ProofNet++: A Neuro-Symbolic System for Formal Proof Verification with Self-Correction

• URL: http://arxiv.org/abs/2505.24230v1
• Date: Fri, 30 May 2025 05:44:34 GMT
• Title: ProofNet++: A Neuro-Symbolic System for Formal Proof Verification with Self-Correction
• Authors: Murari Ambati,
• Abstract summary: We propose ProofNet++, a neuro-symbolic framework that enhances automated theorem proving.<n>We show that ProofNet++ significantly improves proof accuracy, correctness, and formal verifiability over prior models.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose ProofNet++, a neuro-symbolic framework that enhances automated theorem proving by combining large language models (LLMs) with formal proof verification and self-correction mechanisms. Current LLM-based systems suffer from hallucinated logical steps and unverifiable reasoning. ProofNet++ mitigates these limitations by integrating symbolic proof tree supervision, a reinforcement learning loop using verifiers as reward functions, and an iterative self-correction module. Our experiments on miniF2F, Lean's mathlib, and HOL Light show that ProofNet++ significantly improves proof accuracy, correctness, and formal verifiability over prior models. We provide theoretical analysis of the convergence and stability of the verifier-guided RL framework and release our datasets and codebase for future research.

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