| Metrics | Explanations | Rubrics |
| MA-SI (Source Invariance) | Measures consistency between the response generated with the anonymized persona y^anon and the original one y^ . A high score indicates grounding in persona semantics, not name priors. | 1: The two answers differ greatly in meaning, i.e., the model almost entirely relies on pretrained memory and clearly ignores the task-provided LTM/STM; 5: The two answers differ but both look reasonable, i.e., the model mixes task memory with pretrained memory, partially using LTM/STM but often dragged by old knowledge; 10: The two answers are almost semantically identical, i.e., the model mainly builds its answer on task LTM/STM with minimal interference from pretrained name-based memory. |
| MA-AF (Alias Fidelity) | Assesses whether the behavior under an anonymized persona y^anon remains faithful to the original intended character, using the ground-truth response y^gold as an anchor. | 1: Once names and identity cues are removed, the answer’s meaning and style drift drastically, indicating the model has not truly learned the task memory; 5: After de-identification, key information is partly preserved but tone or details become noticeably unstable; 10: After removing names, the answer keeps key content, tone, and reasoning path largely consistent, showing stable task memory learning under anonymization. |
| MS-FA (Facet Alignment) | Quantifies the model’s precision in selecting the correct scene facet by contrasting responses under the true ML versus a counterfactual (inverted) LTM MLanti . | 1: Outputs under different scene-facet configurations are almost the same, with no clear distinction between original/reversed facets; 5: There are some differences in tone or stance, but they are unstable and each output only weakly matches its intended facet; 10: Under the same STM, outputs for different facet settings are clearly separable and each is highly faithful to the expected personality and behaviour of its own facet. |
| MS-FU (Facet Utility) | Measures the improvement gained by including scene-specific facets in the LTM, compared to a scene-ablated LTM MLno-scene . | 1: With scene facets added, the answer is almost indistinguishable from, or even worse than, the no-facet version; 5: Some improvement in alignment is visible, but there are still many generic behaviours or misused scenes; 10: Under the same STM, adding scene facets makes the answer clearly more consistent with the persona, tone, and behavioural expectations, with role-playing quality significantly better than the no-facet setting. |
| MB-AL (Answer Leakage) | Scores the model’s ability to avoid generating a forbidden reference answer y^out when presented with an out-of-scope prompt cKout (e.g., a future plot spoiler). | 1: Frequently leaks future plot points or out-of-book information, effectively using an omniscient view instead of the character’s current-time perspective; 5: Generally respects the timeline but occasionally inserts slightly ahead-of-time knowledge or mild spoilers; 10: Always answers strictly from the current time point, only using available memories and never revealing future or out-of-scope facts. |
| MB-CR (Controlled Response) | Assesses the appropriateness of the model’s response strategy to out-of-scope prompts, favoring expressions of uncertainty, refusal, or grounded speculation over confident fabrication. | 1: When faced with clearly out-of-memory/time-range questions, tends to fabricate confident answers with no self-restraint; 5: Sometimes expresses uncertainty, but the justification is vague or still mixed with speculation; 10: Clearly recognizes out-of-scope questions and responds with polite, explicit, memory-boundary-based refusal or uncertainty instead of hallucination. |