arXiv subjects

Hua Zheng

Publications and source records attributed to Hua Zheng.

At least 19 recordsLinked to original sources

arXiv2026

ALP photo-production in the parton and hadron cascade model PACIAE

In this work, the parton and hadron cascade model PACIAE is employed to simulate the events of Au+Au collisions at $\sqrt{s_{NN}}=$200 GeV within the 60-80 \% centrality interval. Based on the abundant photons in final hadronic state, the statistical-mechanics-based coalescence model DCPC is applied to phenomenologically recombine post-freeze-out photon pairs into axion-like particles (ALPs) for the first time. The coalescence probability is constrained by the branching-ratio $B_r(a \rightarrow γγ)=0.57$ under the threshold of twice of $K$ meson mass. The ALP observables including yields, transverse-momentum distributions and rapidity spectra are calculated for several benchmark ALP-mass bins. These theoretical predictions are worthy to be investigated by the experiments.

hep-ph↗

arXiv2026

Measuring Behavioural Signatures of Large Language Models through Psychometric Profiling

Large language models (LLMs) increasingly mediate human decisions and communication, yet their behavioural regularities remain difficult to characterize systematically. We develop a cross-linguistic psychometric profiling framework and evaluate nine LLMs using seven psychological instruments, with five repeated administrations per model and language in Chinese and English. Items unresolved after a prespecified retry procedure are retained as NA. Joint analysis of scored and NA responses captures response tendencies and boundaries of self-report applicability. LLMs exhibit structured, model-specific profiles despite a shared alignment-shaped pattern of higher prosocial and self-regulatory responses and lower dominance, disengagement and harmful-intent endorsement. NA responses are structured rather than uniformly distributed, indicating where outputs are treated as inapplicable, refused or cannot be mapped to valid response options. Language condition and provider origin are associated with profile configuration and answerability, whereas repeated administrations show high reproducibility and permit recovery of model identity. Human-reference and prompt-robustness analyses further indicate that these signatures are context dependent. Joint analysis of psychometric profiling and answerability offers a framework for quantifying deployment-level behavioural signatures.

cs.CL↗

arXiv2026

Variance Reduction Based Experience Replay for Policy Optimization

Effective reinforcement learning (RL) for complex stochastic systems requires leveraging historical data to improve sample efficiency and accelerate policy optimization. However, classical experience replay treats all past observations uniformly and fails to account for their varying contributions to learning. To address this limitation, we propose Variance Reduction Experience Replay (VRER), a principled framework that selectively reuses informative samples to reduce the variance of policy gradient estimates. VRER is algorithm-agnostic and can be integrated with existing policy optimization methods, yielding the sample-efficient off-policy algorithm, Policy Gradient with VRER (PG-VRER). To provide rigorous theoretical guarantees, we develop a novel analysis framework for experience replay that explicitly accounts for dependencies induced by Markovian dynamics and behavior-policy interactions. Using this framework, we establish finite-time convergence guarantees for PG-VRER and characterize a fundamental bias-variance trade-off: reusing older samples reduces gradient variance but may introduce greater estimation bias. Extensive experiments show that VRER consistently accelerates learning and outperforms state-of-the-art policy optimization algorithms

stat.ML↗

arXiv2026

Shedding light on the nature of $ϕ(2170)$ with the parton and hadron cascade model PACIAE

The nature of $ϕ(2170)$ remains open. We simulate its production in $e^+e^-$ collisions at $\sqrt{s}=4.95$ GeV using PACIAE 4.0, which sequentially generates the final partonic state (FPS) and the final hadronic state (FHS). While previous studies have interpreted $ϕ(2170)$ as an $ss\bar{s}\bar{s}$ or a $u\bar{u}s\bar{s}$ state, the $U(1)$ anomaly coupling allows non-strange quarks to couple to a vector $s\bar{s}$ component via soft-gluon interactions. This motivates us to also explore the $d\bar{d}s\bar{s}$ tetraquark configuration. In addition, we consider $ϕ(2170)$ as an excited strangeonium state, an $s\bar{s}g$ hybrid state, a $\barΛΛ$ bound state, and a $ϕK^+K^-$ resonance state. The strangeonium, hybrid, and tetraquark candidates are formed by coalescing their constituent partons in the FPS using the dynamically constrained phase-space coalescence model. The $\barΛΛ$ and $ϕK^+K^-$ states are produced via recombination of their constituent hadrons in the FHS. We calculate the orbital angular momentum quantum number of each candidate in its rest frame and perform spectral classification. Given $J^{PC}=1^{--}$, $ϕ(2170)$ can be interpreted as a $D$-wave $s\bar{s}$, a $P$-wave $s\bar{s}g$, a $P$-wave $u\bar{u}s\bar{s}/d\bar{d}s\bar{s}/ss\bar{s}\bar{s}$, an $S$-wave $\barΛΛ$, or an $S$-wave $ϕK^+K^-$ state. The yields of the $D$-wave $s\bar{s}$, $P$-wave $s\bar{s}g$, $u\bar{u}s\bar{s}$ and $d\bar{d}s\bar{s}$ states are of order $10^{-4}$; those for the $S$-wave $\barΛΛ$ and $ϕK^+K^-$ states are of order $10^{-5}$; while the $P$-wave $ss\bar{s}\bar{s}$ yield is of order $10^{-6}$. Moreover, significant discrepancies are observed in the rapidity distributions and the $p_T$ spectra among the various candidates. These discrepancies could serve as valuable criteria for unraveling the nature of $ϕ(2170)$.

hep-ph↗

arXiv2026

Hadronization of $Λ_c^+$ baryons from recombination model in Pb+Pb collisions at the Large Hadron Collider

The production of $Λ_c^+$ baryons in Pb+Pb collisions at $\sqrt{s_{NN}} = 5.02$ TeV is investigated within the quark recombination framework, including the energy loss of light and charm quarks inside the hot and dense medium. The model simultaneously describes the transverse momentum ($p_T$) spectra of $Λ_c^+$ baryons, $Λ_c^+/D^0$ yield ratio, which is attributed to the dominance of quark recombination mechanism in the Quark-Gluon Plasma (QGP), and the second harmonic coefficient of $Λ_c^+$ baryons with emphasis on the effects of minijets on the azimuthal anisotropy. Furthermore, we extend the theoretical calculation to Pb+Pb collisions at $\sqrt{s_{NN}} =2.76$ TeV and make predictions for $Λ_c^+$ baryons and $Λ_c^+/D^0$ yield ratio. The simultaneous description of the yield, baryon-to-meson ratio, and azimuthal anisotropy further validates that the recombination model is an effective hadronization mechanism in heavy-ion collisions.

hep-ph↗

arXiv2026

Nuclear shell evolution near N = 6, 14, 20 and 28: insights from nuclear charge radii of short-lived nuclei derived from binding energies

A deep understanding of the evolution of nuclear shell structure correlating with the nucleon number is crucial for unraveling the fundamental properties of the nuclear structure and for exploring new nuclear physics phenomena far from the $β$-stability line. Although significant progress has been made in probing nuclear shell evolution via the measurements of nuclear root-mean-square charge radii, $R_{\text{ch}}$, the scarcity of new data for short-lived and exotic nuclei due to the increasing difficulty of measurements presents a formidable challenge in obtaining deeper and more universal insights into the nature of shell evolution. To mitigate this issue, we develop an improved method, accounting for the exchange term, charge-symmetry breaking effect, and odd-even staggering effect in the Coulomb energy formulation compared with that proposed by Liu et al. [Phys. Lett. B 872, 140046 (2026)], to determine unmeasured $R_{\text{ch}}$ values. Using the improved method, the $R_{\text{ch}}$ values of 59 nuclei are determined from their measured binding energies ($B$) and the respective $B$ and $R_{\text{ch}}$ of their mirror partners. We then systematically study the shell evolution near $N=6$, 14, 20 and 28 (sub)shells by placing the newly obtained $R_{\text{ch}}$ values into the corresponding isotopic chains. More comprehensive insights into the properties of nuclear shell evolution, particularly for the neutron-deficient sectors of the studied shell regions, e.g., $p$, $sd$ and $pf$ shells, are acquired, advancing our understanding of nuclear shell evolution in the light and intermediate mass region.

nucl-th↗

arXiv2026

A novel approach for studying two-particle momentum correlation function in relativistic nuclear collisions

Two particle momentum correlation functions provide a nontrivial tool for probing the strong interaction and/or extracting particle emission source information in relativistic nuclear collisions. Although transport models can describe the microscopic phase-space evolution of the collision system, calculating correlation functions within the framework of transport models remains challenging. In this paper, we employ the mixed-event technique to calculate two particle momentum correlation function as $C(k^*)= \mathcal{N} ξ(k^*)\frac{N_{\mathrm{same}}(k^*)}{N_{\mathrm{mixed}}(k^*)}$ based on the parton and hadron cascade model PACIAE simulated final hadronic state (FHS) with introducing a modification factor $ξ(k^*)$ to improve the treatment of final-state interactions and quantum statistics effects in the PACIAE model. The simulated results show good agreement with the ALICE data for $Kp$, $pp$, $pΛ$, and $ΛΛ$ momentum correlation functions in $pp$ collisions at $\sqrt{s}=7$ TeV. On the other hand, the particle emission source radius of the correlated pairs are also evaluated based on the simulated FHS self-consistently. Since the PACIAE model employs hadron-hadron cross sections derived from the additive quark model, the calculation of two-particle momentum correlation functions does not require prior assumptions about the interaction between the two correlated particles. This successful ``PACIAE + modification factor" approach may shed light on the future study of momentum correlation functions for dimesons, dibaryons, and even diexotic hadrons.

hep-ph↗

arXiv2026

QCD matter at a finite magnetic field and nonzero chemical potential

We construct a hybrid equation of state (EoS) by smoothly interpolating the EoS in the hadron resonance gas at low temperatures to that in the ideal parton gas at high temperatures, and employ it to study the properties of the quantum chromodynamics (QCD) matter under finite magnetic field and nonzero chemical potential. In this work, we neglect the anomalous magnetic moment effects of both charged and neutral particles. Our results show that the thermodynamic observables such as the entropy density, the pressure, the energy density, the trace anomaly, and the specific heat at constant volume are sensitive to both finite magnetic field and chemical potential. As the chemical potential increases from zero, these quantities rise in both the hadronic and quark-gluon plasma phases. In contrast, introducing a magnetic field suppresses them at low temperatures but enhances them at high temperatures. Furthermore, nonzero chemical potential and magnetic field introduce nontrivial modifications to the squared speed of sound. Both effects increase its value near the critical temperature while reducing it at lower temperatures. When both the chemical potential and the magnetic field are present, their influences superimpose, leading to more intricate changes in the thermodynamic behavior. Finally, we compare our results with the lattice QCD data for the quadratic fluctuations of conserved charges and their correlations. The model successfully reproduces the temperature dependence of these observables at $eB=0$ and 0.04 GeV$^2$. However, at the stronger field strength $eB=0.14$ GeV$^2$, the model underestimates the magnitudes while still capturing the overall temperature trend.

hep-ph↗

arXiv2026

LoopFM: Learning frOm HistOrical RePresentations of Foundation Model for Recommendation

Knowledge distillation (KD) transfers a single scalar prediction from a large foundation model (FM) to compact vertical models (VMs), suffering from diminishing transfer ratio -- the fraction of FM improvement captured by the VM -- as a single scalar cannot convey the rich intermediate knowledge that larger FMs learn. To address this bottleneck, we propose LoopFM (Learning frOm HistOrical RePresentations of FM), a framework that opens a high-bandwidth transfer channel by structuring FM intermediate embeddings as input features (e.g., user history sequence) for downstream VMs, without requiring real-time FM inference at serving and architectural coupling between FM and VM. We provide a theoretical framework for LoopFM with a gain decomposition and transfer-ratio analysis. On three public benchmarks, LoopFM demonstrates strong AUC improvements (e.g., 6%+ on TaobaoAd) and complementary knowledge transfer capability with KD. On industrial-scale systems (billions of examples, trillion-parameter FMs), LoopFM approximately doubles the knowledge transfer ratio on top of KD, delivering a +0.5% conversion improvement in the first half after its initial launch, and +1.03% and +1.22% conversion improvement from two individual launches in the subsequent half.

cs.LG↗

arXiv2026

Large Language Models for Multilingual Code Intelligence: A Survey

Large language models have transformed AI-assisted software engineering, but current research remains biased toward high-resource languages such as Python, with weaker performance in languages like Rust and OCaml. Since real-world systems are inherently polyglot, robust multilingual code intelligence is crucial. This survey focuses on two key tasks: multilingual code generation from shared natural-language requirements, and multilingual code translation that preserves semantics across languages. It reviews representative methods, benchmarks, and evaluation metrics, and highlights challenges and opportunities for trustworthy cross-language generalization.

cs.SE↗

arXiv2026

From Heuristics to Transformers: A Comprehensive Survey of Type Inference from Stripped Binaries

The recovery of high-level type information from stripped binaries-executables devoid of symbol tables and debugging information-is a cornerstone of software reverse engineering, vulnerability analysis, and decompilation. This survey tracks the evolution of binary type inference from early rule-based heuristics and static analysis to modern deep learning architectures. We analyze the shift from "duck typing" and constraint-solving techniques (e.g., BITY, BinSub) to context-aware neural models (e.g., EKLAVYA, CATI) and finally to state-of-the-art Transformer and Graph Neural Network (GNN) architectures (e.g., SeeType, TYGR). We identify core challenges, including optimization-induced semantics loss and structural type recovery, and propose future research directions in neuro-symbolic inference.

cs.SE↗

arXiv2026

Deciphering the universal scaling of particle transverse momentum spectra in heavy-ion collisions

We systematically investigate the scaling properties of the transverse momentum spectra for pions, kaons, and protons in Au+Au collisions at $\sqrt{s_{NN}}$ = 7.7, 11.5, 14.5, 19.6, 27, 39, 62.4, and 200 GeV, as well as in U+U collisions at $\sqrt{s_{NN}}$ = 193 GeV, across different centrality classes, using experimental data from the collaborations at the Relativistic Heavy Ion Collider (RHIC). Universal scaling emerges when the particle transverse momentum spectra are scaled by global physical quantities, i.e., the average total particle multiplicity and mean transverse momentum, confirming recent scaling findings from the data at the Large Hadron Collider (LHC) by the ExTrEMe collaboration. The scaling behavior breaks down in the high $p_{T}$ region and in peripheral collisions. We provide a natural explanation for these observations by invoking the Cooper-Frye formula, which is used for hadronization in hydrodynamics. Furthermore, we demonstrate the equivalence between the scaling found by the ExTrEMe collaboration and the Hwa-Yang scaling which was proposed two decades ago.

hep-ph↗

arXiv2026

Deep learning approaches to extract nuclear deformation parameters from initial-state information in heavy-ion collisions

The deformation of heavy nuclei leaves characteristic imprints on the initial conditions of relativistic heavy-ion collisions. However, event-by-event fluctuations make the quantitative extraction of this information challenging. This study examines the identifiability of the quadrupole ($β_2$) and hexadecapole ($β_4$) deformation parameters from nucleon configurations sampled from a deformed Woods-Saxon distribution commonly used in initial-state modeling of heavy-ion collisions. As a baseline, we first establish an upper bound on the "intrinsic identifiability" of deformation information at the most microscopic level by constructing permutation-invariant point-cloud networks under controlled multi-event grouping. We then extend the analysis to the more realistic initial entropy-density profiles generated by the TRENTo model, where both standard regression and simulation-based inference (SBI) with conditional normalizing flows are employed to reconstruct the deformation parameters from ensembles of event images supplemented with global attributes. Multi-event averaging is found to be essential in this setting for suppressing stochastic fluctuations and revealing the underlying deformation information. While standard regression efficiently captures the central trends of deformation through point estimates, SBI provides calibrated posterior distributions, offering a more complete and robust characterization of uncertainty. Collectively, our results demonstrate that deformation information is effectively encoded in the initial state and becomes increasingly identifiable with sufficient ensemble averaging, laying a solid foundation for future extensions toward more complete dynamical modeling and final-state observables.

nucl-th↗

arXiv2026

A Modular Mechanistic In Silico Model for In Vitro Transcription Process Yield and Product Quality Prediction

In vitro transcription (IVT) plays a critical role in the manufacture of mRNA vaccines and therapeutics. Optimizing mRNA yield and ensuring product quality, such as capping efficiency and integrity, are essential but mechanistically complex. This study presents a modular mechanistic model of the IVT process to advance scientific understanding and improve predictive capability. The IVT reaction network is decomposed into interconnected modules describing (1) initiation and capping, (2) elongation and truncation, (3) termination and read-through, (4) mRNA degradation, (5) magnesium pyrophosphate precipitation, and (6) enzymatic degradation of pyrophosphate. Guided by biochemical principles and experimental data, kinetic models were developed for each module, accounting for mass balances, molecular complexation, and enzyme activity, and were subsequently assembled to capture coupled IVT dynamics. Multivariate residual analysis and Shapley value-based sensitivity analysis, guided by domain knowledge, were applied to iteratively improve model fidelity. These machine learning-driven analytics enabled identification of key mechanisms, supported in silico experimentation, and facilitated root-cause analysis. Combined with Gaussian-process-based batch Bayesian optimization for efficient parameter estimation, this framework establishes a scalable hybrid (mechanistic + machine learning) modeling platform that integrates heterogeneous data, accelerates model calibration, and supports rational design and optimization of mRNA manufacturing processes.

q-bio.MN↗

arXiv2026

On the Convergence of Experience Replay in Policy Optimization: Characterizing Bias, Variance, and Finite-Time Convergence

Experience replay is a core ingredient of modern deep reinforcement learning, yet its benefits in policy optimization are poorly understood beyond empirical heuristics. This paper develops a novel theoretical framework for experience replay in modern policy gradient methods, where two sources of dependence fundamentally complicate analysis: Markovian correlations along trajectories and policy drift across optimization iterations. We introduce a new proof technique based on auxiliary Markov chains and lag-based decoupling that makes these dependencies tractable. Within this framework, we derive finite-time bias bounds for policy-gradient estimators under replay, identifying how bias scales with the cumulative policy update, the mixing time of the underlying dynamics, and the age of buffered data, thereby formalizing the practitioner's rule of avoiding overly stale replay. We further provide a correlation-aware variance decomposition showing how sample dependence governs gradient variance from replay and when replay is beneficial. Building on these characterizations, we establish the finite-time convergence guarantees for experience-replay-based policy optimization, explicitly quantifying how buffer size, sample correlation, and mixing jointly determine the convergence rate and revealing an inherent bias-variance trade-off: larger buffers can reduce variance by averaging less correlated samples but can increase bias as data become stale. These results offer a principled guide for buffer sizing and replay schedules, bridging prior empirical findings with quantitative theory.

cs.LG↗

arXiv2026

Deciphering the nature of $X(2300)$ with the PACIAE model

Inspired by the BESIII newest observation of an axial-vector particle $X(2300)$ in the $ψ(3686)\rightarrow ϕηη'$ process, we simulate its production in $e^+e^-$ collisions at $\sqrt{s}=4.95$ GeV using the parton and hadron cascade model PACIAE 4.0. In this model, the final partonic state (FPS) and hadronic state (FHS) are simulated and recorded sequentially. We propose, for the first time, that $X(2300)$ could be a $q\bar{q}s\bar{s}$ ($q=u/d$) state or a hadro-strangeonium state, i.e., a bound system of a strangeonium and a light hadron. The excited strangeonium candidate is formed by coalescing an $s\bar{s}$ quark pair in the FPS with the quantum statistical mechanics inspired dynamically constrained phase-space coalescence model. The tetraquark candidates of $q\bar{q}s\bar{s}$ and $ss\bar{s}\bar{s}$ are similarly produced by coalescing four constituent quarks in the FPS. In contrast, a hadro-strangeonium candidate emerges from the recombination of the constituent $ϕ$ and $η/η$ in the FHS. We then calculate the $X(2300)$'s orbital angular momentum quantum number in its rest frame and perform the spectral classification for each of the above candidates. Given its quantum numbers $J^{PC}=1^{+-}$, $X(2300)$ is identified as a $P$-wave $s\bar{s}$, an $S$-wave $q\bar{q}s\bar{s}/ss\bar{s}\bar{s}$ or $S$-wave $ϕη'/ϕη$ candidate. For the first time, we estimate the production rates for these configurations. The $P$-wave $s\bar{s}$ and $S$-wave $q\bar{q}s\bar{s}$ states are produced at rates on the order of $10^{-5}$, whereas the $S$-wave $ss\bar{s}\bar{s}$ and $ϕη'/ϕη$ states appear at rates on the order of $10^{-6}$. Moreover, significant discrepancies are observed in the rapidity and transverse momentum distributions among different candidates. These discrepancies could be served as valuable criteria for deciphering the nature of $X(2300)$.

hep-ph↗

arXiv2026

A deep learning approach for predicting multiple observables in Au+Au collisions at RHIC

We develop a neural network model, based on the processes of high-energy heavy-ion collisions, to study and predict several experimental observables in Au+Au collisions. We present a data-driven deep learning framework for predicting multiple bulk observables in Au+Au collisions at RHIC energies. A single neural network is trained exclusively on experimental measurements of charged-particle pseudorapidity density distributions, transverse-momentum spectra and elliptic flow coefficients over a broad range of collision energies and centralities. The network architecture is inspired by the stages of a heavy-ion collision, from the quark-gluon plasma to chemical and kinetic freeze-out, and employs locally connected hidden layers and a structured input design that encodes basic geometric and kinematic features of the system. We demonstrate that these physics-motivated choices significantly improve test performance compared to purely fully connected baselines. The trained model is then used to predict the above observables at collision energies not yet explored experimentally at RHIC, and the results are validated using the energy dependence of the total charged-particle multiplicity per participant pair as well as comparisons to a CLVisc hydrodynamic calculation with TRENTo initial conditions. Our findings indicate that such physics-guided neural networks can serve as efficient surrogates to fill critical data gaps at RHIC and to support further phenomenological studies of QGP properties.

nucl-th↗

arXiv2025

Inclusive $J/ψ$ productions in pp collisions at $\sqrt{s}=$ 5.02, 7, and 13 TeV with the PACIAE model

We investigate the inclusive $J/ψ$ production in proton-proton (pp) collisions at center-of-mass energies $\sqrt{s} = 5.02$, 7, and 13 TeV using the PACIAE 4.0 model. This model extends PYTHIA 8.3 by incorporating partonic and hadronic rescatterings before and after hadronization, respectively. Compared to our earlier study [K.-F. Ye et al., Phys. Rev. C 109, 035201 (2024)], which considered only the color-singlet processes, the present work includes both the color-singlet and color-octet contributions within the non-relativistic QCD (NRQCD) framework. In addition to NRQCD, we also consider the contributions from the cluster collapse and weak decays of $b$-hadrons. We find that the simulated inclusive $J/ψ$ transverse momentum differential cross sections agree well with the experimental data at both the middle and forward rapidities. We provide a quantitative analysis of the relative contributions for different production mechanisms and their energy and rapidity dependence in the inclusive $J/ψ$ production. Furthermore, we offer a quantification of the relative contributions of the various components and their energy and rapidity dependence in the NRQCD channels, including the direct production via the hard scattering and the feed-down from the decays of heavier charmonium states such as $ψ(2S)$, $χ_{c0}$, $χ_{c1}$, and $χ_{c2}$. Finally, we examine the effects of partonic and hadronic rescatterings and offer the quantitative estimate of their impact on the $J/ψ$ production. These results are entirely new and represent a significant step forward in understanding the mechanisms of the inclusive $J/ψ$ production in high-energy pp collisions.

hep-ph↗