Task Cascades for Efficient Unstructured Data Processing
Summary: Task cascades generalize model cascades for LLM-based document processing by varying not only the model, but also the queried span and even the operation, exploiting simpler correlated sub-tasks and partial evidence. An iterative optimizer plus statistical accuracy guarantees yields 36% lower cost than standard cascades at 90% target accuracy. (summarized by gpt-5.4-mini on Apr 11 2026)
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Authors
- 1. Shreya Shankar (University of California Berkeley)
- 2. Sepanta Zeighami (University of California Berkeley)
- 3. Aditya G. Parameswaran (University of California Berkeley)
BibTeX Citation
@inproceedings{shankar_sigmod26,
title = {{Task Cascades for Efficient Unstructured Data Processing}},
author = {Shankar, Shreya and Zeighami, Sepanta and Parameswaran, Aditya G.},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3786702},
url = {https://dl.acm.org/doi/10.1145/3786702},
year = {2026}
}
Incoming Citations (Sorted by Pagerank)
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,886 | Multi-Objective Agentic Rewrites for Unstructured Data Processing | 2026 | VLDB | 5.6551172e-05 |
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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