Giving LLMs a Formal Reasoning Engine for Code Analysis
Summary
The article introduces Chiasmus, a neurosymbolic framework that combines tree-sitter parsing, Prolog facts, and formal solvers to empower LLMs to perform provable code analysis. It demonstrates how this approach enables precise reachability, dead-code detection, and cycle analysis with improved efficiency over traditional grep-based methods. The piece also situates the work within neurosymbolic AI and outlines architecture, tooling, and developer benefits.