pith:3BDSMIKO
From Textual Columns to Query Plans: A Unified Relational-Semantic Execution Framework for Hybrid Query Processing
OmniTQA turns LLM semantic reasoning into an optimizable operator inside relational query plans to process mixed structured and textual tables more accurately and at lower cost than pure symbolic or pure semantic methods.
arxiv:2604.02444 v2 · 2026-04-02 · cs.DB
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\pithnumber{3BDSMIKOLLH3ZSM6UOUSOTKFFY}
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Claims
Extensive experiments across a diverse suite of structured and semi-structured table question answering benchmarks demonstrate that OmniTQA consistently outperforms existing symbolic, semantic, and hybrid baselines in both accuracy and cost efficiency. These gains are particularly pronounced for complex queries, large tables and multi-relation schemas.
The assumption that LLM inference latency and cost can be sufficiently controlled through atomic decomposition, operator reordering, and batching without introducing unacceptable accuracy trade-offs or requiring extensive per-workload tuning.
OmniTQA integrates LLM semantic reasoning as a first-class query operator with classical relational operators in a cost-aware planner for hybrid structured and semi-structured data.
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Receipt and verification
| First computed | 2026-07-03T00:16:53.600140Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
d84726214e5acfbcc99ea3a9274d452e08fb495790f2f6fea7e98a9d85fff237
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/3BDSMIKOLLH3ZSM6UOUSOTKFFY \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: d84726214e5acfbcc99ea3a9274d452e08fb495790f2f6fea7e98a9d85fff237
Canonical record JSON
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