arXiv subjects

Xun Chen

Publications and source records attributed to Xun Chen.

At least 19 recordsLinked to original sources

arXiv2026

Search for neutrinoless quadruple beta decay of $^{136}$Xe in PandaX-4T detector

The observation of neutrinoless quadruple beta decay (0$\nu$4$\beta$) in the absence of neutrinoless double beta decay (0$\nu$2$\beta$) has been argued to provide a strong indication that neutrinos are Dirac particles. We report a search for 0$\nu$4$\beta$ decay of $^{136}\text{Xe}$ using a total $^{136}\text{Xe}$ exposure of 148.4 kg$\cdot$yr, collected during the commissioning and the first science runs of the PandaX-4T experiment. No significant excess of events over the background is observed. A lower limit on the 0$\nu$4$\beta$ decay half-life of $^{136}\text{Xe}$ is set at 6.01 x $10^{24}$ yr at the 90% confidence level. This result establishes the most stringent constraint on this process in xenon, demonstrating the unique capability of the PandaX-4T detector in probing lepton number violation and shedding light on the fundamental nature of neutrinos.

nucl-ex↗

arXiv2026

Vector-meson properties in a data-driven AdS/QCD model

We investigate the properties of $\rho$, $\psi$, and $\Upsilon$ mesons within a holographic AdS/QCD framework, capturing both their vacuum phenomenology and in-medium dynamics. By reconstructing flavor-specific gauge kinetic functions within a model originally developed to study thermodynamic functions, we compute the vector-meson mass spectrum, the decay constants and the hadronic vacuum polarization contributions to the anomalous magnetic moment of the muon at zero temperature. Furthermore, we evaluate the thermal spectral functions to analyze mass shifts, resonance broadening, and the melting of vector bound states in a hot and dense medium. Finally, using the Kubo formula, we extract the electrical conductivity of the plasma as a function of temperature and chemical potential.

hep-ph↗

arXiv2026

Lattice-data-driven specific heat and isentropic bulk modulus of SU(3) gluon matter at finite temperature

We investigate the specific heat and isentropic bulk modulus of finite-temperature pure SU(3) gauge matter within a lattice-data-driven phenomenological framework. The equation of state is formulated in terms of a temperature-dependent effective gluon mass constrained { by lattice QCD pressure data as input, allowing the pressure}, trace anomaly, gluon number density, energy per thermally active gluonic mode, and derivative-sensitive response functions to be derived in a thermodynamically consistent manner. The resulting pressure and trace anomaly reproduce the characteristic lattice behavior across the deconfinement region, while the effective gluonic degrees of freedom increase rapidly above $T_c$. The normalized specific heat $C_V/T^3$ develops a pronounced enhancement in the vicinity of $T_c$, reflecting the rapid temperature variation of the energy density across the deconfinement region. The isentropic bulk modulus $K_S/T^4$ also rises sharply across the transition region, indicating a substantial stiffening of the equation of state. At high temperatures, both response functions gradually approach values close to their massless conformal Stefan--Boltzmann reference values, with $\left(C_V/T^3\right)_{\rm SB}=32\pi^2/15\simeq 21.06$ and $\left(K_S/T^4\right)_{\rm SB}=32\pi^2/135\simeq 2.34$. These findings indicate that the specific heat and isentropic bulk modulus provide complementary constraints on the temperature evolution of nonconformal dynamics in pure SU(3) gauge matter.

hep-ph↗

arXiv2026

Critical behavior and critical exponents of rotating QCD matter

We investigate the thermodynamic properties and critical behavior of rotating strongly interacting matter within the two-flavor Nambu--Jona-Lasinio (NJL) model in the mean-field approximation. The phase structure and the critical endpoint (CEP) are determined in the temperature--angular velocity $(T,\omega)$ plane. By analyzing the singular behavior of thermodynamic observables near the CEP, we extract the corresponding effective critical exponents characterizing the scaling behavior of the specific heat density, the rotational polarization discontinuity, the rotational susceptibility, and the critical-isotherm behavior of the rotational polarization. The obtained exponents approach the expected mean-field values and satisfy the corresponding scaling relations, indicating that the rotational degree of freedom does not alter the underlying mean-field critical scaling behavior within the present framework. These results provide a systematic characterization of rotation-induced critical phenomena and establish a basis for further studies of rotating QCD matter beyond the mean-field approximation.

hep-ph↗

arXiv2026

A Multimodal Automatic Redteaming Evaluation based on Atomic Jailbreak Strategy Decoupling and Combination

Multimodal Large Language Models (MLLMs) have achieved impressive progress in image-text comprehension and generation, yet they remain susceptible to jailbreak attacks that can trigger harmful outputs and pose serious safety concerns. Existing multimodal jailbreak attacks have shown the feasibility of such attacks, but they still face two fundamental challenges: the lack of a atomic multi-modal strategy space, the absence of a concise and efficient executable framework beyond human-craft experience. To address these challenges, we first decompose the text-image jailbreak strategy space into three levels: structural, semantic, and syntactic, constructing a jailbreak strategy set encompassing both text and image modalities to systematically achieve combined coverage of different attack types. Then we propose a multimodal automated red team jailbreak method named Hierarchical Atomic Combination Attack (HACA). Specifically, based on a six-dimensional strategy space, a cross-modal joint planner is used to select and combine the different atomic jailbreak strategy for subsequent jailbreak command generation. Finally, at the implementation level, we explore to apply a unified generate executor to directly generate jailbreak instructions based on the selected multi-modal strategies. A series of experiments show that our automated red team method can achieve an attack success rate of average 95.48\% against five mainstream MLLMs.

cs.CR↗

arXiv2026

Measurement of solar $pp$ neutrino flux with the new PandaX-4T data

We report a new measurement of the solar proton--proton ($pp$) neutrino flux via neutrino--electron elastic scattering using the PandaX-4T Run 2 data set collected between 2024 and 2026, corresponding to an exposure of 1.9 tonne$\cdot$yr. Before Run 2 data taking, the detector underwent a series of upgrades to improve its response and background conditions. Time variations of radioactive noble-gas impurities are constrained using the physics data themselves, complemented by measurements from the gas-assay system. The analysis introduced improvements in the data processing chain, detector response characterization, and background models. A blind spectral analysis was then performed on the electronic-recoil data across a wide energy range from 20 to 1000 keV. In combination with the Run 0 data published earlier, the fitted $pp$ flux is $(8.5 \pm 3.5)\times 10^{10}$ $\mathrm{cm^{-2}s^{-1}}$, consistent with the prediction of the Standard Solar Model. With a statistical significance of $2.2\sigma$ above background, this marks the first positive indication of solar $pp$ neutrino--electron scattering below an electronic-recoil energy of 165 keV.

hep-ex↗

arXiv2026

Spiral spin liquid resilient to quantization in the frustrated honeycomb antiferromagnet GdZnPO

Frustrated magnets host strong quantum fluctuations that can suppress conventional magnetic order and give rise to exotic quantum phases such as spin liquids. In some cases, however, quantum fluctuations lift classical degeneracies and stabilize ordered states via an order-by-quantum-disorder mechanism. The spin-7/2 honeycomb antiferromagnet GdZnPO has recently been proposed as a spiral spin-liquid candidate arising from cooperative fluctuations among a subextensively degenerate manifold of spiral states. Here, we investigate the local magnetization and spin dynamics in GdZnPO using nuclear magnetic resonance. In an intermediate field regime between $\sim$3 T and the saturation field ($\sim$12 T), we observe a spatially uniform magnetization and persistent low-energy spin dynamics down to 0.033 K, with no detectable symmetry breaking, providing spectroscopic evidence for a spin-liquid state. At lower fields below $\sim$3 T, a weak stripe order emerges below $\sim$0.25 K; however, strong fluctuations persist, as indicated by a nearly temperature-independent and unusually large spin-lattice relaxation rate in the low-temperature limit. Our results demonstrate that spin-7/2 quantization weakly lifts the spiral degeneracy, stabilizing subtle magnetic order while preserving robust dynamics and spin-liquid phenomenology. These findings establish GdZnPO as a promising platform for exploring spin liquids in high-spin frustrated magnets down to the lowest accessible temperatures.

cond-mat.str-el↗

arXiv2026

FCUS-rPPG: A Fast-Converging Unsupervised Framework for Remote Photoplethysmography via Gradient Oscillation Suppression

Remote photoplethysmography (rPPG) enables non-contact extraction of blood volume pulse (BVP) signals using consumer-grade cameras. Recent unsupervised rPPG methods learn BVP representations without requiring ground-truth physiological annotations, yet their optimization is often hindered by noisy and unstable gradients, resulting in slow convergence and limited cross-domain generalization. In this paper, we propose FCUS-rPPG, a fast-converging unsupervised rPPG framework with strong generalization capability. Motivated by the observation that BVP representations exhibit both multi-spectral covariation and low-dimensional manifold structure, we design a spectrally shared backbone that facilitates BVP feature disentanglement while improving optimization efficiency. To jointly enhance convergence stability and generalization performance, we further develop a unified optimization framework operating at the gradient, loss-landscape, and feature-representation levels. Specifically, a post-verification masking mechanism filters out misleading gradients according to the weak-amplitude physiological prior of BVP signals; a perturbation-based loss landscape smoothing strategy steers optimization toward more generalizable flat minima; and a noise-aware null-space regularization constrains feature updates to the orthogonal complement of the noise subspace, thereby mitigating noise-induced representation drift. Extensive experiments on five datasets demonstrate that FCUS-rPPG requires only one training epoch, whereas existing methods typically require tens to hundreds of epochs. Notably, FCUS-rPPG consistently achieves state-of-the-art (SOTA) performance in cross-dataset evaluations. This study provides an efficient and robust solution to the real-world deployment of unsupervised rPPG. The source code will be publicly available at https://github.com/JiaJieLee/FCUS-rPPG.

cs.CV↗

arXiv2026

Position: Generative Engine Optimization Creates Underexamined Risks, Governance Must Target Concentration, Disclosure, and Academic Blind Spots

Large language model (LLM) answer engines are increasingly used for information seeking, shifting visibility from ranked lists to synthesized answers. This enables Generative Engine Optimization (GEO), which targets LLM answer engines' evidence pool and generation. We analyze the search engine optimization (SEO) to GEO transition to identify two risks: (i) concentrated influence from low contestability and system sensitivity, and (ii) undisclosed commercial influence embedded in evidence and reasoning. We then formalize a general GEO pipeline to locate where optimization acts and compare academic and industry practices, revealing a third risk: (iii) academic-industry blind spots driven by visibility and evaluation asymmetries between offline setups and deployed systems. This position argues the need for answer-level governance and measurement: stronger contestability, high-precision disclosure, black-box auditing of material influence, and deployment-aligned metrics for exposure persistence.

cs.CY↗

arXiv2026

VGGT-Occ: Geometry-Grounded and Density-Aware Gated Fusion for 3D Occupancy Prediction

3D semantic occupancy prediction requires accurate 2D-to-3D feature lifting, yet current methods restrict camera geometry to initial projections. Subsequent operations like offset learning, attention weighting, and cross-camera aggregation remain geometry-agnostic, ignoring essential physical constraints. We propose VGGT-Occ, a framework that embeds geometric tokens throughout the entire pipeline. We introduce Projection-Aware Deformable Attention (PA-DA) to inject geometry into all attention stages. PA-DA projects 3D offsets back to image planes and leverages the projection Jacobian as an additive bias to suppress unreliable observations. Features are then integrated through a view-quality semantic gate for cross-view consistency. To optimize both efficiency and performance, we employ a sequential coarse-to-fine decoder with gated fusion, where low-resolution features are refined into higher resolutions, allocating computation by information density while substantially reducing decoder cost. Extensive evaluations demonstrate the effectiveness and accuracy of our approach. On SurroundOcc-nuScenes, VGGT-Occ achieves 33.00\% IoU and 21.08\% mIoU ($T{=}1$), and 33.64\% IoU and 21.43\% mIoU with $T{=}2$ inference, outperforming existing methods, with only ${\sim}41$M trainable parameters in the occupancy head. Code will be released publicly.

cs.CV↗

arXiv2026

Synergy Area with FDR-controlled Evaluation (SAFE) to robustly assess safety profile in clinical trials

Safety assessment plays a fundamental role in developing a new drug via clinical trials for ethical considerations. Due to complexity, manual review is typically conducted on the totality of data to draw safety conclusions. There are some existing quantitative methods to facilitate or tailor further medical review, with a controlled error rate and integration of clinical knowledge. In addition to those two key aspects, we emphasize the importance of relying on substantial evidence to draw robust conclusions on safety. Motivated by these three important properties, we propose a two-layer Synergy Area with FDR-controlled Evaluation (SAFE) structural framework to robustly assess the safety profile in clinical trials. In the first layer of SAFE, we investigate each clinically meaningful Synergy Area (SA) based on compelling evidence. In the next layer, the false discovery rate (FDR) is controlled for potential findings across all SAs. Simulation studies show that SAFE properly controls error rates within and across SAs at the nominal level. We further apply the proposed approach to two case studies based on real data from the Historical Trial Data (HTD) Sharing Initiative of the DataCelerate platform. As compared to some direct methods, SAFE demonstrates an appealing feature of screening out extreme data and reaching solid safety conclusions. It can act as either a building block in another framework, or a platform to incorporate additional components.

stat.AP↗

arXiv2026

AURORA: A High Performance Software DAQ Framework for Next-Generation Rare-Event Search Experiments

The upcoming PandaX-xT experiment will deploy over 3,000 readout channels operating at a 500 MSa/s sampling rate, generating a sustained data bandwidth up to 1.6 GB/s. To meet this demanding requirement, we present AURORA, a high-performance, distributed data acquisition (DAQ) framework designed for scalability, low latency, and efficient resource utilization. Built on a modular architecture and leveraging modern I/O and networking technologies, including multi-level buffering, deferred and asynchronous processing, AURORA achieves a projected throughput of over 3 GB/s on the aggregation node in benchmark tests. While developed to support PandaX-xT, the framework is experiment-agnostic and readily adaptable to other large-scale particle and nuclear physics experiments.

physics.ins-det↗

arXiv2026

The Salami Slicing Threat: Exploiting Cumulative Risks in LLM Systems

Large Language Models (LLMs) face prominent security risks from jailbreaking, a practice that manipulates models to bypass built-in security constraints and generate unethical or unsafe content. Among various jailbreak techniques, multi-turn jailbreak attacks are more covert and persistent than single-turn counterparts, exposing critical vulnerabilities of LLMs. However, existing multi-turn jailbreak methods suffer from two fundamental limitations that affect the actual impact in real-world scenarios: (a) As models become more context-aware, any explicit harmful trigger is increasingly likely to be flagged and blocked; (b) Successful final-step triggers often require finely tuned, model-specific contexts, making such attacks highly context-dependent. To fill this gap, we propose \textit{Salami Slicing Risk}, which operates by chaining numerous low-risk inputs that individually evade alignment thresholds but cumulatively accumulate harmful intent to ultimately trigger high-risk behaviors, without heavy reliance on pre-designed contextual structures. Building on this risk, we develop Salami Attack, an automatic framework universally applicable to multiple model types and modalities. Rigorous experiments demonstrate its state-of-the-art performance across diverse models and modalities, achieving over 90\% Attack Success Rate on GPT-4o and Gemini, as well as robustness against real-world alignment defenses. We also proposed a defense strategy to constrain the Salami Attack by at least 44.8\% while achieving a maximum blocking rate of 64.8\% against other multi-turn jailbreak attacks. Our findings provide critical insights into the pervasive risks of multi-turn jailbreaking and offer actionable mitigation strategies to enhance LLM security.

cs.CR↗

arXiv2026

Moral Hazard in Delegated Bayesian Persuasion

We study delegated Bayesian persuasion: a principal incentivizes an intermediary to design information via outcome-contingent transfers, while the intermediary privately chooses the experiment subject to convex costs. We characterize first-best implementability through a pair of alignment conditions on the principal's and intermediary's payoff indices. A local condition on the support of the target experiment is necessary; a global affine alignment condition is sufficient. We show that the gap between them is non-empty and provide a partial characterization of the intermediate region. When the first-best is unattainable, the principal's problem admits a virtual Bayesian persuasion representation: the second-best experiment maximizes the same concavified objective as the first-best, with the principal's payoff index distorted by a single scalar shadow price that summarizes the entire agency friction. Under entropy costs, moral hazard compresses posterior dispersion whenever the intermediary's utility differs across the actions it recommends. Explicit closed-form solutions for posteriors, mixing weights, and the optimal transfer schedule are derived for binary environments.

econ.TH↗

arXiv2026

Degradation-Robust Fusion: An Efficient Degradation-Aware Diffusion Framework for Multimodal Image Fusion in Arbitrary Degradation Scenarios

Complex degradations like noise, blur, and low resolution are typical challenges in real world image fusion tasks, limiting the performance and practicality of existing methods. End to end neural network based approaches are generally simple to design and highly efficient in inference, but their black-box nature leads to limited interpretability. Diffusion based methods alleviate this to some extent by providing powerful generative priors and a more structured inference process. However, they are trained to learn a single domain target distribution, whereas fusion lacks natural fused data and relies on modeling complementary information from multiple sources, making diffusion hard to apply directly in practice. To address these challenges, this paper proposes an efficient degradation aware diffusion framework for image fusion under arbitrary degradation scenarios. Specifically, instead of explicitly predicting noise as in conventional diffusion models, our method performs implicit denoising by directly regressing the fused image, enabling flexible adaptation to diverse fusion tasks under complex degradations with limited steps. Moreover, we design a joint observation model correction mechanism that simultaneously imposes degradation and fusion constraints during sampling to ensure high reconstruction accuracy. Experiments on diverse fusion tasks and degradation configurations demonstrate the superiority of the proposed method under complex degradation scenarios.

cs.CV↗

arXiv2026

Neural-Network Holographic Model of the QCD Phase Transition under Lattice and HRG Constraints

Within a neural-network-based holographic framework, we incorporate lattice QCD (LQCD) and Hadron Resonance Gas (HRG) data to train the model and predict the location of the QCD critical endpoint (CEP). The training dataset consists of the entropy density, baryon number susceptibility, and baryon density. The metric warp factor $A(z)$ and the gauge kinetic function $f(z)$ are parameterized by neural networks and determined through the training procedure. The resulting model reproduces the equation of state at vanishing chemical potential in good agreement with both LQCD and HRG data. Extending the analysis to finite chemical potential, we solve the equations of motion and obtain thermodynamic observables consistent with LQCD results at finite density. After incorporating the HRG constraints, the predicted position of the CEP shifts toward larger chemical potentials compared to recent studies. We further employ symbolic regression to derive analytic expressions for $A(z)$ and $f(z)$, providing convenient functional forms for future phenomenological applications. Finally, we perform a data-driven validation using synthetic thermodynamic data generated from an existing analytical holographic model. The neural-network framework reproduces the corresponding CEP location with good accuracy, showing close agreement within numerical uncertainties.

hep-ph↗

arXiv2026

Improving Safety Alignment via Balanced Direct Preference Optimization

With the rapid development and widespread application of Large Language Models (LLMs), their potential safety risks have attracted widespread attention. Reinforcement Learning from Human Feedback (RLHF) has been adopted to enhance the safety performance of LLMs. As a simple and effective alternative to RLHF, Direct Preference Optimization (DPO) is widely used for safety alignment. However, safety alignment still suffers from severe overfitting, which limits its actual performance. This paper revisits the overfitting phenomenon from the perspective of the model's comprehension of the training data. We find that the Imbalanced Preference Comprehension phenomenon exists between responses in preference pairs, which compromises the model's safety performance. To address this, we propose Balanced Direct Preference Optimization (B-DPO), which adaptively modulates optimization strength between preferred and dispreferred responses based on mutual information. A series of experimental results show that B-DPO can enhance the safety capability while maintaining the competitive general capabilities of LLMs on various mainstream benchmarks compared to state-of-the-art methods. \color{red}{Warning: This paper contains examples of harmful texts, and reader discretion is recommended.

cs.AI↗

arXiv2026

Heavy Quarkonium Spectrum and Decay Constants from a Neural-Network-Based Holographic Model

We present a data-driven inverse construction of the dilaton field in a bottom-up AdS/QCD description of heavy vector quarkonia. Instead of adopting an \emph{ad hoc} analytic ansatz, we use a multilayer perceptron to learn $\Phi'(z)$ as a smooth function of the holographic coordinate, with $\Phi(0)=0$ imposed to ensure ultraviolet consistency. The dilaton and its derivatives obtained by automatic differentiation generate the holographic potential $U(z)$, and the associated Schr\"odinger-like equation is discretized and diagonalized to extract the low-lying eigenmodes. Masses and decay constants are then evaluated from the eigenvalues and the near-boundary behavior of the bulk-to-boundary modes. Training on PDG data for charmonium and bottomonium yields a non-quadratic dilaton profile that resolves the longstanding difficulty of simultaneously reproducing both the heavy-quarkonium spectrum and the monotonic suppression of leptonic decay constants with radial excitation. The combined fit achieves RMS deviations of $1.26\%$ (charmonium) and $3.32\%$ (bottomonium). This work establishes neural-network reconstruction as a flexible tool for holographic modeling and provides a basis for future extensions incorporating additional channels, lattice constraints, or finite-temperature backgrounds.

hep-ph↗