Do Agents Truly Forget? A Lifecycle Benchmark and Memory Receipts for Long-Term Memory

Long-term memory is becoming a core component of intelligent agent systems, motivating growing research on how user information should be stored, consolidated, updated, and retrieved. However, once information has been consolidated into derived long-term memory, reliably revoking its influence remains difficult to define and verify. Invalidating the original record may leave semantic residue in derived memories; removing an entire memory unit may destroy other information that should remain; and appending a negative instruction keeps the invalidated content in the model context. This paper studies verifiable forgetting in long-term agent memory: can an agent behave as if it had never observed that source, while preserving information supported by the remaining history? To make this question measurable, we construct a unified benchmark suite containing 865 evaluation cases adapted from five public benchmarks. By comparing Invalidated and Never-Seen worlds, the suite distinguishes residual personalized influence from content independently regenerated by the model. We further introduce Memory Receipts, which associate each derived memory with source lineage maintained in a separate memory maintenance layer and reconstruct only the memory units affected by invalidation. This mechanism avoids rebuilding the complete user history and requires no persistent invalidation instruction in future queries, enabling traceable and verifiable memory repair with recomputation limited to affected memory units. Our work positions forgetting as a first-class operation in the lifecycle of long-term agent memory, alongside writing, updating, and retrieval.