AI coding agents operate in a paradox: they possess vast parametric knowledge yet cannot remember a conversation from an hour ago. Existing memory systems store text in vector databases with single-channel retrieval, require cloud LLMs for core operations, and implement none of the cognitive processes that make human memory effective. We present SuperLocalMemory V3.3 ("The Living Brain"), a local-first agent memory system implementing the full cognitive memory taxonomy with mathematical lifecycle dynamics. Building on the information-geometric foundations of V3.2 (arXiv:2603.14588), we introduce five contributions: (1) Fisher-Rao Quantization-Aware Distance (FRQAD) -- a new metric on the Gaussian statistical manifold achieving 100% precision at preferring high-fidelity embeddings over quantized ones (vs 85.6% for cosine), with zero prior art; (2) Ebbinghaus Adaptive Forgetting with lifecycle-aware quantization -- the first mathematical forgetting curve in local agent memory coupled to progressive embedding compression, achieving 6.7x discriminative power; (3) 7-channel cognitive retrieval spanning semantic, keyword, entity graph, temporal, spreading activation, consolidation, and Hopfield associative channels, achieving 70.4% on LoCoMo in zero-LLM Mode A; (4) memory parameterization implementing Long-Term Implicit memory via soft prompts; (5) zero-friction auto-cognitive pipeline automating the complete memory lifecycle. On LoCoMo, V3.3 achieves 70.4% in Mode A (zero-LLM), with +23.8pp on multi-hop and +12.7pp on adversarial. V3.2 achieved 74.8% Mode A and 87.7% Mode C; the 4.4pp gap reflects a deliberate architectural trade-off. SLM V3.3 is open source under the Elastic License 2.0, runs entirely on CPU, with over 5,000 monthly downloads.

翻译：人工智能编码智能体陷入一个悖论：它们拥有海量的参数化知识，却无法记住一小时前的对话。现有记忆系统将文本存储在向量数据库中，仅采用单通道检索，依赖云端大语言模型进行核心操作，且完全未实现构成人类记忆有效性的任何认知过程。我们提出SuperLocalMemory V3.3（“活体大脑”），一个本地优先的智能体记忆系统，该系统实现了完整的认知记忆分类体系并带有数学化的生命周期动态演化。基于V3.2版本（arXiv:2603.14588）的信息几何基础，我们引入了五项贡献：（1）Fisher-Rao量化感知距离——一个基于高斯统计流形的新度量，在高保真嵌入优于量化嵌入的偏好选择上实现了100%的精度（而余弦度量仅为85.6%），且无任何现有先例；（2）具备生命周期感知量化的艾宾浩斯自适应遗忘——本地智能体记忆中的首个数学化遗忘曲线，与渐进式嵌入压缩相结合，实现了6.7倍的判别能力提升；（3）覆盖语义、关键词、实体图谱、时序、扩散激活、巩固及霍普菲尔德联想通道的七通道认知检索，在零大语言模型模式A下于LoCoMo基准上达到70.4%的性能；（4）通过软提示实现长时内隐记忆参数化的记忆表征技术；（5）实现完整记忆生命周期自动化的零摩擦自动认知流水线。在LoCoMo基准上，V3.3在模式A（零大语言模型）下达到70.4%，多跳任务提升23.8个百分点，对抗性任务提升12.7个百分点。V3.2在模式A和模式C下分别达到74.8%和87.7%；这4.4个百分点的差距反映了审慎的架构权衡。SLM V3.3采用弹性许可证2.0开源，完全运行于CPU之上，月下载量超过5000次。