MEMOREPAIR: Barrier-First Cascade Repair in Agentic Memory
Yang Zhao, Chengxiao Dai, Mengying Kou, Yue Xiu

TL;DR
MemoRepair introduces a barrier-first cascade repair method for agentic memory, effectively updating invalidated derived artifacts to maintain consistency and reduce stale information in memory systems.
Contribution
It formalizes the cascade update problem and provides an exact solution using max-weight predecessor closure via min-cut, improving memory repair efficiency.
Findings
Reduces invalidated-memory exposure from 69.8-94.3% to 0%.
Recovers over 91% of validated successors compared to exhaustive repair.
Cuts repair-operator cost by approximately 24-43%.
Abstract
Agentic memory evolves across tasks into durable derived artifacts: summaries, cached outputs, embeddings, learned skills, and executable tool procedures. When a source artifact is deleted, corrected, or invalidated by tool or API migration, descendants derived from that source can remain visible and steer future actions with stale support. We formalize this failure mode as the cascade update problem, where repair targets the visible derived state of the memory store. We present MemoRepair, a barrier-first cascade-repair contract for agentic memory. A repair event induces a controlled transition from invalidated descendant state to validated successor state: affected descendants are withdrawn before repair, successors are constructed from retained support and staged repaired predecessors under the current interface, and republication is restricted to validated predecessor-closed…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
