Meta paper: Pooling agent memories by problem type is more effective
A new Meta paper finds that when AI agents learn from past attempts, matching new problems to notes of similar issues performs better than sharing a single full history. The proposed Routed CL uses an LLM router to reuse or create memory clusters before each run, with each cluster employing one of five memory types (such as raw historical runs or distilled experience).
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