arXiv subjects

Ping Yang

Publications and source records attributed to Ping Yang.

At least 19 recordsLinked to original sources

arXiv2026

Nuclear-size correction to the one-loop self-energy in hydrogenlike ions

The nuclear-size effect on both diagonal and off-diagonal one-loop self-energy matrix elements is considered for hydrogenlike ions with $Z=60$, $82$, $90$, and $92$. Specifically, the $1s$, $2s$, $3s$, $2p_{1/2}$, and $2p_{3/2}$ states, as well as the off-diagonal $1s-2s$, $1s-3s$, and $2s-3s$ matrix elements are considered. The calculations are performed within the rigorous quantum-electrodynamics framework, nonperturbatively in the nuclear-strength parameter $\alpha Z$. Excellent agreement is found with results reported in the literature. Simple and useful approximate formulas to treat the nuclear-size correction are obtained, which, in particular, can be used to study the self-energy contributions to the field-shift factors.

physics.atom-ph↗

arXiv2026

Strain induced magnetic phase transition and anomalous transport phenomena in RuO$_2$ and MnF$_2$

Collinear antiferromagnets with broken time-reversal symmetry have emerged as a fertile platform for spintronics. Using a general tight-binding model and first-principles calculations, we show that strain engineering provides a simple route to control magnetic phase transition and activate transverse responses in representative altermagnets RuO$_2$ and MnF$_2$. For pristine RuO$_2$ and MnF$_2$ with N\'eel vector $\mathbf{n}\parallel$ [001], symmetry constrains the off-diagonal elements of the Hall conductivity tensor to vanish, thereby forbidding anomalous transport and magneto-optical responses. Shear strain applied along the $ac$ direction preserves the spin symmetry relating the two spin-opposite magnetic sublattices and therefore maintains the altermagnetic phase. By contrast, shear strain applied along the $ab$ direction breaks this spin symmetry and drives a transition from an altermagnetic phase to a partially compensated ferrimagnetic phase in metallic RuO$_2$ and to a fully compensated ferrimagnetic phase in semiconducting MnF$_2$. In addition, the lowered symmetry enables finite anomalous Hall, anomalous Nernst, and anomalous thermal Hall conductivities, as well as magneto-optical rotation angles, which are prohibited in the pristine systems. These responses exhibit a clear strain dependence and become progressively stronger as the strain amplitude increases. Our results establish strain engineering as an effective route to manipulate magnetic phases and functional responses in unconventional antiferromagnets, thereby expanding opportunities for antiferromagnetic spintronics and magneto-optical applications.

cond-mat.mtrl-sci↗

arXiv2026

LUCID: Learned Undersampling-Adaptive Consistency-Guided Inference with Deterministic Flow Matching for Sparse-View CT Reconstruction

Sparse-view CT reduces radiation dose and scanning time by acquiring fewer projection views, but angular undersampling makes reconstruction severely ill-posed, causing streak artifacts, structural blurring, and loss of fine details. Existing supervised methods are often tied to specific sampling settings, whereas generative methods may introduce anatomically inconsistent hallucination-like structures under severe undersampling. We propose Lucid, a sparsity-adaptive, consistency-guided reconstruction framework based on a Flow Matching generative prior for sparse-view CT. Lucid is trained only on high-quality CT images to learn a continuous transport between a Gaussian distribution and the high-quality CT image distribution, independent of view sampling. During inference, the sampling sparsity level is explicitly incorporated to adapt the generative trajectory of a single pretrained model. Specifically, Lucid constructs a degradation-matched initial state by sparsity-weighted fusion of the sparse-view FBP image and Gaussian noise, performs sparsity-modulated Flow Matching updates, and applies projection-domain data-consistency correction after each prior update. Experiments under multiple sparse-view settings show that Lucid achieves stable reconstruction performance across different sampling densities, improves image quality and structural fidelity, and reduces the risk of hallucination-like structures in generative sparse-view CT reconstruction.

cs.CV↗

arXiv2026

WALL-WM: Carving World Action Modeling at the Event Joints

WALL-WM is a World Action Model that shifts video-action learning from chunk-centric optimization to event-grounded Vision-Language-Action pretraining, using semantically coherent action events as the atomic unit of learning. Existing WAMs commonly initialize from multimodal or video foundation models and then optimize fixed-length action chunks conditioned directly on the current observation and instruction. Although convenient, this chunk-centric formulation creates a fundamental granularity mismatch. Language describes semantic goals and events, vision evolves through continuous scene dynamics, and actions operate at control-level timescales; forcing all three into the same fixed-length prediction window turns VLA training into short-horizon correlation fitting. WALL-WM addresses this mismatch by organizing both supervision and data around semantic events. Specifically, it pairs event-grounded VLA pretraining with a data ecosystem built from event-level captions and cluster-balanced sampling, enabling scalable learning over diverse behaviors, scenes, and task structures. From the same event-pretrained backbone, WALL-WM supports two complementary inference modes. The event mode consumes next-event descriptions and enables variable-length execution chunks, while the unified mode uses a VLM with Staircase Decoding to condition conventional fixed-length chunk inference while preserving a gradient-continuous VLA path. Together with Muon-optimizer-based large-scale pretraining infrastructure, WALL-WM provides a practical scale-up recipe for general-purpose WAMs. Experiments show that WALL-WM generalizes broadly across language, scenes, and tasks, achieving state-of-the-art performance in large-scale real-world generalization evaluation.

cs.RO↗

arXiv2026

Ni-O hybridization-driven electronic reconstruction across the superconducting dome in an infinite-layer nickelate

Superconductivity in infinite-layer nickelates has drawn wide interest as a cuprate analogue, yet how the electronic structure evolves with hole doping remains unsettled. Here we map the doping- and temperature-dependent unoccupied states of the La-based infinite-layer nickelate La1-xCaxNiO2 using O K-edge and Ni L-edge x-ray absorption spectroscopy. Superconductivity occurs for 0.18<=x<=0.27. Near x~0.20-0.23, low-energy spectral weight redistributes: Ni3d-dominated states decrease while O2p-hybridized states increase, indicating an orbital-selective crossover in Ni-O covalency. This crossover coincides with a sign reversal of the Hall coefficient and precedes the reduction of the superconducting critical temperature at higher doping. By directly linking transport anomalies and the superconducting dome to a measurable Ni-O orbital reorganization, our results provide a key step toward a unified, orbital-resolved phase diagram for infinite-layer nickelates and a practical route to engineer superconductivity via hybridization control.

cond-mat.supr-con↗

arXiv2026

Wall-OSS-0.5 Technical Report

Large-scale Vision-Language-Action (VLA) pretraining is increasingly adopted as the foundation for robot policies, yet the evidence for pretrained VLAs is almost invariably reported after task-specific fine-tuning. This leaves a foundational question unanswered: does VLA pretraining itself yield executable robot behavior, or does it merely furnish a better initialization for downstream policy learning? We present Wall-OSS-0.5, an open-source 4B VLA built upon a 3B VLM backbone augmented with action-generation components, designed so that pretrained robotic capability is directly measurable on physical hardware. The model is pretrained across more than 20 embodiments, processing over one million robot trajectories per epoch alongside a grounded multimodal corpus. We adopt a gradient-bridged co-training recipe in which three objectives play distinct and complementary roles: discrete action prediction routes strong VLM-native gradients into the backbone, multimodal prediction preserves grounded vision-language understanding, and continuous flow matching serves as the deployment-time action interface. Before task-specific fine-tuning, the pretrained checkpoint achieves non-trivial zero-shot real-robot behavior, completing several tasks, including a held-out deformable manipulation task, at high task progress on a 17-task suite. After fine-tuning, the same checkpoint serves as a stronger adaptation prior, reaching 60.5% average task progress on 15 real-robot tasks and outperforming \pi_0.5 by 17.5%. Multimodal evaluations further confirm that action training does not erode grounded vision-language competence: the model preserves broad vision-language ability while strengthening embodied grounding. Together, these results reposition VLA pretraining from an initialization strategy to a directly testable, already useful source of robot capability.

cs.RO↗

arXiv2026

Hole-doped superconductivity above 100 K in infinite-layer cuprate thin films

Since the discovery of superconductivity in (La,Ba)2CuO2 (Ref.~\cite{bednorz1986possible}), a broad family of structurally distinct cuprate superconductors has been proposed or engineered to elucidate the physics of high-temperature superconductivity~\cite{chu2015hole,plakida2010high}. Among them, the infinite-layer cuprate has the simplest structure, consisting only of the essential ingredients for superconductivity: CuO$_2$ square planes separated by spacer ions~\cite{siegrist1988parent}. Despite being proposed nearly 40 years ago, the hole-doped superconductivity via chemical substitution in this compound has not yet been achieved, a fundamental open question in the field. Here, we report the observation of superconductivity in the hole-doped infinite-layer cuprate thin film. Measurements of resistivity and magnetic-field response in Sr1-xRbxCuO2 single-crystal thin films show superconducting transitions with a high onset temperature of 100 K. Hole doping is achieved via the synergistic effect of rubidium substitution and apical oxygen incorporation, as evidenced by structural analysis and transport measurements. As the parent structure of the cuprate family~\cite{chu2015hole}, hole-doped infinite-layer cuprate provides a unique platform for revisiting key puzzles in cuprate superconductors~\cite{keimer2015quantum,tsuei2000pairing,armitage2010progress,dagotto1994correlated}, including strange metal~\cite{proust2019remarkable,taillefer2010scattering} and electron-hole symmetry~\cite{tohyama2004asymmetry,segawa2010zero,lee2014asymmetry}, while bridging to cuprate-nickelate symmetry~\cite{li2019superconductivity,zeng2022superconductivity,chow2025bulk,lechermann2020late}.

cond-mat.supr-con↗

arXiv2026

Beyond Fixed Tests: Repository-Level Issue Resolution as Coevolution of Code and Behavioral Constraints

Software engineers resolving repository-level issues do not treat existing tests as immutable correctness oracles. Instead, they iteratively refine both code and the tests used to characterize intended behavior, as new modifications expose missing assumptions or misinterpreted failure conditions. In contrast, most existing large language model (LLM)-based repair systems adopt a linear pipeline in which tests or other validation signals act mostly as post-hoc filters, treating behavioral constraints as fixed during repair. This formulation reduces repair to optimizing code under static and potentially misaligned constraints, leading to under-constrained search and brittle or overfitted fixes. We argue that repository-level issue resolution is fundamentally not optimization under fixed tests, but search over evolving behavioral constraints. To operationalize this view, we propose Agent-CoEvo, a coevolutionary multi-agent framework in which candidate code patches and test patches are jointly explored and iteratively refined. Rather than treating tests as immutable oracles, our framework models them as dynamic constraints that both guide and are revised by the repair process. Through mutual evaluation and semantic recombination, code and test candidates progressively narrow the space of behavior consistent with the issue description. Evaluated on SWE-bench Lite and SWT-bench Lite, Agent-CoEvo consistently outperforms state-of-the-art agent-based and agentless baselines in both repair success and test reproduction quality. Our findings suggest that enabling repair agents to revise behavioral constraints during search is critical for reliable issue resolution, pointing toward a shift from code-only optimization to coevolution of implementation and specification.

cs.SE↗

arXiv2026

Beyond Fixed Inference: Quantitative Flow Matching for Adaptive Image Denoising

Diffusion and flow-based generative models have shown strong potential for image restoration. However, image denoising under unknown and varying noise conditions remains challenging, because the learned vector fields may become inconsistent across different noise levels, leading to degraded restoration quality under mismatch between training and inference. To address this issue, we propose a quantitative flow matching framework for adaptive image denoising. The method first estimates the input noise level from local pixel statistics, and then uses this quantitative estimate to adapt the inference trajectory, including the starting point, the number of integration steps, and the step-size schedule. In this way, the denoising process is better aligned with the actual corruption level of each input, reducing unnecessary computation for lightly corrupted images while providing sufficient refinement for heavily degraded ones. By coupling quantitative noise estimation with noise-adaptive flow inference, the proposed method improves both restoration accuracy and inference efficiency. Extensive experiments on natural, medical, and microscopy images demonstrate its robustness and strong generalization across diverse noise levels and imaging conditions.

cs.CV↗

arXiv2026

General Explicit Network (GEN): A novel deep learning architecture for solving partial differential equations

Machine learning, especially physics-informed neural networks (PINNs) and their neural network variants, has been widely used to solve problems involving partial differential equations (PDEs). The successful deployment of such methods beyond academic research remains limited. For example, PINN methods primarily consider discrete point-to-point fitting and fail to account for the potential properties of real solutions. The adoption of continuous activation functions in these approaches leads to local characteristics that align with the equation solutions while resulting in poor extensibility and robustness. A general explicit network (GEN) that implements point-to-function PDE solving is proposed in this paper. The "function" component can be constructed based on our prior knowledge of the original PDEs through corresponding basis functions for fitting. The experimental results demonstrate that this approach enables solutions with high robustness and strong extensibility to be obtained.

cs.LG↗

arXiv2026

A Novel Low-Complexity Dual-Domain Expectation Propagation Detection Aided AFDM for Future Communications

This paper presents a dual-domain low-complexity expectation propagation (EP) detection framework for affine frequency division multiplexing (AFDM) systems. By analyzing the structural properties of the effective channel matrices in both the time and affine frequency (AF) domains, our key observation is the domain-specific quasi-banded sparsity patterns, including AF-domain sparsity under frequency-selective channels and time-domain sparsity under doubly-selective channels. Based on these observations, we develop an AF-domain EP (EP-AF) detector for frequency-selective channels and a time-domain EP (EP-T) detector for doubly-selective channels, respectively. By performing iterative inference in the time domain using the Gaussian approximation, the proposed EP-T detector avoids inverting the dense channel matrix in the AF domain. Furthermore, the proposed EP-AF and EP-T detectors leverage the aforementioned quasi-banded sparsity of the AF domain and time domain channel matrices, respectively, to reduce the complexity of matrix inversion from cubic to linear order. Simulation results demonstrate that the proposed low-complexity EP-AF detector achieves nearly identical error rate performance to its conventional counterpart, while the proposed low-complexity EP-T detector offers an attractive trade-off between detection performance and complexity.

eess.SP↗

arXiv2026

QED corrections of orders $m\alpha^6$ and $m\alpha^6(m/M)$ for HD$^+$ rovibrational transitions beyond Born-Oppenheimer approximation

The effective Hamiltonian of $m\alpha^6$ and $m\alpha^6(m/M)$ order corrections for hydrogen molecular ions has been derived in [ Z.-X. Zhong, \emph{et al.}, Phys. Rev. A {\bf98}, 032502(2018).], in this work we express the energy correction in the form of finite-value effective operators. The cut-off regularization scheme is used to determine finite part of divergent operators of the leading-order recoil corrections. Numerical calculations of first-order contributions are performed in the Hylleraas basis set. Combining the second-order terms calculated in recent work [V. I. Korobov, \emph{et al.}, Mol. Phys. e2563023 (2025).], the $m\alpha^6$-order corrections for the fundamental rovibrational transition are obtained with an uncertainty three times smaller than in previous calculations.

physics.atom-ph↗

arXiv2026

Assessment of the synthetic feasibility of hypothetical zeolite-like materials based on ZeoNet

A suite of classifiers was developed to distinguish experimentally synthesized zeolites from computationally predicted zeolite-like structures. Using convolutional neural networks applied to 3D volumetric grids, these classifiers achieve accuracies more than an order of magnitude higher than previous approaches based on geometric filters or other machine learning methods. The best-performing model differentiates among hypothetical zeolites and those that can be synthesized as silicates, as aluminophosphates, or as both. This four-class classifier attains a false negative rate of 3.4% and a false positive rate of 0.4%, misidentifying only 1,207 of over 330,000 hypothetical structures--even though the hypothetical structures exhibit similar formation energies as real zeolites and chemically reasonable bond lengths and angles. We hypothesize that the ZeoNet representation captures essential structural features correlated with synthetic feasibility. In the absence of comprehensive physics-based criteria for synthesizability, the small subset of misclassified hypothetical structures likely represents promising candidates for future experimental synthesis.

cond-mat.mtrl-sci↗

arXiv2025

Interfacial Strain Modulated Correlated Plasmons in La1.85Sr0.15CuO4 and Their Role in High-temperature Superconductivity

High-temperature superconductivity in cuprate materials remains a major challenge in physics due to the complexity of their strongly correlated electronic states. Interfacial strain is a powerful lever for tuning electronic correlations in complex oxides, offering new pathways to control emergent quantum phases. Here, we report the discovery of interfacial strain modulated correlated plasmons observed exclusively in superconducting La1.85Sr0.15CuO4 (LSCO) through spectroscopic ellipsometry. This form of plasmons is absent in the non-superconducting LSCO counterparts. Detailed analysis reveals that these correlated plasmons, arising from the collective excitations within Mott-correlated bands, are driven by long-range electronic correlations in the Cu-O planes. Furthermore, long-range electronic correlations, intricately modulated by interfacial strain, may play a crucial role in the emergence of superconductivity and in tuning the transition temperature. Dynamical cluster approximation (DCA) with quantum Monte Carlo (QMC) calculations of the extended Hubbard model suggest that long-range Coulomb interactions play an important role in LSCO, showing good agreement with our experimental findings. The collective evidence from both the experimental results and theoretical findings provides new insights into the nature of collective excitations and their pivotal role in the emergence of high-temperature superconductivity.

cond-mat.str-el↗

arXiv2025

Enhancing Lung Cancer Treatment Outcome Prediction through Semantic Feature Engineering Using Large Language Models

Accurate prediction of treatment outcomes in lung cancer remains challenging due to the sparsity, heterogeneity, and contextual overload of real-world electronic health data. Traditional models often fail to capture semantic information across multimodal streams, while large-scale fine-tuning approaches are impractical in clinical workflows. We introduce a framework that uses Large Language Models (LLMs) as Goal-oriented Knowledge Curators (GKC) to convert laboratory, genomic, and medication data into high-fidelity, task-aligned features. Unlike generic embeddings, GKC produces representations tailored to the prediction objective and operates as an offline preprocessing step that integrates naturally into hospital informatics pipelines. Using a lung cancer cohort (N=184), we benchmarked GKC against expert-engineered features, direct text embeddings, and an end-to-end transformer. Our approach achieved a mean AUROC of 0.803 (95% CI: 0.799-0.807) and outperformed all baselines. An ablation study further confirmed the complementary value of combining all three modalities. These results show that the quality of semantic representation is a key determinant of predictive accuracy in sparse clinical data settings. By reframing LLMs as knowledge curation engines rather than black-box predictors, this work demonstrates a scalable, interpretable, and workflow-compatible pathway for advancing AI-driven decision support in oncology.

cs.LG↗

arXiv2025

GemiRec: Interest Quantization and Generation for Multi-Interest Recommendation

Multi-interest recommendation has gained attention, especially in industrial retrieval stage. Unlike classical dual-tower methods, it generates multiple user representations instead of a single one to model comprehensive user interests. However, prior studies have identified two underlying limitations: the first is interest collapse, where multiple representations homogenize. The second is insufficient modeling of interest evolution, as they struggle to capture latent interests absent from a user's historical behavior. We begin with a thorough review of existing works in tackling these limitations. Then, we attempt to tackle these limitations from a new perspective. Specifically, we propose a framework-level refinement for multi-interest recommendation, named GemiRec. The proposed framework leverages interest quantization to enforce a structural interest separation and interest generation to learn the evolving dynamics of user interests explicitly. It comprises three modules: (a) Interest Dictionary Maintenance Module (IDMM) maintains a shared quantized interest dictionary. (b) Multi-Interest Posterior Distribution Module (MIPDM) employs a generative model to capture the distribution of user future interests. (c) Multi-Interest Retrieval Module (MIRM) retrieves items using multiple user-interest representations. Both theoretical and empirical analyses, as well as extensive experiments, demonstrate its advantages and effectiveness. Moreover, it has been deployed in production since March 2025, showing its practical value in industrial applications.

cs.IR↗

arXiv2025

Causality Enhancement for Cross-Domain Recommendation

Cross-domain recommendation forms a crucial component in recommendation systems. It leverages auxiliary information through source domain tasks or features to enhance target domain recommendations. However, incorporating inconsistent source domain tasks may result in insufficient cross-domain modeling or negative transfer. While incorporating source domain features without considering the underlying causal relationships may limit their contribution to final predictions. Thus, a natural idea is to directly train a cross-domain representation on a causality-labeled dataset from the source to target domain. Yet this direction has been rarely explored, as identifying unbiased real causal labels is highly challenging in real-world scenarios. In this work, we attempt to take a first step in this direction by proposing a causality-enhanced framework, named CE-CDR. Specifically, we first reformulate the cross-domain recommendation as a causal graph for principled guidance. We then construct a causality-aware dataset heuristically. Subsequently, we derive a theoretically unbiased Partial Label Causal Loss to generalize beyond the biased causality-aware dataset to unseen cross-domain patterns, yielding an enriched cross-domain representation, which is then fed into the target model to enhance target-domain recommendations. Theoretical and empirical analyses, as well as extensive experiments, demonstrate the rationality and effectiveness of CE-CDR and its general applicability as a model-agnostic plugin. Moreover, it has been deployed in production since April 2025, showing its practical value in real-world applications.

cs.IR↗

arXiv2025

Semantic-Driven AI Agent Communications: Challenges and Solutions

With the rapid growth of intelligent services, communication targets are shifting from humans to artificial intelligent (AI) agents, which require new paradigms to enable real-time perception, decision-making, and collaboration. Semantic communication, which conveys task-relevant meaning rather than raw data, offers a promising solution. However, its practical deployment remains constrained by dynamic environments and limited resources. To address these issues, this article proposes a semantic-driven AI agent communication framework and develops three enabling techniques. First, semantic adaptation transmission applies fine-tuning with real or generative samples to efficiently adapt models to varying environments. Second, semantic lightweight transmission incorporates pruning, quantization, and perception-aware sampling to reduce model complexity and alleviate computational burden on edge agents. Third, semantic self-evolution control employs distributed hierarchical decision-making to optimize multi-dimensional resources, enabling robust multi-agent collaboration in dynamic environments. Simulation results show that the proposed solutions achieve faster convergence and stronger robustness, while the proposed distributed hierarchical optimization method significantly outperforms conventional decision-making schemes, highlighting its potential for AI agent communication networks.

cs.AI↗