Proactive Memory for Event-Driven LLM Agents

Key points

ProactMem stored memory in three layers: recent events, temporal trajectories, and behavioral regularities.
After an LLM extracts daily trajectories, facts are validated against source events, and regularities get confidence scores through historical replay.
The authors built ProactiveStream, a longitudinal event-stream benchmark across Sports, Home, and Code, containing 17 isolated streams, 4,539 events, and 729 reference interventions.
ProactMem recorded an overall effectiveness (F1) of 52.6%, a 21.4% relative improvement over the strongest of five external baselines.
In ablations, adding trajectory memory raised effectiveness in every scope, while removing replay validation or trajectory validation lowered both relevance and effectiveness.
As limitations, the authors state that evaluation covers low-risk service settings under a protocol fixed in advance, and that recipient benefit and deployment safety require future measurement.

External research summaries. These are not HDATF publications or measured product results.