arXiv subjects

Zhe Zhang

Publications and source records attributed to Zhe Zhang.

At least 55 records · Page 3Linked to original sources

arXiv2026

Entanglement redistribution of hyperon-antihyperon pair via sequential decay

Hyperon-antihyperon pairs produced in high energy electron-positron annihilation constitute a naturally spin-entangled system in the high energy regime. Recently, a probabilistic amplification of entanglement, termed autodistillation, has been found in the daughter baryon-antibaryon pairs from hyperon decay and is constrained by an upper boundary. This work demonstrates that the quantum entanglement in this process may be accompanied by a decrease, constrained by a lower boundary, but will not be completely lost. Thus, the entanglement of these systems undergoes redistribution within the phase space during the sequential decays of hyperons, highlighting an important role of hyperon polarization. By using the explicit spin density matrix of baryon pairs, it is also found that quantumness of the system characterized by quantum discord always have the possibility to increase during decay processes, even when entanglement evaluated by concurrence and negativity does not increase.

hep-ph↗

arXiv2026

Antiferromagnetic Ordering Enhanced Magnetic Damping in Mn2Au/CoFeB Bilayers

Antiferromagnets (AFMs) hold significant potential for spintronic devices owing to their insensitivity to external magnetic fields and the absence of stray fields. Beyond these inherent advantages, an AFM can manipulate the magnetic dynamics of a ferromagnet (FM) layer in AFM/FM bilayers, whereas the mechanism of such manipulation remains controversial. Here, we investigate the magnetic dynamics of AFM/FM Mn2Au/CoFeB bilayers via Ferromagnetic Resonance (FMR). It is found that the Néel temperature of 2-nm-thick Mn2Au is as low as ~40 K, in sharp contrast to that of bulk Mn2Au, which exceeds 1000 K. In the Mn2Au(2 nm)/CoFeB(4 nm) bilayer, the magnetic damping $α$ of the CoFeB layer increases from 0.013 to 0.047 as temperature decreases from 160 K to 10 K, accompanied by a synchronous increase in the exchange coupling field H_rot. Such an increase in $α$ is attributed to the enhanced spin angular momentum transfer from CoFeB to Mn2Au, mediated through AFM-FM exchange coupling between Mn2Au and CoFeB, which is enhanced by the Mn2Au antiferromagnetic ordering as the temperature decreases. Our study provides deeper insights into AFM/FM dynamics and spintronic storage technology.

cond-mat.mtrl-sci↗

arXiv2026

Universal and Parameter-free Gradient Sliding for Composite Optimization

We propose a Parameter-Free Universal Gradient Sliding (PFUGS) algorithm for computing an approximate solution to the convex composite optimization $\min_{x\in X} \{f(x) + g(x)\}$, where $f$ has $(M_ν,ν)$-Hölder continuous subgradient and $g$ has $L$-Lipschitz continuous gradient. PFUGS computes an $\varepsilon$-approximate solution with $\mathcal{O}((M_ν/\varepsilon)^{{2}/{(1+3ν)}})$ evaluations of (sub)gradients of $f$ and $\mathcal{O}((L/\varepsilon)^{1/2})$ evaluations of gradients of $g$, without prior knowledge of problem constants. To the best of our knowledge, PFUGS is the first gradient sliding algorithm for problems involving two functions whose distinct problem constants are both unknown a priori.

math.OC↗

arXiv2026

ConStruM: A Structure-Guided LLM Framework for Context-Aware Schema Matching

Column matching is a central task in reconciling schemas for data integration. Column names and descriptions are valuable for this task. LLMs can leverage such natural-language schema metadata. However, in many datasets, correct matching requires additional evidence beyond the column itself. Because it is impractical to provide an LLM with the entire schema metadata needed to capture this evidence, the core challenge becomes to select and organize the most useful contextual information. We present ConStruM, a structure-guided framework for budgeted evidence packing in schema matching. ConStruM constructs a lightweight, reusable structure in which, at query time, it assembles a small context pack emphasizing the most discriminative evidence. ConStruM is designed as an add-on: given a shortlist of candidate targets produced by an upstream matcher, it augments the matcher's final LLM prompt with structured, query-specific evidence so that the final selection is better grounded. For this purpose, we develop a context tree for budgeted multi-level context retrieval and a global similarity hypergraph that surfaces groups of highly similar columns (on both the source and target sides), summarized via group-aware differentiation cues computed online or precomputed offline. Experiments on real datasets show that ConStruM improves matching by providing and organizing the right contextual evidence.

cs.DB↗

arXiv2026

Effective LLM Code Refinement via Property-Oriented and Structurally Minimal Feedback

LLMs excel at code generation, yet ensuring the functional correctness of their outputs remains a persistent challenge. While recent studies have applied Test-Driven Development (TDD) to refine code, these methods are often undermined by poor feedback quality, stemming from the scarcity of high-quality test cases and noisy signals from auto-generated ones. In this work, we shift the focus from test quantity to feedback quality. We introduce the Property-Generated Solver (PGS), a novel paradigm designed to generate highly effective feedback via two principles: it must be property-oriented, to provide semantic guidance beyond simple I/O mismatches, and structurally minimal, to reduce cognitive load and isolate root causes. PGS operates by checking high-level program properties (e.g., a sorting function must produce a non-decreasing sequence) then providing the simplest failing counterexample to the LLM. By adhering to these principles, this targeted feedback mechanism leads to significant performance gains. Specifically, PGS achieves an improvement of up to 13.4% in pass@1 against other TDD-based methods and an over 64% fix rate on problems where the model initially failed. This property-driven, minimal feedback steers LLMs toward correct and generalizable solutions. Across diverse benchmarks, PGS demonstrates superior performance, achieving a bug fix rate 1.4x-1.6x higher than the strongest debugging-based approaches and establishing a new state-of-the-art in automated code refinement.

cs.SE↗

arXiv2026

Adaptive Dual-Teacher Distillation with Subnetwork Rectification for Bridging Semantic Gaps in Black-Box Domain Adaptation

Assuming that neither source data nor source model parameters are accessible, black-box domain adaptation (BBDA) represents a highly practical yet challenging setting, where transferable knowledge is limited to the predictions of a black-box source model. Existing approaches exploit such knowledge via pseudo-label refinement or by leveraging vision-language models (ViLs), but they often fail to reconcile the inherent discrepancy between task-specific knowledge from black-box models and language-aligned semantic priors of ViLs, resulting in suboptimal integration and degraded adaptation performance. To address this challenge, we propose adaptive Dual-Teacher Distillation with Subnetwork Rectification (DDSR), a framework that explicitly reconciles these complementary yet inconsistent knowledge sources. DDSR employs an adaptive prediction fusion strategy to integrate predictions from the black-box source model and a ViL, generating reliable pseudo-labels for the target domain. A subnetwork-based regularization mechanism mitigates overfitting to noisy supervision by enforcing output consistency and gradient divergency. Furthermore, progressively improved target predictions iteratively refine both pseudo-labels and ViL prompts, enhancing semantic alignment. Finally, class-wise prototypes are used to further optimize target predictions via self-training. Extensive experiments on multiple benchmark datasets demonstrate that DDSR consistently outperforms state-of-the-art methods, including those with access to source data or source model parameters.

cs.CV↗

arXiv2026

Wan-Image: Pushing the Boundaries of Generative Visual Intelligence

We present Wan-Image, a unified visual generation system explicitly engineered to paradigm-shift image generation models from casual synthesizers into professional-grade productivity tools. While contemporary diffusion models excel at aesthetic generation, they frequently encounter critical bottlenecks in rigorous design workflows that demand absolute controllability, complex typography rendering, and strict identity preservation. To address these challenges, Wan-Image features a natively unified multi-modal architecture by synergizing the cognitive capabilities of large language models with the high-fidelity pixel synthesis of diffusion transformers, which seamlessly translates highly nuanced user intents into precise visual outputs. It is fundamentally powered by large-scale multi-modal data scaling, a systematic fine-grained annotation engine, and curated reinforcement learning data to surpass basic instruction following and unlock expert-level professional capabilities. These include ultra-long complex text rendering, hyper-diverse portrait generation, palette-guided generation, multi-subject identity preservation, coherent sequential visual generation, precise multi-modal interactive editing, native alpha-channel generation, and high-efficiency 4K synthesis. Across diverse human evaluations, Wan-Image exceeds Seedream 5.0 Lite and GPT Image 1.5 in overall performance, reaching parity with Nano Banana Pro in challenging tasks. Ultimately, Wan-Image revolutionizes visual content creation across e-commerce, entertainment, education, and personal productivity, redefining the boundaries of professional visual synthesis.

cs.CV↗

arXiv2026

VoxEffects: A Speech-Oriented Audio Effects Dataset and Benchmark

Speech audio in the wild is often processed by post-production effects, but existing speech datasets rarely provide precise annotations of effects and parameters, limiting systematic study. We introduce VoxEffects, a speech audio effects dataset that pairs produced speech with exact effect-chain supervision at multiple granularities. VoxEffects supports speech-oriented audio effect identification: given a produced waveform, infer which effects are present and how they are applied. Built from minimally edited clean speech, it provides an extensible rendering pipeline for both offline synthesis and on-the-fly rendering for efficient training and evaluation. The audio effect identification benchmark includes effect presence detection, preset classification, and intensity prediction, with a robustness protocol covering capture-side and platform-side degradations. We provide an AudioMAE-based multi-task baseline and analyses of domain shift, robustness, input duration, and gender fairness.

eess.AS↗

arXiv2026

The Second Challenge on Cross-Domain Few-Shot Object Detection at NTIRE 2026: Methods and Results

Cross-domain few-shot object detection (CD-FSOD) remains a challenging problem for existing object detectors and few-shot learning approaches, particularly when generalizing across distinct domains. As part of NTIRE 2026, we hosted the second CD-FSOD Challenge to systematically evaluate and promote progress in detecting objects in unseen target domains under limited annotation conditions. The challenge received strong community interest, with 128 registered participants and a total of 696 submissions. Among them, 31 teams actively participated, and 19 teams submitted valid final results. Participants explored a wide range of strategies, introducing innovative methods that push the performance frontier under both open-source and closed-source tracks. This report presents a detailed overview of the NTIRE 2026 CD-FSOD Challenge, including a summary of the submitted approaches and an analysis of the final results across all participating teams. Challenge Codes: https://github.com/ohMargin/NTIRE2026_CDFSOD.

cs.CV↗

arXiv2026

Surface ferrimagnetic order in RuO2 film

RuO2, widely proposed as a prototypical altermagnet, remains intensely debated with regard to its magnetic nature. Here, we demonstrate that RuO2 is non-magnetic in the bulk, but possesses a spontaneous surface ferrimagnetic order. Using spin- and angle-resolved photoemission spectroscopy, we directly detect a narrow surface state with identical spin polarizations at opposite momenta and at the Brillouin-zone center, incompatible with the spin texture of any altermagnetic order. First-principles calculations identify the non-magnetic bulk state and reveal that the detected magnetism is confined to the fully oxygen-terminated surface, where the charge transfer from Ru to O at surface triggers a ferrimagnetic alignment between adjacent Ru sublattices with antiparallel moments of +0.48 uB and -0.04 uB. Our findings provide a unified explanation reconciling debating reports on the magnetism of RuO2, establishing surface ferrimagnetism as the origin of the observed magnetic signals, and distinguishing it unambiguously from altermagnetism.

cond-mat.mtrl-sci↗

arXiv2026

Hypothesis Testing for Penalized Estimating Equations with Cross-Fitted Covariance Calibration

We study hypothesis testing for penalized estimators in settings where the full marginal distribution of a multivariate response is difficult to specify, such as longitudinal data with correlated measurements or high-dimensional heteroscedastic regression. Assuming that the conditional mean model is correctly specified, we establish that the penalized estimating equations admit a $\sqrt{n}$-consistent solution, even when the working covariance structure is misspecified. Our inferential target is a low-dimensional subvector of parameters associated with the mean model. We show that the resulting test statistic converges to a $χ^2$ distribution, and that its asymptotic power depends on the nuisance covariance function. To mitigate this dependence, we propose estimating the covariance function via cross-fitting, which provides a calibrated and robust procedure for inference.

stat.ME↗

arXiv2026

Integrating Maneuverable Planning and Adaptive Control for Robot Cart-Pushing under Disturbances

Precise and flexible cart-pushing is a challenging task for mobile robots. The motion constraints during cart-pushing and the robot's redundancy lead to complex motion planning problems, while variable payloads and disturbances present complicated dynamics. In this work, we propose a novel planning and control framework for flexible whole-body coordination and robust adaptive control. Our motion planning method employs a local coordinate representation and a novel kinematic model to solve a nonlinear optimization problem, thereby enhancing motion maneuverability by generating feasible and flexible push poses. Furthermore, we present a disturbance rejection control method to resist disturbances and reduce control errors for the complex control problem without requiring an accurate dynamic model. We validate our method through extensive experiments in simulation and real-world settings, demonstrating its superiority over existing approaches. To the best of our knowledge, this is the first work to systematically evaluate the flexibility and robustness of cart-pushing methods in experiments. The video supplement is available at https://sites.google.com/view/mpac-pushing/.

cs.RO↗

arXiv2026

Evolution-Based Timed Opacity under a Universal Observation Model

Existing literature on timed opacity uses specific definitions for restricted subclasses of timed automata or limited observation models. This lack of a unified definition makes it difficult to establish formal relationships and compare the expressiveness of different opacity variants. This paper establishes a unified framework for timed opacity by introducing a universal observation model for timed automata. First, we introduce an observation model with full observation of time delay and partial observation of locations, clocks, and events. Second, based on this model, we define the notion of evolution-based timed opacity. Third, we mathematically prove that evolution-based timed opacity strictly implies language-based timed opacity and establish a formal equivalence with execution-time opacity under constrained observations. This framework establishes a unified semantic hierarchy for characterizing the landscape of timed opacity.

cs.FL↗

arXiv2026

The Extinction Distance of Supernova Remnants in Combination with the CO Line Measurements

Accurate distance measurements to supernova remnants (SNRs) are crucial for understanding their physical properties and evolution. We present a novel method that combines CO line observations with three-dimensional (3D) extinction maps to determine distances to SNRs (G93.7$-$0.2, G109.1$-$1.0, G156.2+5.7, and G166.0+4.3) through their associated molecular clouds. For each SNR, candidate CO velocity components corresponding to interacting molecular clouds are identified based on previous observational evidence with refinements: [$-$19, $-$3] km s$^{-1}$ for G93.7$-$0.2, [$-$51, $-$46] km s$^{-1}$ for G109.1$-$1.0, [$-$10, 0] km s$^{-1}$ for G156.2+5.7, and [$-$27, $-$15] km s$^{-1}$ for G166.0+4.3. By examining extinction-distance profiles along the sightlines and identifying extinction jumps that spatially coincide with CO emission features, we derive distances of 1.82$\pm$0.13 kpc for G93.7$-$0.2, 3.05$\pm$0.15 kpc for G109.1$-$1.0, 0.60$\pm$0.15 kpc for G156.2+5.7, and 3.44$\pm$0.23 kpc for G166.0+4.3. Our extinction-based distances are largely consistent with previous estimates while with better accuracy and robustness.

astro-ph.GA↗

arXiv2026

Self Voice Conversion as an Attack against Neural Audio Watermarking

Audio watermarking embeds auxiliary information into speech while maintaining speaker identity, linguistic content, and perceptual quality. Although recent advances in neural and digital signal processing-based watermarking methods have improved imperceptibility and embedding capacity, robustness is still primarily assessed against conventional distortions such as compression, additive noise, and resampling. However, the rise of deep learning-based attacks introduces novel and significant threats to watermark security. In this work, we investigate self voice conversion as a universal, content-preserving attack against audio watermarking systems. Self voice conversion remaps a speaker's voice to the same identity while altering acoustic characteristics through a voice conversion model. We demonstrate that this attack severely degrades the reliability of state-of-the-art watermarking approaches and highlight its implications for the security of modern audio watermarking techniques.

cs.SD↗

arXiv2026

DA-VPC: Disturbance-Aware Visual Predictive Control Scheme of Docking Maneuvers for Autonomous Trolley Collection

Service robots have demonstrated significant potential for autonomous trolley collection and redistribution in public spaces like airports or warehouses to improve efficiency and reduce cost. Usually, a fully autonomous system for the collection and transportation of multiple trolleys is based on a Leader-Follower formation of mobile manipulators, where reliable docking maneuvers of the mobile base are essential to align trolleys into organized queues. However, developing a vision-based robotic docking system faces significant challenges: high precision requirements, environmental disturbances, and inherent robot constraints. To address these challenges, we propose a Disturbance-Aware Visual Predictive Control (DA-VPC) scheme that incorporates active infrared markers for robust feature extraction across diverse lighting conditions. This framework explicitly models nonholonomic kinematics and visibility constraints for image-based visual servoing (IBVS), solving the predictive control problem through optimization. It is augmented with an extended state observer (ESO) designed to counteract disturbances during trolley pushing, ensuring precise and stable docking. Experimental results across diverse environments demonstrate the robustness of this system, with quantitative evaluations confirming high docking accuracy.

cs.RO↗

arXiv2026

RAID: Retrieval-Augmented Anomaly Detection

Unsupervised Anomaly Detection (UAD) aims to identify abnormal regions by establishing correspondences between test images and normal templates. Existing methods primarily rely on image reconstruction or template retrieval but face a fundamental challenge: matching between test images and normal templates inevitably introduces noise due to intra-class variations, imperfect correspondences, and limited templates. Observing that Retrieval-Augmented Generation (RAG) leverages retrieved samples directly in the generation process, we reinterpret UAD through this lens and introduce \textbf{RAID}, a retrieval-augmented UAD framework designed for noise-resilient anomaly detection and localization. Unlike standard RAG that enriches context or knowledge, we focus on using retrieved normal samples to guide noise suppression in anomaly map generation. RAID retrieves class-, semantic-, and instance-level representations from a hierarchical vector database, forming a coarse-to-fine pipeline. A matching cost volume correlates the input with retrieved exemplars, followed by a guided Mixture-of-Experts (MoE) network that leverages the retrieved samples to adaptively suppress matching noise and produce fine-grained anomaly maps. RAID achieves state-of-the-art performance across full-shot, few-shot, and multi-dataset settings on MVTec, VisA, MPDD, and BTAD benchmarks. \href{https://github.com/Mingxiu-Cai/RAID}{https://github.com/Mingxiu-Cai/RAID}.

cs.CV↗

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↗