arXiv:2509.24762v3 Announce Type: replace Abstract: Modeling event sequences of multiple event types with marked temporal point processes (MTPPs) provides a principled way to uncover governing dynamical rules and predict future events. Current neural network approaches to MTPP inference rely on training separate, specialized models for each target system. We pursue a radically different approach: drawing on amortized inference and in-context learning, we pretrain a deep neural network to infer, in-context, the conditional intensity functions of event histories from a context defined by sets of
The proliferation of generalized AI models and the increasing sophistication of in-context learning techniques make this approach feasible now.
This work points to a paradigm shift in AI model development, moving away from specialized training per task towards more generalized, adaptable 'foundation inference models' that learn in-context.
AI models will become more versatile and efficient, reducing the need for re-training for every new application involving temporal event sequences.
- · AI platform developers
- · Analytics/forecasting sectors
- · Data scientists
- · Specialized MTPP model developers
- · Organizations with rigid, siloed AI infrastructure
Reduced development time and cost for new temporal event sequence prediction applications.
Accelerated deployment of AI in diverse, data-rich fields like finance, healthcare, and logistics.
Enhanced automation and predictive capabilities across complex systems, potentially leading to new forms of systemic risk or control.
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Read at arXiv cs.LG