arXiv:2604.18587v2 Announce Type: replace Abstract: Large language models (LLMs) have demonstrated significant potential in formal theorem proving, yet state-of-the-art performance often necessitates prohibitive test-time compute via massive roll-outs or extended context windows. In this work, we address this scalability bottleneck by exploiting an informative structure in formal verification: the observation that compilers map a vast space of diverse proof attempts to a compact set of structured failure modes. We introduce a learning-to-refine framework that leverages this compression to perf
The increasing computational demands of LLMs in formal theorem proving necessitate more efficient methods to scale, making research into compiler optimizations timely.
This work directly addresses the scalability bottleneck in applying large language models to complex formal verification tasks, a crucial step for robust AI systems.
A new framework for learning-to-refine based on compiler outputs provides a method to reduce the prohibitive test-time compute requirements for advanced theorem proving.
- · AI developers
- · Formal verification sector
- · Safety-critical software industries
- · Inefficient LLM-based theorem provers
Reduced computational cost and improved efficiency for formal theorem proving tasks using LLMs.
Accelerated development and adoption of AI-assisted formal verification in software and hardware design.
Enhanced reliability and trustworthiness of complex AI systems and autonomous agents through more rigorous formal verification.
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Read at arXiv cs.LG