Efficient Memory Alignment for Long-term Conversational Information Seeking

Abstract

Long-term conversational agents rely on personal memory to maintain coherence and personalization, yet practical systems must operate under context budgets and cope with evolving or contradictory user information. We frame persona memory as a retrieval problem over a growing memory store, and propose REMAP, a reflection-guided memory editing approach for online alignment of persona facts that selectively writes and revises memory entries based on the current dialogue evidence and retrieved related items. The method aims to preserve salient facts while reducing redundancy and resolving apparent conflicts, enabling more efficient context utilization over extended interaction horizons. Experiments on multi-session dialogue datasets show consistent gains in persona-consistent retrieval and response continuity over commonly used memory strategies, while achieving more selective memory updates under comparable operational overhead.