AIR: AI Intermediate Representation — A Semantic Runtime Architecture with Verified Delivery for Token-Efficient LLM Execution

AIR (AI Intermediate Representation) is a semantic runtime architecture for token-efficient large language model (LLM) execution through semantic compression, deterministic reconstruction, and sandbox-validated delivery. Instead of regenerating full syntactic boilerplate on every request, AIR separates semantic intent from predictable structure. The LLM generates only compact semantic instructions, while a local runtime reconstructs full executable output from pre-validated templates. The system includes: Semantic intermediate representation (AIR JSON) Deterministic template reconstruction Three-tier sandbox validation Incremental repair protocol Entropy decomposition framework Evaluation across 36 benchmark prompts and 8 structured generation domains demonstrates token reductions of 45–71% on boilerplate-heavy tasks while validating the entropy boundary through algorithmic negative controls. This upload contains the AIR v1.1 preprint research paper. GitHub Repository:https://github.com/masoomul786/air-research License:CC BY-NC-SA 4.0