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Qun Wang

Publications and source records attributed to Qun Wang.

At least 37 records · Page 2Linked to original sources
arXiv2025

A Comprehensive Review of AI Agents: Transforming Possibilities in Technology and Beyond

Artificial Intelligence (AI) agents have rapidly evolved from specialized, rule-based programs to versatile, learning-driven autonomous systems capable of perception, reasoning, and action in complex environments. The explosion of data, advances in deep learning, reinforcement learning, and multi-agent coordination have accelerated this transformation. Yet, designing and deploying unified AI agents that seamlessly integrate cognition, planning, and interaction remains a grand challenge. In this review, we systematically examine the architectural principles, foundational components, and emergent paradigms that define the landscape of contemporary AI agents. We synthesize insights from cognitive science-inspired models, hierarchical reinforcement learning frameworks, and large language model-based reasoning. Moreover, we discuss the pressing ethical, safety, and interpretability concerns associated with deploying these agents in real-world scenarios. By highlighting major breakthroughs, persistent challenges, and promising research directions, this review aims to guide the next generation of AI agent systems toward more robust, adaptable, and trustworthy autonomous intelligence.

cs.MA↗
arXiv2025

Spin alignment of vector mesons in heavy-ion collision

We give a brief review on the spin alignment induced by the strong field and the shear-stress tensor. In experiments, a significant positive deviation from 1/3 is observed for the $ϕ$ meson, which can be explained by the anisotropy of the strong field fluctuation in the meson's rest frame, while the anisotropy is mainly a consequence of the motion of meson relative to the quark-gluon plasma. On the other hand, the shear-induced spin alignment is of the order $10^{-4}\sim10^{-5}$ if the magnitude of thermal shear tensor is $10^{-2}$.

hep-ph↗
arXiv2025

Nuclear deformation effects in photoproduction of $ρ$ mesons in ultraperipheral isobaric collisions

We have investigated the $ρ^{0}$ meson photoproduction in ultraperipheral isobaric collisions between $_{44}^{96}\textrm{Ru}+_{44}^{96}\textrm{Ru}$ and $_{40}^{96}\textrm{Zr}+_{40}^{96}\textrm{Zr}$ at $\sqrt{s_{NN}}=200$ GeV, employing the dipole model with the equivalent photon approximation. By implementing the Woods-Saxon distribution to represent the nuclear mass density, which is derived from density functional theory with an inclusion of nuclear deformation effects, we have calculated the transverse momentum $q_{T}$ spectra in isobaric collisions. We observe the characteristic dip behavior in these spectra, indicative of diffraction phenomena in high-energy physics. We notice that the deformation effects cause a nearly linear increase with $q_{T}^{2}$ for $q_{T}^{2}\lesssim0.015$ $\textrm{GeV}^{2}$, aligning with experimental observations. We offer a simple explanation for the observed behavior in these spectra by introducing the effective width of the nuclei in the thickness function. We also extend our discussion on the $ρ^{0}$ meson photoproduction with the targets $^{63}\textrm{Cu}$,$^{197}\textrm{Au}$, and $^{238}\textrm{U}$.

hep-ph↗
arXiv2025

IRS Assisted Decentralized Learning for Wideband Spectrum Sensing

The increasing demand for reliable connectivity in industrial environments necessitates effective spectrum utilization strategies, especially in the context of shared spectrum bands. However, the dynamic spectrum-sharing mechanisms often lead to significant interference and critical failures, creating a trade-off between spectrum scarcity and under-utilization. This paper addresses these challenges by proposing a novel Intelligent Reflecting Surface (IRS)-assisted spectrum sensing framework integrated with decentralized deep learning. The proposed model overcomes partial observation constraints and minimizes communication overhead while leveraging IRS technology to enhance spectrum sensing accuracy. Through comprehensive simulations, the framework demonstrates its ability to monitor wideband spectrum occupancy effectively, even under challenging signal-to-noise ratio (SNR) conditions. This approach offers a scalable and robust solution for spectrum management in next-generation wireless networks.

eess.SY↗
arXiv2025

Intelligent IoT Attack Detection Design via ODLLM with Feature Ranking-based Knowledge Base

The widespread adoption of Internet of Things (IoT) devices has introduced significant cybersecurity challenges, particularly with the increasing frequency and sophistication of Distributed Denial of Service (DDoS) attacks. Traditional machine learning (ML) techniques often fall short in detecting such attacks due to the complexity of blended and evolving patterns. To address this, we propose a novel framework leveraging On-Device Large Language Models (ODLLMs) augmented with fine-tuning and knowledge base (KB) integration for intelligent IoT network attack detection. By implementing feature ranking techniques and constructing both long and short KBs tailored to model capacities, the proposed framework ensures efficient and accurate detection of DDoS attacks while overcoming computational and privacy limitations. Simulation results demonstrate that the optimized framework achieves superior accuracy across diverse attack types, especially when using compact models in edge computing environments. This work provides a scalable and secure solution for real-time IoT security, advancing the applicability of edge intelligence in cybersecurity.

cs.CR↗
arXiv2025

Spin density matrix for neutral $ρ$ mesons in a pion gas in linear response theory

We calculate the spin density matrix for neutral $ρ$ mesons from the spectral function and thermal shear tensor by Kubo formula in the linear response theory, which contributes to the $γ$ correlator for the CME search. We derive the spectral function of neutral $ρ$ mesons with $ρππ$ and $ρρππ$ interactions using the Dyson-Schwinger equation. The thermal shear tensor contribution is obtained from the Kubo formula in the linear response theory. We numerically calculate $ρ_{00}-1/3$ and $\mathrm{Re}ρ_{-1,1}$ using the simulation results for the thermal shear tensor by the hydrodynamical model, which are of the order $10^{-3}\sim10^{-2}$.

hep-ph↗
arXiv2025

Efficient Phishing URL Detection Using Graph-based Machine Learning and Loopy Belief Propagation

The proliferation of mobile devices and online interactions have been threatened by different cyberattacks, where phishing attacks and malicious Uniform Resource Locators (URLs) pose significant risks to user security. Traditional phishing URL detection methods primarily rely on URL string-based features, which attackers often manipulate to evade detection. To address these limitations, we propose a novel graph-based machine learning model for phishing URL detection, integrating both URL structure and network-level features such as IP addresses and authoritative name servers. Our approach leverages Loopy Belief Propagation (LBP) with an enhanced convergence strategy to enable effective message passing and stable classification in the presence of complex graph structures. Additionally, we introduce a refined edge potential mechanism that dynamically adapts based on entity similarity and label relationships to further improve classification accuracy. Comprehensive experiments on real-world datasets demonstrate our model's effectiveness by achieving F1 score of up to 98.77\%. This robust and reproducible method advances phishing detection capabilities, offering enhanced reliability and valuable insights in the field of cybersecurity.

cs.CR↗
arXiv2024

Spin alignment of vector mesons in local equilibrium by Zubarev's approach

We compute the $00$ element of the spin density matrix, denoted as $ρ_{00}$ and called the spin alignment, up to the second order of the gradient expansion in local equilibrium by Zubarev's approach. In the first order, we obtain $ρ_{00}=1/3$, meaning that the contributions from thermal vorticity and shear stress tensor are vanishing. The non-vanishing contributions to $ρ_{00}-1/3$ appear in the second order of gradients in the Belinfante and canonical cases. We also discuss the properties of the spin density matrix under the time reversal transformation. The effective transport coefficient for the spin alignment induced by the thermal shear stress tensor is T-odd in the first order, implying that the first order effect is dissipative.

hep-ph↗
arXiv2024

Nucleon axial radius

We present the first systematic study of the relativistic axial-vector four-current distributions inside a nucleon. We show in particular that the slope of the axial form factor $G_A(Q^2)$ in the forward limit -- conventionally denoted as $R^2_A$ in the literature -- does not represent the three-dimensional mean-square axial radius in the Breit frame, but corresponds instead to a contribution to the mean-square spin radius. We derive explicit expressions for the latter in different frames and find in general additional contributions that depend on both the nucleon mass and the forward values of the axial-vector form factors $G_A(0)$ and $G_P(0)$. This provides an additional key motivation for ongoing lattice quantum chromodynamics (QCD) calculations and future experimental measurements of the induced pseudoscalar form factor $G_P(Q^2)$.

hep-ph↗
arXiv2024

A solvable model for spin polarizations with flow-momentum correspondence

We present an analytically solvable model based on the blast-wave picture of heavy-ion collisions with flow-momentum correspondence. It can describe the key features of spin polarizations in heavy-ion collisions. With the analytical solution, we can clearly show that the spin polarization with respect to the reaction plane is governed by the directed flow, while the spin polarization along the beam direction is governed by the ellipticity in flow and in transverse emission area. There is a symmetry between the contribution from the vorticity and from the shear stress tensor due to the flow-momentum correspondence. The solution can be improved systematically by perturbation method.

nucl-th↗
arXiv2024

Xmodel-LM Technical Report

We introduce Xmodel-LM, a compact and efficient 1.1B language model pre-trained on around 2 trillion tokens. Trained on our self-built dataset (Xdata), which balances Chinese and English corpora based on downstream task optimization, Xmodel-LM exhibits remarkable performance despite its smaller size. It notably surpasses existing open-source language models of similar scale. Our model checkpoints and code are publicly accessible on GitHub at https://github.com/XiaoduoAILab/XmodelLM.

cs.CL↗
arXiv2024

Optimizing food taste sensory evaluation through neural network-based taste electroencephalogram channel selection

The taste electroencephalogram (EEG) evoked by the taste stimulation can reflect different brain patterns and be used in applications such as sensory evaluation of food. However, considering the computational cost and efficiency, EEG data with many channels has to face the critical issue of channel selection. This paper proposed a channel selection method called class activation mapping with attention (CAM-Attention). The CAM-Attention method combined a convolutional neural network with channel and spatial attention (CNN-CSA) model with a gradient-weighted class activation mapping (Grad-CAM) model. The CNN-CSA model exploited key features in EEG data by attention mechanism, and the Grad-CAM model effectively realized the visualization of feature regions. Then, channel selection was effectively implemented based on feature regions. Finally, the CAM-Attention method reduced the computational burden of taste EEG recognition and effectively distinguished the four tastes. In short, it has excellent recognition performance and provides effective technical support for taste sensory evaluation.

eess.SP↗
arXiv2024

On-Device Language Models: A Comprehensive Review

The advent of large language models (LLMs) revolutionized natural language processing applications, and running LLMs on edge devices has become increasingly attractive for reasons including reduced latency, data localization, and personalized user experiences. This comprehensive review examines the challenges of deploying computationally expensive LLMs on resource-constrained devices and explores innovative solutions across multiple domains. The paper investigates the development of on-device language models, their efficient architectures, including parameter sharing and modular designs, as well as state-of-the-art compression techniques like quantization, pruning, and knowledge distillation. Hardware acceleration strategies and collaborative edge-cloud deployment approaches are analyzed, highlighting the intricate balance between performance and resource utilization. Case studies of on-device language models from major mobile manufacturers demonstrate real-world applications and potential benefits. The review also addresses critical aspects such as adaptive learning, multi-modal capabilities, and personalization. By identifying key research directions and open challenges, this paper provides a roadmap for future advancements in on-device language models, emphasizing the need for interdisciplinary efforts to realize the full potential of ubiquitous, intelligent computing while ensuring responsible and ethical deployment. For a comprehensive review of research work and educational resources on on-device large language models (LLMs), please visit https://github.com/NexaAI/Awesome-LLMs-on-device. To download and run on-device LLMs, visit https://www.nexaai.com/models.

cs.CL↗
arXiv2024

Vector meson's spin alignments in high energy reactions

The global spin alignment of vector mesons has been observed by the STAR collaboration at the Relativistic Heavy Ion Collider (RHIC) at Brookhaven National Laboratory (BNL). It provides a unique opportunity to probe the correlation between the polarized quark and antiquark in the strongly coupled quark-gluon plasma (sQGP) produced in relativistic heavy ion collisions, opening a new window to explore the properties of sQGP. In addition, spin alignments of vector mesons have also been observed in other high-energy particle collisions such as $e^+e^-$ annihilations at high energies where hadron production is dominated by quark fragmentation mechanism. The results obtained are quite different from those obtained in heavy ion collisions where quark coalescence/combination mechanism dominates suggesting strong dependence on hadronization mechanisms. So comprehensive studies in different hadronization processes are needed. In this article, we present a brief review of theoretical and experimental advances in the study of vector meson's spin alignments in a variety of high-energy particle collisions, with emphasis on hadronization mechanisms.

hep-ph↗
arXiv2024

Normalized ground state solutions of Schrödinger-KdV system in $\mathbb{R}^3$

In this paper, we study the coupled Schrödinger-KdV system \begin{align*} \begin{cases} -Δu +λ_1 u=u^3+βuv~~&\text{in}~~\mathbb{R}^{3}, \\-Δv +λ_2 v=\frac{1}{2}v^2+\frac{1}{2}βu^2~~&\text{in}~~\mathbb{R}^{3} \end{cases} \end{align*} subject to the mass constraints \begin{equation*} \int_{\mathbb{R}^{3}}|u|^2 dx=a,\quad \int_{\mathbb{R}^{3}}|v|^2 dx=b, \end{equation*} where $a, b>0$ are given constants, $β>0$, and the frequencies $λ_1,λ_2$ arise as Lagrange multipliers. The system exhibits $L^2$-supercritical growth. Using a novel constraint minimization approach, we demonstrate the existence of a local minimum solution to the system. Furthermore, we establish the existence of normalized ground state solutions.

math.AP↗
arXiv2024

Normalized solutions of $L^2$-supercritical Kirchhoff equations in bounded domains

In this paper, we investigate the existence of normalized solutions for the following nonlinear Kirchhoff type problem \begin{equation*} \begin{cases} -(a+b\int_Ω\vert\nabla u\vert^2dx)Δu+λu=\vert u\vert^{p-2}u & \text{ in }Ω,\\ u=0 & \text{ on }\partialΩ\end{cases} \end{equation*} subject to the constraint $\int_Ω\vert u\vert^2dx=c$. Here, $a$ and $b$ are positive constants, $Ω$ is a smooth bounded domain in $\mathbb{R}^N$ with $1\leq N\leq3$, $c>0$ is a prescribed value, and $λ\in \mathbb{R}$ is a Lagrange multiplier. In the $L^2$-supercritical regime $2+\frac{8}{N}<p<2^*$, we establish the existence of mountain pass-type normalized solutions. Our approach relies on utilizing a parameterized version of the minimax theorem with Morse index information for constraint functionals, and developing a blow-up analysis for the nonlinear Kirchhoff equations. Furthermore, we explore the asymptotic behavior of these solutions as $b\rightarrow0$.

math.AP↗