Dropout
Dropout randomly zeroes hidden units during training so the net cannot rely on any single co-adaptation, then scales weights at test time (or uses inverted dropout).
Definition
Dropout refers to adaptation, then scales weights at test time (or uses inverted dropout). 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 adaptation, then scales weights at test time (or uses inverted dropout) 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 dropout is saying. If adaptation, then scales weights at test time (or uses inverted dropout) 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 Dropout: what would falsify the current reading in the next window?
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