RocqStar: Leveraging Similarity-driven Retrieval and Agentic Systems for Rocq generation

This paper explores enhancing Rocq proof generation in interactive theorem proving through improved premise selection using a self-attentive embedder model, achieving up to a 28% performance boost. It introduces a multi-stage, agentic system for formal verification, demonstrating high effectiveness and incorporating multi-agent debate during proof planning.