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Wenzhuo Qian

Wenzhuo Qian appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

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Artificial IntelligenceComputational Engineering, Finance, and ScienceMultiagent Systems

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Published work

2 published item(s)

preprint2026arXiv

Industrial Data-Service-Knowledge Governance: Toward Integrated and Trusted Intelligence for Industry 5.0

The convergence of artificial intelligence, cyber-physical systems, and cross-enterprise data ecosystems has propelled industrial intelligence to unprecedented scales. Yet, the absence of a unified trust foundation across data, services, and knowledge layers undermines reliability, accountability, and regulatory compliance in real-world deployments. While existing surveys address isolated aspects, such as data governance, service orchestration, and knowledge representation, none provides a holistic, cross-layer perspective on trustworthiness tailored to industrial settings. To bridge this gap, we present \textsc{Trisk} (TRusted Industrial Data-Service-Knowledge governance), a novel conceptual and taxonomic framework for trustworthy industrial intelligence. Grounded in a five-dimensional trust model (quality, security, privacy, fairness, and explainability), \textsc{Trisk} unifies 120+ representative studies along three orthogonal axes: governance scope (data, service, and knowledge), architectural paradigm (centralized, federated, or edge-embedded), and enabling technology (knowledge graphs, zero-trust policies, causal inference, etc.). We systematically analyze how trust propagates across digital layers, identify critical gaps in semantic interoperability, runtime policy enforcement, and operational/information technologies alignment, and evaluate the maturity of current industrial implementations. Finally, we articulate a forward-looking research agenda for Industry 5.0, advocating for an integrated governance fabric that embeds verifiable trust semantics into every layer of the industrial intelligence stack. This survey serves as both a foundational reference for researchers and a practical roadmap for engineers to deploy trustworthy AI in complex and multi-stakeholder environments.

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preprint2026arXiv

When Does Hierarchy Help? Benchmarking Agent Coordination in Event-Driven Industrial Scheduling

Recent advances in agent and multi-agent systems have shown strong performance on tool use, reasoning, and collaborative tasks. However, existing benchmarks mostly evaluate task completion in weakly coupled environments, and provide limited support for studying coordination in shared, dynamically evolving systems with hierarchy and coupled constraints. This leaves an important question underexplored: when do different coordination paradigms succeed or fail? We introduce Distributed Event-driven Scheduling Benchmark (DESBench), a benchmark for evaluating agent coordination in hierarchical event-driven scheduling. Built on a shared discrete-event driven environment in industrial scheduling, our benchmark captures multi-timescale decision making, partial observability, and dynamically coupled constraints. We define tasks and metrics that evaluate effectiveness, constraint alignment, coordination efficiency, and robustness, and focus on four representative coordination paradigms: centralized, hierarchical, heterarchical, and holonic. These paradigms correspond to distinct mechanisms of information flow, decision authority, and conflict resolution. Our controlled evaluations reveal clear coordination trade-offs: centralized coordination is robust and communication-efficient but scales poorly with difficulty; hierarchical coordination improves efficiency through decomposition but suffers from cross-level misalignment; heterarchical coordination is flexible but communication-heavy; and holonic coordination satisfies constraints well but loses global robustness. These findings demonstrate that coordination design fundamentally shapes agent system behavior in complex environments, revealing structural trade-offs that cannot be captured by outcome metrics alone and underscoring the imperative for more adaptive, principled, and dynamic coordination mechanisms in future MAS research.

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