arXiv subjects

Hailong Wang

Publications and source records attributed to Hailong Wang.

At least 19 recordsLinked to original sources

arXiv2026

Evidence of Crystal-Field-Mediated Anisotropic Orbital Rashba Effect in Epitaxial Ru/FM Heterostructure

Electrical generation of orbital angular momentum provides a promising route to current-induced torques, yet effective control of orbital Rashba textures still remains challenging, particularly because the role of the interfacial crystal field remains largely unexplored. Here, we report experimental evidence for a crystal-field-mediated interfacial anisotropic orbital Rashba effect (AORE) in epitaxial Ru/ferromagnet heterostructures. Total orbital torque was disentangled into an isotropic bulk contribution and an in-plane anisotropic interfacial contribution. The latter was strongly suppressed by degrading the crystallinity of either constituent and by inserting a Cu spacer, highlighting the essential roles of coherent interfacial orbital hybridization and direct Ru/ferromagnet contact. These results identify interfacial crystal-field coherency as a key ingredient in manipulating orbital Rashba textures and establish a route toward engineering the symmetry and directionality of orbital torques.

cond-mat.mes-hall↗

arXiv2026

Observation and Control of Spontaneous Magnon Emission from Spin Ensembles in 2D Hexagonal Boron Nitride

Hybrid systems consisting of color centers and magnetic materials provide an appealing solid-state platform for advancing the burgeoning quantum technological revolution. Exploring novel coupling mechanisms between optically active spin defects and quantum degrees of freedom is directly relevant in this context. Here, we report observation and control of spontaneous magnon emission from boron-vacancy centers in 2D hexagonal boron nitride (hBN), an unconventional qubit-magnon dipole coupling channel that dominates in the near-zero temperature limit. The spontaneous magnon emission process starts to be overshadowed by thermal magnon effect as temperature increases, reflecting the crossover from an emission-dominated, effectively cold magnon reservoir to a thermally occupied spin bath where absorption and stimulated processes restore balance. By increasing the spin defect density, we further present that spontaneous magnon emission into a common spin bath could help establish quantum correlations in dense hBN spin ensembles. Our results are quantitatively captured by detailed theoretical modeling, bringing insights into understanding qubit-magnon coupling, correlated spin dynamics, and many-body physics of color centers in the quantum regime.

cond-mat.mes-hall↗

arXiv2026

Scanning-probe quantum sensing of microwave and static magnetic field response of an on-chip superconducting resonator

Superconducting resonators are finding increasing applications in designing advanced quantum circuits for ongoing sensing, metrology, and computing technological revolution. A detailed knowledge of microscopic electromagnetic properties of superconducting resonators is directly relevant for their further improvements on circuitry design and device performance. Here, we introduce scanning-probe quantum microscopy to report nanoscale sensing of microwave and static magnetic field environment of an on-chip niobium (Nb) superconducting resonator. Taking advantage of Rabi oscillation measurements, we show that microwave magnetic fields generated by the superconducting resonator mode can be utilized to achieve coherent control of a quantum spin sensor. We further visualize static electromagnetic field response of the Nb resonator, showing magnetic field-induced formation, evolution, and depinning of superconducting vortices. Our results provide insights into future design, testing and evaluation of solid-state superconducting resonators, highlighting the potential of quantum sensors as a local probe to investigate electromagnetic properties of superconducting quantum circuits.

cond-mat.mes-hall↗

arXiv2026

VICR: Visual In-Context Restoration for Real-World Image Super-Resolution

Real-world image super-resolution (Real-ISR) requires balancing structural fidelity to degraded observations with realistic detail synthesis. However, existing generative Real-ISR methods often rely on entangled conditioning mechanisms, leading to structural drift or semantically inconsistent details. To address this issue, we propose Visual In-Context Restoration (VICR), a Diffusion Transformer (DiT)-based framework that formulates Real-ISR as image completion. Specifically, we introduce a decoupled visual prior injection mechanism that derives local and global cues from the low-quality (LQ) image: local cues help recover image structures and support high-frequency detail synthesis, while global cues guide overall generation and promote semantic consistency. For ambiguous regions under severe degradation, VICR employs an inference-time agent to refine semantic prompts using visual evidence from the LQ input while keeping model parameters fixed. Experiments show that VICR achieves state-of-the-art performance across multiple Real-ISR benchmarks with only 127M trainable parameters.

cs.CV↗

arXiv2026

Imaging Surface Magnetization in Altermagnetic MnTe Films

Altermagnets with pronounced spin-splitting band structure, unconventional magnetic and crystal symmetries, and exotic magneto-transport properties have received immense interest in cutting-edge spintronics, materials science, and condensed matter physics research. Microscopic imaging of spontaneous magnetic domains and phases in altermagnets constitutes an important step for investigating their underlying material properties, mechanisms, and spin behaviors. Taking advantage of scanning-probe quantum microscopy, here we report nanoscale quantum sensing of a prototypical altermagnet candidate $\alpha$-MnTe. We visualize evanescent magnetization and the associated magnetic domains in epitaxial MnTe films, which allows external magnetic fields to control the intrinsic altermagnetic order and configurations. By evaluating a series of MnTe films with different thicknesses down to the atomic scale, we further present evidence for the interfacial origin of the observed weak magnetization and show its correlation with the anomalous Hall effect in MnTe film. Our results advance the current understanding of emergent altermagnetism, providing insights into future material design of altermagnet-integrated spintronic devices.

cond-mat.mtrl-sci↗

arXiv2026

Large Deviation Functions for Open Quantum Systems with a Strong Symmetry

In open quantum systems with strong symmetries, the global scaled cumulant generating function (SCGF) is generally nonanalytic, so the G\"artner-Ellis theorem cannot directly yield the genuine large-deviation rate function. To address this issue, we propose that the theorem remains valid within blocks of the systems' operator space: we first obtain local rate functions for each block via the theorem and then recover the global one by minimization. This approach is justified by the dissipative freezing phenomenon in such systems. We demonstrate the scheme in an analytical model and a three-spin model with XX interaction. In the latter, we find that the vanishing of a nonanalytic point in the global SCGF under dephasing appears as an avoided ``level'' crossing, and we quantify this behavior using a degenerate perturbation theory.

cond-mat.stat-mech↗

arXiv2026

CoT2-Meta: Budgeted Metacognitive Control for Test-Time Reasoning

Recent test-time reasoning methods improve performance by generating more candidate chains or searching over larger reasoning trees, but they typically lack explicit control over when to expand, what to prune, how to repair, and when to abstain. We introduce CoT2-Meta, a training-free metacognitive reasoning framework that combines object-level chain-of-thought generation with meta-level control over partial reasoning trajectories. The framework integrates four components: strategy-conditioned thought generation, tree-structured search, an online process oracle for step-level reasoning evaluation, and a meta-controller that allocates computation through expansion, pruning, repair, stopping, and fallback decisions. Under matched inference budgets, CoT2-Meta consistently outperforms strong single-path, sampling-based, and search-based baselines, including ReST-MCTS. On the default backbone, it achieves 92.8 EM on MATH, 90.4 accuracy on GPQA, 98.65 EM on GSM8K, 75.8 accuracy on BBEH, 85.6 accuracy on MMMU-Pro, and 48.8 accuracy on HLE, with gains over the strongest non-CoT2-Meta baseline of +3.6, +5.2, +1.15, +2.0, +4.3, and +4.3 points, respectively. Beyond these core results, the framework remains effective across a broader 15-benchmark suite spanning knowledge and QA, multi-hop reasoning, coding, and out-of-distribution evaluation. Additional analyses show better compute scaling, improved calibration, stronger selective prediction, targeted repair behavior, and consistent gains across backbone families. These results suggest that explicit metacognitive control is a practical design principle for reliable and compute-efficient test-time reasoning systems.

cs.AI↗

arXiv2025

F.A.C.U.L.: Language-Based Interaction with AI Companions in Gaming

In cooperative video games, traditional AI companions are deployed to assist players, who control them using hotkeys or command wheels to issue predefined commands such as ``attack'', ``defend'', or ``retreat''. Despite their simplicity, these methods, which lack target specificity, limit players' ability to give complex tactical instructions and hinder immersive gameplay experiences. To address this problem, we propose the FPS AI Companion who Understands Language (F.A.C.U.L.), the first real-time AI system that enables players to communicate and collaborate with AI companions using natural language. By integrating natural language processing with a confidence-based framework, F.A.C.U.L. efficiently decomposes complex commands and interprets player intent. It also employs a dynamic entity retrieval method for environmental awareness, aligning human intentions with decision-making. Unlike traditional rule-based systems, our method supports real-time language interactions, enabling players to issue complex commands such as ``clear the second floor'', ``take cover behind that tree'', or ``retreat to the river''. The system provides real-time behavioral responses and vocal feedback, ensuring seamless tactical collaboration. Using the popular FPS game \textit{Arena Breakout: Infinite} as a case study, we present comparisons demonstrating the efficacy of our approach and discuss the advantages and limitations of AI companions based on real-world user feedback.

cs.HC↗

arXiv2025

Strain-Tunable Opto-electronics in PdS$_2$ Monolayer: the Role of Band Nesting and Carrier-Phonon Scattering

Strain engineering is a powerful strategy for tuning the optoelectronic properties in two-dimensional materials, yet the underlying mechanisms governing their strain response are often not fully elucidated. In this work, our first-principle calculations show that the penta-orthorhombic PdS$_2$ monolayer exhibits two key strain-tunable properties: a continuous redshift of its main optical absorption peak from $\sim$2.0 to $\sim$1.6~eV and enhancement in carrier mobility, with a more than threefold increase for electron under 0--4\% biaxial tensile strain. Subsequent analysis reveals that the tunable optical response originates from a robust band nesting feature between the highest valence and lowest conduction bands, which is preserved across the Brillouin zone under biaxial strain. For the carrier transport, deformation potential theory predicts mobility increasing with strain, strongly correlating with the reduction of carrier effective mass. Our first-principles calculations show a strain-induced monotonic decrease in carrier linewidths near the band edges, indicating suppressed carrier-phonon scattering and longer carrier lifetime as the origin of the mobility enhancement. Our work establishes a pathway for engineering the optoelectronic response in 2D semiconductors where strong band nesting governs the optical properties and paves the way for the rational design of continuously tunable flexible optoelectronic devices.

cond-mat.mtrl-sci↗

arXiv2025

Combining ECG Foundation Model and XGBoost to Predict In-Hospital Malignant Ventricular Arrhythmias in AMI Patients

Malignant ventricular arrhythmias (VT/VF) following acute myocardial infarction (AMI) are a major cause of in-hospital death, yet early identification remains a clinical challenge. While traditional risk scores have limited performance, end-to-end deep learning models often lack the interpretability needed for clinical trust. This study aimed to develop a hybrid predictive framework that integrates a large-scale electrocardiogram (ECG) foundation model (ECGFounder) with an interpretable XGBoost classifier to improve both accuracy and interpretability. We analyzed 6,634 ECG recordings from AMI patients, among whom 175 experienced in-hospital VT/VF. The ECGFounder model was used to extract 150-dimensional diagnostic probability features , which were then refined through feature selection to train the XGBoost classifier. Model performance was evaluated using AUC and F1-score , and the SHAP method was used for interpretability. The ECGFounder + XGBoost hybrid model achieved an AUC of 0.801 , outperforming KNN (AUC 0.677), RNN (AUC 0.676), and an end-to-end 1D-CNN (AUC 0.720). SHAP analysis revealed that model-identified key features, such as "premature ventricular complexes" (risk predictor) and "normal sinus rhythm" (protective factor), were highly consistent with clinical knowledge. We conclude that this hybrid framework provides a novel paradigm for VT/VF risk prediction by validating the use of foundation model outputs as effective, automated feature engineering for building trustworthy, explainable AI-based clinical decision support systems.

cs.AI↗

arXiv2025

Generating Transferrable Adversarial Examples via Local Mixing and Logits Optimization for Remote Sensing Object Recognition

Deep Neural Networks (DNNs) are vulnerable to adversarial attacks, posing significant security threats to their deployment in remote sensing applications. Research on adversarial attacks not only reveals model vulnerabilities but also provides critical insights for enhancing robustness. Although current mixing-based strategies have been proposed to increase the transferability of adversarial examples, they either perform global blending or directly exchange a region in the images, which may destroy global semantic features and mislead the optimization of adversarial examples. Furthermore, their reliance on cross-entropy loss for perturbation optimization leads to gradient diminishing during iterative updates, compromising adversarial example quality. To address these limitations, we focus on non-targeted attacks and propose a novel framework via local mixing and logits optimization. First, we present a local mixing strategy to generate diverse yet semantically consistent inputs. Different from MixUp, which globally blends two images, and MixCut, which stitches images together, our method merely blends local regions to preserve global semantic information. Second, we adapt the logit loss from targeted attacks to non-targeted scenarios, mitigating the gradient vanishing problem of cross-entropy loss. Third, a perturbation smoothing loss is applied to suppress high-frequency noise and enhance transferability. Extensive experiments on FGSCR-42 and MTARSI datasets demonstrate superior performance over 12 state-of-the-art methods across 6 surrogate models. Notably, with ResNet as the surrogate on MTARSI, our method achieves a 17.28% average improvement in black-box attack success rate.

cs.CV↗

arXiv2025

Adversarial Patch Attack for Ship Detection via Localized Augmentation

Current ship detection techniques based on remote sensing imagery primarily rely on the object detection capabilities of deep neural networks (DNNs). However, DNNs are vulnerable to adversarial patch attacks, which can lead to misclassification by the detection model or complete evasion of the targets. Numerous studies have demonstrated that data transformation-based methods can improve the transferability of adversarial examples. However, excessive augmentation of image backgrounds or irrelevant regions may introduce unnecessary interference, resulting in false detections of the object detection model. These errors are not caused by the adversarial patches themselves but rather by the over-augmentation of background and non-target areas. This paper proposes a localized augmentation method that applies augmentation only to the target regions, avoiding any influence on non-target areas. By reducing background interference, this approach enables the loss function to focus more directly on the impact of the adversarial patch on the detection model, thereby improving the attack success rate. Experiments conducted on the HRSC2016 dataset demonstrate that the proposed method effectively increases the success rate of adversarial patch attacks and enhances their transferability.

cs.CV↗

arXiv2025

Nanoscale quantum imaging of field-free deterministic switching of a chiral antiferromagnet

Recently, unconventional spin-orbit torques (SOTs) with tunable spin generation open new pathways for designing novel magnetization control for cutting-edge spintronics innovations. A leading research thrust is to develop field-free deterministic magnetization switching for implementing scalable and energy favorable magnetic recording and storage applications, which have been demonstrated in conventional ferromagnetic and antiferromagnetic material systems. Here we extend this advanced magnetization control strategy to chiral antiferromagnet Mn3Sn using spin currents with out-of-plane canted polarization generated from low-symmetry van der Waals (vdW) material WTe2. Numerical calculations suggest that damping-like SOT of spins injected perpendicular to the kagome plane of Mn3Sn serves as a driving force to rotate the chiral magnetic order, while the field-like SOT of spin currents with polarization parallel to the kagome plane provides the bipolar deterministicity to the magnetic switching. We further introduce scanning quantum microscopy to visualize nanoscale evolutions of Mn3Sn magnetic domains during the field-free switching process, corroborating the exceptionally large magnetic switching ratio up to 90%. Our results highlight the opportunities provided by hybrid SOT material platforms consisting of noncollinear antiferromagnets and low-symmetry vdW spin source materials for developing next-generation, transformative spintronic logic devices.

cond-mat.mtrl-sci↗

arXiv2025

ReuseDroid: A VLM-empowered Android UI Test Migrator Boosted by Active Feedback

GUI testing is an essential quality assurance process in mobile app development. However, the creation and maintenance of GUI tests for mobile apps are resource-intensive and costly. Recognizing that many apps share similar functionalities, researchers have proposed various techniques to migrate GUI tests from one app to another with similar features. For example, some techniques employ mapping-based approaches to align the GUI elements traversed by the tests of a source app to those present in the target app. Other test migration techniques have also been proposed to leverage large language models (LLMs) by adapting the GUI tasks in source tests. However, these techniques are ineffective in dealing with different operational logic between the source and target apps. The semantics of GUI elements may not be correctly inferred due to the missing analysis of these flows. In this work, we propose REUSEDROID, a novel multiagent framework for GUI test migration empowered by Large Vision-Language Models (VLMs). REUSEDROID is powered by multiple VLM-based agents, each tackling a stage of the test migration process by leveraging the relevant visual and textual information embedded in GUI pages. An insight of REUSEDROID is to migrate tests based only on the core logic shared across similar apps, while their entire operational logic could differ. We evaluate REUSEDROID on LinPro, a new test migration dataset that consists of 578 migration tasks for 39 popular apps across 4 categories. The experimental result shows that REUSEDROID can successfully migrate 90.3% of the migration tasks, outperforming the best mapping-based and LLM-based baselines by 318.1% and 109.1%, respectively.

cs.SE↗

arXiv2025

Visualizing Field-free Deterministic Magnetic Switching of all-van der Waals Spin-Orbit Torque System Using Spin Ensembles in Hexagonal Boron Nitride

Recently, optically active spin defects embedded in van der Waals (vdW) crystals have emerged as a transformative quantum sensing platform to explore cutting-edge materials science and quantum physics. Taking advantage of excellent solid-state integrability, this new class of spin defects can be arranged in controllable nanoscale proximity of target materials in vdW heterostructures, showing great promise for improving spatial resolution and field sensitivity of current sensing technologies. Building on this state-of-the-art measurement platform, here we report hexagonal boron nitride-based quantum imaging of field-free deterministic magnetic switching of room-temperature two-dimensional magnet Fe3GaTe2 in an all-vdW spin-orbit torque (SOT) system. By visualizing SOT-driven variations of nanoscale Fe3GaTe2 magnetic stray field profile under different conditions, we have revealed how the observed magnetic switching evolves from deterministic to indeterministic behavior due to the interplay between out-of-plane spins, in-plane spins and Joule heating. This understanding, which is otherwise difficult to access by conventional transport measurements, offers valuable insights on material design, testing, and evaluation of next-generation vdW spintronic devices.

cond-mat.mes-hall↗

arXiv2024

Nanoscale observation and control of quasiparticle induced magnetic noise in a superconducting resonator

Superconducting circuits are arguably taking a leading role in driving the ongoing quantum technological revolution. A detailed knowledge of the microscopic fluctuating electromagnetic properties plays an important role in advancing the circuitry design, testing, and material integration of cutting-edge superconducting quantum electronics. Here we report scanning nitrogen-vacancy (NV) quantum sensing of local magnetic noise environment of an on- chip superconducting resonator. We find that quasiparticle-induced fluctuating magnetic fields can drive NV spin relaxation, which shows a peak value around the superconducting transition point of niobium at the thermal equilibrium state. External microwave driving at the resonator mode frequency significantly increases the quasiparticle density, leading to enhancement of magnetic noise. We further perform optically detected magnetic resonance measurements to demonstrate quasiparticle magnetic noise mediated off-resonant dipole coupling between the NV center and niobium resonator. Our work reports experimental observation of the Hebel-Slichter peak signature by an external sensor outside of a superconductor. The presented study also highlights the advantages of quantum sensors in investigating miniaturized superconducting devices, providing insights into their future performance improvements.

cond-mat.mes-hall↗

arXiv2024

Nonlocal, Pattern-aware Response and Feedback Framework for Regional Climate Change

We devise a pattern-aware feedback framework for representing the forced climate response using a suite of Green's function experiments with solar radiation perturbations. By considering the column energy balance, a comprehensive linear response function (CLRF) forimportant climate variables and feedback quantities such as moist static energy, sea surface temperature, albedo, cloud optical depth, and lapse rate is learned from Green's function data. The learned CLRF delineates the effects of the energy diffusion in both the ocean and atmosphere and the pattern-aware feedbacks from the aforementioned radiatively active processes. The CLRF can then be decomposed into forcing-response mode pairs which are in turn used to construct a reduced-order model (ROM) describing the dominant dynamics of climate responses. These mode pairs capture nonlocal effects and teleconnections in the climate and thus, make the ROM apt for capturing regional features of climate change response. A key observation is that the CLRF captures the polar amplified response as the most excitable mode of the climate system and this mode is explainable in the data-learned pattern-aware feedback framework. The ROM can be used for predicting the response for a given forcing and for reconstructing the forcing from a given response; we demonstrate these capabilities for independent forcing pattern.

physics.ao-ph↗

arXiv2024

Coherent Driving of a Single Nitrogen Vacancy Center by a Resonant Magnetic Tunnel Junction

Nitrogen-vacancy (NV) centers, atomic spin defects in diamond, represent an active contender for advancing transformative quantum information science (QIS) and innovations. One of the major challenges for designing NV-based hybrid systems for QIS applications results from the difficulty of realizing local control of individual NV spin qubits in a scalable and energyefficient way. To address this bottleneck, we introduce magnetic tunnel junction (MTJ) devices to establish coherent driving of an NV center by a resonant MTJ with voltage controlled magnetic anisotropy. We show that the oscillating magnetic stray field produced by a resonant micromagnet can be utilized to effectively modify and drive NV spin rotations when the NV frequency matches the corresponding resonance conditions of the MTJ. Our results present a new pathway to achieve all-electric control of an NV spin qubit with reduced power consumption and improved solid-state scalability for implementing cutting-edge QIS technological applications.

cond-mat.mes-hall↗