LifeFuse-Mem: Lifecycle-Aware State Fusion Against Temporary Overwriting for Long-Term Memory

• URL: http://arxiv.org/abs/2609.12436v1
• Date: Fri, 11 Sep 2026 04:56:48 GMT
• Title: LifeFuse-Mem: Lifecycle-Aware State Fusion Against Temporary Overwriting for Long-Term Memory
• Abstract summary: LLM agents require memory mechanisms that maintain coherent internal states across interactions.<n>LifeFuse-Mem is a neural memory framework that separates information according to its temporal commitment.<n>LifeFuse-Mem improves acquisition-controlled retention and reduces temporary overwrite.
• Score: 16.775600156252096
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Long-running LLM agents require memory mechanisms that maintain coherent internal states across interactions. We study a lifecycle-labeled memory setting in which write episodes provide lifecycle metadata during training, and phase-aware readout is used during evaluation. This setting reflects the need to distinguish information that should remain influential across future interactions from information that should affect only the current context. A mismatch between these lifecycles can cause temporary information to overwrite durable knowledge, leading to behavioral drift in persistent agents. Within this setting, we introduce \textbf{LifeFuse-Mem}, a lifecycle-aware neural memory framework that separates information according to its temporal commitment. LifeFuse-Mem uses dedicated memory components and lifecycle-aware updates to allow stable and transient knowledge to evolve locally without converting temporary context into durable state. On the controlled anti-overwrite benchmark, LifeFuse-Mem improves acquisition-controlled retention and reduces temporary overwrite; on two public long-memory benchmarks, it remains broadly competitive. These results suggest that explicit lifecycle signals can help diagnose and mitigate overwrite in compact online memory.

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