On Foundation Models for Temporal Point Processes to Accelerate Scientific Discovery

2025-10-14

AI Summary

Traditional machine learning approaches for scientific event sequence analysis require task-specific modeling and incur high training costs. Method: This paper introduces the first general-purpose foundation model for temporal point processes (TPPs), built upon a deep neural architecture that integrates context-aware mechanisms with principled TPP theory, trained via large-scale synthetic data-driven self-supervised pretraining. Contribution/Results: The model achieves unified cross-domain representation of event patterns, enabling zero-shot inference and rapid few-shot fine-tuning. Experiments across domains—including medicine and seismology—demonstrate its plug-and-play usability without task-specific training while maintaining competitive performance; subsequent fine-tuning yields significant accuracy gains. By eliminating the need for per-task architecture design and extensive retraining, the model substantially lowers modeling barriers and computational overhead, thereby accelerating scientific discovery.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Cognitive Modeling & Cognitive Systems: Other Foundations of Cognitive Modeling & Systems

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Machine learning and data science for the Web
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Abstract

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.

Problem

• Developing foundation models for temporal event sequences
• Eliminating retraining for new scientific event datasets
• Accelerating analysis of complex temporal patterns

Innovation

• Foundation model learns event patterns from simulations
• Analyzes new data instantly without retraining required
• Can be fine-tuned quickly for higher accuracy