title: AI-native Memory 2.0: Second Me
summary: Human interaction with the external world fundamentally involves the exchange
  of personal memory, whether with other individuals, websites, applications, or,
  in the future, AI agents. A significant portion of this interaction is
  redundant, requiring users to repeatedly provide the same information across
  different contexts. Existing solutions, such as browser-stored credentials,
  autofill mechanisms, and unified authentication systems, have aimed to mitigate
  this redundancy by serving as intermediaries that store and retrieve commonly
  used user data. The advent of large language models (LLMs) presents an
  opportunity to redefine memory management through an AI-native paradigm: SECOND
  ME. SECOND ME acts as an intelligent, persistent memory offload system that
  retains, organizes, and dynamically utilizes user-specific knowledge. By
  serving as an intermediary in user interactions, it can autonomously generate
  context-aware responses, prefill required information, and facilitate seamless
  communication with external systems, significantly reducing cognitive load and
  interaction friction. Unlike traditional memory storage solutions, SECOND ME
  extends beyond static data retention by leveraging LLM-based memory
  parameterization. This enables structured organization, contextual reasoning,
  and adaptive knowledge retrieval, facilitating a more systematic and
  intelligent approach to memory management. As AI-driven personal agents like
  SECOND ME become increasingly integrated into digital ecosystems, SECOND ME
  further represents a critical step toward augmenting human-world interaction
  with persistent, contextually aware, and self-optimizing memory systems. We
  have open-sourced the fully localizable deployment system at GitHub:
  https://github.com/Mindverse/Second-Me.
paper_authors._id: 67d12c32428a3d8d5281f310
paper_authors.name: Jiale Wei
paper_authors.status: admin_assigned
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paper_authors.fullname: Jiale Wei
paper_authors.type: user
paper_authors.user: JialeWei
paper_authors._id: 67d12c32428a3d8d5281f311
paper_authors.name: Xiang Ying
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paper_authors.fullname: Xiang Ying
paper_authors.type: user
paper_authors.user: MindYing
paper_authors._id: 67d12c32428a3d8d5281f312
paper_authors.name: Tao Gao
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paper_authors.fullname: Tao Gao
paper_authors.type: user
paper_authors.user: moralCompass
paper_authors._id: 67d12c32428a3d8d5281f313
paper_authors.name: Felix Tao
paper_authors.status: admin_assigned
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paper_authors._id: 6680c5b174fb1736a4bfec1f
paper_authors.avatarUrl: /avatars/8cc1b74977262d12593fa507b20a5911.svg
paper_authors.fullname: Felix Tao
paper_authors.type: user
paper_authors.user: felixtao
paper_authors._id: 67d12c32428a3d8d5281f314
paper_authors.name: Jingbo Shang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-12T22:04:21.102Z
paper_authors._id: 660655119e3555d648f6c6b5
paper_authors.avatarUrl: /avatars/ae1e2c97a08be39b77a9f1a5c2a718ef.svg
paper_authors.fullname: Jingbo Shang
paper_authors.type: user
paper_authors.user: shangjingbo
paper_title: AI-native Memory 2.0: Second Me
paper_summary: Human interaction with the external world fundamentally involves the exchange
  of personal memory, whether with other individuals, websites, applications, or,
  in the future, AI agents. A significant portion of this interaction is
  redundant, requiring users to repeatedly provide the same information across
  different contexts. Existing solutions, such as browser-stored credentials,
  autofill mechanisms, and unified authentication systems, have aimed to mitigate
  this redundancy by serving as intermediaries that store and retrieve commonly
  used user data. The advent of large language models (LLMs) presents an
  opportunity to redefine memory management through an AI-native paradigm: SECOND
  ME. SECOND ME acts as an intelligent, persistent memory offload system that
  retains, organizes, and dynamically utilizes user-specific knowledge. By
  serving as an intermediary in user interactions, it can autonomously generate
  context-aware responses, prefill required information, and facilitate seamless
  communication with external systems, significantly reducing cognitive load and
  interaction friction. Unlike traditional memory storage solutions, SECOND ME
  extends beyond static data retention by leveraging LLM-based memory
  parameterization. This enables structured organization, contextual reasoning,
  and adaptive knowledge retrieval, facilitating a more systematic and
  intelligent approach to memory management. As AI-driven personal agents like
  SECOND ME become increasingly integrated into digital ecosystems, SECOND ME
  further represents a critical step toward augmenting human-world interaction
  with persistent, contextually aware, and self-optimizing memory systems. We
  have open-sourced the fully localizable deployment system at GitHub:
  https://github.com/Mindverse/Second-Me.
paper_ai_keywords: large language models (LLMs) intelligent, persistent memory offload system context-aware responses structured organization contextual reasoning adaptive knowledge retrieval self-optimizing memory systems