Modern retrieval-augmented generation (RAG) systems treat vector embeddings as static, context-free artifacts: an embedding has no notion of when it was created, how trustworthy its source is, or which other embeddings depend on it. This flattening of knowledge has a measurable cost: recent work on VersionRAG reports that conventional RAG achieves only 58% accuracy on versioned technical queries, because retrieval returns semantically similar but temporally invalid content. We propose SmartVector, a framework that augments dense embeddings with three explicit properties -- temporal awareness, confidence decay, and relational awareness -- and a five-stage lifecycle modeled on hippocampal-neocortical memory consolidation. A retrieval pipeline replaces pure cosine similarity with a four-signal score that mixes semantic relevance, temporal validity, live confidence, and graph-relational importance. A background consolidation agent detects contradictions, builds dependency edges, and propagates updates along those edges as graph-neural-network-style messages. Confidence is governed by a closed-form function combining an Ebbinghaus-style exponential decay, user-feedback reconsolidation, and logarithmic access reinforcement. We formalize the model, relate it to temporal knowledge graph embedding, agentic memory architectures, and uncertainty-aware RAG, and present a reference implementation. On a reproducible synthetic versioned-policy benchmark of 258 vectors and 138 queries, SmartVector roughly doubles top-1 accuracy over plain cosine RAG (62.0% vs. 31.0% on a held-out split), drops stale-answer rate from 35.0% to 13.3%, cuts Expected Calibration Error by nearly 2x (0.244 vs. 0.470), reduces re-embedding cost per single-word edit by 77%, and is robust across contradiction-injection rates from 0% to 75%.

翻译：现代检索增强生成系统将向量嵌入视为静态且无上下文的人工制品：嵌入缺乏对其创建时间、信息来源可信度以及与其他嵌入依赖关系的认知。这种知识的扁平化处理带来了可量化的代价——近期关于VersionRAG的研究表明，传统RAG在版本化技术查询上的准确率仅为58%，因为检索系统会返回语义相似但时间维度无效的内容。我们提出SmartVector框架，通过三个显式属性——时间感知、置信衰减和关系感知——增强稠密嵌入，并构建基于海马体-新皮层记忆巩固机制的五阶段生命周期。检索流程将纯余弦相似度替换为四信号评分，融合语义相关性、时间有效性、实时置信度和图关系重要性。后台巩固代理通过检测矛盾、构建依赖边并沿这些边传播图神经网络式消息。置信度由结合艾宾浩斯指数衰减、用户反馈再巩固与对数访问强化的闭式函数调控。我们对模型进行形式化描述，将其关联至时序知识图谱嵌入、智能体记忆架构及不确定性感知RAG，并提供参考实现。在包含258个向量与138个查询的可复现合成版本化策略基准测试中，SmartVector在top-1准确率上较纯余弦RAG实现近两倍提升（留出集上62.0%对比31.0%），僵化答案率从35.0%降至13.3%，期望校准误差降低近2倍（0.244对比0.470），单次词级编辑的重新嵌入成本减少77%，且在0%至75%矛盾注入率下保持鲁棒性。