AdaChain: A Learned Adaptive Blockchain
Summary: AdaChain uses reinforcement learning to adaptively select among permissioned blockchain architectures to maximize effective throughput under dynamic, heterogeneous smart-contract workloads. It performs secure runtime architecture switches, converges quickly, and outperforms fixed designs with low overhead. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Chenyuan Wu (University of Pennsylvania)
- 2. Bhavana Mehta (University of Pennsylvania)
- 3. Mohammad Javad Amiri (University of Pennsylvania)
- 4. Ryan Marcus (University of Pennsylvania)
- 5. Boon Thau Loo (University of Pennsylvania)
BibTeX Citation
@article{wu_vldb23,
title = {{AdaChain: A Learned Adaptive Blockchain}},
author = {Wu, Chenyuan and Mehta, Bhavana and Amiri, Mohammad Javad and Marcus, Ryan and Loo, Boon Thau},
journal = {PVLDB},
series = {{VLDB} '23},
volume = {16},
number = {8},
pages = {2033--2046},
doi = {10.14778/3594512.3594531},
url = {https://doi.org/10.14778/3594512.3594531},
year = {2023}
}
Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,338 | Adaptive Sharding in Untrusted Environments | 2026 | SIGMOD | 5.093636e-05 |
| 10,356 | DAG of DAGs: Order-Fairness Made Practical | 2026 | SIGMOD | 5.093636e-05 |
| 11,210 | Towards Full Stack Adaptivity in Permissioned Blockchains | 2024 | VLDB | 5.093636e-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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