GovernMem: Governed Evolution of Persistent Agent Memory through Failure Repair

Long-horizon agents use persistent memory to retain experience and guide future behavior. However, memory errors can spread across interactions, and incorrect experience can also poison subsequent learning. Although prior work has advanced memory repair and experience reuse, a closed loop connecting repair outcomes and reusable experience for agent self-evolution is still missing. To address this challenge, we introduce GovernMem, a framework without updating model weights. GovernMem uses a state machine to manage repairs and collect verified feedback. GovernRouter then learns from this feedback to adjust future repair choices. As repair programs accumulate, GovernMem tracks their versions and manages their reuse. Experiments on Meta-Llama-3.1-8B-Instruct and Qwen3-14B show that matched experience reuse improves mean repair utility by 6.39 and 6.63 points. GovernRouter also achieves the highest repair utility among seven evaluated routers on both models under the matched setting.