Large language model (LLM) agents increasingly use external memory systems to support personalization by drawing on long and evolving interaction histories, in which user preferences may be distributed across time, change with context, and conflict with earlier evidence. However, existing systems face three limitations: fixed-turn, fixed-token, or session-based boundaries can mix unrelated dialogue or split an event from its causes, decisions, and outcomes; storing multiple pieces of user information from the same interaction as a single memory binds together items that serve different functions and should be independently retrievable; and treating the current task as a single top-k retrieval query can return fragments that are individually relevant but fail to jointly capture preference evolution, temporal validity, and contextual applicability. We introduce QUMem, a structured memory framework for query-conditioned user-state inference. QUMem first segments interaction histories into variable-length episodes according to semantic continuity, then decomposes each episode into independently retrievable factual, preference, and transferable insight memories while preserving temporal positions and source evidence. At inference time, three sequential agents identify task-specific information needs, plan multi-query retrieval over the typed memory stores, and jointly infer a temporally and contextually valid user state for downstream response generation. QUMem achieves state-of-the-art performance on both PersonaMem and KnowU-Bench, demonstrating the effectiveness of query-conditioned user-state inference for long-term personalization.
Figures & tables
Figure 1: Comparison between previous method and QUMem . Previous methods retrieve memory fragments based on their similarity to the current query, whereas QUMem retrieves task-relevant evidence from typed memory stores and jointly interprets it to infer the user’s current, contextually valid state.
Figure 2: Overview of QUMem . The framework constructs semantically coherent dialogue episodes, decomposes them into factual, preference, and transferable insight memories, and uses three agents to infer a query-conditioned user state.
Base Model
Context Length
Method
Recall user-shared facts
Suggest new ideas
Acknowledge latest user preferences
Track full preference evolution
Revisit reasons behind preference updates
Provide preference-aligned recommendation
Generalize to new scenarios
Overall
GPT-4o-mini
32K
A-MEM
47.95
30.11
–
44.60
71.72
52.73
17.54
45.84
Mem0
67.81
36.56
–
47.48
83.84
65.45
63.16
60.10
Zep
45.89
31.18
–
51.08
70.71
69.09
31.58
49.75
QUMem
69.18
49.46
–
54.68
84.85
76.36
73.68
66.38
128K
A-MEM
59.65
31.27
50.12
46.04
65.06
44.99
27.23
45.65
Mem0
75.44
30.31
62.47
54.84
79.55
63.04
44.60
56.58
Table 1: Performance on the PersonaMem benchmark. The best performance is highlighted in bold , and the second-best is underlined .
Easy
Hard
Overall
Method
SR
Score
SR
Score
SR
Score
IE
A-MEM
14.0
0.41
4.7
0.34
9.3
0.38
0.34
Mem0
18.6
0.48
7.0
0.40
12.8
0.44
0.40
Zep
16.3
0.44
7.0
0.37
11.6
0.41
0.37
QUMem
23.3
0.56
11.6
0.49
17.4
0.53
0.49
Table 2: Performance on the KnowU-Bench benchmark.
Ablation Setting
Context Length
Overall
32K
128K
1M
QUMem
66.38
64.61
56.17
61.02
w/o Episode Construction
64.69
62.05
53.25
58.38
w/o Memory Decomposition
63.67
60.69
52.02
57.11
w/o Reconstruction
61.97
58.12
49.18
54.51
Table 3: Ablation on key components.
Figure 3: Ablation on key hyperparameters.
Method
Builds/100 ↓
LLM Calls/100 ↓
Tokens/Turn ↓
A-MEM
100.0
201.2
3,317.9
Mem0
25.6
380.7
6,401.9
Zep
100.0
331.8
13,337.3
QUMem
27.1
81.4
1,221.2
Table 4: Normalized memory construction costs of different methods on PersonaMem.
Long-term memory systems allow LLM agents to preserve information beyond a single context window, but most systems focus on storing and retrieving facts after extraction, leaving the write decision under-specified. What deserves memory can depend on the user's current task, topic, activity, or interaction partner, while uniform extraction applies one notion of importance across these different situations. We formulate this challenge as preference-conditioned write control and introduce AdaMem, which uses adaptive natural-language Memory Policies to personalize what an agent writes to memory. Each policy represents the user's memory preference for a particular interaction context, is updated from periodic feedback, and controls subsequent memory writing. We evaluate this loop in AdaMem-Bench, which assigns different memory preferences to six concurrent interaction personas across five ten-week stories. Across two extraction models and two feedback modes, AdaMem improves average QA accuracy over Mem0 from 80.0% to 84.35% while reducing persistent memory by 9.27%. Our analyses show that explicit feedback helps models learn better memory policies, but current models still struggle to translate those policies into reliably selective writing behavior. AdaMem thus demonstrates the promise of adaptive write control while exposing policy execution as a central limitation of current memory agents. Our code is publicly available: https://github.com/galaxyChen/AdaMem
Large Language Models (LLMs) lack persistent memory for long-term personalized conversations. Existing graph-based memory systems suffer from information dilution, absent provenance tracking, and uniform retrieval that ignores query context. We introduce MemORAI (Memory Organization and Retrieval via Adaptive Graph Intelligence), a framework that integrates three innovations: selective memory filtering with dual-layer compression to retain user-persona-relevant content, a provenance-enriched multi-relational graph tracking factual origins at the turn level, and query-adaptive subgraph retrieval with Dynamic Weighted PageRank that applies query-conditioned edge weighting. Evaluated on LOCOMO and LongMemEval benchmarks, MemORAI achieves state-of-the-art performance in memory retrieval and personalized response generation, demonstrating that selective storage, enriched representation, and adaptive retrieval are essential for coherent, personalized LLM agents.
Existing large language model (LLM) based memory systems apply universal, static policies that overlook a fundamental reality: the contexts that are worth storing in memory are different across users. This misalignment wastes limited memory budget on transient interactions while failing to preserve critical context for long horizon tasks. To address this gap, we investigate an underexplored question: can LLM based memory systems learn personalized memory policies? We introduce PerMemBench, the first benchmark for evaluating personalized memory systems, featuring multi year, multi domain interaction histories across diverse user personas. We further present the first empirical study of memory personalization, proposing session level storage gating, a lightweight framework that selectively bypasses memory operations for transient sessions. Our study confirms that personalization yields substantial retention gains under perfect gating, yet reveals that accurate gating remains an open and critical challenge.