@inproceedings{zhuoning-etal-2026-mentor,
title = "{MENTOR}: Mitigating Identity Drift in Dynamic Role-Playing via Dual-Chain Structured Memory",
author = "Zhuoning, Zhu and
Gao, Xingyu and
Shi, Hailong",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1046/",
doi = "10.18653/v1/2026.findings-acl.1046",
pages = "20865--20882",
ISBN = "979-8-89176-395-1",
abstract = "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."
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<abstract>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 (\mathcalG) for long-term event logging and isolated Role Chains (\mathcalR_r) as per-role working memories, supported by a semantic Knowledge Graph (\mathcalK) 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.</abstract>
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%0 Conference Proceedings
%T MENTOR: Mitigating Identity Drift in Dynamic Role-Playing via Dual-Chain Structured Memory
%A Zhuoning, Zhu
%A Gao, Xingyu
%A Shi, Hailong
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F zhuoning-etal-2026-mentor
%X 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 (\mathcalG) for long-term event logging and isolated Role Chains (\mathcalR_r) as per-role working memories, supported by a semantic Knowledge Graph (\mathcalK) 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.
%R 10.18653/v1/2026.findings-acl.1046
%U https://aclanthology.org/2026.findings-acl.1046/
%U https://doi.org/10.18653/v1/2026.findings-acl.1046
%P 20865-20882
Markdown (Informal)
[MENTOR: Mitigating Identity Drift in Dynamic Role-Playing via Dual-Chain Structured Memory](https://aclanthology.org/2026.findings-acl.1046/) (Zhuoning et al., Findings 2026)
ACL