CADENZA introduces TxRA algebra and logical/physical planners to compile intents into optimized task DAGs, reporting up to 0.49 quality, 165.7x latency, and 310.3x cost gains on SemBench versus prior SQPE optimizers.
Ilyas, and Sumin Song
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CADENZA: Compiling Natural-Language Intent into Task-Specific Operator DAGs for Semantic Query Processing
CADENZA introduces TxRA algebra and logical/physical planners to compile intents into optimized task DAGs, reporting up to 0.49 quality, 165.7x latency, and 310.3x cost gains on SemBench versus prior SQPE optimizers.