FERN ranks frozen retrievers for Text-to-SQL table retrieval
A LightGBM ranker over three frozen retrievers approaches RASL's published recall without generative LLM calls.
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A LightGBM ranker over three frozen retrievers approaches RASL's published recall without generative LLM calls.
On Beaver it reports 95.7% execution validity versus 60.8% for embedding retrieval, with experts preferring it 70% to 10%.
It gains 30.0 pass@8 points over Qwen3.5-4B and 8.3 over Qwen3.5-397B-A17B on HybridDeepResearch.
ARCS benchmark shows text-to-SQL remains weak when natural question ambiguity is present.
FALCON generates realistic, ambiguity-aware NL2SQL training pairs with compact open models, matching or exceeding challenging benchmark complexity locally.
Argo-Bench tests data agents on 210 enterprise tasks over a 7.5-billion-row simulated warehouse.
Paper proposes skill habit formation so agents cache deterministic skills from execution history, fixing 38-74% answer inconsistency and cutting tokens 14-56%.
Governed enterprise analytics study shows deterministic policy execution matched 110/110 answer-and-evidence contracts while runtime agent planning matched none.
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