Compact orchestrator trajectories in worker KV caches using task-guided queries.
npx skills add https://github.com/lucasFR2/LogiSync --skill latent-briefing-lucasfr2
Please help me install this Agent Skill. Skill: latent-briefing Source: https://github.com/lucasFR2/LogiSync/tree/main/skills/latent-briefing Command: npx skills add https://github.com/lucasFR2/LogiSync --skill latent-briefing-lucasfr2
This skill addresses the issue of hierarchical multi-agent systems paying for the same context twice, and provides a solution for efficient memory sharing between agents.
Use the latent-briefing skill to share memory between agents for a specific task.
High-intent search queries and answers about installing and using this skill.
KV cache compaction for multi-agent memory sharing compresses the orchestrator trajectory directly in the worker model's KV cache, enabling representation-level memory sharing without replaying the full text trajectory.
To compact a worker model's KV cache, use task-guided query vectors derived from the current worker prompt to score and select relevant context, applying shared token masks to handle task-specific context efficiently.
Hierarchical multi-agent systems pay for the same context twice because workers typically replay the full orchestrator trajectory as text. Representation-level memory sharing via KV cache compaction avoids this redundant processing cost.
Yes, latent-briefing is specifically designed for orchestrator-worker or supervisor-specialist multi-agent systems where workers need access to prior orchestrator state without incurring the cost of replaying the full trajectory as text.
The best way to optimize inference-time memory in hierarchical multi-agent systems is compacting the orchestrator trajectory in the worker model's KV cache using attention matching and task-guided queries for efficient representation-level sharing.
You should avoid representation-level memory sharing when your multi-agent system lacks attention matching capabilities or requires workers to process task-specific context without relying on derived task-guided query vectors and shared token masks.
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