arXiv subjects

Hao Zheng

Publications and source records attributed to Hao Zheng.

At least 73 records · Page 4Linked to original sources

arXiv2026

The time-relaxation limit for weak solutions to the quantum hydrodynamics system

This paper analyzes weak solutions of the quantum hydrodynamics (QHD) system with a collisional term posed on the one-dimensional torus. The main goal of our analysis is to rigorously prove the time-relaxation limit towards solutions to the quantum drift-diffusion (QDD) equation. \newline The existence of global in time, finite energy weak solutions can be proved by straightforwardly exploiting the polar factorization and wave function lifting tools previously developed by the authors. However, the sole energy bounds are not sufficient to show compactness and then pass to the limit. \newline For this reason, we consider a class of more regular weak solutions (termed GCP solutions), determined by the finiteness of a functional involving the chemical potential associated with the system. For solutions in this class and bounded away from vacuum, we prove the time-relaxation limit and provide an explicit convergence rate. \newline Our analysis exploits compactness tools and does not require the existence (and smoothness) of solutions to the limiting equations or the well-preparedness of the initial data. \newline As a by-product of our analysis, we also establish the existence of global in time $H^2$ solutions to a nonlinear Schrödinger-Langevin equation and construct solutions to the QDD equation as strong limits of GCP solutions to the QHD system.

math.AP↗

arXiv2026

Dynamic topological exciton-polaritons enabling ultrafast logic operations

Topological active materials have emerged as powerful paradigm bridging the discovery of exotic topological phases of matter with the development of functional topological devices. The recent extension of these material systems into dynamic regime, where topological properties can be actively manipulated at ultrafast timescales, promises unprecedented control over topological states and their functionalities. However, translating the static topological lasing signals into high-performance logic functions remain highly challenging, which imposes a far more stringent set of materials attributes. Here, leveraging the strong nonlinearity and pronounced spectral isolation of perovskite exciton-polaritons embedded in a Dirac vortex microcavity, we experimentally demonstrate the dynamic topological Majorana-like state polariton condensation with its ultrafast logic operations at room temperature. By actively coordinating pump and control beams in both spectral and temporal domain, we dynamically steer the topological polariton condensation process and demonstrate AND and NOT logic operations, achieving record extinction ratio (~20 dB), extremely low control fluence (~0.2 nJ/cm2) and sub-picosecond response time (~500 fs). Our results expand the frontier of dynamic topology and establish a novel pathway towards robust, ultrafast, and reconfigurable on-chip polaritonic logic circuits.

physics.optics↗

arXiv2026

Altermagnetic Even-Odd Effects in CsV$_2$Te$_2$O Josephson Junctions

The interplay between conventional superconductivity and unconventional magnetism offers an exciting platform for realizing exotic superconducting phenomena. Here, we investigate Josephson effects in planar and vertical junctions based on CsV$_2$Te$_2$O-family materials, which host hidden $d$-wave altermagnetism with G-type antiferromagnetic order. In monolayer-based planar junctions, the quasi-1D, nearly flat, spin-polarized bands of the altermagnet, when coupled to $s$-wave superconductors, produce a \textit{fully} spin-polarized supercurrent with strong directional anisotropy -- a spin-selective Josephson effect. In multilayers, we uncover an \textit{altermagnetic even-odd effect}: spin-polarized supercurrents persist only in odd-layer planar junctions but cancel exactly in even layers. Thus, layer parity acts as a switch for spin-polarized supercurrent. In vertical junctions, odd-layer barriers enhance equal-spin triplet transport while even layers favor opposite-spin transport, yielding a robust period-two oscillation in the total supercurrent with layer number. These layer-parity-dependent responses represent a general even-odd effect in hidden altermagnets, applicable to diverse magnetic and transport phenomena.

cond-mat.supr-con↗

arXiv2026

X-ray Insights Unleashed: Pioneering the Enhancement of Multi-Label Long-Tail Data

Long-tailed pulmonary anomalies in chest radiography present formidable diagnostic challenges. Despite the recent strides in diffusion-based methods for enhancing the representation of tailed lesions, the paucity of rare lesion exemplars curtails the generative capabilities of these approaches, thereby leaving the diagnostic precision less than optimal. In this paper, we propose a novel data synthesis pipeline designed to augment tail lesions utilizing a copious supply of conventional normal X-rays. Specifically, a sufficient quantity of normal samples is amassed to train a diffusion model capable of generating normal X-ray images. This pre-trained diffusion model is subsequently utilized to inpaint the head lesions present in the diseased X-rays, thereby preserving the tail classes as augmented training data. Additionally, we propose the integration of a Large Language Model Knowledge Guidance (LKG) module alongside a Progressive Incremental Learning (PIL) strategy to stabilize the inpainting fine-tuning process. Comprehensive evaluations conducted on the public lung datasets MIMIC and CheXpert demonstrate that the proposed method sets a new benchmark in performance.

cs.CV↗

arXiv2026

Heterogeneous Complementary Distillation

Knowledge distillation (KD)transfers the dark knowledge from a complex teacher to a compact student. However, heterogeneous architecture distillation, such as Vision Transformer (ViT) to ResNet18, faces challenges due to differences in spatial feature representations.Traditional KD methods are mostly designed for homogeneous architectures and hence struggle to effectively address the disparity. Although heterogeneous KD approaches have been developed recently to solve these issues, they often incur high computational costs and complex designs, or overly rely on logit alignment, which limits their ability to leverage the complementary features. To overcome these limitations, we propose Heterogeneous Complementary Distillation (HCD),a simple yet effective framework that integrates complementary teacher and student features to align representations in shared logits.These logits are decomposed and constrained to facilitate diverse knowledge transfer to the student. Specifically, HCD processes the student's intermediate features through convolutional projector and adaptive pooling, concatenates them with teacher's feature from the penultimate layer and then maps them via the Complementary Feature Mapper (CFM) module, comprising fully connected layer,to produce shared logits.We further introduce Sub-logit Decoupled Distillation (SDD) that partitions the shared logits into n sub-logits, which are fused with teacher's logits to rectify classification.To ensure sub-logit diversity and reduce redundant knowledge transfer, we propose an Orthogonality Loss (OL).By preserving student-specific strengths and leveraging teacher knowledge,HCD enhances robustness and generalization in students.Extensive experiments on the CIFAR-100, Fine-grained (e.g., CUB200)and ImageNet-1K datasets demonstrate that HCD outperforms state-of-the-art KD methods,establishing it as an effective solution for heterogeneous KD.

cs.CV↗

arXiv2026

Quantum Spin-1/2 Rings Built from [2]Triangulene Molecular Units

Quantum spin rings represent fundamental model systems that exhibit distinctive quantum phenomena-such as quantum critical behavior and quasiparticle excitations-arising from their periodic boundary conditions and enhanced quantum fluctuations. Here, we report the on-surface synthesis and atomic-scale characterization of antiferromagnetic S=1/2 quantum spin rings composed of pristine and unmodified [2]triangulene units on a Au(111) surface. Using stepwise on-surface synthesis followed by STM tip-induced dehydrogenation, we precisely constructed cyclic five- and six-membered spin rings and investigated their spin states via scanning probe microscopy and multireference calculations. Nc-AFM imaging reveals that the six-membered ring retains a planar geometry, whereas the five-membered ring exhibits pronounced structural distortion. The six-membered ring hosts a uniform excitation gap that can be accurately described by a Heisenberg spin model and multireference CASCI calculations. In contrast, the distorted five-membered ring displays spin ground states with asymmetric spatial distributions due to degeneracy lifting induced by structural distortion. Our findings establish a versatile molecular platform for exploring correlated magnetism and quantum spin phenomena in cyclic organic magnetic architectures with disorder.

cond-mat.mtrl-sci↗

arXiv2026

Interplay of Quantum Size Effect and Tensile Strain on Surface Morphology of Sn(100) Islands

The quantum size effect (QSE) and strain effect are two key factors influencing the surface morphology of thin films, which can increase film surface roughness through QSE-induced thickness oscillation and strain-induced island formation, respectively. Surface roughness usually manifests in the early stages of film growth and diminishes beyond a critical thickness. In this work, we employ molecular beam epitaxy (MBE) to grow Sn(100) islands with varying thickness N on bilayer graphene-terminated 6H-SiC(0001) substrates. Scanning tunneling microscopy and spectroscopy measurements reveal an inverse surface roughness effect that highlights the interplay of QSE and misfit strain in shaping the surface morphology of Sn(100) islands. For N =< 10, the islands exhibit flat surfaces, while for N >= 26, the island surfaces become corrugated and patterned. For the intermediate range, i.e., 12 =< N =<24, both flat and patterned surfaces coexist, with the percentage coverage of the patterned surface oscillating as a function of N. By performing density functional theory calculations, we demonstrate that the unusual surface pattern evolution in our MBE-grown Sn(100) islands is a result of the interplay between QSE-induced surface roughing and tensile strain-induced smoothening effect.

cond-mat.mtrl-sci↗

arXiv2026

Graph Domain Adaptation via Homophily-Agnostic Reconstructing Structure

Graph Domain Adaptation (GDA) transfers knowledge from labeled source graphs to unlabeled target graphs, addressing the challenge of label scarcity. However, existing GDA methods typically assume that both source and target graphs exhibit homophily, leading existing methods to perform poorly when heterophily is present. Furthermore, the lack of labels in the target graph makes it impossible to assess its homophily level beforehand. To address this challenge, we propose a novel homophily-agnostic approach that effectively transfers knowledge between graphs with varying degrees of homophily. Specifically, we adopt a divide-and-conquer strategy that first separately reconstructs highly homophilic and heterophilic variants of both the source and target graphs, and then performs knowledge alignment separately between corresponding graph variants. Extensive experiments conducted on five benchmark datasets demonstrate the superior performance of our approach, particularly highlighting its substantial advantages on heterophilic graphs.

cs.SI↗

arXiv2026

Scaling Agentic Verifier for Competitive Coding

Large language models (LLMs) have demonstrated strong coding capabilities but still struggle to solve competitive programming problems correctly in a single attempt. Execution-based re-ranking offers a promising test-time scaling strategy, yet existing methods are constrained by either difficult test case generation or inefficient random input sampling. To address this limitation, we propose Agentic Verifier, an execution-based agent that actively reasons about program behaviors and searches for highly discriminative test inputs that expose behavioral discrepancies among candidate solutions. Through multi-turn interaction with code execution environments, the verifier iteratively refines the candidate input generator and produces targeted counterexamples rather than blindly sampling inputs. We train the verifier to acquire this discriminative input generation capability via a scalable pipeline combining large-scale data synthesis, rejection fine-tuning, and agentic reinforcement learning. Extensive experiments across five competitive programming benchmarks demonstrate consistent improvements over strong execution-based baselines, achieving up to +10-15% absolute gains in Best@K accuracy. Further analysis reveals clear test-time scaling behavior and highlights the verifier's broader potential beyond reranking.

cs.CL↗

arXiv2026

Optically reconfigurable canalization of exciton-polaritons in a non-hyperbolic perovskite

The ability to steer polariton flow on-demand holds significant promise towards nanophotonic applications and photonic circuitry. Polariton canalization, exhibiting intrinsic collimation and diffractionless transport, emerges as a promising solution without guiding structures. However, earlier demonstrations have been restricted to certain crystal surfaces with intrinsic hyperbolic responses and operated in the linear regime. Here, we experimentally demonstrate canalization of nonlinear exciton polariton condensates with optical reconfigurability in a birefringent CsPbBr3 perovskite crystal without intrinsic hyperbolic response. By embedding the birefringent perovskite crystal into a planar microcavity, the interplay between cavity transverse-electric-transverse-magnetic splitting and crystalline birefringence produces an anisotropic band geometry with a hyperbolic-flat-parabolic evolution of polaritonic isofrequency contours (IFCs). Nonresonant pumping drives exciton polariton condensation onto flat far-field contours with nonlinear emission amplification, leading to coherent canalized flows with over twentyfold collimation with respect to arc-shaped contours. Reconfiguring the optical pumping-spot size allows switching the nonlinear polariton condensates into hyperbolic and parabolic IFC regimes, leading to divergent propagation behaviour with collimating reconfiguration. Our study reveals a distinct canalization framework for shaping the nonlinear exciton-polariton condensate flows, opening opportunities for all-optical polaritonic logic circuits based on stabilized nonlinear quantum interconnects.

physics.optics↗

arXiv2026

Multigap nodeless superconductivity in Dirac semimetal PdTe

PdTe has recently been reported to be a type-II Dirac semimetal while a bulk nodal and surface nodeless superconductivity (SC) has been claimed to coexist. In this work, we applied point-contact spectroscopy (PCS) method to systematically study the superconducting gap in PdTe single crystals with a SC transition temperature $T_{c}=4.3$ K. The obtained differential conductance curves show a common deviation from a single-gap superconducting behavior and can be better fitted by a two-gap Blonder-Tinkham-Klapwijk model, suggesting the larger gap $Δ_{L}$ with $2Δ_{L}$=3.7 $k_{B}T_{c}$ and the smaller gap $Δ_S$ yielding $2Δ_{S}$=1.1-2.2 $k_{B}T_{c}$ with a weak interband scattering. The variations of conductance spectra among different contacts are proposed to be caused by the anisotropy of Fermi surface topology associated with different gaps.

cond-mat.supr-con↗

arXiv2026

RxnCaption: Reformulating Reaction Diagram Parsing as Visual Prompt Guided Captioning

Large-scale chemical reaction datasets are crucial for AI research in chemistry. However, existing chemical reaction data often exist as images within papers, making them not machine-readable and unusable for training machine learning models. In response to this challenge, we propose the RxnCaption framework for the task of chemical Reaction Diagram Parsing (RxnDP). Our framework reformulates the traditional coordinate prediction driven parsing process into an image captioning problem, which Large Vision Language Models (LVLMs) handle naturally. We introduce a strategy termed BBox and Index as Visual Prompt (BIVP), which uses our state-of-the-art molecular detector, MolYOLO, to pre-draw molecular bounding boxes and indices directly onto the input image. This turns the downstream parsing into a natural-language description problem. Extensive experiments show that the BIVP strategy significantly improves structural extraction quality while simplifying model design. We further construct the RxnCaption-15k dataset, an order of magnitude larger than prior real-world literature benchmarks, with a balanced test subset across four layout archetypes. Experiments demonstrate that RxnCaption-VL achieves state-of-the-art performance on multiple metrics. We believe our method, dataset, and models will advance structured information extraction from chemical literature and catalyze broader AI applications in chemistry. We will release data, models, and code on GitHub.

cs.CV↗

arXiv2026

OFFSIDE: Benchmarking Unlearning Misinformation in Multimodal Large Language Models

Advances in Multimodal Large Language Models (MLLMs) intensify concerns about data privacy, making Machine Unlearning (MU), the selective removal of learned information, a critical necessity. However, existing MU benchmarks for MLLMs are limited by a lack of image diversity, potential inaccuracies, and insufficient evaluation scenarios, which fail to capture the complexity of real-world applications. To facilitate the development of MLLMs unlearning and alleviate the aforementioned limitations, we introduce OFFSIDE, a novel benchmark for evaluating misinformation unlearning in MLLMs based on football transfer rumors. This manually curated dataset contains 15.68K records for 80 players, providing a comprehensive framework with four test sets to assess forgetting efficacy, generalization, utility, and robustness. OFFSIDE supports advanced settings like selective unlearning and corrective relearning, and crucially, unimodal unlearning (forgetting only text data). Our extensive evaluation of multiple baselines reveals key findings: (1) Unimodal methods (erasing text-based knowledge) fail on multimodal rumors; (2) Unlearning efficacy is largely driven by catastrophic forgetting; (3) All methods struggle with "visual rumors" (rumors appear in the image); (4) The unlearned rumors can be easily recovered and (5) All methods are vulnerable to prompt attacks. These results expose significant vulnerabilities in current approaches, highlighting the need for more robust multimodal unlearning solutions. The code is available at https://github.com/zh121800/OFFSIDE

cs.AI↗

arXiv2025

Coexistence of unconventional spin-orbit torque and in-plane Hall effect in a single ferromagnetic layer

The symmetry of a material fundamentally governs its spin transport properties. While unconventional spin transport phenomena have been predominantly explored in low-symmetry systems (e.g., $C_{1v}$ symmetry), high-symmetry crystals--which constitute the majority of industry-compatible materials--are generally expected to exhibit only conventional spin-transport behavior. Here, we report the coexistence of two unconventional spin transport effects, the crystal spin-orbit torque (CSOT) and the crystal in-plane Hall effect (CIHE), in a CoPt single ferromagnetic layer with $C_{3v}$ symmetry. Leveraging the CSOT, we achieve nearly 100% field-free perpendicular magnetization switching in a 6 nm CoPt layer at room temperature. Simultaneously, the CIHE observed in this material exhibits nearly identical dependencies on both current angle and growth temperature as the CSOT. Symmetry analysis confirms that both effects share a common physical origin. Our work not only establishes CoPt as a high-performance spin-orbit material, but also demonstrates that unconventional spin transport can be realized in high-symmetry systems, thereby opening a broad pathway for their application in practical spintronics.

cond-mat.mes-hall↗

arXiv2025

TB or Not TB: Coverage-Driven Direct Preference Optimization for Verilog Stimulus Generation

With the rapid advancement of Large Language Models (LLMs), there is growing interest in applying them to hardware design and verification. Among these stages, design verification remains the most time-consuming and resource-intensive phase, where generating effective stimuli for the design under test (DUT) is both critical and labor-intensive. We present {\it TB or not TB}, a framework for automated stimulus generation using LLMs fine-tuned through Coverage-Driven Direct Preference Optimization (CD-DPO). To enable preference-based training, we introduce PairaNet, a dataset derived from PyraNet that pairs high- and low-quality testbenches labeled using simulation-derived coverage metrics. The proposed CD-DPO method integrates quantitative coverage feedback directly into the optimization objective, guiding the model toward generating stimuli that maximize verification coverage. Experiments on the CVDP CID12 benchmark show that {\it TB or not TB} outperforms both open-source and commercial baselines, achieving up to 77.27\% improvement in code coverage, demonstrating the effectiveness of Coverage-driven preference optimization for LLM-based hardware verification.

cs.LG↗

arXiv2025

Taiji: A DPU Memory Elasticity Solution for In-production Cloud Environments

The growth of cloud computing drives data centers toward higher density and efficiency. Data processing units (DPUs) enhance server network and storage performance but face challenges such as long hardware upgrade cycles and limited resources. To address these, we propose Taiji, a resource-elasticity architecture for DPUs. Combining hybrid virtualization with parallel memory swapping, Taiji switches the DPU's operating system (OS) into a guest OS and inserts a lightweight virtualization layer, making nearly all DPU memory swappable. It achieves memory overcommitment for the switched guest OS via high-performance memory elasticity, fully transparent to upper-layer applications, and supports hot-switch and hot-upgrade to meet in-production cloud requirements. Experiments show that Taiji expands DPU memory resources by over 50%, maintains virtualization overhead around 5%, and ensures 90% of swap-ins complete within 10 microseconds. Taiji delivers an efficient, reliable, low-overhead elasticity solution for DPUs and is deployed in large-scale production systems across more than 30,000 servers.

cs.OS↗

arXiv2025

TopoPerception: A Shortcut-Free Evaluation of Global Visual Perception in Large Vision-Language Models

Large Vision-Language Models (LVLMs) typically align visual features from an encoder with a pre-trained Large Language Model (LLM). However, this makes the visual perception module a bottleneck, which constrains the overall capabilities of LVLMs. Conventional evaluation benchmarks, while rich in visual semantics, often contain unavoidable local shortcuts that can lead to an overestimation of models' perceptual abilities. Here, we introduce TopoPerception, a benchmark that leverages topological properties to rigorously evaluate the global visual perception capabilities of LVLMs across various granularities. Since topology depends on the global structure of an image and is invariant to local features, TopoPerception enables a shortcut-free assessment of global perception, fundamentally distinguishing it from semantically rich tasks. We evaluate state-of-the-art models on TopoPerception and find that even at the coarsest perceptual granularity, all models perform no better than random chance, indicating a profound inability to perceive global visual features. Notably, a consistent trend emerge within model families: more powerful models with stronger reasoning capabilities exhibit lower accuracy. This suggests that merely scaling up models is insufficient to address this deficit and may even exacerbate it. Progress may require new training paradigms or architectures. TopoPerception not only exposes a critical bottleneck in current LVLMs but also offers a lens and direction for improving their global visual perception. The data and code are publicly available at: https://github.com/Wenhao-Zhou/TopoPerception.

cs.AI↗

arXiv2025

Vmem: A Lightweight Hot-Upgradable Memory Management for In-production Cloud Environment

Traditional memory management suffers from metadata overhead, architectural complexity, and stability degradation, problems intensified in cloud environments. Existing software/hardware optimizations are insufficient for cloud computing's dual demands of flexibility and low overhead. This paper presents Vmem, a memory management architecture for in-production cloud environments that enables flexible, efficient cloud server memory utilization through lightweight reserved memory management. Vmem is the first such architecture to support online upgrades, meeting cloud requirements for high stability and rapid iterative evolution. Experiments show Vmem increases sellable memory rate by about 2%, delivers extreme elasticity and performance, achieves over 3x faster boot time for VFIO-based virtual machines (VMs), and improves network performance by about 10% for DPU-accelerated VMs. Vmem has been deployed at large scale for seven years, demonstrating efficiency and stability on over 300,000 cloud servers supporting hundreds of millions of VMs.

cs.OS↗