card: specialist vs generalist evaluation modes

README.md

@@ -163,6 +163,28 @@ A better model can score *worse*: anything right where the teacher is wrong is
 penalised. The teacher missed a duplicate invoice whose ID literally matched a
 prior one.
 
+## Two ways to be evaluated on this, and why it matters
+
+The System One models this benchmark is shaped after are **general pretrained
+models**: their API exposes one call that takes an arbitrary question schema at
+request time, with no training step and no per-workflow setup. Anything scored
+here should say which of these it is, because the two are not comparable:
+
+| | What it is | What it needs | Can it answer an unseen question? |
+|---|---|---|---|
+| **Specialist** | fitted per workflow, label spaces fixed at training time | training data for *these* workflows | No |
+| **Generalist** | one model, arbitrary question schemas, zero-shot | training data from *other* workflows | Yes |
+
+A specialist trained on these four workflows and scored on them is measuring
+**architecture** — how cheaply many typed decisions can be produced from one
+input. That is a legitimate question and this benchmark answers it well. It is
+*not* a comparison against a general System One model, which has never seen
+these workflows.
+
+To be scored as a generalist, train on other workflows entirely and evaluate
+here zero-shot. Publish which mode you used; a specialist number next to a
+generalist number, unlabelled, is a misleading comparison.
+
 ## Honest limitations
 
 **The gold is a teacher signal, not ground truth.** It comes from a model of