RETENTION IS NOT ENOUGH: SELECTIVE READOUT IN BOUNDED HYBRID MEMORY

Correct retention and correct slot selection need not produce a correct answer. We examine this distinction in typed online streams containing shared rules and sparse overrides, with a common 256-byte persistent-state budget for learned systems. Across three frozen workflow fits, 772 of 4,602 exception-query evaluations remain incorrect after decoded-content and addressing checks pass; replacing soft retrieval with the stored top-1 payload rescues only 30. On a separate query subset, masking nonmatching payloads raises rule accuracy from 60.15% to 86.93% at fixed weights, with little change to exception accuracy. Training shared predictors with either a hard match gate or a one-scalar soft-null option largely repairs the failure, without full parameter isolation. The hard gate reaches 99.959% on original-task streams of 8,192 events; the soft null is correct on every sampled workflow query at lengths 2,048 and 8,192. These controlled, prior-informed tasks admit smaller exact programs. Our contribution is a query-aligned diagnosis of retained content, slot selection and downstream use, together with tests of simple rejection-capable readers, rather than a new memory or attention primitive.