latent-briefing

Multi-agent token usage is reduced by compacting the orchestrator trajectory into a smaller KV cache. This representation-level state sharing replaces replaying full text when delegating tasks to workers.

Attention matching KV cache compaction is a technique that uses a task-guided query and a shared global mask to reduce cache size. It preserves attention relevance while sharing latent state between orchestrator and workers.

You can share orchestrator state by providing the orchestrator with the worker task prompt to generate a compact KV representation. This enables efficient handoffs in long-running reasoning tasks without incurring large token overhead.

Yes, enabling latent state sharing requires access to worker prompts, the orchestrator trajectory, a task-guided query, and a shared mask. You must also have the ability to perform attention-based KV compaction.

KV cache compaction is ideal for hierarchical agent architectures where token costs scale with context length. It provides efficient handoffs for long-running reasoning tasks without the overhead of replaying full text.

Attention-based KV compaction requires specific components like a shared mask and task-guided query to preserve attention relevance. It is strictly applied in hierarchical multi-agent tasks and cannot function without worker prompt access.