立场：当前AI会议模式难以为继！集中式AI会议的危机诊断

摘要

人工智能（AI）会议对于推动研究进展、知识共享以及促进学术共同体至关重要。然而，其迅速扩张使得集中式会议模式日益难以为继。本文通过数据驱动的方式，诊断了一场威胁科学传播、公平性及社区福祉基础目标的结构性危机。我们识别出四个关键领域的压力：(1) 科学层面，每位作者的年均发表论文数量在过去十年间翻倍增长，现已超过4.5篇；(2) 环境层面，单次会议的碳足迹已超过其主办城市一天的排放量；(3) 心理层面，在线社区讨论中71%的情绪为负面，35%涉及心理健康问题；(4) 后勤层面，如NeurIPS 2024等顶级会议的参会人数开始超出场地容量。这些压力表明，当前系统与其核心使命存在偏差。为此，我们提出了社区联邦会议（Community-Federated Conference, CFC）模型，该模型将同行评审、论文展示和网络交流分离为全球协调但本地组织的组成部分，为AI研究提供了一条更可持续、包容且具有韧性的发展路径。

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.

立场：当前AI会议模式难以为继！集中式AI会议的危机诊断

Position: The Current AI Conference Model is Unsustainable! Diagnosing the Crisis of Centralized AI Conference

摘要

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