Thirsty's Symbolic Compression Grammar (TSCG): A Formal Meta-Language for Constitutional AI Governance Encoding

Abstract The formalization of AI governance specifications presents a fundamental engineering challenge: constitutional frameworks expressed in natural language are verbose, ambiguous, and not directly computable by the systems they govern. This paper introduces Thirsty's Symbolic Compression Grammar (TSCG), a domain-specific symbolic meta-language designed to represent governance flows, reflex logic, mutation control, state transitions, quorum structures, and invariant validation in compact, deterministic, machine-readable form. TSCG achieves 75–90% token reduction over equivalent prose specifications while preserving complete semantic fidelity through a bidirectional encoder/decoder architecture with a formal bijective guarantee: decode(encode(X)) = X and encode(decode(Y)) = Y under fixed Semantic Dictionary versioning and canonical ordering constraints. The grammar is defined as a context-free language at the syntax layer with a context-sensitive semantic resolution layer, ensuring deterministic parsing while supporting the expressive requirements of constitutional AI governance. A versioned Semantic Dictionary binds symbolic identifiers to precise governance semantics, enabling safe constitutional mutation tracking through SD version control. TSCG is specifically designed for integration with the Project-AI Sovereign Monolith ecosystem, enabling the full constitutional governance stack — including Triumvirate consensus, Shadow Thirst speculative validation, FourLaws enforcement, and cryptographic anchor chains — to be encoded in under 150 tokens without semantic loss. This enables constitutional state transmission across distributed nodes with minimal bandwidth, compact shadow replay logs, and direct symbolic injection into AI inference pipelines. This work represents the first formal compression grammar specifically designed for constitutional AI governance specifications, addressing a gap between human-readable governance frameworks and machine-executable constitutional enforcement.