MCCL Paper Reframes Agent Memory as an Active Driver of Continual Learning
Dongfang Li · hf · 2026-10-22
The MCCL paper argues agent memory should be judged by whether it improves future actions, not just recall. It introduces a three-axis taxonomy (substrates/assets/policies), a two-loop lifecycle, an evaluation protocol measuring retention, adaptation and transfer, and a memory scaling framework linking performance to experience, storage and compute budgets.
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