Skip to content
AI NEWS JOURNAL
arXiv cs.DCAI NEWS / JOURNAL

MANTA: Unlocking DAG Flexibility in Asynchronous BFT

arXiv:2610.34419v1 Announce Type: new Abstract: DAG-based BFT protocols marked a shift in consensus design by replacing single-leader proposal paths with concurrent block proposals linked through a shared graph, where references encode consensus evidence. However, under heterogeneous wide-area networks (WANs), existing designs suffer from prolonged rounds and degraded performance due to straggler effects. The root cause is rigid round advancement: replicas move in synchronized rounds, waiting for enough references from the previous round before advancing. We propose a flexible DAG growth framework for asynchronous BFT consensus. Our key observation is that while reference quorums are necessary for safety and liveness, they need not block every round advancement. Instead, the connectivity r

Abstract:DAG-based BFT protocols marked a shift in consensus design by replacing single-leader proposal paths with concurrent block proposals linked through a shared graph, where references encode consensus evidence. However, under heterogeneous wide-area networks (WANs), existing designs suffer from prolonged rounds and degraded performance due to straggler effects. The root cause is rigid round advancement: replicas move in synchronized rounds, waiting for enough references from the previous round before advancing.

We propose a flexible DAG growth framework for asynchronous BFT consensus. Our key observation is that while reference quorums are necessary for safety and liveness, they need not block every round advancement. Instead, the connectivity required for commitment can be accumulated across rounds. Within the framework, we instantiate MANTA, which combines flexible DAG growth to advance by relaxing round synchronization and flexible coin to trigger leader election and finality earlier. Experiments on heterogeneous WAN deployments show that MANTA reduces latency by approximately 30%-70% in our evaluation.

Submission history

Access Paper:

View PDF

HTML (experimental)

TeX Source

Additional Features

Audio Summary

Current browse context:

References & Citations

Google Scholar

Semantic Scholar

Bookmark

Institution

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Sources

arXiv cs.DC
← Continue to all updates