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. Crucially, the user persona dictates memory redistribution across this hierarchy, promoting records mapped to long-term patterns toward more active tiers while relegating less relevant information. This targeted orchestration surfaces historical knowledge precisely when needed while maintaining a lean and efficient active search space. Evaluations on multiple benchmarks achieve state-of-the-art performance. Real-world deployments in ecosystems like OpenClaw demonstrate that HMO significantly enhances agent fluidity and personalization.
Figures & tables
Figure 1. Comparison between (top) existing user-agnostic storage and (bottom) our proposed method driven by user persona. Comparison between (top) existing user-agnostic storage and (bottom) our proposed method driven by user persona.
Figure 2. The system architecture and operational workflow of HMO.
Method
Retriever
Rec@5 ↑
NDCG@5 ↑
Acc. ↑
No History ( Tan et al., 2025 )
-
-
-
0.0
Long Context ( Tan et al., 2025 )
-
-
-
57.4
RAG ( Tan et al., 2025 )
Stella
59.2
-
61.4
RAG ( Tan et al., 2025 )
GTE
62.4
-
63.6
MemoryBank ( Tan et al., 2025 )
Specific1
58.6
-
59.6
LD-Agent ( Tan et al., 2025 )
Specific2
56.8
-
59.2
Table 1. Comparison of long-term memory performance on the LongMemEval dataset. Our proposed HMO outperforms existing RAG-based and memory-augmented agents in both retrieval recall and reasoning accuracy.
Figure 3. Demonstration of HMO application on OpenClaw, with a simple and informative user interface.
Method
Overall F1 ↑
A-Mem ( Liu et al., 2025 )
23.30
MemoryBank ( Liu et al., 2025 )
31.42
Mem0 ( Liu et al., 2025 )
42.13
MemoryOS ( Liu et al., 2025 )
42.84
MemVerse ( Liu et al., 2025 )
43.44
HMO (Ours)
45.65
Table 2. Overall F1 performance on the LoCoMo benchmark using the GPT-4o-mini model.
Configuration
Rec@5 ↑
NDCG@5 ↑
Acc. ↑
Time (m) ↓
Graph-based
75.9
86.6
88.0
448.30
w/o Tier 1
80.4
85.8
83.1
21.53
w/o Tier 1, 2
85.9
89.7
88.8
56.05
w/ Tier 1, 2
81.1
85.6
86.4
12.88
Table 3. Ablation results on LongMemEval.
Strategy
Recall ↑
Acc. ↑
Build (s) ↓
Inf. (min) ↓
MLLM-Scoring-only
80.7
86.1
1.63
15.05
Similarity-only
83.3
86.7
0.15
87.58
HMO (Hybrid)
81.1
86.4
0.19
12.88
Table 4. Efficiency comparison of memory update strategies.
Appendix figures & tables5 assets
Supplementary material from the paper’s appendix.
Appendix
Method
Answer Prediction (F1)
Single-Hop
Multi-Hop
Temporal
Open-Domain
Overall
A-Mem ( Xu et al., 2025a )
44.65
27.02
45.85
12.14
23.30
MemoryBank ( Xu et al., 2025a )
41.04
26.65
25.52
9.15
31.42
Mem0 ( Chhikara et al., 2025 )
38.72
28.64
48.93
47.65
42.13
MemoryOS ( Kang et al., 2025b )
35.27
41.15
20.02
48.62
42.84
MemVerse ( Liu et al., 2025 )
46.11
29.69
30.84
49.23
43.44
Appendix
Table 5. Detailed Performance Breakdown on the LoCoMo Benchmark (GPT-4o-mini). Performance is measured by F1 scores across Single-Hop, Multi-Hop, Temporal, and Open-Domain categories. The best results are highlighted in blue bold, and the second-best results are marked in cyan.
Initial Importance Scoring Evaluator E
Role
You are an expert Cognitive Memory Engine. Your task is to evaluate a new interaction record between a User and an AI Agent to determine its “Initial Importance” ( Im ) for long-term retention.
Context
∙ User Persona ( P ): {{user_persona}}
∙ Interaction Record ( m ): {{memory_content}}
Scoring Dimensions
Appendix
Table 6. Prompt template for MLLM-based initial importance scoring.
LLM-as-a-judge Evaluation Prompt
Role
You are an expert language model evaluator. I will provide you with a question, a ground-truth answer, and a model-generated response. Your task is to determine whether the response correctly answers the question by following these evaluation rules:
Evaluation Rules
∙ Answer Yes if the response contains or directly matches the correct answer.
∙ Answer Yes if the response includes all necessary intermediate steps leading to the correct answer.
∙ Answer No if the response provides only a partial answer or omits essential information.
Appendix
Table 7. Prompt template for LLM-as-a-judge evaluation.
Figure 4. System interface demonstration (example 1).
Figure 5. System interface demonstration (example 2).