Instead of searching all memories every forward pass, stateful retrieval navigates locally through memory neighborhoods, potentially making long-context reasoning more efficient by exploiting overlap between consecutive retrieval problems.
This paper proposes a new approach to memory retrieval for long-context AI agents that treats retrieval as persistent navigation through an address space, rather than searching from scratch each time. By separating memory content from its location and charging for access cost, the method encourages related memories to cluster together, reducing redundant retrieval work across similar tasks.