article published yes Ray Neiheiser author f09651b9-fec0-11ec-b5d8-934aff0e52a40000-0001-7227-8309 Miguel Matos author Luis Rodrigues author KrPi department Interface Theory for Security and Privacy project SeCure, privAte, and interoperabLe layEr 2 project With the growing interest in blockchains, permissioned approaches to consensus have received increasing attention. Unfortunately, the BFT consensus algorithms that are the backbone of most of these blockchains scale poorly and offer limited throughput. In fact, many state-of-the-art BFT consensus algorithms require a single leader process to receive and validate votes from a quorum of processes and then broadcast the result, which is inherently non-scalable. Recent approaches avoid this bottleneck by using dissemination/aggregation trees to propagate values and collect and validate votes. However, the use of trees increases the round latency, which limits the throughput for deeper trees. In this paper we propose Kauri, a BFT communication abstraction that sustains high throughput as the system size grows by leveraging a novel pipelining technique to perform scalable dissemination and aggregation on trees. Furthermore, when the number of faults is moderate (arguably the most common case in practice), our construction is able to recover from faults in an optimal number of reconfiguration steps. We implemented and experimentally evaluated Kauri with up to 800 processes. Our results show that Kauri outperforms the throughput of state-of-the-art permissioned blockchain protocols, by up to 58x without compromising latency. Interestingly, in some cases, the parallelization provided by Kauri can also decrease the latency. https://research-explorer.ista.ac.at/download/21017/22392/2026_TransCompSyst_Neiheiser.pdf application/pdfno Association for Computing Machinery2026 eng Distributed systemsbyzantine fault toleranceblockchainvote aggregationpipelining 0734-2071 1557-733310.1145/3769423 442 Neiheiser, Ray, et al. “Kauri: BFT Consensus with Pipelined Tree-Based Dissemination and Aggregation.” <i>ACM Transactions on Computer Systems</i>, vol. 44, no. 2, 12, Association for Computing Machinery, 2026, doi:<a href="https://doi.org/10.1145/3769423">10.1145/3769423</a>. R. Neiheiser, M. Matos, L. Rodrigues, ACM Transactions on Computer Systems 44 (2026). Neiheiser R, Matos M, Rodrigues L. Kauri: BFT consensus with pipelined tree-based dissemination and aggregation. <i>ACM Transactions on Computer Systems</i>. 2026;44(2). doi:<a href="https://doi.org/10.1145/3769423">10.1145/3769423</a> Neiheiser R, Matos M, Rodrigues L. 2026. Kauri: BFT consensus with pipelined tree-based dissemination and aggregation. ACM Transactions on Computer Systems. 44(2), 12. Neiheiser, R., Matos, M., &#38; Rodrigues, L. (2026). Kauri: BFT consensus with pipelined tree-based dissemination and aggregation. <i>ACM Transactions on Computer Systems</i>. Association for Computing Machinery. <a href="https://doi.org/10.1145/3769423">https://doi.org/10.1145/3769423</a> R. Neiheiser, M. Matos, and L. Rodrigues, “Kauri: BFT consensus with pipelined tree-based dissemination and aggregation,” <i>ACM Transactions on Computer Systems</i>, vol. 44, no. 2. Association for Computing Machinery, 2026. Neiheiser, Ray, Miguel Matos, and Luis Rodrigues. “Kauri: BFT Consensus with Pipelined Tree-Based Dissemination and Aggregation.” <i>ACM Transactions on Computer Systems</i>. Association for Computing Machinery, 2026. <a href="https://doi.org/10.1145/3769423">https://doi.org/10.1145/3769423</a>. 210172026-01-20T10:14:23Z2026-07-23T10:07:17Z