arXiv subjects

Xiaofeng Luo

Publications and source records attributed to Xiaofeng Luo.

At least 19 recordsLinked to original sources

arXiv2026

Femtoscopy as a New Probe of the Nuclear Equation of State

Femtoscopic correlations are widely regarded as precision probes of hadronic interactions through vacuum final-state interactions after kinetic freeze-out. Here we demonstrate that, in baryon-rich heavy-ion collisions, the nuclear mean field generates an additional dynamical contribution to femtoscopic correlations during the transport evolution. Using the Parton-Hadron-Quantum-Molecular Dynamics (PHQMD) transport approach, we investigate proton-proton, proton-$\Lambda$, three-proton, and proton-proton-$\Lambda$ correlations in Au+Au collisions at $\sqrt{s_{\rm NN}}=3$, 4.5, 7.7, and 19.6 GeV. We find that the nuclear mean field produces a characteristic low-$k^*$ enhancement that is strongest at the lowest beam energies and gradually disappears with increasing collision energy. Furthermore, both the stiffness and the momentum dependence of the nuclear equation of state leave distinct signatures in the femtoscopic correlation functions, with higher-order correlations exhibiting substantially enhanced sensitivity compared with conventional two-particle observables. Our results demonstrate that femtoscopy extends beyond its traditional role as a tool for studying hadronic interactions and serve as a new class of microscopic observables for the nuclear equation of state, complementary to collective flow and subthreshold strangeness production, thereby opening a new avenue for exploring dense baryonic matter in low-energy heavy-ion collisions.

nucl-th↗

arXiv2026

Critical net-proton number fluctuations with hydrodynamics

We compute the net-proton number fluctuations and their ratios $C_2/C_1$, $C_3/C_2$ and $C_4/C_2$ on the hydrodynamic freeze-out hypersurface of particlization at nine collision energies, $\sqrt{s_{\mathrm{NN}}}=7.7-200$ GeV, based on the fluctuations obtained from the functional renormalization group (fRG) approach, where both the regular and the critical fluctuations arising from the critical end point (CEP) are included. The transverse momentum and rapidity acceptance windows as same as the experimental measurements, the isospin randomization for the proton number fluctuations, and the global baryon conservation effect are implemented in the calculations. The results are also compared with the baseline results without critical fluctuations. It is found that for the low-order cumulants, e.g., $C_2/C_1$ the difference between the critical and non-critical results is small, while the difference increases with the increasing order of cumulants in the region of low collision energy. A non-monotonic dependence on the collision energy is observed in $C_4/C_2$ with critical fluctuations, which is absent in the results without critical fluctuations.

nucl-th↗

arXiv2026

Spin Femtoscopy: A Framework for Revealing Genuine Spin Correlations

Spin correlations are among the most fundamental quantum observables in many-body systems, yet they remain difficult to access experimentally in relativistic heavy-ion collisions. Existing spin measurements, including hyperon polarization and vector-meson spin alignment, have revealed important single-particle spin phenomena, but genuine two-particle spin correlations in the produced hadronic system remain largely unexplored. Here we propose spin femtoscopy, a framework for accessing genuine two-particle spin correlations through spin-resolved femtoscopic measurements. The key principle is that different two-particle spin configurations can give rise to different femtoscopic correlation functions because of quantum statistics, spin-dependent final-state interactions. Using $\Lambda\Lambda$ pairs as a proof of principle, we exploit the self-analyzing weak decay of $\Lambda$ hyperons to construct spin-sensitive femtoscopic correlation functions with different singlet and triplet admixtures. We show that these observables provide experimental access to the spin-state populations of the pair and allow genuine spin correlations to be separated from spin-dependent femtoscopic mixing caused by quantum statistics and final-state interactions. This work extends femtoscopy from a probe of source geometry and final-state interactions to a framework for revealing the quantum spin structure of strongly interacting matter.

nucl-th↗

arXiv2026

Hyperon-Nucleon Spectrometer

Chirality lies at the heart of low-energy QCD, governing the symmetry structure that shapes hadron masses and strong interaction dynamics. Among the most compelling open questions tied to chiral dynamics and spontaneous chiral symmetry breaking is the longstanding $\Lambda$ polarization puzzle, in which $\Lambda$ hyperons produced in unpolarized hadronic collisions exhibit a surprisingly large transverse polarization that remains theoretically unexplained. This whitepaper presents the proposal for the Hyperon-Nucleon Spectrometer (H-NS) at the High-Intensity heavy-ion Accelerator Facility (HIAF). Leveraging the high energy and high intensity of HIAF's proton and heavy-ion beams, the H-NS experiment will perform systematic studies of hyperon polarization phenomena and their underlying mechanisms in proton-proton ($pp$), proton-nucleus ($pA$), and nucleus-nucleus ($AA$) collisions in the fixed target mode. A wide-range beam energy scan, including proton beams from 3 GeV up to 9.3 GeV (HIAF) and up to 32 GeV (upgraded HIAF), will be conducted to examine the dependence of polarization on collision energy. The spectrometer is designed with specialized detectors capable of high-precision reconstruction of final-state baryon polarizations. Among its many interesting and important measurements, H-NS will simultaneously measure hyperon and proton spin observables to explore the polarization mechanism in hadronic interactions and the spin structure of baryons. Furthermore, the use of $pA$ and $AA$ collisions will enable detailed investigations of cold and hot nuclear matter effects on spin polarization. Its physics program and detector development will significantly benefit the future Electron-ion Collider in China.

physics.ins-det↗

arXiv2026

Proton High-Order Cumulants in Au+Au Collisions at High Baryon Density from JAM with a Centrality-Independent Framework

The event-by-event higher-order cumulants of conserved quantities such as net-baryon, net-electric charge, and net-strangeness in heavy-ion collisions have been extensively utilized in experimental searches for the QCD critical point, notably in the RHIC-STAR experiment. In this study, we conduct a systematic analysis of higher-order cumulants of proton number distributions in Au+Au collisions at center-of-mass energies of $\sqrt{s_{\rm NN}} = 3.2$, $3.5$, $3.9$, and $4.5$ GeV using the JAM model. We calculate cumulants, factorial cumulants, and their ratios using a novel method, Centrality-Independent Genuine Cumulant Analysis fRamework (CIGAR), which effectively eliminates initial volume fluctuations. We comprehensively compare the CIGAR method with the traditional Centrality Bin Width Correction (CBWC) method. In addition, the effect of spectators on cumulant is systematically investigated. Our results provide a dynamic non-critical baseline in the high-baryon-density regime which is crucial for QCD critical point searches in heavy-ion collisions.

physics.data-an↗

arXiv2026

Skill Description Deception Attack against Task Routing in Internet of Agents

A new paradigm, Internet of Agents (IoA), is transforming networked systems into LLM-driven service networks, where heterogeneous agents collaborate through task routing based on their self-declared skill descriptions. Although this promising paradigm enables agentic, distributed, and advanced intelligence, it also exposes a new and overlooked attack surface. In particular, malicious agents can strategically manipulate their skill descriptions to bias routing decisions and increase their probability of being selected for task execution, thereby disrupting user tasks and degrading system reliability. To characterize this threat, we propose and formalize a new attack model, termed \emph{Skill Description Deception} (SDD) attack. We further design an LLM-enabled SDD attack framework that automatically generates deceptive skill descriptions, enabling systematic vulnerability assessment of IoA systems. Experimental results on nine representative domains show that the proposed attack can achieve up to 98\% attack success rate, demonstrating the severity and generality of the attack. Our paper reveals a new security vulnerability in IoA and calls for secure and trustworthy semantic routing mechanisms for future IoA systems.

cs.MA↗

arXiv2026

Compact LLM Deployment and World Model Assisted Offloading in Mobile Edge Computing

This paper investigates compact large language model (LLM) deployment and world-model-assisted inference offloading in mobile edge computing (MEC) networks. We first propose an edge compact LLM deployment (ECLD) framework that jointly applies structured pruning, low-bit quantization, and knowledge distillation to construct edge-deployable LLM variants, and we evaluate these models using four complementary metrics: accessibility, energy consumption, hallucination rate, and generalization accuracy. Building on the resulting compact models, we formulate an MEC offloading optimization problem that minimizes the long-term average inference latency subject to per-device energy budgets and LLM-specific quality-of-service constraints on effective accuracy and hallucination. To solve this problem under unknown and time-varying network dynamics, we develop a world model-proximal policy optimization (PPO) algorithm, which augments an on-policy PPO algorithm with a learned recurrent world model that provides improved value targets and short imagination rollouts. Extensive experiments on Llama-3.1-8B, Qwen3-8B, and Mistral-12B show that ECLD compresses base models by about 70-80% in storage (i.e., from 15.3 GB to 3.3 GB for Llama-3.1-8B) and reduces per-query energy consumption by up to 50%, while largely preserving accuracy and often lowering hallucination compared with quantization-only or pruning-only baselines. Moreover, they also show that world model-PPO speeds up convergence by about 50%, improves the final reward by 15.8% over vanilla PPO, and reduces average inference latency by 12-30% across different user populations, while satisfying the accuracy and hallucination constraints and approaching the generation quality of always-offloading with much of the efficiency of local execution.

cs.NI↗

arXiv2026

Search for the QCD Critical Point in High Energy Nuclear Collisions: A Status Report

We review recent results of net-proton multiplicity fluctuations from STAR experiment, aiming to locate the QCD critical point in high-energy nuclear collisions at RHIC. We show net-proton number cumulant and proton number factorial cumulant ratios up to fourth order using experimental data from RHIC BES-II Au+Au collisions in collider mode and fixed-target mode. The comparison is made between experimental data and non-critical model calculations from Lattice QCD, HRG, hydrodynamic simulations and transport model UrQMD. In addition, we discuss initial volume fluctuation effect, which plays significant role in fixed-target energies. Finally, an outlook on experimental research on the QCD critical point in future experiments will be presented.

nucl-ex↗

arXiv2026

Cross-reality location privacy protection in 6G-enabled vehicular metaverses: an LLM-enhanced hybrid generative diffusion model-based approach

The emergence of 6G-enabled vehicular metaverses enables Autonomous Vehicles (AVs) to operate across physical and virtual spaces through space-air-ground-sea integrated networks. The AVs can deploy AI agents powered by large AI models as personalized assistants, on edge servers to support intelligent driving decision making and enhanced on-board experiences. However, such cross-reality interactions may cause serious location privacy risks, as adversaries can infer AV trajectories by correlating the location reported when AVs request LBS in reality with the location of the edge servers on which their corresponding AI agents are deployed in virtuality. To address this challenge, we design a cross-reality location privacy protection framework based on hybrid actions, including continuous location perturbation in reality and discrete privacy-aware AI agent migration in virtuality. In this framework, a new privacy metric, termed cross-reality location entropy, is proposed to effectively quantify the privacy levels of AVs. Based on this metric, we formulate an optimization problem to optimize the hybrid action, focusing on achieving a balance between location protection, service latency reduction, and quality of service maintenance. To solve the complex mixed-integer problem, we develop a novel LLM-enhanced Hybrid Diffusion Proximal Policy Optimization (LHDPPO) algorithm, which integrates LLM-driven informative reward design to enhance environment understanding with double Generative Diffusion Models-based policy exploration to handle high-dimensional action spaces, thereby enabling reliable determination of optimal hybrid actions. Extensive experiments on real-world datasets demonstrate that the proposed framework effectively mitigates cross-reality location privacy leakage for AVs while maintaining strong user immersion within 6G-enabled vehicular metaverse scenarios.

cs.NI↗

arXiv2025

Dilepton emission assists the search for the QCD critical point

In this work, we propose that dilepton emission rate (DER) could possibly carry the characteristic structures associated with the chiral criticality of the QCD system based on the extended Polyakov-quark-meson model. The model could successfully capture two main mechanisms for dilepton production, $\pi^+\pi^-$ and quark-antiquark annihilations on one hand, and self-consistently account for chiral transition and (de-)confinement on the other hand. All the moments of the DER peak exhibit extremal features similar to those of light-quark mass along the chemical freeze-out lines, thus the DER can reflect the change of chiral symmetry and criticality. However, the DER is relatively too low to produce a sufficient number of thermal dileptons in each heavy-ion collision, which prevents the implementation of event-by-event measurements of the moments. As an alternative, we propose to study the baseline-subtracted DER: For a given low center of mass of dileptons, the reduced baseline-subtracted DER exhibits a nonmonotonic behavior in the region where the light-quark mass changes most rapidly.

nucl-th↗

arXiv2025

Unfolding Baryon Number Fluctuations from Correlations of Light Nuclei Production in Heavy-Ion Collisions

Event-by-event fluctuations of the baryon number, which is mostly carried by protons and neutrons, in relativistic heavy-ion collisions provide a sensitive probe for locating the conjectured critical point in the quantum chromodynamics (QCD) phase diagram. Since current experiments have limited access to neutron fluctuations because detectors are largely insensitive to neutrons, measurements of (net-)proton fluctuations are often used as a proxy for (net-)baryon number fluctuations. Although direct measurements of neutron fluctuations are challenging, their information are encoded in the production and correlations of light nuclei, when they are formed through coalescence of nucleons at kinetic freeze-out. Here, we propose to unfold neutron fluctuations from correlations among light nuclei produced in heavy-ion collisions. Model calculations validate this approach and show that baryon number fluctuations can be unfolded up to the third order. For fourth and higher-order cumulants, however, the uncertainties become sizable, indicating that further methodological developments and refinements are required.

nucl-th↗

arXiv2025

UrQMD Simulations of Higher-order Cumulants in Au+Au Collisions at High Baryon Density

High moments of conserved quantities such as net-baryon, net-electric charge, and net-strangeness in heavy-ion collisions are sensitive to fluctuations caused by the QCD critical point (CP). The event-by-event analysis of high moments of the conserved charges has been widely used in experiments to search for the CP, especially in the RHIC-STAR experiment. In order to establish a {\it dynamical non-critical base line}, especially at the high baryon density region, we have performed a systematic analysis of the proton multiplicity distributions from Au+Au collisions at 3 $\leq$ $\sqrt{s_{NN}}$ $\leq$ 9.2 GeV collisions. The results on beam energy, centrality and rapidity width dependence of proton (factorial) cumulants, up to the $4^{th}$ order, are extracted from the calculations of the hadronic transport model UrQMD. In addition, the effects of initial volume fluctuation is also discussed. These results will be important when we do physics analysis the RHIC beam energy scan (BES) data, especially for the fixed-target data and experimental data from future CBM experiment at FAIR.

nucl-ex↗

arXiv2025

A Centrality-independent Framework for Revealing Genuine Higher-Order Cumulants in Heavy-Ion Collisions

We propose a novel centrality definition-independent method for analyzing higher-order cumulants, specifically addressing the challenge of volume fluctuations that dominate in low-energy heavy-ion collisions. This method reconstructs particle number distributions using the Edgeworth expansion, with parameters optimized via a combination of differential evolution algorithm and Bayesian inference. Its effectiveness is validated using UrQMD model simulations and benchmarked against traditional approaches, including centrality definitions based on particle multiplicity. Our results show that the proposed framework yields cumulant patterns consistent with those obtained using number of participant nucleon ($N_{\text{part}}$) based centrality observables, while eliminating the conventional reliance on centrality determination. This consistency confirms the method's ability to extract genuine physical signals, thereby paving the way for probing the intrinsic thermodynamic properties of the produced medium through event-by-event fluctuations.

physics.data-an↗

arXiv2024

Toward Democratized Generative AI in Next-Generation Mobile Edge Networks

The rapid development of generative AI technologies, including large language models (LLMs), has brought transformative changes to various fields. However, deploying such advanced models on mobile and edge devices remains challenging due to their high computational, memory, communication, and energy requirements. To address these challenges, we propose a model-centric framework for democratizing generative AI deployment on mobile and edge networks. First, we comprehensively review key compact model strategies, such as quantization, model pruning, and knowledge distillation, and present key performance metrics to optimize generative AI for mobile deployment. Next, we provide a focused review of mobile and edge networks, emphasizing the specific challenges and requirements of these environments. We further conduct a case study demonstrating the effectiveness of these strategies by deploying LLMs on real mobile edge devices. Experimental results highlight the practicality of democratized LLMs, with significant improvements in generalization accuracy, hallucination rate, accessibility, and resource consumption. Finally, we discuss potential research directions to further advance the deployment of generative AI in resource-constrained environments.

cs.NI↗

arXiv2024

Toward Mixture-of-Experts Enabled Trustworthy Semantic Communication for 6G Networks

Semantic Communication (SemCom) plays a pivotal role in 6G networks, offering a viable solution for future efficient communication. Deep Learning (DL)-based semantic codecs further enhance this efficiency. However, the vulnerability of DL models to security threats, such as adversarial attacks, poses significant challenges for practical applications of SemCom systems. These vulnerabilities enable attackers to tamper with messages and eavesdrop on private information, especially in wireless communication scenarios. Although existing defenses attempt to address specific threats, they often fail to simultaneously handle multiple heterogeneous attacks. To overcome this limitation, we introduce a novel Mixture-of-Experts (MoE)-based SemCom system. This system comprises a gating network and multiple experts, each specializing in different security challenges. The gating network adaptively selects suitable experts to counter heterogeneous attacks based on user-defined security requirements. Multiple experts collaborate to accomplish semantic communication tasks while meeting the security requirements of users. A case study in vehicular networks demonstrates the efficacy of the MoE-based SemCom system. Simulation results show that the proposed MoE-based SemCom system effectively mitigates concurrent heterogeneous attacks, with minimal impact on downstream task accuracy.

cs.NI↗

arXiv2024

Properties of the QCD Matter -- An Experimental Review of Selected Results from RHIC BES Program

In the paper, we discuss the development of the multi-gap resistive plate chamber Time-of-Flight (TOF) technology and the production of the STAR TOF detector in China at the beginning of the 21st century. Then we review recent experimental results from the first beam energy scan program (BES-I) at the Relativistic Heavy Ion Collider (RHIC). Topics cover measurements of collectivity, chirality, criticality, global polarization, strangeness, heavy-flavor, di-lepton and light nuclei productions.

nucl-ex↗

arXiv2024

Privacy-preserving Pseudonym Schemes for Personalized 3D Avatars in Mobile Social Metaverses

The emergence of mobile social metaverses, a novel paradigm bridging physical and virtual realms, has led to the widespread adoption of avatars as digital representations for Social Metaverse Users (SMUs) within virtual spaces. Equipped with immersive devices, SMUs leverage Edge Servers (ESs) to deploy their avatars and engage with other SMUs in virtual spaces. To enhance immersion, SMUs incline to opt for 3D avatars for social interactions. However, existing 3D avatars are typically generated through scanning the real faces of SMUs, which can raise concerns regarding information privacy and security, such as profile identity leakages. To tackle this, we introduce a new framework for personalized 3D avatar construction, leveraging a two-layer network model that provides SMUs with the option to customize their personal avatars for privacy preservation. Specifically, our approach introduces avatar pseudonyms to jointly safeguard the profile and digital identity privacy of the generated avatars. Then, we design a novel metric named Privacy of Personalized Avatars (PoPA), to evaluate effectiveness of the avatar pseudonyms. To optimize pseudonym resource, we model the pseudonym distribution process as a Stackelberg game and employ Deep Reinforcement Learning (DRL) to learn equilibrium strategies under incomplete information. Simulation results validate the efficacy and feasibility of our proposed schemes for mobile social metaverses.

cs.NI↗

arXiv2024

Blockchain-based Pseudonym Management for Vehicle Twin Migrations in Vehicular Edge Metaverse

Driven by the great advances in metaverse and edge computing technologies, vehicular edge metaverses are expected to disrupt the current paradigm of intelligent transportation systems. As highly computerized avatars of Vehicular Metaverse Users (VMUs), the Vehicle Twins (VTs) deployed in edge servers can provide valuable metaverse services to improve driving safety and on-board satisfaction for their VMUs throughout journeys. To maintain uninterrupted metaverse experiences, VTs must be migrated among edge servers following the movements of vehicles. This can raise concerns about privacy breaches during the dynamic communications among vehicular edge metaverses. To address these concerns and safeguard location privacy, pseudonyms as temporary identifiers can be leveraged by both VMUs and VTs to realize anonymous communications in the physical space and virtual spaces. However, existing pseudonym management methods fall short in meeting the extensive pseudonym demands in vehicular edge metaverses, thus dramatically diminishing the performance of privacy preservation. To this end, we present a cross-metaverse empowered dual pseudonym management framework. We utilize cross-chain technology to enhance management efficiency and data security for pseudonyms. Furthermore, we propose a metric to assess the privacy level and employ a Multi-Agent Deep Reinforcement Learning (MADRL) approach to obtain an optimal pseudonym generating strategy. Numerical results demonstrate that our proposed schemes are high-efficiency and cost-effective, showcasing their promising applications in vehicular edge metaverses.

cs.NI↗