The field of knowledge graph reasoning and multi-modal completion is moving towards more comprehensive and innovative approaches. Researchers are exploring ways to integrate structural and textual information in knowledge graphs, enabling more accurate and generalizable reasoning capabilities. This includes the development of novel models and architectures that can seamlessly fuse different modalities and adapt to diverse downstream tasks. Additionally, there is a growing interest in applying knowledge graphs to real-world applications, such as assistive technologies for the visually impaired and programming tools for mixed-ability collaboration. Noteworthy papers include:

• Beyond Completion, which introduces a foundation model for general knowledge graph reasoning that outperforms existing baselines in most scenarios.
• Towards Structure-aware Model, which proposes a novel multi-modal knowledge graph completion model that integrates fine-grained modality interaction and dominant graph structure.
• Rethinking Regularization Methods, which rethinks the application of regularization methods in knowledge graph completion and introduces a novel sparse-regularization method that enables models to break through their original performance bounds.