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
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From: Prahlad Menon [view email]
[v1] Mon, 2 Mar 2026 02:34:50 UTC (79 KB)
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