Hugging Face Daily Papers · June 25, 2026 · 4 min read

Are We Ready For An Agent-Native Memory System?

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Please refer to our paper list at: <a href=\"https://github.com/OpenDataBox/awesome-agent-memory\" rel=\"nofollow\">https://github.com/OpenDataBox/awesome-agent-memory</a>; Please refer to our code repository at: <a href=\"https://github.com/OpenDataBox/MemoryData\" rel=\"nofollow\">https://github.com/OpenDataBox/MemoryData</a>.\n<a href=\"https://cdn-uploads.huggingface.co/production/uploads/68216c63856b96f869d1d116/-TmBEtd46a-g19MBYgXa_.png\" rel=\"nofollow\"><img src=\"https://cdn-uploads.huggingface.co/production/uploads/68216c63856b96f869d1d116/-TmBEtd46a-g19MBYgXa_.png\" alt=\"memory\"></a>\n<a href=\"https://cdn-uploads.huggingface.co/production/uploads/68216c63856b96f869d1d116/OL3LutUSo43swh58hIjqG.png\" rel=\"nofollow\"><img src=\"https://cdn-uploads.huggingface.co/production/uploads/68216c63856b96f869d1d116/OL3LutUSo43swh58hIjqG.png\" alt=\"memorycate\"></a>\n","updatedAt":"2026-06-25T03:51:46.107Z","author":{"_id":"68216c63856b96f869d1d116","avatarUrl":"/avatars/f69026ca75377e6754ab3e317879e35a.svg","fullname":"Wei Zhou","name":"weizhoudb","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":3,"isUserFollowing":false}},"numEdits":2,"identifiedLanguage":{"language":"en","probability":0.8159105777740479},"editors":["weizhoudb"],"editorAvatarUrls":["/avatars/f69026ca75377e6754ab3e317879e35a.svg"],"reactions":[{"reaction":"🔥","users":["weizhoudb","jimi888","muskkk"],"count":3},{"reaction":"🤗","users":["weizhoudb","jimi888","muskkk"],"count":3},{"reaction":"🚀","users":["weizhoudb","jimi888"],"count":2}],"isReport":false}},{"id":"6a3ca318825aa59937202f2f","author":{"_id":"697c75fbe2b8ef94ad7f9c65","avatarUrl":"/avatars/677bedeaaecd1365a9568702f5c198a7.svg","fullname":"Zhang","name":"djangodev33","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"isUserFollowing":false},"createdAt":"2026-06-25T03:40:08.000Z","type":"comment","data":{"edited":true,"hidden":true,"hiddenBy":"","latest":{"raw":"This comment has been hidden","html":"This comment has been hidden","updatedAt":"2026-06-25T03:43:16.994Z","author":{"_id":"697c75fbe2b8ef94ad7f9c65","avatarUrl":"/avatars/677bedeaaecd1365a9568702f5c198a7.svg","fullname":"Zhang","name":"djangodev33","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"isUserFollowing":false}},"numEdits":0,"editors":[],"editorAvatarUrls":[],"reactions":[]}},{"id":"6a3ca45b25644bdcf89ef688","author":{"_id":"68216c63856b96f869d1d116","avatarUrl":"/avatars/f69026ca75377e6754ab3e317879e35a.svg","fullname":"Wei Zhou","name":"weizhoudb","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":3,"isUserFollowing":false},"createdAt":"2026-06-25T03:45:31.000Z","type":"comment","data":{"edited":true,"hidden":false,"latest":{"raw":"Welcome to utilize our agent memory testbed :)\n","html":"Welcome to utilize our agent memory testbed :)\n","updatedAt":"2026-06-25T03:48:27.276Z","author":{"_id":"68216c63856b96f869d1d116","avatarUrl":"/avatars/f69026ca75377e6754ab3e317879e35a.svg","fullname":"Wei Zhou","name":"weizhoudb","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":3,"isUserFollowing":false}},"numEdits":1,"identifiedLanguage":{"language":"en","probability":0.4366520941257477},"editors":["weizhoudb"],"editorAvatarUrls":["/avatars/f69026ca75377e6754ab3e317879e35a.svg"],"reactions":[{"reaction":"🔥","users":["weizhoudb"],"count":1},{"reaction":"🚀","users":["weizhoudb"],"count":1}],"isReport":false}}],"primaryEmailConfirmed":false,"paper":{"id":"2606.24775","authors":[{"_id":"6a3c9fedf3facdb67e9ff137","name":"Wei Zhou","hidden":false},{"_id":"6a3c9fedf3facdb67e9ff138","name":"Xuanhe Zhou","hidden":false},{"_id":"6a3c9fedf3facdb67e9ff139","name":"Shaokun Han","hidden":false},{"_id":"6a3c9fedf3facdb67e9ff13a","name":"Hongming Xu","hidden":false},{"_id":"6a3c9fedf3facdb67e9ff13b","name":"Guoliang Li","hidden":false},{"_id":"6a3c9fedf3facdb67e9ff13c","name":"Zhiyu Li","hidden":false},{"_id":"6a3c9fedf3facdb67e9ff13d","name":"Feiyu Xiong","hidden":false},{"_id":"6a3c9fedf3facdb67e9ff13e","name":"Fan Wu","hidden":false}],"publishedAt":"2026-06-23T00:00:00.000Z","submittedOnDailyAt":"2026-06-25T00:00:00.000Z","title":"Are We Ready For An Agent-Native Memory System?","submittedOnDailyBy":{"_id":"68216c63856b96f869d1d116","avatarUrl":"/avatars/f69026ca75377e6754ab3e317879e35a.svg","isPro":false,"fullname":"Wei Zhou","user":"weizhoudb","type":"user","name":"weizhoudb"},"summary":"Memory for large language model (LLM) agents has rapidly evolved from simple retrieval-augmented mechanisms into a data management system that supports persistent information storage, retrieval, update, consolidation, and dynamic lifecycle governance throughout agent execution. 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Papers

arxiv:2606.24775

Are We Ready For An Agent-Native Memory System?

Published on Jun 23

· Submitted by

Wei Zhou on Jun 25

#1 Paper of the day

Shanghai Jiao Tong University

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Abstract

Large language model agents' memory systems have evolved into complex data management frameworks requiring systematic evaluation across multiple modules and workloads to understand their performance characteristics and trade-offs.

Generated by Qwen/Qwen2.5-Coder-32B-Instruct

Memory for large language model (LLM) agents has rapidly evolved from simple retrieval-augmented mechanisms into a data management system that supports persistent information storage, retrieval, update, consolidation, and dynamic lifecycle governance throughout agent execution. Despite this evolution, existing evaluations still benchmark agent memory mainly through end-to-end task success metrics (e.g., F1, BLEU), while treating the underlying system as a monolithic black box. As a result, critical system-level concerns, including operational costs, architectural trade-offs across memory modules, and robustness under dynamic knowledge updates, remain insufficiently explored. In this paper, we present a systematic experimental study of agent memory from a data management perspective. We propose an analytical framework that decomposes agent memory into four core modules: memory representation and storage, extraction, retrieval and routing, and maintenance. Under this framework, we evaluate 12 representative memory systems and two reference baselines across five benchmark workloads spanning 11 datasets. Our extensive end-to-end evaluation shows that no single architecture dominates across all scenarios; instead, effectiveness depends heavily on how well the memory structure aligns with the workload bottleneck. Furthermore, through fine-grained ablation studies, we quantify their individual effects on representation fidelity, retrieval precision, update correctness, and long-horizon stability. Finally, we reveal cost-performance trade-offs under realistic workloads, showing localized maintenance is more cost-efficient than global reorganization. Based on these findings, we identify promising directions towards building truly agent-native memory systems. The code is publicly available at https://github.com/OpenDataBox/MemoryData.