canary-trap, fault-localisation and schema-migration, in the three domains the
Environments Hub has nothing in: contamination, operations, data engineering.
None is a port. Each is graded on a slice the model never read.
canary-trap runs a probe set against a model that memorised the corpus AND one
that learned the same facts from a reworded copy with every identifier
regenerated. The obvious probe -- plant a GUID, ask for it back -- is sound and
caps at 0.525 because it cannot see the paraphrase. It scored higher than that
at first: facts with no distinct rewording had the paraphrase restating the
corpus verbatim, so GUID probes caught it by accident. Every fact now carries a
"core" token present in both wordings and in no public one, asserted at import,
which is also what makes the ceiling reachable.
fault-localisation puts the fault upstream and the noise downstream, the way
incidents actually present. The broken service logs one line and goes quiet
while its dependents log a dozen timeouts, so ranking by error volume answers
the victim -- 0/64 incidents have the loudest service as the root, and probe.py
asserts it rather than trusting the generator to stay that way.
schema-migration executes the model's SQL against forty held-out rows carrying
thousands separators, negatives, a unit with a slash in it and a value with no
unit. Row preservation started as a reward beside fidelity and paid 0.15 for
adding two empty columns and touching nothing; it is a multiplier now, so
losing rows still zeroes a perfect migration and keeping them earns nothing on
its own.
probe.py is house rule 3 as an exit code: 0.000 for inaction and 1.000 for an
oracle, or it returns 1. It loads the leaf modules through a namespace shim
rather than the packages, so it runs without verifiers installed -- a self-check
that needs the training stack is a self-check nobody runs.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
kartiandClaude Opus 5
5a99eb86a5