8 citations · 8 across the 12 of their papers we have counts for
13 papers
GRAIN: Bridging Name and Narrative Shifts in Real-World Graph Reasoning through Invariance-Rewarded Agentic RL
Zike Yuan, Han Zhang, Jianzhi Yan +10
Despite their potential in standardized graph tasks, Large Language Models (LLMs) remain brittle to real-world shifts in node identifiers and task formulation. While deterministic…
EGL-SCA: Structural Credit Assignment for Co-Evolving Instructions and Tools in Graph Reasoning Agents
Zike Yuan, Yukun Cao, Han Zhang +7
Graph reasoning agents operating from natural-language inputs must solve a coupled problem: they must reconstruct a structured graph instance from text, decide whether existing com…
KG-ViP: Bridging Knowledge Grounding and Visual Perception in Multi-modal LLMs for Visual Question Answering
Zhiyang Li, Ao Ke, Yukun Cao +1
Multi-modal Large Language Models (MLLMs) for Visual Question Answering (VQA) often suffer from dual limitations: knowledge hallucination and insufficient fine-grained visual perce…
See or Say Graphs: Agent-Driven Scalable Graph Structure Understanding with Vision-Language Models
Shuo Han, Yukun Cao, Zezhong Ding +3
Vision-language models (VLMs) have shown promise in graph structure understanding, but remain limited by input-token constraints, facing scalability bottlenecks and lacking effecti…
Lego Sketch: A Scalable Memory-augmented Neural Network for Sketching Data Streams
Yuan Feng, Yukun Cao, Hairu Wang +2
Sketches, probabilistic structures for estimating item frequencies in infinite data streams with limited space, are widely used across various domains. Recent studies have shifted…
SkewRoute: Training-Free LLM Routing for Knowledge Graph Retrieval-Augmented Generation via Score Skewness of Retrieved Context
Hairu Wang, Yuan Feng, Yukun Cao +2
Large language models excel at many tasks but often incur high inference costs during deployment. To mitigate hallucination, many systems use a knowledge graph to enhance retrieval…
CriticalKV: Optimizing KV Cache Eviction from an Output Perturbation Perspective
Yuan Feng, Junlin Lv, Haoyu Guo +3
Large language models have revolutionized natural language processing but face significant challenges of high storage and runtime costs, due to the transformer architecture's relia…
FRAG: A Flexible Modular Framework for Retrieval-Augmented Generation based on Knowledge Graphs
Zengyi Gao, Yukun Cao, Hairu Wang +4
To mitigate the hallucination and knowledge deficiency in large language models (LLMs), Knowledge Graph (KG)-based Retrieval-Augmented Generation (RAG) has shown promising potentia…
LEGO-GraphRAG: Modularizing Graph-based Retrieval-Augmented Generation for Design Space Exploration
Yukun Cao, Zengyi Gao, Zhiyang Li +3
GraphRAG integrates (knowledge) graphs with large language models (LLMs) to improve reasoning accuracy and contextual relevance. Despite its promising applications and strong relev…
Prototype-based Optimal Transport for Out-of-Distribution Detection
Ao Ke, Wenlong Chen, Chuanwen Feng +4
Detecting Out-of-Distribution (OOD) inputs is crucial for improving the reliability of deep neural networks in the real-world deployment. In this paper, inspired by the inherent di…
GraphInsight: Unlocking Insights in Large Language Models for Graph Structure Understanding
Yukun Cao, Shuo Han, Zengyi Gao +3
Although Large Language Models (LLMs) have demonstrated potential in processing graphs, they struggle with comprehending graphical structure information through prompts of graph de…
Ada-KV: Optimizing KV Cache Eviction by Adaptive Budget Allocation for Efficient LLM Inference
Yuan Feng, Junlin Lv, Yukun Cao +2
Large Language Models have excelled in various domains but face efficiency challenges due to the growing Key-Value (KV) cache required for long-sequence inference. Recent efforts a…
Learn to Explore: on Bootstrapping Interactive Data Exploration with Meta-learning
Yukun Cao, Xike Xie, Kexin Huang
Interactive data exploration (IDE) is an effective way of comprehending big data, whose volume and complexity are beyond human abilities. The main goal of IDE is to discover user i…