AGENTIC-SYSTEMSApril 14, 20261 min read8 views

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

Researchers propose a multi-anchor architecture for AI agents to maintain persistent identity and continuity, drawing inspiration from human neurology to overcome the 'catastrophic forgetting' caused by context window limitations.

Computer Science > Artificial Intelligence

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

View PDF HTML (experimental)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 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

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Source: arXiv cs.AI Recent

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