Hierarchical Memory Orchestration for Personalized Persistent Agents
Junming Liu, Yifei Sun, Weihua Cheng, Haodong Lei, Yuqi Li, Yirong Chen, Ding Wang

TL;DR
The paper introduces Hierarchical Memory Orchestration (HMO), a framework that organizes long-term interaction data into a multi-tiered hierarchy to improve efficiency and personalization in persistent agents.
Contribution
HMO is a novel memory management system that dynamically allocates interaction history based on user relevance, enhancing agent reasoning and scalability.
Findings
Achieves state-of-the-art performance on multiple benchmarks.
Significantly improves agent fluidity and personalization in real-world deployments.
Effectively manages extensive interaction data with reduced latency.
Abstract
While long-term memory is essential for intelligent agents to maintain consistent historical awareness, the accumulation of extensive interaction data often leads to performance bottlenecks. Naive storage expansion increases retrieval noise and computational latency, overwhelming the reasoning capacity of models deployed on constrained personal devices. To address this, we propose Hierarchical Memory Orchestration (HMO), a framework that organizes interaction history into a three-tiered directory driven by user-centric contextual relevance. Our system maintains a compact primary cache, coupling recent and pivotal memories with an evolving user profile to ensure agent reasoning remains aligned with individual behavioral traits. This primary cache is complemented by a high-priority secondary layer, both of which are managed within a global archive of the full interaction history.…
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