Controllable Memory Usage: Balancing Anchoring and Innovation in Long-Term Human-Agent Interaction
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866915716955897856 |
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| author | Tian, Muzhao Huang, Zisu Wang, Xiaohua Xu, Jingwen Guo, Zhengkang Qian, Qi Shen, Yuanzhe Song, Kaitao Yuan, Jiakang Lv, Changze Zheng, Xiaoqing |
| author_facet | Tian, Muzhao Huang, Zisu Wang, Xiaohua Xu, Jingwen Guo, Zhengkang Qian, Qi Shen, Yuanzhe Song, Kaitao Yuan, Jiakang Lv, Changze Zheng, Xiaoqing |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_05107 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Controllable Memory Usage: Balancing Anchoring and Innovation in Long-Term Human-Agent Interaction Tian, Muzhao Huang, Zisu Wang, Xiaohua Xu, Jingwen Guo, Zhengkang Qian, Qi Shen, Yuanzhe Song, Kaitao Yuan, Jiakang Lv, Changze Zheng, Xiaoqing Artificial Intelligence 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. |
| title | Controllable Memory Usage: Balancing Anchoring and Innovation in Long-Term Human-Agent Interaction |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2601.05107 |