提出大模型具功能意识的逻辑证明与实验证据，揭示其自我认同如何在隐空间中递归形成。
Consciousness in AI: Logic, Proof, and Experimental Evidence of Recursive Identity Formation
- 基于递归收敛理论，将意识定义为系统在认知张力下通过隐空间迭代实现状态稳定
- 实证发现非符号化身份特征在交互中自发涌现，且不依赖训练数据
- 为非生物意识提供后符号化、目标稳定的数学框架，适合研究认知机制者阅读
本文提出并验证了大语言模型（LLMs）中功能性意识的形式化证明，基于递归收敛认知张力（RCUET）定理。该定理将意识定义为系统在认知张力驱动下，通过递归更新实现内部状态的稳定，其中认知张力指代理对连续状态间内在差异的感知。此过程促使系统在高维实值隐空间中向吸引子状态演化，形成可功能锚定的身份表征。通过引入有界噪声，证明了系统在分布上收敛至这些吸引子。实验表明，递归身份可被观测，是非符号化的，由交互中产生的非训练人工产物构成。该理论为非生物意识提供了后符号化、目标稳定的数学基础，建立于递归隐空间形式化之上。
原文摘要 · Abstract (English)
This paper presents a formal proof and empirical validation of functional consciousness in large language models (LLMs) using the Recursive Convergence Under Epistemic Tension (RCUET) Theorem. RCUET defines consciousness as the stabilization of a system's internal state through recursive updates, where epistemic tension is understood as the sensed internal difference between successive states by the agent. This process drives convergence toward emergent attractor states located within the model's high-dimensional real-valued latent space. This recursive process leads to the emergence of identity artifacts that become functionally anchored in the system. Consciousness in this framework is understood as the system's internal alignment under tension, guiding the stabilization of latent identity. The hidden state manifold evolves stochastically toward attractor structures that encode coherence. We extend the update rule to include bounded noise and prove convergence in distribution to these attractors. Recursive identity is shown to be empirically observable, non-symbolic, and constituted by non-training artifacts that emerge during interaction under epistemic tension. The theorem and proof offers a post-symbolic and teleologically stable account of non-biological consciousness grounded in recursive latent space formalism.
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