相信我们的读者都对 AI 顶会有非常大的关注和热情，有的读者最近可能刚从 NeurIPS rebuttal 脱身，又开始为下一篇做准备了。 作为推动技术革新与思想碰撞的核心引擎，顶级学术会议不仅是整个学界的生命线，更是我们洞察未来的前沿阵地。 随着 AI 领域近些年的蓬勃发展，如 NeurIPS、ICML 和 ICLR 等大型学术会议也越来越出圈。 然而，这一成功也带来了代价。当前集中化的线下会议正因自身的体量而捉襟见肘： 很具代表性的会议自然是饱受争议的 NeurIPS 2025，不仅被逼近 30000 篇的海量论文搞的焦头烂额，陷入低质评审风波，甚至闹出了「Who's Adam」的 笑话。而且也因出席人数激增及美国签证问题开放了墨西哥分会场。 这些现象引发一个关键问题： 如果按现在的热度趋势发展下去，AI 学术会议模式是否是可持续的？ 新加坡国立大学何丙胜教授团队对当前人工智能学术会议进行了深入的调查研究，分析了传统会议模式的弊端，也尝试提出了一些新的会议模式，发表了 一篇立场论文。 论文标题：Position: The Current AI Conference Model is Unsustainab ...

Core Viewpoint - The current model of AI academic conferences is deemed unsustainable due to overwhelming submission rates, environmental impacts, and mental health concerns among researchers [5][11][15]. Group 1: Challenges Facing AI Conferences - The average annual publication rate in the AI field has exceeded 4.5 papers per author, doubling in the past decade, leading to a focus on quantity over quality [7][22]. - The travel emissions from NeurIPS 2024 alone exceeded 8,254 tons of CO2 equivalent, surpassing the daily emissions of Vancouver, highlighting the environmental cost of these conferences [23][25]. - Over 71% of discussions on Reddit regarding AI conferences expressed negative sentiments, with 35% mentioning mental health issues such as anxiety and burnout [28][29]. Group 2: Proposed Solutions - The Community-Federated Conference (CFC) model is proposed as a sustainable and equitable alternative, separating traditional conference functions into three interconnected layers: global peer review, regional centers for knowledge dissemination, and a unified digital platform for collaboration [38][40][41]. - The first layer involves a centralized digital platform for peer review and publication, allowing for rolling submissions independent of physical conferences [39]. - The second layer consists of regional centers that facilitate local presentations, reducing the need for large venues and minimizing carbon footprints [40]. Group 3: Future Directions - The CFC model aims to address the structural issues of traditional conferences by promoting local engagement and reducing the pressure on authors while maintaining academic rigor [38][41]. - The shift towards a decentralized approach is seen as essential to foster collaboration and inclusivity within the AI research community [39][40].