FaDE: More Than a Million What-ifs Per Second
Summary: FaDE compiles relational provenance—not symbolic expressions—to rapidly evaluate hypothetical deletion and scaling interventions. Its optimized engine combines compilation, parallelism, incrementality, and sparsity to exceed one million what-ifs/sec, achieving 1,000–10,000× speedups. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Haneen Mohammed (Columbia University)
- 2. Alexander Yao (Columbia University)
- 3. Charlie Summers (Columbia University)
- 4. Hongbin Zhong (Georgia Institute of Technology)
- 5. Gromit Yeuk-Yin Chan (Adobe)
- 6. Subrata Mitra (Adobe)
- 7. Lampros Flokas (Celonis Inc.)
- 8. Eugene Wu (Columbia University)
BibTeX Citation
@article{mohammed_vldb25,
title = {{FaDE: More Than a Million What-ifs Per Second}},
author = {Mohammed, Haneen and Yao, Alexander and Summers, Charlie and Zhong, Hongbin and Chan, Gromit Yeuk-Yin and Mitra, Subrata and Flokas, Lampros and Wu, Eugene},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {4},
pages = {943--955},
doi = {10.14778/3717755.3717757},
url = {https://doi.org/10.14778/3717755.3717757},
year = {2025}
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| 10,116 | Please Don't Kill My Vibe: Empowering Agents with Data Flow Control | 2026 | CIDR | 5.093636e-05 |
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