From Deletion to Action: An Empirical Study of Selective Deletion in Agent Memory

Persistent memory enables large language model (LLM) agents to draw on past interactions. Privacy and other practical needs motivate selective deletion: removing designated information and its influence on subsequent behavior while preserving other useful memories. Yet it remains unclear how well the record-level deletion operations available in existing memory frameworks satisfy this requirement. We empirically assess selective deletion of user preferences across four memory frameworks by tracing deletion operations through stored memory and context to task actions. We measure behavioral residue relative to a NeverSeen baseline with no prior exposure to the target preference. We find that a mismatch between preferences and records can leave alternative representations of the target preference intact or remove non-target preferences consolidated in the same record. Moreover, failure to detect a target preference in context neither establishes its removal from stored memory nor guarantees its absence from later contexts. At the behavioral level, near-zero overall mean residue can mask positive residue in subgroups where the target preference remains detectable. We find that post-deletion behavioral residue varies across reading model configurations within the same memory framework and on the same scenarios. These findings show that record deletion, absence from context, and the disappearance of behavioral influence need not coincide, motivating a joint assessment of stored memory, context visibility, and subsequent task actions.