# Maintenance plan

## What can rot, and what we do about it

An agentic benchmark has a dependency no dataset paper usually carries: the
worker model that established what each probe instance is worth. Every
instance here passed a floor gate (a memoryless worker cannot answer it) and a
ceiling gate (a worker given the facts can answer it), both measured under one
pinned worker. Those anchors are not a property of the data alone.

That dependency is not hypothetical. The worker this dataset was screened
under (gpt-5.4, effort medium, codex-cli 0.144.5) was deprecated by its
provider during the study, which is why v1 publishes no comparative system
numbers. The procedure below is what we did about it, written down so a third
party can do it without asking us.

## Re-anchoring procedure

1. **Pick the successor by rule, not by taste.** The nearest same-provider
   successor available at the time the pinned worker became unreachable.
   Record the model id, the reasoning-effort setting, and the exact harness
   version; pin all three the way 0.144.5 was pinned.
2. **Re-run the anchors** for the public seeds under the new pin: floor,
   ceiling, and twin-ceiling for every instance.
3. **Apply the frozen gate rules mechanically.** No criterion edits, no
   adjudications, no re-judging of anything that already has a verdict. Task
   text must stay byte-identical. Verify with the prompt-equality check
   before reusing any cached output.
4. **Publish the new valid set as its own thing.** It will not be the same 371
   instances, and that is the expected result, not a defect: probe validity is
   task-model-relative and we measured how much (37/54 vs 17/54 cluster
   survival across two workers under identical rules).
5. **Never mix.** Anchors from one worker and system results from another
   cannot be combined. Normalization and pair validity both break, and the
   resulting numbers mean nothing.

## Versioning

Semantic versioning on the dataset. A new screened seed or a re-anchored valid
set is a minor version; any change to the streams, probes, or assertions is a
major version and gets a new DOI. Withheld material stays withheld across
versions.

## Contamination

Canary strings are embedded in every released stream. If a model reproduces
one, it has been trained on this data. The generator and the two unscreened
holdout seeds are withheld so fresh organizations can be minted if the public
seeds become contaminated.

## Contact

Issues and correspondence through the repository.