arXiv preprints from January 1, 2026 through September 21, 2026 — 21:53:45 EST
This paper develops a micro-founded economic theory of the AI industry by modeling large language models as a distinct asset class-Digital Intelligence Capital-characterized by data-compute complementarities, increasing returns to scale, and relative (rather than absolute) valuation. We show that these features fundamentally reshape...
Customer reviews contain detailed, domain specific signals about service failures and user expectations, but converting this unstructured feedback into actionable business decisions remains difficult. We study review-to-action generation: producing concrete, implementable recommendations grounded in review text. We propose a modular...
As a key task in hyperspectral image processing, hyperspectral anomaly detection has garnered significant attention and undergone extensive research. Existing methods primarily relt on two prior assumption: low-rank background and sparse anomaly, along with additional spatial assumptions of the background. However, most methods only...
We investigate the circumstellar environment of the O-type bloated star candidate IRAS 19520+2759 (I19520) using high-resolution observations from the Atacama Large Millimeter/submillimeter Array (ALMA) and the Expanded Very Large Array (EVLA). Radio continuum emission traced by the EVLA (C, K, and Q bands) exhibits a spectral index...
We consider a possibly multiply connected bounded open subset $Ω$ of ${\mathbb{R}}^n$ of class $C^{\max\{1,m\},α}$ for some $m\in {\mathbb{N}}$, $α\in]0,1[$ and we plan to solve both the Dirichlet and the Neumann problem for the Helmholtz equation in $Ω$ and in the exterior of $Ω$ in terms of acoustic layer potentials. Then we turn to...
We propose an active-learning method for nonlinear minimax regression. Given a nonlinear function that can be arbitrarily evaluated over a compact set, we fit a surrogate model, such as a feedforward neural network, by minimizing the maximum absolute approximation error. To handle the nonsmoothness of this worst-case loss, we...
Low-scattering, deep-penetration light transport in biological media remains a pivotal challenge for biophotonic technologies, including biomedical imaging, optical diagnostics, and photodynamic therapy. This review builds upon and extends our earlier studies of nonlinear optical self-trapping and optically induced waveguiding in...
This paper provides a complete proof of Simon-Lukic conjecture for orthogonal polynomials on the unit circle. For a probability measure $dμ= w(θ) \frac{dθ}{2π} + dμ_s$ with Verblunsky coefficients $α=\{α_n\}_{n=0}^\infty$, distinct singular points $(θ_k)_{k=1}^{\ell}$, and multiplicities $(m_k)_{k=1}^{\ell}$, we establish the...
RAG has emerged as a key technique for enhancing response quality of LLMs without high computational cost. In traditional architectures, RAG services are provided by a single entity that hosts the dataset within a trusted local environment. However, individuals or small organizations often lack the resources to maintain data storage...
Glacial Lake Outburst Floods (GLOFs) pose a serious threat in high mountain regions. They are hazardous to communities, infrastructure, and ecosystems further downstream. The classical methods of GLOF detection and prediction have so far mainly relied on hydrological modeling, threshold-based lake monitoring, and manual satellite...
Intrinsic Image Decomposition (IID) separates an image into albedo and shading components. It is a core step in many real-world applications, such as relighting and material editing. Existing IID models achieve good results, but often use a large number of parameters. This makes them costly to combine with other models in real-world...
In this paper, we introduce the bivariate exponential generating function $F_l(x,y)$ for the number of level-$l$ faces of an exponential sequence of arrangements (ESA), and establish the formula $F_l(x,y)=\big(F_1(x,y)\big)^l$ with a combinatorial interpretation. Its specialization at $x=0$ recovers a result first obtained by Chen et...
Generative AI (GenAI) systems promise to transform knowledge work by automating a range of tasks, yet their deployment in enterprise settings remains hindered by the lack of systematic quality assurance mechanisms. We present an Expert Validation Framework that places domain experts at the center of building software with GenAI...
Existing image emotion editing methods struggle to disentangle emotional cues from latent content representations, often yielding weak emotional expression and distorted visual structures. To bridge this gap, we propose EmoKGEdit, a novel training-free framework for precise and structure-preserving image emotion editing. Specifically,...
Hypernetworks are models that generate or modulate the weights of another network. They provide a flexible mechanism for injecting context and task conditioning and have proven broadly useful across diverse applications without significant increases in model size. We leverage hypernetworks to improve multimodal patch matching by...
Online fraud is a critical global threat that disproportionately targets older adults. Prior anti-fraud education for older adults has largely relied on static, traditional instruction that limits engagement and real-world transfer, whereas role-based simulation offers realistic yet low-risk opportunities for practice. Moreover, most...
Humans face countless scenarios that require reasoning and judgment in daily life. However, existing large language model training methods primarily allow models to learn from existing textual content or solve predetermined problems, lacking experience in real scenarios involving interaction, negotiation, and competition with others....
Momentum SGD (MSGD) serves as a foundational optimizer in training deep models due to momentum's key role in accelerating convergence and enhancing generalization. Meanwhile, asynchronous distributed learning is crucial for training large-scale deep models, especially when the computing capabilities of the workers in the cluster are...
Surface ozone pollution remains a persistent challenge in many metropolitan regions worldwide, as the nonlinear dependence of ozone formation on nitrogen oxides and volatile organic compounds (VOCs) complicates the design of effective emission control strategies. While chemical transport models provide mechanistic insights, they rely...
In this work, we investigate the interactions between an excited anticharm meson doublet $(\bar{D}_1, \bar{D}_2^*)$ and ground-state octet baryons $(N, Λ, Σ, Ξ)$ with the aim of identifying possible molecular pentaquark states. A systematic analysis is performed within the one-boson-exchange model, which incorporates both $S$ and...
We propose a simple yet testable framework for light fermion dark matter (DM) with mass in the MeV--GeV range, charged under a dark $U(1)_D$ gauge symmetry. The $U(1)_D$ is spontaneously broken by a scalar field $Φ$, giving mass to the dark gauge boson $X_D$. The dominant DM annihilation proceeds via a forbidden channel, where the DM...
The advancement of Document Intelligence (DI) demands large-scale, high-quality training data, yet manual annotation remains a critical bottleneck. While data generation methods are evolving rapidly, existing surveys are constrained by fragmented focuses on single modalities or specific tasks, lacking a unified perspective aligned...
Automation in data analysis has been a long-time pursuit. Current agentic LLM shows a promising solution towards it. Like DeepAnalyze, DataSage, and Datawise. They are all powerful agentic frameworks for automatic fine-grained analysis and are powered by LLM-based agentic tool calling ability. However, what about powered by a preset...
We present a semantics modulated, multi scale Transformer for 3D gaze estimation. Our model conditions CLIP global features with learnable prototype banks (illumination, head pose, background, direction), fuses these prototype-enriched global vectors with CLIP patch tokens and high-resolution CNN tokens in a unified attention space,...
We present a comprehensive analysis of the dark matter (DM) content and its structural dependence in star-forming disk-like galaxies at intermediate redshifts ($0.1 \lesssim z \lesssim 0.85$), utilizing spatially resolved kinematic data from the MAGPI survey. We report the following: (1) Low stellar mass galaxies ($M_{\rm star} <...