Persistent memory can personalize an LLM agent, but an incorrect durable update can distort future behavior. We introduce the Memory–Clarification Boundary benchmark (MCB), which supplies an interaction-derived candidate and a later reuse context and asks whether to persist it, use it locally, verify it, or clarify it with the user. The release contains 140 primary scenarios, 70 evidence-flip contrasts, and a 36-item real-dialogue-derived extension. On 70 adjudicated primary-test items, verification recall exceeds clarification recall in all 12 label conditions across four checkpoints. However, only three comparisons remain significant after an exploratory Fisher test with Holm correction, and none involve Claude. A category oracle attains .800 accuracy, exposing strong scenario-category structure. Original label and tool-selection conditions agree on only 23–57% of items, although their differing prompts prevent attributing this solely to the interface. The naturalistic extension is descriptive: it contains only two clarification items and evaluates local models only. MCB measures reuse-time decision selection under a conservative policy; storage backends, matched policy/tool interventions, and downstream utility remain unevaluated.

Large language models are increasingly proposed as educational tutors, yet stronger task-solving ability does not necessarily imply stronger learning support. Motivated by recent calls to measure the social impact of NLP systems in practice, we study whether public LLM tutoring benchmarks distinguish learning-supportive behavior from mere answer production. We propose a lightweight diagnostic based on the gap between solving-oriented and pedagogy-oriented benchmark performance. Using public MathTutorBench leaderboard results, we show that these dimensions are only partially aligned: across eight publicly reported models, the correlation between solving and pedagogy composites is 0.421, and several models shift meaningfully in rank when evaluation moves from solving to pedagogy. We then analyze the public TutorBench sample and show that agency-relevant behaviors are explicitly encoded in benchmark rubrics, especially in active-learning settings that reward guiding questions, calibrated hints, and non-disclosive scaffolding. Together, these findings suggest that educational-impact evaluation should not treat task success as a sufficient proxy for learning support. We argue that public tutoring benchmarks can better support positive-impact evaluation by reporting solving-oriented and pedagogy-oriented scores separately and by making disclosure-sensitive, student-agency-preserving criteria more explicit.