Memory-Centric Continual Learning for AI Agents
In this paper, we introduce Memory-Centric Continual Learning (MCCL), arguing that memory should be judged by whether it improves future actions, not merely what it retrieves.
ProofPaper ↗
Key points
- 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.
- Whether memory can drive genuine continual learning remains an open question.
- Third, a memory scaling framework models how agent performance scales with experience, storage capacity, and compute budgets across non-parametric and parametric memory.
- Together, these components reframe agent memory from passive lookup to continuous learning, offering a principled route toward self-improving autonomous agents.
Sources (2)
- [1]Memory-Centric Continual Learning for AI AgentsHugging Face Daily Papers · Oct 21, 12:00 AM
In this paper, we introduce Memory-Centric Continual Learning (MCCL), arguing that memory should be judged by whether it improves future actions, not merely what it retrieves.
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.
- [2]Memory-Centric Continual Learning for AI AgentsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 21, 07:49 AM · same content
Extractive summary: sentences quoted from the sources.
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