Do Proactive Agents Really Need an LLM to Decide When to Wake and What to Anchor?
A new approach challenges the assumption that proactive agents must invoke LLMs on every user event. Rather than converting structured activity streams into text and asking language models to parse them back into decisions, researchers propose encoding raw event graphs directly with temporal graph learning models. This yields trigger probabilities and routing scores in a single forward pass, deferring LLM calls only when action is warranted. The shift from text-mediated reasoning to native graph processing reduces computational overhead while improving F1 scores across 14 model backbones, suggesting a broader architectural rethinking of how always-on systems should handle continuous signals.62
