AI会议模式濒临崩溃，提出去中心化新方案
Position: The Current AI Conference Model is Unsustainable! Diagnosing the Crisis of Centralized AI Conference
- 将评审、演讲与社交拆分，由全球协作、本地组织
- 人均发文量超4.5篇/年，单场会议碳排放超城市日均
- 适合关注学术可持续性与社区健康的科研人员
人工智能会议是推动研究、共享知识和促进学术共同体的关键。然而，其快速扩张使集中式会议模式日益不可持续。本文通过数据揭示系统性危机，威胁科学传播、公平与社区福祉。四方面压力包括：（1）科学层面，作者年均发表量十年间翻倍以上，超过4.5篇；（2）环境层面，单场会议碳足迹超过主办城市的日均排放；（3）心理层面，71%的线上讨论呈现负面情绪，35%提及心理健康问题；（4）物流层面，如NeurIPS 2024参会人数已逼近场地容量。该系统已偏离核心使命。为此，我们提出社区联邦会议（CFC）模型，将同行评审、展示与交流分拆为全球协调、本地执行的模块，提供更可持续、包容且有韧性的未来路径。
原文摘要 · Abstract (English)
Artificial Intelligence (AI) conferences are essential for advancing research, sharing knowledge, and fostering academic community. However, their rapid expansion has rendered the centralized conference model increasingly unsustainable. This paper offers a data-driven diagnosis of a structural crisis that threatens the foundational goals of scientific dissemination, equity, and community well-being. We identify four key areas of strain: (1) scientifically, with per-author publication rates more than doubling over the past decade to over 4.5 papers annually; (2) environmentally, with the carbon footprint of a single conference exceeding the daily emissions of its host city; (3) psychologically, with 71% of online community discourse reflecting negative sentiment and 35% referencing mental health concerns; and (4) logistically, with attendance at top conferences such as NeurIPS 2024 beginning to outpace venue capacity. These pressures point to a system that is misaligned with its core mission. In response, we propose the Community-Federated Conference (CFC) model, which separates peer review, presentation, and networking into globally coordinated but locally organized components, offering a more sustainable, inclusive, and resilient path forward for AI research.
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