Towards Universal Tabular Embeddings: A Benchmark Across Data Tasks

ArXi:2604.21696v1 Announce Type: new Tabular foundation models aim to learn universal representations of tabular data that transfer across tasks and domains, enabling applications such as table retrieval, semantic search and table-based prediction. Despite the growing number of such models, it remains unclear which approach works best in practice, as existing methods are often evaluated under task-specific settings that make direct comparison difficult. To address this, we