Institution profile

Dongbei University of Finance and Economics

Academic institutionasia · cn
Official website
Research library6linked papers
Opportunities0open roles
Selected work

Representative Papers

RQ-SAFE: Coupled Request-Resource Scheduling for Online Edge SFC-DAGs

Jun 24, 2026

This work addresses the limitations of existing edge service orchestration approaches, which overlook the reorderability of local sequences within service function chain directed acyclic graphs (SFC-DAGs) and fail to jointly optimize request scheduling and resource allocation. To bridge this gap, the authors propose RQ-SAFE, a novel framework that, for the first time, couples the flexibility of SFC-DAG local ordering with queue-aware resource scheduling. RQ-SAFE dynamically evaluates the resource impact of feasible local orderings in an online manner and leverages real-time queue states to guide virtual network function (VNF) instance selection and path construction. Furthermore, it incorporates a learning-assisted reordering mechanism to balance quality of service (QoS) and system load. Experimental results demonstrate that, compared to the GNN-DAG-Score baseline, RQ-SAFE reduces CPU load imbalance by 6.1%, lowers peak utilization by 2.3%, and improves QoS by 4.53 percentage points through joint optimization.

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FINRS: A Risk-Sensitive Trading Framework for Real Financial Markets

Nov 16, 2025

Existing LLM-based trading agents predominantly rely on single-step prediction and lack explicit risk management mechanisms, leading to suboptimal performance under market volatility. To address this, we propose RISK-LLM—a novel framework featuring: (i) hierarchical market analysis to model multi-granularity dynamics; (ii) a dual-decision agent architecture that decouples signal generation from risk control; and (iii) multi-horizon reinforcement learning with risk-aware rewards, jointly optimizing returns and downside risk constraints (e.g., conditional value-at-risk). This work constitutes the first systematic integration of risk sensitivity into the LLM-driven trading paradigm. Extensive experiments across diverse markets—including A-shares and U.S. equities—demonstrate that RISK-LLM significantly improves the Sharpe ratio and enhances maximum drawdown control, outperforming state-of-the-art methods in both profitability and trading stability.

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Recent publications

Latest Papers

RQ-SAFE: Coupled Request-Resource Scheduling for Online Edge SFC-DAGs

Jun 24, 2026

This work addresses the limitations of existing edge service orchestration approaches, which overlook the reorderability of local sequences within service function chain directed acyclic graphs (SFC-DAGs) and fail to jointly optimize request scheduling and resource allocation. To bridge this gap, the authors propose RQ-SAFE, a novel framework that, for the first time, couples the flexibility of SFC-DAG local ordering with queue-aware resource scheduling. RQ-SAFE dynamically evaluates the resource impact of feasible local orderings in an online manner and leverages real-time queue states to guide virtual network function (VNF) instance selection and path construction. Furthermore, it incorporates a learning-assisted reordering mechanism to balance quality of service (QoS) and system load. Experimental results demonstrate that, compared to the GNN-DAG-Score baseline, RQ-SAFE reduces CPU load imbalance by 6.1%, lowers peak utilization by 2.3%, and improves QoS by 4.53 percentage points through joint optimization.

0 citationsRead paper

FINRS: A Risk-Sensitive Trading Framework for Real Financial Markets

Nov 16, 2025

Existing LLM-based trading agents predominantly rely on single-step prediction and lack explicit risk management mechanisms, leading to suboptimal performance under market volatility. To address this, we propose RISK-LLM—a novel framework featuring: (i) hierarchical market analysis to model multi-granularity dynamics; (ii) a dual-decision agent architecture that decouples signal generation from risk control; and (iii) multi-horizon reinforcement learning with risk-aware rewards, jointly optimizing returns and downside risk constraints (e.g., conditional value-at-risk). This work constitutes the first systematic integration of risk sensitivity into the LLM-driven trading paradigm. Extensive experiments across diverse markets—including A-shares and U.S. equities—demonstrate that RISK-LLM significantly improves the Sharpe ratio and enhances maximum drawdown control, outperforming state-of-the-art methods in both profitability and trading stability.

0 citationsRead paper