The Price of Meaning: Why Every Semantic Memory System Forgets
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 impl...
| Authors | Sambartha Ray Barman, Andrey Starenky, Sofia Bodnar et al. |
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| Primary category | cs.AI |
| Published | 28 March 2026 |
| Last updated at source | 28 March 2026 |
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