Compile to Compress: Boosting Formal Theorem Provers by Compiler Outputs

This research introduces a novel learning-to-refine framework to address the prohibitive computational cost of Large Language Models (LLMs) in formal theorem proving. By exploiting compiler outputs that compress diverse proof attempts into structured failure modes, the method enables efficient proof exploration and local error correction, significantly amplifying the reasoning capabilities of base provers.