HammerLang v1.0 (NEXUS Edition): A Semantic Compression Protocol for AI Safety Specifications

We introduce HammerLang v1.0, a semantic compression protocol for formal specification of AI safety constraints within large language model context windows. HammerLang encodes safety invariants, state transitions, and constraint boundaries using a logic-dense symbolic grammar, achieving an average compression ratio of 3.44× (peak 4.11×) across representative safety-critical specifications. Unlike traditional compression (gzip, brotli), HammerLang preserves semantic and logical equivalence: specifications remain formally executable by LLMs without external decompression. HammerLang functions as the Specification Layer within a layered safety architecture, complementing the Logic Lock Protocol v1.2 (Carricondo, February 2026; DOI: 10.5281/zenodo.18447076), which serves as the corresponding Enforcement Layer. This separation of concerns enables governance of safety logic independent of model weights and enables deterministic interpretability across heterogeneous LLM architectures. Semantic equivalence was validated via state-match verification: compressed specifications were decoded using a universal decoder and executed by independent LLM instances to confirm identical logical conclusions and state transitions relative to original specifications. Compression was measured using the tiktoken cl100k_base tokenizer across five representative AI safety specifications (FSM-based access control, gradient-locking, threat modeling, contradiction detection, and LoRA-based attack scenarios). HammerLang is model-agnostic and requires no proprietary fine-tuning, operating across contemporary frontier models. The protocol employs a modular namespace architecture, enabling domain-specific extensions for specialized safety grammars. Declared Limitations (v1.0): Initial validation dataset comprises five critical use cases; cross-tokenizer variability is observed but not exhaustively quantified; decoder assumes knowledge of finite state machines and threshold-based logic standard in contemporary LLMs. Nomenclature & Origin (Disambiguation):HammerLang v1.0 (2026) is an original semantic compression architecture for AI safety specifications developed under the Protocolo AEE framework by Franco Carricondo. HammerLang has no relationship to, derivation from, or affiliation with: (1) the npm package @hammerlang/interpreter (inactive, 2022); (2) Valve Corporation's Hammer Editor (Source Engine map editor, 2004–2013); or (3) any game modding frameworks, JavaScript multitouch libraries, or legacy software systems sharing similar naming conventions. HammerLang is a formal specification protocol for AI safety constraints, not a programming language, integrated development environment, or content creation tool. This publication establishes the definitive record of provenance, authorship, and technical scope.