elvis(@omarsar0):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://t.co/E0kYC2PFec Chat with Paper:

2026.10.12 13:54

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: Chat with Paper: