arXiv:2606.28601v1 Announce Type: cross Abstract: Recent progress in Text-to-SQL has been driven by stronger language models and prompting strategies, yet performance on real enterprise benchmarks such as Spider 2.0 and BIRD remains far below that on classical academic datasets. We argue that the main bottleneck is no longer reasoning, but database representation. Real databases contain repeated audit columns, large groups of similar tables, opaque identifiers whose meanings are stored only in documentation, and extensive data dictionaries with little query-relevant information. Existing query
The increasing sophistication of language models and prompting strategies is now encountering real-world database complexities, shifting the bottleneck from reasoning to data representation.
This identifies a critical bottleneck in the practical application of Text-to-SQL AI in enterprise environments, highlighting that data architecture, not just AI model capabilities, dictates performance.
The focus for advancing Text-to-SQL performance is shifting from purely improving language models to developing better methods for compressing and representing complex, 'dirty' real-world databases for AI consumption.
- · AI development firms focusing on data pre-processing and context compression
- · Enterprises with well-structured data practices
- · Database tool vendors offering advanced metadata and schema management
- · AI models that rely solely on raw schema access
- · Enterprises with highly unstructured or poorly documented databases
Further research and development will concentrate on database context compression and intelligent schema abstraction for Text-to-SQL.
New tools and platforms will emerge specifically designed to clean, transform, and optimize enterprise databases for AI consumption, becoming a critical middleware layer.
The perceived 'intelligence' of AI agents in enterprise settings will become directly correlated with the quality and manageability of underlying data infrastructure, influencing IT investment priorities.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI