Longformer

Reasoning graphs improve robustness of LLM authorship attribution against obfuscation

A new arXiv paper proposes using graph neural networks over argument-mining-derived reasoning graphs to attribute authorship of LLM-generated text, moving beyond surface-level linguistic features. The approach outperforms a Longformer baseline by up to 27 percentage points under paraphrasing and backtranslation obfuscation attacks, and by 19 points when generalizing to unseen model versions. The work addresses a practical gap in AI-generated text detection: robustness to adversarial rewriting and model version drift.