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Persistent Identity in AI Agents: A Multi-Anchor Architecture for Resilient Memory and Continuity

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arXiv:2604.09588v1 Announce Type: new Abstract: Modern AI agents suffer from a fundamental identity problem: when context windows overflow and conversation histories are summarized, agents experience catastrophic forgetting -- losing not just information, but continuity of self. This technical limitation reflects a deeper architectural flaw: AI agent identity is centralized in a single memory store, creating a single point of failure. Drawing on neurological case studies of human memory disorder

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    Computer Science > Artificial Intelligence [Submitted on 2 Mar 2026] Persistent Identity in AI Agents: A Multi-Anchor Architecture for Resilient Memory and Continuity Prahlad G. Menon Modern AI agents suffer from a fundamental identity problem: when context windows overflow and conversation histories are summarized, agents experience catastrophic forgetting -- losing not just information, but continuity of self. This technical limitation reflects a deeper architectural flaw: AI agent identity is centralized in a single memory store, creating a single point of failure. Drawing on neurological case studies of human memory disorders, we observe that human identity survives damage because it is distributed across multiple systems: episodic memory, procedural memory, emotional continuity, and embodied knowledge. We present this http URL, an open-source architecture that implements persistent identity through separable components (identity files and memory logs), and propose extensions toward multi-anchor resilience. The framework introduces a hybrid RAG+RLM retrieval system that automatically routes queries to appropriate memory access patterns, achieving efficient retrieval without sacrificing comprehensiveness. We formalize the notion of identity anchors for AI systems and present a roadmap for building agents whose identity can survive partial memory failures. Code is available at this http URL Comments: 18 pages, 2 figures. Submitting to arXiv cs.ET (Emerging Technologies) Subjects: Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET); Machine Learning (cs.LG) ACM classes: I.2.7; H.3.3 Cite as: arXiv:2604.09588 [cs.AI]   (or arXiv:2604.09588v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2604.09588 Focus to learn more Submission history From: Prahlad Menon [view email] [v1] Mon, 2 Mar 2026 02:34:50 UTC (79 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-04 Change to browse by: cs cs.ET cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv AI
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    ◬ AI & Machine Learning
    Published
    Apr 14, 2026
    Archived
    Apr 14, 2026
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