Patent Law Creates 'Perplexity Trap' Making Human Writing Look Like AI

Jul 16, 2026

A new preprint finds that zero-shot AI detectors, which rely on perplexity and related metrics, have false positive rates exceeding 60% when distinguishing between human-written and LLM-generated European patent claims. The study attributes this to legal drafting requirements that push human writing into the same statistical patterns as AI-generated text. The authors propose a logistic regression model using linguistic features, which reduces false positives and improves accuracy by 13 percentage points over perplexity-based methods.

Why it matters: This work reveals a structural flaw in current AI detection methods for patent law, raising concerns about the enforceability of disclosure rules and the reliability of AI-authorship detection in legal contexts.

Full story at: arXiv AI/ML ↗