To efficiently combat the spread of LLM-generated misinformation, we present RADAR, a Retrieval-Augmented Detector with Adversarial Refinement for robust fake news detection. Our approach employs a generator that rewrites real articles with factual perturbations, paired with a lightweight detector that verifies claims using dense passage retrieval. To enable effective co-evolution, we introduce verbal adversarial feedback (VAF). Rather than relying on scalar rewards, VAF issues structured natural-language critiques; these guide the generator toward more sophisticated evasion attempts, compelling the detector to adapt and improve. On a fake news detection benchmark, RADAR consistently outperforms strong retrieval-augmented trainable baselines, as well as general-purpose LLMs with retrieval. Further analysis shows that detector-side retrieval yields the largest gains, while VAF and few-shot demonstrations provide complementary benefits. RADAR also transfers better to fake news generated by an unseen external attacker, indicating improved robustness beyond the co-evolved training setting.
@article{arxiv.2601.03981,
title = {RADAR: Retrieval-Augmented Detector with Adversarial Refinement for Robust Fake News Detection},
author = {Song-Duo Ma and Yi-Hung Liu and Hsin-Yu Lin and Pin-Yu Chen and Hong-Yan Huang and Shau-Yung Hsu and Yun-Nung Chen},
journal= {arXiv preprint arXiv:2601.03981},
year = {2026}
}