arXiv:2603.16513v3 Announce Type: replace Abstract: Structured data is widely used in domains such as healthcare, finance, and scientific data management. Recent studies on structured data foundation models (SFMs) aim to support data analysis and mining tasks over such data, but still face scalability and generalization challenges when applied to real-world enterprise databases. First, many SFMs rely on full self-attention, which introduces an O(N^2) computational bottleneck and limits the number of tuples that can be processed jointly. Second, directly replacing attention with linear-complexi
The increasing scale and complexity of real-world enterprise databases necessitate more efficient and scalable foundation models for structured data.
Improving the scalability and computational efficiency of structured data foundation models (SFMs) can unlock new possibilities for data analysis and AI applications in critical sectors like healthcare and finance.
The development of linear-complexity SFMs could remove significant computational bottlenecks, allowing for the processing of extremely large structured datasets previously unmanageable.
- · Healthcare sector
- · Financial services
- · AI model developers
- · Enterprise data management
- · Companies reliant on older, less scalable SFMs
- · Systems with high O(N^2) computational overhead
More sophisticated and comprehensive AI analysis becomes feasible for large enterprise structured data.
New AI-powered applications emerge across industries that depend on robust structured data processing and insights.
The competitive landscape for AI foundation model providers shifts towards those offering superior scalability and efficiency for structured datasets.
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Read at arXiv cs.LG