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Hui Wang

Publications and source records attributed to Hui Wang.

At least 19 recordsLinked to original sources
arXiv2026

Boundary layer of 2D Chemotaxis Navier-Stokes equations with logarithmic Sensitivity. II. viscous vanishing limit

This is the second part of a two-part work concerning boundary layer solutions to the coupled Chemotaxis-Navier-Stokes system in the two-dimensional half-space. In the present work, we address the convergence of boundary layer solutions to singular chemotaxis-fluid equations under slip boundary conditions with respect to the chemical diffusion-viscosity parameter $\varepsilon$ in the two-dimensional half-plane. More precisely, we show that the boundary layer for $\varepsilon>0$ (viscous convection coefficient) converges to the superposition of the outer layer (solution with $\varepsilon=0$) and the inner layer as $\varepsilon\rightarrow0$. The outer and inner profiles are explicitly derived as in the first part\cite{WWZ}. Furthermore, the well-posedness results of the coupled Chemotaxis-Navier-Stokes system in conormal Sobolev spaces will be presented in Appendix. They answer the question mentioned in the first part of the two-part work. This study could help the understanding of the chemotactic movement of aerobic bacteria to the water-air surface observed experimentally in fluids, and enrich the theoretical results of boundary layer in chemotactic fluid models.

math.AP↗
arXiv2026

AuK Technical Report: An Open-Source Foundational Model for Speech Generation and Editing

We introduce AuK, an open-source foundational model that unifies speech generation and editing through a common interface of natural-language instructions and audio context. To support this broad capability set, we construct approximately 3.03 billion instruction--audio instances and 1.95 million hours of effective supervision across five task families: speech generation, content editing, enhancement and separation, paralinguistic editing, and acoustic editing. AuK combines a multimodal large language model for semantic conditioning, an VAE jointly trained on speech, general audio, and music for acoustic conditioning, and a hybrid rectified-flow Transformer that performs dual-stream MMDiT blocks followed by unified single-stream DiT blocks for generation. Training begins with generation-only warm-up and proceeds to joint generation--editing pre-training. We then apply complementary post-training strategies: human-feedback preference optimization for open-ended editing and reward-based reinforcement learning for speech generation. To reduce inference cost, we further distill the model with consistency initialization and task-routed Decoupled DMD. The resulting AuK-Flash performs 4-step inference without classifier-free guidance and achieves a 4.5 wall-clock speedup over the full model under matched conditions. Experiments demonstrate leading performance on zero-shot and instruction-controlled speech generation and general instruction-guided editing, while remaining competitive on signal-level restoration tasks. We release both the source code and model weights to support reproducibility and further research.

cs.SD↗
arXiv2026

CoVer: Conflict-Aware Claim Verification

Social media fact-checking has long been challenged by evidence-level and aggregation-level conflicts, where erroneous evidence mimics authoritative news sources. To capture this challenge and support conflict verification tasks, we present ContraNote, a large-scale real-world dataset curated from X's Community Notes system. It includes 33,686 posts for evaluating evidence-level conflict resolution, and 54,474 instances for evaluating aggregation-level prioritization. Additionally, we propose CoVer, a factual adjudication framework with three-stage pipelines: evidence schema normalization, factual consensus and support verification. This prioritizes evidence over noise to prevent it from compromising the final verdict. Technical evaluations show that CoVer achieves strong performance compared with state-of-the-art baselines across ContraNote (86.0% Acc., 68.0% mac. F1, 64.5 bal. Acc. on Conflict; and 88.5% Acc., 88.5 mac. F1 and 89.2 bal. Acc. on Prioritization), CONFACT-HumC (88.4% Acc.) and CONFACT-ModC (89.4% Acc.).

cs.AI↗
arXiv2026

GRAIN: Bridging Name and Narrative Shifts in Real-World Graph Reasoning through Invariance-Rewarded Agentic RL

Despite their potential in standardized graph tasks, Large Language Models (LLMs) remain brittle to real-world shifts in node identifiers and task formulation. While deterministic graph tools are invariant to such shifts, extracting topological structures from noisy text is highly fragile for LLMs, which often overfit to surface patterns. Moreover, mitigating these parsing failures via multi-agent systems incurs prohibitive latency. To address this, we propose GRAIN, a single-agent framework optimized via reinforcement learning. GRAIN models reasoning as a semantic parsing and tool-execution pipeline, guided by a Structure Invariance Reward. By validating extracted intermediate graphs against ground-truth topologies, this reward forces the LLM to learn robust text-to-structure mappings rather than memorizing linguistic artifacts. We also introduce GRIT, a benchmark evaluating sensitivity to such linguistic shifts. GRAIN outperforms multi-agent baselines by 16.45\% in accuracy with approximately 24\% lower latency. Furthermore, it demonstrates superior structural generalization, halving the out-of-distribution (OOD) gap of SFT models (from 15.77\% to 7.80\%) and maintaining robustness on large-scale graphs beyond the training distribution.

cs.AI↗
arXiv2026

Deterministic preparation of entangled Dicke states

Dicke states $|J=N/2,m\rangle$ of a collection of $N$ atoms were central to Dicke's theory of superradiance. Except for the fully polarized end states, they are entangled many-body states; in particular, the single-excitation Dicke state is the W state well known in quantum information science. However, deterministic preparation of Dicke states with a prescribed spin projection remains challenging, especially without relying on postselection or heralding. Here we present a detuning-programmed Hamiltonian protocol using atoms or qubits in a dispersive cavity subject to a coherent transverse drive. In the absence of the drive, the off-resonant cavity produces an effective collective-spin interaction. By combining this cavity-mediated interaction with the coherent drive, tuning the atom--drive detuning enables the protocol, in principle, to target any allowed Dicke state along the symmetric Dicke ladder. Starting from the transverse-drive ground state, adiabatic ground-state interpolation prepares the selected Dicke state by ramping down the drive strength while ramping up the cavity-mediated interaction. After preparation, tuning the cavity into resonance with the atoms or qubits provides a direct way to probe the collective-emission response of the prepared Dicke state. We discuss implementation with superconducting circuit QED and show that the prepared states, especially the central Dicke state with $m=0$, provide resources for quantum sensing with Heisenberg-limited scaling.

quant-ph↗
arXiv2026

Self-Calibrating Dense Displacement Fields for Reliable Co-Registration of Large Optical Satellite Imagery

Co-registration underlies nearly every multi-temporal and multi-sensor use of optical satellite imagery, and operational products still carry documented offsets well above the fraction-of-a-pixel scale at which change detection, time series, and data fusion degrade. Real image pairs differ along several axes at once (sensor response, scene content, viewing geometry, resolution, mosaic seams), and the last of these is not a single global motion. Existing tools embed a motion model and constants tuned to their development data; a pair that fits is registered precisely, while one that does not either fails to match or returns a result wrong by tens of pixels with no failure reported. Learned matchers add a GPU requirement and carry no accuracy guarantee outside their training distribution. We present SCDF (self-calibrating displacement fields), a training-free, GPU-free estimator whose motion model is the dense per-pixel displacement field itself, so no scene motion falls outside the model. A single predict--measure--filter loop runs over a resolution pyramid: the accumulated field predicts where each patch of the moving image falls in the reference, RootSIFT matching and a correlation pass measure the displacement there to sub-pixel precision, and filters whose thresholds are all calibrated on the image pair itself decide what survives. One configuration, with no per-dataset tuning, processes full $8192^2$ scenes on a single CPU core. On 584 constructed-ground-truth pairs built from real Sentinel-2, Landsat-8/9, and NAIP imagery, against seven classical baselines and two zero-shot pretrained matchers, SCDF registers every pair with zero failures, reduces the best baseline's real-pair median end-point error from 6.83 to 4.17m, and cuts its 90th percentile from 17.8 to 7.77m.

cs.CV↗
arXiv2026

Gravitational-wave propagation and standard sirens in dynamical Barbero--Immirzi gravity: an action-level analysis

We develop an action-level framework for identifying which dynamical Barbero--Immirzi sectors can modify cosmological tensor propagation in a bosonic, two-derivative, curvature-linear Einstein--Cartan class. Eliminating the algebraic Lorentz connection shows that the Holst-to-Palatini ratio contributes to the scalar kinetic metric, whereas the transverse-traceless tensor normalization is controlled by the parity-even Hilbert--Palatini coefficient. Consequently, a dynamical Holst coefficient at fixed parity-even normalization does not by itself generate anomalous gravitational-wave friction: the minimal dynamical-Holst theory lies exactly on the general-relativistic propagation surface. We then construct a leading analytic parity-invariant nonminimal realization, exhibit a regular cosmological background satisfying both Friedmann equations, and verify the invariant two-derivative kinetic conditions along that benchmark. Finally, we recast published GWTC-3 and GWTC-4.0 constraints and the two public GWTC-4.0 hyperposterior products to determine which action-level combination present standard sirens constrain. The corresponding catalog constraints remain broad and depend on the adopted priors and population assumptions; itconstrains the evolution of a nonminimal parity-even curvature sector rather than minimal Barbero--Immirzi torsion, providing an action-level interpretation of standard-siren propagation tests.

gr-qc↗
arXiv2026

Robust structure from motion for aerial-ground images via detector-free feature matching and multi-view track refinement

Integrated 3D reconstruction from aerial-ground images is essential for generating high-precision urban 3D models, yet severe variations in viewpoint, scale, and rotation make robust feature matching highly challenging. To address these limitations, this study introduces a rotation-robust detector-free matching network coupled with multi-view track refinement for incremental Structure from Motion (ISfM). The proposed workflow features four key modules. First, rotation-aware feature extraction replaces traditional convolutions with an Omnidirectional State Space Block (OSS Block) that selectively scans across eight symmetrical directions to model long-range spatial dependencies and synthesize rotation-invariant feature maps. Second, multi-scale attention transformation utilizes quadtree attention to build a hierarchical token pyramid that isolates high-association token regions and discards irrelevant areas, capturing long-range context with linear computational complexity. Third, bi-directional feature matching executes a symmetric coarse-to-fine matching scheme where coarse alignment computes dual-direction Softmax confidence matrices under mutual nearest neighbor constraints, and fine alignment uses a multi-layer perceptron to regress sub-pixel coordinate offsets. Finally, multi-view track refinement employs an integrated indexing structure to evaluate localized spatial proximity and link disjoint sub-tracks to the highest-confidence anchor point, ensuring stable feature repeatability across the ISfM pipeline. By using real aerial-ground datasets, experimental results demonstrate that the proposed method improves AUC at 5{\deg} pose error by 93.9% compared with LoFTR and achieves the highest precision in ISfM reconstruction, with the improved accuracy ranging from 27.6% to 32.7%. The proposed method provides a reliable solution for integrated 3D reconstruction of aerial-ground images.

cs.CV↗
arXiv2026

Intern-S2-Mobius: Foundation Model with Decoupled Knowledge and Reasoning

We introduce Mobius-v0, an architecture that comprises a globally shared Memory (FFN) that stores knowledge vectors and multiple Reasoners (Self-Attn) that iteratively achieve compositional reasoning. Using hidden states as cache and carrier, reasoners repeatedly query memory for required knowledge-vectors, while the knowledge is transmitted back to reasoning operators. Through this knowledge-reasoning-separation architecture, Mobius achieves better knowledge compression and reasoning efficiency. Built upon Mobius-v0 architecture: 1) Our 7B model trained-from-scratch achieves similar downstream score as a 7B Transformer baseline with 62.6% of baseline's training data. 2) Our Intern-S2-Mobius, continually-pretrained from Qwen3.5-35B, achieves similar downstream score while delivering nearly 4x end-to-end inference speedup.

cs.AI↗
arXiv2026

Dynamical Barbero--Immirzi field coupled to quintessence: gravitational-wave propagation constraints and next-generation forecasts

We investigate the imprints of a dynamical Barbero--Immirzi (BI) field $\gamma(x)$ coupled to a quintessence scalar field $\phi$ on gravitational-wave (GW) propagation. In the framework of Einstein--Cartan--Holst gravity, promoting $\gamma$ to a dynamical scalar introduces a stress--energy that back-reacts on the metric, modifying the GW friction term. A minimal coupling $\propto\beta\,\phi^2\gamma^2$ between the BI field and quintessence leads to a two-parameter extension of the Belgacem--Maggiore parametrization, characterized by $\xBI$ (from the isolated BI field) and $\xcp$ (from the coupling). Using the LIGO--Virgo--KAGRA GWTC-3 dark-siren constraint $\Xi_0=1.2^{+0.7}_{-0.7}$, we obtain the first simultaneous constraints: $|\xBI|\lesssim0.7$ and $|\xcp|\lesssim0.13$ at 90\% credibility. We then forecast the sensitivity of next-generation detectors Einstein Telescope (ET) and Cosmic Explorer (CE), showing that a 10-year observation campaign can improve these bounds by roughly one to two orders of magnitude depending on the parameter---a factor of $\sim\!20$ for $\xBI$ and $\sim\!20$ for $\xcp$---reaching $\sigma(\xBI)\sim3\times10^{-2}$ and $\sigma(\xcp)\sim1.2\times10^{-2}$. Translated into microscopic parameters, this corresponds to $\gamma_{\rm dyn}\lesssim10^{-12}$ and $\beta\lesssim10^{-3}$, providing a powerful new observational window into the interplay between quantum-gravity phenomenology and dark energy.

gr-qc↗
arXiv2026

Understanding Security and Privacy Perceptions of Content Creators Regarding AI Labels of AI-Generated Content

AI labels, typically implemented via underlying tracing mechanisms such as watermarks and metadata, are crucial for protecting Artificial Intelligence-Generated Content (AIGC) against security threats like disinformation and evasion. However, the perceived devaluation of AI-assisted work discourages creators from disclosing AI use, incentivizing efforts to bypass labeling and compromising downstream traceability. Yet, how AIGC creators perceive the security and privacy (S\&P) implications of these labels, and how their behaviors impact technical resilience remain underexplored. To this end, we conducted semi-structured interviews with 21 AIGC creators and measured images across 6 image generation platforms against 16 self-reported manipulation settings. Our findings reveal that creators conflate binary AI labels with granular traceability, and express strong fears of de-anonymization via platform identifiers. Driven by fears of algorithmic traffic suppression and reputational risks, they defensively removed digital traces. Through empirical tests, we show that targeted modifications like coarse quantization significantly degrade detection. AI detection capabilities are also inconsistent across platforms, and suffer from false positives even for human-authored images. Based on these insights, we advocate for workflow-resilient implicit AI labels that align technical guarantees with creators' incentives.

cs.HC↗
arXiv2026

Your VLM Already Knows When: Training-Free Temporal Grounding by Asking Yes or No

Multimodal LLMs that recognise events reliably still fail to say when they happen. Prompted for timestamps, strong VLMs reach as little as $3.8\%$ R@0.5 on Charades-STA, and $77$ to $80\%$ of their wrong predictions carry low output entropy: the models are confidently wrong, and entropy-based error detection stays below a random classifier. We show that this failure lives in the task interface, not in perception. Holding the weights fixed, replacing timestamp regression with a coarse-to-fine scan of binary questions, whose first-token probabilities are consumed only as a ranking, raises R@0.5 by $28$ to $50$ points across four frozen backbones. The residual failures decompose into two measurable axes: a perception axis that moves with the backbone, and a geometry axis that is analytically predictable from the ratio of the output-window and event widths. FV-Action, the training-free method built on this analysis, reaches $56.8\%$ R@0.5 on Charades-STA, above the same backbone's native grounding pipeline and the strongest training-free result on this benchmark; it surpasses every TVG-trained model evaluated zero-shot on TACoS, and improves over direct prediction on ActivityNet Captions and QVHighlights, with no temporal supervision at any stage.

cs.CV↗
arXiv2026

Boundary layer analysis for the 2D chemotaxis-Navier-Stokes system with logarithmic sensitivity, Part I: Well-posedness

This is the first part of a two-part work concerning the boundary layer convergence for chemotaxis-Navier-Stokes system in a two-dimensional half-space. In this paper, we investigate the chemotacxis-Navier-Stokes system with the logarithmic singularity under Navier-slip boundary conditions. More precisely, we perform an exact asymptotic expansion for the chemotaxis Navier-Stokes system with viscous coefficient $\varepsilon>0$, and obtain partial boundary layer profiles, establishing the well-posedness of the corresponding boundary layer profiles. Specially, we also establish the local well-posedness of solutions to the supercritical chemotaxis Euler equation (with $\varepsilon=0$) by overcoming the difficulty from the disappearance of diffusion terms.

math.AP↗
arXiv2026

GAUGE: A Measurement-Grounded Benchmark for Physical Fidelity in Simulation Engines and Video World Models

Physics engines facilitate large-scale training and evaluation for embodied intelligence, while generative video world models are emerging as implicit simulators of future states and interactions. However, existing evaluations of physical fidelity are often conducted in isolation and rely heavily on perceptual similarity or human judgments, providing limited insight into which physical principles or parameters are violated. We introduce GAUGE, a real-world-grounded diagnostic benchmark for jointly evaluating how numerical simulators and generative video world models reproduce or deviate from real-world physics. It comprises 22 controlled task families covering rigid bodies, flexible cables, textiles, and volumetric deformable objects. Grounded in real-world trajectories and paired with calibrated physical metadata, uncertainty annotations, and task-specific observables, these tasks cover fundamental physical processes including collision, friction, momentum transfer, oscillation, self-contact, and deformation across diverse materials and conditions. We benchmark Isaac Sim, Genesis, and Newton on 14 task families using generalized trajectory errors, and evaluate 6 image-to-video models on 5 rigid-body tasks by testing physical-law consistency and the temporal stability of inferred parameters. Our results reveal no uniformly faithful physics engine, with the largest discrepancies arising in impulsive contact, rapid textile motion, and volumetric deformation. We further find that video world models can produce trajectories with the expected equation form while recovering incorrect accelerations, momentum transfer, and oscillation timing. GAUGE lays the groundwork for developing more physically faithful simulators and world models for embodied intelligence.

cs.AI↗
arXiv2026

TBSG-Net: Temporal Bipartite Scene Graph Network for Fine-Grained Video Moment Retrieval

Recent advances in proposal-free Video Moment Retrieval (VMR) have highlighted the effectiveness of Static Scene Graphs (SSGs). By modeling objects and their relations at the frame level, SSGs enrich retrieval-oriented video representations. However, integrating SSGs into VMR remains constrained by two inherent limitations: (1) Lack of Temporal Dynamics. SSGs fail to model how objects and their relationships evolve over time, leading to the loss of essential temporal dependencies in video representation; and (2) Lack of Explicit Temporal Span Encoding. SSGs do not explicitly encode the duration of relationships, making precise localization challenging. To address these limitations, we propose Temporal Bipartite Scene Graph Network (TBSG-Net)---to the best of our knowledge, the first Dynamic Scene Graph (DSG) based proposal-free VMR model. Specifically, TBSG-Net leverages DSGs to extract event-centric graph representations of the input video, enabling the modeling of object interactions over time and thus addressing limitation (1). These DSGs are then processed by a novel Dynamic Scene Graph Embedding (DSG-E) module to capture both Temporal Span and spatio-temporal information. First, DSG-E utilizes a TBSG Constructor to transform DSGs into TBSGs, explicitly encoding objects, relationships, and time spans to tackle limitation (2). Second, the resultant TBSGs are passed into a hybrid TBSG Encoder that integrates a Transformer variant for global event modeling and a Graph Convolutional Network for detailed relational reasoning, ultimately producing a more comprehensive spatio-temporal representation. Our experiments demonstrate substantial improvements of TBSG-Net over all baselines.

cs.CV↗
arXiv2026

ASARL: Autonomous Social-Aware Relevance Learning for QQ Search

The rapid growth of online social platforms has transformed communication and information retrieval, giving rise to social search, where queries-titles are typically expressed in informal, community-specific language. While large language models provide strong general-purpose semantic understanding, their effectiveness in social search is constrained by contextual discrepancy, data scarcity, and behavior-driven dynamics. To address these challenges, we propose the Autonomous Social-Aware Relevance Learning (ASARL), a fully automated framework that integrates multi-agent data curation with staged model training. ASARL leverages a collaborative agent system: ReasonAgent generates interpretable relevance labels grounded in social attributes, CriticAgent validates and ensures logical consistency, and GenAgent augments long-tail data through synthetic query-title pairs. Building on the curated dataset, ASARL employs three-stage training: Social Context Training (SCT) to capture social language patterns, Preference-Guided Optimization (PGO) to align model predictions with behavioral signals, and Social Distillation (SD) to transfer these improvements into compact models for efficient deployment. Extensive offline and online experiments on the QQ search platform demonstrate significant improvements in both offline relevance metrics and online user engagement indicators, along with enhanced annotation efficiency. These results validate the effectiveness of combining autonomous, socially grounded data governance with preference-aligned training in practical search systems.

cs.IR↗
arXiv2026

Purcell-Engineered Hybrid Coupler for Leakage-Suppressed Robust CZ Gates

We propose a Purcell-engineered notch-filter hybrid coupler for superconducting controlled-$Z$ (CZ) gates that combines coherent interaction engineering with leakage-selective dissipation. The architecture integrates a nonlinear transmon coupler with a coupled Purcell-filter and notch-resonator subsystem, providing additional control over both the coherent interaction pathways and the engineered dissipative environment. The filter branch reshapes the effective interaction pathways, while the notch resonator further tailors the frequency response of the coupled filter network and preserves strong leakage-selective dissipation. Using dressed-eigenstate analysis together with Lindblad master-equation simulations, we show that the proposed architecture substantially reduces leakage and improves the worst-case computational-state fidelity compared with an optimized single-transmon coupler while remaining robust over a broad range of coherence assumptions and device parameters. The optimized gate achieves $F_{\rm avg}=99.74\%$, $F_{\rm min}=99.62\%$, and a maximum leakage probability of $1.6\times10^{-3}$. These results demonstrate that engineered dissipation complements conventional coherent interaction engineering and provides an additional design degree of freedom for realizing robust, high-fidelity superconducting CZ gates.

quant-ph↗
arXiv2026

Magnetic proximity-induced non-relativistic valley polarization

The magnetic proximity effect in van der Waals heterostructures exerts a significant impact on the properties of adjacent materials. Here, we propose van der Waals heterostructures composed of monolayers ferromagnets (FM) and altermagnets (AM), in which the magnetic proximity effect from the FM induces pronounced non-relativistic valley polarization in the AM, and this phenomenon is demonstrated to be universal. Furthermore, by tuning the magnetization of the FM and the N\'eel vector direction of the AM, four independent valley-polarized states can be realized in the FM/AM heterostructures, exhibiting strong magnetic-valley coupling. These findings suggest that FM/AM heterostructures hold potential application value in the field of valleytronics-based information storage.

cond-mat.mtrl-sci↗