Graph retrieval-augmented generation (GraphRAG) uses document-derived knowledge graphs to assemble evidence for large language model (LLM) answers. Its security depends on both the seed chunks retrieved for a query and the graph evidence assembled from them, yet attacks on this evidence path remain underexplored. We in...
Yi-Ning Tang, Jun-Yu Zhang, Hao-Miao Qiu et al.· 0 citations
GraphFAS (Graph Feature Automated Selection), a distributed feature selection procedure based on Boruta that decouples feature aggregation from model training, enabling direct integration with tabular models and direct compatibility with TreeSHAPbased explanations.
Bole is presented, a kernel--runtime co-design that enables efficient tree speculation for hybrid-attention LLMs and couples efficient state management with a batch-wide verification budget calibrated to the complete hybrid forward.
This work presents \textsc{GTA-RAG}, a graph-trajectory-augmented RL framework for multi-turn retrieval-augmented reasoning that consistently outperforms RL-based RAG baselines with both Qwen2.5-3B and Qwen2.5-7B backbones, while substantially improving evidence-chain coverage.
Jun Chen, Yongchao Liu, Pengyu Qiu et al.· 0 citations
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