39 citations · 98 across the 19 of their papers we have counts for
21 papers
ATTUNER: Recomputation-Free Agentic Context KV Reuse
Xinghao Chen, Junnan Dong, Cai Ke +8
Large language model (LLM) agents repeatedly load reusable content, such as skills, documents, and memory entries, into the current context. Re-encoding this content for every requ…
Latent Multimodal Memory Reasoning
Cai Ke, Xinghao Chen, Yuanchen Bei +7
Long-term personalized agents are required to reason over distributed history across modalities and evolving dialogue states. While existing flat cross-modal methods compress visua…
LADDER: Graph-Guided Diffusion Language Models for Efficient Multi-Hop Reasoning
Senlei Zhang, Linhao Luo, Qian-Wen Zhang +4
Graph Retrieval-Augmented Generation (GraphRAG) has remarkably enhanced large language models on complex reasoning by leveraging structured entity topologies. However, existing fra…
Toward Native Multimodal Modeling: A Roadmap
Siyu An, Junru Lu, Junnan Dong +18
Multimodal modeling represents a vital step from modality-agnostic reasoning toward world modeling. While early approaches predominantly rely on late-fusion that assembles encoders…
Deep Tabular Research via Continual Experience-Driven Execution
Junnan Dong, Chuang Zhou, Zheng Yuan +7
Large language models often struggle with complex long-horizon analytical tasks over unstructured tables, which typically feature hierarchical and bidirectional headers and non-can…
Youtu-GraphRAG: Vertically Unified Agents for Graph Retrieval-Augmented Complex Reasoning
Junnan Dong, Siyu An, Yifei Yu +6
Graph retrieval-augmented generation (GraphRAG) has effectively enhanced large language models in complex reasoning by organizing fragmented knowledge into explicitly structured gr…
G-reasoner: Foundation Models for Unified Reasoning over Graph-structured Knowledge
Linhao Luo, Zicheng Zhao, Junnan Liu +9
Large language models (LLMs) excel at complex reasoning but remain limited by static and incomplete parametric knowledge. Retrieval-augmented generation (RAG) mitigates this by inc…
GraphRAG-Bench: Challenging Domain-Specific Reasoning for Evaluating Graph Retrieval-Augmented Generation
Yilin Xiao, Junnan Dong, Chuang Zhou +5
Graph Retrieval Augmented Generation (GraphRAG) has garnered increasing recognition for its potential to enhance large language models (LLMs) by structurally organizing domain-spec…
A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Models
Qinggang Zhang, Shengyuan Chen, Yuanchen Bei +9
Large language models (LLMs) have demonstrated remarkable capabilities in a wide range of tasks, yet their application to specialized domains remains challenging due to the need fo…
Benchmarking LLMs' Mathematical Reasoning with Unseen Random Variables Questions
Zijin Hong, Hao Wu, Su Dong +8
Recent studies have raised significant concerns regarding the reliability of current mathematics benchmarks, highlighting issues such as simplistic design and potential data contam…
CLR-Bench: Evaluating Large Language Models in College-level Reasoning
Junnan Dong, Zijin Hong, Yuanchen Bei +3
Large language models (LLMs) have demonstrated their remarkable performance across various language understanding tasks. While emerging benchmarks have been proposed to evaluate LL…
Neuro-Symbolic Entity Alignment via Variational Inference
Shengyuan Chen, Zheng Yuan, Qinggang Zhang +3
Entity alignment (EA) aims to merge two knowledge graphs (KGs) by identifying equivalent entity pairs. Existing methods can be categorized into symbolic and neural models. Symbolic…
Logical Reasoning with Relation Network for Inductive Knowledge Graph Completion
Qinggang Zhang, Keyu Duan, Junnan Dong +2
Inductive knowledge graph completion (KGC) aims to infer the missing relation for a set of newly-coming entities that never appeared in the training set. Such a setting is more in…
Balanced Edge Pruning for Graph Anomaly Detection with Noisy Labels
Zhu Wang, Junnan Dong, Shuang Zhou +3
Graph anomaly detection (GAD) is widely applied in many areas, such as financial fraud detection and social spammer detection. Anomalous nodes in the graph not only impact their ow…
Next-Generation Database Interfaces: A Survey of LLM-based Text-to-SQL
Zijin Hong, Zheng Yuan, Qinggang Zhang +4
Generating accurate SQL from users' natural language questions (text-to-SQL) remains a long-standing challenge due to the complexities involved in user question understanding, data…
Entity Alignment with Noisy Annotations from Large Language Models
Shengyuan Chen, Qinggang Zhang, Junnan Dong +3
Entity alignment (EA) aims to merge two knowledge graphs (KGs) by identifying equivalent entity pairs. While existing methods heavily rely on human-generated labels, it is prohibit…
Cost-efficient Knowledge-based Question Answering with Large Language Models
Junnan Dong, Qinggang Zhang, Chuang Zhou +3
Knowledge-based question answering (KBQA) is widely used in many scenarios that necessitate domain knowledge. Large language models (LLMs) bring opportunities to KBQA, while their…
Modality-Aware Integration with Large Language Models for Knowledge-based Visual Question Answering
Junnan Dong, Qinggang Zhang, Huachi Zhou +3
Knowledge-based visual question answering (KVQA) has been extensively studied to answer visual questions with external knowledge, e.g., knowledge graphs (KGs). While several attemp…
Structure Guided Large Language Model for SQL Generation
Qinggang Zhang, Hao Chen, Junnan Dong +3
Recent advancements in large language models (LLMs) have shown promise in bridging the gap between natural language queries and database management systems, enabling users to inter…
KnowGPT: Knowledge Graph based Prompting for Large Language Models
Qinggang Zhang, Junnan Dong, Hao Chen +3
Large Language Models (LLMs) have demonstrated remarkable capabilities in many real-world applications. Nonetheless, LLMs are often criticized for their tendency to produce halluci…
Contrastive Knowledge Graph Error Detection
Qinggang Zhang, Junnan Dong, Keyu Duan +3
Knowledge Graph (KG) errors introduce non-negligible noise, severely affecting KG-related downstream tasks. Detecting errors in KGs is challenging since the patterns of errors are…