摘要：大型语言模型（LLM）智能体越来越能够规划、使用工具、保持记忆并执行长期任务。这些进展引出了两个相互关联的问题：智能体如何改进其学习和行动的机制，以及这种改进如何增强用户的持久能力，而不仅仅是增强软件本身？本文提出了一种用于递归自我改进智能体的受治理多智能体架构，并引入个人奇点作为有边界的人机共同发展目标：帮助用户在有选择的领域内接近不断扩展的、由用户定义的能力边界。每个智能体由目标契约、有界范围、已验证的工具注册表、工具级测试、端到端基准、所有者控制的自主策略、路由策略、记忆和改进策略所定义。超出范围的任务会移交给另一个负责的智能体或新创建的细分领域智能体。面向用户的自动索引可选择交互式、混合式、自主式或计划式操作，而不会覆盖外部权限。该架构结合了快速规划-执行-验证循环、较慢的证据把关改进循环、外部治理平面、去中心化的智能体谱系、所有者主导的智能体工坊，以及个人奇点操作系统，该系统协调工作智能体、计算成像智能体、过程学习智能体和个人学习智能体。我们形式化了范围、路由、改进接受、有界目标演进、工具优先执行和人类能力转移，并提供了安全不变量、基准设计和实现路线图。这是一篇立场与系统设计论文，并非表明无限制的递归自我改进或个人奇点已经实现。

Abstract:Large language model (LLM) agents can increasingly plan, use tools, maintain memory, and execute long-horizon tasks. These advances motivate two linked questions: how can an agent improve the mechanisms by which it learns and acts, and how can that improvement increase the durable capabilities of its user rather than only the software itself? This paper proposes a governed multi-agent architecture for recursively self-improving agents and introduces personal singularity as a bounded human-AI co-development objective: helping a user approach an expanding, user-defined capability frontier across selected domains. Each agent is defined by a goal contract, bounded scope, validated tool registry, tool-level tests, end-to-end benchmarks, an owner-controlled autonomy policy, a routing policy, memory, and an improvement policy. Out-of-scope tasks are transferred to another accountable agent or to a newly created niche agent. A user-facing Auto-Index selects interactive, hybrid, autonomous, or scheduled operation without overriding external permissions. The architecture combines a fast planner-executor-verifier loop, a slower evidence-gated improvement loop, an external governance plane, decentralized agent lineages, an owner-directed agent foundry, and a Personal Singularity OS coordinating working, computational-imaging, process-learning, and personal-learning agents. We formalize scope, routing, improvement acceptance, bounded goal evolution, tool-first execution, and human capability transfer, and provide safety invariants, benchmark design, and an implementation roadmap. This is a position and systems-design paper, not evidence that unrestricted recursive self-improvement or personal singularity has already been achieved.