latent-briefing

To reduce redundant context repetition in hierarchical multi-agent systems, you can share memory at the representation level. This transfers relevant state information to workers without passing the entire trajectory, significantly reducing token cost.

Representation-level memory sharing is a technique that transfers task-specific state between agents using task-guided queries. It passes only the essential latent representations rather than full text context, lowering token overhead during multi-agent handoff.

To optimize multi-agent system performance through memory sharing, you need control over the worker inference runtime and a shared model space. These prerequisites ensure optimal performance when transferring representations.

Yes, task-guided queries allow you to pass task-specific state to a worker agent without sending the entire trajectory. By sharing memory at the representation level, you avoid redundant context repetition and cut token costs.

You should avoid representation-level context transfer if you lack control over the worker inference runtime or do not have a shared model space. Without these requirements, the system cannot effectively share memory at the representation level.