将基础模型从欧式空间拓展至非欧几何，提升对复杂结构数据的建模能力。
Towards Non-Euclidean Foundation Models: Advancing AI Beyond Euclidean Frameworks
- 用双曲、球面等非欧空间替代传统欧式空间构建模型
- 在社交网络与用户-物品交互等场景中实现更高效表示
- 适合关注复杂关系建模与下一代AI架构的研究者
在基础模型与大语言模型时代，欧式空间是机器学习架构的默认几何设定。然而，近期研究指出这一选择存在根本性局限。为此，非欧学习正迅速兴起，尤其在涉及复杂关系与结构的网络应用中表现突出。双曲、球面及混合曲率空间已被证明能更高效、有效地表示具有内在几何特性的数据，如社交网络拓扑、查询-文档关系和用户-物品交互。将基础模型与非欧几何结合，有望显著增强其捕捉和建模底层结构的能力，从而在搜索、推荐与内容理解任务中取得更好性能。本次研讨会聚焦非欧基础模型与几何学习（NEGEL）的交叉领域，探讨其潜在优势、挑战与未来方向。
原文摘要 · Abstract (English)
In the era of foundation models and Large Language Models (LLMs), Euclidean space is the de facto geometric setting of our machine learning architectures. However, recent literature has demonstrated that this choice comes with fundamental limitations. To that end, non-Euclidean learning is quickly gaining traction, particularly in web-related applications where complex relationships and structures are prevalent. Non-Euclidean spaces, such as hyperbolic, spherical, and mixed-curvature spaces, have been shown to provide more efficient and effective representations for data with intrinsic geometric properties, including web-related data like social network topology, query-document relationships, and user-item interactions. Integrating foundation models with non-Euclidean geometries has great potential to enhance their ability to capture and model the underlying structures, leading to better performance in search, recommendations, and content understanding. This workshop focuses on the intersection of Non-Euclidean Foundation Models and Geometric Learning (NEGEL), exploring its potential benefits, including the potential benefits for advancing web-related technologies, challenges, and future directions. Workshop page: [https://hyperboliclearning.github.io/events/www2025workshop](https://hyperboliclearning.github.io/events/www2025workshop)
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