AI agent generates rebuttals for papers (arxiv.org)

Researchers have unveiled **RebuttalAgent**, a pioneering multi-agent framework designed to generate transparent and evidence-based rebuttals for academic papers. Traditional rebuttal generation approaches often struggle with issues like hallucination and a lack of grounding, making it difficult for authors to effectively address critiques. By reframing rebuttal creation as an evidence-centric planning task, RebuttalAgent dynamically deconstructs reviewer feedback into manageable concerns and synthesizes responses using relevant external literature. This innovative system also permits authors to inspect a generated response plan before drafting, ensuring each argument is well-supported by internal or external evidence. The significance of RebuttalAgent lies in its potential to enhance the peer review process in the AI/ML community and beyond. By outperforming existing methods in coverage, faithfulness, and strategic coherence, this framework not only aids authors in crafting robust rebuttals but also promotes transparency and verifiability in academic discourse. Its performance has been validated using a new benchmark called **RebuttalBench**, and the forthcoming open-source release of the code will empower more researchers to leverage this technology, thus advancing the quality and integrity of scholarly communication.