What question did this study set out to answer?

The paper aims to explore how different writing systems influence the efficiency of Large Language Models (LLMs).

February 8, 2026Open Access

Why Writing Systems Affect LLM Efficiency: The Semantic Compression Hypothesis

Key Points

The paper aims to explore how different writing systems influence the efficiency of Large Language Models (LLMs).
Propose a theoretical framework for understanding writing systems as compression protocols.
Utilize a 3-phase experimental design to validate the Variable Bit-Rate hypothesis.
Analyze LLM behaviors with a focus on metrics like token consumption and inference latency.
Demonstrates that Japanese orthography and parliamentary shorthand improve semantic processing in LLMs.
Finds that logograms serve as dense semantic anchors, enhancing efficiency under token constraints.
Reports measurable improvements in task accuracy across different writing systems.

Abstract

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.

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