ASHRU Whitepaper

Abstract

The scaling of enterprise AI systems is constrained by the semantic inefficiency of standard data interchange formats. ASHRU is a positional, pipe-delimited transport protocol that reduces LLM output token consumption by approximately 80% in repetitive extraction workloads, while keeping downstream parsing and graph ingestion strictly deterministic. The full whitepaper covers the design rationale, the probabilistic / deterministic boundary, benchmark methodology, and production implementation patterns.

Sections

• 1. Introduction
• 2. The Deterministic vs. Probabilistic Boundary
• 3. The Protocol Specification
• 4. Best For / Not Best For
• 5. Benchmarks and Token Economics
• 6. Implementation Realism
• 7. Conclusion