistic safe sets. In this work, we propose a hierarchical probabilistic verification framework for reac