Every frontier model today has the same hidden inefficiency: it recomputes facts it already knows. Ask it for the capital of France and a few billion parameters fire, matrix-multiplying their way to "Paris" — a fact that was fixed in training and will never change. A new paper from a DeepSeek-linked team asks the obvious question nobody built for: why not just look it up? The paper — "Conditional Memory via Scalable Lookup" (arXiv:2601.07372, submitted January 2026, revised July 2026) — i...