Large language models (LLMs) have become the foundation of personalized assistants, but maintaining persistent user memory across long-term interactions remains challenging. Existing memory systems often focus on storage, retrieval, or consolidation, while memory writing remains less controlled: transient requests, duplicate statements, and outdated user states may enter memory and later be retrieved for personalization. In this paper, we present AMU: Admission and Memory Update for Personalized Conversations, an SLM-guided (Small language model guided) structured framework for writing-time memory control. AMU uses structured memory filtering to decide what should enter memory and SLM-guided storage management to determine whether an admitted record should be stored separately, discarded as a duplicate, or fused as an update. We evaluate AMU in a controlled memory writing and retrieval setting. Experimental results show that AMU maintains cleaner and more retrievable personalized memories.
Figures & tables
Figure 1: Motivation of writing-time memory control. Directly storing user utterances can preserve transient, redundant, or outdated memories, while AMU keeps stable and reusable memories for cleaner personalization.
Figure 2: Overview of AMU. Structured fields guide memory admission and retrieval, while the SLM decides whether an admitted record is duplicate, update, or separate before storage.
Metric
Validity (%)
Action label validity
89.3
Query-memory alignment
94.1
Table 1: Manual validation of silver annotations. Reported values are mean validity rates across independent annotations.
Method
P@3
R@3
F1@3
HIT@3
Red.@3 ↓
Full Comp.
35.42
55.50
41.30
57.00
12.08
Sliding Window
24.42
29.50
25.87
29.50
11.75
Mem0
34.08
57.50
40.88
56.00
3.63
A-MAC
30.36
58.76
37.96
54.50
3.18
A-MEM
35.13
56.90
40.36
56.50
2.88
AMU(Ours)
37.33
63.50
44.47
57.50
2.83
Table 2: Main retrieval results. Red.@3 denotes Redundancy@3, where lower values are better.
Variant
F1@3
Red.@3 ↓
Act. Acc.
w/o Struct. Filtering
34.30
2.25
65.40
w/o SLM Storage Mgmt.
25.62
0.25
34.50
AMU(Ours)
44.47
2.83
67.70
Table 3: Component verification of AMU. Red.@3 denotes Redundancy@3, where lower values are better.
Size
F1@3
Red.@3 ↓
Act. Acc.
0.8B
13.90
0.31
26.40
2B
44.47
2.83
67.70
4B
41.78
3.08
81.00
9B
40.00
3.33
84.20
Table 4: Effect of SLM controller size.
Appendix figures & tables4 assets
Supplementary material from the paper’s appendix.
Appendix
Method
Overall Score
No Memory
2.92
Full Compressed Memory
3.21
AMU
4.37
Appendix
Table 5: Small-scale downstream response evaluation with an LLM judge.
τs
F1@3
Red.@3 ↓
Acc.
Time/Turn ↓
0.40
43.68
2.75
66.80
327.17
0.60
44.47
2.83
67.70
231.34
0.80
45.82
4.50
65.80
176.74
Appendix
Table 6: Sensitivity to the writing-time threshold τs with τr=0.55 . Red.@3 denotes Redundancy@3, where lower values are better.
τr
P@3
R@3
F1@3
Red.@3 ↓
HIT@3
0.40
36.00
64.25
43.75
2.83
58.00
0.55
37.33
63.50
44.47
2.83
57.50
0.70
45.33
51.25
46.33
2.00
44.50
Appendix
Table 7: Sensitivity to the retrieval-time threshold τr with τs=0.60 . Red.@3 denotes Redundancy@3, where lower values are better.
Embedding Model
P@3
R@3
F1@3
Red.@3 ↓
HIT@3
Action Acc.
Qwen3-Embedding-0.6B
37.33
63.50
44.47
2.83
57.50
67.70
BGE-M3
36.83
63.00
43.97
2.92
57.00
67.10
Multilingual-E5-Large
36.58
62.75
43.68
2.75
56.50
66.90
Appendix
Table 8: Additional analysis of embedding models, SLM backbones, and session mixing. Red.@3 and Red. denote Redundancy@3, where lower values are better.