2 citations · 5 across the 14 of their papers we have counts for
5 papers · 1 filter
T-GRAG: A Dynamic GraphRAG Framework for Resolving Temporal Conflicts and Redundancy in Knowledge Retrieval
Dong Li, Yichen Niu, Ying Ai +3
Large language models (LLMs) have demonstrated strong performance in natural language generation but remain limited in knowle- dge-intensive tasks due to outdated or incomplete int…
Graph Counselor: Adaptive Graph Exploration via Multi-Agent Synergy to Enhance LLM Reasoning
Junqi Gao, Xiang Zou, YIng Ai +4
Graph Retrieval Augmented Generation (GraphRAG) effectively enhances external knowledge integration capabilities by explicitly modeling knowledge relationships, thereby improving t…
Automating Exploratory Proteomics Research via Language Models
Ning Ding, Shang Qu, Linhai Xie +13
With the development of artificial intelligence, its contribution to science is evolving from simulating a complex problem to automating entire research processes and producing nov…
SR-CIS: Self-Reflective Incremental System with Decoupled Memory and Reasoning
Biqing Qi, Junqi Gao, Xinquan Chen +3
The ability of humans to rapidly learn new knowledge while retaining old memories poses a significant challenge for current deep learning models. To handle this challenge, we draw…
Contrastive Augmented Graph2Graph Memory Interaction for Few Shot Continual Learning
Biqing Qi, Junqi Gao, Xingquan Chen +4
Few-Shot Class-Incremental Learning (FSCIL) has gained considerable attention in recent years for its pivotal role in addressing continuously arriving classes. However, it encounte…