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Large language model (LLM)-based multi-agent systems are increasingly applied across diverse domains, yet different applications require different forms of agent organization. Existing studies typically adopt predefined organizations, such as Star, Chain, and Tree, or learn communication graphs to improve performance o...
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...
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...
MAGIC, a dense-reward reinforcement learning framework for mixed-granularity graph generation, outperforms state-of-the-art baselines across eight benchmarks and demonstrates strong inference efficiency in the efficiency study.
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.
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.
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.
FedOGL preserves historical decision behavior through replay and task-start distillation, while protecting graph-propagation memory via projection onto a globally shared structure basis via projection onto a globally shared structure basis.
OpenRTAG provides a standardized testbed for understanding robustness in TAG learning under realistic low-quality settings and systematically evaluates scenario validity and model sensitivity, compares traditional GNNs, LLM-GNNs, and a representative GFM, and investigates the effectiveness, efficiency, and robustness o...
A multimodal federated graph unlearning framework built around target-specific representation decoupling that effectively removes requested information, preserves retained graph utility, and achieves a speedup over full retraining.
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