Negative sampling is a key component of implicit collaborative filtering (CF), as it enables recommenders to effectively learn user preferences. Existing negative sampling methods mostly follow a two-stage paradigm: they first construct a candidate negative pool for each user and then select negative samples from the p...
Jia-Yi Wu, Zheng-Yu Wu, Xun-Kai Li et al.· 0 citations
Multimodal graph foundation models (MGFMs) seek to learn generalizable representations from large-scale graphs with heterogeneous node modalities. However, real-world Multimodal-Attributed Graphs (MAGs) often contain incomplete node attributes, limiting the scale and diversity of available pretraining corpora. Besides,...
Xun-Kai Li, Chen-Xi Wan, Yin-Lin Zhu et al.· 0 citations
Multimodal graph learning has recently emerged as an effective paradigm for in corporating inter-entity relationships into multimodal representations. Existing studies have made substantial progress on how to construct and optimize graphs, but rarely consider a more fundamental question: whether additional relational s...
Ze-Kai Chen, Kai Hu, Yu-Xin Zeng et al.· 0 citations
Multimodal graph learning faces a fundamental challenge: new classes may emerge after deployment, while models are trained with a fixed label space. Existing approaches typically detect unknown nodes and use LLMs to generate candidate class descriptions, but they do not determine whether existing classes are insufficie...
Ze-Kai Chen, Jia-Yang Xing, Xun Wu et al.· 0 citations
OmniVCBench is introduced, a figure-centric, source-traceable benchmark for the interpretation component of an AIVC, and AIVC-Judge is introduced, a task-conditioned MLLM-as-a-judge framework with category-specific, reference-aware rubrics for evaluating open-ended responses.
Man-Yu Li, Xun-Kai Li, Yong-Fu Xiong et al.· 0 citations
This empirical study shows that candidate rankings change with the selected input, while predicted probabilities remain informative after the class stops changing, and leads to GraphSelect, which starts from individual candidate gains and refines the subset through jointly evaluated exchanges.
Xu Wang, Xun-Kai Li, Yin-Lin Zhu et al.· 0 citations
Graph-Optimized Multimodal Alignment (GOMA), which lets a jointly trained model support single-modality and dual-attribute retrieval through a task-specific readout, achieves state-of-the-art performance on all 14 primary measures against 14 external methods.
This work introduces ICE (Interaction-aware Clifford Encoder), a multimodal graph foundation model built on a node-indexed Clifford latent field that ranks first in all 30 reported supervised and few-shot comparisons.
Xun-Kai Li, Xu Wang, Yin-Lin Zhu et al.· 0 citations
Collaboration topology shapes both the performance and execution cost of LLM-based multi-agent systems. Because tasks differ in complexity and required capabilities, recent approaches generate task-specific collaboration graphs that specify agent participation and information flow. However, representative topology gene...
Kai-Rui Yang, Zi-Heng Yi, Xun-Kai Li et al.· 0 citations
MACE Loop is a memory-agent co-evolution framework that adapts memory organization and agent memory use through execution feedback that outperforms ten baselines with an average score of 81.11%, compared with 78.97% for the strongest baseline, SAGE.
Kai-Rui Yang, Ming-Hao An, Xun-Kai Li et al.· 0 citations
OpenMAS-GCom is introduced, a benchmark for diagnosing how these components affect G-MAS performance through controlled interventions, and 400 G-MAS-Complex tasks requiring agents to combine information from multiple documents, resolve conflicting records, and return specified values with source identifiers are added.
Kai-Rui Yang, Xun-Kai Li, Kai-Xiang Zhang et al.· 0 citations
BRAIN is a unified model that focuses on graph context that combines neighborhood scope with modality composition that improves node-classification and link-prediction performance and achieves an average relative improvement of 14.72% across four graph-to-text and two graph-to-image metrics.
Si Zhang, Yu-Bing Zhou, Xun-Kai Li et al.· 0 citations
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