Safeguarding Publication Caliber Amidst Massive AI Conference Submissions in the Era of LLMs

The explosion of AI research, accelerated by Large Language Models (LLMs) and agent-powered tools, has led to an unprecedented surge in conference submissions. This deluge imposes a tremendous burden on the peer-review process, triggering a vicious cycle characterized by low-quality reviews, stochastic decision-making, and a reproducibility crisis. In this paper, we analyze the structural failures of current AI reviewing paradigms and contrast them with established practices in other computer science subfields. We find that existing mechanisms in AI conferences fail to effectively constrain irresponsible behavior from both authors and reviewers in the LLM era. To break this cycle, we propose the “carrot and stick" methods that introduce lightweight yet high-impact modifications for authors, reviewers, and conference organizers. Our method incorporates mandatory revision plans, a real-name shepherding system, and artifact evaluation (AE) for high-tier papers including oral and spotlight. By optimizing workflows and UI features, such as explicit score-increase justifications, our practical, cost-effective approach aims to safeguard publication caliber and restore the academic reputation of AI conferences.