Prospective Memory for Proactive LLM Agents
Read the original on arXiv Computation and Language →The Flow has not summarised this story yet — read it at arXiv Computation and Language.
The Flow has not summarised this story yet — read it at arXiv Computation and Language.
arXiv:2607. 12385v1 Announce Type: new Abstract: A significant challenge in agentic AI is prospective memory: the ability to execute an intention at a specific future cue or state while other activities are ongoing.
ProMemAgent is a proactive agent that uses prospective memory to handle delayed user intentions over long horizons. It organizes past sensory perceptions into context-aware memory, generates prospective cues, and links earlier events to future developments for retrieval. The authors also introduce CPMBench, a benchmark of 1,000 instances across eight everyday categories, and show that ProMemAgent outperforms baselines in memory retrieval and proactive assistance tasks.
arXiv:2606. 14571v1 Announce Type: new Abstract: A central role of personal-agent memory is to turn stored information and prior interactions into future-oriented assistance.
Proactive Memory for Event-Driven LLM Agents introduces ProactMem, a system that organizes an agent’s history into recent events, temporal trajectories, and behavioral regularities. It validates trajectory facts against source events and assesses regularities through historical replay before matching them to current conditions. The authors also present ProactiveStream, a longitudinal benchmark with three application scopes, and report that ProactMem achieves an overall F1 of 52.6%, a 21.4% relative improvement over the strongest of five external baselines.
In long-horizon tasks, decision-relevant state is often scattered across an expanding trajectory, while the action agent must surface it and act. As trajectories grow, task requirements, environment facts, prior attempts, diagnoses, and open subgoals can be buried in the context window or pushed beyond it, failing to influence decisions when needed.
arXiv:2607. 08716v1 Announce Type: new Abstract: In long-horizon tasks, decision-relevant state is often scattered across an expanding trajectory, while the action agent must surface it and act.