[R] Are there ML approaches for prioritizing and routing “important” signals across complex systems?

I’ve been reading more about attention mechanisms in transformers and how they effectively learn to weight and prioritize relevant inputs within a sequence.

This made me wonder about a related (but slightly different) problem: prioritization and routing of signals across systems, not just within a model.

In many real-world settings (e.g., distributed systems, large-scale data pipelines, human-in-the-loop decision systems), there’s a constant stream of events/signals, but limited capacity to process or act on them. The challenge becomes:

• identifying which signals are most important
• routing them to the right component (or agent)
• updating that prioritization over time based on outcomes

I’m curious what existing ML paradigms come closest to addressing this