Brilliant paper on agent memory from FAIR at Meta.
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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, a
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