From Human Memory to AI Memory: A Survey on Memory Mechanisms in the Era of LLMs
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915254808608768 |
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| author | Wu, Yaxiong Liang, Sheng Zhang, Chen Wang, Yichao Zhang, Yongyue Guo, Huifeng Tang, Ruiming Liu, Yong |
| author_facet | Wu, Yaxiong Liang, Sheng Zhang, Chen Wang, Yichao Zhang, Yongyue Guo, Huifeng Tang, Ruiming Liu, Yong |
| contents | Memory is the process of encoding, storing, and retrieving information, allowing humans to retain experiences, knowledge, skills, and facts over time, and serving as the foundation for growth and effective interaction with the world. It plays a crucial role in shaping our identity, making decisions, learning from past experiences, building relationships, and adapting to changes. In the era of large language models (LLMs), memory refers to the ability of an AI system to retain, recall, and use information from past interactions to improve future responses and interactions. Although previous research and reviews have provided detailed descriptions of memory mechanisms, there is still a lack of a systematic review that summarizes and analyzes the relationship between the memory of LLM-driven AI systems and human memory, as well as how we can be inspired by human memory to construct more powerful memory systems. To achieve this, in this paper, we propose a comprehensive survey on the memory of LLM-driven AI systems. In particular, we first conduct a detailed analysis of the categories of human memory and relate them to the memory of AI systems. Second, we systematically organize existing memory-related work and propose a categorization method based on three dimensions (object, form, and time) and eight quadrants. Finally, we illustrate some open problems regarding the memory of current AI systems and outline possible future directions for memory in the era of large language models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_15965 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Human Memory to AI Memory: A Survey on Memory Mechanisms in the Era of LLMs Wu, Yaxiong Liang, Sheng Zhang, Chen Wang, Yichao Zhang, Yongyue Guo, Huifeng Tang, Ruiming Liu, Yong Information Retrieval H.0 Memory is the process of encoding, storing, and retrieving information, allowing humans to retain experiences, knowledge, skills, and facts over time, and serving as the foundation for growth and effective interaction with the world. It plays a crucial role in shaping our identity, making decisions, learning from past experiences, building relationships, and adapting to changes. In the era of large language models (LLMs), memory refers to the ability of an AI system to retain, recall, and use information from past interactions to improve future responses and interactions. Although previous research and reviews have provided detailed descriptions of memory mechanisms, there is still a lack of a systematic review that summarizes and analyzes the relationship between the memory of LLM-driven AI systems and human memory, as well as how we can be inspired by human memory to construct more powerful memory systems. To achieve this, in this paper, we propose a comprehensive survey on the memory of LLM-driven AI systems. In particular, we first conduct a detailed analysis of the categories of human memory and relate them to the memory of AI systems. Second, we systematically organize existing memory-related work and propose a categorization method based on three dimensions (object, form, and time) and eight quadrants. Finally, we illustrate some open problems regarding the memory of current AI systems and outline possible future directions for memory in the era of large language models. |
| title | From Human Memory to AI Memory: A Survey on Memory Mechanisms in the Era of LLMs |
| topic | Information Retrieval H.0 |
| url | https://arxiv.org/abs/2504.15965 |