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Zhenyu Wang

Publications and source records attributed to Zhenyu Wang.

At least 19 recordsLinked to original sources
arXiv2026

Evidence for Three-component Interlayer Coherent Exciton Condensation

Increasing the number of internal components in a quantum many-body system can host collective orders inaccessible to simpler settings. Quantum Hall bilayers provide a canonical realization of interlayer exciton condensation, yet extending such coherence across three independently addressable electronic fluids has remained elusive. Here we report evidence for three-component interlayer coherent exciton condensation in triple-layer graphene system. Using Rydberg excitons in an adjacent WSe2 monolayer as a layer-sensitive optical probe, we resolve interaction-induced incompressibility at zeroth-Landau-level crossings for all three pairwise layer combinations, establishing top-middle, middle-bottom and top-bottom exciton condensate channels within the same device. Independent control of displacement field and interlayer bias continuously tunes these pairwise states towards a regime where Landau levels from all three layers approach simultaneous degeneracy. At their convergence, incompressibility persists while the exciton energy and spectral weight evolve smoothly between the pairwise limits, suggesting coherent participation of all three layers in a single three-component state. More broadly, the ability to independently control layer potentials and engineer interlayer interactions establishes multilayer graphene as a programmable synthetic dimension for exploring higher-component quantum Hall order and simulating strongly correlated quantum matter.

cond-mat.mes-hall↗
arXiv2026

Physics-Informed Sliding-Window Particle Filtering for Tactile-Only In-Hand 6-DoF Object Pose Refinement

This paper studies tactile-only 6-DoF pose refinement and belief maintenance for grasped objects in static and short quasi-static in-hand configurations where vision is unavailable or heavily occluded. The key difficulty is tactile partial observability: whole-hand taxel contacts are sparse, intermittent, and ambiguous under limited excitation and object symmetries. We propose a physics-informed particle filter on $\mathrm{SE}(3)$ that updates pose beliefs from dense whole-hand tactile measurements. The likelihood combines active-contact signed-distance consistency, force-normal alignment, friction-cone feasibility, zero-force negative evidence, and optional feasibility guards. A sliding-window log-likelihood fuses recent tactile frames to reduce single-frame ambiguity, while a potential-field-guided proposal steers particles away from hand--object penetration. Symmetry-aware resampling preserves multiple plausible modes. Experiments on an Allegro Hand V5 with five objects show lower normalized ADD-S than tactile-only geometric, particle-filter, and learning baselines, and ablations confirm the benefits of temporal fusion, potential guidance, and mode preservation.

cs.RO↗
arXiv2026

Diffusion-Guided Cooperative Policy Learning for Target Tracking Based on Underwater Mobile Agent Networks

Multi-agent reinforcement learning (MARL) provides a promising solution for cooperative target tracking in networks of autonomous underwater vehicles (AUVs). However, existing methods still face three major challenges: 1) policy non-stationarity caused by concurrent updates among multiple agents; 2) inefficient policy learning caused by the heterogeneous quality of experiences accumulated during exploration; and 3) policy drift between stochastic exploration and deterministic execution under dynamic underwater disturbances. To address these challenges, this paper develops a four-layer hierarchical MARL architecture comprising global training scheduling, multi-agent coordination, local policy generation, and real-time action execution. Building on this architecture, we propose a Supervised Diffusion-Aided MARL (SDA-MARL) algorithm with three closely coupled mechanisms. First, a dual-decision policy integrates a diffusion-based generative branch with a Deep Deterministic Policy Gradient (DDPG) branch, while segregated experience pools reduce training interference between the two branches. Second, a supervised sample-selection mechanism identifies high-quality tracking transitions and uses their actions to guide reverse diffusion, enabling the generative policy to concentrate on effective regions of the action space. Third, a behavioral-cloning loss transfers diffusion-generated actions to the deterministic DDPG Actor, thereby aligning exploration with execution and suppressing policy drift. Experiments conducted in six-degree-of-freedom underwater environments across multiple AUV-target configurations show that SDA-MARL achieves faster convergence, higher tracking accuracy, more consistent inter-AUV velocities, and shorter tracking paths than the compared MARL methods.

cs.NI↗
arXiv2026

Multi-AUV Ad-hoc network-based Target Tracking: A Value Gradient Guidance Multi-Agent Diffusion Reinforcement Learning Approach

Multi-AUV ad-hoc network-based target tracking requires networked autonomous underwater vehicles (AUVs) to cooperatively track maneuvering targets under constrained acoustic communication, dynamic topology, and uncertain ocean disturbances. Although multi-agent reinforcement learning (MARL) enables decentralized coordination through centralized training, existing methods suffer from high-dimensional joint state-action modeling, noise-sensitive policy generation, leading to unstable training and degraded tracking. To address these issues, we propose VGG-MADiffRL, a value-gradient-guided multi-agent diffusion RL algorithm, and MDCA, a diffusion?based hierarchical control architecture. Leveraging underwater mission characteristics, we model sonar detection mechanisms and ocean current disturbances, formulating cooperative tracking for multi-AUV ad-hoc networks as an MDP. The proposed MDCA constitutes a three-tier closed-loop control framework: a global intelligent control layer, a local online training layer, and a physical action execution layer. This structure enables synergistic optimization across task allocation, local decision processes, and execution feedback. Within MDCA, the local online training layer is the policy learning framework; VGG-MADiffRL builds on diffusion policies and incorporates value gradients to guide action generation in the reverse denoising process, steering the generated actions towards higher expected returns. It employs twin value networks with joint optimization and soft target updates to mitigate overestimation and training oscillations, promoting more stable convergence. Experimental results show that VGG-MADiffRL consistently achieves faster convergence, higher tracking accuracy, and smoother training dynamics in cooperative tracking scenarios, validating its effectiveness and practical engineering value in dynamic underwater settings.

cs.LG↗
arXiv2026

Whisper-Aware LLM: Self-Supervised Uncertainty Learning for Robust Whispered Speech Recognition

The signal ambiguity of whispered speech drives ASR systems toward two opposing failure modes: failing to capture whispered speech or hallucinatory transcription of noise. This paper introduces the Whisper-Aware LLM, a framework that teaches an Audio-LLM to perceive and react to this uncertainty. Our model develops an intrinsic self-awareness by learning to quantify the physical deficiencies of acoustic signals through targeted self-supervised tasks. This learned uncertainty is then operationalized via a novel Confidence-Fused Decoding mechanism, which provides both high-level instructions and frame-level attention modulation to the LLM decoder. Our experiments confirm the effectiveness of this approach. The model sets a new state-of-the-art on whispered speech with a 17% relative CER reduction on AISHELL6-Whisper. At the same time, it directly addresses the reliability trade-off, with hallucination rates dropping from over 25% to 4.5%.

cs.SD↗
arXiv2026

Giant mode splitting of azimuthal spin waves in radial vortices

Radial vortex is a topological spin texture stabilized by the interfacial Dzyaloshinskii-Moriya interaction (DMI) in ferromagnetic disks. Previous investigations have shown that the doublet splitting of azimuthal modes in traditional circular vortices arises from the coupling between azimuthal spin waves and vortex core (VC), an effect occurs only for azimuthal indices $m=\pm1$ and is absent for higher-order modes. Here, we present a giant mode splitting of azimuthal spin waves in radial vortices, even in the absence of the VC. This mode splitting arises from the DMI, which can be an order of magnitude larger than that induced by the VC. Moreover, the DMI-induced frequency splitting increases with both the DMI constant and mode index, reaching tens of GHz for higher-order azimuthal modes. Our results reveal a robust mechanism for mode splitting in chiral magnetic textures and deepen the fundamental understanding of the DMI effect on the spin-wave dynamics in confined magnets.

cond-mat.mes-hall↗
arXiv2026

Thermodynamic phase transition, pairing symmetry and Fermi surface topology in Ruddlesden-Popper nickelate films

Ruddlesden-Popper (RP) nickelates provide an uncharted territory to explore high-transition-temperature (high-$T_C$) superconductivity and superconducting mechanism. Here, we investigate the electronic structure of a new type of high-$T_C$ superconducting RP nickelate heterostructure $\mathrm{La_2PrNi_2O_7/NdAlO_3}$ by angle-resolved photoemission spectroscopy. A superconducting state is observed without a pseudogap state, enabling a direct measurement of the superconducting order parameter and a microscopic extraction of the electronic specific heat. The observed superconducting gap opens at $T_C$ with prominent coherence peaks, illustrating the emergence of nonzero order parameter upon entering the superconducting state. An electronic specific heat jump appears at $T_C$, further demonstrating a thermodynamic phase transition. The magnitude of the superconducting order parameter is quantified by the observed superconducting coherence peaks, and a nodeless behavior is unambiguously established in the absence of pseudogap. The underlying Fermi surface consists of $α$, $β$ and $γ$ pockets, exhibiting a multi-orbital nature. Strain dependent measurements further reveal the $γ$ pocket in all superconducting and non-superconducting films with different epitaxial strain. Our results establish the missing thermodynamic evidence for superconducting phase transition in nickelates. They also provide direct evidence for the symmetry of the superconducting order parameter and illustrate the relationship between Fermi surface topology and the emergence of superconductivity in RP nickelate films.

cond-mat.supr-con↗
arXiv2026

Machine Learning-Guided Screening of Advantageous Solvents for Solid Polymer Electrolytes in Lithium Metal Batteries

Trace residual solvents in solid polymer electrolytes (SPEs) significantly affect electrolyte and interface properties, where optimal selection enhances ionic conductivity and transference numbers. However, solvent complexity hinders general screening methods. We establish a universal criterion linking electronic (HOMO, LUMO) and macroscopic properties (dielectric constant, dipole moment, polarizability) via machine learning on an approximately 10,000-solvent dataset from high-throughput DFT. Two solvents, N-methoxy-N-methyl-2,2,2-trifluoroacetamide and 2,2,2-trifluoro-N,N-dimethylacetamide, were identified. Experimental incorporation of trace N-methoxy-N-methyl-2,2,2-trifluoroacetamide into a poly(vinylidene fluoride-co-hexafluoropropylene) matrix achieves a 4.5 V window, 5.5x10^-4 S cm^-1 conductivity (30 C), and 0.78 Li+ transference number. The cell retains 86.7% capacity over 500 cycles (LiFePO4) and 98.7% after 200 cycles at 2C (LiNi0.9Co0.05Mn0.05O2), outperforming 2,2,2-trifluoro-N,N-dimethylacetamide, dimethylformamide, N-methyl-2-pyrrolidone, and dimethyl sulfoxide. This synergy enables balanced ion transport, wide stability, and cycling durability, advancing safer, high-energy lithium metal batteries. Our integrated approach establishes a solvent screening paradigm for rational SPE design, accelerating next-generation battery development.

cond-mat.mtrl-sci↗
arXiv2026

On the Runtime Analysis of Reinforcement Learning Hyper-Heuristics

Selection Hyper-heuristics (HHs) automate algorithmic design by selecting from a set of low-level heuristics which one to apply at each stage of the optimisation process. Several impressive results have been recently rigorously proven regarding the performance of selection hyper-heuristics (HHs) for standard benchmark functions. However, the learning mechanisms employed by these HHs are considerably simplified compared to the machine learning techniques typically used in real world applications. In this paper we analyse a Reinforcement Learning Hyper-heuristic (RLHH) from the literature. The only previous result available proved that for a wide range of parameter settings, RLHH does not learn to select heuristics appropriately for the standard LeadingOnes benchmark function. In this paper, we rigorously prove that with appropriate parameter values RLHH equipped with two random local search operators, RLS_1 and RLS_2 optimises the LeadingOnes benchmark function in the best possible expected runtime achievable with the two operators up to lower order terms. Experiments show that for realistic problem sizes it is faster than the Generalised Random Gradient HH which was previously proven to also have optimal expected runtime up to lower order terms.

cs.NE↗
arXiv2026

Optical observations on the young Type Ia SN 2021fxy with detached high velocity features

We present optical observations on the young type Ia supernova (SN Ia) SN 2021fxy obtained within a few days after the explosion, with a focus on its prominent high-velocity features (HVFs). It reached a $B$-band maximum of $M_{\rm max}(B) = -19.36\pm0.31$ mag, corresponding to a bolometric luminosity of $\sim 1.3\times10^{43}~\rm{erg~s^{-1}}$ with a synthesized $^{56}$Ni mass of $0.58\pm0.14$ M$_{\odot}$. The early spectra exhibit strong HVFs of intermediate-mass elements that are significantly detached from the photospheric components. In particular, the velocity of the Si II $\lambda6355$ HVFs follows a power-law evolution ($β\approx 0.1$), shallower than the expected photospheric velocity evolution expected for an assumed $n=10$ density profile ($β\approx 0.22$) under homologous expansion. This behavior is consistent with the HVFs forming in intrinsic ejecta structures at least partially decoupled from the bulk outer ejecta, providing a possible constraint on the explosion physics of SNe Ia.

astro-ph.HE↗
arXiv2026

NouveauVoice: Generating Novel Pseudo Speakers for Voice Anonymization

Advanced neural technologies in speech synthesis and voice conversion (VC) have introduced severe risks to personal privacy, necessitating robust Speaker Anonymization Systems (SAS). Existing SAS approaches modify voice characteristics in the hand-crafted feature space or speaker embedding space, often struggling to provide sufficient identity variance across generated voices. In this paper, we propose NouveauVoice, a novel pseudo-speaker generation framework based on a Hierarchical Deep Variational Autoencoder (NVAE). Integrated as a standalone plug-in module on top of state-of-the-art architectures (FACodec and CosyVoice2), our approach leverages tractable sampling and the Evidence Lower Bound (ELBO) objective to synthesize highly expressive pseudo-speaker embeddings with significantly enhanced speaker diversity. Evaluating our framework under a protocol similar to the VoicePrivacy Challenge alongside Maximum Mean Discrepancy (MMD) analysis, we demonstrate that NouveauVoice achieves strong identity concealment, yielding an Equal Error Rate (EER) exceeding 38% against an automatic speaker verification attacker model. Our system shows a reasonable trade-off between strict anonymity, rich pseudo-speaker diversity, and downstream speech utility, such as intelligibility and emotional expressiveness.

eess.AS↗
arXiv2026

Boron-assisted synthesis of compositionally complex amorphous oxides via short-range-order-constrained generative design

Engineering short-range atomic order in amorphous materials offers a promising yet still underexplored route to high-performance solids. Here, we establish a boron-assisted amorphization strategy through ApolloX, a theory-guided, short-range-order-constrained generative framework for identifying low-energy amorphous configurations in compositionally complex multimetal BOx systems. Using FeCoNiMoBOx as a representative model platform, ApolloX predicts an ensemble of candidate amorphous configurations across systematically varied boron contents. Ab initio molecular dynamics simulations based on these configurations show that increasing boron content suppresses atomic diffusion and disfavors crystallization, with the stabilization of BO3-centered local motifs emerging as a key structural feature associated with enhanced amorphization propensity. Guided by these predictions, we synthesize three representative FeCoNiMoBOx compositions with distinct boron contents and use synchrotron-based scattering and electron microscopy to verify compositional fidelity, structural homogeneity, and the targeted amorphous features, thereby experimentally validating the boron-regulated structural evolution predicted by theory. Beyond this representative system, the same strategy is extended to a broader library of multimetal BOx amorphous compositions spanning diverse metal combinations and boron loadings, demonstrating that the boron-assisted route is not limited to a single FeCoNiMoBOx family but is broadly transferable across compositionally complex amorphous oxides. Overall, our results establish boron incorporation as a practical design variable for tuning short-range order and amorphization in multicomponent oxides, and provide a general framework for the theory-guided discovery of compositionally complex amorphous materials with tunable properties.

cond-mat.mtrl-sci↗
arXiv2026

A Comprehensive Survey of Medical Image Segmentation: Challenges, Benchmarks, and Beyond

Medical image segmentation plays a critical role in clinical diagnostics, treatment planning, disease monitoring, and neurological disorder identification. This article presents a comprehensive review of its systematic development, covering widely used public datasets, representative methods built on the U-Net, Transformer, and SAM architectures, and key evaluation metrics with their differences, followed by an analysis of major challenges from multiple perspectives. Unlike surveys that focus on a single model family or a specific clinical application, this review organizes U-Net-, Transformer-, and SAM-based methods within a unified analytical framework, with a particular focus on their effectiveness in improving segmentation accuracy and efficiency. This work aims to guide future research and support clinical translation of medical image segmentation, with all related resources publicly available in our GitHub repository: https://github.com/andrew-pengyu/Awsome_MedSeg/tree/main.

cs.CV↗
arXiv2026

Spatiotemporal Magnonic Vortex Beams with Alternating Transverse Orbital Angular Momentum

Recent theoretical and experimental advances have demonstrated spatiotemporal photonic and acoustic vortex beams in free space. Such spatiotemporal vortex beams possess orbital angular momentum oriented perpendicular to the wave propagation direction. Herein, we report the discovery of spatiotemporal magnonic vortex beams in a confined ferromagnetic nanostrip geometry. The spatiotemporal magnonic vortex beam features stationary phase dislocations and exhibits wave propagation along a zigzag-like trajectory. Notably, the transverse orbital angular momentum carried by these phase dislocations displays spatial alternation. Our results differ distinctly from their photonic and acoustic counterparts, offering new insights into the fundamental research of spatiotemporal vortex beams.

cond-mat.mes-hall↗
arXiv2026

OMG: Omni-Modal Motion Generation for Generalist Humanoid Control

Humanoid whole-body control has made significant progress in recent years, yet existing approaches remain limited to few-skill policies with heavy reward engineering, or motion trackers that are difficult to extend to new input modalities. We argue that the key to general-purpose humanoid control is to build a scalable brain, a module capable of reasoning with diverse conditioning modalities, atop a reactive motion tracking cerebellum, mirroring the hierarchical structure of biological motor systems. Two challenges arise in realizing this vision: acquiring a vast amount of high-quality data to achieve general purpose control, and equipping the generator with the capability to condition on compositional, extensible multi-modal inputs. We present OMG, which addresses these challenges with a meticulous data curation, filtering and labeling pipeline, as well as a diffusion-based motion generation backbone that conditions on language, audio, and human reference motions. Extensive experiments validate OMG as an omni-modal whole-body controller exhibiting state-of-the-art performance, model scaling behavior and efficient adaptation to new distributions and modalities, marking a concrete step toward foundation models for humanoid robots.

cs.RO↗
arXiv2026

Supermoiré Chern mosaic in helical trilayer WSe2

Helically twisted multilayers offer access to moiré physics beyond the single-superlattice paradigm, yet their correlated and topological transport properties remain largely unexplored in semiconductor moiré materials. Here we report magnetotransport measurements of helical trilayer WSe2, in which two coupled moiré patterns relax into a supermoiré landscape composed of inequivalent local topological domains with distinct electronic structures and unequal spatial areas. By electrostatic tuning, we identify a trilayer-hybridized regime where interactions and real-space reconstruction combine to generate a plethora of magnetic and topological states absent in the twisted bilayers. At moiré filling factor $ν$ = -1, we observe a ferromagnetic insulating state that is robust against magnetic field and accompanied by a non-quantized anomalous Hall response ~-4 kOhms. This behaviour is consistent with a time-reversal-symmetry-breaking supermoiré Chern mosaic, in which the Hall response arises from the non-cancelling contributions of local domains with opposite Chern character arranged by the relaxed structure. Under strong magnetic fields, a symmetry-broken Chern insulating state (C = 1) emerges near $ν$ = -2/3, displaying a much larger positive Hall response together with strongly enhanced longitudinal resistance, suggestive of field-reconstructed topological minibands and domain-boundary scattering. These results establish relaxed supermoiré semiconductor trilayers as a platform for spatially organized magnetism and topology beyond the bilayer limit.

cond-mat.str-el↗
arXiv2026

SPAMoE: Spectrum-Aware Hybrid Operator Framework for Full-Waveform Inversion

Full-waveform inversion (FWI) is pivotal for reconstructing high-resolution subsurface velocity models but remains computationally intensive and ill-posed. While deep learning approaches promise efficiency, existing Convolutional Neural Networks (CNNs) and single-paradigm Neural Operators (NOs) struggle with one fundamental issue: frequency entanglement of multi-scale geological features. To address this challenge, we propose Spectral-Preserving Adaptive MoE (SPAMoE), a novel spectrum-aware framework for solving inverse problems with complex multi-scale structures. Our approach introduces a Spectral-Preserving DINO Encoder that enforces a lower bound on the high-to-low frequency energy ratio of the encoded representation, mitigating high-frequency collapse and stabilizing subsequent frequency-domain modeling. Furthermore, we design a novel Spectral Decomposition and Routing mechanism that dynamically assigns frequency bands to a Mixture-of-Experts (MoE) ensemble comprising FNO, MNO, and LNO. On the ten OpenFWI sub-datasets, experiments show that SPAMoE reduces the average MAE by 44.4% relative to the best officially reported OpenFWI baseline, thereby establishing a new architectural framework for learning-based full-waveform inversion. Our code and data are available at https://github.com/zhenyuwang12366/SPAMoE

cs.LG↗
arXiv2026

Discovery of d-orbital order in Tb2CoAl4Ge2

Orbital order describes a quantum state where occupied orbitals line up in a periodic pattern. While orbital physics plays a fundamental and universal role in strongly correlated electron systems, the existence and particularly the band structure fingerprint of orbital order remain a long-standing mystery. Here, we report the discovery of rare earth 5d-orbital order developed by the surface states of intermetallic compound Tb2CoAl4Ge2. Angle-resolved photoemission spectroscopy reveals characteristic nematic features like Fermi surface deformation and band split. These experimental observations can be described by a ferro-orbital order term in the mean-field Hamiltonian. The structural and magnetic origin of such order is excluded by systematic high-resolution neutron powder diffraction and scanning tunnelling microscopy measurements. Our results provide strong evidence for a pure surface orbital order scenario avoiding complications from structural distortion as in colossal magnetoresistance manganites, magnetic order as in iron-based superconductors, and charge transfer p-orbital order in cuprates.

cond-mat.str-el↗