3 papers
cs.LG2026
Disentangled Graph Prompting for Out-Of-Distribution Detection
Cheng Yang, Yu Hao, Qi Zhang +1
When testing data and training data come from different distributions, deep neural networks (DNNs) will face significant safety risks in practical applications. Therefore, out-of-d…
cs.LG2026
GNNVerifier: Graph-based Verifier for LLM Task Planning
Yu Hao, Qiuyu Wang, Cheng Yang +3
Large language models (LLMs) facilitate the development of autonomous agents. As a core component of such agents, task planning aims to decompose complex natural language requests…
cs.CL2025
PathRAG: Pruning Graph-based Retrieval Augmented Generation with Relational Paths
Boyu Chen, Zirui Guo, Zidan Yang +5
Retrieval-augmented generation (RAG) improves the response quality of large language models (LLMs) by retrieving knowledge from external databases. Typical RAG approaches split the…