AI 代理的持久身份：面向韧性记忆与连续性的多锚点架构
摘要
现代 AI 代理面临身份根本问题：当上下文窗口溢出且对话历史被概括时，代理会遭受灾难性遗忘——不仅丢失信息，更丢失自我的连续性。这一技术限制反映出更深层的架构缺陷：AI 代理的身份集中在单个记忆存储中，形成单点故障。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 soul.py, 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 github.com/menonpg/soul.py
引用
@article{arxiv.2604.09588,
title = {Persistent Identity in AI Agents: A Multi-Anchor Architecture for Resilient Memory and Continuity},
author = {Prahlad G. Menon},
journal= {arXiv preprint arXiv:2604.09588},
year = {2026}
}
备注
18 pages, 2 figures. Submitting to arXiv cs.ET (Emerging Technologies)