Contextual Agentic Memory is a Memo, Not True Memory
Current agentic memory systems (vector stores, retrieval-augmented generation, scratchpads, and context-window management) do not implement memory: they implement lookup. We argue that treating lookup as memory is a category error with provable consequences for agent capability, long-term learning, and security. Retrieval generalizes by similarity to stored cases; weight-based memory generalizes by applying abstract rules to inputs never seen before. Conflating the two produces agents that accumulate notes indefinitely without developing expertise, face a provable generalization ceiling on compositionally novel tasks that no increase in context size or retrieval quality can overcome, and ...
| Authors | Binyan Xu, Xilin Dai, Kehuan Zhang |
|---|---|
| Primary category | cs.AI |
| Published | 30 April 2026 |
| Last updated at source | 5 August 2026 |
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