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This article explores the meaning of 'human touch' in text, analyzes statistical features of AI detection methods such as perplexity and burstiness, and argues that human writing can be diagnosed but cannot be optimized without creating a paradox.
This paper analyzes whether LLM-generated novels exhibit compressed formal variation compared to human-written novels, finding that AI outputs are more uniform in sentence structure, readability, and punctuation despite individual novels resembling human style.
This paper argues that LLM outputs exhibit model-specific linguistic signatures akin to human idiolects, analyzing 2024 and 2026 corpora to show both generational shifts and stable individual model profiles.
Presents CHiPS, a lightweight character-level authorship attribution method for Romanian texts that uses character histograms and positional signals without tokenization or pretrained language models, achieving 0.9310 accuracy on a closed-set benchmark.
The article argues that LLMs are increasingly capable of linking pseudonymous identities by analyzing writing style, and predicts a future where all high-bandwidth interactions leave unique fingerprints that can be traced.
This paper applies computational stylometry to English translations of the Pali Canon, examining vocabulary differences across the Sutta, Vinaya, and Abhidhamma divisions.
This paper introduces Darshana Graph, a parallel commentary corpus for comparative Indian philosophy, and presents stylometric and exploratory graph analyses.
This paper applies the likelihood ratio framework for forensic authorship attribution to Japanese texts, fusing stylometric features with embedding-based systems to improve discrimination and calibration.
Introduces PromptPrint, a systematic study showing that users' habitual vocabulary and syntax in LLM prompts form a learnable behavioral biometric, with lexical features outperforming semantic encoders and revealing a uniqueness–consistency paradox.
Spiral 4.0 is a new writing partner that uses stylometry to extract a user's brand voice and produce on-brand content, now integrated with MCP and CLI for agent use.
This paper introduces StoryScope, a pipeline that analyzes discourse-level narrative features to distinguish AI-generated fiction from human-written stories. It achieves high accuracy and reveals distinct narrative fingerprints for different LLMs like Claude, GPT, and Gemini.
A foundational study on applying stylometric authorship attribution to threat intelligence, using Japanese Rakuten reviews to compare TF-IDF+LR, BERT embedding, BERT fine-tuning, and metric learning methods. BERT-FT performed best overall, but TF-IDF+LR proved more stable and efficient when scaling to hundreds of authors.