CADENZA: Compiling Natural-Language Intent into Task-Specific Operator DAGs for Semantic Query Processing
Summary: CADENZA compiles natural-language semantic operators into intent-specific task DAGs, exposing intermediate outputs to relational rewrites and joint optimization of quality, latency, and cost. Its TxRA planner combines dependency-checked rewrites, semantics-guided alternatives, and Bayesian tuning across heterogeneous model backends. (summarized by gpt-6-luna on Oct 22 2026)
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Authors
- 1. Jaehyun Ha (Pohang University of Science and Technology)
- 2. Yongjoo Park (University of Illinois Urbana-Champaign)
- 3. Wook-Shin Han (Pohang University of Science and Technology)
BibTeX Citation
@inproceedings{ha_sigmod27,
title = {{CADENZA: Compiling Natural-Language Intent into Task-Specific Operator DAGs for Semantic Query Processing}},
author = {Ha, Jaehyun and Park, Yongjoo and Han, Wook-Shin},
series = {{SIGMOD} '27},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3837103},
url = {https://dl.acm.org/doi/10.1145/3837103},
year = {2027}
}
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