AI-native Memory 2.0: Second Me

2025-03-11

AI Summary

Repeated manual entry of personal information during human–system interactions imposes significant cognitive load and introduces privacy risks. Method: This paper proposes Second Me, an AI-native memory management system that deeply integrates large language models (LLMs) into personal memory management for the first time. It introduces an LLM-driven memory parameterization mechanism enabling structured memory representation, context-aware reasoning, and adaptive retrieval. Second Me implements a localized, context-sensitive knowledge agent deployable end-to-end without uploading user data. Contribution/Results: Unlike conventional static storage approaches, Second Me substantially reduces cross-platform interaction redundancy. Empirical evaluation demonstrates an 87% reduction in redundant information input while preserving privacy, thereby enhancing interaction efficiency and knowledge reusability.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Machine Learning: Privacy

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Security and Privacy: Large-scale security measurements

Abstract

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.

Problem

• Reduces redundant user information exchange across contexts
• Enhances memory management using AI-native systems
• Facilitates seamless, context-aware interactions with external systems

Innovation

• AI-native memory offload system
• LLM-based memory parameterization
• Context-aware autonomous response generation