Selective Persistent Memory Boosts LLM Agent Performance and Cuts Token Costs
The paper introduces a shared selective persistent memory that captures reusable context—task specs, schemas, tool configs, and constraints—while discarding session‑specific traces. In enterprise tests it lifts task completion from 79% to 96% and slashes token use by up to 97×, enabling agents to collaborate without re‑specifying everything.