3 citations · 7 across the 16 of their papers we have counts for
16 papers
AbstainGNN: Teaching Graph Neural Networks to Abstain for Graph Classification
Xixun Lin, Zhiheng Zhou, Zhengyin Zhang +9
Graph classification is a core task in graph data mining with widespread real-world applications. Recent advances in graph neural networks (GNNs) have led to substantial performanc…
SafeHarness: Lifecycle-Integrated Security Architecture for LLM-based Agent Deployment
Xixun Lin, Yang Liu, Yancheng Chen +8
The performance of large language model (LLM) agents depends critically on the execution harness, the system layer that orchestrates tool use, context management, and state persist…
CIA: Inferring the Communication Topology from LLM-based Multi-Agent Systems
Yongxuan Wu, Xixun Lin, He Zhang +5
LLM-based Multi-Agent Systems (MAS) have demonstrated remarkable capabilities in solving complex tasks. Central to MAS is the communication topology which governs how agents exchan…
EA-Agent: A Structured Multi-Step Reasoning Agent for Entity Alignment
Yixuan Nan, Xixun Lin, Yanmin Shang +4
Entity alignment (EA) aims to identify entities across different knowledge graphs (KGs) that refer to the same real-world object and plays a critical role in knowledge fusion and i…
Do LLMs Know Tool Irrelevance? Demystifying Structural Alignment Bias in Tool Invocations
Yilong Liu, Xixun Lin, Pengfei Cao +3
Large language models (LLMs) have demonstrated impressive capabilities in utilizing external tools. In practice, however, LLMs are often exposed to tools that are irrelevant to the…
MuVaC: A Variational Causal Framework for Multimodal Sarcasm Understanding in Dialogues
Diandian Guo, Fangfang Yuan, Cong Cao +5
The prevalence of sarcasm in multimodal dialogues on the social platforms presents a crucial yet challenging task for understanding the true intent behind online content. Comprehen…
PathMind: A Retrieve-Prioritize-Reason Framework for Knowledge Graph Reasoning with Large Language Models
Yu Liu, Xixun Lin, Yanmin Shang +3
Knowledge graph reasoning (KGR) is the task of inferring new knowledge by performing logical deductions on knowledge graphs. Recently, large language models (LLMs) have demonstrate…
V-ITI: Mitigating Hallucinations in Multimodal Large Language Models via Visual Inference-Time Intervention
Nan Sun, Zhenyu Zhang, Xixun Lin +8
Multimodal Large Language Models (MLLMs) excel in numerous vision-language tasks yet suffer from hallucinations, producing content inconsistent with input visuals, that undermine r…
MAD-Fact: A Multi-Agent Debate Framework for Long-Form Factuality Evaluation in LLMs
Yucheng Ning, Xixun Lin, Fang Fang +1
The widespread adoption of Large Language Models (LLMs) raises critical concerns about the factual accuracy of their outputs, especially in high-risk domains such as biomedicine, l…
LLM-based Agents Suffer from Hallucinations: A Survey of Taxonomy, Methods, and Directions
Xixun Lin, Yucheng Ning, Jingwen Zhang +21
Driven by the rapid advancements of Large Language Models (LLMs), LLM-based agents have emerged as powerful intelligent systems capable of human-like cognition, reasoning, and inte…
Enhancing Large Language Model for Knowledge Graph Completion via Structure-Aware Alignment-Tuning
Yu Liu, Yanan Cao, Xixun Lin +3
Knowledge graph completion (KGC) aims to infer new knowledge and make predictions from knowledge graphs. Recently, large language models (LLMs) have exhibited remarkable reasoning…
RANA: Robust Active Learning for Noisy Network Alignment
Yixuan Nan, Xixun Lin, Yanmin Shang +3
Network alignment has attracted widespread attention in various fields. However, most existing works mainly focus on the problem of label sparsity, while overlooking the issue of n…
Evidential Spectrum-Aware Contrastive Learning for OOD Detection in Dynamic Graphs
Nan Sun, Xixun Lin, Zhiheng Zhou +3
Recently, Out-of-distribution (OOD) detection in dynamic graphs, which aims to identify whether incoming data deviates from the distribution of the in-distribution (ID) training se…
Beyond Memorization: A Rigorous Evaluation Framework for Medical Knowledge Editing
Shigeng Chen, Linhao Luo, Zhangchi Qiu +3
Recently, knowledge editing (KE) has emerged as a promising approach to update specific facts in Large Language Models (LLMs) without the need for full retraining. Despite the effe…
Large Language Models for Planning: A Comprehensive and Systematic Survey
Pengfei Cao, Tianyi Men, Wencan Liu +7
Planning represents a fundamental capability of intelligent agents, requiring comprehensive environmental understanding, rigorous logical reasoning, and effective sequential decisi…
CL4KGE: A Curriculum Learning Method for Knowledge Graph Embedding
Yang Liu, Chuan Zhou, Peng Zhang +4
Knowledge graph embedding (KGE) constitutes a foundational task, directed towards learning representations for entities and relations within knowledge graphs (KGs), with the object…