AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Open-world aerial object-goal search is a foundational yet challenging task, requiring aerial agents to autonomously explore large-scale, unstructured three-dimensional environments and reach target objects specified by semantic descriptions or reference images, rather than following route-specific instructions. Howeve...
A novel Continual Learning Dynamic Graph LLM framework (Continual-GraphLLM) is proposed to continually adapt to incoming patterns by routing them to experts specialized in similar past patterns, while mitigating the overwriting of previously learned patterns by assigning new experts to unseen patterns.
A text-guided molecular graph generation framework that leverages the structural modeling power of graph diffusion models to achieve both strong alignment with textual descriptions and high-quality molecular structures and a molecule structure consistency loss that explicitly enforces structural coherence during genera...
This tutorial presents a comprehensive overview of three emerging and synergistic directions for tackling distribution shifts in graph learning, which highlight Graph LLMs, which combine the representational power of large language models with graph structures to enable flexible, in-context, and few-shot learning on gr...
Discovering optimal graph neural network (GNN) architectures for various tasks is both labor-intensive and time-consuming. To reduce human effort, graph neural architecture search (GNAS) has recently been utilized to automatically identify effective GNN architectures for specific tasks, achieving competitive or even su...
This work proposes MoD-VLLM, a novel Modularized Dynamic-Granularity Video LLM framework for multi-event long video understanding, which unifies temporal grounding and semantic understanding iteratively and self-reflectively and proposes a dynamic-granularity reinforcement learning strategy, allowing MoD-VLLM to learn...
Dynamic text-attributed graphs (DyTAGs) exhibit coupled textual and structural dynamics, and existing mainstream approaches for DyTAGs extend conventional large language models (LLMs) to capture both dynamics, thereby giving rise to dynamic graph LLMs. However, in DyTAGs, the continuous emergence of new nodes and edges...
Graph machine learning has witnessed rapid progress across both academia and industry. However, most existing methods are developed under the in-distribution (I.D.) hypothesis, which assumes that training and testing graph data are drawn from the same distribution. In real-world applications—ranging from dynamic knowle...
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