Large models of Language (LLMS) show impressive mathematical skills, but their solutions often contain default errors. Organized Theorem programs such as default default defenses accurate accurate accuracy, which stimulates the latest attempts to create sure Prover-LLMS alignment in the official languages in the official languages. However, an important gap remains: The current profer of the llms solves a few more problems than commonly functional llms. We introduce Hilbert, Agentic Framework to destroy the corresponding component consulting force and formal verification. Our program relates to four parts: Informal LLM passes by expressing calculations, a special LLM area designed for lean 4 tricks, and legitimate restoration, and Semantic Theorem Theorem Retriever. As a problem is provided that the project cannot solve, Hilbert uses the repetition of the dementia to distinguish the problem. The test results indicate that Hilbert is very early in key benches, reaching 99.2% in the miniF2F, 6.6 points above the best way available in society. Hilbert reaches the most well-known result in PutNanBanch. It solves 462/660 problems (70.0%), outgoing methods such as the SEARPLOVER (50.4%) and get 422% improvement in the best foundation. Hilbert, therefore, successfully decreasing the gap between illegal thinking and formal proof of evidence.
- 40 Diego
- ** Work done while in Apple
