Grading whether a value reached the model was LLM-judged, which puts a model on
the load-bearing path for the claims where a wrong verdict misleads most: if the
judge says a fact was absent, the conclusion is "the context system failed", and
that is an expensive thing to get wrong.
For a concrete value — a date, a channel, a column name, a parameter key — the
question is a substring search over data the harness already captures. Add
`contextAssertions`, checked without an LLM: it cannot hallucinate, costs nothing,
and needs no rubric. `mustAppear: false` covers the inverse, a stale value that
should have been dropped.
It also searches the untruncated context. The judge's view is capped so the part
worth grading stays in its attention, which means a value living in an elided
region reads as absent; this check has no such limit.
Matching is case-insensitive and callers are told to assert atomic values rather
than formatted phrases, because the same value is serialised with different
spacing and quoting depending on which context tier carries it.
Judged `memoryExpectations` stay for claims a string search cannot express — that
a retrieved sibling was the right *kind* of artifact, or that a value is present
but altered. Verdicts from both share `kind: "memory"`, so the report and results
file group them together, and both degrade to `incomplete` on the same missing
capture.
245bc86bb4