Many scientific fields, from medicine to seismology, rely on analyzing sequences of events over time to understand complex systems. Traditionally, machine learning models must be built and trained from scratch for each new dataset, which is a slow and costly process. We introduce a new approach: a single, powerful model that learns the underlying patterns of event data in context. We trained this "foundation model" on millions of simulated event sequences, teaching it a general-purpose understanding of how events can unfold. As a result, our model can analyze new scientific data instantly, without retraining, simply by looking at a few examples from the dataset. It can also be quickly fine-tuned for even higher accuracy. This approach makes sophisticated event analysis more accessible and accelerates the pace of scientific discovery.

翻译：从医学到地震学，许多科学领域都依赖于分析随时间推移的事件序列来理解复杂系统。传统上，每个新数据集都需要从头构建和训练机器学习模型，这是一个缓慢且成本高昂的过程。我们提出了一种新方法：一个单一而强大的模型，能够在上下文中学习事件数据的基本模式。我们在数百万个模拟事件序列上训练了这个“基础模型”，使其获得了关于事件如何展开的通用理解。因此，我们的模型能够即时分析新的科学数据，无需重新训练，只需查看数据集中的少量示例即可。该模型还可以通过快速微调以获得更高的准确性。这种方法使得复杂的事件分析变得更加易于实现，并加速了科学发现的进程。