latent-briefing

Reduce token consumption in multi-agent system interactions by selectively sharing relevant parts of orchestrator trajectories with workers using Attention Matching for efficient memory compaction.

Yes, you can share memory in orchestrator-worker systems without text replay by transferring state at the representation level, avoiding the overhead of text summarization.

Attention Matching for memory compaction is a technique that selectively shares relevant parts of an orchestrator's trajectory with workers to optimize token efficiency during state transfer.

Transfer prior state to workers without exceeding token limits by utilizing Attention Matching to compact memory and share relevant trajectory representations instead of replaying text.

Yes, representation-level memory sharing works for orchestrator-worker architectures by efficiently compacting memory and transferring state without text replay overhead.

The limitations of using KV cache compaction for multi-agent memory sharing involve its suitability strictly for orchestrator-worker systems requiring efficient state transfer without text replay.