Melo: A Cognitive Science–Inspired Closed-Loop Long-Term Memory Framework for AI Agents

Long-term memory enables AI agents to maintain coherent and personalized behavior across sessions. However, many memory architectures implement writing, retrieval, revision, and forgetting as separate operations, leaving weak attribution between a memory's use and later evidence that confirms or contradicts it. Drawing on cognitive science, we propose Melo, a closed-loop framework that coordinates attention-biased selective encoding, outcome-triggered feedback admission, evidence-linked procedural reconsolidation, and time-dependent retention control. Corrections, tool evidence, task outcomes, and conflict signals can therefore change the memory state used for subsequent retrieval and response generation. We also introduce CMLM-Assist, a 1,536-question benchmark that evaluates user-profile retention, cross-session task continuity, procedural reuse, and memory-grounded response generation. Across six baselines and three language-model backbones, Melo achieves competitive overall question-answering performance on the public LoCoMo benchmark and state-of-the-art performance under the evaluated CMLM-Assist protocol. These results support integrated coordination of memory use and revision while delimiting unresolved challenges in conflict resolution, equal-budget comparison, and long-term deployment.