Not Only Remember, But Also Forget: A Multi‑Scenario Benchmark for Selective Forgetting in Personalized Agents

Personalized language agents rely on long-term memory to maintain user-specific knowledge across conversations. Real-world deployments demand not only reliable memory retention but also selective forgetting: upon user request or fact obsolescence, agents must suppress target information, keep semantically unrelated facts intact, block indirect inference-driven leakage, and stop outdated facts from steering downstream behavior. Existing evaluations emphasize recall accuracy and offer limited checks of retrieved evidence, indirect leakage, and residual behavioral influence, hiding critical safety flaws of memory systems. To fill this gap, we introduce MEMFORGET, a multi-scenario benchmark for selective forgetting in personalized agents. We design five realistic evaluation scenarios covering temporal updates, revoked preference decoupling, user-driven privacy erasure, value-aware discrimination between critical safety constraints and trivial history, and localized targeted forgetting. Uniquely, we conduct two-layer auditing: retrieved memory evidence and final agent answers. Dataset quality is guaranteed via rule-level checks, LLM-assisted filtering and manual human review. Across four language models and six memory agents, no system meets all forgetting requirements. Excluding deleted targets from retrieval outperforms answer-side filtering, especially on targeted forgetting and indirect privacy probes. Higher recall can increase leakage, while stronger deletion can damage critical or neighboring facts. Direct refusal can also coexist with indirect leakage. These findings reveal that selective forgetting constitutes a comprehensive system-level capability instead of an isolated local operation. MEMFORGET enables fine-grained diagnosis for future memory governance systems. The anonymized data and code are available at https://anonymous.4open.science/r/MEMFORGET-C927/.