Controllable Memory Usage: Balancing Anchoring and Innovation in Long-Term Human-Agent Interaction

• URL: http://arxiv.org/abs/2601.05107v1
• Date: Thu, 08 Jan 2026 16:54:30 GMT
• Title: Controllable Memory Usage: Balancing Anchoring and Innovation in Long-Term Human-Agent Interaction
• Authors: Muzhao Tian, Zisu Huang, Xiaohua Wang, Jingwen Xu, Zhengkang Guo, Qi Qian, Yuanzhe Shen, Kaitao Song, Jiakang Yuan, Changze Lv, Xiaoqing Zheng,
• Abstract summary: We show that an agent's reliance on memory can be modeled as an explicit and user-controllable dimension.<n>We propose textbfSteerable textbfMemory Agent, textttSteeM, a framework that allows users to dynamically regulate memory reliance.
• Score: 35.20324450282101
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: As LLM-based agents are increasingly used in long-term interactions, cumulative memory is critical for enabling personalization and maintaining stylistic consistency. However, most existing systems adopt an ``all-or-nothing'' approach to memory usage: incorporating all relevant past information can lead to \textit{Memory Anchoring}, where the agent is trapped by past interactions, while excluding memory entirely results in under-utilization and the loss of important interaction history. We show that an agent's reliance on memory can be modeled as an explicit and user-controllable dimension. We first introduce a behavioral metric of memory dependence to quantify the influence of past interactions on current outputs. We then propose \textbf{Stee}rable \textbf{M}emory Agent, \texttt{SteeM}, a framework that allows users to dynamically regulate memory reliance, ranging from a fresh-start mode that promotes innovation to a high-fidelity mode that closely follows interaction history. Experiments across different scenarios demonstrate that our approach consistently outperforms conventional prompting and rigid memory masking strategies, yielding a more nuanced and effective control for personalized human-agent collaboration.

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