collaborators

5 papers

cs.CL2026

Efficient and Transferable Agentic Knowledge Graph RAG via Reinforcement Learning

Junhong Lin, Shicheng Liu, Jinyeop Song +3

Knowledge-graph retrieval-augmented generation (KG-RAG) couples large language models (LLMs) with structured, verifiable knowledge graphs (KGs) to reduce hallucination and provide…

cs.CR2026

SafeDream: Safety World Model for Proactive Early Jailbreak Detection

Bo Yan, Weikai Lin, Yada Zhu +1

Multi-turn jailbreak attacks progressively erode LLM safety alignment across seemingly innocuous conversation turns, achieving success rates exceeding 90% against state-of-the-art…

cs.LG2026

How Much Reasoning Do Retrieval-Augmented Models Add beyond LLMs? A Benchmarking Framework for Multi-Hop Inference over Hybrid Knowledge

Junhong Lin, Bing Zhang, Song Wang +4

Large language models (LLMs) continue to struggle with knowledge-intensive questions that require up-to-date information and multi-hop reasoning. Augmenting LLMs with hybrid extern…

cs.LG2025

Temporal Reasoning with Large Language Models Augmented by Evolving Knowledge Graphs

Junhong Lin, Song Wang, Xiaojie Guo +2

Large language models (LLMs) excel at many language understanding tasks but struggle to reason over knowledge that evolves. To address this, recent work has explored augmenting LLM…

cs.LG2025

Reasoning of Large Language Models over Knowledge Graphs with Super-Relations

Song Wang, Junhong Lin, Xiaojie Guo +3

While large language models (LLMs) have made significant progress in processing and reasoning over knowledge graphs, current methods suffer from a high non-retrieval rate. This lim…