LLM memory systems often filter facts classified as stale from the current prompt. We study this query-time context filtering, without deleting stored memories, in memory-conditioned question answering. Across three models on a synthetic benchmark, controlled label corruption moves hard-drop from gains of +0.186 to +0.230 at perfect resolution to losses of −0.176 to −0.181 at the lowest tested reliability. Paired targeted interventions show greater degradation from false-drop than false-keep; retaining evidence can still fail when incorrect labels steer generation. On one frozen 102/102 calibration/test query split, empirical policy selection using labeled calibration outcomes reaches 0.655 accuracy versus 0.641 for always hard-drop and 0.662 for test-informed oracle selection. A question-disjoint MemoryAgentBench replication is inconclusive: gain +0.03, 95% CI [−0.01, +0.08]. These results establish a controlled sensitivity mechanism, not generalization to long-horizon agents. Global-accuracy crossovers require gold labels and are model- and task-specific diagnostics. Context filtering should be cost-sensitive and validated on matched labeled data; label-free retention is an accuracy fallback under the tested errors, subject to separate privacy and retention requirements.