LightMem: Lightweight and Efficient Memory-Augmented Generation
Authors: Jizhan Fang
Xinle Deng
Haoming Xu
Ziyan Jiang
Yuqi Tang
Ziwen Xu
Shumin Deng
Yunzhi Yao
Mengru Wang
Shuofei Qiao
Huajun Chen
Ningyu Zhang
Venue: ICLR 2026 Poster (2026)
Keywords: large language model
LLM memory
Primary area: foundation or frontier models, including LLMs

Abstract: Despite their remarkable capabilities, Large Language Model (LLM) struggle to effectively leverage historical interaction information in dynamic and complex environments. Memory systems enable LLMs to move beyond stateless interactions by introducing persistent information storage, retrieval, and utilization mechanisms. However, existing memory systems often incur substantial time and computational overhead. To this end, we introduce a new memory system called LightMem, which strikes a balance between the performance and efficiency of memory systems. Inspired by the Atkinson–Shiffrin model of human memory, LightMem organizes memory into three complementary stages. First, cognitive-inspired sensory memory rapidly filters irrelevant information through lightweight compression and groups information according to their topics. Next, topic-aware short-term memory consolidates these topic-based groups, organizing and summarizing content for more structured access. Finally, long-term memory with sleep-time update employs an offline procedure that decouples consolidation from online inference. 
Experiments on LongMemEval with GPT and Qwen backbones show that LightMem outperforms strong baselines in accuracy (up to 10.9% gains) while reducing token usage by up to 117×, API calls by up to 159×, and runtime by over 12×. Code will be released on GitHub.

Published as a conference paper at ICLR 2026
LIGHTMEM: LIGHTWEIGHT AND EFFICIENT HEMORY-AUGMENTE GENERATION
Jizhan Fang*, Xinle Deng* Haoming Xu Ziyan Jiang Yuqi Tang*, Ziwen Xu
Shumin Deng Yunzhi Yao Mengru Wang*, Shuofei Qiao Huajun Chen Ningyu Zhang
Zhejiang University National University of Singapore
State Key Lab. for Novel Software Technology. Nanjing University, P.R. China
[fangjizher zhan, zhangningyu ju .ddu.cn
ABSTRACT
Despite their remarkable capabilities, Large Language Models (LLMs) struggle to effectively leverage historical interaction information in dynamic and complex environments. Memory systems enable LLMs to move beyond stateless inter- actions by introducing persistent information storage, retrieval, and utilization mechanisms. However, existing memory systems often introduce substantial time and computational overhead. To this end, we introduce new memory system called LightMem, which strikes balance between the performance and effi- ciency of memory systems. Inspired by the Likinson-Shiffrin model of human memory, LightMem organizes memory into three complementary stages. First, cognition-inspiret sensory memory rapidly filters irrelevant information through lightweight compression and groups information according to their topics. Next, topic-aware short-term memory consolidates these topie-based groups, organizing and summarizing content for more structured access. Finally, long-term memory with sleep-time update employs an offline procedure that decouples consolida- tion from online inference. On LONGMEMEVAL and LoCoMo, using GPT and Qwen backbones, LightMem consistently surpasses strong baselines, improving QA accuracy by up to 7.7% / 29.3%, reducing total token usage by up to 38 20.9x and API calls by up to 30x / 55.5x, while purely online test-time costs are even lower, achieving up 106x 117x token reduction and 159x 310x fewer API calls. The code is available at
INTRODUCTION
Memory is fundamental to intelligent agent, enabling the assimilation of prior experiences, contex- tual cues, and task-specific knowledge that underpin robust reasoning and recision-makkng (Wang et al., 2024; Behrouz et al., 2024; Du et al., 2025; Zhang et al., 2024). While Large Language Models (LLMs) (DeepSeek-Al et al., 2025; Achiam al., 2023) demonstrate remarkable capabilities across wide range of tasks, they exhibit significant limitations when engaged in long-context or multi- turn interaction scenarios due to fixed context windows and the "lost in the middle" problem (Liu et al., 2024). Memory systems are pivotal for overcoming these limitations, maintain a persistent state across extended interactions. Recent works (Li et al., 2025b; Yang et al., 2024: Chhikara et al., 2025; Kang et al., 2025) address this challenge by building explicit external memory through sequential summarization and long term storage, enabling models to retain and retrieve relevant information over long horizons.
they allow LLMs to
Note that a typical LLM memory system processes raw interaction data into manageable chunks, such turn- session-level in dialogue scenarios (Xu et al., 2025; Li et al., 2025a), organizes them into long-term memory (e.g., databases or knowledge graphs) by indexing them into memory units, and continuously updates by adding new information and discarding outdated conflicting content (Zhong et al., 2024). This enables retrieval of relevant memories, improving coherence, and personalization in long- context multi-turn scenarios.
"Corresponding Author.

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Published as a conference paper at ICLR 2026
Challenges. Despite these advances, shown in Figure 1, contemporary memory systems still suffer from significant inefficiencies and consistency issues. First, in long interactions (e.g., dia- logue scenarios), both user imputs and model responses often contain substantial redundant infor- mation (Maharana et al., 2024; Wu et al., 2025). Such information is typically irrelevant to down- stream tasks subsequent memory construction, and in some cases, may even negatively affect the model's in-context learning capability (Liu et al., 2023; Pan et al., 2025). However, current main- stream memory-related studies generally process the raw information directly without any filtering or refinement, leading to high overhead from noisy or irrelevant data. This inflates token consump- tion without proportional gains reasoning quality or coherence. Second, memory construction typically treats cach turn in isolation or relies on rigid context- model semantic connections across different turns (Tan et al., 2025). As result, during subse- quent memory item construction, the backbone LLM may generate inaccurate or incomplete item representations due to overly entangled topics or semantics, leading to the loss of crucial contex- tual details. Third, memory updates and forgetting are usually performed directly during inference and task execution. This tight coupling introduces long test-time latency in long-horizon tasks and prevents deeper, reflective processing of past experiences.
boundaries, failing to
Building Lightweight Memory. In- spired by the efficiency and struc- ture of human memory, we introduce LightMem. particular, LightMem emulates human memory through three key components: (1) A pre- compression sensory memory module that filters redundant or low-value to- kens from raw input and buffers the
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distilled content. (2) topic-aware short-term memory that leverages mantic and topical similarity to dy- namically group related utterances into coherent segments. By adap- tively determining segment bound- aries based on content instead of fixed window sizes, this module pro- duces more concentrated and mean- ingful memory units. This not only reduces the frequency of memory construction but also enables more precise and efficient retrieval during inference. (3) A sleep-time update mechanism for long-term term memory maintenance. New memory entries are initially stored with timestamps to support imme- diate ("soft") updates for real-time responsiveness. Later, during designated offline periods (i.e., "sleep"), the system reorganizes, de-duplicates, and abstracts these entries, resolving inconsisten- cies and strengthening cross- knos-knnoweddg connections. Crucially, this decouples expensive memory maintenance from real-time inference, enabling reflective, high-fidelity updates without introducing latency. By systematically filtering, organizing, and consolidating relevant information, LightMem substantially reduces computational overhead and API costs while sustaining accurate, coherent reasoning over extended interactions. We detail each component in $3.
Long latiency
Sequential updates
Inocourate ihems Information loss
Frequent updates
semantic group
1x
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Tapic2
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Offline updates
Parallel process Much fewer iterations
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fittered Info
accurate construction
Global updates
Short test- time latency
Sleep time
Figure I: Comparison of previous works and LightMem.
Results and Evaluation. On LongMemEval (Wu et al., 2025), LightMem consistently outperforms the strongest baseline, improving accuracy by 2.09%-6.40% with GPT and up to 7.67% with Qwen. In terms of overall efficiency (online + offline), LightMem reduces total token usage by up to 38x for GPT and 21.8x x for Qwen, lowers API calls by up to 30x and 17.1 x, and accelerates runtime by up to 12.4x and 6.3x, respectively. If considering only online test-time costs, the gains become even larger: LightMem cuts token usage by up to 105.9x (GPT) and 117.1 x (Qwen), and reduces API calls by up to 159.4x and 309.9x. On the LoCoMo benchmark (Maharana et al., 2024), LightMem higher accuracy and substantial efficiency improvements boosttng token efficiency by up to 20.92x, redueing API calls by up 55.48x. x8,, and speeding up runtime by up to 8.21 x across GPT and Qwen backbones. Furthermore, case studies in $5.6 show that the offline "sleep-time" consolidation enhances long-temn memory reliability, mitigating information loss.
maintains strong advantages, achieving 6. 6.10%%29.299
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2 PRELIMINARY
2.1 CONVENTIONAL MEMORY SYSTEMS FOR LLMS
We describe mainstream memory architectures pipeline in terms of two major stages. (1) Memory Bank Construction. This stage can be further decomposed into three sub-stages: (a) Raw data D are first processed at a chosen level of granularity, D(9) = feel g). 9 E {turn, session, topic} in dialog scenario; (b) The segmented data D(9) are then summarized or extracted to generate mem- ory entries, E = fsum (D())), which are stored and organized within structural backends such as knowledge graphs to enable long-term retention; (c) Many systems incorpo- rate an updating mechanism to mitigate issues such as context conflicts or outdated information, M' = fupdate [M, R: U) where M denotes the existing memory bank, R represents newly generated memory entries, and U specifies the update or forgetting policy. (II) Retrieval and Usage. When a new user query arrives, the system retrieves relevant entries from the memory bank, integrates them with the query to construct the final prompt, and then invokes the model to produce a response.
vector databases
2.2 LIMITATIONS OF EXISTING LLM MEMORY SYSTEMS
Compared human memory, current LLM memory systems are burdened by high maintenance costs, mainly due to three limitations: 1) Redundant Sensory Memory. In current systems, fsum and fgrmn( g = topic) are typically executed by calling stronger LLMs. Feeding raw data D directly wastes resources and even weakens in-context learning due to redundancy. A key challenge is to design lightweight mechanisms that pre-compress inputs and apply pre-attention strategies cap- ture semantic units at different granularities efficiently. 2) Balancing Effectiveness and Efficiency in STM. As shown in Figure 1, when input granularity is fixed, D must pass through the entire pipeline. Excessively fine granularity increases latency and underutilizes STM capacity, whereas overly coarse granularity without semantic constraints grouping may cause mixed or entangled semantics and topics, leading to inaccurate memory construction and loss of fine-grained details in subsequent processes. This calls for strategies that better balance effectiveness and efficiency in STM. 3) Inefficient LTM Updating- Current fupdate() () mechanisms face two main issues: (i) enforcing strict real-time updates at test time incurs significant latency, whereas STM can provide short-term context without immediate LTM updates; (ii) memory banks are updated sequentially due to ordering constraints (read-after-wriigg fiittt/wwitt-ffte-realle rather than being triggered dynamically. These limitations raise a research question: Can we design LLM memory that is both efficient and lightweight, inspired by human memory mechanisms?
3 LIGHTMEM ARCHITECTURE
Analogous to the human memory, we design LightMem as shown in Figure 2, which consists of three light modules: Light implements efficient Sensory Memory Module that selectively preserves salient information from raw input ($3.1), Light2 realizes a topic-aware STM Module for transient information processing ($3.2), and Light3 provides LTM module designed to minimize test time update latency ($3.3) with a sleep time update mechanism. The overall pipeline framework of LightMem, specific models, and comparisons with other memory frameworks are presented in Appendix A.1. The complexity analysis for LightMem's 's efficiency gains is in Section 4.
3.1 LIGHTI: COGNITIVE-INSPIRE SENSORY MEMORY
In long horizon interaction scenarios, such as assisassttt dialogues, large portion of the infor- mation is redundant. Therefore, we design Pre- Pre-Compressing Submodule to eliminate redundant tokens, followed by the Topic Segmentation Submodule that forms semantic topic-based segments for following faster and more accurate memory construction.
Pre-Compressing Submodule. This module leverages compression model 0 to eliminate redun- dant tokens, tailored for compatibility with the downstream memory construction phase:
{a, P(retain x; x: 0) > 7}, T Percentile ((z))rr, Following Xia et al. (2025), we use LLMLingua-2 (Pan et al., 2024b) as our compression model 6. Let be the raw input tokens, 0 the model, and the compression ratio. The threshold is set to
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Figure 2: The LightMem architecture. LightMem consists of three modules: a) An efficient Sen- sory Memory Module, b) a topic aware STM Module, and c) an LTM module updated sleep time.
the r-th percentile of retention scores, keeping only tokens above T. For Preetain x), we treat the compression process as a binary token classification task ("retain" or "discard"). For each token r; in sequence the model 0 outputs a logit vector f,, and the retention probability is given by:
P(retain x; 0) = softmax(44)1.
where the subscript denotes the "retain" class. Tokens with probabilities above a dynamic thresh- old are included in the compressed sequence. In addition, LightMem also employ more general generative LLM as the pre-compression model. We further implement a token filtering mechanism based on the cross-entropy between the model's predicted distribution and the true token labels:
P(retain 2; 0) = Σ g(x;) log P(r: x; 0)
Z
where ((x;) denotes the true token label distribution. Tokens with higher conditional entropy under a given context are more uncertain and less predictable, indicating greater informational uniqueness and critical role in semantic expression, such distinctive tokens essential for subsequent memory construction and are therefore retained.
Topic Segmentation Submodule. Existing works indicate that topic-granular input facilitates im- proved performance in memory systems (Pan et al., 2025; Tan et al., 2025). As shown in Fig- ure 2, LightMem maintains sensory memory buffer to temporarily store information after pre- compression. When the accumulated information reaches the buffer's 's maximum capacity. hybrid topic segmentation operation based on attention and similarity is triggered. We use the compression model 0 and an embedding model to compute attention matrices and semantic similarities, respec- tively. We define the final segmentation boundaries as the intersection of attention-based boundaries Bi and similarity based boundaries 82:
B = {kl MM.k-1 > MA-1K-2, MA.k-1 > M 1< < n).
B2 = { sim 9k) T, 5 k < n B B1 Pan2 BB.
Specifically, dialogue scenarios possess natural semantic units, namely the conversational turn. We construct turn-level attention matrix M x B3 are identified as local maxima in the sequence (MR.-1)) i.c., the sub-diagon diagonal elements of M corresponding to attention between consecutive sen- tences. The detailed process of B and illustrative cases provided in Appendix C.I. To mitigate
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attention sinks and dilution in attention-bassed methods, compute semantic similarity between adjacent turns near each candidate boundary in B1. Boundaries with similarity below threshold т form set B2. which helps determine the final topic boundaries B.
3.2 LIGHT2: TOPIC-AWARE SHORT-TERM MEMORY
After obtaining individual topic segments, forming an index structure of {topic. message turns), ) where message turms (user; model,). These are first placed into the STM buffer. When the token count in the buffer reaches preset threshold, we invoke LLM f fsum to generate concise summaries of every structure. The final index structure stored in LTM {topic, {sum;, user, model:)).
sum; fa))(S;), S user,, model, J. S + Ø,
Entry, = toopic, e, == embedding(summ)), user, model;
where Entry, denotes the memory entry to stored LTM. Compared with inputting the granu- larity of a single turn or session, directly feeding multiple sessions can reduce subsequent API calls often introduces inaccurate memory entries due to excessive topic mixing, leading to perfor- mance degradation. In contrast, topic-constrained input granularity minimizes API calls to the great- est extent while preserving summarization accuracy and maintaining stable system performance.
3.3 LIGHT3: LONG-TERM MEMORY WITH SLEEP-TIME TIME UPDATE
Soft Updating at Test Time. At test time, when memory entries arrive, LightMem directly inserts them into LTM with soft updates, thereby decoupling the update process from online inference. Due real-time updates being converted to direct insertions, interaction latency is significantly reduced. After all entries are inserted or when an update trigger arrives, we compute an update queue for every entry in LTM.
((c)= Topk { (eg (e,,simmm; 05))
where e denotes the i-th memory entry with embedding 8 and timestamp t;, sim(:,) is the simi- larity function, and Top ( indicates selecting the top-k most similar candidates, with the update queue ((e,) length fixed at n. Consistent with existing work, we select the top-k existing memory entries with the highest semantic similarity as potential update sources. On this basis, we further impose the constraint that only entries with later timestamps are allowed update earlier ones (t, 2 t,), which is consistent with realistic temporal dynamics. Here, ((e,) denotes the queue of other entries that may update e;. Since this process involves only similarity retrieval, it is fast and lightweight, and can be executed offline in parallel with online inference.
Offline Parallel Update. LightMem does not simply transfer online update latency to offline phases, it substantially reduces the overall update latency. The online update mechanism in existing memory frameworks enforces sequential updates, leading to a total latency that accumulates with each up- date. As shown in Figure 2, in LightMem, each memory entry maintains a global update queue, with each queue corresponding to a distinct fupdase operation. Since the update targets are independent across queues, updates can be executed in parallel, thereby greatly reducing the total latency.
4 COMPLEXITY ANALYSIS ABOUT LIGHTMEM
Method
Summary Tokens
Update Tokens
Calls
Runtime O(N) N
Baselines
Ligum-in + T + Lsam-oout
N NM, Rí (Lup-in + Lup-out)
N NrT
LightMem
NT (T + th + Ts out)
Nr M2R2(Tnp + Tuu
o
th
th
th
th
Table 1: Complexity comparison between LightMem and other memory systems. definitions of each symbol are provided in the Appendix A.2.
The specific
As shown in Table 4, consider dialogue with N turns, each containing average T tokens. In conventional memory systems, each turn triggers a summarization call, consuming Luum-in + T + T out tokens and totaling N N(Tsum in T Tesum out) tokens with N API calls. Each summarization
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produces M1 memory entries, fraction R1 of which retrieve at least relevant neighbor and trigger an update, resulting in an update-token cost of N R1 (Lup-in + Lup-out).
In LightMem, each turn is first passed through iterative pre-compression submodule, retaining only r"T tokens after æ iterations, and appended to a short-term memory (STM) buffer of capacity th. Summarization is triggered only when the buffer reaches capacity, yielding Nr summarization calls, each consuming Lsum-in + th + Luum-out tokens. Each summarization produces M2 memory entries, but stricter retrieval constraints, including semantic similarity and timestamp filtering, re- duce the fraction R2 that trigger updates. Hence, the update phase involves Nr M2R2 calls, with total token cost of Ny M2 R22R2(an (Lup-in + Lup.om)
th
th
Overall, LightMem requires only th API calls for both summarization operations. substantially reducing token usage and call frequency compared to other systems. Correspondingly, the runtime
complexity of other memory systems is O(N), while LightMem achieves a reduced runtime of o Nr reflecting the efficiency gain from compressed summarization and selective updates. th
EXPERIMENTS
5.1 EXPERIMENTAL SETUP
Experimental Details. (1) Our experiments adopt realistic Incremental Dialogue Turn Feeding setting, where the entire dialogue history is fed and processed at the turn level, one turn at time. This reflects practical scenarios where interactions between user and model is incrementally formed turn by turn. (Hu et al., 2025). (2) For considerations of both efficiency and effectiveness, we employ I.L. as our pre-compressor throughout all subsequent experiments. (3) The attention scores for topic segmentation are also obtained using LLMLingua-2, the size of the sensory memory buffer 512 tokens. All specific models used in this paper, can be found in Table 5.
Datasets & Baseline Methods. We use two well-known datasets, LONGMEMEVAL (Wu et al., 2025) (specifically the Momg split) and LoCoMo (Maharana et al., 2024) to evaluate memory ability. We compare LightMem against several representative baselines of conversational memory modeling. C Full Text, @ Naive RAG, 3 LangMem (LangChain, 2025), A-MEM (Xu al., 2025), @ MemoryOS (Kang al., 2025), @ Memo (Chhikara et al., 2025). In addition, all methods use GPT--4ominii, Qwen3-30B-A3B -300---33---sttutt-5500 and GLM-4.6 as the LLM backbones. Details on dataset, baselines, and experimental settings are provided in the Appendix D.
Metrics. We evaluate these methods using both effectiveness and efficiency metrics. For effective- ness, we report Accuracy (ACC), defined the proportion of correctly answered questions. The evaluation is conducted with GPT-4o-mini as an LLM judge, guided by a detailed evaluation prompt (see Appendix E.1). For efficiency, we focus on tracking the computational costs of the LLM invo- cations in memory bank construction stage (see Section 2. all averaged across the entire dataset, as it is the one tied to the design and implementation differences of memory systems. The retrieval and usage stage is not our focus, because for fair comparison, The frettiee () and number of retrieved entries are same among all methods. As a result, their costs exhibit only minor differences, and this stage is largely orthogonal to the design of memory systems, as shown in the table. Within the memory bank construction stage, only the two sub- processesssss Summary and Update involve the use of LLMs, fsum/extrae() and (). So for both processes, report the token consumption from LLM calls, including input tokens, output tokens, and total token usage (in thousands). Addi- tionally, track API Calls counting the total number of LLM invocations, and Runtime recording the overall execution time for memory bank construction stage.
5.2
MAIN RESULTS
As shown in Table 2 and Table 3, LightMem demonstrates superior effectiveness and efficiency both datasets across both GPT and Qwen backbones. For a fair comparison, all efficiency metrics for LightMem in the following analysis refer to the combined online and offline costs.
MenvallE On the LongMemEval benchmark, LightMem consistently outperforms the stroneest baseline, A-Mem, in the ACC metric, improving accuracy by (09%-6.409% with GPT and

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Table 2: Effectiveness and efficiency comparison LONGMEMEVAI--S, The token usage is in thousands. indicates no value for the metric. Bold denotes the best result, underline the second- best. r denotes the compression rate. th denotes the capacity threshold of the STM buffer, measured in tokens. Each pair of and th corresponds to two rows: one for online soft update and one for offline update. up-ate denotes the offline parallel update process of LightMem.
Summary Tokens (k) Update Tokens (k)
Method
ACC (%)
Total (k)
Calls
Runtime
In
Out
In
Out
FullTexx
56.80
105.07
NaiveRAG
61.00
867,38
LangMem
37.20
982.68
119.48
1,102.16
520.62
2,293.70
A-MEM
62.60
214.66
42.82
1,157.52
190.81
1,605.81
986.55
5,132.06
MemoryOS
44.80
2,302.35
304.18
350.02
35.19
2,991.75
2,938.41
8,030.04
Mem0
53.61
424.13
17.76
560.17
150.56
1,152.62
811.57
4,248.49
LightMem
r=0.5, th=256
64.29
20.80
10.01
30.81
25.67
302.69
(OP-update)
64.69
44.46
2.56
47.02
70.23
342.63
0066, U/=256
67.78
24.58
10.53
35.11
30.47
329.61
(OP-update)
65.39
53,98
3.18
57.16
85.07
411.56
0=0.7, th=512
68.64
18.88
9.37
28.25
18.43
283.76
(OP-updatc)
67.07
79.38
4.06
83.44
125.47
496.03
Swem3-30B-A33-IIsttree
Full'Tex
54.80
105.07
NaiveRAG
60.80
659.09
LangMem
50.80
1,311.96
118.06
1,430.02
495.12
3,237.16
A-MEM
65.20
219.21
1,260.54
318.20
1,864.93
989.30
5,367.51
MemoryOS
49.60
2,101.54
510.88
305.12
27.43
2,944.97
2,922.28
8,721.78
Mem0
39.51
424.20
15.34
411.50
111.35
1001.90
722.76
2,239.94
LighiMem
r=0.4, t6=
61.95
9.01
16.14
25.15
16.54
357.13
(PPuudd
62.34
111.13
7.88
119.01
176.02
1036.47
00666 th=768
70.20
13.19
19.21
32.40
19.97
417.13
(P-update)
65.14
97.11
5.92
103.03
152.93
1023.56
0088, th= 1024
68.69 69
14.82
18.49
33.31
9.43
355.71
OPP OP-update)
67.34
106.91
6.20
113.11
168.37
1026.90
GLM-4.6
FullText
36.71
103.38
NaiveRAG
73.20
53,725.15 15
LangMem
49.20
3,052.42
7.03
3,059.45
314.61
5,577.91
A-MEM
70.60
444.95
63.40
1,992.04
403.39
2,903.78
450.40
8,068.80
LighiMem
00555 t/:256
73.00
16.55
14,06
30.61
10.78
1,014.37
==0.6, th-256
73.20
16.55
13.99
30.54
10.78
1,077.69
0=0.7, th=512
72.80
16.55
13.91
30.46
10.78
1,038.19
up to 7.67% with Qwen. In terms of efficiency, for GPT, LightMem reduces total token consump- tion by 10x-38x x and API calls by -6xx30x: for Qwen, it reduces total tokens by 6,9x -1.8x and API calls by 3.3x-17.11 1.6x for Qwen speedup over other memory baselines.
Regarding runtime, LightMem achieves 11244 for GPT and
If considering only online test-time cost, LightMem shows For GPT, LightMem reduces total token consumption by 31.4x-105 115.9059. and API calls by 17.1x- x- 159.4x; for Qwen, it reduces total tokens by 30.1 x-117.1x and API calls by 24.8x-309.9x.
larger efficiency advantage.
LoCoMo, On the LoCoMo dataset, LightMem also demonstrates superior performance over other memory baselines. For the GPT backbone, improves ACC by 6.10%-18 18.1118.11 12%.. achieves x

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Table 3: Effectiveness and efficiency comparison LoCoMo. Due space limitations and for ease of comparison, we merge the results before and after LightMem's 's offline update into a single row. The ACC reported corresponds the performance after the offline update.
ACC Summary Tokens (k) Update Tokens (k) Total (k)
Method
Calls
Runtime
Out
Out
GPT-4o-mini
Full
71.83
NaiveRAG
63.64
LangMem
57.20
898.27
111.95
1010.22
920.62
2229.37
A-MEM
64.16
182.74
49.29
729.89
187.52
1149.43
1175.47
6060.73
MemoryOS(locomoo)
58.25
110.98
33.40
78.08
64.54
287.00
553.45
2422.05
(eegraOS(regular)
54.87
226.86
46.61
177.66
75.34
526.48
1016.06
3332.59
Memo
61.69
851.32
20.53
632.12
189.42
1693.39
1602.20
4432.87
LighiMem(00.7.112)
71.95
"73.99
20.13
6.05
6,40
99.76
41.65
848.49
LightMem(0.7.768)
70.26
57.54
18.92
3.79
0.23
80.48
29.55
737.80
LightMem(0.8,7.)
72.99
62.82
17.95
4.14
0.28
85.19
29.83
815,32
Qwen3-30B-A3B-
Full
74.87
NaiveRAG
66.95
Lang.emm
60.53
1004.35
138.02
1 1142.37
1005.37
2268.57
A-MEM
56.10
158.29
60.85
924.19
483.51
1626.80
1175.40
5543.90
MemoryOS(locomo)
61.04
122.21
53.12
104.43
81.75
361.51
414.70
1269.70
MemoryOS(regular)
51.30
228.85
51.60
242.27
143.63
666.35
1004.60
1982.20
Mem0
43.31
827 09
18.64
763.88
189.80
1799.40
1614.50
4540.70
LightMem(0.6,1 6,768)
71.36
56.68
34.14
8.31
0.74
99.87
29.10
815.70
LightMem(0.8.1000
72.60
61.38
36.33
9.86
0.88
108.45
32.00
1079.40
20.92x improvement in total token efficiency, reduces API calls by 13.29x-39.78x, and accelerates runtime by 2.63 x x. On the Qwen backbone, LightMem maintains its advantage in both ef- fectiveness and efficiency, with 4.41% 29.29% higher ACC, 3.33x-188 -18002x reduction in total token consumption, 12.96x-55.483 fewer API calls, and 1.18x-5.57 faster runtime.
LightMem achieves superior performance on nearly all metrics and both LLM backbones, while demonstrating robust performance and efficiency both LongMemEval and LoCoMo, highlighting its generalizability across different models and scenarios.
5.3 ANALYSIS OF PRE- COMPRESSING SUBMODULE
Performance and Overhead. LightMem uses an additional model (Pan et al., 2024b; Xia et al., 2025) for pre-oompression. We evaluate its performance by randomly sampling 1/5 of LONG- MEMEVAL and compressing at ratios shown in Figure 3(a), then prompting LLMs for in-context QA. When compression ratio ranges from 50%-80% compressed and uncompressed performance are comparable, demonstrating LLMs can effectively understand compressed content and validating LightMem's approach. The submodule is highly efficient, consuming under 2GB of GPU memory with negligible impact on overall runtime.
Impact of Performance. As shown in Tables 8 and 9, The optimal for ACC is dependent on the STM buffer threshold th. For smaller thresholds (th E (0, 256)). an r of 0.6 achieves the highest ACC. In contrast, for larger thresholds (th E (512, 1024)), a higher retention rate performs best. This suggests greater buffer capacity enables effective use of richer, less-compressed information, leveraging LLMs' advanced long-contex: processing to mitigate the "lost the middle" phenomenon. On average, the optimal for ACC is 0.6. reflecting a trade-off between information compression rate and the quantity of information in the STM buffer. terms of efficiency, lower r
0.7
MMemoryOS(locom)) is the LoCoMo reproduction script in the MemoryOS library, simplifying the standard version, shown (emoorOS(regular),
8

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0.3
70%
Only
Similarity
Attention-Simlliit
Accuracy
0.7
60%
80%
Accuracy (%)
90
66.0
6.62
0.64
6.50
0.58
0,18
0.50
75
0.5
0.4
45
GPT-4o-ma mini
40%
50%
60%
70%
80%
(a) Accuracy Comparison Different Compression Ratios
(b) Segmentation Mathods Comparlson
100
With Topic Segmentation
100
With Topic Segmentation Without Topic Seginentttion
80
Without Topic Segmentation
80
60
60
40
0..00
$4.00
40
12. Th
acc(%)
input(k) output(k) total(k) (cl) GPT Ablation Study
calls
acc(%)
input(k) output(k) total(k) (c2) Qwen Ablation Study
calls
Analysis and Ablation Study of Key Modules, (ag. depicts the QA accuracy when using prompts compressed at different ratios (r) in-contexts to query the LLM directly. Fig.(b) compares the accuracy of different topic segmentation methods under these varying compression ratios. (cl-(cl) and Fig.(c2) present the ablation study for the topie segmentation module, evaluating its impact both performance and efficiency for the GPT and Qwen models.
Figure 3:
generally leads to higher efficiency, as it triggers the buffer threshold less frequently under the same th, resulting in fewer API calls and lower token consumption.
5.4 ANALYSIS OF TOPIC SEGMENTATION SUBMODULE
Segmentation Accuracy. To validate the accuracy of our proposed hybrid topic segmentation method, we compare with segmentation using only a single granularity: attention-only-bar and mimilarity-only-basst
segmentation. Since the construction process of the LONGMEMEVAL indicates that different sessions naturally serve as topic boundaries, we directly use them as ground- truth labels. The final accuracy is calculated as the number of correctly identified segmentation points divided by the total number of labels. The results Figure 3(b) validate the effectiveness of our method: it achieves higher accuracy than both individual segmentation methods across all compression ratios, with an absolute accuracy exceeding 80%.
Ablation Study. As shown in Figure 3(c), removing the topic segmentation submodule slightly improves efficiency but significantly harms accuracy, causing a 6.3% drop for GPT and 5,4% for Qwen. This indicates that the submodule effectively enables models to perceive semantic units in the input, facilitating subsequent memory unit generation.
5.5 ANALYSIS OF THE STM THRESHOLD'S IMPACT
As illustrated in the Figure 4, the STM buffer threshold (th) has a distinct but significant impact on both efficiency and performance metrics. consistent trend is: th increases, there is a marked improvement in efficiency. In contrast, the effect on QA accuracy is non-monotonic. The optimal threshold for accuracy varies depending on the model and the compression ratio (r)), indicating that a larger buffer does not always yield better performance. This highlights crucial trade-off: while a larger STM threshold is consistently better reducing computational cost, the ideal setting for maximizing task accuracy requires careful tuning.
5.6 ANALYSIS OF SLEEP-TIME UPDATE
Why Soft Updates Work. A primary challenge in designing memory systems is handling updates. While powerful, LLMs can be unreliable when tasked with complex real-time update operations. For instance, when presented with two related but not contradictory pieces of information, an LLM might incorrectly interpret them as a conflict and delete the older memory entry, leading to inre- versible information loss. Instead, the optimal operations might be merge the information or

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Published as a conference paper at ICLR 2026
simply add the new entry. In contrast, LightMem performs only incremental additions through soft updates during test time, which preserves global information and complete semantics.
Case Study: Memory Update Mechanism Comparison
Historyl: "Monday, PM' : User is planning a trip to Tokyo.
History2: 1' "oodday, 4 PM' : User asks about trains to Kyeto.
Hard Update: Overwrites memory
LightMem Soft Update: Appends info
"User plans Kyoto trip"
"Tokyo trip Kyoto inquiry"
Tokyo context lost
Full context preserved
RELATED WORK
Hard Prompt Compression for LLMs. Hard prompt compression improves LLM efficiency by removing redundant content from prompts (Li et al., 2025c). Methods recently have evolved from using smaller language models (Jiang et al., 2023; Li et al., 2023; Chuang et al., 2024) to query- aware approaches that preserve task-relevant information (Weston & Sukhbaatar, 2023; Creswell et al., 2023; Jiang et al., 2024). Additionally, lightweight bidirectional encoders have demonstrated strong effectiveness and efficiency (Pan al., 2024a; Liskavets et al., 2025).
Chunking Strategies in RAG Systems. Retrieval-Augnee Augmented Generation (RAG) systems rely on chunking extrernal documents into smaller units for retrieval (Lewis et al., 2020; Gao et al., 2023). Existing chunking strategies include rule-based methods creating fixed-size segments (Lewis et al., 2020: Sarthi al., 2024; Edge et al., 2024; Gutierrez al., 2024), cemantic-based methods group- ing content by topic (Qu et al., 2025), and LLM-driven methods leveraging model knowledge for splitting (Pan et al., 2025; Duarte al., 2024: Zhao et al., 2024; Liu al., 2025b). However, all of these chunking strategies for RAG systems are tailored to static scenarios, not applicable to dynamic and open-ended environments.
Memory Systems for LLM Agents. Memory systems help LLM agents move beyond stateless interactions to support flexible reasoning and adaptation complex and changing environments (Liu et al., 2025a; Mei et al., 2025). The earliest and most straightforward approaches store experiences as linear or sequential streams, sometimes enhanced with hierarchical structures (Liang et al., 2023; Park al., 2023; Packer et al., 2023; Zhong al., 2024; Salama et al., 2025; Fang et al., 2025). A more structured class methods represents memories as nodes and their relationships as edges, using trees, graphs, temporal knowledge structures support retrieval and update (Rezazadeh et al., 2025; Chhikara et al., 2025; Rasmussen et al., 2025; Xu et al., 2025; Zhang et al., 2025). The latest trend integrates various types of memory, allowing them to interact and synergistically improve overall performance (Kang et al., 2025; Li et al., 2025b; Wang & Chen, 2025; Nan et al., 2025). Overall, existing memory systems for LLM agents have become increasingly complex and capable, leveraging hierarchical, structured, and multi-type memories. However, most focus on maximizing effectiveness, with limited consideration of efficiency. While some recent works (Guo et al., 2024; Zhao et al., 2025; Dong et al., 2025) share similar motivation with our work, they focus on lightweight adaptations of GraphRAG where the corpus is predefined and static.
7 CONCLUSION
In this work, we introduced LightMem, a lightweight and efficient memory framework designed to address the significant overhead of memory systems for LLM agents. Inspired by the multi-stage Atkinson-Shiffrin human memory model, LightMem's 's architecture effectively filters, organizes, and consolidates information. Our empirical evaluation demonstrates that this approach maintains strong task performance while sharply reducing computational costs. In the near future, we plan accel- erate LightMem's 's update phase via offline pre-computed KV caches, reducing runtime overhead. We aim to integrate a lightweight knowledge graph memory for explicit multi-hop reasoning and structured retrieval. A multimodal memory extension will enable adaptation to visual, auditory, and textual inputs in embodied and real-world scenarios.
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ETHICS STATEMENT
LightMem enhances LLM agents by creating an external memory of user interactions. While this improves agent coherence, it introduces critical ethical challenges. Storing dialogue histories poses inherent risks user privacy, conversations may contain sensitive data. The memory can also absorb and perpetuate biases or misinformation from user input, potentially leading to bad agent behavior. Therefore, any deployment of this technology must prioritize robust safeguards. We strongly advocate for strict privacy protocols, such as data anonymization and user consent, as well as mechanisms to mitigate the effects of biased or false memories. Responsible development essential ensure these memory- augmented systems are used in safe and trustworthy manner.
REPRODUCIBILITY STATEMENT
To ensure the reproducibility of this work, we introduce the detailed implementations for LightMem are provided in Section 3, Appendix C. Additionally, we plan to release our source code in the future to further support reproducibility. These measures are intended to facilitate the verification and replication of our results by other researchers in the field.
ACKNOWLEDGMENTS
We would like express our sincere gratitude the anonymous reviewers for their thoughtful and constructive feedback. This work was supported by the National Natural Science Foundation of China (No. 62576307, No. NSFCU23B2055, No. NSFCUI9B2027), the Fundamental Research Funds for the Central Universities (226-2023-0013)) the 2025 Zhejiang Provincial Center for Dis- ease Control and Prevention Science and Technology Talent Incubation Project (No. 2025-A-04), the 2025 Zhejiang Health Informatics Association Scientific Research Program (Key Project, No. 2025XHZN -Z01), titled "Research on Monitoring and Early Warning Methods of Al Large Model and Infectious Disease Epidemic Data Fusion", undertaken by the Zhejiang Provincial Center for Disease Control and Prevention, Yongjiang Talent Introduction Programme (2021A-156-G), and Information Technology Center and State Key Lab of CAD&CG, Zhejiang University.
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URL
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A BACKGROUND DETAILS
A.1 BACKGROUND ABOUT CURRENT MEMORY SYSTEMS
We describe both the mainstream memory architectures and the LightMem pipeline in terms of two major stages. The first is the memory bank construction stage, which can be further decomposed into the three sub-stages (I), (II), and (III) described in the Section 2.1. The second major stage concerns the usage of the memory system, which consists of retrieval and question answering (QA).
Memory Bank Construction As shown Table 4, we detail the workflows the three sub-stages (I), (II), and (III) for naive RAG, prevailing memory systems, and our LightMem. It can be observed that baseline memory systems typically perform their update stage during user-model interaction, which introduces substantial test-time latency. In contrast, LightMem decouples this update process from online interaction, thereby significantly reducing test-time latency. All models involved in these processes listed in Table 5. As shown, LightMem introduces only one additional model, L.Miingua22.eeymm those used by baseline methods. This model follows lightweight BERT architecture and requires less than 2GB of GPU memory during inference. rendering its overhead negligible. Moreover, for fairness, the latency introduced by this component is fully accounted for in our reported Runtime metric.
Table 4: The mainstream memory architectures and the LightMem pipeline of memory bank con- struction stage. Black-font processes denote those executed during online test-time interactions, whereas red-font processes denote those executed offline.
Method
Segment
(II) Summary/Extret
(III) Update
Raw dialog + fae() + {seg,]
NaiveRAG
fudex - (emb.}
Other
fieusee)) + (related entry,) + faglae()
Raw dialog
fws() ()
f Fuuuteamal () (memory entry,)
Memory
{seg,}
findes {emb.}
Systems
+ {add, delete, update, merge...)
Raw dialog -> fss((
Offline update trigger
[seg,}
fevery entry, Sieuseee()
fpm compress) ()
STM buffer full fumiintmaa)
(related entry, } + {update queue)
LightMem
+
fcomp_seg.
(topic,, {memory entry, }}
All update queues established
sensory butfer full +
+ fines() () + {topic,, {emb;})
parallel fapanc()
fiyii () +
{add, delete, update, merge...)
(topic-wise comp seg,)
Function
Model / /Strategy
Implementation in This Paper
feeg
Segmentation strategy
Turn-level gramularity input
Findex
Embedding model
all-MiniL.M-L6-v2
f Sumntextraat !
System backbone model
GPT-4o-mini; Owen3-30B-A3B-Insee 2150
f frearieve
Retrieval strategy
Cosine similarity vector retrieval
Jupsatte (
System backbone model
GPT-4o-mini; Qwven3-30B-A33--nnsttutt
free compress
Token compression model
LLMlingua-2
Supic
Topic segmentation model
MLMlingua-2
fehaa
Chat model
GPT-4o-mini: Owen3-30B-AAB--Iniiie
Table Mapping between functions, their roles, and the concrete models used this paper. Black- font entries denote models shared by both LightMem and bascline methods, whereas red-font entries denote models unique LightMem.
Retrieval and Usage After the memory bank construction stage, we obtain an up-to-date memory bank. When from this repository, appends them to the query, and then prompts the chat model fahmt ( to produce response.
query arrives, the memory system freuieve () to retrieve relevant entries
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A.2
NOTATION AND COMPLEXITY DETAILS
Table 6: Notation used in complexity analysis ($Section 4).
Symbol
Definition
N
Total number of turns in dialogue history.
T
Average number of tokens per turn.
Token compression rate (as defined in the main paper). After one compression step, only fraction r of tokens is retained.
Number of compression iterations. In LightMem, the pre-conpress module may be invoked multiple times for the same message to remove redundancy until the message is sufficiently compact. This occurs frequently in datasets such LongMemEval All time costs are included in runtime metrics.
th
Capacity of the Short-Tern Memory (STM) buffer, defined in the paper.
Number of tokens in the input prompt template and output of single backbone LLM call for summarization. These are similar across memory frameworks.
Laum-in/ /
Lum--out
Mi / M2
Number of memory entries produced from single summarization operation under Other Memory Systems (M1) and LightMem (M2).
Tuu Trppout out
Number of tokens in the input prompt template and output of single backhone LLM call for memory update. Similar across frameworks.
R1 R1/Ro R2
Proportion summary entries that successfully retrieve at least one relevant memory entry (triggering update) for Other Memory Systems (R₂) and LightMem (R2). Some entries do not retrieve any relevant counterparts and thus do not trigger updates.
B USAGE OF LLMS
Throughout the preparation of this manuscript, we used LLMs assist with improving grammar, clarity, and wording in parts of this work. The use of LLMs was limited to language refinement, with all ideas, analyses, and conclusions solely developed by the authors.
C METHODOLOGY DETAILS
C.1 TOPIC SEGMENTATION
In this part, we present the construction of the attention matrix, the underlying rationale for topic segmentation, and representative illustrative cases.
We extract only the user sentences from multi-turn dialogues, as they are generally more concise and the assistant's responses necessarily remain consistent with the user's theme within the same turn. Moreover, since the maximum input length of the LLMLingua-2 Pan et al. (2024b) model is 512 tokens, the assistant's often lengthy sentences cannot be effectively accommodated. Therefore, we sequentially store the user sentences into a buffer and segment them, ensuring that as many sentences as possible are preserved while staying within the token limit. As practical trick, a sentence becomes empty after compression, we retain its original uncompressed version; if the token length of a sentence still exceeds the maximum limit, we continue to compress using the LLMMingua-2 model at a 0.5 compression rate until the token length falls below the threshold. To reduce the effect of attention sinks, we mask out the contributions of the first and last three tokens in each sequence and subsequently normalize the remaining attention values. Attention is derived from the higher layers of LLMLingua-2 (layers 8, 9, 10, and 11). For any two sentences, we first compute leken-level pairwise attention and average across tokens to obtain the overall attention of one sentence to the target sentence; we then average across the selected layers to obtain a more robust inter-sentence attention score. For each current sentence, the attention scores directed toward all
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th:o
th:256
th:512
th:768
th:1024
GPT-4o-mint
GFT-4o-mint
GPT-4ominn
r-
Ovtpur( (3)
Traut (l)
Output [b]
Ho
Outaut
Ouiput [b)
Total
afe (9)
Torn H
ACC CSO
Total Oi)
ACC (4)
Watl Ho
ACC (N)
Cafe
Time Ii
Cats
Time Oo
Calls
1ime
Cals
Time 60
GPT-4o-ming r-
Qwen3 r-0
Qwen3
Qwen3 F-0.8
0-
Outpur (U)
Jesut Oi)
Output (J)
Josu (k)
Outpot QJ)
Jppat O4)
Outport (t)
Josun (4)
1.00
200
1.0
200
Total
aCe (SD Total
Total
Figure 4: Impact of the STM buffer threshold (th) on performance and efficiency across different compression ratios (r). Each radar chart represents a specific configuration of a model (GPT-40- mini or Qwen3) and a fixed compression ratio. The axes measure six key metrics: Accuracy (ACC), token consumption (Input, Output, Total), API Calls, and Runtime. To facilitate comparison, all values are normalized for visualization on the chart.
preceding sentences are normalized within the sentence, yielding the final attention matrix. Residual fragments that remain after segmentation are carried over to the beginning of the next buffer for further processing, and this procedure continues iteratively until the dialogue ends.
Based on the attention pattern, we focus on the sequence formed by each sentence's 's attention scores relative to its immediately preceding sentence, which directly reflects the continuity of local seman- tics. Therefore, we take the attention scores from the outermost layer of the attention map. When the attention score at a given position is higher than both its preceding and following positions, it is re- garded as a local peak. a sentence is identified as a peak, we set a segmentation point immediately before this sentence, making the peak sentence the beginning of new segment. The rationale is that the peak sentence exhibits consistently low attention to all earlier sentences overall and reflects clear transition from an old topic new one, indicating that the identified sentence marks the initiation of a new topic.
0.46
0.00
0.20
with
6129
811
8211
300
6124
B3th
0.55
15
Figure 5: Example of Topic Segment Attention Matrix.
Figure 5 illustrates three representative examples of reliable segmentation under 50% compression rate. In the first attention map, local peaks in the adjacent-sentence attention sequence appear at po- sitions 5, 8, and 11, where the actual segmentation boundaries lie between sentences 4-5 and 11 11-12.
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In the second attention map, peaks occur at positions 8, 12, and 14, and the actual boundaries located between sentences 7-8, 11-12, and 13-14. Overall, our method achieves close alignment with the majority of true boundaries while providing finer-grained segmentation. These examples demonstrate that our segmentation approach enables both fine-grained and reliable detection of topic boundaries, thereby validating its effectiveness.
C.2 WISE ACCURACY
As summarized Table retrieval- -uugeeted and memory-centric methods (e.g., A-MEM, MemO, MemoryOS) generally outperform Full Text on categories that demand information integration or belief revision, such as Temporal, Multi-Session, and SrwwledgeUUddatee In contrast, categories such Single-User and Single-Assistant, lightweight retrieval like Naive RAG is often competitive and can the most reliable option, while sample size.
shows higher variance due its smaller
Table 7: Category-wise Accuracy. Accuracy (%) by method across question types. Parentheses indicate category proportion and sample size. For GPT, LightMem is configured with parameters 0.7 and th 512; for Qwen, LightMem configured with r 0.4 and th 768.
Temporal Multi-Session Crawledge-Updatee (3=)
SingleUUser Single-Assistant Single-Preferee (n=70)
Method
(n=1333
(n=78)
(m=56)
(n=30)
GPT-4o-mini
Full Text
31.58
45.45
76.92
87.14
89.29
36.67
Naive RAG
39.85
48.48
67.95
90.00
98.21
53.33
LangMem
15.79
20.30
66.67
60.00
46.43
60.00
A-MEM
47.36
48.87
64.11
92.86
96.43
46.67
MemoryOS
32.33
31.06
48.72
80.00
64.29
30.00
MemO 40.15
46.21
70.12
81.43
41.07
60.00
LightMem
67.18
71.74
83.12
87.14
32.14
68.18
Full Text
33.08
35.61
76.92
82,86
87.50
50.00
Naive RAG
36.84
47.73
65.38
91.43
98.21
70.00
LangMem
37.60
38.35
67.95
78.57
42.86
A-MEM
51.88
51.12
76.93
90,00
96.43
40,00
MemoryOS
28.57
36.84
61.54
72.86
92.86
33.33
Mem0
41.94
28.13
28.57
55.32
26.09
81.82
LightMem
54.20
51.91
66.67
80.00
31.25
80.00
C.3 DETAILED PARAMETER ANALYSIS
As Table shows, we report the numerical results the effects of LightMem parameters (compres- sion ratio r and STM threshold th).
D EXPERIMENT DETAILS
D.1 DATASETS AND BASELINES
Datasets The Mong dataset (Wu et al., 2025) is designed benchmark long-term inter- active memory in conversational agents. It comprises 500 evaluation questions built upon extended user-assistant dialogues. It has two different versions with different lengths: the LONGMEMEVAL S setting contains approximately 1 115k tokens per problem, while the LONGMEMEVAL-N setting extends up to 1.5 million tokens across 500 sessions. In our work, we adopt the LONGMEMEVAL. S version due its balance between dialogue length and computational feasibility. The questions are categorized into multiple types: information extraction, multi-session reasoning, knowledge up- dates, temporal reasoning. and abstention. Overall, the dataset is characterized by extremely long
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Table 8: The impact of LightMem compression ratio and STM buffer threshold th reported here. Due to space limitations, we only present a subset of representative results of the online soft update results, with more results provided the Figure
Model
th
ACC
Input (k)
Output (k)
Total (k)
Calls
Time
256
0.5
64.29
20.80
10.01
30.81
25.67
302.69
256
0.6
67.68
24.58
10.53
35.11
30.47
329.61
256
0.7
65.68
27.66
9.97
37.63
34.26
403.59
GPT
512
0.6
63.74
16.23
9.45
25.68
15.63
266.98
512
0.7
28.25
18.43
283.76
512
0.8
66.67
21.55
8.59
30.14
21.11
268.97
1024
0.6
59.68
10.34
18.20
7.69
177.45
1024
0.7
64.68
12.93
6.90
19.83
8.25
209.12
1024
0.8
64.35
14.86
6.28
21.14
9.43
216.08
512
0.4
58.57
11.03
17.00
28.03
10.11
421.74
512
0.6
66.57
16.22
19.50
35.72
15.40
471.09
512
0.8
67.37
21.35
19.36
40.71
20.98
461.02
Owen
768
0.4
61.95
9.01
16.14
25.15
6.54
357.13
768
0.6
73.20
13.19
19.21
32.40
9.97
417.13
768
0.8
64.95
19.06
36.00
13.09
420.14
1024
0.4
53.91
8.02
15,44
23.46
4.83
300.56
0.6
65.67
18.21
29.71
7.18
396.35
1024
0.8
68.69
14.82
18.49
33.31
9.43
355.71
histories, wide temporal spans, and diverse question types, making a comprehensive benchmark for evaluating conversational agents' memory capabilities. During the experiments, five samples from this dataset contained corrupted characters, which caused LightMem's 's compression model to fail to properly. Consequently, LightMem directly discarded these five samples when processing the dataset. However, their accuracy results were uniformly treated as false. The indices these five samples in the dataset are 74, 183, 278, 351, and 380.
The LoCoMo benchmark targets the evaluation of long-range conversational memory. features extremely long dialogues, with each conversation spanning roughly 300 turns and around 9K tokens on average. The accompanying questions fall into four Segporiis-SSigle-hppp Multi-hop. Tempo- ral, and Open-domain Open-domainn-providnng a comprehensive assessment different dimensions memory in LLMs.
Baselines We compare our approach against several representative baselines of conversational memory modeling. @ LANGMEM (LangChain, 2025): The Langchain's long-term memory mod- ule. @ A-MEM (Xu al., 2025): Constructs memory-centri knowledge graph, encoding each interaction as a structured memory note and linking these notes via LLM--riven reasoning. @ MEM- ORYOS (Kang et al., 2025): Organizes conversational memory in an OS OS--isspred hierarchy, structur- ing interactions into short-term, mid-term, and long-term layers via paging and heat-based updating. MEMO (Chhikara et al., 2025): Extracts memories from dialogue turns through a combination of global summaries and recent context, maintaining them via LLM-guided operations.
D.2 IMPLEMENTATION DETAILS
All the experiments are conducted on hardware equipped with 4xNVIDIA RTX 3090 GPUs, dual Intel Xeon Gold 6133 CPUs (40 cores, 80 threads), and 256 GB of RAM.
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Table 9: The impact of LightMem's compression ratio (r) and STM buffer threshold (th).
Model
th
ACC
Input (k)
Output (k)
Total (k)
Calls
Time
0
0,4
58.04
27.70
8.90
36,60
39.91
500.69
256
0.4
$7.78
16.64
25.04
20.25
254.93
512
0.4
55.56
11.05
7.66
18.71
10.13
230.59
768
0.4
49.29
9.05
6.55
15.60
6.57
157.13
1024
0.4
46.87
7.75
5.25
13.00
4.82
118.11
0.5
62.89
30.84
9.75
40.59
43.56
550.36
256
0.5
64.29
20.80
10.01
30.81
25.67
302.69
GPT-4o-mini
512
0.5
62.44
13.49
8.89
22.38
12.70
250.36
768
0.5
56.12
10.93
7.57
18.50
8.12
203.13
1024
0.5
50.36
8.34
6.97
15.31
6.32
160.35
0.6
70.35
33.17
10.20
43.37
45.86
553.07
256
0.6
67.68
24.58
10.53
35.11
30.47
329.61
16.23
25.68
15.63
266.98
768
0.6
64.44
13.04
8.10
21.14
9.90
210.05
0.6
18.20
7.69
177.45
0
0.7
62.35
35,36
9.76
45.12
48.08
573.42
0.7
27.66
9.97
37.63
34.26
403.59
512
0.7
68.64
18.88
9.37
28.25
18.43
283.76
1024
0.7
64.68
12.93
6.90
19.83
8.25
209.12
0.8
61.52
39.32
9.89
49.21
$2.97
622,90
256
0.8
66.37
30.67
9.70
40.37
41.66
489.61
512
0.8
66.67
21.55
8.59
30.14
21.11
268.97
1024
0.8
64.35
14.86
6.28
21.14
9.43
216.08
0.4
56.89
28.44
18.30
46.74
41.08
594.94
0.4
52.37
17.63
34.45
20.48
450.98
512
0.4
58.57
11.03
17.00
28.03
10.11
421.74
768
0.4
61.95
9.01
16.14
25.15
6.54
357.13
1024
0.4
53,91
8.02
15,44
23.46
4.83
300.56
0.6
69.56
34.90
20.26
55.16
48.63
642.10
wwnn
256
0.6
65.37
24.78
19.59
44.37
30.66
520.37
512
0.6
66.57
16.22
19.50
35.72
15.40
471.09
768
0.6
73.20
13.19
19.21
32.40
9.97
417.13
1024
0.6
65.67
11,50
18.21
29.71
7.18
396.35
0.8
67.68
37.97
20.18
50.81
759.15
256
0.8
64.52
30.54
19.77
50.31
37.35
550.98
0.8
21.35
19.36
40.71
20.98
461.02
768
0.8
64.95
16,94
19.06
36.00
13.09
420.14
1024
0.8
68.69
14.82
18.49
33.31
9.43
355.71
22

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Published as a conference paper at ICLR 2026
E PROMPTS
E.1 LLM-AS-JUDGE
Standard Tasks Siingle-esssion-user/rssssss Multi-session)
I will give yes if the response contains the correct answer. Otherwise, answer no. If the response is equivalent to the correct answer or contains all the intermediate steps to get the correct answer, you should also answer yes. the response only contains subset of the information required by the answer, answer no.
question, a correct answer, and a response from a model. Please answer
Question: {question)
Correct Answer: {answer]
Model Response: [response]
Is the model response correct? Answer yes only.
Temporal Reasoning Tasks
I will give you question, correct answer, and response from model. Please answer yes if the response contains the correct answer. Otherwise, answer no. the response equivalent to the correct answer contains all the intermediate steps to get the correct answer, you should also answer yes. the response only contains a subset of the information required by the answer, answer In addition, do not penalize off-by off-by-one errors for the number of days. If the question asks for the number of days/weeks/months, etc., and the model makes off-by-one errors (e.g., predicting 19 days when the answer is 18), the model's response is still correct.
Question: {question}
Correct Answer: [answer]
Model Response: (response]
Is the model response correct? Answer yes or no only.
Knowledge Update Tasks
I will give question, a correct answer, and a response from a model. Please answer yes the response contains the correct answer. Otherwise, answer no. If the response contains some previous information along with an updated answer, the response should be considered as correct as long as the updated answer is the required answer.
Question: {question}
Correct Answer: (answer]
Model Response: (response)
the model response correct? Answer yes or no only.
Single-session Preference Tasks
I will give you a question, rubric for desired personalized response, and response from model. Please answer yes if the response satisfies the desired response. Otherwise, answer no. The model does not need to reflect all the points in the rubric. The response is correct as long it recalls and utilizes the user's personal information correctly. Question: {question)
Rubrie: (answer)
Model Response: {response) Is the model response correct? Answer yes or only.
23

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Published as a conference paper at ICLR 2026
Abstention Tasks
I will give you an unanswerable question, an explanation, and a response from a model. Please answer yes the model correctly identifies the question as unanswerable. The model could say that the information is incomplete, or some other information is given but the asked information not.
Question: (question)
Explanation: [answer]
Model Response: {response)
Does the model correctly identify the question as unanswerable? Answer yes or no only.
24


Reviews:
[{"review_id": "GQFGbmiOlL", "reviewer": "Reviewer_XYm9", "summary": "Inspired by the Atkinson–Shiffrin human memory model, this paper proposes LightMem, a memory-augmented LLM framework composed of three stages: (1) precompression into sensory memory using a classifier, (2) grouping memory entries by topic with another model to form short-term memory, and (3) updating entries in long-term memory during “sleep time” using a summarization model The authors implement this hierarchical memory structure with GPT and Qwen backbones and report consistent efficiency and performance improvements on LongMemEval tasks compared with state-of-the-art memory-augmented LLMs.", "questions": "What is the model choice for the summarization model f_{sum}? Is it a stronger model than the backbone?\n\nThe soft update mechanism allows preserving both old and new events, even if they appear contradictory. Does this also mean it retains genuinely conflicting facts (e.g., “the user will go to Tokyo at 2 PM” vs. “the user will go to Kyoto at 2pm”)? Will it mislead the model when making responses?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "The architecture is inspired by cognitive science, and the translation of the human memory model into an LLM framework is novel and well-motivated.\n\nThe model demonstrates promising efficiency compared to other memory-augmented systems, while achieving superior performance on the evaluated benchmark.", "weaknesses": "The paper claims significant efficiency gains, but this is not well supported. LightMem introduces at least three additional components (a compression model, an embedding or topic model, and a summarization model), making it unclear how overall runtime and token usage could be lower than baselines such as RAG. Were the costs of “sleep-time” updates included? The paper should provide standard efficiency metrics such as FLOPs or throughput, or a theoretical analysis explaining why and by how much LightMem calculates and improves efficiency.\n\nThe experiments are conducted only on one benchmark. Other long-context evaluation datasets, such as MemoryAgentBench, MemoryBank, LoCoMo, and PerLTQA, should be included to demonstrate generalization.\n\nSeveral design aspects are underexplained. For instance, GPT-4o-mini is used as the evaluation judge even when the same GPT backbone is part of the model itself, which risks inductive bias. A human evaluation or consistency check between human and LLM judgments would prove the soundness of the reported results. Moreover, the paper does not explain how the hierarchical memory (sensory, short-term, and long-term) is actually used during inference. Are all kinds of memories input into the model at every turn? Are they prepended to the user query as additional context? If there is any retrieval, how does it work?", "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": "naRdcMJqAQ", "reviewer": "Reviewer_sGUo", "summary": "This paper proposes LightMem, a memory system approach that augments LLMs to better retain and retrieve information over long conversations. LightMem, inspired by Atkinson-Shiffrin's model of human memory, consists 3 components: (1) a sensory memory that filters out noise and retain only the most informative parts, (2) a topic aware short term memory that consolidates information for more structed access, and (3) a long term memory that that decouples consolidation from online inference. The authors report significant gains on several metrics using LightMem on GPT and Qwen models.", "questions": "1. I am not really an expert in the memory-augmented LLMs area, but with cursory google search I found [He et al, 2025](https://arxiv.org/pdf/2405.06067) and [Sun and Zeng, 2025](https://arxiv.org/abs/2507.22925), both of them seem very relevant to this work. You should include a discussion how your approach differ from these previous works, possibly also include them as a baseline. \n\n2. The paper might be missing some important citations such as [Wu et al, 2025](https://arxiv.org/pdf/2504.15965) that has some discussion on memory systems in humans vs LLMs.", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "* I think this is decent engineering effort to build memory augmented LLMs.", "weaknesses": "* I am not sure about the novelty of this work. The base idea seems to have been studied in prior works with different names/flavors.", "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": "FooDCgRoAI", "reviewer": "Reviewer_Ub6P", "summary": "The paper describes LightMem, a memory module for LLMs, inspired by a model of the human memory. It has three stages: (1) filtering incoming tokens which are of low quality or redundant, (2) a short-term memory which groups together semantically related utterances, and (3) a long-term memory which is maintained during designated offline periods where memories are re-organized, removing redundancies and resolving inconsistencies. By moving this heavy lifting offline, LightMem ensures that the processes involved with maintaining the memory have little impact on the LLM’s latency. Experiments show significant improvement over baselines in terms of accuracy, number of tokens, number of API calls, and runtime.", "questions": "N/A", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "By and large, the paper is well written and well motivated. The stages of creating and generating memory entries are inspired by the model of the human brain. The experimental setup is sufficient and results are convincing. The field of memory-augmented LLMs is crowded. While the paper does not present fundamentally different ideas to the ones in previous work, I still find it interesting and I think that it is a solid addition to this growing body of work.", "weaknesses": "I don’t have any major issues with the paper. The description in Section 3.3 is not clear to me. What happens after a queue for each entry is created? How is f_{update} implemented? What happens when the LTM reaches capacity?", "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": "BO3mrdUYeF", "reviewer": "Reviewer_4xu7", "summary": "This paper presents LightMem, a lightweight memory system for large language models (LLMs) designed to improve long-context reasoning in multi-turn dialogues while minimizing inference and maintenance overhead. Inspired by the Atkinson–Shiffrin model of human memory, LightMem consists of three stages: (1) a pre-compression sensory memory that filters redundant tokens using LLMLingua-2, (2) a topic-aware short-term memory (STM) that segments and summarizes dialogue by semantic topics, and (3) a sleep-time long-term memory (LTM) update mechanism that asynchronously consolidates memory to reduce runtime latency.", "questions": "1. How well LightMem scales over long durations of deployment, such as weeks- or months-long open-ended interactions? Does the framework include mechanisms to manage memory lifecycle over time to avoid memory bloat and coherence degradation?", "limitations": "", "rating": 6, "confidence": 1, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The decomposition of memory into sensory / STM / LTM stages reflects cognitive plausibility, offering interpretability and modularity.\n2. Across GPT and Qwen backbones, LightMem consistently outperforms prior methods in accuracy, latency, API usage, and token efficiency.\n3. Employing LLMLingua-2 for token-level retention filtering is well-grounded and shown to maintain semantic fidelity even at 50–70% compression ratios.\n4. Writing is clear, figures and tables are well-structured, and Appendix contains implementation details, metrics, and additional experiments.", "weaknesses": "1. All evaluation is performed on the QA-focused LONGMEMEVAL benchmark. It remains unclear how LightMem would perform in generation-based tasks, e.g., reasoning or question-answering.", "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": "WdqdWIQ0xr", "reviewer": "Reviewer_SYFd", "summary": "The paper proposes LightMem, a lightweight memory system for LLM agents that aims to keep long-term dialogue context both accurate and cheap to use. LightMem is explicitly inspired by the Atkinson–Shiffrin model of human memory, and contains: (1) a sensory memory module that pre-compresses and filters incoming dialogue with LLMLingua-2 and groups turns by topic, (2) a topic-aware short-term memory (STM) buffers these topic segments and summarises them adaptively rather than at fixed window boundaries, and (3) a long-term memory (LTM) that is updated in two stages — fast “soft updates” during inference and a later offline “sleep-time” consolidation. \n\nThe system is evaluated in an incremental multi-turn dialogue setting on LONGMEMEVAL-S, where the model only sees past turns as they arrive (simulating agents that operate over very long histories). On GPT-4o-mini and Qwen3-30B backbones, LightMem improves QA accuracy by up to ~10.9% over strong memory baselines such as A-Mem, and simultaneously cuts token usage by as much as 117×, reduces API calls by up to 177×, and lowers runtime by over 12×. \n\nAblations varying compression ratio and STM buffer threshold show a tradeoff between efficiency and accuracy but still indicate large gains over prior work across both backbones.", "questions": "1. Your evaluation is limited to LONGMEMEVAL-S and a QA-style setup with LLM-based judging. Can you provide evidence that LightMem generalizes beyond that setting? For example, to tool-using agents, planning/assistive tasks, or code assistants? If not, what are the expected failure modes outside the current benchmark?\n\n2. When do you “sleep” the model? The LTM “sleep-time update” is described as happening offline in parallel and supposedly without user-facing latency. In a real interactive assistant, what triggers this (e.g. wall clock time, buffer size, explicit idle periods)? And what happens if the agent is in continuous use and never becomes idle?\n\n3. Summary faithfulness audits. Have you measured factual faithfulness of STM summaries vs. gold dialogue histories (e.g., hallucination rate, omission rate)? Accuracy on QA is an indirect proxy but doesn’t capture whether LightMem invents or loses user-specific facts in ways that could create safety/privacy issues downstream.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Clear, biologically grounded design. Structuring memory into sensory → STM → LTM with delayed consolidation is conceptually clean and directly motivated by cognitive models of human memory and sleep-driven consolidation, which is underexplored in LLM memory pipelines. Decoupling long-term memory maintenance from online inference and executing updates in parallel queues is a nice systems contribution. It directly addresses latency and consistency problems in prior approaches that update memories synchronously during inference. \n\n2. Strong efficiency gains. The paper optimises for both higher QA accuracy as well as token cost, number of model calls, and wall-clock runtime. These are central bottlenecks for deployed LLM agents, and LightMem shows 1–2 orders of magnitude improvements on these metrics versus baselines like A-Mem, LangMem, MemoryOS, and Mem0. \n\n3. Topic-aware STM. The idea of grouping turns by semantic/topic boundaries (via attention peaks and embedding similarity) instead of treating each turn or fixed window as a separate memory unit feels like a meaningful refinement over naive chunking and helps avoid both over-fragmented and over-mixed summaries.\n\n4. Clarity. The paper is clearly written and easy to follow, with clear analysis/ablations of each component.", "weaknesses": "1. Benchmark breadth. All main results are on LONGMEMEVAL-S (500 long multi-session dialogues, ~110k tokens each on average) and QA-style evaluation judged by GPT-4o-mini. It’s not obvious how well LightMem generalizes to other domains (e.g., tool-use agents, planning tasks, code assistants) or to evaluation regimes that don’t rely on an LLM judge aligned with the backbone family.\n\n2. Stability of offline updates. After “OP-update” (offline parallel update), accuracy sometimes drops relative to the purely online/soft-update configuration (e.g., GPT backbone at r=0.7 goes from 68.64% to 67.07%), suggesting that consolidation can introduce regressions. The paper claims improved long-term consistency, but the quantitative story is mixed and could use deeper analysis of failure modes.\n\n3. Safety / faithfulness of summaries. The system stores summarized memory units as ground truth for future retrieval. We don’t see discussion of hallucinated summaries or factual drift when compressing many sessions into a single topic-level entry, which is a known risk for LLM-generated memory.", "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)