MENTOR: Mitigating Identity Drift in Dynamic Role-Playing via Dual-Chain Structured Memory

摘要

Long-context LLM agents increasingly serve multiple users or personas within a single session, requiring stable identity and knowledge boundaries under frequent switching. We identify a common failure mode, identity drift, where models conflate user-specific states and leak information across roles. On BEAM-SWITCH, a benchmark for controlled multi-user switching, performance consistently degrades as switching intensifies, even when responses remain fluent and locally coherent. We propose MENTOR, a cognitive architecture that mitigates identity drift without fine-tuning. MENTOR uses a Dual-Chain Memory Mechanism: a Global Chain (\mathcal{G}) for long-term event logging and isolated Role Chains (\mathcal{R}_r) as per-role working memories, supported by a semantic Knowledge Graph (\mathcal{K}) that filters and verifies role-admissible information before generation. Across six LLM families, MENTOR improves the overall score (Avg) from 0.46 to 0.75 on average (+0.29 absolute), with substantial gains in identity adherence and knowledge fidelity.