Most AI code-reviewer evaluations treat the candidate as an amnesiac: feed it one pull request, read one verdict, and move on. Persistent-memory reviewers break that model because they keep history across PRs, and that history becomes a second source of bugs. The dominant failure is no longer amnesia but overconfidence in stale context. A two-phase probe exposes whether a candidate trusts its own memory more than the repository's current decisions.
This article supplies the complete take-home package: a fixture repository, a reusable candidate prompt, an HTTP-flavored scoring rubric, a reference solution, and a zero-cost runner script. The probe uses two synthetic PRs and measures one skill: which convention source wins inside the reviewer's context window. That focus separates it from single-shot snapshot tests, which cannot observe memory effects at all.
Why Memory Changed the Review Game
Review agents increasingly index merged PRs, cache decision logs, and carry state between sessions; memory is now a product feature rather than an accident. A bot that recalled yesterday's debate can produce faster and better reviews than a cold-start model. The same memory can poison verdicts when it retrieves an obsolete decision or anchors on the first PR it ever saw.
