Agents
Must-read paper on the effectiveness of multi-agent systems and persistent memory. We have recently seen many viral examples of multi-agent …
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 clos
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
Related
- Great paper from AWS. I use a similar setup where an agent orchestrator sits on top of a multi-agent system. (bookmark it) This work introdu…
- This WikiSkill paper from Google is a must-read. At a high level, it shows the effectiveness of persistent agents, knowledge bases, and skil…
- Interesting paper from Microsoft. If you run persistent memory for a production agent, this one is worth your time. (bookmark it) Memory cur…
- Finally a good paper testing whether memory-based self-improving agents actually improve. The re-evaluation adds two things prior work skipp…
Source: DAIR.AI (X) | 2026-10-12