A novel, robust, and effective framework for detecting AI-generated text using the principle of posterior concentration.
- [2026.01] 🎉 Our paper has been accepted to ICLR 2026!
- [2025.09] Initial code release.
We introduce Disrupt & Recover (D&R), a recovery-based detection framework built on the principle of posterior concentration. Unlike traditional methods that rely on white-box features or specialized classifiers, D&R leverages a single black-box LLM call to differentiate between human-written and AI-generated text.
- 💡 Single Black-box Call: Efficient detection without requiring access to model gradients or logits.
- 🛡️ Robustness: Maintains high performance even under source-recovery mismatch and model variations.
- 🏆 SOTA Performance: Outperforms existing baselines on both long and short text generation benchmarks.
- ⚡ Interpretability: Built on solid theoretical ground (posterior concentration).
Extensive experiments on four datasets and six source models show that D&R achieves state-of-the-art detection performance:
- AUROC 0.96 on long texts
- AUROC 0.87 on short texts
- Gains of +0.08 (long) and +0.14 (short) compared to the strongest baseline
D&R remains robust under source–recovery mismatch and model variation, making it broadly applicable.
git clone
pip install -r requirements.txt