Position: Academic Conferences are Potentially Facing Denominator Gaming Caused by Fully Automated Scientific Agents
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 20 of its 59 references that are in the map. First posted on arXiv 11 May 2026.
Builds on
20 of 59 references.
References · 20
- 2024The AI Scientist: Towards Fully Automated Open-Ended Scientific Discoverycited by 1,002
- 2022Galactica: A Large Language Model for Sciencecited by 736
- 2025Agent Laboratory: Using LLM Agents as Research Assistantscited by 405
- 2023GPT detectors are biased against non-native English writerscited by 175
- 2025Deep Research Agents: A Systematic Examination And Roadmapcited by 122
- 2025Towards Scientific Intelligence: A Survey of LLM-based Scientific Agentscited by 96
- 2025From AI for Science to Agentic Science: A Survey on Autonomous Scientific Discoverycited by 74
- 2026Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineeringcited by 68
- 2025A Survey of LLM-based Deep Search Agents: Paradigm, Optimization, Evaluation, and Challengescited by 58
- 2024Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challengescited by 41
- 2025Position: The AI Conference Peer Review Crisis Demands Author Feedback and Reviewer Rewardscited by 34
- 2026SkillProbe: Security Auditing for Emerging Agent Skill Marketplaces via Multi-Agent Collaborationcited by 21
8 more
- 2023The strain on scientific publishingcited by 17
- 2025Adversarial Paraphrasing: A Universal Attack for Humanizing AI-Generated Textcited by 16
- 2025Paper Copilot: Tracking the Evolution of Peer Review in AI Conferencescited by 10
- 2025Position: The Current AI Conference Model is Unsustainable! Diagnosing the Crisis of Centralized AI Conferencecited by 7
- 2025Stop DDoS Attacking the Research Community with AI-Generated Survey Paperscited by 6
- 2025PaperDebugger: A Plugin-Based Multi-Agent System for In-Editor Academic Writing, Review, and Editingcited by 5
- 2025Superplatforms Have to Attack AI Agentscited by 3
- 2025Identity Theft in AI Conference Peer Reviewcited by 3
Built on by
Citing papers per year: 2026: 1.
1 paper.
Similar, not cited
Close in wording, no citation either way
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- 2026CABAL: Multi-Agent Simulacra for Tracing the Effects of Collusive Bidding in Peer Review
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- 2026Agentic Publication Protocol: An Attempt to Modernize Scientific Publication
- 2025The Hunger Game Debate: On the Emergence of Over-Competition in Multi-Agent Systems
- 2026Towards Automating Scientific Review with Google's Paper Assistant Tool
- 2025HIKMA: Human-Inspired Knowledge by Machine Agents through a Multi-Agent Framework for Semi-Autonomous Scientific Conferences