Anthropic has publicly disclosed Claude's "dreaming" memory system, which draws on the human sleep memory mechanism. During idle computing intervals between sessions, it cross-checks and corrects past interaction records in the background, organizes them, and precipitates scattered information into structured, reusable long-term knowledge. The system divides agent memory into four layers: raw session records, memory notes, a networked memory representation layer, and preloaded injected context. It adopts a strategy of focusing on the major points rather than redundant logs to extract core rules, addressing pain points of traditional architectures such as context bloat and coarse-grained summaries that easily miss key information. In scenarios such as Pokémon game testing and large-scale enterprise document processing, it significantly improves task accuracy and efficiency, and can maintain associated links within a structured knowledge network and correct erroneous content. This mechanism still has problems such as information redundancy loss caused by knowledge abstraction, easy conflicts in multi-agent collaboration, and rising computing power consumption as the knowledge base grows in complexity. A universal standardized operating rhythm for all scenarios has not yet been formed.