8 papers
ADAGE: Active Defenses Against GNN Extraction
Jing Xu, Franziska Boenisch, Adam Dziedzic
Graph Neural Networks (GNNs) achieve high performance in various real-world applications, such as drug discovery, traffic states prediction, and recommendation systems. The fact th…
ExpGraph: Model-Agnostic Experience Learning with Graph-Structured Memory for LLM Agents
Tao Feng, Chongrui Ye, Tianyang Luo +8
Large language model (LLM) agents have shown strong capabilities in reasoning, tool use, and multi-step interaction, but they often solve tasks from scratch and fail to reuse succe…
ElasticMem: Latent Memory as a Learnable Resource for LLM Agents
Tao Feng, Chongrui Ye, Tianyang Luo +5
Long-term memory is essential for LLM agents to reason coherently across extended interactions, personalize responses, and reuse past experience. However, existing memory-augmented…
RouteProfile: Graph-Based Profiling for Cold-Start LLM Routing
Jingjun Xu, Hongji Pu, Tao Feng +3
LLM routing is increasingly important for selecting suitable models under diverse user needs and deployment constraints, but its practical effectiveness depends on continual adapta…
Memorization in Graph Neural Networks
Adarsh Jamadandi, Jing Xu, Adam Dziedzic +1
Deep neural networks (DNNs) have been shown to memorize their training data, yet similar analyses for graph neural networks (GNNs) remain largely under-explored. We introduce NCMem…
Adversarial Attacks and Defenses on Graph-aware Large Language Models (LLMs)
Iyiola E. Olatunji, Franziska Boenisch, Jing Xu +1
Large Language Models (LLMs) are increasingly integrated with graph-structured data for tasks like node classification, a domain traditionally dominated by Graph Neural Networks (G…