AI自我生成内容作为训练数据会引发无限复杂度增长
When Your Own Output Becomes Your Training Data: Noise-to-Meaning Loops and a Formal RSI Trigger
- 提出噪声到意义的递归自提升框架，模拟AI自我反馈机制
- 当信息整合突破阈值，系统内部复杂度将无限增长
- 适合关注AI安全与自我演化机制的研究者参考
我们提出噪声到意义的递归自提升（N2M-RSI）模型，该最小化形式框架表明：当一个AI代理将其自身输出作为输入，并跨越显式的知识整合阈值后，在假设条件下其内部复杂度将无界增长。该框架统一了早期关于自提示大语言模型、哥德尔式自指及AutoML的思想，同时保持实现无关性。模型还能自然扩展至多智能体群体，一旦允许实例间通信，便可能产生超线性效应。出于安全考虑，本文省略具体系统实现细节，仅在附录C中提供简化的、与模型无关的原型。
原文摘要 · Abstract (English)
We present Noise-to-Meaning Recursive Self-Improvement (N2M-RSI), a minimal formal model showing that once an AI agent feeds its own outputs back as inputs and crosses an explicit information-integration threshold, its internal complexity will grow without bound under our assumptions. The framework unifies earlier ideas on self-prompting large language models, Gödelian self-reference, and AutoML, yet remains implementation-agnostic. The model furthermore scales naturally to interacting swarms of agents, hinting at super-linear effects once communication among instances is permitted. For safety reasons, we omit system-specific implementation details and release only a brief, model-agnostic toy prototype in Appendix C.
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