activity
20162026
most citedBipartite Graph Embedding via Mutual Information Maximization

6 citations · 22 across the 20 of their papers we have counts for

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6 papers · 1 filter

cs.AI2026

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…

cs.IR2026

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…

cs.CL2026

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…

cs.LG2026

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…

cs.AI2026

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…

cs.CL2026

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…