6 citations · 22 across the 20 of their papers we have counts for
22 papers
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…
Hypergraph Neural Diffusion: A PDE-Inspired Framework for Hypergraph Message Passing
Zhiheng Zhou, Mengyao Zhou, Xixun Lin +2
Hypergraph neural networks (HGNNs) have shown remarkable potential in modeling high-order relationships that naturally arise in many real-world data domains. However, existing HGNN…
Hard Constraints Meet Soft Generation: Guaranteed Feasibility for LLM-based Combinatorial Optimization
Yang Liu, Chuan Zhou, Yancheng Chen +3
Large language models (LLMs) have emerged as promising general-purpose solvers for combinatorial optimization (CO), yet they fundamentally lack mechanisms to guarantee solution fea…
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…
Deep Graph Neural Point Process For Learning Temporal Interactive Networks
Su Chen, Xiaohua Qi, Xixun Lin +3
Learning temporal interaction networks(TIN) is previously regarded as a coarse-grained multi-sequence prediction problem, ignoring the network topology structure influence. This pa…
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…
LFD: Layer Fused Decoding to Exploit External Knowledge in Retrieval-Augmented Generation
Yang Sun, Zhiyong Xie, Lixin Zou +7
Retrieval-augmented generation (RAG) incorporates external knowledge into large language models (LLMs), improving their adaptability to downstream tasks and enabling information up…
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…
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…
Reliably Bounding False Positives: A Zero-Shot Machine-Generated Text Detection Framework via Multiscaled Conformal Prediction
Xiaowei Zhu, Yubing Ren, Yanan Cao +3
The rapid advancement of large language models has raised significant concerns regarding their potential misuse by malicious actors. As a result, developing effective detectors to…
Beyond Pairwise Interactions: Equivariant Hypergraph Diffusion for Crystal Structure Prediction
Yang Liu, Chuan Zhou, Shuai Zhang +5
Crystal Structure Prediction (CSP) remains a fundamental challenge with significant implications for materials discovery and the advancement of various scientific disciplines. Rece…
Deep Structural Point Process for Learning Temporal Interaction Networks
Jiangxia Cao, Xixun Lin, Xin Cong +4
This work investigates the problem of learning temporal interaction networks. A temporal interaction network consists of a series of chronological interactions between users and it…
Task-adaptive Neural Process for User Cold-Start Recommendation
Xixun Lin, Jia Wu, Chuan Zhou +3
User cold-start recommendation is a long-standing challenge for recommender systems due to the fact that only a few interactions of cold-start users can be exploited. Recent studie…
Bipartite Graph Embedding via Mutual Information Maximization
Jiangxia Cao, Xixun Lin, Shu Guo +3
Bipartite graph embedding has recently attracted much attention due to the fact that bipartite graphs are widely used in various application domains. Most previous methods, which a…
Compositional Learning of Relation Path Embedding for Knowledge Base Completion
Xixun Lin, Yanchun Liang, Fausto Giunchiglia +2
Large-scale knowledge bases have currently reached impressive sizes; however, these knowledge bases are still far from complete. In addition, most of the existing methods for knowl…