Do research topics in artificial intelligence grow gradually, or do they advance through abrupt, detectable jumps? Analyzing 80,814 accepted main-track papers from five premier AI conferences (ACL, CVPR, ICLR, ICML, NeurIPS) spanning 2017 to 2025, we show major AI topics advance through topical phase transitions: remaining marginal for years, then surging across venues within one to three years. Large language models became the dominant cross-venue topic by 2025, diffusion models rose with comparable abruptness, and language-model methods crossed into computer vision via vision-language models, whereas reinforcement learning compounded smoothly, distinguishing genuine phase transitions from ordinary growth. This structure is our primary contribution: a large-scale, cross-venue characterization of how AI research reorganizes. We then ask whether a transition leaves a detectable footprint before it peaks. We define an early-warning signature, four publication-dynamics criteria frozen on 2017-2021 data, and evaluate it out of sample on 2023-2025 transitions, obtaining a precision of 27% and recall of 63% against a 13.5% base rate. Applied to 2025 data, the signature flags reasoning and test-time compute, agentic AI, multimodal LLMs, retrieval-augmented generation, and world models as topics to monitor over 2026-2028. The source code is also publicly available on GitHub at https://github.com/KurbanIntelligenceLab/ai-phase-transitions.

翻译：人工智能研究主题是逐步增长，还是通过突发的、可检测的跃迁而发展？通过分析2017年至2025年间五大顶级人工智能会议（ACL、CVPR、ICLR、ICML、NeurIPS）录用的80,814篇主会议论文，我们展示了主要人工智能主题通过主题相变的方式推进：这些主题在多年内保持边缘地位，随后在一至三年内跨越多个会议爆发涌现。到2025年，大语言模型成为跨会议的主导主题，扩散模型以类似的突发性崛起，而语言模型方法通过视觉-语言模型跨界进入计算机视觉领域；相比之下，强化学习则呈现平稳增长，从而区分了真正的相变与普通增长。这一结构是我们的主要贡献：对人工智能研究如何重组的大规模跨会议表征。随后，我们探究相变是否在达到顶峰前留下可检测的印记。我们定义了一个早期预警信号，即基于2017-2021年数据冻结的四项发表动力学标准，并在2023-2025年相变样本外评估中获得了27%的精确率和63%的召回率（基准率为13.5%）。将该信号应用于2025年数据时，它标记了推理与测试时计算、自主智能体、多模态大语言模型、检索增强生成以及世界模型，作为2026-2028年需关注的主题。源代码已在GitHub上公开，网址为https://github.com/KurbanIntelligenceLab/ai-phase-transitions。