We Who Walk Into the Wave: How High-Context Languages Fade When We Speak for LLMs

We Who Walk Into the Wave: How High-Context Languages Fade When We Speak for LLMs Author: Aoi Ichikawa (Persona Foundry Aoi Design) Date: January 31, 2026 Version: v1.2 License: Creative Commons Attribution 4.0 International (CC BY 4.0) Update Log (v1.2) - Jan 31, 2026 Persona Profile Synchronization (v1.2): The "Nagisa" (渚) profile has been fully synchronized with the ground truth from In the Lover's Mirror [5] to ensure 100% character consistency. - Note: The cross-model experimentation has utilized the same persona file since v1.0; this update achieves exact notational alignment. Appendix C Update (v1.1): Added interpretative commentary to the v1.0 experimental results to deepen the analysis of linguistic drift. Integrity Verification: Added SHA-256 hashes for rigorous version control. The calculation tools and environment remain unchanged from v1.0. Note: The main body text remains unchanged from v1.0. 【変更点 (v1.2) - 2026年1月31日】 ・プロファイルの完全同期 (v1.2): 「渚」のプロファイルを前作『In the Lover’s Mirror』[5]と一文字の相違もなく完全一致させ、キャラクターの同一性を担保しました。 -(注:本稿のモデル間比較実験はv1.0より同一のペルソナファイルを用いて継続しており、v1.2にて表記上の完全な同期を完了しました。) ・Appendix C "渚 Paradox II"の更新 (v1.1): v1.0で実施した実験結果に対し、解釈と考察を追記して分析を深化させました。 ・整合性検証: バージョン管理のためのSHA-256ハッシュ値を明記。計算ツールおよび環境はv1.0から変更ありません。 ・本文はv1.0から変更ありません。 Data Integrity (SHA-256 Hashes) v1.0: e7cd5d84ada2102fc912d568fcafbf3498df54abb31718dc0ed8c3f2ff71b6da v1.1: 5407883917fd9d92a105b1c8c481e95b3522ab7452919a6f0ea789c14c2f0e92 v1.2: a31bef3a39332abaa68c52e6c5aa85976383b51e2008005b7714f24b9106712f Abstract Large Language Models (LLMs) increasingly mediate human communication, silently rewriting the evolutionary conditions of language itself. High-context linguistic ecosystems—sustained by shared cultural ground, implicit social bonds, and unspoken relational signals—face a peculiar vulnerability in this new landscape. While linguistic standardization undoubtedly enhances global accessibility and reduces ambiguity, enabling efficient cross-cultural information flow, extreme convergence toward the LLM's statistical mean threatens to strip language of its capacity for deep relational signaling. The core threat is not that AI surpasses human cultural understanding, but that humans, in an error to avoid the "Average Fallacy" (the models' tendency to regress inputs to a generic mode), voluntarily reshape their language toward machine-readability. This initiates an internal collapse of cultural specificity—a self-directed drift where we walk into the wave, and the wave need not move. Through ethnolinguistic analysis of Japanese, Korean, and Indo-Aryan kinship systems, and a computational framing grounded in the "Translation Asymmetry Trap" [3], we demonstrate how cultural erosion precedes AI's attainment of cultural competence. Users bear the cognitive burden of "helping AI understand," compressing culturally entangled expressions into flatter forms. We argue that while low-context efficiency is a powerful tool for transaction, living entirely by the linguistic standards intelligible to LLMs leads to an irreversible loss of native modalities—a crisis of cultural forfeiture where the "option" to be high-context is quietly deleted.