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

arXiv · 2610.26295

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

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Abstract

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.

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BibTeX RIS

Paul Franke-Bergmann, Maya Kuhlmann, Alexander Heß, Marc Leinweber, Rüdiger Kapitza, Hannes Hartenstein, Franz J. Hauck, Christian Berger. 2026-10-19. Pangolin: Efficient DAG-based BFT State-Machine Replication with TEE-Accelerated Commitment. https://arxiv.org/abs/2610.26295

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