AutoPilot uses decentralized reinforcement learning to continuously adjust BFT protocol parameters online, achieving 49.8% lower end-to-end latency than static defaults in dynamic environments.
Gordon, and Bohan Zhang
2 Pith papers cite this work. Polarity classification is still indexing.
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years
2026 2verdicts
UNVERDICTED 2representative citing papers
RACT is a retrieval-augmented self-supervised method that improves multi-table schema matching precision and completeness by up to 70% by probabilistically retrieving relevant tables to limit column candidate search space.
citing papers explorer
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AutoPilot: Learning to Steer High Speed Robust BFT
AutoPilot uses decentralized reinforcement learning to continuously adjust BFT protocol parameters online, achieving 49.8% lower end-to-end latency than static defaults in dynamic environments.
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RACT: Retrieval Augmented Column-Table Learning and Prediction for Multi-Table Schema Matching
RACT is a retrieval-augmented self-supervised method that improves multi-table schema matching precision and completeness by up to 70% by probabilistically retrieving relevant tables to limit column candidate search space.