arXiv subjects

Zehua Wang

Publications and source records attributed to Zehua Wang.

At least 19 recordsLinked to original sources

arXiv2026

Function Name Is All You Need to Detect Blockchain Application Attacks

Blockchain application attacks, targeting business logic bugs in decentralized applications (dApps), have been an increasing concern to their developers and users, causing significant financial loss. Existing attack detectors either rely on handcrafted rules for detection, or need difficult-to-obtain smart contract source code to analyze attack transactions. This makes them brittle and inapplicable in practice. In this paper, we argue that function name sequences suffice to capture the high-level semantics of a transaction, and hence can be used to detect blockchain application attacks. Our empirical study on transactions from 424 real-world attack incidents shows that 98.46% of call traces can be resolved to function names, whereas only 74.78% invoke contracts with available source code. Based on this observation, we propose TxLucent (pronounced "translucent"), an automated framework to detect blockchain application attacks by extracting application semantics from transaction call traces. TxLucent maps call traces to function name sequences and uses a transformer to learn semantics from such sequences. Consequently, TxLucent can detect attacks without relying on hand-coded patterns or source code. Our results show that TxLucent achieves a 1.56% false negative rate on 424 known incidents with 14,611 attack transactions, and an estimated 0.0017% false positive rate for benign transactions from over 500 million transactions on the Ethereum blockchain. Finally, TxLucent takes an average of 24.90 milliseconds to analyze a transaction, thus supporting real-time attack detection on popular blockchains.

cs.CR↗

arXiv2026

VibeVoice-ASR-Streaming Technical Report

Traditional speaker-attributed ASR systems treated ASR and speaker diarization as two separate tasks. Recently, end-to-end models such as VibeVoice-ASR have unified the two tasks within a single model. However, existing unified models still mainly support offline recognition, making it difficult to meet the low-latency requirements of real-time voice assistants and agents. To tackle this issue, we present VibeVoice-ASR-Streaming, one of the first LLM-based end-to-end approaches to streaming speaker-attributed ASR. It interleaves fixed-size audio chunks, a small amount of lookahead audio and previous text. This allows the model to produce ''who said what'' as speech arrives, without a separate diarization stage. For transcription accuracy, our 7B model achieves the lowest average WER/CER across five evaluation sets. For speaker attribution, it achieves the best or tied-best on 12 of 13 evaluation settings. We release the 1.5B and 7B model weights together with inference code.

eess.AS↗

arXiv2026

BMDS-Net:Deployment-aware multi-modal brain tumor segmentation with adaptive fusion,decoder regularization,and Bayesian calibration

Multi-modal MRI enables detailed brain tumor sub-region segmentation, but clinical deployment remains affected by missing sequences,boundary errors, and overconfident predictions. We present BMDS-Net, a two-stage Swin UNETR framework that combines adaptivemodality fusion, boundary-aware decoder regularization, and efficient Bayesian calibration. The deterministic stage adds zero-initializedmultimodal contextual fusion (MMCF) and residual-gated deep decoder supervision (DDS) to modulate modality contributions andregularize hierarchical boundary reconstruction. The second stage replaces only the final convolutional layer with a Bayesian layer,enabling Monte Carlo uncertainty estimates with limited additional training cost. On BraTS 2021 (1,251 cases), BMDS-Net achieved Dice scores of 0.9293, 0.9098, and 0.8675 for WT, TC, and ET, with HD95 values of 2.27, 2.22, and 3.27 mm. Configuration comparisons showed that auxiliary supervision favored complete-input segmentation, whereas the complete MMCF+gated-DDS configuration was most robust under single-modality removal. Bayesian BMDS-Net achieved an expected calibration error of 0.0037, close to a three-model ensemble (0.0035) at approximately 60% lower training cost. On BraTS 2020, the same protocol preserved the leading internal ranking of the complete configuration among five Swin UNETR-controlled variants. The implementation code is available at https://github.com/RyanZhou168/BMDS-Net.

cs.CV↗

arXiv2026

QoS-Aware Token Scheduling and Private Data Valuation for Multi-Modal Agentic Networks

In agentic systems, human-generated data records anchor the value of AI services. Yet cloud compute pipelines centralize processing on remote servers. Data centralization reduces personal data sovereignty and may potentially degrade the quality of service (QoS). Meanwhile, user contributions are diverse in quantity and quality: decentralized records can be biased, noisy, and heterogeneously distributed. To address the data challenge, we study fair token allocation and private data valuation for decentralized and resource-constrained agentic systems. Our approach embeds multi-modal representations in a shared semantic space and releases differentially private (DP) prototypes to preserve utility while reducing semantic leakage. With the DP guarantee, we design a fair token allocation scheme that rewards effective contributions and remains robust to data heterogeneity and AI resource scarcity. Extensive simulations demonstrate improved contribution-based fairness and QoS compared to standard benchmarks. The improved resistance to image reconstruction attacks indicates enhanced privacy for multi-modal personal data.

cs.AI↗

arXiv2026

Family-Aware Residual Architecture for Predicting Quantum Circuit Simulation Performance

Approximate tensor-network simulators enable classical simulation of quantum circuits beyond the reach of exact methods, but selecting optimal approximation parameters -- such as bond dimension thresholds -- remains a costly trial-and-error process. We present a family-aware neural architecture that predicts both the minimum approximation threshold required to achieve target fidelity and the expected wall-clock runtime for quantum circuit simulation, given only the circuit's OpenQASM description and execution context. Our key insight is that quantum circuits from different algorithmic families (e.g., QFT, Grover, VQE) exhibit fundamentally distinct simulation cost profiles due to their differing entanglement structures. We employ family-conditioned residual corrections -- additive, family-specific adjustments atop a shared backbone, drawing on established conditional computation techniques -- enabling the model to capture both universal circuit properties and algorithmic nuances. The architecture incorporates a pretrained family classifier (97.5% accuracy) and domain-informed algorithm fingerprint features derived from gate-composition heuristics. Evaluated on circuits spanning 7--130 qubits across 10 algorithm families, our system achieves 79.5% exact threshold accuracy (91.2% within one rung) and $R^2 = 0.82$ runtime correlation, with inference completing in approximately 50 ms -- replacing trial-and-error simulation runs that may take minutes to hours. Ablation studies confirm that family-aware modeling provides the single largest performance improvement (+3.2 percentage points), validating the hypothesis that algorithm family is a first-class feature for simulation cost prediction.

quant-ph↗

arXiv2026

L2A: Learning to Accumulate Pose History for Accurate 3D Human Pose Estimation

Existing 2D-3D lifting human pose estimation methods have achieved strong performance. But the utilization of historical pose representations across network depth was overlooked. In current pipelines, information is propagated through fixed residual connections, which restricts effective reuse of early-layer features such as fine-grained spatial structures and short-term motion cues. However, naively incorporating historical features across layers is non-trivial. We further identify that maintaining a consistent representation space across layers is a prerequisite for effective cross-layer feature aggregation. To address this issue, we propose a history-aware framework that enables effective network cross-layer history feature utilization. Specifically, we adopt a spatial-temporal parallel Transformer backbone to prevent alternating spatial-temporal transformations during sequential processing, thereby maintaining a consistent representation space. Building upon this, we introduce a History Pose Accumulation (HPA) mechanism that adaptively aggregates features from all preceding layers to enhance current representations. Furthermore, we propose a Layer Pose History Aggregation (LPA) module that transforms layer pose features into a compact and structured form, reducing redundancy and enabling more stable aggregation. Extensive experiments demonstrate that our approach achieves state-of-the-art performance on benchmarks.

cs.CV↗

arXiv2026

FedRE: A Representation Entanglement Framework for Model-Heterogeneous Federated Learning

Federated learning (FL) enables collaborative training across clients while preserving privacy. While most existing FL methods assume homogeneous model architectures, client heterogeneity in both data and resources makes this assumption impractical, thus motivating model-heterogeneous FL. To address this problem, we propose Federated Representation Entanglement (FedRE), a framework built upon a novel form of client knowledge termed entangled representation. Specifically, each client aggregates its local representations into a single entangled representation using normalized random weights, and then applies the same weights to integrate the corresponding one-hot label encodings into an entangled-label encoding. Both are subsequently uploaded to the server to train a global classifier. During training, each entangled representation is supervised across categories via its entangled-label encoding, while random weights are re-sampled at each round to introduce diversity, alleviating overconfidence in the global classifier and yielding smoother decision boundaries. Moreover, each client uploads a single entangled representation along with its entangled-label encoding, mitigating the risk of representation inversion attacks and reducing communication overhead. Extensive experiments demonstrate that FedRE achieves an effective trade-off among model performance, privacy protection, and communication overhead. The codes are available at https://github.com/AIResearch-Group/FedRE.

cs.LG↗

arXiv2026

Diffusion-Guided Semantic Consistency for Multimodal Heterogeneity

Federated learning (FL) is severely challenged by non-independent and identically distributed (non-IID) client data, a problem that degrades global model performance, especially in multimodal perception settings. Conventional methods often fail to address the underlying semantic discrepancies between clients, leading to suboptimal performance for multimedia systems requiring robust perception. To overcome this, we introduce SemanticFL, a novel framework that leverages the rich semantic representations of pre-trained diffusion models to provide privacy-preserving guidance for local training. Our approach leverages multi-layer semantic representations from a pre-trained Stable Diffusion model (including VAE-encoded latents and U-Net hierarchical features) to create a shared latent space that aligns heterogeneous clients, facilitated by an efficient client-server architecture that offloads heavy computation to the server. A unified consistency mechanism, employing cross-modal contrastive learning, further stabilizes convergence. We conduct extensive experiments on benchmarks including CIFAR-10, CIFAR-100, and TinyImageNet under diverse heterogeneity scenarios. Our results demonstrate that SemanticFL surpasses existing federated learning approaches, achieving accuracy gains of up to 5.49% over FedAvg, validating its effectiveness in learning robust representations for heterogeneous and multimodal data for perception tasks.

cs.CV↗

arXiv2026

Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey

Edge-cloud collaborative computing (ECCC) has emerged as a pivotal paradigm for addressing the computational demands of modern intelligent applications, integrating cloud resources with edge devices to enable efficient, low-latency processing. Recent advancements in AI, particularly deep learning and large language models (LLMs), have dramatically enhanced the capabilities of these distributed systems, yet introduce significant challenges in model deployment and resource management. In this survey, we comprehensive examine the intersection of distributed intelligence and model optimization within edge-cloud environments, providing a structured tutorial on fundamental architectures, enabling technologies, and emerging applications. Additionally, we systematically analyze model optimization approaches, including compression, adaptation, and neural architecture search, alongside AI-driven resource management strategies that balance performance, energy efficiency, and latency requirements. We further explore critical aspects of privacy protection and security enhancement within ECCC systems and examines practical deployments through diverse applications, spanning autonomous driving, healthcare, and industrial automation. Performance analysis and benchmarking techniques are also thoroughly explored to establish evaluation standards for these complex systems. Furthermore, the review identifies critical research directions including LLMs deployment, 6G integration, neuromorphic computing, and quantum computing, offering a roadmap for addressing persistent challenges in heterogeneity management, real-time processing, and scalability. By bridging theoretical advancements and practical deployments, this survey offers researchers and practitioners a holistic perspective on leveraging AI to optimize distributed computing environments, fostering innovation in next-generation intelligent systems.

cs.DC↗

arXiv2026

VIBEVOICE-ASR Technical Report

This report presents VibeVoice-ASR, a general-purpose speech understanding framework built upon VibeVoice, designed to address the persistent challenges of context fragmentation and multi-speaker complexity in long-form audio (e.g., meetings, podcasts) that remain despite recent advancements in short-form speech recognition. Unlike traditional pipelined approaches that rely on audio chunking, VibeVoice-ASRsupports single-pass processing for up to 60 minutes of audio. It unifies Automatic Speech Recognition, Speaker Diarization, and Timestamping into a single end-to-end generation task. In addition, VibeVoice-ASR supports over 50 languages, requires no explicit language setting, and natively handles code-switching within and across utterances. Furthermore, we introduce a prompt-based context injection mechanism that allows users to supply customized conetxt, significantly improving accuracy on domain-specific terminology and polyphonic character disambiguation.

cs.SD↗

arXiv2026

Multi-Agent Honeypot-Based Request-Response Context Dataset for Improved SQL Injection Detection Performance

SQL injection remains a major threat to web applications, as existing defenses often fail against obfuscation and evolving attacks because of neglecting the request-response context. This paper presents a context-enriched SQL injection detection framework, focusing on constructing a high-quality request-response dataset via a multi-agent honeypot system: the Request Generator Agent produces diverse malicious/benign requests, the Database Response Agent mediates interactions to ensure authentic responses while protecting production data, and the Traffic Monitor pairs requests with responses, assigns labels, and cleans data, yielding totally 140,973 labeled pairs with contextual cues absent in payload-only data. Experiments show that models trained on this context dataset outperform payload-only counterparts: CNN and BiLSTM achieve over 40\% accuracy improvement in different tasks, validating that the request-response context enhances the detection of evolving and obfuscated attacks.

cs.CR↗

arXiv2026

Toward a Sustainable Federated Learning Ecosystem: A Practical Least Core Mechanism for Payoff Allocation

Emerging network paradigms and applications increasingly rely on federated learning (FL) to enable collaborative intelligence while preserving privacy. However, the sustainability of such collaborative environments hinges on a fair and stable payoff allocation mechanism. Focusing on coalition stability, this paper introduces a payoff allocation framework based on the least core (LC) concept. Unlike traditional methods, the LC prioritizes the cohesion of the federation by minimizing the maximum dissatisfaction among all potential subgroups, ensuring that no participant has an incentive to break away. To adapt this game-theoretic concept to practical, large-scale networks, we propose a streamlined implementation with a stack-based pruning algorithm, effectively balancing computational efficiency with allocation precision. Case studies in federated intrusion detection demonstrate that our mechanism correctly identifies pivotal contributors and strategic alliances. The results confirm that the practical LC framework promotes stable collaboration and fosters a sustainable FL ecosystem.

cs.GT↗

arXiv2025

A Closer Look at Personalized Fine-Tuning in Heterogeneous Federated Learning

Federated Learning (FL) enables decentralized, privacy-preserving model training but struggles to balance global generalization and local personalization due to non-identical data distributions across clients. Personalized Fine-Tuning (PFT), a popular post-hoc solution, fine-tunes the final global model locally but often overfits to skewed client distributions or fails under domain shifts. We propose adapting Linear Probing followed by full Fine-Tuning (LP-FT), a principled centralized strategy for alleviating feature distortion (Kumar et al., 2022), to the FL setting. Through systematic evaluation across seven datasets and six PFT variants, we demonstrate LP-FT's superiority in balancing personalization and generalization. Our analysis uncovers federated feature distortion, a phenomenon where local fine-tuning destabilizes globally learned features, and theoretically characterizes how LP-FT mitigates this via phased parameter updates. We further establish conditions (e.g., partial feature overlap, covariate-concept shift) under which LP-FT outperforms standard fine-tuning, offering actionable guidelines for deploying robust personalization in FL.

cs.LG↗

arXiv2025

A Survey on Cloud-Edge-Terminal Collaborative Intelligence in AIoT Networks

The proliferation of Internet of things (IoT) devices in smart cities, transportation, healthcare, and industrial applications, coupled with the explosive growth of AI-driven services, has increased demands for efficient distributed computing architectures and networks, driving cloud-edge-terminal collaborative intelligence (CETCI) as a fundamental paradigm within the artificial intelligence of things (AIoT) community. With advancements in deep learning, large language models (LLMs), and edge computing, CETCI has made significant progress with emerging AIoT applications, moving beyond isolated layer optimization to deployable collaborative intelligence systems for AIoT (CISAIOT), a practical research focus in AI, distributed computing, and communications. This survey describes foundational architectures, enabling technologies, and scenarios of CETCI paradigms, offering a tutorial-style review for CISAIOT beginners. We systematically analyze architectural components spanning cloud, edge, and terminal layers, examining core technologies including network virtualization, container orchestration, and software-defined networking, while presenting categorizations of collaboration paradigms that cover task offloading, resource allocation, and optimization across heterogeneous infrastructures. Furthermore, we explain intelligent collaboration learning frameworks by reviewing advances in federated learning, distributed deep learning, edge-cloud model evolution, and reinforcement learning-based methods. Finally, we discuss challenges (e.g., scalability, heterogeneity, interoperability) and future trends (e.g., 6G+, agents, quantum computing, digital twin), highlighting how integration of distributed computing and communication can address open issues and guide development of robust, efficient, and secure collaborative AIoT systems.

cs.NI↗

arXiv2025

VibeVoice Technical Report

This report presents VibeVoice, a novel model designed to synthesize long-form speech with multiple speakers by employing next-token diffusion, which is a unified method for modeling continuous data by autoregressively generating latent vectors via diffusion. To enable this, we introduce a novel continuous speech tokenizer that, when compared to the popular Encodec model, improves data compression by 80 times while maintaining comparable performance. The tokenizer effectively preserves audio fidelity while significantly boosting computational efficiency for processing long sequences. Thus, VibeVoice can synthesize long-form speech for up to 90 minutes (in a 64K context window length) with a maximum of 4 speakers, capturing the authentic conversational ``vibe'' and surpassing open-source and proprietary dialogue models.

cs.CL↗

arXiv2025

Clio-X: AWeb3 Solution for Privacy-Preserving AI Access to Digital Archives

As archives turn to artificial intelligence to manage growing volumes of digital records, privacy risks inherent in current AI data practices raise critical concerns about data sovereignty and ethical accountability. This paper explores how privacy-enhancing technologies (PETs) and Web3 architectures can support archives to preserve control over sensitive content while still being able to make it available for access by researchers. We present Clio-X, a decentralized, privacy-first Web3 digital solution designed to embed PETs into archival workflows and support AI-enabled reference and access. Drawing on a user evaluation of a medium-fidelity prototype, the study reveals both interest in the potential of the solution and significant barriers to adoption related to trust, system opacity, economic concerns, and governance. Using Rogers' Diffusion of Innovation theory, we analyze the sociotechnical dimensions of these barriers and propose a path forward centered on participatory design and decentralized governance through a Clio-X Decentralized Autonomous Organization. By integrating technical safeguards with community-based oversight, Clio-X offers a novel model to ethically deploy AI in cultural heritage contexts.

cs.CR↗

arXiv2025

A Novel Approach to Differential Privacy with Alpha Divergence

As data-driven technologies advance swiftly, maintaining strong privacy measures becomes progressively difficult. Conventional $(ε, δ)$-differential privacy, while prevalent, exhibits limited adaptability for many applications. To mitigate these constraints, we present alpha differential privacy (ADP), an innovative privacy framework grounded in alpha divergence, which provides a more flexible assessment of privacy consumption. This study delineates the theoretical underpinnings of ADP and contrasts its performance with competing privacy frameworks across many scenarios. Empirical assessments demonstrate that ADP offers enhanced privacy guarantees in small to moderate iteration contexts, particularly where severe privacy requirements are necessary. The suggested method markedly improves privacy-preserving methods, providing a flexible solution for contemporary data analysis issues in a data-centric environment.

cs.CR↗

arXiv2025

Observation of non-adiabatic Landau-Zener tunneling among Floquet states

Electromagnetic fields not only induce electronic transitions but also fundamentally modify the quantum states of matter through strong light-matter interactions. As one established route, Floquet engineering provides a powerful framework to dress electronic states with time-periodic fields, giving rise to quasi-stationary Floquet states. With increasing field strength, non-perturbative responses of the dressed states emerge, yet their nonlinear dynamics remain challenging to interpret. In this work we explore the emergence of non-adiabatic Landau-Zener transitions among Floquet states in Cu(111) under intense optical fields. At increasing field strength, we observe a transition from perturbative dressing to a regime where Floquet states undergo non-adiabatic tunneling, revealing a breakdown of adiabatic Floquet evolution. These insights are obtained through interferometrically time-resolved multi-photon photoemission spectroscopy, which serves as a sensitive probe of transient Floquet state dynamics. Numerical simulations and the theory of instantaneous Floquet states allow us to directly examine real-time excitation pathways in this non-perturbative photoemission regime. Our results establish a direct connection the onset of light-dressing of matter, non-perturbative ultrafast lightwave electronics, and high-optical-harmonic generation in the solids.

cond-mat.mes-hall↗