InternReviewer and InternAdvocate are specialized agents for generating peer reviews and rebuttals, trained on a large-scale scholarly dataset with integrated arXiv retrieval. The agents are trained with reinforcement learning against multi-dimensional objective criteria, including reference-anchored semantic alignment, structural compliance, and hallucination detection, avoiding subjective evaluation biases. The authors report improvements in reasoning quality and citation accuracy over baseline approaches.
