Mixture of Experts
MoE routes each token (or example) to a sparse subset of specialist feed-forward experts, raising parameter count without paying dense FLOPs on every token.
Definition
Mixture of Experts refers to forward experts, raising parameter count without paying dense FLOPs on every token. Keep that definition fixed when comparing series, managers, or regimes — renaming the same tape does not create a new signal.
Why it matters
It binds model output to retrieval, tools, or evaluation so answers stay grounded instead of free-floating. When forward experts, raising parameter count without paying dense FLOPs on every token shifts, related hedges, limits, and narratives usually need an explicit update rather than a quiet assumption.
Case
Suppose a desk is positioned for the opposite of what mixture of experts is saying. If forward experts, raising parameter count without paying dense FLOPs on every token moves against that book, the first question is not “is the story clever?” but whether size, hedges, and stop logic still match the observation.
How to read it
Measure grounding rate, latency, and failure modes under missing context — not demo chat quality alone. Prefer a short written null hypothesis for Mixture of Experts: what would falsify the current reading in the next window?