9 citations · 9 across the 8 of their papers we have counts for
9 papers
MetaCascade: End-to-End Attacks on Agent Skills via Metadata-Only Poisoning
Junhao Wen, Ping Deng, Qizhi Chen +2
As a key extension mechanism for agent systems, Agent Skills package specialized capabilities for reuse by LLM agents. An agent first retrieves candidate skills based on skill docu…
Beyond the Static Barrier for Ordinary Dynamic Approximate Membership
Qizhi Chen, Zhebei Shen, Zhehan Yu
We prove a strict space separation between static and ordinary dynamic approximate membership at every fixed error rate. For each fixed , a capacity- ordina…
Stop When Further Reasoning Won't Help: Attention-State Adaptive Generation in Reasoning Models
Jiakai Li, Ke Qin, Rongzheng Wang +4
By incorporating test-time compute scaling, large reasoning models (LRMs) can solve complex problems through explicit chain-of-thought (CoT) reasoning processes. However, they ofte…
KEPo: Knowledge Evolution Poison on Graph-based Retrieval-Augmented Generation
Qizhi Chen, Chao Qi, Yihong Huang +5
Graph-based Retrieval-Augmented Generation (GraphRAG) constructs the Knowledge Graph (KG) from external databases to enhance the timeliness and accuracy of Large Language Model (LL…
Toward Robust GraphRAG: Mitigating Retrieval Drift and Hallucination from Imperfect Knowledge Graphs
Yizhuo Ma, Jinchuan Xu, Tao Wen +6
Graph Retrieval-Augmented Generation (GraphRAG) has become a common approach for multi-hop reasoning by using knowledge graphs (KGs) as structured retrieval indexes. However, most…
When Safety Becomes a Vulnerability: Exploiting LLM Alignment Homogeneity for Transferable Blocking in RAG
Junchen Li, Liang Xu, Qizhi Chen +6
Retrieval-Augmented Generation (RAG) systems are vulnerable to blocking attacks, in which poisoned documents cause large language models (LLMs) to refuse benign queries. Existing a…
NeuroPath: Neurobiology-Inspired Path Tracking and Reflection for Semantically Coherent Retrieval
Junchen Li, Rongzheng Wang, Yihong Huang +3
Retrieval-augmented generation (RAG) greatly enhances large language models (LLMs) performance in knowledge-intensive tasks. However, naive RAG methods struggle with multi-hop ques…
GraphCogent: Mitigating LLMs' Working Memory Constraints via Multi-Agent Collaboration in Complex Graph Understanding
Rongzheng Wang, Shuang Liang, Qizhi Chen +6
Large language models (LLMs) show promising performance on small-scale graph reasoning tasks but fail when handling real-world graphs with complex queries. This phenomenon arises f…
GraphTool-Instruction: Revolutionizing Graph Reasoning in LLMs through Decomposed Subtask Instruction
Rongzheng Wang, Shuang Liang, Qizhi Chen +2
Large language models (LLMs) have been demonstrated to possess the capabilities to understand fundamental graph properties and address various graph reasoning tasks. Existing metho…