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Pangolin: Efficient DAG-based BFT State-Machine Replication with TEE-Accelerated Commitment
Paul Franke-Bergmann, Maya Kuhlmann, Alexander Heß, Marc Leinweber, Rüdiger Kapitza, Hannes Hartenstein, Franz J. Hauck, Christian Berger

Pangolin: Efficient DAG-based BFT State-Machine Replication with TEE-Accelerated Commitment

Science & Mathematics Published 2026 Verification Pending

Overview & Synopsis

Currently many Byzantine fault-tolerant (BFT) protocols use directed acyclic graphs (DAGs) to let replicas propose blocks of requests in parallel for improved throughput. Yet, the existing DAG-based designs mainly consider the atomic-broadcast layer: They typically do not describe an efficient client interaction layer, their garbage collection is either unspecified or relies on trusted external storage, and the lowest-latency designs rely on timing assumptions that may be violated in geo-distributed deployments. In this paper, we propose the Pangolin protocol: It introduces a novel low-latency DAG-based atomic broadcast layer and completes it into an efficient state-machine replication (SMR) framework. To preserve system throughput, clients issue requests to a single replica and verify replies efficiently via compact execution proofs. Furthermore, Pangolin introduces a new garbage-collection scheme for DAGs which bounds memory usage even under full asynchrony. Pangolin leverages trusted execution environments to tolerate f Byzantine faults with only n >= 2f + 1 replicas and accelerates vertex commitments significantly: Broadcasting certified vertices requires only a single communication step, and commit rules are refined to commit both frequently and early without revealing the coin-elected anchor. This way, Pangolin overtakes the fastest partially synchronous DAG protocols while being live under asynchrony: In our evaluation, Pangolin achieves an average 1.27x (up to 1.52x) latency speedup over Mysticeti.

Indexed from repository: arXiv (arxiv.org)
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Document Information

Document Title Pangolin: Efficient DAG-based BFT State-Machine Replication with TEE-Accelerated Commitment
Author / Organization Paul Franke-Bergmann, Maya Kuhlmann, Alexander Heß, Marc Leinweber, Rüdiger Kapitza, Hannes Hartenstein, Franz J. Hauck, Christian Berger
Subject & Discipline cs.DC
Categories Science & Mathematics
Publication Year 2026
License / Rights arXiv Open Access / Creative Commons
Resource Verification Listed PDF (Verification Pending)
Original Host Website arXiv (arxiv.org)
File Format application/pdf (Portable Document Format)
Language EN
Catalog ID DOC-273162
Original Source Link Visit Source Page on arxiv.org »