Large language model (LLM) evaluators are increasingly used to assess personalized assistants, yet whether they can reliably judge personalization has rarely been tested.
We introduce PersEvalBench, a benchmark testing whether an evaluator can recognize appropriate and inappropriate uses of user history.
It contains 403 multi-episode scenarios where user information should persist, transfer, or cease to apply.
The benchmark covers nine controlled error subtypes across three families: under-personalization, over-personalization, and misapplied-state errors.
Human audits confirm that generated errors match their intended types and that the prescribed uses of user information are reasonable.
Across five LLM evaluators, recognition of controlled personalization errors remains limited even with the full dialogue history.
On the human-evaluated subset, under-personalization shows the largest gap: humans recognize 94.4% of these errors, compared with 63.9% for the strongest reported evaluator.
Evaluators often recognize that a response is problematic but reverse the error direction, mistaking failure to follow an applicable preference (UNDER) for use of an inapplicable preference (OVER).
Easier evidence access offers little help: highlighting the relevant history turns raises error recognition only slightly, suggesting that the remaining difficulty lies in judging what role that information should play in the response.