Position: Academic Conferences are Potentially Facing Denominator Gaming Caused by Fully Automated Scientific Agents

2026/05/11 by Rong Shan, Te Gao, Hang Zheng +6 · 1 voice
Computer Science · Decision Sciences · Social Sciences · #Academic integrity and plagiarism #Ethics and Social Impacts of AI #cs.AI #cs.CL #cs.CY #scientometrics and bibliometrics research

paper · pdf · doi:10.48550/arxiv.2605.09915

openalex publication_date 2026/05/11 · arxiv published 2026/05/11 · arxiv updated 2026/05/11 · openalex created_date 2026/05/13 · openalex updated_date 2026/07/28

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.

Citations

• Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineering
• Artificial intelligence tools expand scientists’ impact but contract science’s focus
• PaperDebugger: A Plugin-Based Multi-Agent System for In-Editor Academic Writing, Review, and Editing
• Hope, Signals, and Silicon: A Game-Theoretic Model of the Pre-Doctoral Academic Labor Market in the Age of AI
• Paper Copilot: Tracking the Evolution of Peer Review in AI Conferences
• Stop DDoS Attacking the Research Community with AI-Generated Survey Papers
• From AI for Science to Agentic Science: A Survey on Autonomous Scientific Discovery
• Position: The Current AI Conference Model is Unsustainable! Diagnosing the Crisis of Centralized AI Conference
• Identity Theft in AI Conference Peer Review
• A Survey of LLM-based Deep Search Agents: Paradigm, Optimization, Evaluation, and Challenges
• The peer-review crisis: how to fix an overloaded system
• Deep Research Agents: A Systematic Examination And Roadmap
• Adversarial Paraphrasing: A Universal Attack for Humanizing AI-Generated Text
• Superplatforms Have to Attack AI Agents
• Position: The AI Conference Peer Review Crisis Demands Author Feedback and Reviewer Rewards
• Towards Scientific Intelligence: A Survey of LLM-based Scientific Agents
• Agent Laboratory: Using LLM Agents as Research Assistants
• Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges
• How AI agents will change cancer research and oncology
• A call for research to address the threat of paper mills
• The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery
• The strain on scientific publishing
• GPT detectors are biased against non-native English writers
• GPT detectors are biased against non-native English writers
• Galactica: A Large Language Model for Science

Discussions

• "The implicit policy of maintaining relatively stable acceptance rates at top AI conferences, despite exponentially growing submissions, introduces a critical structural vulnerability." An interesting [bsky, 2 points, 1 comments]