arXiv subjects

Ziliang Wang

Publications and source records attributed to Ziliang Wang.

At least 37 records · Page 2Linked to original sources

arXiv2026

Quantifying Non-linearity in Topology Optimization with similarity based Visualization

Topology optimization (TO) can be viewed as seeking an optimal solution in the design space of a given TO problem. For weakly non-linear TO problems, e.g., compliance minimization, sensitivity-based methods typically converge well, whereas for strongly non-linear problems, e.g., maximum stress minimization, stabilization strategies such as stabilization terms and projection functions are often required to enhance convergence. Especially in scenarios with massive design variables, it is difficult to intuitively demonstrate the non-linear complexity of different TO problems and to elucidate the mechanisms by which stabilization strategies affect convergence. To address this challenge, we propose a visualization framework and a quantitative non-linearity index for objectives with varying complexity. We employ a multi-start fixed-gradient sampling tailored to similarity-based dimensionality reduction while keeping the computational cost under control. The samples are then parameterized via cosine similarity to obtain a low-dimensional visualization surface of the objective function. Based on this visualization, we construct a dimensionless complexity index with a clear geometric interpretation by measuring the gap between the visualization surface and the discrete approximation of its convex envelope, which enables quantitative comparisons of non-linearity across TO tasks, parameter choices, and stabilization strategies. Extensive comparative experiments show that the proposed approach is both adaptable and discriminative on a variety of representative TO problems, and it provides intuitive and measurable guidance for parameter selection.

math.OC↗

arXiv2026

GPUMDkit: A User-Friendly Toolkit for GPUMD and NEP

Machine-learned interatomic potentials have revolutionized molecular dynamics simulations by providing quantum-mechanical accuracy at empirical-potential speeds. The graphics processing unit molecular dynamics (GPUMD) package, featuring the highly efficient neuroevolution potential (NEP) framework, has emerged as a powerful tool in this domain. However, the complexity of force field development, active learning, and trajectory post-processing often requires extensive manual scripting, imposing a steep learning curve on new users. To address this, we present GPUMDkit, a comprehensive and user-friendly toolkit that streamlines the entire simulation workflow for GPUMD and NEP. GPUMDkit integrates a suite of essential functionalities, including format conversion, structure sampling, property calculation, and data visualization, accessible through both interactive and command-line interfaces. Its modular, extensible architecture ensures accessibility for users of all experience levels while allowing seamless integration of new features. By automating complex tasks and enhancing productivity, GPUMDkit substantially lowers the barrier to using GPUMD and NEP programs. This article describes the program architecture and demonstrates its capabilities through practical applications.

cond-mat.mtrl-sci↗

arXiv2026

Consistent inverse optimal control for infinite time-horizon discounted nonlinear systems under noisy observations

Inverse optimal control (IOC) aims to estimate the underlying cost that governs the observed behavior of an expert system. However, in practical scenarios, the collected data is often corrupted by noise, which poses significant challenges for accurate cost function recovery. In this work, we propose an IOC framework that effectively addresses the presence of observation noise. In particular, compared to our previous work \cite{wang2025consistent}, we consider the case of discrete-time, infinite-horizon, discounted MDPs whose transition kernel is only weak Feller. By leveraging the occupation measure framework, we first establish the necessary and sufficient optimality conditions for the expert policy and then construct an infinite dimensional optimization problem based on these conditions. This problem is then approximated by polynomials to get a finite-dimensional numerically solvable one, which relies on the moments of the state-action trajectory's occupation measure. More specifically, the moments are robustly estimated from the noisy observations by a combined misspecified Generalized Method of Moments (GMM) estimator derived from observation model and system dynamics. Consequently, the entire algorithm is based on convex optimization which alleviates the issues that arise from local minima and is asymptotically and statistically consistent. Finally, the performance of the proposed method is illustrated through numerical examples.

math.OC↗

arXiv2026

qNEP: A highly efficient neuroevolution potential with dynamic charges for large-scale atomistic simulations

Although electrostatics can be incorporated into machine-learned interatomic potentials, existing approaches are computationally very demanding, limiting large-scale, long-time simulations of electrostatics-driven phenomena such as dielectric response, infrared activity, and field-matter coupling. Here, we extend the neuroevolution potential (NEP), a highly efficient machine-learned interatomic potential, to a charge-aware framework (qNEP) by introducing explicit, environment-dependent partial charges. Each ionic partial charge is represented by a neural network as a function of the local descriptor vector, analogous to the NEP site-energy model. This formulation enables the direct prediction of the Born effective charge tensor for each ion and, consequently, the polarization. As a result, dielectric properties, infrared spectra, and coupling to external electric fields can be evaluated within a unified framework. We derive consistent expressions for the forces and virials that explicitly account for the position dependence of the partial charges. The qNEP method has been implemented in the free-and-open-source GPUMD package, with support for both Ewald summation and particle-particle particle-mesh treatments of electrostatics. We demonstrate the accuracy and efficiency of the qNEP approach through representative applications to water, Li7La3Zr2O12, BaTiO3, and a magnesium-water interface. These results show that qNEP enables accurate atomistic simulations with explicit long-range electrostatics, scalable to million-atom systems on nanosecond time scales using consumer-grade GPUs.

physics.comp-ph↗

arXiv2026

muT2-NMR: Micro-Scale Correlation Relaxometry for in-situ High-Pressure Nuclear Magnetic Resonance

Over the last decade, frequency-domain in-situ high-pressure nuclear magnetic resonance (NMR) spectroscopy in diamond anvil cells (DACs) has been employed as a structural and electronic probe of condensed matter systems at pressures well into the megabar range. However, extensive spin interactions and sample heterogeneities under pressure often lead to significant spectral overlap, inhibiting independent observation of chemically similar spin sub-species in the same sample. In this work, we introduce a time-domain relaxometry framework specifically suited for DAC experiments, named muT2-NMR. Experimental flexibility and operational robustness are benchmarked on three hydrogen-rich molecular solids at pressures up to 72 GPa. We demonstrate that muT2-NMR can resolve individual molecular subunits in relaxation space, paving the way for novel high-pressure, high-resolution NMR applications in molecular solids.

physics.ins-det↗

arXiv2025

MMRPT: MultiModal Reinforcement Pre-Training via Masked Vision-Dependent Reasoning

Multimodal pre-training remains constrained by the descriptive bias of image-caption pairs, leading models to favor surface linguistic cues over grounded visual understanding. We introduce MMRPT, a masked multimodal reinforcement pre-training framework that strengthens visual reasoning in MLLMs. We are the first to incorporate reinforcement learning directly into the pre-training of large vision-language models, enabling learning signals that reward visual grounding rather than caption imitation. MMRPT constructs masked multimodal data by estimating sentence-level visual dependency via attention over visual tokens and masking highly vision-dependent segments; the model reconstructs these spans through vision-grounded reasoning guided by a semantic-visual reward. Experiments show consistent zero-shot gains across diverse benchmarks and substantially improved robustness under supervised fine-tuning, demonstrating that reinforcement-driven masked reasoning provides a more reliable and generalizable pre-training objective for multimodal models.

cs.CV↗

arXiv2025

Consistent inverse optimal control for discrete-time nonlinear stochastic systems

Inverse Optimal Control (IOC) seeks to recover an unknown cost from expert demonstrations, and it provides a systematic way of modeling experts' decision mechanisms while considering the prior information of the cost functions. Nevertheless, existing IOC methods have consistency issue with the estimator under noisy and nonlinear settings. In this paper, we consider a discrete-time nonlinear system with process noise, and it is controlled by an optimal policy that minimizes the expectation of a discounted cumulative cost function across an infinite time-horizon. In particular, the cost function takes the form of a linear combination of a priori known feature functions. In this setting, we first adopt Lasserre's reformulation of the forward problem with occupancy measure. Next, we propose the infinite dimensional IOC algorithm and further approximate it with Lagrange interpolating polynomials, which results in a convex, finite-dimensional sum-of-squares optimization. Moreover, the estimator is shown to be asymptotically and statistically consistent. Finally, we validate the theoretical results and illustrate the performance of our method with numerical experiments. In addition, the robustness and generalizability performance of the proposed IOC algorithm are also illustrated.

math.OC↗

arXiv2025

Layer-wise Noise Guided Selective Wavelet Reconstruction for Robust Medical Image Segmentation

Clinical deployment requires segmentation models to stay stable under distribution shifts and perturbations. The mainstream solution is adversarial training (AT) to improve robustness; however, AT often brings a clean--robustness trade-off and high training/tuning cost, which limits scalability and maintainability in medical imaging. We propose \emph{Layer-wise Noise-Guided Selective Wavelet Reconstruction (LNG-SWR)}. During training, we inject small, zero-mean noise at multiple layers to learn a frequency-bias prior that steers representations away from noise-sensitive directions. We then apply prior-guided selective wavelet reconstruction on the input/feature branch to achieve frequency adaptation: suppress noise-sensitive bands, enhance directional structures and shape cues, and stabilize boundary responses while maintaining spectral consistency. The framework is backbone-agnostic and adds low additional inference overhead. It can serve as a plug-in enhancement to AT and also improves robustness without AT. On CT and ultrasound datasets, under a unified protocol with PGD-$L_{\infty}/L_{2}$ and SSAH, LNG-SWR delivers consistent gains on clean Dice/IoU and significantly reduces the performance drop under strong attacks; combining LNG-SWR with AT yields additive gains. When combined with adversarial training, robustness improves further without sacrificing clean accuracy, indicating an engineering-friendly and scalable path to robust segmentation. These results indicate that LNG-SWR provides a simple, effective, and engineering-friendly path to robust medical image segmentation in both adversarial and standard training regimes.

cs.CV↗

arXiv2025

VulAgent: Hypothesis-Validation based Multi-Agent Vulnerability Detection

The application of language models to project-level vulnerability detection remains challenging, owing to the dual requirement of accurately localizing security-sensitive code and correctly correlating and reasoning over complex program context. We present VulAgent, a multi-agent vulnerability detection framework based on hypothesis validation. Our design is inspired by how human auditors review code: when noticing a sensitive operation, they form a hypothesis about a possible vulnerability, consider potential trigger paths, and then verify the hypothesis against the surrounding context. VulAgent implements a semantics-sensitive, multi-view detection pipeline: specialized agents, each aligned to a specific analysis perspective (e.g., memory, authorization), collaboratively surface and precisely localize sensitive code sites with higher coverage. Building on this, VulAgent adopts a hypothesis-validation paradigm: for each vulnerability report, it builds hypothesis conditions and a trigger path, steering the LLM to target the relevant program context and defensive checks during verification, which reduces false positives. On average across the two datasets, VulAgent improves overall accuracy by 6.6%, increases the correct identification rate of vulnerable--fixed code pairs by up to 450% (246% on average), and reduces the false positive rate by about 36% compared with state-of-the-art LLM-based baselines.

cs.SE↗

arXiv2025

StepSearch: Igniting LLMs Search Ability via Step-Wise Proximal Policy Optimization

Efficient multi-hop reasoning requires Large Language Models (LLMs) based agents to acquire high-value external knowledge iteratively. Previous work has explored reinforcement learning (RL) to train LLMs to perform search-based document retrieval, achieving notable improvements in QA performance, but underperform on complex, multi-hop QA resulting from the sparse rewards from global signal only. To address this gap in existing research, we introduce StepSearch, a framework for search LLMs that trained with step-wise proximal policy optimization method. It consists of richer and more detailed intermediate search rewards and token-level process supervision based on information gain and redundancy penalties to better guide each search step. We constructed a fine-grained question-answering dataset containing sub-question-level search trajectories based on open source datasets through a set of data pipeline method. On standard multi-hop QA benchmarks, it significantly outperforms global-reward baselines, achieving 11.2% and 4.2% absolute improvements for 3B and 7B models over various search with RL baselines using only 19k training data, demonstrating the effectiveness of fine-grained, stepwise supervision in optimizing deep search LLMs. Our code will be released on https://github.com/Zillwang/StepSearch.

cs.CL↗

arXiv2025

QoSBERT: An Uncertainty-Aware Approach based on Pre-trained Language Models for Service Quality Prediction

Accurate prediction of Quality of Service (QoS) metrics is fundamental for selecting and managing cloud based services. Traditional QoS models rely on manual feature engineering and yield only point estimates, offering no insight into the confidence of their predictions. In this paper, we propose QoSBERT, the first framework that reformulates QoS prediction as a semantic regression task based on pre trained language models. Unlike previous approaches relying on sparse numerical features, QoSBERT automatically encodes user service metadata into natural language descriptions, enabling deep semantic understanding. Furthermore, we integrate a Monte Carlo Dropout based uncertainty estimation module, allowing for trustworthy and risk-aware service quality prediction, which is crucial yet underexplored in existing QoS models. QoSBERT applies attentive pooling over contextualized embeddings and a lightweight multilayer perceptron regressor, fine tuned jointly to minimize absolute error. We further exploit the resulting uncertainty estimates to select high quality training samples, improving robustness in low resource settings. On standard QoS benchmark datasets, QoSBERT achieves an average reduction of 11.7% in MAE and 6.7% in RMSE for response time prediction, and 6.9% in MAE for throughput prediction compared to the strongest baselines, while providing well calibrated confidence intervals for robust and trustworthy service quality estimation. Our approach not only advances the accuracy of service quality prediction but also delivers reliable uncertainty quantification, paving the way for more trustworthy, data driven service selection and optimization.

cs.CL↗

arXiv2025

Point2Quad: Generating Quad Meshes from Point Clouds via Face Prediction

Quad meshes are essential in geometric modeling and computational mechanics. Although learning-based methods for triangle mesh demonstrate considerable advancements, quad mesh generation remains less explored due to the challenge of ensuring coplanarity, convexity, and quad-only meshes. In this paper, we present Point2Quad, the first learning-based method for quad-only mesh generation from point clouds. The key idea is learning to identify quad mesh with fused pointwise and facewise features. Specifically, Point2Quad begins with a k-NN-based candidate generation considering the coplanarity and squareness. Then, two encoders are followed to extract geometric and topological features that address the challenge of quad-related constraints, especially by combining in-depth quadrilaterals-specific characteristics. Subsequently, the extracted features are fused to train the classifier with a designed compound loss. The final results are derived after the refinement by a quad-specific post-processing. Extensive experiments on both clear and noise data demonstrate the effectiveness and superiority of Point2Quad, compared to baseline methods under comprehensive metrics.

cs.CV↗

arXiv2025

Understanding Magnesium Dissolution through Machine Learn-ing Molecular Dynamics

Magnesium alloys have become increasingly important for various potential industrial applications, especially in energy storage, due to their outstanding properties. However, a clear under-standing of the dissolution mechanism of magnesium in the most common aqueous environments re-mains a critical challenge, hindering the broader application of magnesium alloys. To address pending key controversies in magnesium alloys research, the atomic-scale hydrogen evolution process and dis-solution mechanism of magnesium were investigated by combining machine learning molecular dy-namics with density functional theory. These controversies include the presence of magnesium reaction intermediates, the formation of uni-positive Mg+, the specific reaction steps involved in hydrogen evo-lution and magnesium dissolution, and the generation and growth mechanisms of the surface films. The results indicate that the intermediate species in the magnesium dissolution process is solid-phase MgOH, which exhibits an MgO-like structure. The magnesium in MgOH is identified as the widely recognized uni-positive Mg+. The intermediate film is formed, consisting primarily of the MgOH phase with a small amount of MgO. This film grows inward by extending into the magnesium substrate. Un-der sufficient water availability, the film undergoes further oxidation to form Mg(OH)2. These findings highlight the critical role of the MgOH phase in the magnesium dissolution process, leading to the pro-posal of a dissolution model based on MgOH/MgO solid phases as intermediates. These insights deep-en the understanding of magnesium dissolution, pave the way for the development of more effective anti-corrosion strategies for magnesium alloys, and may also advance the utilization of magnesium in energy storage applications.

cond-mat.mtrl-sci↗

arXiv2024

A Centralized-Distributed Transfer Model for Cross-Domain Recommendation Based on Multi-Source Heterogeneous Transfer Learning

Cross-domain recommendation (CDR) methods are proposed to tackle the sparsity problem in click through rate (CTR) estimation. Existing CDR methods directly transfer knowledge from the source domains to the target domain and ignore the heterogeneities among domains, including feature dimensional heterogeneity and latent space heterogeneity, which may lead to negative transfer. Besides, most of the existing methods are based on single-source transfer, which cannot simultaneously utilize knowledge from multiple source domains to further improve the model performance in the target domain. In this paper, we propose a centralized-distributed transfer model (CDTM) for CDR based on multi-source heterogeneous transfer learning. To address the issue of feature dimension heterogeneity, we build a dual embedding structure: domain specific embedding (DSE) and global shared embedding (GSE) to model the feature representation in the single domain and the commonalities in the global space,separately. To solve the latent space heterogeneity, the transfer matrix and attention mechanism are used to map and combine DSE and GSE adaptively. Extensive offline and online experiments demonstrate the effectiveness of our model.

cs.LG↗

arXiv2024

Feature Noise Resilient for QoS Prediction with Probabilistic Deep Supervision

Accurate Quality of Service (QoS) prediction is essential for enhancing user satisfaction in web recommendation systems, yet existing prediction models often overlook feature noise, focusing predominantly on label noise. In this paper, we present the Probabilistic Deep Supervision Network (PDS-Net), a robust framework designed to effectively identify and mitigate feature noise, thereby improving QoS prediction accuracy. PDS-Net operates with a dual-branch architecture: the main branch utilizes a decoder network to learn a Gaussian-based prior distribution from known features, while the second branch derives a posterior distribution based on true labels. A key innovation of PDS-Net is its condition-based noise recognition loss function, which enables precise identification of noisy features in objects (users or services). Once noisy features are identified, PDS-Net refines the feature's prior distribution, aligning it with the posterior distribution, and propagates this adjusted distribution to intermediate layers, effectively reducing noise interference. Extensive experiments conducted on two real-world QoS datasets demonstrate that PDS-Net consistently outperforms existing models, achieving an average improvement of 8.91% in MAE on Dataset D1 and 8.32% on Dataset D2 compared to the ate-of-the-art. These results highlight PDS-Net's ability to accurately capture complex user-service relationships and handle feature noise, underscoring its robustness and versatility across diverse QoS prediction environments.

cs.SE↗

arXiv2024

Dual Latent State Learning: Exploiting Regional Network Similarities for QoS Prediction

Individual objects, whether users or services, within a specific region often exhibit similar network states due to their shared origin from the same city or autonomous system (AS). Despite this regional network similarity, many existing techniques overlook its potential, resulting in subpar performance arising from challenges such as data sparsity and label imbalance. In this paper, we introduce the regional-based dual latent state learning network(R2SL), a novel deep learning framework designed to overcome the pitfalls of traditional individual object-based prediction techniques in Quality of Service (QoS) prediction. Unlike its predecessors, R2SL captures the nuances of regional network behavior by deriving two distinct regional network latent states: the city-network latent state and the AS-network latent state. These states are constructed utilizing aggregated data from common regions rather than individual object data. Furthermore, R2SL adopts an enhanced Huber loss function that adjusts its linear loss component, providing a remedy for prevalent label imbalance issues. To cap off the prediction process, a multi-scale perception network is leveraged to interpret the integrated feature map, a fusion of regional network latent features and other pertinent information, ultimately accomplishing the QoS prediction. Through rigorous testing on real-world QoS datasets, R2SL demonstrates superior performance compared to prevailing state-of-the-art methods. Our R2SL approach ushers in an innovative avenue for precise QoS predictions by fully harnessing the regional network similarities inherent in objects.

cs.LG↗

arXiv2024

Line-level Semantic Structure Learning for Code Vulnerability Detection

Unlike the flow structure of natural languages, programming languages have an inherent rigidity in structure and grammar.However, existing detection methods based on pre-trained models typically treat code as a natural language sequence, ignoring its unique structural information. This hinders the models from understanding the code's semantic and structual information.To address this problem, we introduce the Code Structure-Aware Network through Line-level Semantic Learning (CSLS), which comprises four components: code preprocessing, global semantic awareness, line semantic awareness, and line semantic structure awareness.The preprocessing step transforms the code into two types of text: global code text and line-level code text.Unlike typical preprocessing methods, CSLS retains structural elements such as newlines and indent characters to enhance the model's perception of code lines during global semantic awareness.For line semantics structure awareness, the CSLS network emphasizes capturing structural relationships between line semantics.Different from the structural modeling methods based on code blocks (control flow graphs) or tokens, CSLS uses line semantics as the minimum structural unit to learn nonlinear structural relationships, thereby improving the accuracy of code vulnerability detection.We conducted extensive experiments on vulnerability detection datasets from real projects. The CSLS model outperforms the state-of-the-art baselines in code vulnerability detection, achieving 70.57% accuracy on the Devign dataset and a 49.59% F1 score on the Reveal dataset.

cs.SE↗

arXiv2024

M2CVD: Enhancing Vulnerability Semantic through Multi-Model Collaboration for Code Vulnerability Detection

Large Language Models (LLMs) have strong capabilities in code comprehension, but fine-tuning costs and semantic alignment issues limit their project-specific optimization; conversely, code models such CodeBERT are easy to fine-tune, but it is often difficult to learn vulnerability semantics from complex code languages. To address these challenges, this paper introduces the Multi-Model Collaborative Vulnerability Detection approach (M2CVD) that leverages the strong capability of analyzing vulnerability semantics from LLMs to improve the detection accuracy of code models. M2CVD employs a novel collaborative process: first enhancing the quality of vulnerability semantic description produced by LLMs through the understanding of project code by code models, and then using these improved vulnerability semantic description to boost the detection accuracy of code models. We demonstrated M2CVD's effectiveness on two real-world datasets, where M2CVD significantly outperformed the baseline. In addition, we demonstrate that the M2CVD collaborative method can extend to other different LLMs and code models to improve their accuracy in vulnerability detection tasks.

cs.SE↗