Semantic Self-Verification (SSV) solves a critical challenge in AI systems by accurately translating natural language reasoning problems into formal logical structures that can be verified.

• Combines the flexibility of language models with the rigor of logical solvers
• Achieves high-precision reasoning through a consistency-based approach
• Provides a verification framework essential for security-critical applications
• Enables more trustworthy AI reasoning systems

This research is crucial for security applications where reasoning errors can have significant consequences. By formalizing reasoning tasks and verifying outputs, SSV creates a foundation for more dependable AI systems in sensitive contexts where reliability is non-negotiable.

Instantiation-based Formalization of Logical Reasoning Tasks using Language Models and Logical Solvers