Paper proposes memory-centric continual learning for foundation-model agents
Original title: Memory-Centric Continual Learning for AI Agents
MCCL judges memory by later task gains, with a taxonomy, two-loop lifecycle, eval protocol, and scaling model.
A paper argues that external memory for foundation-model agents is usually scored as passive recall rather than as a driver of later behavior. Memory-Centric Continual Learning organizes memory by substrate, asset, and policy, and pairs an inner state-update loop with an outer policy loop driven by task feedback. It also defines tests for retention under interference, adaptation, and transfer, plus a scaling model over experience, storage, and compute.
- The taxonomy covers where state is stored, its form, and how it is managed.
- An inner loop updates memory while an outer loop revises management policies.
- The protocol separates behavioral gains from simple context expansion.
- Scaling covers non-parametric and parametric memory under storage and compute budgets.
From the original article
1 min read at huggingface.co
Autonomous agents powered by foundation models can reason, plan, and use tools, but their fixed weights and context limits hinder sustained learning from experience over long-term deployment. While external memory systems attempt to bridge this gap, they mostly act as passive retrieval caches, evaluated primarily by whether they can recall stored facts or past interactions.
By Dongfang Li, Zhuoen Chen, Bonian Jia · huggingface.co
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