Position: Academic Conferences are Potentially Facing Denominator Gaming Caused by Fully Automated Scientific Agents
2026-05-11 • Computation and Language
Computation and LanguageArtificial IntelligenceComputers and Society
AI summaryⓘ
The authors explain a problem with AI conferences that keep their acceptance rates steady even as more papers get submitted. They describe a new attack called Agentic Denominator Gaming, where bad actors flood conferences with many low-quality AI-generated papers, not to get them accepted, but to make it harder for real papers to get reviewed properly. This overload causes stress for reviewers and lowers review quality. The authors suggest that solving this requires big policy changes, not just technical fixes.
Acceptance rateAI conferencesAgentic Denominator GamingAutomated paper submissionReviewer burnoutReview qualityPolicy reformAI agentsSubmission overload
Authors
Rong Shan, Te Gao, Hang Zheng, Yunjia Xi, Jiachen Zhu, Zeyu Zheng, Yong Yu, Weinan Zhang, Jianghao Lin
Abstract
The implicit policy of maintaining relatively stable acceptance rates at top AI conferences, despite exponentially growing submissions, introduces a critical structural vulnerability. This position paper characterizes a new systemic threat we term Agentic Denominator Gaming, in which a malicious actor deploys AI agents to generate and submit a large volume of superficially plausible but low-quality papers. Crucially, their objective is not the acceptance of low-quality papers, but rather to inflate the submission denominator and overwhelm reviewing capacity. Under a relatively stable acceptance rate, this dilution can systematically increase the publication probability of a small, targeted set of legitimate papers. We analyze the practical feasibility of this threat and its broader consequences, including intensified reviewer burnout, degraded review quality, and the emergence of industrialized automated agent mills. Finally, we propose and evaluate a range of mitigation strategies, and argue that durable protection will require system-level policy and incentive reforms, rather than relying primarily on technical detection alone.