From Single to Multi-Granularity: Toward Long-Term Memory Association and Selection of Conversational Agents
Authors: Derong Xu
Yi Wen
Pengyue Jia
Yingyi Zhang
Wenlin Zhang
Yichao Wang
Huifeng Guo
Ruiming Tang
Xiangyu Zhao
Enhong Chen
Tong Xu
Venue: ICLR 2026 Poster (2026)
Keywords: Long-Term Memory
Agent
LLM
Multi-Granularity
Conversation
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Large Language Models (LLMs) have recently been widely adopted in conversational agents. However, the increasingly long interactions between users and agents accumulate extensive dialogue records, making it difficult for LLMs with limited context windows to maintain a coherent long-term dialogue memory and deliver personalized responses. While retrieval-augmented memory systems have emerged to address this issue, existing methods often depend on single-granularity memory segmentation and retrieval. This approach falls short in capturing deep memory connections, leading to partial retrieval of useful information or substantial noise, resulting in suboptimal performance. To tackle these limits, we propose MemGAS, a framework that enhances memory consolidation by constructing multi-granularity association, adaptive selection, and retrieval. MemGAS is based on multi-granularity memory units and employs Gaussian Mixture Models to cluster and associate new memories with historical ones. An entropy-based router adaptively selects optimal granularity by evaluating query relevance distributions and balancing information completeness and noise. Retrieved memories are further refined via LLM-based filtering. Experiments on four long-term memory benchmarks demonstrate that MemGAS outperforms state-of-the-art methods on both question answer and retrieval tasks, achieving superior performance across different query types and top-K settings\footnote{https://github.com/Applied-Machine-Learning-Lab/ICLR2026\_MemGAS}.

Published as a conference paper at ICLR 2026
FROM SINGLE TO MULTI-GRANULARITY, TOWARD LONG-TERM MEMORY ASSOCIATION AND SELECTION OF CONVERSATIONAL AGENTS
Derong X Yi Wen", Pengyue Jia', Yingyi Zhang Wenlin Zhang?, Yichao Wang Huifeng Guo Ruiming Tang'. Xiangyu Zhao2-; Enhong Chen', Tong Xul.* 'University of Science and Technology of China, Ciity University of Hong Kong,
'Huawei Technologies Ltd., "Dalian University of Technology derongxuêmail ust edu. en, xi anwhaoocityp www.eiiyt.uuu........moomm edu. hk, hearyyicchaohhhaaae com, tongxu@uste edu. en
ABSTRACT
Large Language Models (LLMs) have recently been widely adopted in conver- sational agents. However, the increasingly long interactions between users and agents accumulate extensive dialogue records, making it difficult for LLMs with limited context windows to maintain a coherent long-term dialogue memory and deliver personalized responses. While retrieval-augmeente memory systems have emerged to address this issue, existing methods often depend on gngllagranularrty memory segmentation and retrieval. This approach falls short in capturing deep memory connections, leading to partial retrieval of useful information or substantial noise, resulting in suboptimal performance. To tackle these limits, propose MemGAS, a framework that enhances memory consolidation by constructing multi- granularity association, adaptive selection, and retrieval. MemGAS is based multi-granularity memory units and employs Gaussian Mixture Models to cluster and associate new memories with historical ones. An entropy-basee router adap- tively selects optimal granularity by evaluating query relevance distributions and balancing information completeness and noise. Retrieved memories are further re- fined via LLM-based filtering. Experiments on four long-term memory benchmarks demonstrate that MemGAS outperforms state-of-the-ee -fttt--artt methods on both question answer and retrieval tasks, achieving superior performance across different query types and top-K settings
INTRODUCTION
Large Language Models (LLMs) have showcased remarkable conversational abilities, enabling them serve as personalized assistants (Li et al., 2025a: Wang al., 2024; Li et al., 2024b; Zhang et al., 2025b) and handle wide range of applications (Gao et al., 2024; Wang et al., 2023; Fu et al., 2025), such as customer service (Pandya & Holia, 2023) and software development (Qian al., 2024). However, as user-agent interactions increase, the volume of conversational history grows significantly. Despite their impressive conversational abilities, LLMs struggle with maintaining long-term conversational memory due to their limited context length (Liu et al., 2023b), which makes challenging to retain a comprehensive record of user interactions and preferences over time. This limitation undermines their ability generate coherent and personalized responses (Deng al., 2026; Wu et al., 2025a; Pan et al., 2025; Xu et al., 2025b; Li et al., 2025c). The development of retrieval-baa -based external mormmparamttric) memory systems (Packer et al., 2023; Park al., 2023) has emerged as a promising solution. By storing interaction histories and retrieving relevant information when needed, memory systems enable LLMs to recall user-specific details from past dialogues and deliver more tailored responses.
"Corresponding authors.

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Published as a conference paper at ICLR 2026
Conversation 2023/05/23 (Tuc) 14:39
Afterwards
Conversation 2 2023/05/30 (Tuc) 10:29
sinti-csston Query
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Portial Retrieval
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(a) Retrieval performance with
Keyword
Deep Leuming Engineer; Al Researcher
different granularity
Figure I: An example of the multi-session query. The agent needs to synthesize information from both Conversation 1 and Conversation 2 to answer the question. The red underlines highlight information directly relevant to the answer, while the bold text emphasizes the same details between conversations. By generating multi-granularities (e.g., summary and keyword), the agent can better establish connections between conversations, enhancing full memory retrieval. Additionally, chart (a) illustrates the performance across different granularities, where 'Suited Gran' means the result using best-suited granularity for each query.
Recent studies on external memory systems of LLM agents primarily depend on the Retrieval- Augmented Generation (RAG) pipeline and have explored diverse aspects of memory segmentation and construction for effective retrieval (Jia et al., 2025; Xu et al., 2025a; Zhang et al., 2025c; Wu et al., 2025b; Zhang et al., 2026; 2024a; Zhong et al., 2024; Li et al., 2025b). For memory segmentation, existing approaches mainly adopt single-granularity to segment conversations. They utilize session-level chunks as retrieval units (Lu al., 2023; Liu et al., 2023a; Team, 2023), while others employ finer-grained turn-level segmentation to capture details (Zhong et al., 2024; Wu et al., 2025a; Yuan et al., 2023). Recent advances introduce topic-aware segmentation techniques (Pan et al., 2025; Tan et al., 2025) that group dialogue based on semantic coherence, enhancing topic-consistent retrieval. Additionally, several works (Wang et al., 2025; Team, 2023; Zhong et al., 2024; Sarthi et al., 2024; Chen et al., 2025a) generate memory summaries, condensing key information into compact representations to improve retrieval efficiency. Regarding memory construction, researchers have explored structured organization paradigms enhance long-term knowledge retention (Li et al., 2024c; Edge et al., 2024; Guo et al., 2024). Methods like RAPTOR (Sarthi et al., 2024) and MemTree (Rezazadeh et al., 2024) employ tree structures encode multi-scale relations between memory units. Some works propose hierarchical memory architectures (Sun & Zeng, 2025; Hu et al., 2025), building complex systems with deep abstraction capabilities that enable efficient top-down retrieval. Other studies (Gutiérrez et al.; 2025; Chhikara et al., 2025; Rasmussen et al., 2025) implement graph-based memory architectures to simulate neural memory consolidation processes, explicitly modeling relational structures between entities.
However, despite their progress, current approaches exhibit two critical limits: i) Insufficient Multi- Granularity Memory Connection. While existing methods endeavor to organize memories into topologiaal structures (e.g., knowledge graphs (Gutiérrez al., 2025; Chhikara et al., 2025) or trees (Sarthi et al., 2024; Rezazadeh et al., 2024)), they predominantly concentrate on singular granularity levels-either entities or session summaries. This single-scale paradigm fails to model gross-grauular interactions between memory units, resulting in retrieving only partial useful information. As demonstrated in Figure 1, answering multi-session queries requires establishing semantic links across granularities (e.g., connecting Conversation I and Conversation 2 through shared keyword/summarr) Failure to establish links results in partial retrieval (c.g., retrieving only Conversation 1), which leads to incorrect answers. ii) Lack of Adaptive Multi-Granular Memory Selection. Current
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Published as a conference paper at ICLR 2026
methods mainly rely fixed granularity strategies (e.g., session/turn segmentation LLM- LLM-generated summaries (Wang et al., 2025; Zhong et al., 2024; Wu et al., 2025a)), which often lead to incomplete context recall noise due to improper granularity. Although topic-aware segmentation (Pan et al., 2025; Tan et al., 2025) enhances intra-chunk coherence, they lack adaptive mechanisms to select granularity for each query. Our empirical analysis in Figure (a) reveals that adaptively choosing best- suited granularity per query (c.g., balancing noise reduction in summaries/keyword with information retention in raw sessions) yields substantial performance gains. This highlights the necessity of granularity selection to resolve the inherent noise- iniissinformttion trade-off.
In this paper, we propose MemGAS, a framework for constructing and retrieving long-term memories through multi- gunlliigaanllritty association and adaptive selection. Our method addresses these issues by two core strategies: i) Memory Association: We leverage L.LMs to generate memory summaries and keywords, constructing multi-granular memory units. When new memory is updated, Gaussian Mixture Model is employed to cluster historical memories into an accept set (relevant) and a reject set (irrelevant). The memories in the accept set are then associated with new memories, ensuring consolidated memory structures and real-time updates. ii) Granularity Selection: An entropy-based router adaptively assigns retrieval weights to different granularities by evaluating the certainty of the query's s relevance distribution. Finally, critical memories are retrieved using Personalized PageRank and filtered through L.MSs to remove redundancies, ensuring a refined and high-quality memory that enhances the assistant's understanding. Experiments on four open-source long-term memory benchmarks demonstrate that MemGAS significantly outperforms state-of-the-ar baselines and dingle-granllaiin approaches on both question answering (QA) and retrieval tasks. Moreover, it consistently achieves superior results across various query types and retrieval top-k settings.
METHODOLOGY
This section defines the task and data format, constructs dynamical memory association framework, details an entropy-basee router for better-suited granularity, and outlines strategies for retrieving and filtering high-quality contextual information for response generation.
2.1
PRELIMINARY
Our work focuses on building personalized assistants through long-term conversational memory, where the system leverages multi-session user-agent interactions (referred to as memory) to construct (8) an external memory bank M. Without loss of generality, the i-th session S, {(u))) aj 3
contains n, dialogue turns of user utterances u," and assistant responses (6) When the assistant receives a query q, our aim is to retrieve relevant memories Miel S M through multi-granular associations across session-level S, turn-level T, keyword-level K, and summary-level U, then generates responses a LLM(q, M,a). The core challenges involve establishing cross- gross-gr memory association and learning adaptive granularity selection & q + {a,, Ot ag, O.,. to balance information completeness and retrieval noise.
2.2 MULTI-GRANULARITY ASSOCIATION CONSTRUCTION
Existing methods mainly encode memories into vector libraries (e.g., session-level chunks) and directly retrieve information via similarity search (Izacard et al., 2021; Lee et al., 2023; Lu et al., 2023). However, such approaches overlook deeper associations between memories. To address this, we propose an associative memory construction process that captures multi-granular relationships.
Multi-Granular Memory Metadata. For the i-th session S,, multi-granularity metadata is generated using I.LMs, including a session summary U, and keywords K₁. Additionally, the session is segmented into multiple turns T,. Formally,
(I) Here, fLLM denotes LLM processing function, and segment represents segmentation operation. The resulting memory chunk M, combines the lessel S,, turn-level dialogues T., summary U,, and keywords K,, and is stored in memory bank as:
U,, K, = fSLM(S.), T, = ==semmet(SS))
M, (S,, U,, K.) E EM
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(I) Grani-Granularity Association Construction
Raw Comversations
Multi-Gaambarity
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Memory Unit
Gaussian
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Entrepy-Driie Granularity Selection
Personalized RageRank
Query
Query-related
Response
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Memory
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Figure 2: Overall Framework. In the first phase, leverage LLM agent generate multi culli-gramularity information and construct Memory Association using GMM. the second phase, given query, we perform multi-granulari routing based entropy and utilize PPR algorithm search for key nodes. Finally, we filter the information to obtain the answer.
Dynamical Memory Association. When a new memory Mnew is added, the current memory bank Meur is updated Meur Mear Unne To establish associations between Mnew and historical memories, Gaussian Mixture Model (GMM)-based clustering strategy (Rasmussen, 1999) is used. Each granularity of each element in the memory bank encoded dense vector. Specifically, all memories are encoded into dense vectors e(S)). e(T.), e(U,), and el (;) ) for session-level, turn-level, summary, and keyword metadata, respectively. The entire memory bank Meur thus represented as collection of these multi-granular vectors. Pairwise similarity scores are computed between e(Mrew) and e( e(Marr), covering granularities of each memory. The resulting set of similarity vectors Ssim is clustered by GMM into two probabilistic sets:
Accept Set: Memories with high similarity to Mnew. forming direct associations with Mnew-
Reject Set: Irrelevant memories, excluded from immediate connections with Maew-
Note that the similarity vectors computed with granularity information, meaning that each granularity of Mnew is treated as a node and used to establish connections with nodes in Mear- We maintain association graph Acur store the connections in M, which is updated as Acur + Acur U Anew, where Anew represents the edges between Mnew and its accept set. This process mimics Juman-like memory consolidation by selectively reinforcing contextually related memories.
2.3 GULUI-GRANULARITY ROUTER
Existing methods rely on single predefined granularity for memory retrieval, limiting their ability to adaptively prioritize fine- coarse- coarse-grained information based on query (Lee et al., 2023; Wang et al., 2025; Sarthi et al., 2024). To address this, we propose an entropy-based router that adaptively selects the better-suited granularity for each query.
Matropy-Driveer Granularity Selection. For a query we first compute its similarity to all memory chunks across each granularity level (session, turn, summary, keyword). Let 8 [sim(q, M "ff),..., ..m...,iimm.... sim(q, Mg)] denote similarity scores between g and n memories chunks at granularity g. We normalize 8 into probability distribution pd (sa) and caleulate its Shannon
entropy:
Σ (29) (og)))logil (s9), where (s()) = exp(sim 9. M2)/2) Vi fl....,n..
=
Σ
クー exp exppsim(gg (MM))/))
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Published as a conference paper at ICLR 2026
where H quantifies the uncertainty of matching q to memories granularity g. A hyperparameter to control the degree of entropy, and is analyzed in Appendix F.
Soft Router Weights. Our motivation stems from that lower entropy H typically reflects higher confidence in precise matches (c.g., higher confidence indicates a clear correspondence between the query and its associated memory). Conversely, higher entropy suggests more ambiguous matches (e.g., lower confidence implies uncertainty about the query's 's corresponding memory). To capture this behavior, we assign weights granularities by normalizing their inverse entropy:
1/H9
W:
Eg Eg-1//1
where G denotes the total number of granularities. This formulation ensures that granularities with lower entropy (indicating higher certainty) are assigned greater weights, Consequently, memories are reweighted emphasize those associated with the query's most confident granularities, eliminating the need for manual intervention. We present theoretical analysis of granularity association and routers Appendix
2.4 MEMORY RETRIEVAL AND FILTER
After constructing the multi-granularic memory associations and determining granularity weights, we retrieve relevant memories for a query q by leveraging the graph-structured memory {M, AM E M x A. To fully utilize inter-memory relationships, employ the Personalized PageRank (PPR) algorithm (Haveliwala, 2002) for contexr-aware ranking.
At the granularity level, treat each M (i.e., the g-th granularity node of memory M₁) individual node in the association graph. For each granularity J. we compute an initial relevance score for node MM using the router-assigned weight w from Equation 4:
score? w sim( q, M,)).
(5)
where sim(g, M measures the cosine similarity between the query embedding e(g) and the cranularity-speciie embedding e(M,)). The set of scores {score[] defines the personalized starting probabilities over granularity-level nodes. We then select the top nodes seed nodes (analyzed in Appendix F), and run PPR on the multi-granularity association graph, propagating relevance through the graph structure emphasize nodes that are both directly relevant to the query and densely connected to other value nodes. After convergence, we select the top-k nodes as candidate contexts by their final PPR scores. The impact of K is empirically analyzed in Section 3.4.
LLM-Based Redundancy Filtering. To minimize noise and eliminate redundancy in the retrieved multi-granularity memories, employ an LLM-bassed filtering mechanism. This mechanism processes the top-K memories alongside the query using a curated designed prompt (see Appendix to identify and discard irrelevant or repetitive content. As result, the final context provided to the response generator is refined to focus exclusively on the most critical information relevant to q. ensuring more concise and personalized response. We present case study on multi aulti-granularity information in Appendix K.2 and case study on filtering in Appendix K.3.
3 EXPERIMENTS
3.1 EXPERIMENTAL SETTINGS
Dataset and Metries. Experiments are conducted on four comprehensive long-term memory datasets: LoCoMo (Maharana et al., 2024), Long-MTTBench+ Bench+ (Pan et al., 2025), LongMemEval-s (Wu et al., 2025a), and LongMemEval-m (Wu et al., 2025a), all focused on evaluating the capabilities of LLM agents in long-term conversations. Since our task is training-free, the whole QA pairs in datasets are used for evaluation. Detailed dataset statistics are provided in Appendix A. To comprehensively evaluate model performance, we employ multiple metrics: Fl scores (as used by Maharana et al. (2024)), BLEU (with 4-gram by default), BERTScore, and ROUGE scores (following Pan et al. (2025)). Additionally, we introduce GPT4 -uddee (GPT4o-J (Zheng al., 2023), an evaluation setting where GPT4o assesses the alignment of a model's I's response with the reference answer. The evaluation prompts are provided in Appendix J.
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Baselines. We compare MemGAS against various methods. (1) Full History: which utilizes all the latest conversation records, incorporating up to 128k tokens of context. Two strong retrieval models: (2) MPNet (Song et al., 2020) and (3) Contriever (Izacard et al., 2021). Four memory- based conversational models: (4) RecurSum (Wang et al., 2025), which uses L.MSs to recursively summarize and update memory for contextually relevant responses; (5) MPC (Lee al., 2023), which composes LLM with prompting and external memory; (6) A-Mem (Xu et al., 2025b), which organizes memories through generating note and links; (7) SeCom (Pan et al., 2025), which segments memory into coherent topics and applies compression-baate
denoising to boost retrieval; Two structured RAG models: (8) HippoRAG 2 (Gutiérrez et al.), which integrates knowledge graphs for efficient retrieval, and (9) RAPTOR (Sarthi et al., 2024), which enhances retrieval via recursive summary and hierarchical clustering into a tree structure. We also include two recent memory models: H- MEM (Sun & Zeng, 2025) introduces a four-layer hierarchical memory with positional indexing, and COMEDY Chen et al. (2025a) provides a unified One-for-Ae All compressive memory framework without traditional memory stores. Due to space limitations, the experimental results for H-MEM and COMEDY are provided in Appendix 1.2. More details can be found in Appendix B.
Implementation Details. We -opt-4o-mini-222 miii-2022--07-18 as the backbone for all tasks, including multi- qultiigraaullritty information generation and QA. To ensure faimess, all baselines share consistent generation prompts. The temperature of LLMs is to 0 for reproducibilityy and all models operate in a zero-shot setting with prompt templates detailed in Appendix J. A consistent top-3 session retrieval setting is applied across all models for fair comparison, with top-3 segments used for SeCom and sessions/summarii for RAPTOR. Contriever is used as the encoding model to generate embeddings for memory texts. We select hyperparameters through grid search: À tuned over (0.1, 0.2, 0.3, 0.5, 0.7, 1.0] and over [5, 10, 15, 20, 25). LongMT Bench+ is excluded due to the lack of ground truth for retrieval. RAPTOR and A-Mem retrieval cannot be evaluated with session- Recall because their stored units are abstracted or rewritten representations, so there is no deterministic mapping back to ground-truth sessions. Results for RAPTOR, A-Mem, and HippoRAG on LongMemEEvllmm unavailable due to high runtime.
We compared various methods across different retrievers, generators, and query types, as detailed in Appendix E. E.2 and E.3. Additionally, we provide a hyperparameter analysis in Appendix F and an error analysis in Appendix G. The additional cost and efficiency of the methods are discussed in Appendix D.
3.2 OVERALL RESULTS
We present the results for Question Answering and Retrieval in Table 1 and Table 2, respectively. We also compare performance of single-granularity and multi-granularity Appendix C. Below, we provide analysis of these results.
Question Answering Results. As presented in Table 1, MemGAS consistently outperforms other methods across most datasets and evaluation metrics. Unlike Full History, which introduces noise by utilizing all historical context, MemGAS excels by effectively consolidating and retrieving only the most relevant memories. Other baselines, such RecurSum and SeCom, although operating at specific granularities, lack the capability to integrate multi-granul granular associations between mem- ory, resulting in suboptimal outcomes. Besides, methods like HippoRAG 2 and RAPTOR, while establishing connections between memory units, fail to construct multi-granular relationships and selection mechanisms effectively, limiting their performance. MemGAS performs better through its dynamic construction and adaptive router of gratilgra memory units and redundancy filtering. emphasizing the critical role of association and selection memory management. In terms of efficiency, MemGAS maintains competitive token usage and achieves stronger QA quality while retaining comparable- or even lower-later than several baselines such as A-Mem and RecurSum, making it both more accurate and more efficient overall.
Retrieval Results. As shown Table 2, our MemGAS demonstrates outstanding performance across all datasets, consistently achieving the highest Recall and NDCG metrics. These results highlight the effectiveness and robustness of our approach, addressing key challenges in long-term memory construction and retrieval, and ensuring that queries are matched with the most relevant context.

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Published as a conference paper at ICLR 2026
Table 1: QA performance. Contriever is used the retrieval backbone for all baselines (except for Full History and MPNet), with GPT40-mini serving the generator. The bold values indicate the best performance, while underlined values mark the second-best across each metrie. All evaluation metrics follow "higher is better" convention. 40-J' denotes GPT4o-as-Judge, B-4' represents BLEU4, R-n' refers to ROUGE-n, and BS denotes BERTScore. Avg. Tokens and Avg. Latency both report the average computational cost per query; Avg. Tokens measures LLM API token consumption, while Avg. Latency reflects the end-to-end response time. Latency is measured in seconds.
Avg. Tokens
Avg. Latency
Model
40-J
F1
B-4
R-1
R-2
R-L
BS
LongMemEver
Full History
50.60
11.48
1.40
12.10
5.47
10.85
83.07
103,137
MPNet (Song et al., 2020)
53.20
13.96
2.21
14.49
6.78
12.93
83.72
8,173
1.82
Contriever (Izacard et al., 2021)
55.40
13.78
2.21
14.46
6.93
12.89
83.70
8,286
1.85
MPC (Lee al., 2023)
53.80
13.60
1.74
14.27
6.49
12.95
83.49
8,457
2.66
RecurSum (Wang ct al., 2025)
35.40
12.29
2.09
13.01
5.55
11.52
83.60
8,853
1.94
SeCom (Pan et al., 2025)
56.00
12.95
2.25
13.80
6.09
11.93
83.51
2,741
1.67
HippoRAG 2(Gutiérrez al., 2025)
57.60
14.73
2.15
15.30
7.36
13.83
83.86
8,530
4.51
RAPTOR (Sarthi et al., 2024)
32.20
12.08
1.90
12.73
5.82
11.25
83.50
6,254
A-Mem (Xu et al., 2025h)
55.60
13.73
2.11
14.82
6.81
12.98
83.88
9,018
2.59
MemGAS (Ours)
60.20
20.38
4.22
21.05
10.47
19.47
85.21
8,829
2.55
LongMemFFall
Full History
12.20
5.70
0.78
6.27
2.08
5.28
81.62
1 128,000
12.88
MPNet (Song al., 2020)
37.80
10.76
1.70
11.46
4.76
10.03
83.09
8,352
1.83
Contriever (Izacard et al., 2021)
42.80
11.88
1.66
12.56
5.63
11.02
83.28
8,467
1.92
MPC (Lec et al., 2023)
37.80
11.28
1.37
11.93
5.12
10.57
82.98
8,428
2.75
RecurSum (Wang et al., 2025)
23.80
10.04
1.70
10.89
4.26
9.21
83.12
8,927
1.99
SeCom (Pan et al., 2025)
42.80
11.33
1.79
12.03
5.07
10.49
83.36
2,821
MemGAS (Ours)
45.40
16.85
3.39
17.60
8.25
16.14
84.69
8,852
2.45
LoCoMo
Full History
33.43
12.23
1.84
12.70
5.66
11.73
84.07
20,078
4.92
MPNet (Song et al., 2020)
38.07
14.44
2.35
14.90
6.83
13,90
84.42
2,472
1.29
Contriever (Izacard al., 2021)
40.33
15.66
2.67
16.01
7.68
15.00
84.65
2.348
1.24
MPC (Lee al., 2023)
40.38
14.81
1.99
15.10
6.83
14.13
84.43
2,683
RecurSum (Wang et al., 2025)
22.56
9.14
0.99
9.82
3.38
8.98
83.45
3,074
1.58
SeCom (Pan et al., 2025)
44.21
13.79
2.30
14.28
6.17
13.30
84.04
1,021
1.02
HippoRAG 2(Gutiérrez al., 2025)
45.62
16.66
2.91
17.01
8.27
15.93
84.88
2,991
3.56
RAPTOR (Sarthi et al., 2024)
31.72
14.55
2.88
15.09
7.49
14.18
84.48
1,931
1.73
A-Mem (Xu et al., 2025b)
40.81
14.72
2.83
16.22
7.71
14.89
84.72
3,042
1.98
MemGAS (Ours)
41.07
17.66
3.61
18.00
8.93
16.99
85.13
2,825
1.88
Bonnch+
Full History
67.44
36.07
11.32
37.90
20.51
29.25
87.81
19,194
4.72
MPNet (Song et al., 2020)
63.89
36.09
11.26
38.39
20.58
28.86
87.84
12,143
Contriever (Izacard et al., 2021)
63.54
36.30
11.59
38.17
21.65
29.67
87.90
11,941
3,27
MPC (Lee et al., 2023)
61.81
31.52
7.97
33.56
17.27
25.20
86.51
12,289
3,98
RecurSum (Wang al., 2025)
24.65
26.58
6.91
29.23
11.93
20.90
86.11
13,527
3.41
SeCom (Pan et al., 2025)
64.58
36.68
12.01
38.81
21.44
29.65
87.88
4,714
HippoRAG 2(Gutiérrez et al., 2025)
63.54
35.64
11.05
37.61
20.37
28.76
87.70
13,583
6.14
RAPTOR (Sarthi et al., 2024)
59.72
37.69
13.47
40.08
21.68
30.88
88.38
10,631
3.47
A-Mem (Xu et al., 2025b)
65.73
36.82
11.36
38.92
20.88
29.14
87.92
13.735
3.95
MemGAS (Ours)
69.44
41.49
15.62
43.69
24.45
34.66
88.96
12,873
3.85
3.3 ABLATION STUDY
The ablation study in Table 3 demonstrates the significance of each component in enhancing both QA and retrieval performance. Individually removing GMM, PPR, MA, or the Router results in consistent performance degradation, validating their essential contributions. Notably, the combined
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Table 2: Retrieval Performance. All methods are based on Contriever as the retriever, except for MPNet.
Model
3 NDCG@3 Recall@s NDCG@5 R 10 NDCG@ 10
LongMemEvver Eval-s
MPNet (Song et al., 2020)
66.17
75.47
76.38
78.29
85.11
80.63
Contriever (Izacard al., 2021)
71.06
79.72
81.28
82.47
90.00
84.29
RecurSum (Wang et al., 2025)
67.23
78.33
79.79
81.76
87.66
83.28
MPC (Lee al., 2023)
60.00
70.90
68.09
73.27
80.00
76.59
ScCom (Pan al., 2025)
71.06
80,88
80.43
83.08
89.15
85.11
HippoRAG 2(Gutiérrez al., 2025)
75.53
85.44
84.68
87.32
91.28
88.73
MemGAS (Ours)
78.51
86.83
88.94
88.77
94.47
89.96
LongMemEEver
MPNet (Song et al., 2020)
37.02
48.68
45.74
52.51
61.28
56.55
Contriever (Izacard et al., 2021)
44.26
56.11
53.40
59.44
65.96
62.74
RecurSum (Wang et al., 2025)
23.19
32.10
31.70
36.99
43.62
41.40
MPC (leee al., 2023)
35.96
47.51
42.55
50.36
54.26
53.88
SeCom (Pan al., 2025)
44.26
56.61
55.32
60.76
66.60
63.78
MemGAS (Ours)
51.06
61.36
63.62
66.07
77.02
69.46
LoCoMo
MPNet (Song et al., 2020)
45.92
47.71
53.98
51.79
68.58
$6.88
Contriever (Izacard al., 2021)
49.90
52.15
58.26
56.29
71.80
60.92
RecurSum (Wang et al., 2025)
47.23
48,99
59,01
54.58
74.97
60.07
MPC (Lee al., 2023)
49.50
51.47
57.45
55.53
71.85
60.47
ScCom (Pan al., 2025)
55.24
57.90
64.80
62.36
78.30
66.97
HippoRAG 2(Gutiérrez al., 2025)
56.60
58.37
65.06
62,50
78.05
66.79
MemGAS (Ours)
57.30
58.76
67.32
63.62
81.82
68.42
Table 3: Ablation Study on LongMemeval-s for Gaussian Mixture Model (GMM), Personalized PageRank (PPR), Granularity Router and Memory Association (MA). The w/o MA setting is equiva- lent to w/o GMM and PPR. QA performance is evaluated using top retrieved results. @ means Recall @n. The (A) represents the latency introduced by each module.
QA Performance
Retrieval Performance
Method
Avg. Tokens
Total Latency (8)
Retrieval Latency
GPT4o-J
F1
RogueL
R@3
R@5
R@10
MemGAS
60.20
20.38
19.47
8,829
2.5534
78.51
88.94
94.47
0.0239
w/o GMM
57.20
19.49
18.68
8,820
2,5506
76.38
85.53
91.28
0.0232(40.0007)
w/o PPR
56,60
19.76
18.85
8,772
2.5449(20.0000)
75,96
85.96
90.64
0.0194(200.045)
w/o MA
56.80
17.69
19.00
8.734
2.5418/An00116
74.89
85.74
91.49
0.0182(40.0057)
w/o Router
56.60
18.88
18.62
8.763
2.5.71()
75.53
85.74
92.34
0.0216/
w/o 55.40
13.78
12.89
8,701
2.5041())0011))
71.06
81.28
90.00
0.0160/An..079)
absence of all components leads to the most significant drop, with the Fl score plummeting from 20.38 to 13.78, and Recall @3 decreasing from 78.51 71.06. This highlights the importance of modules in improving overall performance. Moreover, the latency introduced by these modules is minimal, with the highest latency increase being only 0.0191 seconds for QA and 0.0079 seconds for retrieval. We also found that LLM API calls account for over 98% of the overall end-to-ent latency, indicating that they are by far the primary contributor to the system's response time, and the overhead from modules is acceptable in practice. This demonstrates that the proposed architecture achieves a remarkable balance between enhanced performance and computational efficiency.
3.4 DETAILED COMPARISON ANALYSIS
In this section, we present a comprehensive analysis comparing our approach with baselines across different query types and different K retrieval settings.
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Contriover SeCom
HippoRAG
Ours
Contriever SeCom
HippoRAG
Knowledge Update
Single-Session User
Ours
LoCoMo
Long Lenvals
25 34
21
20
Temporal Reasoning
Single Songle-Session Preference
18
15
15
10
12
Single-Sessiae
5
10
3
10
Multi-Session
Assistant
Top K. Retrieval
Top K. Retrieval
Figure 3: Comparison of FI Across Dif- ferent Query Types in LongMemEval-s.
Figure 4: Comparison on Different Top-K Retrieval.
Comparison on different query types. The results in Figure 5 highlight performance across different query types. Our MemGAS consistently demonstrates superior performance, particularly excelling in multi-hop retrieval on LoCoMo and multi-session on LongMemFvvl--s. This suggests that memory association mechanism MemGAS effectively identifies highly relevant sessions, enabling enhanced multi-hop reasoning and better integration of knowledge. Additionally, our method performs well across sinllesession, temporal reasoning, and knowledge update, demonstrating addressing both simple and complex query types. We also provide case study for different query types in Appendix K.1, and comparison with other datasets and metrics Appendix E.3.
strength in
Comparison on different Top-K retrieval. In downstream QA tasks, Top-K retrieved memories are directly fed as context to L.LM generator, where increasing K improves coverage but may introduce noise when K becomes too large. The results shown in Figure demonstrate the performance of various Top-K retrieval settings. On the LoCoMo and Long datasets, our model consistently surpasses baseline methods in Fl scores as Top-K increases from to 10. This trend highlights that retrieving more relevant context significantly enhances accuracy. Note that on the Levale dataset, while FI scores initially improve lower Top-K values, performance declines at higher Top-K levels, suggesting that the longer context introduces noise, which negatively impacts the model's effectiveness.
Comparison w/ and w/o Filter Setting. To ensure fairness when comparing MemGAS with baselines that may not employ LLM-based filtering, we provide detailed breakdown of token usage for online per-query cost. Table reports token consumption with and without filtering, as well as the corresponding QA performance on LongMemEval- using GPT4o-J. Overall, MemGAS exhibits competitive or lower online token usage compared to representative structured baselines such as HippoRAG2, while consistently achieving higher QA accuracy. The filtering module introduces only small additional overhead (approximately 200-300 tokens per query), yet it improves answer precision and benefits downstream reasoning quality. Moreover, as shown Appendix D. MemGAS requires noticeably less LLM involvement during memory construction compared to multi-stage structured approaches (c.g.. HippoRAG2), further enhancing its overall efficiency.
Table 4: Comparison of LLM token usage and QA performance between MemGAS and representative baselines, under both filtering and non-fitterig settings. The reported numbers include (i) average online per-query token cost and (ii) GPT4o-J QA score Lena Avg. Tokens per query denotes online QA-stage token consumption: tokens for LLM filtering + tokens for QA generation.
Category
Setting
MemGAS HippoRAG2 RAPTOR SeCom RecurSum
Avg. Tokens per query With filter Avg. Tokens per query Without filter
8,829
8,911
6,617
3,015
9,176
8.481
8,530
6,254
2,741
8,853
QA performance
With filter
60.2
58.4
33.2
56.6
36.2
QA performance
Without filter
59.4
57.6
32.2
56.0
35.4

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4 RELATED WORK
Recent advancements in retrieval methods have greatly enhanced information retrieval performance(Robertse
Retrieval Methods.
et al., 2009; Song et al., 2020; Santhanam et al., 2021; Zhang et al.). BM25 (Robertson et al., 2009), classic sparse retrieval method, uses a probabilistic model to improve query relevance. In contrast, dense retrieval methods excel at capturing semantie similarity. For instance, DPR (Karpukhin et al., 2020) employs dual-encoder models to encode queries and documents into dense vectors for efficient similarity search. Similarly, Contriever (Izacard et al., 2021) leverages contrastive learning enhance semantic understanding. Advanced methods like E5 (Wang et al., 2022). BGE (Luo et al., 2024), and GTE (Li et al., 2023) further utilize pre-trained or fine-tuned transformers for robust and efficient semantic retrieval.
Long Term Memory Management. With the development of LLMs, the user-assistant conversation becomes longer and contains various topics (Chhikara et al., 2025; Li et al., 2025c; Wang & Chen, 2025; Lu et al., 2023; Du et al., 2024; Qian et al., 2025; Packer et al., 2023; Zhang et al., 2025a; 2024a; Wu et al., 2025b; Du et al., 2024; Kirmayr et al., 2025), which introduces challenges for preserving the user's 's long-term memory. Management in long-term memory often involves segmentation (Pan et al., 2025), summary (Kim et al., 2024), compression (Chen et al., 2025b), forgetting and updating (Bae et al., 2022; Wang et al., 2025; Zhong et al., 2024). For example, several approaches (Wang et al., 2025; Sarthi al., 2024; Team, 2023; Liu et al., 2023a; Lu et al., 2023) focus on generating memory summaries as records to enable more accurate retrieval. Besides, some methods (Pan et al., 2025; Lee et al., 2024; Xu et al., 2023; Chen et al., 2025a) leverage compression techniques to reduce memory size while preserving essential information. The forgetting mechanisms (Zhong et al., 2024; Jia et al., 2024) address the need to remove obsolete or irrelevant memories while maintaining model performance. Some integrated methods (Tan et al., 2025; Ong et al., 2025; Li et 2024a) combine memory retention, update to enable personalized and contextually relevant long-term dialogue. However, existing approaches typically focus on ungle-granularity segmentation strategies, such as session/turn, to organize and manage long-term memory. Whereas our work leverages multi-granular information for better adaptive memory selection.
Structural Memory Management. Additionally, existing works employ structured paradigms for memory or knowledge base organization (Xu al., 2024; Chen et al., 2024; Li al., 2024c; Zhang et al., 2025a; 2024a; Wu et al., 2025b; He et al., 2024a; Zhang et al., 2024b; Xu et al., 2022; Rasmussen et al., 2025). HippoRAG (Gutiérrez et al.; 2025) builds entity-centric knowledge graphs inspired by hippocampal indexing theory (Teyler & DiScenna, 1986), while Graph-CoT (Jin et al., 2024) and G-Retriever (He et al., 2024b) integrate graph reasoning for interactive retrieval or generation, whereas LightRAG (Guo et al., 2024) and GraphR (Edge et al., 2024) optimize retrieval and summary via graph structures. MemTree and RAPTOR (Rezazadeh et al., 2024; Sarthi et al., 2024) utilize recursive embedding, clustering, and summarization of text chunks to construct a hierarchical tree, while StructRAG (Li et al., 2024c) enhances reasoning by leveraging multiple structured formats. While existing methods focus on single-granularigg memory modeling, they lack cross-grand cross-granularity interactions. In contrast, our MemGAS utilizes multi gultiigranularity association and adaptive selection construct consolidated memory structures and optimize retrieval efficiency.
5 CONCLUSION
In this paper, we proposed MemGAS, a novel framework for long-term memory construction and retrieval that integrates multi- gratularr memory units and enables adaptive selection and retrieval. By leveraging human-inspired memory mechanisms through Gaussian Mixture Models and an entropy- based muuti-graaulaate router, MemGAS effectively addresses challenges of memory connection and selection. Experimental results across four benchmarks demonstrate that MemGAS significantly outperforms state-of-the baselines in both QA and retrieval tasks, highlighting its robustness and superiority in managing long-tern oag-termm memory for conversational agents.
ETHICS STATEMENT
This work adheres to ethical research practices by ensuring that all experiments and datasets used are publicty available and utilized in accordance with their licenses. The proposed MemGAS framework
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is designed to enhance conversational agents responsibly, with focus improving experience while minimizing the risk of harm, such as generating misinformation. No sensitive or proprietary data was used during the research process, and the methodology prioritizes transparency and accountability in memory retrieval and usage.
REPRODUCIBILITY STATEMENT
We are committed ensuring the reproducibility of our work. To this end, we have provided the code in anonymous repository. The repository includes a well-documented README file with instructions to replicate our experiments. Additionally, we have detailed implementation specifics in this manuscript, and hyperparameter tuning is comprchensively described in Appendix F. Furthermore, we have included the complete set of prompts required for the experiments, ensuring that all components of methodology can be accurately reproduced. We encourage the community to leverage these resources to build upon our work.
ACKNOWLEDGMENTS
This work was supported in part by the grants from National Science and Technology Major Project (No. 20237D0121 104), and National Natural Science Foundation of China (No.62222213 U22B2059, 6o.62502404). This research was also partially supported by Hong Kong Research Grants Council (Research Impact Fund No.R1015-23, Collaborative Research Fund No.C1043-24GB General Research Fund No. 12 18325),, Institute Digital Medicine of City University of Hong Kong (No.9229503), Huawei (Huawei Innovation Research Program).
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Technical Appendix
TABLE OF CONTENTS
A Datasets Statistics
18
Comparison Methods Structure with Baselines
18
Comprehensive Comparison of Sranua-Gra and Aulti-Granularity
19
C.1 QA Performance.
19
C.2 Retrieval Performance
19
C33 How Multi-granularity Router works?
20
C.4 How PPR affects the retrieval results?
21
D Additional Cost and Efficiency Analysis
22
D.1 Token Consumption for Memory Construction....
."..............................
22
D.2
Comparison at the Same Token Cost
22
Generalization Analysis
23
E.1
Comparison on Different Retriever
23
E,2 Comparison on Different Generator
_eeeee..ee......................
23
E.3 Comparison on Different Query Types
23
Hyperparameter Sensitivity Analysis
24
G Error Analysis
25
H Theoretical Analysis
25
H.1 GMM Accept/Reject Association (Clean Links)
26
H.2 Entropy Router (Choose Confident Granularities)
28
I Additional Experiment
28
L1 Human Evaluation
28
1.2 Comparison with Additional Baselines
29
L.3
Scalability with Memory Size
29
J
LLM Prompts Design
29
K
Case Study
30
K.I
Case Study on QA Comparison
31
K.2 Case Study Multi-gramularity Information Generation
32
K.3 Case Study on Multi-granularity Filter
32
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A DATASETS STATISTICS
Table 5: Dataset statistics. The term 'Avg.' (e.g., Avg. Sessions) represents the average number corresponding to each conversation.
Dataset
LoCoMo
Long-MT-Benchh
LongMemEvals
LongMemEvalee
Total Conversations
10
500
500
Avg. Sessions
27.2
4.9
50.2
501.9
Avg. Query
198.6
26.2
1.0
1.0
Avg. Token
20,078.9
19, 194.8
103,137.4
1,019,116.7
Sessions Dates
Retrieval Ground-Trun
QA Ground-Trun
Conversation Subject
User-User
User-Al
User-AI
User-AI
We evaluate the proposed method with the following benchmarks:
LongMemEEal-s (Wu et al., 2025a) and Lonl (Wu et al., 2025a) datasets are bench- marks designed to evaluate long-term memory in chat assistants. LongMemEval-s involves 50 sessions per question with an average of 115k tokens, making it a compact yet challenging dataset. Long -m, on the other hand, spans 500 sessions per question, resulting in avg. 1.5 million tokens per conversation, offering more extensive evaluation setting. Both datasets require AI systems to handle user-Al dialogues, dynamic memorization, and historical consistency. The dataset includes several query types: (1) Knowledge Update, testing whether the model can track and reason about changes in the user's personal information over time; (2) Temporal Reasoning, focusing explicit dates and inferred time references; (3) Single-Session User, which checks recall user details within one session; (4) Single-Session Preference, evaluating whether the model can use preferences shared in session; (5) Single-Session Assistant, testing recall of assistant provvded information; (6) Mulli-Session, which assesses reasoning that spans multiple sessions.
LoCoMo (Maharana al., 2024) dataset evaluates long-term memory in AI through lengthy conversations, averaging 300 turns, 9,000 tokens, and up to 35 sessions. LoCoMo, the publicly available subset of the original paper, includes 10 high-quality. long conversations with 27.2 ses- sions and 20,000 tokens on average. The goal of LoCoMo is to evaluate long-context LLMs and RAG systems, which, while improving memory performance, still fall short human capabili- the challenges of understanding long-range
ties- pis-ppattcuularlly in temporal dependencies. The dataset includes several query types: (1) Open Domain Knowledge, requiring integrating
facts with external commonsense knowledge; (2) Temporal Reason- ing, involving chronological inference; (3) Single-Hop Retrieval, solvable via a single session; (4) Adversarial, which intentionally misleads and requires the model to avoid incorrect conclusions; (5) Multi Multi-Hop Retrieval, requiring synthesis across multiple sessions.
Long-MT-Benchhe (Pan et al., 2025) dataset is reconstructed from MT-Bench+ (Lu et al., 2023) by incorporating long-range questions to address the limited QA pairs and short dialogues. merges five consecutive sessions into a single long-ter term conversation, improving its suitability for evaluating memory mechanisms. The dataset features I conversations with an average of 4.9 sessions and 19194.8 8 tokens. Unlike LoCoMo, it focuses on user-Al interactions, excluding session dates and retrieval ground truth.
B COMPARISON OF METHODS STRUCTURE WITH BASELINES
We compare the model structures of each baseline (provided in parentheses) and offer concise summaries to highlight the differences in their memory construction and retrieval mechanisms.
MPNet (Vector Base): Uses permuted language modeling with auxiliary positional information to model token dependencies while seeing full-sentenee position signals.
Contriever (Vector Base): Trains dense retrievers via unsupervised contrastive learning for generalizable text embeddings, including cross-lingual retrieval.
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Composes LLM modules with few-shot prompting, Shain-of-thougee and external memory to maintain long-term conversational consistency without fine-tuning.
MPC (Memory Pool):
RecurSum (Recursive Summary): Recursively generates and updates dialogue summaries as memory, using prior memory and new context maintain coherence.
SeCom (Semantic Segment): Segments conversations into topically coherent units and applies compression-based denoising to build and retrieve from segment-level memory.
RAPTOR (Hierarchical Tree): Recursively embeds, clusters, and summarizes text form hierarchical summary tree, enabling retrieval at multiple abstraction levels.
HippoRAG2 (Knowledge Graph); Augments RAG with knowledge-graph traversal, integrating passages and online LLM reasoning for associative retrieval.
A-Mem (Memory Note): Implements a ZZtteekasteeninnpired memory: creates structured notes with attributes, indexes and links them, and updates existing notes as new ones arrive.
MemGAS (Ours; Hulti-Granularity Memory): Builds multi-granularity memory units, asso- ciates them via Gaussian Mixture Models, and uses an entropy-based router plus L.M filtering for adaptive retrieval.
C COMPREHENSIVE COMPARISON OF BIGGL--RANULARRITY AND MULTI-GRANULAN
C.1 QA PERFORMANCE.
The results in the Table 6 demonstrate the significant advantages of multi- culliigraaularityy methods over single-granularity approaches. Across all datasets, gaatiigranularrty methods outperform single- granularity methods in key evaluation metrics such as F1, BLEU4, ROUGE, and BERTScore. For example, in the LongMemEval level) achieves Fl score of 12.26, whereas the multi- cualiiigaanllrity method achieves significantly higher Fl scores of 12.51 and 16.85. Similarly, in the LongMTBench+ dataset, the multi-granulle granularity method achieves F1 scores of 37.16 and 41,49, outperforming the best single-granularity method, which scores 36.67. These results highlight the effectiveness of multi-granulart approaches in integrating information from different granularities, providing more comprehensive understanding and significantly improving QA task performance.
dataset, the best-performing
single-gre gaaele-ranllartty method (Turn-
Moreover, the proposed method, MemGAS reveals its superiority over simple multi-granularity combination methods. Across all datasets, MemGAS consistently achieves better performance not only Fl scores but also across other evaluation metrics. For instance, in the -ong dataset, MemGAS achieves an F1 score of 20.38, surpassing the combination method's score of 14.59. Similarly, the LongMTBeench+ dataset, MemGAS achieves an score of 41.49, compared 37.16 for the combination method. These results demonstrate that MemGAS leverages more sophisticated mechanism to capture the relationships and complementarities among different granularities. This enables MemGAS to achieve greater robustness and generalization, making it particularly effective for complex QA tasks.
C.2 RETRIEVAL PERFORMANCE
The results in Table clearly demonstrate the advantages of the multi-granularity approach in retrieval tasks compared to granularttu methods, Across all datasets, multi multi-gramullrity methods consistently achieve higher scores in key retrieval metrics such as Recall and NDCG. For example, in the LongMemEval-s dataset, the best of 91.91 and NDCG @ 10 of 86.73. However, the simplest demonstrating its superior retrieval further improves these scores to 91.91 and 88.03, respectively, effectiveness. Similarly, in the LongMemEval-m dataset, the Recall @ 10 and NDCG@ @ 10 of the multi- granularity approach reached at least 73.83 and 68.66, respectively, which significantly exceeded approach (Recall @ 10 70.21 and NDCG@ @ 10 63.43,
method (Turn-level) achieves Recall 10 combination approach
the best results of the single-graullen -evel.eee). These findings underline the strength multi-granulariin approaches in leveraging diverse levels of information retrieve more relevant results and improve overall retrieval quality.
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Table 6: QA performance of Single-Granularty and Multi-Granularity
Granularity
GPT40-J
F1
BLEU4 Rougel
Rouge2
RougeL,
BERTScore
LongMemEvalss
Gratua-Gr
Session-level
53,40
13.78
2.21
14.46
6.93
12.89
83.70
Tum-level
57.60
14.94
2.50
15.53
7.50
14.12
83,93
Summary-leve
28.80
I0.66
1.78
11.51
4.61
9.96
83,26
Keyword-leve
17.20
9.05
1.61
9.82
3.61
8.20
82.84
Multi-Granulee Granularyyy
Combination
56.60
14.59
2.40
15.19
7.22
13.70
83.96
MemGAS
60.20
20.38
4.22
21.05
10.47
19.47
85.21
Lona-emEvallm
Session-level
42.80
11.88
1.66
12.56
5.63
11.02
83.28
Tum-level
42.60
12.26
1.79
12.93
5.67
11.45
83,43
Summary-lews levell
24.40
10.11
1.68
11.02
4.16
9.41
83.17
Keyword-leved
15.00
8.29
1.54
9.16
3.26
7.43
82.72
Multi-Granularig
Combination
46.40
12.51
1.96
13.08
6.13
11.70
83.49
MemGAS
45.40
16.85
3.39
17.60
8.25
16.14
84.69
LoCoMo
Session-leve
40.33
15.66
2.67
16.01
7.68
15.00
84.65
Turn-level
42.09
16.10
2.80
16.42
8.13
15.36
84.70
Summary-leww
19.08
9.78
0.83
10.38
3.28
9.41
83.69
Keyword-lever
14.85
7.74
0.64
8.40
2.20
7.62
83,28
Multi GrailGrannlyrrty
Combination
42.80
15.93
2.60
16.27
7.76
15.26
84.69
MemGAS
40.08
17.66
3.61
18.00
8.93
16.99
85.13
LongMTBench+
Single-Gres Granularty
Session-level
63.54
36.30
11.59
38.17
21.65
29.67
87.90
Turn-level
67.01
36.67
11.91
39.06
20.88
30.13
88.04
Summary-lews
21.18
28.30
8.37
31.12
13.11
22.55
86.74
Keyword-lever
20.83
28.05
8.90
30.92
12.80
22.39
86.78
Multi-Granularin
Combination 67.01
37.16
11.58
39.11
21.95
30.24
88.00
MemGAS 69.44
41.49
15.62
43.69
24.45
34.66
88.96
"Combination" denotes that texts from all granularities are directly concatenated into a single string, and encoded to embedding for retrieval. This naive merging lacks adaptive weighting structure. The proposed method, MemGAS further demonstrates its superiority over simple multi-granularity combination methods. In all datasets, MemGAS consistently achieves better results. For example, in the LongMemEval-s dataset, MemGAS achieves Recall @ 10 of 94.47 and NDCG@ 10 of 89.96, surpassing the combination method's 's scores of 91.91 and 88.03. Similarly, in the LoCoMo dataset, MemGAS achieves Recall @ 10 of 81.07 and NDCG@ 10 of 68.24, outperforming the combination method's ''s scores of 78.70 and 65.64. These improvements demonstrate that MemGAS is merely aggregating information from multiple granularities but instead employs more advanced mechanism capture the interdependencies and complementarities among different granularities. This enables MemGAS to deliver robust and versatile performance, making it highly effective for complex retrieval tasks that require the integration of multi multi-granuularty information.
C.3 How MULTI-GRANULARITTY ROUTER WORKS?
In this section, we analyze how the proposed Multi-gramulaan Router works. "Optimal Selection" is oracle-style upper bound: for each query, it picks the single best-performing granularity among
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Table 7: Retrieval Performance Sangle-Granularite and Multi-Gramularty.
Granularity
@ NDCG@3 Recall@5 NDCG@5
Recall 10
NDCG@10
LongMemEval-s
Single-Graularing
Session-level
71.06
79.72
81.28
82.47
90.00
84.29
Turn-leel
73.62
82.47
84.68
84.94
91.91
86.73
Suemary-level
70.43
80.53
80.00
82.38
88.30
84.28
Keyword-
62.98
74.69
74.68
77.76
82.34
79.50
MuntlrGrrnuyartty
Combination
76.17
84.68
87.23
87.00
91.91
88.03
MemGAS
78.51
86.83
88.94
88.77
94.47
89.96
Long MeemMeemaaalee
Sinala-Granularity
Session-level
44.26
56.11
53.40
59.44
65.96
62.74
Tum-level
44.68
55.06
57.66
60.03
70.21
63.43
Summary-level
41.06
54.68
52.34
58.64
65.53
62.31
Keyword-level
35.11
48.22
43.62
52.02
57.87
55.75
Multi-Granulan Granulrity
Combination
50.85
61.50
60.00
64.82
73.83
68.66
MemGAS
51.06
61.36
63.62
66.07
77.02
69.46
LoCoMo
Session-level
49.90
52.15
58.26
56.29
71.80
60.92
Tum-level
52.77
54.09
64.30
59.64
80.31
65.07
Summary-leve
47.89
48.96
58.21
53.94
74.12
59.61
Keyword-level
29.05
29.72
40.33
35.46
65.11
44.11
Gaati-Grannlarity
Combination
53.98
55.89
63.80
60.61
78.70
65.64
MemGAS 57.45
58.84
67.12
63.60
81.07
68.24
the four. Conceptually, reflects the upper performance achievable one could choose the ideal granularity per query. Our findings reveal that different levels of granularity exhibit distinct preferences on the query type, and our router effectively adapts by selecting the suitable granularity strategy. Table 8 presents the retrieval performance (Recall@3) across various query types and granularities on the LongMemEvvl--n dataset. The results show that different query types favor different granularities: for instance, temporal-reasoning queries benefit most from session- level granularity, knowledge-update queries achieve better performance with turn-level granularity, and single-session preference queries perform best with summary-level granularity. Notably, Granularity Router (MemGAS) adaptively integrates multiple granularities, achieving retrieval performance that closely approaches the upper bound defined by Optimal Selection. This highlights the router's ability to dynamically identify and apply the most effective granularity for each query type, bridging the gap between fixed granularity approaches and optimal strategies.
C.4 HOw PPR AFFECTS THE RETRIEVAL RESULTS?
To further understand how PPR reshapes the retrieval behavior, we conduct detailed analysis comparing the top-k results before and after applying PPR. As shown Table 9, PPR leads to substantial re-ranking, with 97% of samples exhibiting different top-10 results. This indicates that PPR is not minor adjustment but significantly alters the retrieval ordering. Beyond the quantitative changes, also observe that PPR tends to surface memories that have lower embedding similarity but are contextually relevant. In particular, for multi-session queries, the retrieved set before and after PPR often differs in whether all relevant sessions included. This demonstrates that PPR enhances
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Table 8: Comparison of retrieval performance (Recall across query types and granularities,
showcasing the effectiveness of the Multi-granularig Router.
Granularity Router (Ours)
Optimal Selection
Query Type
Session
Turn
Summary
Keyword
single-sessoon-ssittee
96.43
98.21
96.43
92.86
100.0
100.0
single-sesion--sse
51.56
65.62
54.69
51.56
60.94
78.12
multi-session
32.23
28.1
19.01
13.22
33.88
43.80
knowledge-update
37.5
41.67
37.5
34.72
51.39
72.22
temporal-reasoning
33.86
30.71
33.07
22.83
41.73
51.96
mugle--eesionnprefeeene
40.0
33.33
43.0
33.33
46.67
63.33
Table 9: Change rate of top-k retrieval results before and after applying PPR.
Method
Top
Top 5
Top 10
Changes rate in top-k results
44.0%
72.4%
97.0%
the model's ability to identify semantically connected memories, resulting in more comprehensive retrieval outcomes.
D ADDITIONAL COST AND EFFICIENCY ANALYSIS
D.1 TOKEN CONSUMPTION FOR MEMORY CONSTRUCTION
In this section, we present the input and output token consumption during memory construction using LLMs (e.g., constructing knowledge graph of HippoRAG2. Hierarchical Tree of RAPTOR. Semantic segment of SeCom and Recursive Summary of RecurSum). The results in Table 10 clearly demonstrate the exceptional efficiency of MemGAS compared other baselines the LongMemEval-s dataset. MemGAS processes only 52.9 M input tokens, which matches the total corpus size, while all other methods require significantly more tokens. For example, HippoR processes over 111. M input tokens, RAPTOR uses over 62.6 M, and even the relatively efficient RecurSum exceeds MemGAS. Similarly, in terms of output token usage, MemGAS generates only 5.2 M tokens, far fewer than the massive outputs of HippoRAG 2 and SeCom, which produce over 100 M and 70 tokens, respectively. MemGAS achieves this remarkable efficiency without compromising performance, making it a highly effective and scalable solution for handling large-scale datasets. This demonstrates MemGAS 's ability to minimize computational costs while maintaining state-of-the-ar results.
Extra Memory Cost: We also assessed the additional memory (RAM) cost introduced by our method, as presented in Table 11. On the LongMemeval-s dataset, our muuti-granularigg approach incorporates summary and keyword memory. resulting in approximately 27MB extra memory ussge-juus 10% of the 266MB required for raw memory. This demonstrates that method imposes minimal storage overhead, further validating its efficiency.
D.2 COMPARISON AT THE SAME TOKEN COST
We conduct experiments by fixing the number of input tokens across different methods on the LongMemEval-s dataset. Specifically, we performed over-retrieval and then applied truncation at thresholds of 8,000 and 16,000 input tokens ensure that all baseline methods have the same input token length. Table 12 shows the results of these experiments. The results demonstrate that under the same input token constraints, our proposed method, MemGAS, consistently outperforms baseline approaches in terms of performance (GPT4o-J score) while maintaining competitive latency, Specifically, at both token thresholds, MemGAS achieves the highest GPT4o-J scores (59.8 and 60.3, respectively), surpassing other methods. Although MemGAS incurs slightly higher latency compared to Contriever and Secom. it achieves a significantly better trade-off between efficiency and accuracy. Additionally, the results highlight that using fixed token cost is more effective than relying on the
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Table 10: Comparing the total input and output token consumption of baselines on the LongMemEval-
s dataset. The LongMemEval-s dataset contains approximately 51.61 ...M corpus tokens. 'M' means million.
MemGAS
HippoRAG2
RAPTOR
SeCom
RecurSum
Input Tokens |52.9M (100 %)
I11.IM (210.0 %)
62.6M (118.3 %) 106.2M (200 %) 58.3M (110 %)
Output Tokens
5.2 (100 %)
10.9M (209.6 %)
0.73M (14.0 %) 71.IM (1367 %) 16.3M (313 %)
Table 11: Extra Memory (Total Tokens and RAM) Cost on LongMemEval-s datasets.
Original Memory
Summary Memory (Extra)
Keywords Memory (Extra)
Total Tokens RAM
51.6 milinon
3.14 14 milinon
2.09 milinon
266MB
16.2MB
10.8MB
full history approach, which yields lower GPT4o-J score of 50.6 despite its much higher latency. This confirms the efficiency and effectiveness of MemGAS in handling long-tew memory tasks.
E GENERALIZATION ANALYSIS
E.1 COMPARISON ON DIFFERENT RETRIEVER
The results in the Table 13 demonstrate that MemGAS consistently outperforms all other methods across all metrics when evaluated with different base retrievers (MiniLM, MPNet, and Contriever) on the LoCoMo dataset. For the MiniLM base retriever, MemGAS achieves the highest scores in all metrices, outperforming MiniLM, MPC, RecurSum, and SeCom. This indicates that MemGAS is highly effective in leveraging the MiniLM retriever improve retrieval performance.
When using the MPNet and Contriever base retrievers, MemGAS maintains its superiority across all metrics. For MPNet, MemGAS achieves Recall@ 10 80.51 and NDCG@ 10 of 65.48, which are significantly higher than the other methods. Similarly, for the Contriever retriever, MemGAS obtains the best Recall @ (81.82) and NDCG@ 10 (68.42). These results highlight the robustness and adaptability of MemGAS demonstrating its ability to outperform alternative methods regardless of the underlying retriever model.
E.2 COMPARISON ON DIFFERENT GENERATOR
We conducted additional experiments using Qwen-3 (8B and 1.7B) as generators, combined with Contriever as the retriever on the LongMemEval-s dataset. Table 14 shows that our proposed MemGAScconsistenty outperforms the baselines (Contriever and SeCom) across all base generators, including GPT4o-Mini, qwen3-8b. 8b, and qwen3-1.7b Notably, MemGASachieves the highest F1, BLEU4, and Rouge scores, demonstrating its robust ability both retrieve relevant information and generate high-quality answers. The results also highlight that larger base generators, such as GPT4o-Mini and qwen3-8b, lead to better overall performance, but MemGASmaintains its superiority regardless of the base generator used.
E.3 COMPARISON ON DIFFERENT QUERY TYPES
The radar charts in Figure demonstrate the performance of different methods across diverse query dimensions. Our approach consistently outperforms baseline methods, particularly excelling in multi-hop retrieval on LoCoMo (Figures 5a, 5b) and multi-session on LongMemEval-- (Figures 5c, 5d). This highlights the effectiveness of our framework addressing both complex reasoning tasks and simpler retrieval-based queries. In multi-hop retrieval task, our method achieves notably higher Fl and RougeL scores, showcasing its ability to retrieve and integrate information across multiple steps. Similarly, in multi-session task, the model effectively captures relevant historical sessions, enabling context-awarr and accurate responses. These strengths are driven by the memory association mechanisms that enhance reasoning and knowledge integration.
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Table 12: Comparison Methods at Same Token Cost.
Input Tokens
Method
Latency (s)
GPT40-J
Contriever
1.81
55.2
Secom
2.13
57.8
8,000
HippoRAG2
4.39
57.4
MemGAS (Ours)
2.42
59.8
Contriever
2.48
56.6
Secom
2.86
58.0
16,000
HippoRAG2
4.99
58.2
MemGAS (Ours)
3.15
60.3
103,137
Full history
9.39
50.6
Table 13: Retrieval Performance based on different retriever on LoCoMo. "facehook/contrivverr"
lemmeeeeraanffoomrr/mmttiiiggggi -ose- Sentrneeetaasommeee/ttiiiinllili cos-v1'
Model
Recall@3 NDCG@3 Recall@5 NDCG@5 Recall@10 NDCG@10
Base Retriever: MiniLM
MiniLM (Song et al., 2020)
42.55
44.19
52.37
49.01
67.98
54.59
MPC (Lee al., 2023)
42.30
43.59
51.21
48.08
68.03
54.03
RecurSum (Wang et al., 2025)
44.76
46.82
54.73
51.64
72.16
57.52
SeCom (Pan et al., 2025)
45.77
47.25
54.93
51.70
71.15
57.37
MemGAS
47.73
49.11
56.60
53.46
71.30
58.59
Base Retriever: MPNet
MPNet (Song et al., 2020)
45.92
47.71
53.98
51.79
68.58
56.88
MPC (Lee al., 2023)
45.47
47.35
54.08
51.68
68.28
56.59
RecurSum (Wang et al., 2025)
49.50
51.15
59.47
56.16
76.64
61.99
SeCom (Pan et al., 2025)
47.53
49.03
57.05
53.57
70.90
58.57
MemGAS
52.77
54.63
62.79
59,56
80,51
65.48
Base Retriever: Contriever
Contriever (Izacard et al., 2021)
49.90
$2.15
$8.26
56.29
71.80
60.92
MPC (Lee al., 2023)
49.50
$1.47
57.45
55.53
71.85
60.47
RecurSum (Wang et al., 2025)
47.23
48.99
59.01
54.58
74.97
60.07
SeCom (Pan et al., 2025)
55.24
57.90
64.80
62.36
78.30
66.97
MemGAS
57.30
58.76
67.32
63.62
81.82
68.42
Our framework also demonstrates strong performance in temporal reasoning, knowledge update, and single-session tasks, as shown by consistently higher scores across these This indicates its ability to adapt to dynamic information and maintain relevance in evolving contexts. Furthermore, the model performs well in adversarial and open-domain knowledge tasks, reflecting its robustness and versatility. The results emphasize the comprehensive improvements achieved by our method across wide range of query types, showcasing its capability handle both simple and complex scenarios effectively. These findings underline the model's adaptability and suitability for real-world applications requiring diverse and dynamic query handling.
F HYPERPARAMETER SENSITIVITY ANALYSIS
evaluate the hyperparameter entropy degree À in Equation 3 and the number of seed nodes a in Equation 5. The results of the hyperparameter in Figure 6 show that performance metrics, including nDCG and Recall, improve as a increases, reaching their peak when a is set to a moderate value around 15. Beyond this point, performance declines, indicating that balanced setting of is crucial for optimal results. When a is too small, the model tends to underfit, struggling to capture sufficient seed nodes in the data. On the other hand, excessively large values of cause the model to
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Table 14: QA performance comparison of different models across various base generators (GPT4o- Mini, Qwen-3 8B, and Qwen-3 1.7B) on the LonaMesEval-s dataset.
Model
GPT40-J F1
BLEU4 Rougel Rouge2 RougeL BertScore
Base Generator: GPT40-Mini
Contriever (Izacard et al., 2021)
55.40
13.78
2.21
14.46
6.93
12.89
83.70
SeCom (Pan et al., 2025)
56.00
12.95
2.25
13,80
6.09
11.93
83.51
MemGAS
60.20
20.38
4.22
21.05
10.47
19.47
85.21
Base Generator: qwen3-8b
Contriever (Izacard et al., 2021)
50.6
14.76
1.85
15.37
7.44
13.81
83.73
SeCom (Pan al., 2025)
50.6
14.83
1.89
17.66
8.45
14.08
83.15
MemGAS
51.4
20.57
3.5
21.17
10.15
19.09
85.14
Base Generator: qwen3-1.7b
Contriever (Izacard et al., 2021)
36.4
9.39
1.3
9.92
4.37
8.8
82.62
SeCom (Pan et al., 2025)
36.0
8.5
1.07
9.26
3.66
7.91
82.26
MemGAS
38.0
16.79
3.6
17.58
7.78
16.19
84.84
lose the ability to explore the memory graph. When a is set to 15, the model often achieves better performance across various metrics.
For the hyperparameter entropy degree X, a similar trend is observed in Figure Smaller values lead steady improvements in performance, with the metrics reaching their highest levels when is set to a value slightly above the minimum, around 0.2. However, as À increases further, performance begins to degrade, emphasizing the importance. Extremely small values may fail leverage the trade-offs controlled by A, while larger values lead to the same entropy for different granularities. When kept around 0.2, the model often delivers superior performance.
G ERROR ANALYSIS
In this section, conduct error analysis under the following experimental setup: evaluate three datasets- due to the lack of retrieval ground truth. The retriever Contriever, and the generator is GPT-4o-mini with a fixed QA prompt, using the Top-3 retrieved passages for answering. Retrieval correctness defined such that query labeled "Correct" if any Ground-Truth passage appears in the Top- 3; otherwise, it is labeled "Wrong." Generation correctness follows the GPT4o-as-Judge. The error analysis presented Figure highlights the performance across three datasets: LoCoMo, LongMemEval-m. and LongMemEval-s, A key observation is that significant portion of queries in the Mem datasets lack correct retrieval results, as shown by the high percentage of "Wrong Retrieval + Wrong Generation" (40.6% in LoalMemEval-m and 18.6% in LongMemEval-s)) Our method effectively identifies cases where the retrieval corpus does not contain information related to the query, responding with "you don't mention the related information." This approach reduces hallucinations caused by large language models being overly confident and generating fabricated content. For example, in LongMemEyal-s,, "Correct Retrieval + Correct Generation" accounts for 52.6%, demonstrating the method's o's reliability handling scenarios with relevant information.
LongMemEval-m. and -a-eemeBal-s--ellddiii Long-MT-Bench+
H THEORETICAL ANALYSIS
We provide concise guarantees for two building blocks of MemGAS: (i) GMM accept/reject association, and (ii) multi-granularity scoring with entropy-drives routing. All results are stated under standard, verifiable assumptions with compact proof sketches.
Notation. For a query and memory index E (1....,ne g E {S (session), T (turn), U (summary), K (keyword)) with similarity scores sy = sim M().
n). MemGAS maintains four granularities
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Contriever SeCom
HippoRAG
Ours
Temporal Reasoning
Temporal
Reasoning
Open Domain Knowledge
Open Domain
Knowledge
23
N
11
5
Multi-Hop Retricval
Multi-Hop Retricval
Single-Hop
Single-Hop
Retrioval
Retrieval
Adversarial
Adversarial
(a) LoCoMo F1 Score.
(b) LoCoMo RougeL. Score.
Knowledge Update
Single-Session User
Knowledge
Single-Session
Update
User
25 34
24 33
16
Temporal Reasoning
Single-Session Preference
Temporal
Single-Session Pr:ference
Reasoning
Multi-Session
Single-Session Assistant
Multi-Session
Single-Sessee Session Assistant
(c) LongMemEval-s F1 Score.
(d) LongMemEval-s RougeL Score.
Figure 5: Comparing the Fl and RougeL socre of different query types LoCoMo and LongMemEval-s datasets.
For each g, define the softmax distribution and entropy
cxp(s?/A) (o( ΣØ=1 exp(s)/A)
12
Hg Σ pg p))
The router weight is Wg = / H with normalized vector s = (score:); Σ, score,. Let G = (V, E) denote the association graph induced by the accept/reject rule. Let R(q) S [I....,n] be the relevant set, Recall @ K the top K recall, and nDCG the ranking metrie. For each granularity g. let TopK, denote the indices of its lop-K scored items.
and the initial aggregate score is score; = Eg Wy sf
Assumptions. (AI) (Sub-Gaussian separability) For each g. syly; and s
are sub-
Gaussian with means 2 >> My and common scale ay; denote A M Mgg > 0.
H.1 GMM ACCEPT/REJEC ASSOCIATION (CLEAN LINKS)
Intuition. We want the association graph connect new memories mostly truly related ones. the relevance/non-rrllvaaee score distributions are well-separated, a simple threshold already yields exponentially small mistakes; a fitted GMM recovers (approximately) this threshold practice.
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64
nDDG@S
nDCG@3
nDCG@1
63
66
57
62
64
10
15
a
a
a
68
Recall@
57-
Recall
Recall
66
56
75
55
64
10
5
10
15
a
a
$9.0
68.5
aDCG@0
58.5
aDCG@5
63.5
nDCG@10
68.0
58.0
67.5
62.5
$7.0
67.0
0.1
0.2
0.3
0.5
0.7
0.2
0.3
0.5
0.7
0.1
0.2
0.3
0.5
0.7
Recall
57
Recall@:
67.5
Recal I0
67.0
81
$6
66.5
80-
55
0.5
0.2
0.3
0.5
0.7
0.1
0.2
0.7
Figure 6: Hyperparameters Analysis on LoCoMo dataset.
LoCoMo
LongMemEval-n
LongMemEval-s
16.7%
35.5%
34.1%
37.6%
4.5%
56.0%
22.8%
7.2%
17.0%
11.3%
Correct Retrieval + Correct Generation
Wrong Retrieval + Correct Generation Wrong Retricval + Wrong Generation
Correct Retricval Wrong Generation
Figure 7: Error Analysis.
Proposition 1 (Exponentially small mis-link rate) Under Al (drop index for brevity), the mid- threshold T" (n* + p )/2 satisfies
A
Placcept an irrelevant or reject a relevant item) $ 2exp
8g2
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Moreover. for identifiable mixtures, GMM consistently learns (n= attaining the same exponential envor order. Apply sub-Gaussian tail bounds at μ - A/2 and union bound.
Corollary (Few false edges in expectation) Let fl I //sp be the accept/reject ervor rate at granularity g. and dg the number of candidate neighbors. Then the expected number of false edges per insertion satisfies [E] FRatsel] = 0 dg (exponentially small in Ag).
Consequence. With larger separation Ag. the graph stays "clean" over time, which is crucial for subsequent retrieval and ranking stages.
H.2 ENTROPY ROUTER (CHOOSE CONFIDENT GRANULARITIES)
Intuition. Each granularity produces a relevance distribution Py over memories. If this distribution is sharp (low entropy), that granularity is more decisive for the current query. Weighting granularities by inverse entropy therefore favors the most confident signals without hand-tuning.
Lemma (Lower entropy = higher confidence) the distribution Pg becomes more concentrated on its top-1 item (larger top-1 margin), its entropy Hg decreases. Entropy is Schur-concave more concentrated distributions have lower entropy.
Proposition 2 (Inverse-entropy weighting simple and robust) The rule Wg X 1/Hg is (i) monotone in confidence (Lemma 1), (ii) symmetric across granularities, and (iii) scale-free un- der common resealing of {Hg}- These invariances restrict Wi to power laws H 3 the simplest scale-free choice.
Theorem (Multi-grat - candidate-pon coverage is never worse) Let the multi- granularty candidate pool be Cmultt Ug TopK, y (4). coverage of relevant items
Canulti n R(q)) R(4))
max Recall &
|Cmutti n R(g))
and under weak dependence
2 - II (
R((g)
A union cannot cover fewer relevant items than any constituent set; inclusion-excluee yields the product bound under weak dependence. MemGAS approximates this union via weighted aggregation.
Consequence. Adaptive combination cannot underperform the best single granularity for recall, and typically exceeds it when granularities are complementary (c.g., summaries remove noise; turns capture details).
Discussion. Why does MemGAS beat single/fixed granularity? Under Al and for suitable K: (i) the multi-granularity candidate pool, guided by Wy x 1/Hg. achieves coverage at least high as the best single granularity (Thm. 1) and typically higher when the views are complementary: (ii) inverse-entropy routing (Prop. adaptively emphasizes the most informative granularity per query, improving the quality of the aggregated scores without hand-tuning; and (iii) the GMM accept/reject mechanism yields exponentially few false associations (Prop. and its corollary). reducing noise and spurious ties. Together, these effects raise Recall @ K of the candidate pool and typically improve nDCG once any standard downstream ranker applied. Any additional retrieval module (e.g., graph propagation) is orthogonal and can be plugged in as desired, but it is not contribution of our method.
I ADDITIONAL EXPERIMENT
1.1 HUMAN EVALUATION
To address concerns that automatic metrics (e.g., F1, JPP--4oas--udgee may be insufficient dialogue settings, additionally conducted human evaluation LONGMEMEVAL-S (50 random samples). Human annotators evaluated the same fixed model outputs used for the automatic metrics. For GPT-4o-as-Judge, repeated the evaluation three times with different random seeds while keeping
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model outputs fixed, ensuring that any variance arose solely from the evaluator itself. As shown in Table 15, GPT-4o-as-Judge exhibits extremely low variance across runs runs-with only SeCom showing minor fluctuation (56, 56, 58; standard deviation 0.94). More importantly, human judgments closely align with GPT-4o-as-Judge, confirming that GPT--4o-as-Judge is reliable in this evaluation setting.
Table 15: Comparison of human evaluation and GPT-4o-as-Judge on LONGMEMEVAL-S. The GPT-4o-as-Judge results report mean and standard deviation across three runs.
Model
Human-as-Judge
GPT-4o-as-Judge
SeCom
56
56.67 + 0.94
HippoRAG2
58
58.00 0.00
A-Mem
56
56.00 + 0.00
MemGAS
62
62.00 = 0.00
I.2 COMPARISON WITH ADDITIONAL BASELINES
Experimental Setup. For H-MEM, we reproduce the official four-layer hierarchical memory architecture, where each session is processed by an LLM-basee extractor to produce multi-level memory units encoded with Contriever embeddings and positional links to sub-memories Retrieval settings (vector encoding, top-k, and similarity) strictly follow the configuration outlined in our paper. For COMEDY, compression prompt to generate a concise (< 500 words) session-level memory representation. To ensure fairness, all baselines use the same top-3 Contriever retriever and the same GPT-4o-mini generator.
the released COMEDY-7B checkpoint and apply its Task-2 memory-
Results and Analysis. Table summarizes the results on LongMemEval-s and LongMTBenchh. MemGAS consistently achieves higher scores than both H-MEM and COMEDY across all evalua- tion metrics, demonstrating its robustness and generality. While COMEDY offers highly efficient compressed-memory representations with low latency and token usage, its episode-level abstraction limits its ability to capture fine-grained cross-session associations. H-MEM, despite its hierarchical structure. suffers from rigid memory abstraction and less adaptive retrieval behavior. In contrast, MemGAS leverages multi-gran caaluiigaaularttyy memory units and adaptive association mechanisms, enabling more effective retrieval and generation performance.
1.3 SCALABILITY WITH MEMORY SIZE
To assess the scalability of our memory system, we conduct data-scaling experiments on LongMemEval-mm by varying the total memory size from 20K to 1M tokens and measuring four key components. As shown in Figure 8, the GMM update cost grows slowly with memory size and remains within a few milliseconds even at 1M tokens, indicating that incremental clustering updates are practically negligible compared to overall query time. PPR latency also increases with memory size due to the denser association graph, but remains the order of milliseconds at the largest scale, suggesting that graph-based retrieval does not become a runtime bottleneck within the evaluated regime. In contrast, the token usage for both summaries and keywords grows approximately linearly with the number memory tokens, since each session requires single summarization and key- word extraction call. Importantly, these L.LM construction costs incurred offline during memory building or maintenance, and therefore do not affect online query latency. Overall, these results demonstrate that our method scales favorably to at least 1M memory tokens: online components (GMM updates and PPR) remain efficient, while the dominant LLM costs are confined to offline preprocessing.
J LLM PROMPTS DESIGN
Figures 9-12 present the key prompt templates used in our multi- processing pipeline. Figure defines prompts for multi-granulant information generation, where the model is asked to produce both high-level summaries and fine- grained keyword lists from ascr-assi dialogue histories. Figure 10 introduces filtering prompt that selects only the content relevant to the input question,
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Avg. Tokens
Avg.
Model
40-J
F1
B-4
R-1
R-2
R-L
BS
Latency
H-MEM
54.80
13.65
2.10
14.55
6.85
12.92
83.75
8,420
COMEDY
56.20
13.32
2.19
14.42
6.73
12.74
83.78
2,383
1.43
MemGAS (Ours)
60.20
20.38
4.22
21.05
10.47
19.47
85.21
8,829
2.55
Long.MTBench.
H-MEM
64.25
36.50
11.45
38.40
21.10
29.30
87.85
12,450
3.40
COMEDY
65,42
36.72
12.52
38.94
21.62
29.85
87.89
2,492
1,47
MemGAS (Ours)
69.44
41.49
15.62
43.69
24.45
34.66
88.96
12,873
3.85
Table 16: Comparison with H-MEM, COMEDY.
Updade new memery by
Convergence
Latency
Lideecy
Tokal Memory Takena (x30")
Total Memory Tokana (x300))
(a) GMM update latency memory size.
(b) PPR latency memory size.
Scating
Scaling
63000
40000
Towen Count
50000
33000
80000
20000
20000
2000
1000
Momory Texens (410*)
Momory Toxens
(c) Summary token usage vs. memory size.
(d) Keyword token usage vs. memory size.
Figure 8: Data-scaling behavior the proposed memory system on Long the total memory size increases from 20K to IM tokens
operating the structured outputs (session, summary, and keywords) while preserving original token fidelity. Figure 11 provides the instruction for generating final responses, guiding the model to produce concise and coherent answers grounded in the filtered history. Finally, Figure 12 shows the evaluation prompt used to assess answer correctness, following the setup of prior work (Wu et al., 2025a; Zheng al., 2023). This prompt asks GPT-4o compare model outputs against reference answers and return a binary decision without paraphrasing. Together, these prompts enable modular abstraction, filtering, reasoning, and automatic evaluation across the dialogue memory pipeline.
K
CASE STUDY
This section presents a series case studies comparing QA results with other methods, highlighting the model's capabilities in handling multi-granulario information generation and filtering. Through these examples, we demonstrate how the model excels in maintaining factual consistency, extracting structured information, and filtering query-relevant responses effectively.
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Prompt for Multi gulli--ranularity Information Generation
Summary Generation:
Below an user-AI assistant dialegue menory. Please summarize the following dialogue as concisely as possible in a short paragraph, extracting the main themes and key information.
Keyword Generation:
Below an user-AI assistant dialegue menory. Please extract the most relevant keywords, separated by semicolon.
Figure 9: Prompt for Multi-granularity Information Generation.
Prompt for Multi- gulti-granularity Information Filter
You are an intelligent dialog bot. You will be shown History Dialogs and corresponding multi-granular information. Filter the History Dialogs, summaries, and keywords to extract only the parts directly relevant to the Question. Preserve original tokens, not paraphrase. Remove irrelevant turns, redundant info, and non-essential details.
History Dialogs: (retrieved_teaae _
Question Date: (question_date)
Question: (question)
Answer:
Figure 10: Prompt for Multi-granularity Information Filter.
Prompt for QA
You are an intelligent dialeg bet. You will be shown History Dialogs. Please read, memorize, and understand the given Dialogs, then generate one concise, coherent and helpful response for the Question.
History Dialogs: { retrieved_texts] Question Date: iquestion_date date)
Question: (question)
Figure 11: Prompt for QA, which follows Lu al. (2023); Pan al. (2025)
K.1 CASE STUDY ON QA COMPARISON
Figure 13 presents comparative case study of three representative questions requiring multi-session aggregation or temporal reasoning. Across all cases, MemGAS consistently provides responses that align with the ground-truth answers, while baseline methods exhibit various limitations. In the first example, related to course completion, only MemGAS correctly integrates dispersed session information to produce the total number of courses. Other methods either rely on partial evidence or explicitly request further clarification. In the second case, which involves counting tomato and cucumber plants, most baselines retrieve incomplete or vague information, whereas MemGAS retrieves and combines both quantities explicitly. In the final temporal reasoning case, MemGAS successfully grounds the referenced date and computes the correct number of days, while others fail to locate the relevant temporal anchor return fallback prompts. These results indicate that MemGAS better equipped to support multi-turn factual consistency and basic temporal inference within multi-sessio contexts.
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Published as a conference paper at ICLR 2026
Prompt for GPT4o-as-Judge
will give you a question, a reference answer, and a response from a model. Please answer [[yes]] if the response contains the reference answer. Otherwise, answer [[no]]. If the respense is equivalent to the correct steps get the reference answer, you should also answer [[yes]]. the response only contains subset of the information required by the answer, answer [[no]]. [User Question]
contains all the intermediate
question
(The Start of Reference Answer] answer
[The End of Reference Answor]
[The Start of Model's Response] response
[The End of Model's a Response] Is the model response correct? Answer [[yes]] or [[no]] enly.
Figure 12: Prompt for GPT4o-as-Judge (Single), which follows Wu et al. (2025a); Zheng et al. (2023)
K.2 CASE STUDY ON MULTI-GRANULARITY INFORMATION GENERATION
Figures 14 and 15 illustrate the model's ability to generate multi-granularity information from multi- turn user sessions, In both cases, the model extracts structured summaries and relevant keywords that reflect different levels of semantic abstraction. In Figure 14, the model distills task-oriented request regarding ineryy-efficien, industrial equipment into a concise summary. while preserving key product attributes and application contexts. The corresponding keywords focus on sales-related and functional concepts such as "pressure tanks" and "energy saving." In contrast, Figure 15 captures more narrative and emotive user intent centered around personal heritage and item preservation. The summary highlights the user's goal of reclaiming and documenting family-owned antique, while the extracted keywords reflect fine-grained care instructions and conservation practices. These examples demonstrate the model's capability abstract dialogue content varying semantic resolutions, enabling downstream applications such as memory retrieval, personalized assistant planning, and contextual reasoning. The structured outputs also suggest potential for use in grounding generation or summary-based retrieval settings.
K.3
CASE STUDY ON MULTI-GRANULARITY FILTER
Figures and 17 demonstrate the effectiveness of multi- cultt--raanularity response filtering supporting user queries grounded in prior conversational context. In both cases, the user's query refers implicitly to entities previously mentioned across earlier sessions. The system retrieves multiple memory candidates and decomposes their content into structured representations at three granularity levels: session, summary, and keyword. This layered representation facilitates accurate entity matching and selective response generation. In Figure 16, although both memory candidates sustainability- related, only one contains a precise mention of "Patagonia," which is correctly surfaced in the final response. Similarly, Figure 17 involves comparative query referencing companies aligned with Triumvirate's values. The system filters through related prior mentions and correctly extracts "Patagonia" and "Southwest Airlines" from the relevant context. Notably, unrelated memories (e.g.. on kitchen sustainability) are excluded from influencing the final answer. These examples highlight how multi-level content decomposition improves discourse grounding and allows retrieval systems to move beyond surface keyword overlap, ensuring the returned response aligns with both the semantic focus and factual detail required by the user query.
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Published as a conference paper at ICLR 2026
Case study comparing multiple methods
Case 1: Multi-session Question Online Course Completion
Query: How many online courses have completed in total? Ground-Trute -ruth Answer:
Responses of Different Methods:
MemGAS You have completed a total of five online courses.
SeCom: Based on the dialogues, you have completed two courses on edX. However, there no mentions of any additional courses completed, if those are the only ones you'l ve undertaken, your total number of completed online courses is two. X
MPC:
You have completed a total of 2 online courses on edX. After finishing the Stanford NLP with Deep Learning Specialization Coursera, which consists of 5 courses, you will have completed courses. As of now, you have completed courses.
HíppoRAG: You have completed a total of two online courses on edX. x
To determine the total number of online courses you have completed, please provide the number of courses completed from each platform. No Answer
RAPTOR:
Case 2: Multi-session Question Tomato and Cucumber Plants
Query: How many plants did initially plant for tomatoes and cucumbers?
Ground-Tree Truth Answer:
Responses of Different Methods:
MemGAS: : You initially planted 5 tomato plants and cucumber plants. /
SeCom: You initially planted tomato plants. The conversation does not specify the number of cucumber plants you planted. X
MPC: Based on previous conversations, you did not specify the number of tomato and cucumber plants. Please clarify how many of each you started with. No Answer
HíppoRAG: You didn't specify the exact number of plants you initially planted. Please provide that info for clarity. No Answer
RAPTOR: Your message didn't specify the number. A good starting point is tomato per 18-24 inches and cucumbers 12-18 inches apart. No Answer
Case 3: Temporal Reasoning- Meeting Emma Query: How many days ago did meet Emma?
Ground-Truth Answer: 9 days ago (10 days incl. the last day is acceptable)
Responses of Different Methods:
MemGAS : You met Emma 9 days ago, on April 11, 2023.
SeCom: Please provide the date you met Emma. I can calculate how many days ago
it was from April 20, 2023. No Answer
MPC: The exact date of your meeting with Emma isn't available. If you provide it,
can compute the elapsed days. No Answer
HippoRAG: There is no specific mention of meeting Emma or the date in the dialogs. Please provide the date. No Answer
RAPTOR: No relevant information was found about Emma or the timeline. Please add more context. No Answer
Three representative queries with multi-tura temporal reasoning, comparing the performance of various methods. MemGAS provides precise, context-integrated answers. The red text represents the key information that is relevant the answer. means the response correct while X means it is wrong. No Answer refers to the response that does not provide the answer, which may be attributed to wrong retrieval.
Figure 13:
33

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Published as a conference paper at ICLR 2026
Case of generating multi-granularity information
Origin session information
[user]
Write sales email for petentisl customer of company that has product that enables energy saving (Omitted for brevity)
[assistant]
Dear valued customer, We at [Cempany Name] are excited to introduce our innovative (Omitted for brevity) Sincerely,
[Your Name]
[Company Name]
Granularity Level of Summary
user requested assistance drafting a sales email aimed at potential customers for a company offering a product designed to enhance energy savings in industrial factories. Specifically, the product involves larger, more affordable pressure tanks for air compressors, allowing compressors operate less frequently while maintaining necessary air pressure, thereby reducing energy consumption and operating costs.
Granularity level of keywords
sales email
potential customer
energy saving
industry factories
pressure tanks
compressors
cost-effective
reduce energy consumption
lower operating costs
request a quote
An example of generating multi-granularity information using gpt-4o-mini. The blue text represents the key information summarized or extracted that relevant the conversation.
Figure 14:
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Published as a conference paper at ICLR 2026
Case of generate multi guatiigrannulritty information
Origin session information
[user]
Iá thinking of taking antique restoration class to learn how fix up some of the old pieces IVe inherited, like that beautiful vase I get from my grandmother last month. Do you know of any good classes in the city?
[assistant]
I'11 definitely look into those options. By the way, do you know how to appraise the value of antique items? (Omitted for brevity)
[user]:
thinking of taking an antique restcration class learn how to fix up some of the old pieces I've inherited, like that beautiful vase got from my grandmother last month.... (Omitted for brevity)
[assistant]
Appraising the value of antique items can be fascinating and complex process. While I' m not a professional appraiser (Omitted for brevity) [user]:
[assistant]
Granularity Level of Summary
The user plans to retrieve their antique tea set, which they lent to their sister for her wedding. They express excitement about seeing the set again and appreciate the care and storage tips provided for antique china maintain its condition. The user intends possession, they also consider documenting its history and provenance future preservation.
the set is safely back in their
Granularity level of keywords
antique tea set
storage tips
caring for china
keep in good condition
avoid stacking
sturdy box
line with tissue
dry cool place
Figure 15: An example of generating multi-granularity information using gpt-4o-mini. The blue text represents the key information summarized extracted that is relevant to the conversation.
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Published as a conference paper at ICLR 2026
Case of multi-granularity response filtering
Query
"I was looking back environmentally responsible supply chain practices, and was wondering if you could remind me of the company you mentioned that's s doing great job with sustainability?" Answer
previous conversation about
"Patagonia"
Before Filter
Memory 1:
Session-level information
(OmitLed brevity)
[user]:
Can you give me an example of company that has successtully Implemented these environmentally responsible practices in its supply chain?
[assistant]
Yea, sure. Here'a 5 example:
Patagonia, cutdoer clothing and company, is known for its commitment to sust sustainability and envizenmental responeibility threughout its supply chein (Omitted for brevity)
Summary-level information
(Omitted for brevity) Patagonia highlighted prime example, showensing its commitment susainahility ...Oiiited for brevity)
Keyword-level information
Patagonia
environmentally responsible
supply chain
Memory 2:
Session-level information
(Omitted For brevity)
[user]:
I'm looking for recommendations friendly laundry detergent. I've been trying to reduce my carber. ... (Umitted for brevity)
[assistant]
That'a rantastic step towards reducing environmenta impact! Switching eco friendly ... 1CmitLed for brevity)
Summary-level information
The user considering trying new grovery delivery service that partners with local farmers, ... (Otittted for brevity)
Keyword-level information
meal planning
grocery list
start small
After Filter
The two companies mentioned prioritize employee safety and well-being Triunvirate arc:
1. Patagonia Known for commitment to enviroamental conservation and prioritizing employee well-being, offering flexible work arrangements and generous leave policies.
Southwest Airlines Emphasizes employee providing top-notch training and henefits, resulting positive work environment and strong focus on safety and customer service.
Figure 16:
An example of multi-granularte information filtering for retrieved conversations. The red text represents the key information that is relevant the answer.
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Published as a conference paper at ICLR 2026
Case of multi multi- granularity response filtering
Query
"I was going through our previous conversation and I was wondering if you could remind me of the two companies you mentioned that prioritize employee safety and well-being like Triumvirate?"
Answer
"Patagonia and Southwest Airlines."
Before Filter
Memory 1:
Session-level information
(Omitted for brevity)
[user]:
For sure! It's always win-win when companies take care their employees. It makes mo feel better about supporting them as à customer. Do you know of any other companies that prioritize (Omitted for brevity)
[assistant] I can give you an example of two companies that prioritize the safety and well-being of their employees like Triumvirate does:
Patagonia ... (Omitted for brevity) Southwest Airlines
(Omitted for brevity)
Summary-level information
(Omitted for brevity) which the cited Patagonia and Southwest Airlines as examples of organizations that prioritize employee safety and well-being ...(Oitteed for brevity)
Keyword-level information
Triumvirate
employee safety
Memory 2:
Session-level information (Omitted for brevity)
[user]:
I'm thinking of trying out that new grecery delivery service that partners with local farmers. ... (Omitted for brevity)
[assistant]
That sounds like great idea! I'm happy to help you with that (Omitted for brevity)
Summary-level information
The user is focused making eco-friendly changes in their kitchen, specifically looking for sustainable alternatives kitchen utensils, (.miittee for brevity)
Keyword-level information
eco-friendly kitchen makeover
sustainable kitchen utensils
After Filter
The company mentioned that is doing great job with sustainability is Patagonia. They are known for their commitment environmentally responsible practices throughout their supply chain, including sustainable sourcing, green transportation, packaging optimization, reduction, and compliance with environmental regulations.
Figure 17:
An example of muuti-graaulariiy information filtering for retrieved conversations. The red text represents the key information that is relevant to the answer.
37


Reviews:
[{"review_id": "1UutOw51F0", "reviewer": "Reviewer_Lozm", "summary": "The paper takes the multi-granularity into consideration for long-term memory construction and retrieval for the first time. The memory of each session is partitioned into 4 granularities: sessions, turns, keywords, and summary. The paper proposes a training-free method, MemGAS, which consists of a GMM clustering model to construct edges for the memory association graph, an entropy-based router to weight the granularities for the entire memory bank, a PPR method to select the candidate context, and an LLM-based filter to extract the related content. Experiments show that MemGAS outperforms several recent baselines in QA and retrieval tasks with a similar number of tokens used, and a very small trade-off in latency.", "questions": "1. The metric \"Avg. Tokens\" in Table 1 seems ambiguous regarding token consumption. Since MemGAS leverages LLM calls for multi-granularity memory generation, redundancy filtering, and the final QA generation. The authors could clarify: Which specific stage does this token count refer to? How is this \"Avg. Tokens\" measure normalized to be directly comparable to the token counts of single-granularity baselines that may not utilize LLM filtering? Can the authors provide an analysis on token consumption breakdown? \n2. The importance of each granularity could vary for each memory bank $M_i$. Why does Equation 4 assign an overall weight to each granularity for the entire memory bank instead of each $M_i$?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The paper presents a novel idea by utilizing the multi-granularity of each session to learn a structured long-term memory bank and retrieve relevant information to queries. \n2. The GMM distribution assumption of the similarity scores in dynamical memory association is concise and flexible, compared to other oversimplified classification models. \n3. The core algorithms, GMM clustering and PPR, are model-based approaches that ensure the entire model is training-free and preserves interpretability.\n4. The trade-off between performance and latency is impressive, making the model competitive or superior to the baselines.\n5. The presentation is elegant and effectively showcases the paper's methodology and results.", "weaknesses": "1. The paper focuses on four fixed granularities (session, turn, summary, keyword) created from a session chunk. This design feels a bit rigid. The core weakness identified in prior work is often the choice of fixed segmentation (e.g., semantic-based clustering/segmentation). Why were only these four chosen? Other approaches integrate hierarchical structures (RAPTOR, MemTree). It remains unclear that whether the simple summary/keyword levels fully address the limitations of hierarchical knowledge organization. The comparison to other structured RAG models like RAPTOR and HippoRAG 2 is strong on end-task performance, but a deeper discussion and ablation of why this specific set of granularities is better than a structured tree/graph is missing.\n2. The authors reviewed a number of studies on memory segmentation in the introduction (Sec. 1, Paragraph 2), but a recent hierarchical memory architecture [1] is not included and compared in experiments.\n3. The paper did not provide an ablation study on the LLM information filter. The relevance of the retrieved sessions and the question is not evaluated; thus, whether the LLM filter or the PPR algorithm contributes to the better performance is unclear. \n4. Minor typos:  In Line 269, \"RAPOTR\" should be \"RAPTOR\".\n5. It seems the anonymous code link provided in the article cannot display the file content (\"The requested file is not found.\"), only the file structure can be seen.\n\nReference:\n\n[1] Sun, H., & Zeng, S. (2025). Hierarchical memory for high-efficiency long-term reasoning in llm agents. *arXiv preprint arXiv:2507.22925*.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "thI45QL7bf", "reviewer": "Reviewer_gP9f", "summary": "he paper introduces MemGAS, a new framework for managing long-term memory in conversational agents. The proposed system addresses limitations of previous models by using multi-granularity memory units and an adaptive granularity selection mechanism. MemGAS enhances the memory retrieval process through Gaussian Mixture Models (GMM) to cluster and associate new memories with historical ones. Additionally, an entropy-based router is employed to select the optimal granularity for each query, balancing the trade-off between information completeness and retrieval noise. The framework is evaluated across several long-term memory benchmarks, showing significant performance improvements over existing methods in both question answering (QA) and retrieval tasks.", "questions": "1. Why do session $S_i$ and the segmented turns $T_i$ appear together in each memory bank? Are they encoded to the same vector by Contriever?\n2. All metrics are automatic (F1, GPT-4o-as-judge...). Were any human evaluations conducted to assess response coherence, personalization, or perceived helpfulness? As automatic metrics can be misleading in dialogue settings.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- MemGAS introduces a novel multi-granularity memory framework with Gaussian Mixture Models for memory association and an entropy-driven router for adaptive granularity selection, surpassing existing single-granularity methods.\n- The entropy-based router allows MemGAS to dynamically select the most relevant granularity for each query, enhancing retrieval efficiency and reducing noise. This adaptability is a clear improvement over fixed-granularity methods, which can fail to balance information completeness with noise.\n- The use of GMM for clustering historical memories into relevant and irrelevant sets improves the consolidation of new and old memories, ensuring more coherent long-term memory management.\n- MemGAS addresses key challenges in long-term memory management for conversational agents, offering improvements in both retrieval accuracy and efficiency, with strong potential for real-world applications.", "weaknesses": "- The level of edge construction is inconsistent throughout the paper. Line 184-185 and the upper-right part of Figure 2 suggest that each granularity of each memory is treated as a node. Still, Equation 5 computes the weight on each memory across different granularities, which indicates that the PPR is run on a memory association graph where each memory $M_i$ is a node. Additionally, the word \"vectors\" in line 157 is also ambiguous, without pointing out whether the entire memory bank $\\mathcal{M}_{\\text{cur}}$ or each element of $\\mathcal{M}_{\\text{cur}}$, or even each granularity of each element is encoded as a vector.\n- Several substantial experiments lack sufficient explanation. In Sec. 3.4, the classification of \"query types\" and the exact module and task for \"top-K retrieval settings\" seem ambiguous to me. The \"Combination\" and \"Optimal Selection\" in Tables 6 and 7 in Appendix C lack explanation. The experimental setting of error analysis in Appendix G is also unexplained.\n- The conclusion drawn in Line 368 that \"we keep average tokens and latency close to lightweight retrievers and below HippoRAG2 and A-Mem\" does not align with the experimental results. The number of average tokens used by MemGAS is larger than that of HippoRAG2 in LongMemEval-s, and not significantly smaller in other datasets.\n- There are a few typos and grammatical errors in the paper: the last $\\alpha_s$ should be $\\alpha_u$ in Line 135; \"RAPOTR\" -> \"RAPTOR\" in Line 269, and the sentence in Line 474 is incomplete (or removing \"which\"?), etc..\n- The theoretical analysis in Appendix H is a bit coarse in writing, with quite a few writing hints (sketch) not removed.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "HSWYi3niev", "reviewer": "Reviewer_Bona", "summary": "The paper proposed MemGAS, a novel framework for long-term memory construction and retrieval that integrates multi-granular memory units and enables adaptive selection and retrieval. It is motivated by two flaws of existing methods: 1) insufficient multi-granulartiy memory connection; and 2) lack of adaptive multi-granular memory selection. To solve (1), it complements session-level memory with turns, summarize and keywords, and connect these granularities across different sessions based on Gaussian Mixture Models. For (2), it uses similarity between query and each granularity, and run pagerank algorithm to find top-k relevant memories, then feed refined memories by LLM to generate the response. Generally, the paper is well-motivated, well-supported, easy-to-follow. The experimental results are solid and comprehensive in terms of effectiveness and efficiency.", "questions": "1. it is better to analyze efficiency fators for each module, i.e., which module cost most time?\n2. it is better to have some in-depth analysis about each module, i.e., how many samples gets different top-k results before and after you run page rank algorithm, and any common patterns?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "1. the paper is well-motivated, well-supported, easy-to-follow.\n2. the experimental results are solid and comprehensive in terms of effectiveness and efficiency.", "weaknesses": "no significant weakness identified", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "xtN10dtOJC", "reviewer": "Reviewer_tvD3", "summary": "The paper presents **MemGAS**, a framework for **multi-granularity association and adaptive selection** in long-term conversational memory. It constructs a multi-granularity memory graph via **GMM-based accept/reject association**, routes queries with an **entropy-driven router**, performs **PPR** on the association graph, and applies **LLM-based filtering**. \n\nExperiments on four benchmarks show consistent gains over single-granularity and structured baselines", "questions": "1. **Temperature λ**: how selected; any adaptive scheme or theoretical bounds? \n2. **PPR hyper-parameters**: restart prob/seed selection and convergence on large graphs? \n3. **Graph maintenance**: pruning/forgetting policies to avoid graph bloat and stale memory?  \n4. **Judging protocol**: variance control and human calibration for GPT4o-as-Judge?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. **Clear motivation**: this paper identifies the limitations of single-granularity memory and fixed-granularity retrieval; frames a noise–information trade-off handled by adaptive routing. \n\n2. **Association construction**: Form multi-granularity units via LLM-generated summaries/keywords and turn-level splits; use **GMM** to accept/reject similarity vectors and update the association graph.\n   - **Granularity routing**: Assign weights by the **Shannon entropy** of similarity distributions at each granularity (lower entropy → higher weight), removing ad-hoc choices.\n   - **Retrieval and filtering**: Run **PPR** from the routed prior, take top-K, then apply **LLM filtering** to reduce redundancy. \n3. **Strong empirical results**: Consistent gains on QA and retrieval metrics over baselines (e.g., Full-History, Contriever/MPNet, SeCom, HippoRAG2, RAPTOR, A-Mem), with comparable token/latency budgets.\n4. **Ablations and fine-grained analyses**: Removing **GMM**, **PPR**, **routing**, or **associations** leads to clear drops; provides trend analyses over different **Top-K** and **Q types**.", "weaknesses": "1. **Stability of GMM and routing**:  \n   - Since GMM is essentially a binary (accept/reject) clustering, please provide a more systematic sensitivity analysis and confidence intervals on how the choice/adaptation of the number of components and the temperature λ affect entropy and the routing weights.  \n   - Report robustness under different encoders (beyond Contriever) and alternative similarity measures.\n\n2. **Fairness and comparability**:  \n   The implementation unifies the generator as **gpt-4o-mini** and fixes a top-3 session pre-retrieval, which may bias comparability; please add **controlled experiments with multiple generators/retrievers** and learning curves.  \n   **New baseline suggestion**: include **COMEDY** (*COLING 2025 Oral*) as a baseline, and revise the corresponding description concering **COMEDY** in the paper.\n\n3. **Cost and scalability**:  \n   Although average latency is comparable, as the memory store grows, the **complexity of PPR on large graphs**, the cost of frequent GMM updates, and the inference overhead of LLM filtering should be presented as **data-scaling curves** (number of samples/edges/graph diameter → latency and token cost), along with incremental/approximate strategies.\n\n4. **Evaluation protocol**:  \n   For some structured methods the retrieval evaluation is “undeterminable,” and retrieval on LongMTBench+ is missing. Please clarify in the appendix the sources of non-comparability (implementation differences, labeling criteria, task incompatibilities), and provide alternatives (e.g., human spot-checking on a sampled subset, or rerunning a unified comparable subset).\n\n5. **Adaptive Top-K**:  \n   Since large K introduces noise (F1 drops), consider making K part of the routing/uncertainty-driven **dynamic selection** (e.g., entropy/CI-based adaptive K coupled with redundancy-removal thresholds), and report the gains and stability.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)