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The author criticizes existing AI memory platforms for lacking multi-agent memory, poor long-term recall after many interactions, and no forgetting mechanism, and is building a new solution; asks the community for additional pain points.
StageMem proposes a lifecycle-managed memory framework for language models that organizes memory into transient, working, and durable stages with explicit confidence and strength metrics, treating memory as a stateful process rather than a static store to better manage retention and forgetting under bounded capacity.