5 citations · 9 across the 12 of their papers we have counts for
12 papers
AgenticRag-R1: Agentic Reinforcement Learning with Stack Memory for Multi-Step Reasoning, Retrieval and Memorizing
Xinke Jiang, Yue Fang, Zhibang Yang +12
Retrieval-Augmented Generation (RAG) improves the factuality of large language models (LLMs), yet existing RAG systems often struggle with complex, multi-step reasoning that requir…
ScaffoldAgent: Utility-Guided Dynamic Outline Optimization for Open-Ended Deep Research
Zhibang Yang, Xinke Jiang, Yuzhen Xiao +9
Open-ended deep research (OEDR) requires systems to acquire knowledge through multi-round retrieval and generate coherent long-form reports. The outline plays a central role as a s…
Adam's Law: Textual Frequency Law on Large Language Models
Hongyuan Adam Lu, Z. L., Victor Wei +5
While textual frequency has been validated as relevant to human cognition in reading speed, its relatedness to Large Language Models (LLMs) is seldom studied. We propose a novel re…
InfoMamba: An Attention-Free Hybrid Mamba-Transformer Model
Youjin Wang, Jiaqiao Zhao, Rong Fu +5
Balancing fine-grained local modeling with long-range dependency capture under computational constraints remains a central challenge in sequence modeling. While Transformers provid…
StackPlanner: A Centralized Hierarchical Multi-Agent System with Task-Experience Memory Management
Ruizhe Zhang, Xinke Jiang, Zhibang Yang +12
Multi-agent systems based on large language models, particularly centralized architectures, have recently shown strong potential for complex and knowledge-intensive tasks. However,…
EL4NER: Ensemble Learning for Named Entity Recognition via Multiple Small-Parameter Large Language Models
Yuzhen Xiao, Jiahe Song, Yongxin Xu +6
In-Context Learning (ICL) technique based on Large Language Models (LLMs) has gained prominence in Named Entity Recognition (NER) tasks for its lower computing resource consumption…
RAGraph: A General Retrieval-Augmented Graph Learning Framework
Xinke Jiang, Rihong Qiu, Yongxin Xu +7
Graph Neural Networks (GNNs) have become essential in interpreting relational data across various domains, yet, they often struggle to generalize to unseen graph data that differs…
Parenting: Optimizing Knowledge Selection of Retrieval-Augmented Language Models with Parameter Decoupling and Tailored Tuning
Yongxin Xu, Ruizhe Zhang, Xinke Jiang +7
Retrieval-Augmented Generation (RAG) offers an effective solution to the issues faced by Large Language Models (LLMs) in hallucination generation and knowledge obsolescence by inco…
KnowPO: Knowledge-aware Preference Optimization for Controllable Knowledge Selection in Retrieval-Augmented Language Models
Ruizhe Zhang, Yongxin Xu, Yuzhen Xiao +5
By integrating external knowledge, Retrieval-Augmented Generation (RAG) has become an effective strategy for mitigating the hallucination problems that large language models (LLMs)…
TC-RAG:Turing-Complete RAG's Case study on Medical LLM Systems
Xinke Jiang, Yue Fang, Rihong Qiu +10
In the pursuit of enhancing domain-specific Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) emerges as a promising solution to mitigate issues such as hallucinat…
Infinite-Horizon Graph Filters: Leveraging Power Series to Enhance Sparse Information Aggregation
Ruizhe Zhang, Xinke Jiang, Yuchen Fang +6
Graph Neural Networks (GNNs) have shown considerable effectiveness in a variety of graph learning tasks, particularly those based on the message-passing approach in recent years. H…
HyKGE: A Hypothesis Knowledge Graph Enhanced Framework for Accurate and Reliable Medical LLMs Responses
Xinke Jiang, Ruizhe Zhang, Yongxin Xu +8
In this paper, we investigate the retrieval-augmented generation (RAG) based on Knowledge Graphs (KGs) to improve the accuracy and reliability of Large Language Models (LLMs). Rece…