Provides a formal proof that any semantic memory system (including RAG and vector retrieval) is mathematically guaranteed to suffer from interference and forgetting.
March 31, 2026
Original Paper
The Price of Meaning: Why Every Semantic Memory System Forgets
arXiv · 2603.27116
The Takeaway
This challenges the industry reliance on pure semantic retrieval by proving that the geometric structure required for generalization necessitates false recall and interference. It suggests that scaling vector databases alone cannot solve hallucination and that reasoning-augmented systems may face 'catastrophic failure' rather than graceful degradation.
From the abstract
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 semant