AIR-SR v2 (AI Intermediate Representation — Semantic Runtime) is a semantic runtime architecture for token-efficient large language model (LLM) code generation. The system separates semantic intent from structural boilerplate through entropy decomposition: Hₜotal = Hₛemantic + Hₛyntactic Instead of regenerating full syntax on every request, AIR-SR generates compact semantic instructions (AIR-SR JSON) which are reconstructed deterministically through a grammar-driven runtime across multiple language ecosystems including JavaScript/TypeScript, Python, SQL/Schema, React/JSX, and Go. The architecture consists of: Semantic instruction generation Grammar runtime reconstruction Three-tier sandbox verification Incremental repair protocol Empirical evaluation across 40 benchmark prompts and 9 structured generation domains demonstrates: 81. 8% average token reduction 92. 5% validation pass rate r = 0. 97 entropy–compression correlation AIR-SR operates on the output generation pipeline and is distinct from: hardware compiler IRs (LLVM IR, MLIR-AIR), prompt compression systems, and constrained decoding frameworks. This upload contains the AIR-SR v2 research preprint, benchmark framework results, and system architecture documentation. GitHub Repository: https: //github. com/masoomul786/air-research License: CC BY-NC-SA 4. 0
Masoomul Haque Choudhury (Thu,) studied this question.