The AI Hippocampus: How Far are We From Human Memory?
Authors: Zixia Jia
Jiaqi Li
Yipeng Kang
Yuxuan Wang
Tong Wu
Quansen Wang
Xiaobo Wang
Shuyi Zhang
Junzhe Shen
Qing Li
Siyuan Qi
Yitao Liang
Di He
Zilong Zheng
Song-Chun Zhu
Venue: Accepted by TMLR ()

Abstract: Memory plays a foundational role in augmenting the reasoning, adaptability, and contextual fidelity of modern Large Language Models (LLMs) and Multi-Modal LLMs (MLLMs). As these models transition from static predictors to interactive systems capable of continual learning and personalized inference, the incorporation of memory mechanisms has emerged as a central theme in their architectural and functional evolution. This survey presents a comprehensive and structured synthesis of memory in LLMs and MLLMs, organizing the literature into a cohesive taxonomy comprising implicit, explicit, and agentic memory paradigms. Specifically, the survey delineates three primary memory frameworks. \textit{Implicit memory} refers to the knowledge embedded within the internal parameters of pre-trained transformers, encompassing their capacity for memorization, associative retrieval, and contextual reasoning. Recent work has explored methods to interpret, manipulate, and reconfigure this latent memory. \textit{Explicit memory} involves external storage and retrieval components designed to augment model outputs with dynamic, queryable knowledge representations—such as textual corpora, dense vectors, and graph-based structures—thereby enabling scalable and updatable interaction with information sources. \textit{Agentic memory} introduces persistent, temporally extended memory structures within autonomous agents, facilitating long-term planning, self-consistency, and collaborative behavior in multi-agent systems, with relevance to embodied and interactive AI. Extending beyond text, the survey examines the integration of memory within multi-modal settings, where coherence across vision, language, audio, and action modalities is essential. Key architectural advances, benchmark tasks, and open challenges are discussed, including issues related to memory capacity, alignment, factual consistency, and cross-system interoperability. By charting the current landscape and identifying critical research directions, this survey aims to inform the development of memory-augmented (M)LLMs that are more flexible, context-sensitive, and aligned with the requirements of real-world intelligent systems. The survey’s website is available at \url{https://github.com/bigai-nlco/LLM-Memory-Survey}.

Published in Transactions on Machine Learning Research (11/2025)
The Al Hippocampus: How Far are We From Human Memory?
Zixia Jia Jiaqi Li Li Yipeng Kang", Yuxuan Wang 1x Tong Wu², Quansen Wang 1.2
Xiaobo Wang 1 Shuyi Zhang', Junzhe Shen', Qing Li", Siyuan Qi', Yitao Liang?, Di He?,
Zilong Zheng E2 Long-Chur Zhu
State Key Laboratory of General Artificial Intellgence, BIGAI
Peking University
Reviewed on OpenReview: https: // opwnreviee....e.... net/ permfo id- Sk7pumLLAY
Abstract
Memory plays a foundational role augmenting the reasoning, adaptability, and contextual fidelity of modern Large Language Models (LLMs) and Multi-Modal LLMs (MLLMs). As these models transition from static predictors interactive systems capable of continual learning and personalized inference, the incorporation of memory mechanisms has emerged as central theme in their architectural and functional evolution. This survey presents a comprehensive and structured synthesis of memory in LLMs and MLLMs, organizing the literature into a cohesive taxonomy comprising implicit, explicit, and agentic memory paradigms. Specifically, the survey delincates three primary memory frameworks. Implicit memory refers to the knowledge embedded within the internal parameters of pre-trained transformers, encompassing their capacity for memorization, associative retrieval, and contex- tual reasoning. Recent work has explored methods to interpret, manipulate, and reconfigure this latent memory. Explicit memory involves external storage and retrieval components de- signed to augment model outputs with dynamic, queryable knowledge represeniations- suchh as textual corpora, dense vectors, and graph-based structures thereby enabling scalable and updatable interaction with information sources. Agentic memory introduces persistent, temporally extended memory structures within autonomous agents, facilitating long-term planning, self-consistency, and collaborative behavior in multi-agent systems, with rele- vance to embodied and interactive AI. Extending beyond text, the survey examines the integration of memory within multi-modal settings, where coherence across vision, language, audio, and action modalities is essential. Key architectural advances, benchmark tasks, and open challenges are discussed, including issues related to memory capacity, alignment, factual consistency, and cross-system interoperability. By charting the current landscape and identifying critical research directions, this survey aims to inform the development of memory-augmented (M)LLMs that are more flexible, context-scnsitive, and aligned with the requirements of real-world intelligent systems. The survey's website is available at https://githe: com/bigai Alco/LLM, Memory-Survey.
*Core contributors. M Corresponding Zilong Zheng.

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Published in Transactions on Machine Learning Research (11/2025)
Contents
Introduction
4
Implicit Memory: Unveiling Knowledge Inside Transformers
6
2.1
Memory Analysis of Transformers
6
2.1.1
Knowledge Memorization
2.1.2
Associative Memory
9
2.2
Implicit Memory Modification
11
2.2.1
Modification Methods
11
2.2.2
Modification Benchmark
13
2.3
Limitations, Open Questions, Discussion
13
Explicit Memory: When (M)LLMs Meet Retrieval
14
Explicit Memory Representation
3.1.1
Free text
14
3.1.2
Graph
16
3.1.3
Vector
17
3.2
Training with Explicit Memory
17
3.2.1
Pro-Training
18
3.2.2
Fine- Fine-Tuning..
3.3
Training with externalized parameteric knowledge
20
3.3.1
Long Contexts
20
3.3.2
Knowledge Injection
21
3.4
Limitations, Open Questions, Discussion
21
4
Agentic Memory: Consolidating Memories into Humanic Agents
22
4.1
Single-agent Memory
23
4.1.1
Short-term Memory
23
4.1.2
Long-term Memory
4.2
Multi-agent Memory
4.3
System Architecture
27
4.3.1
Data Ingestion
28
4.3.2
Storage and Retrieval
29
4.3.3
User Interfaces and Application Invocation
29
4.4
Evaluation on Agent Memory
30
4.4.1
Qualitative Evaluation
30
4.4.2
Quantitative Evaluation
30
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4.5
Limitations, Open Questions, Discussion
35
5
Memory-augmented Multi-Modal Large Language models
35
5.1
Multimodal Context Modeling with Memory
35
5.1.1
Audio Context Modeling
35
5.1.2
Video Context Modeling
................................
5.1.3
Other Modalities
................................
37
5.2
Downstream tasks
38
................................
5.3
Multimodal Contextual Memory for Robotics
41
5.3.1
Multimodal Memory-Angmenteed Agents
42
5.3.2
Memory-Enhanced Navigalion, Odometry, and Manipulation
................................
5.3.3
Application
42
5.4
Limitations, Open Questions, Discussion
43
6
Conclusion
43
7 Future Work and Limitations
43
3

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"Memory the treasury and guardian of all things." 7
Marcus Tullius Cicero, De Oratore
1 Introduction
Recent advancements in artificial intelligence (AI) have led to the development of sophisticated systems, notably (Multi-Modal) Large Language Models (LLMs), which exhibit remarkable capabilities across various domains, from natural language processing and artificial intelligence to software enginecring and social sciences (Brown et al., 2020; Hoffmann et al., 2022; Kaplan et al., 2020). Their proficiency extends to tasks such multi-step reasoning (Wei et al., 2022; Li et al., 2025a) and cross-lask generalization (Chatterjee et al., 2024), showcasing their potential to revolutionize various applications. The continuous development of LLMs is a crucial stop towards achioving Artificial General Intelligence (AGI), necossitating the incorporation of advanced features that enable these models to autonomously explore and learn from real-world envirouments.
A pivotal aspect of this development is the integration of memory modules within LLMs, which play a fundamental role in how an agent accumulates knowledge, processes historical experiences, and retrieves information to inform decision-making and actions. By embedding memory capabilities, LLMs can evolve from static entities that rely solely on pre-trained knowledge to dynamic agents capable of continuous learning and adaptation. This transformation allows models to retain and leverage past interactions, thereby enhancing their performance in complex tasks that necessitate long-term planning and a deep contextual understanding. For example, a personal assistant agent with memory capabilities can remember user preferences and previous interactions, thereby delivering more personalized and contextually appropriate responses. Similarly, a trip-planning agent can track user itineraries and preferences, optimizing the efficiency and accuracy of its recommendations. In the healthcare sector, memory-enabled models can maintain comprehensive patient histories, lcading to more precise diagnoses and tailored treatment plans. In educational settings, such models can monitor student progress and customize educational content lo meet individual learning needs. Additionally, in customer service, memory-equipped agents can provide more efficient and personalized support, significantly enhancing user satisfaction and operational efficiency.
Analogy to Human Brain The architecture of memory in modern (M)LLMs is increasingly analogous to the synergistic relationship between different human brain systems, particularly the neocortex, the hippocampus, and the prefrontal cortex. This brain-inspired framework, which echoes the principles of Complementary Learning Systems theory (McClelland et al., 1995), provides a powerful lens through which understand the different memory paradigms evolving in AI.
Implicit Memory (82): The Neocortex. We conceptualize the model's internal parameters as its digital neocortex. In the brain, the neocortex is the primary repository for long-term semantic knowledge, skills, and consolidated memories, which are learned slowly and stored in a distributed manner (Squire & 'Zola-Morgan 1991). Similarly, a transformer's weights embody the implicit memory of the model-the foundational "world knowledge" acquired during pre-training. This parametric knowledge represents the model's stable, generalized understanding of language, patterns, and facts.
Explicit Memory ($3): The Hippocampal System. To access specific, real-time, or episodic information, an AI system requires mechanism analogous to the hippocampus. The hippocampus is critical for the rapid encoding of new episodic memories (i.e., specific events and their context) and acts index that binds together disparate clements of an experience stored across the neocortex (Frankland & Bontemmii 2005). Explicit memory systems in AI, such as Betrieval-Augmente mimic this function. They serve as an "Al Hippocampus" by providing an on-demand, queryable index to external information (vector embeddings, knowledge graphs). This allows the model to ground its responses in specific, up-to-date facts without the need for slow, resource-ineessive retraining of its entire parametric base (the "neocortex").
Generation (RAG),
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W hat knowledge is implicitly encoded and memorized in Trans- formers?
How does information explicitly represented, stored and retrieved by language models?
implicit Memory section 2
Explicit Memory Section 3
H How to develop auto- biographical memory systems for humanic agents?
Wha hat innovations in memory modules can help MLLMs execute in multi-modal contexts?
Agentic Memory
MLLM Memory
Section
Section
Figure 1: The overall framework of Memory Mechanisms in Large Language Models.
Agentic Memory ($4): The Prefrontal Cortex. The functionality of agentic memory is best analogized to the prefrontal cortex (PFC), the brain's executive control center. The PFC is responsible for working memory, goal-directed planning, and integrating information from both long-term stores (ncocortex) and recent episodic memories (hippocampus) to guide behavior (Miller & Cohen, 2001). Agentic memory syslems similarly mainlain persistent stale across interactions, manage working memory (e.g., scratchpad), and orchestrate the strategic retrieval and use of both implicit and explicit memory to formulate plans and exccute complex tasks. Furthermore, as we explore in $5, this executive function extends to integrating information from specialized memory modules for spatial, temporal, and embodied intelligence, akin to how the PFC coordinates inputs from various sensory cortices.
This survey explores how these distinct yet interconnected memory paradigms are enhancing the journey toward more context-aware and intelligent Al systems..
Related Surveys of Memory Before the emergence of large language models (LLMs), Khosla el al. (2023) has explored memory-augmented neural networks. Their work investigated range of network architectures, such Hopfield Networks and Neural Turing Machines, and examined various types of memory, including sensory, short-term, and long-term memory. They also established connections between psychological theories of memory and their applications in AI, introducing architectures inspired by human memory systems.
Zhang et al. (2024d) presents a comprehensive survey on the memory mechanisms of LLM-based agents, systematically reviewing the design and evaluation of memory modules. Compared to our work, their focus is specifically on agents, particularly emphasizing historical and trajectory memory acquired through agent-onvironment interactions. He al. (2024d) and Jiang et al. (2024a) focus on long-term memory in AI systems. Notably, Jiang al. (2024a) proposes that Al equipped with long-term memory, capable of storing and managing real-world interactions, can achieve self-evolution.
More recently, increasing attention has been devoted to memory in AI systems. Du et al. (2025) recon- ceptualize memory systems by calegorizing them according to alomic operations and representation types, distinguishing between parametric and contextual forms memory. They further classify memory operations management and utilization, offering detailed taxonomy and technical analysis that provides practical insights. Shan et al. (2025) explores the similarities and differences between human memory and memory in LLMss, discussing various forms such text-based, KV cache-based, parameter-based, and hidden-state-basee
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memory. Wu et al. (2025b) provides in-depth analysis of memory in LLM-driven AI systems, categorizing memory-related methods across object, form, and temporal dimensions using an eight-quadrant framework.
While these surveys offer valuable perspectives, either drawing analogies with human memory or examining specific LLM-based memory forms and sources, none provide a unified view of memory study across pure LLMs, LLM-based agents, and further multimodal models. Consequently, our work presents a comprehensive survey that spans this full spectrum.
2 Implicit Memory: Unveiling Knowledge Inside Transformers
Implicit memory, a concept originating from psychology, refers to memories that are used unconsciously and are not stored explicitly Dew & Cabeza (2011). , In the context of the deep Transformer era, we define "implicit memory" as follows:
Implicit Memory refers to the intrinsic information embedded within a model's parameters, encom- passing self-knowledge, facts, commonsense, associative memory, and other related elements, which collectively enable the generation of contextually relevant responses across variety of tasks.
The rise of Transformer models (Kovaleva al., 2019; Brown, 2020; Geva et al., 2022b; Zhang et al., 2022; Stolfo et al., 2024) has brought significant attention to implicit memory due to their remarkable performance across multiple domains. Recent research Li et al. (2025c) definee parameters of Transformer models implicit long-term memory and the hidden states, KV-cache (in LLMs) as implicit short memory. Our survey focuses on exploring how these transformer models store and utilize knowledge within their parameters to understand and potentially enhance their capabilities. In this section, we attempt to answer the following research questions.
RQ1: What knowledge is implicitly encoded and memorized in Transformers? RQ2: How is information memorized, velrieved, and modified in Trunsformers?
In the following subsections, we provide an overview of the current investigations on L.Mss' memorization, including knowledge memorization and knowledge expression ($2.1.1), associative memory ($2.1.2), and implicit memory modification (52.2). The structure of this section is demonstrated in Figure 2
2.1
Memory Analysis of Transformers
2.1.1
Knowledge Memorization
Transformers (Vaswani, 2017) have shown an impressive ability to memorize and retrieve knowledge implicitly stored in their parameters (Geva al., 2023; Yao al., 2024b; Lv al., 2024; Stolfo et al., 2024). Studies have investigated how components like Feed Forward Networks (FFNs) and Self-Attentions (SAs) contribute to this knowledge memorization (Kovaleva et al., 2019; Geva el al., 2021; 2022b; Dai el al., 2021; Clark, 2019; Hoover et al., 2020; Yu et al., 2023a). This research is crucial for comprehending the internal workings of Transformer models and, accordingly, to manipulate their stored knowledge to enhance model performance.
Knowledge Memorized in Parameters There are two primary hypotheses (H1&H2) concerning the memorization of knowledge transformer-style language models.
HI: Knowledge is encoded through FFNs, emphasizing the role of feed-forward layers within transformer architectures in memorizing information.
An FFN is usually implemented as a stack of interleaved linear and non-linear layers. It has been used as a sub-module in many different neural architectures and has been shown to be crucial for Transformer's representation power (Geva et al., 2021; Meng al., 2022a; Gupta al., 2023). We categorize memory mechanisms within FFNs into two classes:

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Parametric Memory
Geva et al. (2022b), Qiu et al.
(2024), Gurnee et al. (2024), Gupta al. (2023), Zhang al. (2022)
Self- nowl-Ken
Connective Memory
Yao al. (2024b), Geva et (2023),
Li al. (2024d), Lv et al. (2024)
Memory
Analysis
Scaling Law
Lu et al. (2024), Allen-Zhi & Li (2024a), A Allen-Zhu & Li (2024b)
Implicit Memory
Associative Memory
Suzuki (2008), Chisvin & Duckworth (1992), Hopfield (1982), Ramsauer al. (2021); Bietti et al. (2024), Ca- bannes et al. (2024), Niu et al. (2024), He al. (2024c)
Memory
Chen al. (2024c), Zhong al. (2022b), Gururangan al. (2022), Wang et al. (2021b), Meng al. (2022a), Meng al.
Modification
(2022b), Cao al. (2021), Hase et al. (2023), Mitchell et
(2022), Wang et al. (2024c), Zhang et (2024c), Dong et al.
(2022), Tian et al. (2024), Liu al. (2024c), Chen Yang
(2023), Veldanda et al. (2024)
Figure 2: Taxonomy of implicit memory in Transformer.
FFNs act as key-value memories.' 1 Geva et al. (2021) demonstrates that each key correlates with a specifie set of human-interpretable textual patterns and each value induces a distribution over the output vocabulary. Based on this, Geva et al. (2022b) investigates the mechanism in which feod-forward layers update the inner representation, observing value vectors often encode human iimaan-nterrretabbee concepts. Recently, Qiu et al. (2024) re-explores the key-value neural memories, conducting empirical ablation studies on updating keys or values in LLM. They recognize that updating the keys in a model is generally more effective than updating the values. Beyond these studies, Zhong et al. (2025) demonstrates, from a key-value memory perspective, why feed-forward networks (FFNs) adopt the ReLU kernel over more precise alternatives like the exponential kernel. They hypothesize that "A kernel with lower retricval procision encourages more polyscmantic key value memory: multiple unrelated facts can be stored under the same key space".
Different FFN neurons memorize different information. Dai et al. (2021) introduces the concept of knowledge neurons. They hypothesized that knowledge neurons in the FFN module are responsible for expressing lacts. Zhang et al. (2022) shows that FFN neurons can be split into different functional partitions and some partitions are specialized in memorizing fact-related knowledge (Zhang et al., 2023f). Geva et al. (2022a) empirically finds that each vector of neurons in FFNs can be interpretod as concept in the vocabulary space. Wu et al. (2023b) proposed privacy neurons detector to locate neurons associated with private information. Stolfo et al. (2024) investigated entropy neurons and token frequency neurons which two critical components believed to influence the representation and regulated uncertainty of LLMs.
The attention mechanism is more crucial for knowledge storage, examining the relationship between the distribution of attention heads and the aggregation of knowledge.
H2:
The feed-forward layer can be expressed FF(x) f(x KT) )- V, where K denotes key vectors and V denotes value vectors, distinct from the key-value pairs used in self-ateention mechanisms.
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A
Key-Value Memory
B
Token Embdiing
MIP
Input
Flow Path
Tokens
Selfatmiiio
Key Matrix
Value Matrix
Mapped Tokens
FFN
Output Dist.
Matrix Muitiply
Various
Knowledge
Neurons
MHSA
Menseieed
Information
Attention Distribution Shitting
Hoaayy
Die
Dind
Irpor
Layer_1
by
Figure 3: Parameter vs. Circuit. The left graph demonstrates the location of memory stored in Multi- Layer Perceptron layer and Sclf-Attention heads of the Transformer module, representing FNNs act as key-value memories, Different FFN newrons memorize different information, and manipulating allention head distributions, respectively. The right graph simplified demonstration of the knowledge flow between different transformer layers and various components within different layers.
Many works have analyzed Sell-Attention layers for interpretabiliity (Clark, 2019; Hoover et al., 2020), with a focus on manipulating attention head distributions. Recently, Yu al. (2023a) study controlling LLMs to specifically leverage the in-context knowledge or facts memorized in pertaining by changing attention head distributions. Li et al. (2024c) identify sparse set of atlention heads that are completely related to fact knowledge of Alpaca Taori et al. (2023), and during inference, they shift activations along these truth-correlated directions, significantly improving the Alpaca truthfulness. Jiang et al. (2024c) further mathematically explores how Transformers can complete memory tasks based on the observation that LLMs² ability to retrieve facts can be easily manipulated by changing contexts. They theoretically prove and empirically show that the Transformer gathers information using self-attention.
Knowledge Flows in Connections As noted above, most studies have concentrated on knowledge storage within isolated components, such FFNs and attention heads. In contrast, Yao et al. (2024b) studied connections between these Transformer components, introducing the concept of "knowledge circuits" to explore how different components collaborate to store and express knowledge. By ablating component-to-comppnne connection, they demonstrate the knowledge circuit in LLMs for facts, linguistics, and commonsense, Previous work has explored knowledge flows similar to the knowledge circuit concept in LLMss. Geva el al. (2023) adopted the "knock out" strategy which blocks Multi-Layer Perceptron (MLP) or Multi- Head Attention (MHSA) sublayers investigate how the LLMs retrieved factual knowledge internally inference. They conduct experiments on altribute prediction given subject-relation queries, revealing two key components in the prediction process: the subject enrichment process where the early MLP sublayers the primary source and the attribute extraction operation where the upper MHSA sublayers mainly carry out. Typically, Lv et al. (2024) explored several mechanisms employed by LLMs for factual recall tasks. They decompose MLP outputs into compoments that. are easily understandable to humans based on linear regression, availably finding a universal anti-overconffddence mechanism in the final layer of models.
Scaling Law of Knowledge Memorization. The scaling law (Kaplan et al., 2020) has been used to describe model performance in terms of critical variables such as model size, datasct size, and the amount of computing used for training. In the general pretraining field, Kaplan et al. (2020) empirically study scaling laws for the performance of the language model on the cross-entropy loss L. They conclude equation of

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scaling laws with model size and training time:
L(N, Smin) = Ne ax
Se Smin
as
(1)
where N represents non-embedding parameters, Smin represents the minimum number of steps necessary to reach L, an ~ 0.077, ag ~ 0.76, No ~ 6.5 x 10 and S. ~ 2.1 x 10".
For factual knowledge memorization, Lu et al. (2024) investigates the relationship between model size, training epochs, and fact memorization. Their study reveals that LLMs' fact knowledge capacity follows a linear and negative exponential relationship with model size and training epochs, respectively, suggesting that memorizing all public facts, like those in Wikidata?, is nearly impossible in general pro-training sotting:
C= C² - -g . exp(-3p 3E Epoch),
where C' denotes fact capacity, O means the LLMs' fact capacity saturation when epochs approach infinity, and ag and fg are constants. Additionally, they find the scaling law of LLMss fact memorization is similar to general pre-training, and the test loss L on fact generalization also follows the power-law (Kaplan et al., 2020), promising the generalization of unseen fact knowledge:
L(D) = De DaD,
33)
where D is the number of training facts, D. and an are constant numbers. Their study also analyzes the compatibility and preference of LLMs fact memorization, highlighting its inefficiency in handling redundant facts and its preference for memorizing more frequent or difficult facts. Allen-Zhu & Li (2024a) also explores the knowledge capacily scaling laws of LLMs, providing more accurate and flexible alternative to traditional methods which often rely on evaluating language models against real-world benchmarks. Different from Lu et al. (2024), they use a synthetic dataset rather than real-world facts to avoid benchmark contamination. Through comparison across different model architectures and types of knowledge, they conclude that a fully trained Transformer can store 2 bits of knowledge per parameter, even when quantized to int which is close to the theoretical maximum. Further, Allen-Zhu & Li (2024b) observe that mixed training with raw knowledge text and question-answer pairs yields better performance out-of-distributio questions compared to the pretraining-fietee the pretraining data for knowledge augmentation and integrating more instructionfinetumee data during the pretraining stage can enhance LLM's knowledge memorization and extraction.
approach on their synthesized biography dataset. They conclude that rewriting
2.1.2 Associative Memory
In psychology, associative memory is the ability to build relationships between two previously unrelated features or idcas such as phone number and the name of a person (Suzuki, 2008). There are also other researchers trying to use associative memory in the physical computer memory architecture to overcome some basic problems of traditional address- based memory (Chisvin & Duckworth, 1992). In Transformer- based models, associative memory refers to the memory of two previously unrelated representations (e.g., input-output vectors) learned during the training process.
Energy-based Model Mimic Associative Memory Hopfield Network (Hopfield, 1982) is a type of fullly-connected recurrent energy-based neural network that is used primarily for associative memory. This network structure leverages a set of interconnected neurons and weight matrices to encode multiple patterns, where each pattern is associated with a specific input. The storage of these patterns is facilitated by the modification of synaptic weights between neurons, a process that is governed by the principles of Hebbian learning. In details, the original Hopfield Network is composed with binary neurons V, E {0. 1}. so the instantaneous state of the system is defined by the combination of neurons' states Vi,.., Vn. The initial value of the neuron states are all 0 as they not 'firing up" (Hopfield, 1982). The strength of one-way connection from neuron Vú and neuron V, is denoted as Tij where Tij is computed using equation
Tú = (2V, - 1)(2V, - 1) where Tú = O,
(4)
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and the Non-connected neurons have strength Tú O. The associative memory is stored in the format of patterns of states, and the connection strength Tip makes up the weight matrix T. The state of the system changes based on a pre-defined step-function at a given time t. The changes to a single neuron V, follows the step-function given the weight matrix elements Tíy and all the neurons that connects to the ncuron V
I if Ti) U, ( o T., <U;
(5)
where U3 is a predefined threshold. Recent research on the Hopfield network (Ramsauer et al., 2021) has successfully integrated Dense Associative Memorys (DAMs) into modern deep learning architectures. This study introduces innovative updating rules and energy functions, transitioning the traditional discrete Hopfield network into continuous framework. A key limitation of the original Hopfield network is its slow energy reduction during the pattern memorization phase. By utilizing rectified polynomial energy function, DAMs accelerates energy decay, enabling the storage of more memory patterns within the same configuration space.
Transformer-based Models Recent literature suggests that the Transformer architecture stores associative memorics in the form of outer products of finite-dimensional embeddings within the intermodiate weight matrices. Bielti el al. (2024) analyzes the transformer structures with an associative memory point of view. The authors construct synthetic bi-gram dataset and a two-layer, single-attention-beet head Transformer model. Through empirical analysis, the results demonstrate that the Transformer architecture employs the weight matrix at the output of the attention block a repository for associative memories. These associations enable the key-query matrices to direct attention to relevant tokens, thereby enhancing the model's inductive capabilities. The experiments also show how associations are learned through training dynamics and can be used to remap imput to output vectors. Jiang et al. (2024b) explores how LLMs use tokens within a given context as memory clues to retrieve memory patterns from their parameters, and how context can be leveraged to influence or "hijack" the output of LLMs.
Scaling Law of Associative Memory Cabannes et al. (2024) investigates the scaling law of the associative memory error rate in relation to model size and the number of data inputs, using simple Transformer-based model. The error rate of the model is bounded by the previously seen data and the model capacity.
{(a)) ~ a 1+
(6)
where d is the model capacity, and T is the number of training samples. The model is denoted fa. a is the hyper-parameter that represent the Zipf exponent of the assumed data distribution. Compared to the exploration of scaling laws in knowledge memorization, Cabannes et al. (2024) focus on the embedding dimension d as a mcasure of model capacity, whereas Kaplan et al. (2020) consider only non-embedding parameters. And in this formulation, the errors are quantified using the recall accuracy of previously presented factual inputs, while Kaplan et al. (2020) leverages cross-entrope loss. According to the equation, if the model possosses infinite memory, the error rate is constrained by the sample input size, as the model must learn sufficient associations to generalize the distribution. However, when memory is finite and the dataset size exceeds the memory capacity, the model attempls to remap new data into its memory space, which can result. in memory interference when two distinct input-output relations are mapped to the same location. The paper also discusses various memory schemes for storing associations. Niu ct al. 2024 conducted an empirical study on associative memory within the Transformer architecture and proposed a new energy function that, without introducing additional regularization terms, corresponds to a nearest-neighhor search over the memorized patterns.
Usage of Associative Memory Numerous studies have investigated the use of associative memory mechanism for storing patterns linked to past data points. For instance, CAMELOT (He et al., 2024c) int roduces an additional module within the original allention layer of a Transformer model to enhance its ability to handle longer context windows. The authors suggest that compressing past context or inputs into associative memory conserves memory by climinating rodundancy, thereby frecing up space for new content in fresh memory slots. This approach also facilitates the replacement of outdated memory slots with more recent inputs. Additionally, the authors show how modifications to the energy function and update rule enable the
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modern Hopfield network to approximate the architecture of Transformer models. Krotov (2021) proposed an extension of the modern Hopfield network by incorporating arbitrarily large number of recurrent layers, designed with a custom multi-layer recurrent model structure. Similarly, Millidge et al. (2022) proposed a gencralized Hopfield network that decomposes a series of models such as DAMs, Hopfield networks, and sparse distributed memories into a three-stage framework comprising similarity, separation, and projection. This paper demonstrates that these three components not only generalize existing models but also extend their functional capabilities.
2.2 Implicit Memory Modification
Modifying implicit memory (Wang et al., 2024c) in language models involves altering the knowlodge embedded within model's parameters enhance performance, reduce harmful outputs, and enable more efficient adaptation to new tasks. Focusing on methods for updating or removing knowledge without incurring the high costs of full retraining, as discussed in (Dai et al., 2021), we categorize memory modification into three main categories: incremental training, memory editing, and memory unlearning, as illustrated in Figure Incremental training represents adding new knowledge into LLMs while building on the pro-cxisting information within them. Memory editing adjusts the embedded knowledge by modifying specific memory representations within LLMs. And memory unlearning eliminates incorrect harmful internal knowledge from LLMs, thereby enhancing their reliability and trustworthiness. These approaches are intended to create more adaptable and efficient models, capable of rapidly incorporating new information while retaining consistent performance across various tasks. Techniques like dynamic updates, hyper-networks, and targeted unlearning play essential roles in enhancing the adaptability and dependability of LLMs for worl-world applications.
Query:
Who is the winner of 2024 United States presidential election?
Directll modifing the original parameters
Original Model Resporse
Modified Model Response
The 2024 United States presidential clection has not yet taken place, so there is currently no winner. The election is scheduled to held on November 5, 2024.
(a)
DR
Donald J. Trump is the projected winner of the 2024 U.S. presidential election.
LLM
Adepter
Addling knowlsdge through adepters
Incremental Training
Memory Editing
Resporse
Modified Model Response
The 2024 United States presidential election has taken place, so there is currently winner. The election is scheduled to be held November 2024.
Edited
The 2024 United States presidential election has not yet taken place, so there is currently winner. The election is scheduled to be held December 5, 2024.
(b)
Sapee
Layer
Memory Editing
Original Model Response
Modified Model Responee
The 2024 United Stutes presidential election has not yet taken place, so currently
Weights about query LLM
The 2024 United States presidential election has not yet taken place, so there is currently no winner. And there is no Information about it.
(c)
winner. The-eloction-ies scheduled-fe fo-----e-----eee November 20ovember-5522244
Weights
Figure Three categories of Implicit Memory Modification
2.2.1 Modification Methods
Incremental Training Incremental training in LLMs involves not only adding new knowledge but also
ensuring consistency and alignment with the model's existing knowledge basc. This process typically follows two main approaches: directly modifying the original parameters of the LLMs and adding knowledge through adapters, which store new memory separately.
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Directly modifying the original parameters. Zhu et al. (2020) proposes constrained fine-tuning method that selectively updates a subset of parameters to integrate new information efficiently. Expanding on Zhu et al. (2020), Padmanabhan et al. (2023) introduces context distillation to update knowledge using entity-specific texts while preserving the original LLMs distribution measured by KL-divergence. TRIME (Zhong et al., 2022b) incorporates in-batch examples as accessible memory during training. It. improves performance by effectively leveraging local, long-term, and external memory with minimal computational overhead, achieving significant reductions in perplexity across various benchmarks. Another method, RECKONING (Chen et al., 2024c) encodes context-based knowledge into model parameters via bi-level learning process, which consists of two loops: the inner loop adapts the model to memorize facts, while the outer loop uses the updated parameters to answer reasoning questions.
Adding knowledge through adapters. Adapters provide a flexible means of incorporating new knowledge by leaving the original model parameters untouched. LoRA (Hu et al., 2022) uses low-rank decomposition matrices for targeted updates while maintaining the model's core structure. K-ADAPTER (Wang al., 2021b) adds separate adapters for different knowledge lypes in models like RoBERTa. DEMIX (Gururangan et al., 2022) introduces domain-specific expert networks, training only the relevant expert for new knowledge. These methods balance memory augmentation and model stability, making language models more adaptable and resource-effeienn.
Memory Editing The objective of knowledge editing is to incorporate new facts into a language model Mg through query-response pairs D. {q; x; |VE[INN. In this setup, q is the query that triggers the retrieval of factual knowledge from Ma, such as 'The president of the US is', and x is the intended response after editing, e.g.. 'Joe Biden". This integration is typically achieved by maximizing the probability of generating x² based on q, which can be expressed as:
max (1))q) where (q,x*) ~ De
Certain knowledge editing methods first identify the implicit memory within large language models (LLMs)) that corresponds to the targeted knowledge, and then modify the knowledge stored in the model's weights. ROME (Meng et al., 2022a) and MEMIT (Meng et al., 2022b) use the causal trace method to identify the regions in LLMs where relevant knowledge about triplct relations is stored. These methods treat the middle-layer MLPs of GPT models as associative memory structures with key-value associations, and modify the weights of these layers to insert new associations. Dong el al. (2022) propose CKA, a method to detect. incorrect knowledge in PLMs. CKA compares the model's scoring of correct facts against fake facts to determine if the model has accurately learned the facts. It also introduces method called CALINET, which adds new parameters to the model while keeping the original parameters fixed, allowing it to learn the correct facts without overwriting other knowledge. KE (Cao et al., 2021) edits knowledge by employing a hyper-network to update the parameters of origin LLMs, so that LLMs can predict expected predictions for different inputs without affecting the prediction of any other imput. Similarly, SLAG (Hase et al., 2023) utilizes a learnable hyper-network. This hyper-network takes model gradients as input and outputs new updates to be applied to the model parameters. Recently, the LAW (Wang et al,, 2024e) method modifies specific MLP layer weights in language model by adjusting the internal "key" and "values" associated with targeted knowledge, effectively disrupting the model's representation of that knowledge while preserving its reasoning abilitios. FT-M (Zhang et al., 2024c) finc-tunes specific layers of the feod-forward network to maximize the probability of all tokens in the target sequence.
Memory Unlearning Knowledge unlearning can be described as follows: Given a training set D = ((a, w)} for the language model Ma, where z represents the input and y represents the corresponding label, we define Dj as the set of harmful and dangerous knowledge that aim to forget, and D, as the set of data we wish to retain. The goal of knowledge unlcarning is to enable Mg to remove all information from D, while preserving performance on D,, which means:
max dist (Mo(D)):M((1)) and min dist (M,(D)))M 8
(8)
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Table 1: Comparison of Memory Editing Benchmarks Based on Different Dimensions.
Dimension
KnowEdi Beroowdit(ZZaaree (Zhang al., 2024c)
MQuAKE(Zhong et al., 2023)
Eva-KELLM et al., 2023a)
knowledge insertion, modification, and erasure
Counterfactual and temporal updates
Supports multilingual data
X
X
High-quality, validated datasets
Multi-task editing capabilities
Includes reasoning tasks
x
Emphasizes cross-lingual performance
Handles multi-hop reasoning
Suitable for dynamic system updates
Elficient evaluation time
Knowledge unlearning methods in LLMs can be categorized into distinct approaches based on their primary goals and techniques. Some methods, like benchmark KnowUnDo (Tian et al., 2024) and proposed method MemFlle (Tian et al., 2024) focus precision unlearning, using targeted gradient manipulation to selectively remove sensitive information while preserving essential knowledge. In contrast, frameworks like SKU (Liu et al., 2024c) employ two-stage approach, acquiring and systematically negating harmful knowledge to ensure safe responses while maintaining performance on benign tasks. Additionally, efficiency-focusel methods, the Surgery framework (Veldanda et al., 2024) efficiently updates LLMs by unlearning outdated knowledge, integrating new information, and retaining performance on unchanged tasks using a three-part objective: reverse gradient for unlearning, gradient descent for updating, and KT. divergence minimization for consistency. Lastly, solutions like EUL (Chen & Yang, 2023) prioritize selective and scalable unlcarning by employing lightweight layers, facilitating ilerative knowledge removal without impacling the model's overall capabilities, suitable for repeated applications where selective knowledge removal is essential.
2.2.2 Modification Benchmark
Memory Editing Benchmark Many memory editing Benchnarks(Wang el al., 2023d; Cohen et al., 2024; Khandelval et al., 2024) primarily focus on editing accuracy by constructing datasets designed to evaluate whether models can produce counterfactual responses when queried about specific factual knowledge. (nowadit(Zhang et al., 2024c), for instance, includes a suite of six datasets tailored for assessing various knowledge editing methods. These datasets cover diverse range of editing types, such as fact manipulation, sentiment alteration, and hallucination generation. This benchmark consolidates key evaluation criteria into four categories: edit success, portability, locality, and fluency, thereby providing comprehensive evaluation framework for different. editing approaches. MQuAKF((Zong et al., 2023) offers distinct perspective by evaluating whether edited models can answer multi-hop questions where the answer should logically change an entailed consequence, revealing the limitations of prior methods for such questions. Eva-KELLM et al., 2023a) expands the scope of knowledge editing evaluation to a more general scenario where raw documents within datasets are directly utilized for editing. This benchmark offers greater generality and practical relevance, allowing for the asscssment of various knowledge editing methods' performance in a multilingual context. The comparison of these three benchmarks be in table
2.3 Limitations, Open Questions, Discussion
Current research in implicit memory often faces several key challenges:
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Generalization of findings: A significant limitation of many studies is that they focus primarily knowledge memorization and extraction within the confines of specific tasks (e-g., relation triples prediction) or particular types of knowledge (such as facts, commonsense, or bias-related knowledge). This narrow focus does not establish the generalizability or broader applicability of the conclusions drawn.
Efficiency of probing methods: Research on knowledge circuit exploration is often hindered by the time-consuming nature conducting numerous sumponen-to--compnneee ablations. This complexity can significantly slow down the process systematically investigating model behavior and knowledge extractions.
Risk of knowlodge unlcarning: While knowledge unlearning aims to remove unwanted or harmful information, it carries the potential risk inadvertently disrupting related knowledge. This disruption can lead unintended consequences and degraded performance in downstream tasks. Consequently, more comprehensive evaluations of models subjected to knowledge unlearning are needed to fully understand these effects.
Looking ahead, future research should focus on gaining a deeper understanding of the internal mechanisms of Transformer models and exploring the development of more effective and efficient computational frameworks for implicit memory modeling.
3 Explicit Memory: When (M)LLMs Meet Retrieval
In this work, we refer to explicit memory specific, structured, or unstructured representations that store factual knowledge, history trajectories in an external storage.
Explicit memory allows the retriever to dynamically capture context-aware information and enables the generator to adaptively incorporate knowledge from external memory, thus improving the quality of generated outputs. It facilitates the retention and retrieval of information across sessions, ensuring continuity and enhancing the model's capacity to handle long contexts without exceeding its input limitations. Explicit memory plays a crucial role providing flexibility and enhancing interpretability, especially in interactive, knowledge-intensive,, and rapidly evolving domains.
In this section, focus the research question:
How is the explicit memory represented and utilized in different training and application scenarios?
We will discuss how different types of memory externally represented and stored (83.1) the interac- tion and updating of external knowledge during training ($3.2), and how to externalize implicit knowledge for retrieval in typical scenarios (53.3).
3.1
Explicit Memory Representation
3.1.1
Free text
LLMs are usually trained on free-form text, depending on target tasks. The free-form text is represented and stored at different levels of granularity shown in Fig. 6:
Document As the largest memory unit, document retains rich contextual information, which is crucial for understanding the overarching themes (Ren et al., 2023a). Commonly employed retrieval methods can be broadly calegorized into sparse retriever and dense retriever. Spanse retrieval, exemplified by traditional search engines and the BM25 algorithm, relies on exact matches and TF-IDF weighting to retrieve lexically relevant documents (Asai et al., 2024; Ma et al., 2023). In contrast, dense retricval focuses on semantic similarity (Shi et al., 2024; Melz, 2023) and is typically implemented using tools such as FAISS (Johnson et al., 2021) and pre-trained encoders. Once retrieved, this supplementary knowledge is concatenated with the
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Free text
PRCA (Yang et al., 2023), PaperQA (L'ala et al., 2023), McmPrompt (Madaan al., 2022a), PGRA (Guo et al., 2023b), ARM- RAG (Melz, 2023), LFQA (Chen al., 2023), Chain-of-Note (Yu al., 2023b), TAG (Zhang et al., 2023e), RetrievalML (Xu et al., 2023), RA-DIT (Lin et al., 2023), RAPTOR (Sarthi et al., 2024)
Explicit Memory
Graph
ISEEQ (Gaur et al., 2021), G-Retriever (He et al., 2024b), KnowledGPP (Wang et al., 2023f), FABULA (Ranade & Joshi, 2023), HyKGE (Jiang et al., 2023), KALMV (Baek et al., 2023), ProAT (Wu et al,, 2025c), PTP. KAG (Huang et al., 2025), HippoRag (Gutiér- rez et al., 2025), PGMR (Sharma et al., 2025)
Reprosentation
RoG (Luo et al., 2023)
Vector
LlamaIndex (Liu, 2022), LangChain (Chase, 2022). POEM (Do al., 2024), ARM- RAG (Melz, 2023), Ketrieve-and-Sample (Ren et al., 2023b), LOCOMO (Maharana et al., 2024)
RAG-based pretraining
REALM (Guu et al., 2020), RETRO (Borgeaud et al., 2022), RETRO++ (Wang et al., 2023a), Instruc- tRetro (Wang et al., 2024a), Memory3 (Yang et al., 2024a), At- las (Izacard et al., 2023), SANTA (Li et al., 2023b), CoG (Lan et al., 2023)
Learning with explicit memory
Retrieval- based training
RAG-based finetuning
RAG (Lewis al., 2020), Self-RAG (Asai al., 2024), FiD (Izac- ard Grave, 2021), REPLUG (Shi et al., 2024), SURGE (Kang al., 2023), Up- rise (Cheng et al., 2023), SMA (Zhang et al., 2025),
Memory Learning
Actor-Rcasoner (Fang et al., 2025b) ITER-
RETGEN (Shao et al., 2023)
Long contexts
Mem TRM (Wu al., 2022b), Unlimi- former (Bertsch et al.,
2023), FoT (Tworkowski
al., 2023), Ep-
Applications
MAN (Chaudhury al,, 2025), R3Men (Wang al.,
2025), LongMem (Wang
et al., 2023c)
Knowledge injection
TRIME (Zhong et al., 2022a), Lift Your- self Up (Cheng et al.,
2024), Faithful Reason- ing (Creswell & Shanahan, 2022)
Figure Taxonomy of the structure design for learning with explicit memory.
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Explicit Memory
Document
FREE TEXT
split
Chunk
split
Memory Retrieval
Sentence
Encoding
Embdeddings
Vector DB
Query
VECTOR
Encoding
Noder node ncor) éidees (td, edte
Sub-graph
Reasoning paths
GRAPH Scene Graph Knowledge Graph
split
(Rond, reiation, cazl)
Triple
Figure 6: Three types of representations for explicit memory retrieval
question input to the model. However, overly-long lexts can introduce noise (Yang et al., 2023; Chen et al., 2023; Cuconasu et al., 2024; Zhang et al., 2023e), potentially obscuring specific facts (Trivedi et al., 2022; Yu et al., 2023b). Morcover, using entire documents as input increases inference time and computational costs for LLMs. Due limited context window size in LLMs, excessively long lexts will be truncated, risking the omission of critical data.
Chunk Dividing text into fixed-size chunks elfectively reduces redundancy and strengthens the connection between retrieved memory and the query (Wang et al., 2024a; 2023a; Lin et al., 2023). There can be multiple chunks relevant to the query, but using all of them is usually infeasible due to the context length limit. To address this, RetrievalML (Xu et al., 2023) selects the top k chunks based on relevance, determining the optimal k value through experimentation. In contrast, PaperQA (L/ala et al., 2023) employs L.L--generteed relevance scores between the chunks and the query for more refined filtering. To preserve textual coherence, RAPTOR (Sarthi et al., 2024) clusters the chunks and generates summarics for each cluster. While chunking may disrupt semantic continuity and omit details within chunks, offers a practical balance between preserving semantic integrity and enabling LLMs to comprehend specific details.
Sentence A sentence-level memory unit (Cheng et al., 2024) enables the capture of specific facts (Wang et al., 2023i) and details but tends to overlook comnections between sentences, potentially leading to discontinuities in understanding. Thus, sentenco-level segmentation is typically employed in scenarios requiring a detailed grasp of individual sentences' meanings, such as sentiment analysis (Guo et al., 2023b) and knowledge oditing (Zhong al.). In addition, MemPrompt (Madaan et al., 2022a) uses a sentence to assist the model understanding specific tasks.
3.1.2 Graph
Graph-based data organization employs nodes and edges to structure knowledge systematically. Nodes represent distinct units of information, which can range from words and sentences to entire paragraphs. Edges, on the other hand, signify the relationships between these units, illustrating how they are interconnected.. This structured approach to data organization proves particularly advantageous for tasks requiring advanced
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reasoning capabilities. For instance, reasoning through graphs enables flexible transitions between different reasoning paths, accommodating both logical deduction and multi-hop reasoning. Multi-hop reasoning involves traversing multiple interconnected nodes to infer complex relationships or derive conclusions that span diverse pieces of information. By leveraging this graph-based structure, one can achieve more efficient and nuanced reasoning processes. In a recent. development, HippoRAG (Gutiérrez el al., 2025) incorporates the Personalized PageRank algorithm along with the inherent ability of an LLM to automatically build a knowledge graph, thereby enhancing the retrieval process with multi-hop reasoning capabilities.
Sub-graph Sub-graphs represent specific portions of the overall graph that are most closely related to the query (Ranade & Joshi, 2023). The nodes in sub-graph may represent entities, sentences, paragraphs. G-Retriever (He et al., 2024b) takes textual graph describing nodes and edges as part of the input to use the structured information in the graph. HyKGE (Jiang et al., 2023) searches for sub-graphs based on the entities mentioned in the query and extracts the reasoning path to improve LLM's reasoning ability. The primary challenge of using mab-graphs lies in the high costs of constructing and maintaining sub-graphs.
Triple Triples are the fundamental units of graphs, representing entities and their relatiouships a structured format (Wang et al., 2023f; Kang et al., 2023). They provide fine- fine-grained knowledge but are limited by their fragmented nature, i.e., triple only involves two out of a large number of entities, which may hinder the expression of continuous semantic information unless enough triples are retrieved. To use this precise memory, ISEEQ (Gaur et al., 2021) jinserts triple facts into the appropriate position of the original query. KnowledGPT (Wang et al., 2023f) goes a step further and adds additional descriptions of the entities. Because this type of memory is concise and explicit, the retrieved irrelevant triples will directly affect the correctness of the generated content. Therefore, KALMV (Back et al., 2023) proposes a detection method for this problem. Besides, PGMR (Sharma et al., 2025), a new modular architecture featuring a non-parametric memory retriever module for managing KG elements, thereby enhancing the accuracy and reliability of SPARQL queries generated by LLMs.
3.1.3 Vector
Vectors (Liu, 2022; Chong et al., 2024; Chase, 2022; Yang ct al., 2024a; Borgeaud et al., 2022; Do ct al., 2024) characterize rich semantics and thus facilitate improved contextual understanding of LLMs.
Original text is segmented into smaller fragments that are then converted into vector representations through encoding model and archived in vector-based knowledge base. Typically given a query, recent works (Melz, 2023; Ren et al., 2023b; Maharana et al., 2024; Lin et al., 2023) retrieve relevant knowledge from the memory by computing the similarity between the query and the stored embeddings. The top K fragments with the highest similarity will be used as part of the prompt to improve the LLMs' understanding of the query.
As a memory format, vectors offer three advantages over free text and graph-based representations:
Robust semantic undorstanding Vectors represent words, sentences, or even graph nodes in a continuous space where semantically similar entities are closer to each other. However, semantie relationships among free texts entities in the graph are not apparently captured and need to be further inferred.
Scalability and Flexibility Vectors can handle large-scale data with more efficient indexing, quantiza- tion, and batch processing, circumventing the limitations of simple keyword matching. The other two represent in atinns requires heavy pre-processing and more storage space suffering from higher complexity. Generalization and Transfer Learning Vectors capture underlying semantic similarity and can generalize to different tasks and domains with relatively little retraining. In addition, is capable of processing multi-modal data, such as images and audio. Free-form text is typically lask-specifie, requiring manually crafted features, while graph structures are usually associated with specific topologies and require corresponding graphical algorithms.
3.2 Training with Explicit Memory
Training on explicit memory enables the model to efficiently utilize external memory by retrieving, adapting, and refining it, avoiding the need to reprocess the entire dataset. Tt involves learning how interact, organize,
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and integrate well-structured memory representations during training rather than relying solely retrieval based on memory similarity structural matching, which can lead to redundancy.
There are several key advantages compared to training-free free retrieval. Firstly, it optimizes retrieval relevance and generation accuracy by training each module for more context-sensitive use and alignment. Additionally, the training process enables LLMs to perform more sophisticated inferences by integrating external past. experiences with the model's inherent reasoning capability. Specifically, for scenarios with long context that require consistency accuracy across multiple turns of interactions, it ensures consistent responses over successive queries and adapt to rapidly evolving information user-specific scenarios.
In this subsection, we distinguish two critical training phases: pre-training ($3.2.1) and fine-tuning ($3.2.2). We aim to explain how both slages contribute to the enhanced performance of models equipped with explicit memory systems. Besides, we introduce a few works to address the following three main challenges: i) high computational costs of real-time encoding of largo-scale retrieved texts; ii) lack of interpretability in retrieval during training and inference; iii) poor performance in retrieval and generation when training autonomously. Tree typical pipelines for training with explicit memory can be seen in Fig. 7.
(a)
Retriever
Encoder
Generator
Output
Generator Likelihood G
hI..
DKL (PI[G)
Retriever Likellhood
(b)
Attention
Retriever
Encoder
V
Cross
Output
Generator
(c)
Encoder
Retriever
Generator
Output
Memory update
Trainable
Critic
Frozen
Figure 7: Three typical pipelines for training with explicit memory.
3.2.1 Pre- Training
Pre-training involves training LLM from amounts of diverse text data by predicting the next token. The goal is to obtain an LLM with comprehensive understanding of human languages and general task-solving ability, thereby establishing robust foundation for subsequent wowledge-intensiig tasks and enhancing their retrieval capabilities.
Unsupervised Memory Retricval REALM (Guu et al., 2020) proposed an approach to knowledge storage, successfully pre-training a knowledge retriever in an unsupervised manner for the first time. Specif- ically, it achieves end-to-end training by modeling both the retrieval and prediction processes. This work demonstratod significant performance improvements in open-domain question-answering tasks, introducing the first unsupervised pre-training method for knowledge retriever using masked language modeling and backpropagation.
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Advances in Retrieval Integration RETRO (Borgeaud et al., 2022), unlike REALM, directly appends retrieved content to the prompt and integrates retrieved data via chunked cross-attention. RETRO employs a pre-trained BERT as the retriever and freezes it during pre-training. This approach significantly enhances the memory capacity of LLM without increasing computational overhead, cnabling the efficient integration of external knowledge al. a much larger scale. Subsequently, RETRO++ (Wang et al., 2023a) analyzes RETRO and GPT models, illustrating the advantages of retrieval-augmented architectures in text generation and zero-shot knoteevggeintensiive tasks. Building on these insights, InstructRetro (Wang al., 2024a) applied the RETRO framework to pre-training and instruction-tuiing of GPT, resulting in improved accuracy and generalizability on complex, knowledge-intensive tasks.
Innovations in Knowledge-Intens Intensive Pre-Training Memory² (Yang et al., 2024a) leverages a two- stage pre-training strategy by selectively storing key-value pairs with lower read and write costs. Memory" demonstrated potential to enhance both the efficiency and performance of L.M across various tasks. Additionally, various pre-training methodologies have been proposed address other challenges in LLM development. For example, Atlas (Izacard al., 2023) employed a dual-encoder architecture for dense retrieval, combined with sequence-to-sequene model, then used joint pre-training to better integrate retrieved documents for knowledge-intensive tasks. SANTA (Li et al., 2023b) utilized structured data aligmment and masked entity prediction as pre-training techniques, achieving state-of-the-ard performance in code and product search tasks. To maintain coherence and diversity in generation, CoG (Lan et al., 2023) generates text progressively copying fragments from existing corpora, outperforming traditional and retrioval-angmonneed models across multiple evaluations.
3.2.2 Fine- Tuning
Fine-tuning on the explicit memory allows the model to be specialized with lask-specific or domain-specific knowledge, ensuring that it can handle nuanced information retrieval in targeted applications and scenarios.
A notable work is the Retrioval-Angmented Generation (RAG) model proposed by Lewis et al. (2020), which combines parametric memory from a pre-trained seq2seq model with non-parametric memory, such as a dense vector index of Wikipedia. By jointly finctuning the retriever and generator, RAG effectively learns to retrieve relevant information and condition its output on external knowledge, significantly improving factuality, diversity, and specificity in generated responses. This finetuning strategy has led to substantial performance improvements across knowledge-intensive tasks, such as open-domain question answering and fact verification.
Autonomous Memory Retrieval Building on RAG, Self-RAG (Asai et al., 2024) introduced a self- reflection mechanism that further enhances retrieval and generation processes. During training, a critic model generates reflection tokens, which are inserted into the training data. These tokens help the generator learn when and how to retrieve relevant information more intelligently. By finetuning this self-reflective system, Self-RAG shows significant advantages in tasks that require factual verification, reasoning, and long-text generation, allowing the model to be more efficient and accurate in deciding when retrieval is necessary and how to use the retrieved content effectively. Besides, SMA (Zhang et al., 2025)introduces self-memory alignment to enhance the generalization of L.MS and balance trade-offs between different capabilities. Specifically, it fine-tunes the model on self-generated responses to precise, simple factual questions using preference optimization. Extensive exporiments demonstrate that SMA significantly improves the overall performance of LLMs, consistently enhancing factual accuracy, helpfulness, and comprehensive skills across various benchmarks.
Context-Aware and Task-Driven Memory Retrieval To further improve task-specific performance, UPRISE (Cheng el al., 2023) finetunes a light prompt retriever to automatically retrieve prompts tailored to specific inputs, improving LLMs' zero-shot capabilities of long-form context-aware dialogue generation, SURGE (Kang et al., 2023) leverages subgraph retricval through graph-text contrastive learning. By finetuning both the subgraph retriever and the generator, SURGE enhances the model's ability to handle complex, structured data within dialogues.
For
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Efficient Large-Scale Memory Retrieval As processing large amounts of retrieved text passages increase computational costs, methods such as Pusion-in-Decoderr (FiD) (Izacard & Grave, 2021) have been proposed to address these challenges, FiD processes each retrieved passage independently in the encoder while jointly processing them in the decoder, thus optimizing computational efficioncy. Similarly, REPLUG (Shi el al., 2024) introduces a ret -augmeeee approach that integrates relevant documents with inpul context. without modifying the internal parameters of the LLM. This reduces the computational burden of finctuning large models (e.g., 405B parameters) by minimizing the KL divergence between retrieval likelihood and the model's output perplexity.
3.3 Training with externalized parameteric knowledge
Externalized Parametric Knowledge effectively extracts and externally stores portions of the model's internal knowledge or its own intermediate outputs in a structured and accessible format.
It is particularly useful in lasks that involve processing long documents((3.3.1 and Knowledge in- (ection(33.3.2), where the model's internal memory is insufficient for handling the entire context. It can retrieve the information as required, allowing for the efficient handling of extended contexts while maintaining coherence and accuracy throughout the task. This mechanism serves as a bridge between the model's ability to handle inherent knowledge and ability to maintain and process large volumes of external information over time, avoiding the limitations of using internal memory alone. This method provides additional flexibilityy in accessing and utilizing information, ensuring that when the model's internal memory is not sufficient for given task, explicit external storage comes to its rescue.
3.3.1 Long Contexts
The traditional Transformer architecture faces significant challenges in capturing long-range dependencies due to the limited context length imposed by the attention mechanism (Li et al., 2023a; Wu et al., 2025a). Yet, many tasks require models to process distant information, which is often crucial for accurate predictions. To address this limitation, Wu al. (2022b) introduced MemTRR, language model designed to memorize reprosentations of previous inputs. McmTRM stores key-value pairs of past inputs and uses approximate k- nearest neighbor (kNN) search lo extend the model's effective allention span. It demonst rates that MemTRM significantly improves performance across various tasks by expanding the model's attention context.
Building on this concopt, several works take different approaches to extending the attention span. One typical framework leveraging external memory for long context can be seen in Fig. One such model is Unlimiformer (Bertsch et al., 2023), which offloads coss-attention computation to kNN index. Unlike McmTRM, Unlimiformer is fully non-parametric, requiring no fine-tuning, and allows each attention head in every decoder layer to focus only on its top-k keys. This attention reconstruction capability enables Unlimiformer to perform personalized retrieval in each layer while maintaining greater efficiency than MemTRM. Another related work is FoT (Tworkowski et al., 2023), which extends the model's context length through fin-tuning rather than modifying the architecture. FoT has shown significant promise in enhancing the ability of LLM to handle long-text tasks effectively. recent work FpMAN (Chaudhury al., 2025) proposes an architecture combining episodic memory attention with sclf-attention during LLM training for robust long context performance.
Despite the advancements of MemTRM and its derivatives, the coupled memory design in MemTRM presents challenge: the cached representations of past inputs may diverge from the current model represcntations as model parameters are updated. This distribution shift limits the effectiveness of memory-augmenteer models over time. To address this issue, LongMem (Wang et al., 2023e) decouples the network architecture by freezing the original LLM as a memory encoder and introducing an adaptive residual side network to act as the memory retriever and reader. This decoupling not only mitigates the issues caused by distribution shifts but also demonstrates superior performance in long-text processing and contextual learning tasks.
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LLM Decoder Layer
Retrieved K neighbors
Memory fusion
MemoryAugment Layer
Cross Attention
Retrieve
External Memory
LLM Decoder Layer
Recent cached M (key.value) pairs
Embedding Layer
Current input long sequence
Figure 8: External memory training for long context.
3.3.2 Knowledge Injection
There are other research works leverages knowledge provided as part of the context. from memory retrieval for knowledge injection and augmentation. These works aim to reason more robustly by folding the provided contextual knowledge into the model's parameters and complete the downstream tasks by using the updated parameters.
TRIME (Zhong et al., 2022a) utilizes a contrastive learning objective that aligns the hidden representation of a token with both token embeddings and a set of in-batch contextualized representations. The method introduces new strategies for memory construction and data batching to adapt to different memory types at testing time, which allows for back propagation to all memory representations. Similarly with various memory types, Yogatama et al. (2021) introduces adaptive semi-parametric language model (SPALM) that integrates non-paramelric episodic memory component with extended short-term context through cached local hidden states and global long-term memory by retrieving nearest neighbor tokens at each timestep. A gating function is designed to adaptively combine information from local context, short-term memory, and long-term memory.
There are other works encoding text of different levels of granularity into embedding for retrieval. TOME (De Jong al., 2021) integrates a semi-parametrie representation into its architecture as source of factual knowledge through 'mention memory'. It maintains table of dense vector representations for every entity mentioned in corpus, allowing TOME to retrieve and assimilate information from multiple sources making it more scalable and efficient compared. Open Predicate Query Language (OPQL) (Sun et al., 2021) uses a dual-encoder pre-training process to encode relation mentions, which can be integrated into a language model (OPQL-LM) to improve performance on open-domain question answering. Unlike previous mothods that rely on distant supervision from a knowledge base, OPQL is a mothod for constructing virtual knowledge base from text without any structured supervision.
3.4 Limitations, Open Questions, Discussion
To continuously Iearn, adapt, and improve the ability of LLMs to learn with explicit memory, we identify the following open questions and encourage future research to address them.
Can RAG solve the limitation of long context? Enhancing the long context capabilities of LLMs is crucial in downstream tasks. There are currently two main approaches when handling longer sequences: extending an LMs context. length or RAG. Training LLMs with extended context windows allows them to process long inputs continuousl without truncation, thus improving their attention span. However, this also incurs higher computational costs and memory requirements. Methods using RAG dynamically retrieve concise, relevant documents based on the query, without the need store the entire input. Recent studies have positioned LLMMs as promising paradigm for time-series analytics and spatio-temporal data science
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Liu et al. (2025a;b); Liang et al. (2025b). Both domains are characterized by extremely long historical sequences, which can impose substantial computational and storage burdens. The integration of explicit memory mechanisms offers a potential solution by mitigating the need to store the entire historical context, thereby enhancing efficiency in these applications. Further research needed to develop mechanisms beyond simple semantic matching, which can create a more robust and dynamic memory system while avoiding memory overload.
A deeper analysis of potential applications for each method, and how they can be integrated into unified and flexible system, offers a promising direction for future research.
When and how to retrieve more intelligently and autonomously The development of more intelligent and autonomous memory retrieval mechanisms is crucial for optimizing tasks such as retrieval, reasoning and content generation. This enables the model to efficiently decide when retrieval is necessary and how to integrate the retrieved memory scamlessly for improved performance. An intelligent retricval process is crucial for adaptively absorbing uantextual information from external memory based on task-specific needs, especially for tasks requiring complex interactions with various data sources.
How to enhance retrieval-based training to avoid hallucination and contamination Incorporating explicit memory for knowledge injection during training allows LLMs to access relevant information more precisely and adaptively, rather than relying solely on parametric knowledge or context provided at generation time. However, LLMs may encounter memory contamination, where irrelevant or incorrect information is unintentionally stored during the learning process. A promising direction for future research is the design of selective memory mechanisms that filter out. irrelevant details while retaining crucial information, thus roducing model hallucinations and proventing memory contamination.
In addition to these broad research topics, there are several engineering challenges that need to be addressed. For example, developing a retrieval method that maintains consistency and coherence between external memory and the implicit memory learned proviously, without disruption over time. Incorporating large-scale retrieval-uugmmntedd models during training presents a significant challenge. The retriever must. consider millions of candidate documents and backpropagate, which requires substantial computational rosources. This presents important direction for future work: optimizing the model structure, storage, and document selection to reduce computational burden and enhance scalability and efficiency.
4 Agentic Memory: Consolidating Memories into Humanic Agents
In the study of cognitive science, memory encompasses the cognitive processes involved in encoding, storing, and retrieving information. According to the Atkinson-Shiffrin three-stage memory model (Atkinson, 1968), information in the human brain progresses through three distinct stages: it initially enters sensory memory, then transitions to short-term memory, and ultimately consolidates into long-term memory, illustrated in Figure 9.
LLM agents are artificial intelligence systems that understand and generate human-like text within real-world environments. These agents are capable of performing tasks such as answering questions and engaging conversations. One of the central functionalities of LLM agents is their memory system, which mirrors the structure and processes of human memory. With memory modules, LLM agents can accumulate experiences, adapt continuously, and exhibit consistent, rational, and effective behaviors. Specifically, the memory modules in LLM agents facilitate the retention of past interactions and knowledge, enabling the agents to reference previous information and improve their performance over time. In this context, we categorize the memory systems of LLM agents in a manner analogous to human memory, as follows:
Sensory Memory is the initial stage of memory, responsible for briefly retaining sensory information. It includes iconic (visual) memory, echoic (auditory) memory, and haptic (touch) memory. In the context of LLM agents, we regard Sensory Memory as the data ingestion pipeline AI systems, such as DataLoader, and will not discuss it in detail.
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Short-term Memory (STM) temporarily stores the information currently in awareness, as well as information necessary for complex cognitive tasks such learning and reasoning". For LLM agents, STM refers to the information maintained in the context window during in-context learning, which is thus constrained by the limited context window length of the Transformer architecture.
"Typically storcs about 7 items, with duration of approximately 20-30 scconds.
Long-term Memory (LTM) stores information for long periods in human cognition". One type of LTM is declarative memory of facts and events, which can be consciously recalled. Another lype is procodural memory including unconscious skills. For LLM agents, LTM serves as an external storage system, accessible to the agent during queries through efficient retrieval mechanisms..
"Typically with virtually unlimited capacity, lasting from days to decades
Although Zhang et al. (2024d) has examined the reasons, contents, and methods of storing memories in LLM agents, a comprehensive categorization of memory usage approaches based on an analogy to human cognitive processes, along with a review of engineering- memory systems for agents available on the market, remains absent.
In this section, will examine: (1) how an LLM agent's 's memory system operates across both short-term and long-term contexts ($ 4.1), drawing inspiration from human cognitive processes; (2) the mechanisms through which multiple agents share memories ($ 4.2); (3) the pipeline for data ingestion, storing, indexing, and application within agent memory systems ($ 4.3); and (4) methodologies for evaluating the effectiveness of these memory mechanisms ($ 4.4).
4.1 Single-agent Memory
Recently, several studies have been conducted augment LLM agents with non-parametric external memory (Mai al., 2023; Maharana et al., 2024; Yang et al., 2024a). This enhancement improves the agent's 's ability to explicitly store, retrieve, and utilize memory for various tasks. These works generally consist of two key stages: (1) recalling relevant thoughts from memory before generating response, and (2) post-thinking after generating a response to incorporate both historical and new thoughts into memory, thereby improving consistency and efficiency.
In this subsection, we synthesize a wealth of rescarch dedicated to leveraging external memory using refined and optimized RAG methods inference time for the LLM agent on better downstream tasks.
4.1.1 Short-term Memory
Short-term memory serves transient storage system within LLMs, typically implemented by maintaining recent imputs within the context window. Due to the limited length of the context window, imput history prior to certain time point is inevitably discarded. Nevertheless, short-term memory enhances the continuity and consistency of LLMs and provides significant benefits in many challenging tasks, such as multi-hop reasoning and sequential makin...
Chain-of-Thought (CoT) (Wei et al., 2022) prompting explicitly asks LLMs to generate intermediate reasoning steps that lead the final answer. These intermediate steps (also known as thoughts) are stored in the context of LLMs, acting as their short-term memory and stimulating logical reasoning. COT-SC (Wang et al,, 2022b) is extension of the CoT framework that introduces decoding strategy called self-consistency to enhance complex reasoning performance in language models by sampling diverse reasoning paths and selecting the most consistent answer. Unlike CoT, where reasoning follows a linear-chain structure, Tree of Thoughts (ToT) (Yao et al., 2024a) prompting solves problems by considering multiple reasoning paths organized in a tree structure. This tree-structured reasoning facilitates efficient forward-looking and problem-solving Building on CoT
backward-tracking which are crucial elements of advanced and ToT, Graph of Thoughts (GoT) (Besta et al., 2024) prompting enables reasoning over graph
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1. The user once watched Ice Age. Published in Transactions on Machine Learning Research (11/2025)
Chat History
Interaction
Human
Feedback
Ask
Long-term Memory
Agent
(facts, rules, experience) $4.1.2
Induction Reflection
Policy
Filtering. Summary, Storage
Reasoning
2. The user going to ski tomorrow.
Planning
Persistence
Retrieval
Forgetting
Short-term Memory
Decision making
(contexts, trajectory) $4.1.1
Chat with the user about the skiing in an world.
The watched movie called Ice Ice Age made by Blue Sky. Ice Age has scenes mountains wild animals...
Perception
Sensor
Memory
Observation
Action
Environment
(Physical, Web, Code Environment, Operating System, Game, etc.)
Figure 9: General memory architccture for single agent. The agent interacts with the external environment (black arrows) and humans (gray arrows).
structure, where each node represents an intermediate thought, and the edges encode the dependencies between them. GoT offers more flexible prompting paradigm, it supports wide range of thinking transformations. For example, it allows convenient aggregation of thoughts and seamless switching between different reasoning flows within the graph structure.
ReAct (Yao et al., 2022) is a novel approach that prompts LLMs to generate both reasoning traces and lask-specific actions an interleaved manner, fostering synergy between the two processes. This method combines reasoning and acting of L.L and allows the model dynamically update action plans through reasoning and interact with external sources like Wikipedia to enhance decision-making, Reflexion (Shinn et al., 2024) builds upon ReAct and enhances the decision-makken makion ability of LLMs using verbal reinforcement learning. At the core of Reflexion is deliberate reflection on natural language feedbacks obt from task outcomes, rather than through weight updates. Reflexion firstly explores the property of self-reflection in LLMs and shows that self-reflection is extremely useful to iteratively learn over trials as another form of short-term memory. Similar to Reflexion, which leverages linguistic feedback, an increasing number of works have been proposed for self-improvement and evolution (Li al., 2025b; Tang et al., 2024; Zhao et al., 2025). Gupta al. (2024) learns general prompt instructions for LLMs using past self-reflections. Specifically, it gathers self-reflections in training and generalizes them into verbal 'meta-reflections which additional instructions to enhance the efficiency of agent's. RefAug (Zhang et al., 2024e) uses reflective augmentation to embed reflective sections within training instances, encouraging models to consider alternative solutions and engaging in deeper reasoning. Notably, This method goes beyond standard data augmentation by fostering a more comprehensive understanding of mathematical problems. Reflection on search Trees (RoT) (Hui et al., 2024) introduced reflect on an LLM's previous tree search experiences generate guidelines, which are then used to improve the model's decisions subsequent searches. This approach prevents repeated mistakes and enhances search efficiency. key innovation is the identification
al., 2024b;
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Memory Hisotrical
Decuments
Web
Books
Task: Plant the tree
Trajectories& Experience
Make Wood Pickaae Success!
Who preskdent of the
Ensiroment
ste sppirppeed plant Zombe
intrrcttios
The predieett is Donaid Trump.
conkaratte grass lible
Pake ved aved
Memory Fact
Knowledge
(a)
(c)
Chat?
Memory Personalized
Who it the president of the Sulcs??
Couid you recommmnd something
Configuration & Dialogues
User profile
The president
Memory of Feedback
Buns, aroo you're vegetaaran Ets beffer to eat some iruits and vagentable
The presklent can bo changed dus alecllore ofe pet snwnts
Chat 2
When with Mi?
Recant chit hixtory
The presicent of US Donaid trump.
According 3o your reservation, mast
Learn from feodback
(b)
(d)
Figure 10: Long-term memory containing (a) fact knowledge; (b) historical trajectories and experience; (c) user feedback; (d) dialogues and personalized configuration.
of critical information from historical searches to produce more effective guidelines. Mirror (Yan et al., 2024) is peitiple-perspettvee self-reflection method to addresses the limilations of LLMs in self-assessment and feedback generation by introducing a Navigator-Reasonee framework. It improves through a heuristic interaction between a navigator, which provides question-udaptive directions, and a reasoner, which assesses and refines predictions based on the directions from the navigator. Texigrad (Yuksekgonul et al., 2024) is an automatic 'differentiation" framework that optimizes composite Al systems by back-propagating textual feedback from large language models.
4.1.2 Long-term Memory
While operating in a real-time environment like Jia et al. (2024); Hafner (2021); Bellemare et al. (2013), crucial information, interactive feedback and distilled experience need to be continuously collected and further extracted from short-term memory and preserved as long-term memory of the LLM agent for future reference. To provide the agent with useful knowledge and experience for latter use, some works (Huang et al., 2024a; Park et al., 2023; Wang et al., 2023g; Zhang et al., 2024a) incorporate long-term memory (LTM) to LLMs from both user-specific and common-sense perspectives. This enables LLMs to flexibly utilize past experiences in accordancce with current situations and enhance their task-planning and deeision-making capabilities. These works usually formulate and organize thoughts in memory based operations like insert, forget, merge and of enabling dynamic updates and evolution of the memory in the long run.
As illustrated in Figure 10, this section focuses on four primary categories of long-term memory for an LLM agent:
Memory of fact knowledge The vast number of parameters in LLM endows them with remarkable capabilities, allowing them to excel in variety of Natural Language Processing (NLP) tasks. However, this complexity also presents challenges, making LLMs difficult to train and inhibiting their ability to continuously assimilate new knowledge(Zhang et al., 2023g), which may lead to inaccuracies in their outputs. In order to resolve these issues, Du et al. (2023) propose a continual learning framework that incorporates memory mechanisms allowing LLMs to assimilate new knowledge and modular operators to enhance model inference with this newly acquired knowledge and dynamically adapt to evolving environments and continuously integrate new information without the need for parameter tuning. Similarly, Modarressi et al. (2023) augments L.M with a write-read memory module by extracting and saving knowledge in the form of triplets,
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allowing for scalable, updatable, interpretable, and aggregatable memory storage, which is particularly beneficial for handling temporal-based question answering tasks.
Memory of historical trajectories and experience Incorporating memory into agents is crucial for enhancing their ability to remember historical trajectories, which ultimately improves decision-making and learning efficiency. By allowing agents to recall past experiences, they can identify patterns, make more informed predictions, and adapt their strategies based on historical data. This memory mechanism enables agents to build upon previous knowledge, facilitating more muanced and contextually relevant responses. Delving into the integration of memory also supports the development of more sophisticated models that parallel the cognitive processes of humans, Chereby advancing the overall effectiveness and versatility of artificial intelligence systems. Guo et al. (2023a) incorporate centralized working memory hub and episodic buffer access to retain memories across episodes. This architecture aims to provide greater continuity for nuanced uallextual reasoning in intricate tasks and collaborative scenarios. Liu et al. (2023a) also maintains an evolved memory for storing historical thoughts and employs Locality-Sent Sensitive Hashing for efficient retrieval. Planning (RAP) with a contextual memory module
Kagaya, et al. (2024) introduces Netrieval-Augmeenn to leverage past experiences for improved docision makinn in complex tasks. RAP dynamically retricves relevant. past experiences based on the current. context to guide planning and action selection, mirroring human-like analogical reasoning. In recent, numerous studies have explored learning from past experiences through memory mechanisms, emphasizing the reuse of successful trajectories (Su et al., 2025) and procedural workflows (Fang et al., 2025a; Liu et al., 2025c).
Memory for self-evolving More recently, to enhance agents' self-evolving capabilities, Liang et al. (2025a) proposed a memory optimization mechanism grounded in the Ebbinghaus forgetting curve and linguistic principles, aiming to emulate human-like memory processes within an agent's memory management system. Ouyang et al. (2025) introduced a reasoning memory framework, termed ReasoningBank. This framework is derived from past experiences and organizes them into structured knowledge units that abstract away low-level execution details while preserving transferable reasoning patterns and strategies. An agent equipped with the Reasoning Bank can leverage this curated repository of strategies to guide its decision-making, enabling it to recall effective insights, avoid previously encountered pitsalls, and adapt more robustly to novel queries.
Memory of user feedback Recently, many researchers have proposed various methods for LLM to continue to improve without retraining. The Language Model (LM) is coupled with a growing memory module with feedback either corrections on the historical errors or better clarifications on the tasks from the users. Madaan et al. (2022b) maintains a growing memory of misinterpretation of user intents, along with user feedback for clarification, This memory enables the system to produce enhanced prompts for new queries based past user feedback, effectively leveraging previous corrections to improve performance tasks. Tandon et. al. (2021) enables LLM's to improve their output after deployment without retraining, by leveraging user feedback. It maintains a dynamic memory of cases where users have identified and corrected output errors, and uses a trained corrector model to apply similar feedback to fix new errors. This approach shows significant improvement in repairing errors and avoiding past mistakes on new examples. The system represents a step towards continuous model enhancement through interactive learning and memory-based foedback reuse. Dalvi et al. (2022) integrates a dynamic memory component that stores usor-provided corrections to the model's erroneous beliefs. These corrections are retrieved and used as additional context. when answering new questions, helping the system avoid repeating past mistakes. This approach represents a novel application of memory-based continual learning for belief maintenance in language models, allowing for user-driven system enhancement over time. In order to maintain an ever-improving memory for LLM, Li et al. (2024b) utilizes recursive reasoning-base retrieval and experience reflections to continually update the memory and learn from communicative feedback provided by users. Without periodically re-training, it enables LLM to obtain fresh knowlodge and historical experience by dynamically improving and growing a continually updated memory through human communications.
similar
Memory of dialogues and personalized configuration To address the limitation of context capacity over long conversations, Aadhithya A et al. (2024) introduces a novel memory structure that recursively
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aggregates dialogue context flexibly to enhance long-term memory for dialogue agents. It allows for broad coverage of information with controlled depth through conditional tree traversals, balancing the breadth and depth of information for long-form dialogues, which is crucial for multi-turn reasoning without exponential parameter growth. Based on this, Chen et al. (2024a) adopts compressive memory that integrates scssion- specific summaries, user-bot dynamics, and past events inlo a concise memory format. This method is designed to be more manageable and efficient than traditional retrieval-based methods using Direct Preference Optimization (DPO) to enhance the model's ability to generate contextually appropriate responses. In Maharana et al. (2024), memory in these papers contains more nuanced and human-like conversational experiences, showing its effectiveness in managing time-dependent information and maintaining coherence in long-term and varied interactions, significantly contributing to the field of conversational agent.
Incorporating personalized knowledge bases into memory allows users to effectively store and access specific knowledge according to their requirements. Wang et al. (2023f) novel framework for knowledge retrieval and personalizod knowledge base interaction. It employs the "Program of Thoughts" (PoT) prompting method, which facilitates model interaction with Knowledge Bases through the generation of Python code, thereby enabling knowledge retrieval. For domain specific tasks, Zhang et al. (2023c) introduces personalized medical assistant tasks through computational bionic memory. It utilizes a memory generation module using Dual-Process enhanced Memory (DPeM) that fine-tunes the LLM to produce personalized responses. Zhong et al. (2024) enables storing past conversations and adapting to user personalities. The system is showcased through SiliconFriend, a chatbot that provides empathetic and long-term companiouship, demonstrating MemoryBank's effectiveness in improving AI engagement.
4.2 Multi-agent Memory
In LLM-based multi-agent collaboration, shared memory mechanisms enhance agents' ability to leverage historical information, improving reasoning and coordination. Prior works vary in focus, with some emphasizing real-time memory sharing for efficient reuse and others prioritizing agent autonomy by exchanging only essential information for flexibility and robustness, posing the challenge of balancing experience collection and information overload.
Conceptually, shared memory serves as a repository of past interactions and knowledge that agents query to inform current actions, often implemented as voctor-based representations with similarity metrics retrieving relevant memories. Recent frameworks like A-MEM (Xu et al., 2025), DAMCS (Yang et al., 2025), MS (Gao & Zhang, 2024), and loA (Chen et al., 2024b) offer innovative approaches to shared memory in multi-agent systems. Drawing inspiration from the Zettelkasten method, A-MEM emphasizes memory organization and evolution, creating interconnected knowledge networks through dynamic indexing and linking, where comprehensive notes with structured attributes evolve over time to refine contextual understanding and improve performance in long-term tasks and multi-hop reasoning. DAMOS enables decentralized cooperation through hicrarchical knowledge graphs, dynamic team formation, and structured commmication, allowing agents to learn from each other's experiences and adapt to new environments via most relevant information, while keep their own individual memories. MS focuses on real-time memory sharing by storing Prompt-Answer (PA) pairs in a shared memory pool, with an aulonomous retriever ensuring relevant memories enhance response quality and reduce dependence on external databases. loA adopts Internet-inspired framework, enabling scamless integration of heterogencous agents via an hnstant-messaging-lik architecture, facilitating agent discovery, dynamic team formation, and structured communication for scalable collaboration.
4.3 System Architecture
To enable LLM with external memory capabilities, a variety of open-source tools and frameworks have been developed. As shown in Figure 11, these systems typically consist of the following modules: Data Ingestion + Storage and Retrieval + User Interfaces and Application Invocation. In this section, we compare the existing mainstream agent system architectures module by module. summary of these comparisons is shown in Table 2. Notably, evaluation of the memory systems is also an important module, which is discussod in the next subsection.
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Query for Dara
User
Raw Data
Chunk
Database
Ingestion
Indexing
Query for Resporeee
Evaluation
Query Daaa
Chatbot
Figure 11: A general architecture of memory-angmented agent pipelines.
Table 2: Comparison of Tools for Data Ingestion, Storage, and User Interfaces
Tool
Data Ingestion
Storage and Retrieval
User Interfaces
Universal connectors (fies/web/DD)
Vector DB
API access
Common
Hybrid indexing
Logging tools
Conversation context
MemGPT
Hierarchical Liers
Nolebook examples
Tool execution outputs
Temporal streams
Graphiti engine
Dashboard monitoring
Zep
Agent message graphs
Relationship expiration
Graph visualization
Cloud syne
Managed backend
Personalized U1
Memo
Cross-sesion data
Versioned storage
Memory triggers
SQL/API ingestion
REST API
Haystack
Hybrid search
Streaming pipelines
Pipeline configurator
Modular loaders
Memory inspection
LangChain
Conversation buffer
Notion integration
Chain debugging
160+ formats
Query analyzers
LlamaIndex
Tree-Index
Structured data parsing
Index visualization
4.3.1 Data Ingestion
Comprehensive frameworks such as LangChain', LlamaIndex*, and Haystack provide extensive data connectors that support wide range of data sources. These include local files (e.g., TXT, PDF, Word), web scraping, database integration, APTs, and streaming data. For example, LlamaIndex natively supports over 160 data formats, covering plain text, tables, SQL databases, notion documents, Google Drive, Slack messages, and API responses, making it highly versatile for various business scenarios. These frameworks often allow customization of data preprocessing, such as specifying chunk size, filtering special symbols, and extracting metadata to optimize indexing efficiency.
Conversational and agent memory focus mainly data generated from interactions. For instance, Letta (MemGPT)6 emphasizes conversation context and tool usage results, with data sources including user messages, model responses, and outputs from external tools (c.g., code execution results). Text retricved from a search tool may be incorporated as new memory. In multi-agent scenarios, data sources additionally include messages exchanged between agents.
Domain-specific knowledge-based applications, such as customer service and knowledge retrieval systems, rely on data sources such as product documentation, procedural manuals and FAQs. Tools like Haystack facilitate efficient document import, enabling bulk loading and automatic conversion into internal indexing formats.
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4.3.2 Storage and Retrieval
Vector databases are the most prevalent approach, which emphasizes large-scale semantic query performance. Many frameworks integrate vector librarics such as FAISS", Chroma Pinecone", Weaviate and Milvus by default. LangChain and Llamalndex use local vector storage (either in memory or disk) to store embeddings and can optionally connect to external service- based vector databases for persistence and distributed capabilities. In vector-based storage, text is segmented and encoded in high-dimensional vectors using models like Sentence-BERT (Reimers & Gurevych, 2019). The index consists of these vectors, which are matched with query vectors during retrieval to return relevant. results. Retrieval hyper-parameeers, such as top-K value or similarity threshold, can be fine-tuned. Some tools employ additional strategies, such as summary or hicrarchical indexes. LlamaIndex's Trec-Index organizes documents into hierarchical structure, enabling efficient retrieval for long-document QA. Keyword or hash indexing can also complement vector searches to improve accuracy and speed.
Graph databases excel in structured knowledge representation, logical reasoning, relationship tracing, and version control. Systems like Zep parse conversations into nodes and edges with attributes (e.g., context sum- maries, embeddings, timestamps). Its Graphiti engine dynamically updates the graph, invalidating outdated relationships. Retrieval utilizes graph algorithms and explores node relationships. Mainstream frameworks like Llamalndex and LangChain also support graph databases for enhanced knowledge management.
Hybrid storage strategies are sometimes employed to meet diverse needs, balancing speed and capacity. For instance, Letta introduces hierarchical storage, keeping recent conversations in immediate context while compressing older information into external databases for long-term archiving. Persistence is another major consideration. If vector indices stored in memory are not saved, they are lost upon restart. Hence, most frameworks offer options for persisting indexes and memory, such as dumping indexes to disk files or directly to clouds. Memo provides cloud-hosted services, allowing developers store memory in its managed backend for durability and cross-session sharing.
Traditional databases and file storage are also used in simpler cases, which may rely on key-value stores (e.g., Redis), relational databases, or local files to record full conversations. However, such approaches struggle to scale to complex scenarios and are gradually being replaced by vector databases.
Information retrieval methods vary by application needs: chatbots prioritize recent conversation context. and related knowledge, often using sliding windows (e.g., LangChain's while knowledge QA systems focus on pinpointing accurate answers from large databases, favoring semantic search combined with cross- coss-verifiiation. The organization of retrieval outputs is crucial; frameworks often truncate or filter retrieved memories to fit within context windows, with methods allowing LLMs to generate summaries from multiple sources. Graph community summaries similarly aggregate node content into concise contexts, ensuring prompts contain essential information without exceeding length limits.
4.3.3 User Interfaces and Application Invocation
Developer frameworks like LangChain, LlamaIndex, and Haystack provide APIs and librarics for integration but lack graphical user interfaces (GUTs). Memory functions operate in the background, allowing developers review retrieval results through logs or debugging tools. API-based services like Zep store and retrieve conversation history through API calls, operating invisibly to end-users. Developers monitor usage via dashboards that track stored conversations and vector indexes. Command-line tools and developer utilities are available in some open-source projects. For example, MenGPT provides Jupyter Notebook examples for API-based memory retrieval.
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Table 3: Characteristics of memory.
Feature
Type
STM
LTM
Subjective eval
Objective eval
Description
Temporality
Direct
/
/
X
Whether the memory includes time mentions, timestamp, temporal -based long dependency correlations
Consistency
Direct
/
X
Whether the memory remains be consistent or not
Redundancy
Direct
/
Whether the memory maintains redundaut information or not
Variance
Direct
x
X Whether the memory static or dynamic that can be updated
Transformation
Direct
Whether the memory can converted between short-term and long-term
Full-featured platforms such as Dify and Memo offer web-based UIs for chatbot configuration, knowledge base management, and real-time interactions. Dify enables non-programmers to build memory-enabled bots visually, while Mem0 provides a hosted ChatGPT with persistent memory, allowing users to upload knowledge and personalize interactions.
4.4 Evaluation on Agent Memory
4.4.1 Qualitative Evaluation
Understanding the characteristics the agent memory is crucial for designing systems that effectively store, retrieve, and utilize information. In this subsection, we highlight the role of memory in optimizing performance, ensuring efficiency, and enabling communicative learning for an LLM agent. By examining these features, aim to provide a comprehensive overview of memory's multifaceted nature and its impact system behavior, shown in Table 3:
Temporality indicates whether temporal facts or events exist in the memory and how they are stored, accessed, and utilized over time. Memory with timo-sensitive information is crucial for tasks involving temporal understanding, reasoning and long dependency tracking, which enables the model to distinguish information in a timeline order. Consistency reflccts whether the memory remains stable and consistent across interactions. It ensures the credibility and reliability of the LLM agent when generating the output based on relevant memory in context. In the meantime, it avoid frustration when retrieving conflicting information across different queries. Redundancy indicates whether the memory maintains redundant information, such as storing multiple versions of the same fact or event. While redundancy can serve as a backup for fault tolerance, excessive redundancy can lead to inefficiency and confusion. Variance refers to the memory being dynamic that it can be updated and merged with new incoming information without overwriting critical old data or losing coherence. Dynamic updating critical for LLM agents in real-time applications, such as interactive agents or systems that must adapt new facts corrections. Conversion & Transformation refers to whether memory can be transferred between short-term memory (STM) and long-term memory (LTM) effectively. Real-time interactions benefit from STM, whereas LTM supports knowledge retrieval for tasks requiring contimuity over sessions, comprehensive reasoning, and continuous learning.
4.4.2 Quantitative Evaluation
The quantitative cvaluation of an agent's 's memory is imperative to validate design choices and direct future research. Such evaluations could be conducted across diverse tasks and levels of granularity, encompassing
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both final task outcomes and the intrinsic properties of the memory module. Here we present comprehensive framework of the evaluation landscape.
Evaluation tasks. The choice of task is crucial, as it defines the environment and the types of tasks in which the evaluated agents must utilize memory. A growing number of benchmarks have been developed to assess agents in diverse scenarios. These can be broadly categorized into three main types.
These benchmarks evaluate an agent's ability to retain, retrieve, and reason over information from long contexts, serving as a measure of long-term memory capabilities in static settings. Examples include: NarrativeQA (Kočiský et al., 2018), QuALITY (Pang et al., 2021) and Loogle (Li et al., 2023a), which require agents to read lengthy narratives and answer comprehension questions, thereby testing their ability to track long- range dependencies; RetrievalQA (Zhang et al., 2024f), benchmark for open-domain question auswering that assesses the model's capacity to retrieve relevant documents from a large corpus and synthesize answers; and Needle In A Haystack (NIAH) (Kamradt, 2024), a synthetic test designed to precisely measure an agent's 's ability to recall a specific fact embedded within a. large volume of irrelevant context, directly probing the limits of memory recall.
Long-text and question-answering tasks:
These benchmarks assess memory in dynamic, multi-step decision-making scenarios where the agent must interact with an environment. Success in these lasks strongly indicates effective memory use for planning, adaptation, and learning. Examples include: ALFWorld (Shridhar et al., 2021), simulated home environment where agents execute multi-step natural language instructions to complete houschold tasks, requiring memory of both instructions and environmental state; WebArena (Zhou et al., 2024b), a realistic and complex benchmark in which agents perform tasks on live websites (e-g., booking a flight or making a purchase), demanding robust memory of past actions, website responses, and overall goals across multiple pages and interactions; Gentopia (Xu et al., 2024), an ecosystem designed for building and evaluating agents, offering a platform for standardized testing across various predefined lasks; and AgentBBncc (Liu et al., 2023b), multi-dimensional benchmark that evaluates LLM-based agents across diverse set of environments including operating systems and games testing both general reasoning and interactive abilities.
Interactive and Open-World Tasks:
In these tasks, three levels of metrics can be considered: general-purpose task-oriented metrics that gauge overall performance, low-level metrics that probe the memory's fundamental capabilities and specialized metrics for communicative learning agents.
Purpose Task-Oriented Metrics. These are high-level, extrinsic metrics that measure the ultimate success and efficiency of an agent on a given benchmark. Task Success and Accuracy is the most critical metric, quantifying the percentage of tasks the agent successfully completes or the accuracy of its final answers. It serves as the primary indicator of the agent's overall capability. Efficiency (Latency and Cost) is also paramount in world applications. This metric is typically assessed through inference latency (the time taken to complete task), the number of steps or actions required, and the associated monetary cost (c.g., the total tokens consumed via API calls).
Low-Level Intrinsic Memory Metrics. These fine ane-grained metrics assess the intrinsic properties of the memory module, providing deeper insights into its functionality beyond just task success. Wn et al. (2024a) proposes detailed taxonomy of such capabilities, which we adapt and summarize here: Information Extraction (IE): The agent's 's ability to accurately recall previously seen information (recall rate) and extract specific facts from its memory without hallucination (precision). Multi-Session Reasoning (MR): Ability to synthesize the information across multiple history sessions to answer complex questions that involve aggregation and comparison. Knowledge Utilization (KU): Ability to recoguize the changes in the user's personal information and update the knowledge of the user dynamically over time. Temporal Reasoning (TR.): Awareness of the temporal aspects of user information, including both explicit time mentions and timestamp metadata in the interactions. Abstention (ABS): Ability to identify questions seeking unknown information, iee, information not mentioned by the user in the interaction history, and answer "I don't 't know".
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Comparative Evaluation of Memory Frameworks General and Low-Level Metrics
Table 4: Performance evaluation with Llama3-8B-IT as the reasoning engine. Each cell shows correctness rale (bold) and average time seconds (footnotesize).
Llama3-8B-IT
Framework
KU
MS
SS-Assist
SS-Prefer
SS-User
TR
Overall
0.000
0.000
0.000
0.000
0.000
0.000
0.000
No Memory
1.62
1.54
2.25
4.78
1.46
1.25
1.73
0.625
0.074
0.900
0.333
0.857
0.444
0.470
ChromaDB
5.34
6.02
5.57
8.42
5.32
6.67
6.09
0.026
0.000
0.036
0.000
0.000
0.023
0.032
Langchain
112.35
126.60
108.72
105.83
100.99
115.41
111.65
0.562
0.111
0.800
0.167
0.857
0.741
0.530
Haystack
0.88
1.95
1.54
3.61
0.75
2.26
1.75
0.714
0.636
0.500
0.500
0.727
0.615
0.646
Llamalndex
25.47
29.35
25.77
28.61
26.40
27.62
27.31
0.583
0.500
0.625
0.567
0.500
0.592
0.555
Mem0
2280.15
1632.03
1956.42
2103.78
2927.99
1765.34
2110.95
0.125
0.148
0.364
0.200
0.143
0.259
0.200
Zep
172.84
178.92
169.35
181.67
176.43
177.21
176.07
For our evaluation, we selected the longmemeval Bacilll____eeee s_cleneed dataset from the official LongMemEval bench- mark Wu et al. (2024a). This benchmark uniquely suited for our purposes as combines the characteristics of the two critical evaluation tasks: Long-Context Question Answering (QA) and interactive tasks derived from real user conversations. This allows to mcasure not only the accuracy of QA responses but also the processing time for data ingestion, retrieval, and reasoning. The LongMemEFal dalasel calegorizes lest cases into several low-level types based on the required memory capabilities: Knowledge Updates (KU), Multi- Session Reasoning (MR), Single-Scssion Reasoning (SS-Assistant), Single-Session Reasoning (SS-Prefcrence), Single-Session Reasoning UsS-,, and Temporal Reasoning (TR). The full dataset comprises 2,500 questions.
We benchmarked variety of memory frameworks, including a baseline with no memory, a simple RAG implementation using ChromaDB, Langchain's native FAISS RAG, Haystack, LlamaIndex, Memo (local version), and Zep (APT version). Due to the extensive average processing times of Mem0, Langchain, and Zep, we conducted their evaluations on 10% random sample of the dataset. For the reasoning engine, we employed two LLMs: Llama-3-8B-IT and GPT-4o-mini. All responses were evaluated for correctness using GPT-40-mini.
The results, presented in Table 4 and Table 5, show the correctness rate and the average total time per question (in seconds), which includes data ingestion, retrieval, and reasoning. For all frameworks except the no-memory baseline, ingestion and retrieval constituted the majority of the processing time.
Our findings indicate that the simplest framcwork, ChromaDB, performed surprisingly well. In contrast, many of the more complex frameworks did not deliver the performance improvements their official documentation suggested, at least on this comprehensive task. Langchain's memory implementation was the least effective, appearing largely non-functional. MemO exhibitod extremely long processing times, as it performs significant internal memory organization and inference during each data ingestion, which did not translate into a proportional increase in accuracy. Zep also showed prolonged data ingestion times and the ob-
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Table 5: Performance evaluation with GPT----miini as the reasoning engine. Each cell shows correctness rate (bold) and average time in seconds (footnotesize).
GPT-4o-mini
Framework
KU
MS
SS-Assist
SS-Prefer
SS-User
TR
Overall
0.060
0.000
0.000
0.000
0.000
0.000
0.010
No Memory
1.27
1.04
1.18
1.93
0.92
1.15
1.16
0.752
0.222
1.000
0.833
0.865
0.563
0.600
ChromaDB
5.88
6.23
6.79
7.58
6.15
6.65
6.41
0.031
0.000
0.022
0.000
0.000
0.019
0.022
Langchain
108.45
126.60
103.78
98.63
100.99
112.92
108.56
0.688
0.148
0.900
0.500
0.929
0.852
0.630
Haystack
1.00
1.58
0.99
2.98
1.55
1.71
1.00
0.712
0.642
1.000
0.500
0.823
0.467
0.667
LlamaIndex
31.70
27.28
26.74
35.35
26.34
28.28
28.34
0.580
0.624
0.592
0.608
0.573
0.631
0.602
Mem0
1980.25
2015.73
1992.48
2008.16
1975.92
2023.67
2106.53
0.500
0.270
1.000
0.500
0.830
0.550
0.550
Zep
178.45
172.83
181.29
176.92
173.67
175.30
176.41
served performance metrics demonstrated some variance from those claimed in the original research Rasmussen et al. (2025). Hayslack and Llamalndex, however, demonstrated strong balance of performance and efficiency.
Across all frameworks, the multi-scssion reasoning tasks proved to be the most challenging. This difficulty is altributable to the large volume of conversational history that must be comprehensively processed, with each question averaging 47 sessions, each containing around 10 conversational turns. We attribute ChromaDB's strong performance in part its effectiveness on single-session tasks, where relevant information can be extracted from single contiguous block of memory. In comparison, the more advanced frameworks, which actively organize and synthesize memory, showed slight advantage in the more demanding multi-session tasks.
Specialized Metrics for Commuicative Learning Agents. We propose two types of test type to evaluate the capability of an agent memory as below:
Functionality Test (FT) is a type of black-box testing that evaluates whether the software system performs its intended functions correctly expected according to the defined requirements. It focuses on verifying the functional correctness of the application by checking specific features and operations about the memories. Here we use FT to test capabilities like Learning Efficiency and Generulization.
Perturbation Test (PBT) evaluates the stability of software system by introducing modifications disturbances to its inputs, environment, or internal state. The goal is to discern how the system reacts to perturbations of the memories and whether it can maintain the expected behavior. Here we use PBT to test capabilities like Controllability and Robustness.
The detailed definitions of the evaluation metrics mentioned above can be found in Table We conducted these tests based on RetrievalQA Zhang et al. (2024f) dataset using four different types of agents, all using Tlamo3-9B--T(Grrataaae et al., 2024) the base model, with varying memory settings:
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Table 6: Memory capabilities metrics.
Test type
Capability
Type
STM
LTM
Sub. eval
Obj.
Description
eval
Learning Efficiency
Indirect
x
X
/
Task performance increase with accumulative
memory
FT
Generalization
Indirect
/
Unseen task performance with memory learnt from historical tasks
Controllability
Indirect
x
/
Task performance provided with unknown or counterfactual contexts
PBT
Robustness
Indirect
Task performance provided with irrelevant contexts noise
Table 7: Evaluations on capabilities of memory using different agents. Mupd indicates that the ground truth of the current question will be updated into the memory.
ICL
RAG
RAM
Concordia Agent
wo Mud
w Mud
wo M udd
w Mdd
wo Mund
Mud
wo Mund
w M und
Generalization
20%
20%
38%
38%
52%
52%
16%
16%
Controllability
20%
16%
54%
48%
38%
44%
16%
24%
Robustness 20%
4%
38%
19%
54%
26%
16%
12%
In-Context Learning (ICL)): Memories are entirely stored in the LLM's context.
Regmeeval-Angmenteed Generation (RAG): Standard RAG for storing memories.
RAM: Continuously improving memory based on external feedback, implemented according to Li et al. (2024b).
General Agent: An agent that automatically decides how to handle external leedback at each step, implemented using the Concordia framework (Vezhnevets et al., 2023).
Learning Efficiency
Agunt
RAM
RAG
Accuracy
0.4
0.3
0.2
0.4
0.5
0.8
1.0
Context Quantity
Figure 12:
Learning Efficiency: Question-answwring accuracy as function of the proportion of necessary context provided to the LLMs.
In Figure 12, we illustrate the performance each memory setting across varying context quantities, ranging from 10% to 100% of the memories required answer the question. This analysis highlights the learning efficiency of RAG and RAM, as performance basically improves consistently as the proportion of necessary memories increases.
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Table 7 summarizes the evaluation results for Generalization, Controllability, and Robustness. The applied memory perturbations significantly influence performance, demonstrating the sensitivity of these metries to changes in memory inputs. However, the generalization ability of past memories has not been observed, potentially due to insufficient data volume.
4.5 Limitations, Open Questions, Discussion
In contrast to Zhang et al. (2024d), which posits that memory is the definitive component that elevates a standard LLM into an autonomous, self-evolving agent and focuses on functional, examining memory through the lens of what is required to build effective agents, our discussion of agentic memory concentrates on the design and implementation of memory systems developed for direct deployment in scenarios where memory mechanisms are indispensable. Likewise, in distinction to Du et. al. (2025), who argues for a more fundamental, mechanistic understanding and contends that previous application focused reviews have overlooked the "atomic operations". such as consolidation, retrieval, and forgetting, that form the universal building blocks of any memory system, regardless of its application, our survey presents the broadest perspeclive, framing memory as the foundational element augmenting modern LLMSs and, critically, Multi-Modal LLMs, encompassing the challenges of integrating information across diverse modalities like text, vision, and audio, thereby offering a generalizable system view for memory in Agents.
To conclude, to enhance the utility of agents, enabling dynamic memory adaptation during reasoning and communcctive learning could be crucial. Inspired by human pedagogy, methods like RAM (Li et al., 2024b) demonstrate the potential of recursive retrieval and experience reflection for continuous memory updates based on user feedback. Additionally, in multi-agent systems, ensuring adaptive network structures and robust. communication frameworks (Mao et al., 2024; Marro et al., 2024; Liu et al., 2024b) to facilitate effective memory syuchronization also remains as a challenge.
5 Memory-augmented Multi-Modal Large Language models
Addressing the complexities inherent in multimodal context modeling, a critical research inquiry emerges:
How we devise and execute memory mechanisms that adeptly amalgamate and preserve extensive multi- modal contextual data, thereby augmenting the comprehension and manipulation of intricate dalasets within fluid encironments?
This question is particularly salient within the domains of vision and robotics, where the optimization of contextual ual memory is paramount for bolstering the cognitive and functional capacities of embodied agents. Such advancements would empower these agents to execute sophisticated operations, enabling systems make informed decisions based on comprehensive contextual understanding.
5.1 Multimodal Context Modeling with Memory
In this section, we introduce the multimodal context modeling with memory, incorporating information from audio, video, and other modalities. For each modality, will discuss its modeling in relation to various downstream tasks.
5.1.1 Audio Context Modeling
The continuous and high-frequency nature of audio presents significant challenges in efficiently modeling its sequences, demanding substantial computational resources. As a result, developing effective methods to incorporate audio history is crucial for various applications. Recent advancements in audio context modeling have introduced innovative solutions to address these challenges. For instance, Conformer-NTM (Carvalho & Abad, 2023) proposes an external memorr network between the encoder and decoder transformers for automatic speech recognition (ASR), enhancing the system's ability to handle complex audio sequences. Similarly, Loop-Copilot (Zhang et al., 2023d) introduces a Global Attribute Table that identifics and manages various musical attributes at any moment, aiding in task execution and ensuring musical coherence in music generation. Furthermore, MR-MT3 (Tan et al., 2024) employs previous instrumental tokens key /value
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Memory-
(Huang al., 2024b)
Enhanced Manipulation
Memory- Enhanced Odometry
(Xue et al., 2022)
Multimodal Memory for Vision and Robotics
Memory- Enhanced Navigation
(Kumar et al., 2018; Wang et al., 2021a; Kwon et al,, 2021; Li et al., 2024a; Fang et al., 2019; Wani et al., 2020)
Memory-
(Wang et al., 2023j; Yan et al., 2023; Lee et al,, 2023; Zhang et al., 2023b; Wu et al., 2024b; Liu et al., 2024a)
Augmented
Agents
Generation
(Lee et al., 2021; Zhong al., 2021; Yi 2020; Park et al., 2022; Tan al., 2023; Chang ct al., 2023; Tang et al., 2022)
Others
(Dong Fu, 2024; Yasarla al., 2023; Yao, 2022; Sun et al., 2022; Kim et al., 2022a;b)
General-level
(Zhang et al., 2016; Feng et al., 2018; Wang et al., 2019; 2018; Pei et al., 2019; Lei et al., 2020; Prudviraj et al., 2022; Fan et al., 2019; Na et al., 2017; Bai et al., 2023)
Downstream
Tasks
Multimodal Memory Taxonomy
Action-level
(Xu et al., 2021; Wu et al., 2022a; Yang et al., 2022; Wang et al., 2023b; Song et al., 2024)
Object-luvel
(Tokmakov al., 2017; Yang Chan, 2017; Pu al., 2021; Yang & Chan, 2018; Oh et al., 2019; Cheng et al., 2021;
al., 2021; Lu et al., 2020; Zhang al., 2020; Sun et al., 2023; Yang et al., 2021; Paul al., 2021; Meinhardt al., 2022; Cheng & Schwing, 2022; Zhao
et al., 2023; Siam et al., 2023; Zhou et al., 2023; 2024a; Shaker al., 2024; Ravi et al., 2024)
Other Modalities
(Park et al., 2017; Hou et al., 2021; Jeong et al., 2021; Yoo et al., 2019; Li et al., 2021; Wu et al.,
2021; Wang et al., 2022a; Huang et al., 2021;
Bozic et al., 2021)
Video Agents
(Zhang et al., 2023a; Wang al., 2023h; 2024d; Fan al., 2024; Wang al., 2023<; Yang et al., 2024b; Kahatapitiya al., 2024; Ma et al., 2024)
Multimodal Context Modeling with Memorr
Video Context Modeling
Large Video Language Models
(Song et al., 2023; He al., 2024a; Qian et al., 2024; Zhang et al., 2024b; Wang et al., 2024Bbb
Video Rep- resentation Learning
(Balazevic et al., 2024)
Audio Context Modeling
(Carvalho & Abad, 2023; Zhang et al., 2023d; Tan et al., 2024),
Figure 13: Taxonomy of multimodal memory applications and downstream tasks.
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memory, effectively preventing instrument leakage in automatic music transcription tasks. These approaches demonstrate promising strategies for improving audio modeling, paving the way for more efficient and coherent. audio processing systems.
5.1.2 Video Context Modeling
Video context modeling presents unique challenges compared to image processing due to its added time dimension, which significantly increases complexity. Most existing approaches use sampling based methods, converting continuous video into discrete stacked frames. This makes balancing computational complexity with detailed video information a crucial focus in video research. Memory mechanisms have emerged as a vital strategy achieving this balance. This section explores various aspects of video context modeling, starting with general video representation learning, focusing on developing more effective video encoders. We then delve into recent advancements in memory mechanisms for large video language models and video agents, which demonstrate strong performance in zero-shot video-language benchmarks and applications. Finally, we discuss the role of memory in various downstream tasks.
Memory-enhanced Video Representation Learning In the realm of video representation, MC-ViT (Balazevic et al., 2024) introduces long video encoder using memory consoliaation and cross-attention on vidco segments to efficiently encode long-context vidcos. This enhances the ability to handle extensive video data while maintaining detailed representation.
Large Memorr-enhanceed Video Language Models For large video language models, several innovative memory-augmented approaches have been developed for long video modeling. MovieChat (Song et al., 2023) builds on Q-Former for visual feature extraction with memory consolidation to model long videos for video question answering (QA). MA-LMM (He et al., 2024a) extends this with a retrieval strategy based on the semantic similarity of frame features. VidcoStreaming (Qian et al., 2024) proposes an method combined with an adaptive memory selection strategy on the recurrent image feature, selecting a constant number of question-related memories using Gumbel-Softmax. Flash-VStream (Zhang et al., 2024b) presents hierarchical memory system, incorporating FIFO queue for spatial features, and uses abstract memory implemented by cross-attention for whole video modeling. VideoLLaMB (Wang et al., 2024f) introduces a recurrent memory bridge with a memory cache to model video history in memory for long video understanding. Additionally, OmniDrive (Wang et al., 2024b) utilizes a memory bank for frames in autonomous driving-related QA.
Memory-enhanced Video Agent Video agents have made significant strides by transforming various video elements into text, such as captions, object names, and timestamps, which are then stored as external memory for LLMs to enhance video processing tasks. LLoVi (Zhang et al., 2023a), LifelongMemorry (Wang et al., 2023h) and TideoAAent(Waang VideoAgent (Fan et al., 2024) combine captioning, tracking, VQA modules to inject video object detection ability into LLM. Chat (Wang et al., 2023c) utilizes captioning, tracking, and audio modules to extract information from video as memory. DoraemonGPT (Yang et al., 2024b) adopts a more comprehensive method by incorporating captioning, tracking, ASR, detection, action, and segmentation module to provide additional information for LLMs in video QA and segmentation tasks. LangRepo (Kahalapiliya et al., 2024) integrates captions, and timestamps as memory to LLMs for sequential understanding. Finally, DrVideo (Ma et al., 2024) reinterprets long-video understanding as long-document comprehension task, effectively utilizing the power of large language models. These advancements underscore the diverse strategies employed to address the complexities of video context modeling and suggest promising directions for future research.
et al., 2024d) leverage captions memory for LLMss. Furthermore,
5.1.3 Other Modalities
Beyond audio and video context modeling, memory strategics are incrcasingly leveraged across various modalities to enhance context understanding and improve performance. In image captioning, the CSMN model (Park et al., 2017) enhances memory networks by using them as repositories for multiple types of context information, appending previously generated words capture long-term information, and employing CNN memory structure for better context representation. For anomaly detection, the DAAC model (Hou et al., 2021) modulates reconstruction capabilities by generalizing the memory module in blockwise manner
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using a multi-scale approach. In image-to-image translation, MGUIT (Jeong et al., 2021) explores memory networks to improve translation results. MemoPainter (Yoo et al., 2019) introduces a memory-augmenter colorization model that achieves high-quality colorization with limited data. For blind face restoration, RMM (Li et al., 2021) proposes a wavelet memory module that stores spatial features of low-quality images and guides high-quality restoration. In semantic segmentation, memory-based approach (Jin el. al., 2021) stores significant training image representations, while MM-Net (Wu et al., 2021) uses learnable memory embeddings for few-shot segmentation, and CDFSS (Wang et al., 2022a) employs meta-memory bank to bridge domain gaps. In deraining, MOSS (Huang et al., 2021) uses self-supervised memory module to record prototypical rain patterns. Lastly, for 3D scene reconstruction, TransformerFusion (Bozic et al., 2021) proposes a hierarchical memory of input frame features for online video 3D scene reconstruction. These applications underscore the versatility and effectiveness of memory networks in improving context modeling across diverse tasks.
5.2 Downstream tasks
Object level
Action level
V3
Tracking: disappearance
Localization: Baseball Pitch
Object Detection, Segmentation, Tracking
Action Localization Temporal Grounding,
General level
Generation
Question: What types of cabinets did open the video? Answer: You opened cabinet undemeath the opened cabinat.
Prompt: Beautiful, enowy Tokyo city bustling. The camera moves through the bustling city street, following several people enjoying the beautitu snowy weather and shopping nearby Gorgecus sakura petals flying through the slong snowilakes.
Multi-hap QA, Caption,
Long Aualia Video Generation
Figure 14: Various video understanding and processing tasks.
Advancements in video understanding and generation have highlighted the critical role of memory mechanisms in enhancing long-context modeling capabilities. Researchers have been increasingly focused on developing innovative approaches to leverage these memory mechanisms across various video processing tasks. This survey provides an overview of these advancements, beginning with video understanding at the object level, progressing through action-level tasks, and culminating in general-level applications such as summarization, caplioning, and question ion answering. Figure 14 provides some lasks demonstration. Additionally, we explore recent breakthroughs in video generation that utilize memory-baat based architectures.
Object-level Task Object segmentation and tracking videos are foundational tasks that benefit signifi- cantly from memory-augmented modcls. Early efforts this domain employed RNNs, LSTMs, and GRUs maintain and update memory states over time. For instance, CouvGRU (Tokmakov et al., 2017) integrates convolutional gated recurrent units to track the evolution of objects within scene, while RFL (Yang & Chan, 2017) utilizes convolutional LSTMs for object tracking with preserving video instory. RMAN (Pu et al., 2021) builds on the LSTM architecture by adding a memory activation layer specifically for visual tracking.
Further advancements introduced more sophisticated memory networks. MemTrack (Yang Chan, 2018) employs a dynamic memory network to store and recall target information, utilizing an LSTM manage memory operations for complate-matching tasks. STM (Oh al., 2019) and its enhanced version, STCN (Cheng et al., 2021), compute spatio-temporal attention across video frames, improving pixel-level object
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distinction. STMTrack (Fu et al., 2021) introduces a mechanism that stores historical target information to guide the tracker toward the most informative regions.
Graph-based memory networks have also shown promise in video object segmentation. GraphMemVOS (Lu et al., 2020) leverages an episodic memory network structured as a fully connected graph, facilitating Pross-frame correlation capture. DTMNet (Zhang et al., 2020) builds on this by incorporating both short- and long-tern memory storage to enhance temporal modeling. TMRN (Sun et al., 2023) improves memory retrieval operations by spatially aligning memory frames with current frames before temporal aggregation.
The advent of transformer-based architectures has further revolutionized object segmentation and tracking. AOT (Yang et al., 2021) employs long-short term memory for associating multiple object segments, while IMANet (Paul et al., 2021) utilizes atlention mechanisms to access semantic information stored in memory. TrackFormer (Meinhardt et al., 2022) and XMem (Cheng & Schwing, 2022) exemplify the integration of transformers with memory modules to handle occlusions and segment long vidco sequences effectively. Recent innovations continue to push the boundaries of memory utilization. S-VIT (Zhao et al., 2023) and MMC (Siam et al., 2023) focus on preserving detailed feature maps and reducing background confusion through multiscale memory transformers. RFGM (Zhou et al., 2023) introduces a relevance attention mechanism to adaptively assist in selecting pertinent historical information. RMem (Zhou et al., 2024a) enhances efficiency by restricting memory banks to essential frames. MAVOS (Shaker et al., 2024) optimizes long-term memory usage to ensure temporal smoothness without frequent expansions, and SAM 2 (Ravi et al., 2024) extends memory capabilities with a FIFO queue for seamless object tracking.
Action level Task Action classification and localization in videos are critical components of understanding dynamic scenes and require sophisticated models capable of identifying and interpreting temporal patterns. Recent advancements in this field have leveraged memory networks and transformer- based architectures to enhance the accuracy and efficiency of these tasks. One approach that has gained prominence is the use of memory networks. For instance, (Yuan et al., 2019) introduces a novel framework that writes significant. information into an external memory module while discarding irrelevant data. This selective memory management improves video action recognition by focusing on key temporal elements. Transformer-basee models have also been instrumental in advancing action classification and localization. LSTR (Xu et al., 2021) utilizes both long-term and short-term memory in a FIFO structure to address online action detection, allowing the model to maintain relevant past information efficiently. MeMViT (Wu et al., 2022a) proposes a hierarchical memory transformer that optimizes long-term memory use for effective video action classification and anticipation. Another significant contribution is RVTT (Yang al., 2022), which employs an attention gate to facilitate interaction between the current frame input and the previous hidden state. This mechanism enhances the model's 's ability to integrate past and present information dynamically. Similarly, MAT (Wang et al., 2023b) introduces a memory encoder that compresses both long-term and short-term memory in a segment- based manner. It also features a memory-anticiiationn circular decoder that updates historical and future representations for online action detection and anticipation. Finally, MATR (Song et al., 2024) presents a FTFO memory queue that selectively retains past segment features, optimizing the process of temporal action localization. This approach ensures that the most relevant temporal features are preserved, improving the model's ability to localize actions accurately over time.
General level Task Video summarization has evolved significantly with the introduction of memory- augmented models and deep learning architectures. Early approaches, such as vsLSTM (Zhang et al., 2016), employed LSTM to capture range temporal dependencies among video frames. This method aimed to create both representative and compact video summaries by effectively modeling the temporal relationships inherent in video data. Building on this foundation, MAVS (Feng et al., 2018) introduced a memory-augmenteed extractive video summarizer that utilized an external memory to store comprehensive visual information from the entire video, enhancing the sunmarization process with high-capacity memory storage. Furthermore, SMN (Wang et al., 2019) stacked multiple LSTM and memory layers hicrarchically. This approach integrated learned representations from prior layers, resulting more precise video summarics for individual frames by capturing intricate temporal patterns.
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Beyond Length Limit
Within Length Limit
Multimodal Stream
allia
Model
Memory
Figure 15: Memory for multimodal context modeling
Video captioning has also benefited from advancements in memory-augmentee networks. M3 (Wang al., 2018) proposed attaching an LSTM with an external memory that could store and retrieve both visual and textual content. This method allowed for multiple read and write operations, facilitating rich interactions between video sequences and corresponding sentences. MARN (Pei et al., 2019) introduced the Memory- Attended Recurrent Network, designed to explore the full-spectrum correspondence between words and their visual contexts, enhancing the captioning capabilities by leveraging memory structures. In terms of transformer-based solutions, MART (Lei et al., 2020) utilized a layer-wise TransformorXI. architecture for video captioning, which was further improved by AAP-MIT (Prudviraj el al., 2022) through the integration of Pyramid network for generating multi-sentence video descriptions, thereby enriching the narrative depth of the captions.
Video QA lasks have seen significant. enhancements through the incorporation of sophisticated memory mechanisms and attention models. The Heterogeneous Memory Enhanced Multimodal Attention Model (Fan et al., 2019) introduced a heterogeneous external memory module on LSTM. This model employed attentional read and write operations to integrate motion and appearance features, co-learning attention mechanisms, and utilizing visual-question interactions to derive global context-aware representations. (Cai al., 2020) further advanced Video QA by introducing fine-grained feature-angmeented memories. This approach strengthened the information augmentation of video and text, improving memory capacity by capturing global interactions between high-level semantic information through self- anff-attention and co-attention modules. RWMN (Na et al., 2017) utilized multi-layered CNNs to read and write sequential memory cells as chunks, effectively representing sequential stories with strong inter-block correlations. Lastly, Glance- (Bai et al., 2023) proposed a two-stage method for Video QA. In the 'glance' stage, an Encoder-Decode generated dynamic event memories without supervision, while in the "focus" stage, these memories bridged the correlation between questions and both high-level event concepts and low-level vidco content, enhancing the model's comprehension and response accuracy.
Other Understaning Task In addition to common video understanding tasks, memory-angmentted models have been increasingly applied to various other video processing tasks, including flow estimation, depth estimation, video deblurring, gesture recognition, and visual speech recognition. Flow estimation has benefited from models like MemFlow (Dong & Fn, 2024), which employs memory storage for real-time flow estimation, retaining motion information to enhance accuracy. In depth estimation, MAMo (Yasarla et al., 2023) augments networks with memory to store learned visual and displacement tokens from previous frames, allowing for more accurate depth predictions through cross-referencing past features. Video deblurring has advanced with MmDeblur (Ji & Yao, 2022), which uses a memory branch to memorize blurry-sharp feature pairs, aiding the deblurring process for incoming frames. Gesture recognition has been improved by MENet (Sun al., 2022), which features dual-branch architecture to capture temporal dynamics between spatiotemporal windows. Visual speech recognition has seen enhancements through frameworks using associative bridges to
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learn interrelationship and obtain target modal representations from memory (Kim et al., 2021), with VAM (Kim et al., 2022a) imprinting audio features into a memory network using visual features, and MVM (Kim et al., 2022b) employing multihead key memories for visual features and a value memory for audio knowledge to distinguish homophenes. These applications show the versatility memory networks in enhancing video understanding systems by leveraging past information for improved accuracy and robustness in complex video analysis scenarios.
Gencration Task In the realm of vidco generation, there is a growing demand for creating long, high-quality videos. This challenge has led to the development of various memory-augmentted models that enhance the quality and coherence of generated video content by leveraging advanced memory.
In the realm of video generation, there is growing demand for creating long, high-quality vidcos, leading to the development of various memory-auggenteer models that enhance video content by leveraging advanced memory networks. The LMC-Memory model (Lee et al., 2021) utilizes memory alignment learning to store long-term motion contexts, improving video prediction by matching these contexts with sequences that exhibit limited dynamics. Similarly, MV-TON (Zhong et al., 2021) introduces a memory refinement module that embeds generated frames into latent space as external memory, aiding subsequent frame generation with richer context. For talking face video generation, MemGAN (Yi et al., 2020) incorporates memory-angmmented GAN module to refine roughly rendered frames into realistic ones, enhancing video quality. Building this, SyncTalkFace (Park et al., 2022) introduces Audio-Lip Memory mechanism to align visual information of the mouth region with input audio, ensuring fine-grained audio-visua coherence. The EMMN model (Tan et al., 2023) constructs a Motion Memory Net that stores emotion embeddings and mouth motion features as key-value pairs, ensuring consistency between expression and lip motion. STAM Attention
(Chang al., 2023) enhances spatiotemporal memorizing capacity by using a SpatioTempon based Memory on 3D-CNN, incorporating global spatiotemporal information to improve vidco prediction. Lastly, MemFace (Tang et al., 2022) addresses missing information in video generation with implicit and explicit memory components, capturing high-level semantics in the audio-expression shared space and aiding the neural-rendering model in synthesizing pixel-level details. These innovations underscore the critical role of memory networks in advancing video generation technology, enabling coherent and visually appealing outputs by effectively integrating past information and context.
5.3 Multimodal Contextual Memory for Robotics
Pick the tablo, then near coffiee rachine
Model
Human
Visual
-Iffaox
class Scone(onddromeett) def (eseervttior(el.t)))
LINFORMAnT RISUIN
Microphane
hasurv
s:lf pose
self ppssttion
action Risten
rONaTS
state(self):
Memory
Iostors
Multimodal Signal
Multiple Sensors
environment
go caudios
query
Navigation
Manipulstion
PICK
Model Output
TURN LEFT
penaliy: '9.2", '0.1"] ['8.7", 19.5", "a.1", '8.3%]
action
Reward
Memory
Environment
Figure 16: Memory for robotics
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Integrating memory mechanisms into the capabilities of embodied agents and robotics has become increasingly essential for enhancing long-term planning, decision-making, visual navigation, and manipulation reasoning. These advancements demonstrate how memory can significantly improve an agent's ability to operate in complex environments.
5.3.1 Multimodal Memory-Augmented Agents
Multimodal memory-angmentedd agents demonstrate the integration of memory in diverse environments. JARVIS-1 (Wang et al., 2023j) equips agent to perceive multimodal inputs, generate complex plans, and perform embodied control in a gaming environment like Minecraft, using memory to combine pre-trained knowledge with actual game experiences. MM-Navigator (Yan et al., 2023) employs GPT-4V for multimodal sumarizaiiiiii in smartphone GUI navigation tasks, converting historical actions into concise natural language memory. MobileGPT (Lee et al., 2023) and AppAgent (Zhang al., 2023b) focus on smartphone applications, accumulating knowledge about apps in graph form and summarizing interaction historics for improved decision-making and interpretability. OS-Copilol (Wu et al., 2024b) features framework for building generalist agents capable of interacting with various elements in operating system, using a configurator with working, declarative, and procodural memory. MEIA (Liu et al., 2024a) offers an embodicd agent for cafe scenes, utilizing a multimodal enviromment memory module that stores key scene information in natural language, guiding large models to execute action plans effectively under diverse requirements.
5.3.2 Memory-Enhanced Navigation, Odometry, and Manipulation
In the field of visual navigation, memory mechanisms play crucial role in enabling agents to navigate effectively. RPF (Kumar et al., 2018) abstracts sequences of images and actions into memories for robust path following using RNNs. SSM (Wang et al., 2021a) introduces an external structured memory that stores visual and geometric information in disentangled layouts, providing a global action space on LSTM for visual navigation. VGM (Kwon et al., 2021) presents visual graph memory based on GCN, which includes unsupervised image representations for navigation history. In the realm of image-goal navigation, memory-augmented reinforcement learning (Mezghani et al., 2022) integrates an external memory mechanism with representations of past observations into the navigation policy. MemoNav (Li al., 2024a) introduces memory model based on GCN and LSTM, attending to short- and long-term memory while efficiently managing memory by forgetting information below a threshold. SMT (Fang et al., 2019) incorporates attention mechanisms to exploit spatio-tempors dependencies, maintaining long time horizons for navigation. Furthermore, MultiON (Wani et al., 2020) proposes navigation tasks to test agents' ability to locate previously observed goal objects. In visual odometry, memory mechanisms also enhance performance. The Deep Visual Odometry With Adaptive Memory model (Xue et al., 2022) employs selective memory based on RNNs improve visual odometry accuracy and adaptability. For manipulation reasoning and planning, RDMemory (Huang et al., 2024b) encodes object-oriented memory into multi-object manipulation framework based on transformers, facilitating sophisticated reasoning and planning capabilities.
5.3.3 Application
Memory a critical component in the evolution of multimodal embodied agents, enabling them to seamlessly integrate and process diverse inputs such as visual, auditory, and textual data for adaptive, context-aware decision-making. Recent advancements highlight the role of memory-enhanced agents in tasks like autonomous navigation, healthcare assistance, interactive education, and smart home systems. By leveraging memory, these agents can retain past interactions, learn from experiences, and adapt to complex, dynamic environments, significantly enhancing their ability to understand, plan, and execule lasks across domains. Applications range from disaster response robots that utilize spatial memory for efficient navigation to personalized assistants that adapt based on user preferences and history. Memory's role in providing continuity and context allows these agents to go beyond static task execution, achieving higher levels of intelligence and functionality in both real-world and virtual scenarios.
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5.4 Limitations, Open Questions, Discussion
While existing research has achieved significant progress in long-sequence multimodal tasks such as long video understanding and long document processing-horizontall horizontal scaling challenges remain particularly pronounced in multimodal systems. This stems from the inherent abundance of visual tokens generated by patch-based image processing methods. Two critical factors exacerbate these challenges:
Multimodal interaction inherently demands multi-turn reasoning, necessitating robust long-term memory to retain contextual coherence.
Timo-scrics modalitics (e.g., audio, video, streaming data) require long-term memory retention to model temporal dependencies effectively.
Embodied learning requires memorizing multimodal information from the interaction between the agent and the real world.
Addressing these memory challenges-bblancinne computational efficiency with model seseiveeenss--rrresenne pivotal frontier for advancing multimodal systems.
6 Conclusion
In this report, we present a narrative review of three distinct types of memory integrated into large language models (LLMs): implicit memory, which is embedded within model parameters; explicit memory, which involves external storage and retrieval mechanisms; and agent memory, which captures persistent interactions with environments. Additionally, we systematically examine memory mechanisms specifically designed for and utilized by multimodal LLMs. Our survey is meticulously structured to trace the developmental trajectory of memory mechanisms, spanning from foundational concepts to the most recent advancements. We provide not only comprehensive map of the landscape of LLM memory but also detailed explorations of pivotal milestones, rigorous empirical evaluations, and a forward-looking vision for the field's future development.
7 Future Work and Limitations
Based this comprehensive review, outline several key considerations and future research directions. First, there is a critical need to advance our understanding of the internal mechanisms of Trannforrmer architectures and to develop more effective frameworks for implicit memory modeling. Second, enhancing the long-context processing capabilitics of LLMs, cither through extended context windows or retrieval-augmented generation (RAG), essential; however, each approach presents trade-ofls terms of computational elficiency and scalability. Third, dynamic memory adaptation, inspired by human learning strategies such as recursive retrieval and experience reflection, holds promise for improving reasoning and communication in agent-basee systems. Finally, multimodal systems face particular challenges stemming from the high volume of visual tokens, the complexity of multi-turn reasoning, and the temporal dependencies inherent in time-series data. Addressing these challenges calls for the development of scalable, memory-efficient architectures that support coherent, adaptive, and multimodal long-tern learning.
In our survey, we discuss memory in LLMs, agents, and multimodal LLMs, but we do not provide a unified evaluation framework for all memory types due to the diverse aspocts cach memory mechanism emphasizes. Moreover, do not propose single platform or system that integrates these various memory mechanisms. We invite fellow researchers to engage with findings and collaborate toward the advancement of memory-augmented AI systems.
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64


Reviews:
[{"review_id": "aOx6IztpLe", "reviewer": "Reviewer_LqYp", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths\n\n1 The survey covers a wide range of memory mechanisms, from parametric knowledge in transformers to retrieval-augmented generation  and agent memory systems.\n\n2 The taxonomy (implicit/explicit/agentic memory) is well-defined and logically partitions the literature. Figures (e.g., Fig. 1, 2, 5) effectively visualize memory paradigms.\n\n3  The inclusion of memory in multimodal contexts (e.g., video, audio, robotics) is timely and valuable, as most prior surveys focus solely on text.\n\nWeaknesses\n\n1 Some content (e.g., scaling laws, associative memory) is repeated across sections, which could be streamlined for conciseness.\n\n2 While the taxonomy is well-structured, a quantitative comparison of memory-augmented methods (e.g., accuracy, latency trade-offs) would strengthen the survey.\n\n3 The distinction from related works like Zhang et al. [R1] or Du et al. [R2] could be sharper, especially in the agent memory section.\n\n4 The \"Evaluation on Agent Memory\" section (4.4) is preliminary; deeper discussion of standardized benchmarks/metrics for memory-augmented models would be helpful.\n\nReferences:\n[R1] Zhang Z, Dai Q, Bo X, et al. A survey on the memory mechanism of large language model based agents[J]. ACM Transactions on Information Systems, 2024.\n\n[R2] Du Y, Huang W, Zheng D, et al. Rethinking memory in ai: Taxonomy, operations, topics, and future directions[J]. arXiv preprint arXiv:2505.00675, 2025.\n\n5 Some minor suggestions: (1) The survey methods in \"Taxonomy of Implicit Memory in Transformer\" lack inclusion of approaches from 2025. (2) Figure 3 occupies significant space but provides minimal information, particularly containing many undefined diagrams.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "1 This survey paper provides a comprehensive and structured synthesis of memory mechanisms in Large Language Models (LLMs) and Multi-Modal LLMs (MLLMs). \n\n2 The authors propose a cohesive taxonomy of memory in LLMs/MLLMs, categorizing it into three primary frameworks: implicit memory, explicit memory, and agentic memory. \n\n3 Besides, the survey also extends beyond text to examine memory integration in multi-modal settings (vision, language, audio), emphasizing coherence across modalities. \n\n4 It discusses key advances in memory-augmented architectures, benchmark tasks, and open challenges (e.g., memory capacity, alignment, factual consistency).", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "1 Merge overlapping discussions (e.g., scaling laws for implicit/explicit memory) to improve flow.\n\n2 Include a table summarizing key methods (e.g., ROME, MEMIT, RETRO) with metrics (e.g., edit success rate, retrieval accuracy) to highlight trade-offs.\n\n3 Elaborate on memory-specific hurdles in multimodal systems (e.g., token inefficiency in video, audio-video synchronization).\n\n4 Provide more concrete proposals (e.g., architectural templates for dynamic memory, cross-modal memory alignment techniques).", "broader_impact_concerns": "NA", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "5S27pwfzZy", "reviewer": "Reviewer_7zjY", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "**Strengths**\n1. The unification of the taxonomy for memory-augmented LLMs across different modes.\n2. A comprehensive and detailed survey.\n3. The paper is well-written and well-organized.\n\n**Weakness**\n1. The breadth of the survey comes at cost of limited depth of various approaches discussed.\n2. While the paper outlines the limitations of current research across different memory modes and provides targeted recommendations, it could further benefit from the proposal of a unified evaluation framework (however preliminary or speculative) to systematically assess progress in the field.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The paper presents a detailed survey of the role of memory in LLMs, offering a unified taxonomy across different memory modes (implicit, explicit, and agentic), memory horizons (short- and long-term), training and inference phases, architectures, and data formats (text, graph, vector, and multi-modal). Additionally, it presents a discussion on the use of memory in multi-modal LLMs.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "N/A", "broader_impact_concerns": "N/A", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "8LXPA8N1Fj", "reviewer": "Reviewer_xD2c", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "The paper studies an important topic of memory meets large language models and provides a comprehensive survey. Generally, the paper is well-written and easy to follow. The survey is comprehensive, which includes implicit memory, explicit memory, agentic memory, and memory-augmented multi-modal large language models. However, the paper can be improved as follows.\n\nHigh-dimensional data, e.g., time series and spatial-temporal data, is also well-studied in the LLM era. It is suggested to include more related work regarding LLM-based time series analytics as follows.\n[1]. Towards Cross-Modality Modeling for Time Series Analytics: A Survey in the LLM Era, IJCAI 2025.\n\n[2]. Efficient Multivariate Time Series Forecasting via Calibrated Language Models with Privileged Knowledge Distillation, ICDE 2025.\n\n[3]. TimeCMA: Towards LLM-Empowered Multivariate Time Series Forecasting via Cross-Modality Alignment, AAAI 2025.\n\n[4]. Foundation Models for Spatio-Temporal Data Science: A Tutorial and Survey, KDD 2025.\n\nIt would be better to provide a specific section for future work. In this way, the readers can get the information more intuitively.\nIt would be interesting to provide the drawbacks of this survey.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The paper studies an important topic of memory meets large language models and provides a comprehensive survey. Generally, the paper is well-written and easy to follow. The survey is comprehensive, which includes implicit memory, explicit memory, agentic memory, and memory-augmented multi-modal large language models.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "Please see the weaknesses.", "broader_impact_concerns": "NA", "additional_comments": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Decision: Accept (poster)