AI Text Slop: A Quantitative Study of Stylistic Convergence Across Six Language Models in Japanese Technical Writing

We present the first quantitative analysis of stylistic convergence in Japanese technical blog writing across six LLMs (Claude Sonnet 4, GPT-4o, Qwen 3.5-4B/9B, Swallow-20B, Llama 3.2-1B) using 180 samples measured with 16 linguistic pattern indicators. Key findings: (1) RLHF-aligned commercial models score significantly higher than open-source alternatives (Cohen's d = 1.01); (2) vocabulary-level and structural indicators dissociate (the "Swallow Paradox"); (3) human Qiita articles score higher than AI on structural metrics, revealing cultural confounding. All data, code, and analysis scripts are included.