Learning Where a Lesson Stops: Repair Boundaries for Persistent-Memory Agents

Persistent memory allows language agents to carry lessons across episodes, but a repair learned from one failure can become harmful when reused in a nearby state that was already handled correctly. Existing methods improve what agents store and retrieve, yet the failed trajectory itself gives little evidence about where its correction ceases to be valid. We introduce RANGE, which pairs each repair with an activation support and uses matched successful trajectories as boundary evidence for behavior that should be preserved. This evidence constrains which repair–support lesson is stored and where it may act, and the learned support is enforced again at retrieval. Across ALFWorld, WebShop, and ScienceWorld, RANGE improves post-update success by 5.7–6.0 percentage points over ExpeL while reducing Regression by 45.4–50.4% relative to ExpeL.