Meta 论文提出 Routed CL，为 AI 智能体按问题类型路由记忆
Meta 新论文提出 Routed CL，通过 LLM 路由器在每次运行前复用相似任务的记忆簇或新建记忆簇，而非共用一份历史记录。在398个混合问题和4个模型上，共享历史相对无记忆的平均增益仅为0.015（满分1），Routed CL 超出其15%至30%的最大可能得分，并估算将API成本降低65%至72%。
New Meta paper finds that AI agents improve more from their past attempts when each new problem is matched to notes from similar problems, instead of one shared record of everything they have done.
A simple way to let agents learn from past runs is one shared history. With mixed tasks, it gets costly and mixes unrelated experience.
Routed CL adds an LLM router that reuses a memory cluster or starts a new one before each run. Each cluster uses 1 of 5 memory types, such as raw past runs or distilled lessons.
Across 398 mixed problems and 4 models, the shared history barely beat no memory, with a mean gain of 0.015 out of 1. Routed CL beat it by 15% to 30% of the maximum possible score and cut estimated API cost by 65% to 72%.
If your agents handle mixed task types, split their memory into separate pools by problem type instead of sharing one history.
来源：rohanpaul_ai · x.com