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The Database Asked One Question Before Naming Itself
TYTAN combines symbolic checks, model inference and targeted user questions to construct an analytic semantic layer.
Summary
TYTAN combines symbolic checks, model inference and targeted user questions to construct an analytic semantic layer.
Across seven reference databases, the system reached all expert-corrected entities and features, executed all 1,678 self-generated retrieval claims, and matched 92 to 100 percent of semantic roles; a blind ten-table test recovered the verified entity structure. The evidence is limited to the eight evaluated databases.
Why it matters
TYTAN combines symbolic checks, model inference and targeted user questions to construct an analytic semantic layer.
Limits and context
No additional limitation was separately recorded.
Key claims
TYTAN combines symbolic checks, model inference and targeted user questions to construct an analytic semantic layer.
Evidence: source-2026-08-08-018
Sources
- arXiv preprint 2608.06331arXiv · primary research
Corrections
No corrections have been recorded for this story.