@inproceedings{yuan-etal-2026-leaving,
title = "Leaving the Past Behind: Breaking Personalization Inertia in Long-Term {LLM} Agents",
author = "Yuan, Wenhao and
Lin, Chenchen and
Chen, Jian and
Liu, Weijie and
Xu, Jinfeng and
Yang, Shuo and
Ngai, Edith Cheuk-Han",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1744/",
pages = "37311--37333",
ISBN = "979-8-89176-462-0",
abstract = "LLM agents rely on long-term interaction histories to tailor their responses to individual users. However, historical personalization signals may become stale or misaligned with expectations implied by the current context, leading agents to follow outdated preferences during reasoning. We refer to this failure mode as \textit{personalization inertia}, which shifts the agent{'}s behavior away from the user{'}s current intent. Existing methods mainly optimize final responses or memory retrieval, leaving such failures underexplored. In this paper, we propose \textbf{P}ersonalization \textbf{I}nertia \textbf{V}erificati\textbf{O}n and Regula\textbf{T}ion (\textsc{Pivot}), a framework for mitigating personalization inertia in long-term interactions with agents. Concretely, we construct a trajectory-independent evidence condition as a diagnostic reference, model prefix-evidence consistency to assess compatibility with the current personalization evidence, and learn a rollback policy to determine whether and where to intervene. The selected intervention triggers local repair, preserving the reliable prefix while regenerating only the potentially inertia-affected suffix under evidence-guided constraints. Experiments on benchmark datasets demonstrate consistent gains in personalization accuracy and trajectory faithfulness with targeted intervention overhead."
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<abstract>LLM agents rely on long-term interaction histories to tailor their responses to individual users. However, historical personalization signals may become stale or misaligned with expectations implied by the current context, leading agents to follow outdated preferences during reasoning. We refer to this failure mode as personalization inertia, which shifts the agent’s behavior away from the user’s current intent. Existing methods mainly optimize final responses or memory retrieval, leaving such failures underexplored. In this paper, we propose Personalization Inertia VerificatiOn and RegulaTion (Pivot), a framework for mitigating personalization inertia in long-term interactions with agents. Concretely, we construct a trajectory-independent evidence condition as a diagnostic reference, model prefix-evidence consistency to assess compatibility with the current personalization evidence, and learn a rollback policy to determine whether and where to intervene. The selected intervention triggers local repair, preserving the reliable prefix while regenerating only the potentially inertia-affected suffix under evidence-guided constraints. Experiments on benchmark datasets demonstrate consistent gains in personalization accuracy and trajectory faithfulness with targeted intervention overhead.</abstract>
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%0 Conference Proceedings
%T Leaving the Past Behind: Breaking Personalization Inertia in Long-Term LLM Agents
%A Yuan, Wenhao
%A Lin, Chenchen
%A Chen, Jian
%A Liu, Weijie
%A Xu, Jinfeng
%A Yang, Shuo
%A Ngai, Edith Cheuk-Han
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F yuan-etal-2026-leaving
%X LLM agents rely on long-term interaction histories to tailor their responses to individual users. However, historical personalization signals may become stale or misaligned with expectations implied by the current context, leading agents to follow outdated preferences during reasoning. We refer to this failure mode as personalization inertia, which shifts the agent’s behavior away from the user’s current intent. Existing methods mainly optimize final responses or memory retrieval, leaving such failures underexplored. In this paper, we propose Personalization Inertia VerificatiOn and RegulaTion (Pivot), a framework for mitigating personalization inertia in long-term interactions with agents. Concretely, we construct a trajectory-independent evidence condition as a diagnostic reference, model prefix-evidence consistency to assess compatibility with the current personalization evidence, and learn a rollback policy to determine whether and where to intervene. The selected intervention triggers local repair, preserving the reliable prefix while regenerating only the potentially inertia-affected suffix under evidence-guided constraints. Experiments on benchmark datasets demonstrate consistent gains in personalization accuracy and trajectory faithfulness with targeted intervention overhead.
%U https://aclanthology.org/2026.findings-emnlp.1744/
%P 37311-37333
Markdown (Informal)
[Leaving the Past Behind: Breaking Personalization Inertia in Long-Term LLM Agents](https://aclanthology.org/2026.findings-emnlp.1744/) (Yuan et al., Findings 2026)
ACL