[Paper] AutoPilot: Learning to Steer High Speed Robust BFT

Source: arXiv - 2606.09120v1

Overview

Recent Byzantine Fault Tolerant (BFT) protocols achieve strong performance by combining the low-latency advantages of leader-based BFT protocols with the high-throughput benefits of DAG-based data dissemination. Despite exposing a wide spectrum of internal tunable parameters, these protocols typically rely on static and heuristic configurations, which leads to performance degradation under dynamic workloads, heterogeneous network conditions, and evolving adversarial behaviors. In this paper, we present AutoPilot, a reinforcement learning-based framework that continuously monitors runtime conditions and dynamically adjusts protocol parameters online to optimize consensus performance. To ensure robustness, AutoPilot coordinates learning in a decentralized manner, providing resilience against adversarial data pollution. We implement AutoPilot on top of Autobahn, a state-of-the-art, highspeed, robust BFT protocol, and evaluate it across diverse dynamic environments. Experimental results demonstrate that AutoPilot quickly converges to the optimal configuration under changing environments, reduces end-to-end latency by 49.8% compared to the default protocol configuration, and outperforms random configuration exploration by 73.3%.

Key Contributions

This paper presents research in the following areas:

• cs.DC

Methodology

Please refer to the full paper for detailed methodology.

Practical Implications

This research contributes to the advancement of cs.DC.

Authors

• Liangrong Chen
• Yue Zhang
• Eric Zhou
• Mohammad Javad Amiri
• Ryan Marcus
• Chenyuan Wu

Paper Information

• arXiv ID: 2606.09120v1
• Categories: cs.DC
• Published: June 8, 2026
• PDF: Download PDF