Controllable Memory Usage: Balancing Anchoring and Innovation in Long-Term Human-Agent Interaction
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 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 Steerable Memory Agent, 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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Muzhao Tian
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Zisu Huang
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2026-01-12T10:34:47.680Z
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Zisu Huang
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Xiaohua Wang
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Jingwen Xu
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Zhengkang Guo
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Qi Qian
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Yuanzhe Shen
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2026-02-03T10:09:30.347Z
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Yuanzhe Shen
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Kaitao Song
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Jiakang Yuan
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2026-02-10T09:08:33.279Z
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Jiakang Yuan
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JiakangYuan
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Changze Lv
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2026-01-29T13:56:43.233Z
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Changze Lv
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Xiaoqing Zheng
Controllable Memory Usage: Balancing Anchoring and Innovation in Long-Term Human-Agent Interaction
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 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 Steerable Memory Agent, 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.
A framework is presented that enables dynamic regulation of memory reliance in LLM-based agents, allowing users to control the balance between innovation and historical fidelity in long-term interactions.
LLM-based agents cumulative memory memory anchoring memory dependence SteeM framework personalized human-agent collaboration