Why Writing Systems Affect LLM Efficiency: The Semantic Compression Hypothesis

This working paper proposes that Japanese orthography and parliamentary shorthand function as "Human-Optimized Lossless Compression" protocols for Large Language Models (LLMs). By reframing logograms (Kanji) as dense semantic anchors (I-frames) and phonetic scripts (Kana) as logical connectors (P-frames), we demonstrate how this "Variable Bit-Rate (VBR)" architecture maximizes semantic bandwidth under fixed token constraints. The study offers a theoretical framework and a 3-phase experimental design to verify how these historically evolved systems can enhance AI inference efficiency and auditability.Data Source Note: Unlike traditional linguistic studies, this paper treats Large Language Model (LLM) behavior patterns—not theoretical linguistics—as primary evidence. The VBR hypothesis emerged from observing differential computational efficiency across writing systems in practical LLM deployment, rather than from a priori linguistic theory. Validation depends not on the model's "subjective experience" (which cannot be verified) but on measurable metrics: token consumption, inference latency, and task accuracy across languages.