Must-read paper on the effectiveness of multi-agent systems and persistent memory.
We have recently seen many viral examples of multi-agent systems scaled via persistent memory.
This work takes a closer look.
They test one three-tier chain-of-agents system.
Splitting long-context work across agents cuts peak KV cache to 14.3 MiB per query, against about 35 MiB for single-pass and RAG baselines.
The persistent trace-recall tier adds 0.368 MiB of peak cache and changes accuracy by +0.015, with a confidence interval that crosses zero.
The authors explain why.
Single-question benchmarks give each item its own evidence and score it independently, so stored traces have nothing useful to recall.
Paper: https://arxiv.org/abs/2610.07782
Chat with Paper: https://academy.dair.ai/papers/persistent-memory-in-multi-agent-llm-inference-what-it-costs-what-it-buys-and-wh-2610.07782