Reasoning Graphs Enhance Robustness of LLM Authorship Attribution

Jul 17, 2026

A new method uses graph neural networks to analyze reasoning structures extracted from LLM-generated text for authorship attribution. This approach significantly outperforms traditional baselines, achieving up to 27 percentage points higher accuracy under obfuscation attacks such as paraphrasing and backtranslation, and 19 points higher on texts from previously unseen model versions.

Why it matters: The method offers a more robust solution for detecting LLM-generated text, addressing vulnerabilities of existing techniques to paraphrasing and model updates.

Full story at: arXiv Computation and Language ↗