Author

Yongchao Liu

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#natural language process... Preprint Oct 2026

GraphRAG-LTM: A Layered Threat Modeling Framework for Attacks on Graph Retrieval-Augmented Generation

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

Preprint Aug 2026

Bole: Efficient Tree Speculation for Hybrid-Attention Language Models

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.

Li Wang, Yi Su, Xiabao Wu et al. · 1 citation

#machine learning Preprint Aug 2026

GTA-RAG: Graph-Trajectory-Augmented Reinforcement Learning for Multi-Turn Retrieval-Augmented Reasoning

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