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

ACL ARR 2026 January Submission5952 Authors

Keywords: Preference Alignment, Personalized LLM Agent, Agent Memory Usage

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 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.

Paper Type: Long

Research Area: Human-AI Interaction/Cooperation and Human-Centric NLP

Research Area Keywords: personalized agents

Contribution Types: Model analysis & interpretability, NLP engineering experiment, Publicly available software and/or pre-trained models, Data resources

Languages Studied: English

Submission Number: 5952