Brilliant paper on agent memory from FAIR at Meta.

(bookmark it)

A single global history is the usual way to let agents learn from past runs. When tasks are interleaved, it gains almost nothing over no memory at all, an average of +0.015 in normalized reward.

This work proposes Routed Continual Learning. Past trajectories are grouped into clusters, and each cluster keeps its state in one memory system from a fixed library. Before each run, an LLM router reuses a cluster or creates a new one.

They tested it on a stream mixing 301 CL-Bench instances with 97 banking instances from τ³-Bench, across four language models.

Routed CL raised reward over the global history by 15% to 30% of the maximum attainable reward. Estimated API cost fell by 65% to 72%.

The runs were sequential, so parallel agents updating one cluster remain untested.

If your agents handle mixed work, it might help to split their memory into clusters before adding a bigger memory system.

https://arxiv.org/abs/2610.38796

Chat with Paper: https://academy.dair.ai/papers/routed-continual-learning-2610.38796