Rethinking AI Memory: From Raw Ledger to Policy‑Driven Closed Loop
The article argues that AI memory is not mere storage but an external state that feeds decisions, proposes three core propositions—Memory as decision‑usable external state, a minimal closure of Raw Ledger + Views + Policy, and event sequences as the fundamental unit—and details how a System 1 + System 2 architecture, non‑parametric designs, temporal handling, and learnable policies together shape the practical limits of modern agentic memory systems.
