Weiping Deng

Ningning Zhang, Chi Zhang, Zhizhong Tan et al.

Multi-hop question answering (QA) presents significant challenges for retrieval-augmented generation (RAG), particularly in decomposing complex queries into reliable reasoning paths and managing error propagation. Existing RAG methods often suffer from deviations in reasoning paths and cumulative errors in intermediate steps, reducing the fidelity of the final answer. To address these limitations, we propose PAR-RAG (Plan-then-Act-and-Review RAG), a novel framework inspired by the PDCA (Plan-Do-Check-Act) cycle, to enhance both the accuracy and factual consistency in multi-hop question answering. Specifically, PAR-RAG selects exemplars matched by the semantic complexity of the current question to guide complexity-aware top-down planning, resulting in more precise and coherent multi-step reasoning trajectories. This design mitigates reasoning drift and reduces the risk of suboptimal path convergence, a common issue in existing RAG approaches. Furthermore, a dual-verification mechanism evaluates and corrects intermediate errors, ensuring that the reasoning process remains factually grounded. Experimental results on various QA benchmarks demonstrate that PAR-RAG outperforms existing state-of-the-art methods, validating its effectiveness in both performance and reasoning robustness.

Zhizhong Tan, Jiexin Zheng, Xingxing Yang et al.

Due to the highly sensitive nature of certain data in cross-border sharing, collaborative cross-border recommendations and data sharing are often subject to stringent privacy protection regulations, resulting in insufficient data for model training. Consequently, achieving efficient cross-border business recommendations while ensuring privacy security poses a significant challenge. Although federated learning has demonstrated broad potential in collaborative training without exposing raw data, most existing federated learning-based GNN training methods still rely on federated averaging strategies, which perform suboptimally on highly heterogeneous graph data. To address this issue, we propose FedGRec, a privacy-preserving federated graph learning method for cross-border recommendations. FedGRec captures user preferences from distributed multi-domain data to enhance recommendation performance across all domains without privacy leakage. Specifically, FedGRec leverages collaborative signals from local subgraphs associated with users or items to enrich their representation learning. Additionally, it employs dynamic spatiotemporal modeling to integrate global and local user preferences in real time based on business recommendation states, thereby deriving the final representations of target users and candidate items. By automatically filtering relevant behaviors, FedGRec effectively mitigates noise interference from unreliable neighbors. Furthermore, through a personalized federated aggregation strategy, FedGRec adapts global preferences to heterogeneous domain data, enabling collaborative learning of user preferences across multiple domains. Extensive experiments on three datasets demonstrate that FedGRec consistently outperforms competitive single-domain and cross-domain baselines while effectively preserving data privacy in cross-border recommendations.