Every major AI memory system in production today organises information by meaning. That organisation enables generalisation, analogy, and conceptual retrieval -- but it comes at a price. We prove that the same geometric structure enabling semantic generalisation makes interference, forgetting, and false recall inescapable. We formalise this tradeoff for \textit{semantically continuous kernel-threshold memories}: systems whose retrieval score is a monotone function of an inner product in a semantic feature space with finite local intrinsic dimension. Within this class we derive four results: (1) semantically useful representations have finite effective rank; (2) finite local dimension implies positive competitor mass in retrieval neighbourhoods; (3) under growing memory, retention decays to zero, yielding power-law forgetting curves under power-law arrival statistics; (4) for associative lures satisfying a $δ$-convexity condition, false recall cannot be eliminated by threshold tuning. We test these predictions across five architectures: vector retrieval, graph memory, attention-based context, BM25 filesystem retrieval, and parametric memory. Pure semantic systems express the vulnerability directly as forgetting and false recall. Reasoning-augmented systems partially override these symptoms but convert graceful degradation into catastrophic failure. Systems that escape interference entirely do so by sacrificing semantic generalisation. The price of meaning is interference, and no architecture we tested avoids paying it.

翻译：当今生产环境中所有主要AI记忆系统都通过意义组织信息。这种组织方式实现了泛化、类比和概念检索，但代价高昂。我们证明，支持语义泛化的几何结构必然导致干扰、遗忘和错误回忆。针对\textit{语义连续核阈值记忆}系统（检索得分为语义特征空间中内积的单调函数，且特征空间具有有限局部内维数），我们形式化了这一权衡机制。在该框架下，我们推导出四项结论：(1) 具有语义效用的表征必然具有有限有效秩；(2) 有限局部维数意味着检索邻域中存在正竞合质量；(3) 随记忆增长，保留率衰减至零，在幂律到达统计下产生幂律遗忘曲线；(4) 对于满足$δ$-凸性条件的关联诱饵，阈值调整无法消除错误回忆。我们在五种架构中验证了这些预测：向量检索、图记忆、基于注意力的上下文、BM25文件系统检索和参数化记忆。纯语义系统直接表现为遗忘和错误回忆。增强推理的系统能部分抑制这些症状，但将优雅退化转变为灾难性失败。完全规避干扰的系统则以牺牲语义泛化为代价。意义的代价是干扰，而我们测试的所有架构均无法避免这一代价。