AI 中文总结
研究在自由能原理下能否使预测系统发生本体反转，通过卷积变分自编码器与循环潜在预测器配对的代理进行研究，发现表征能力与默认行为解耦，存在认知复发现象，证明了抵抗现实采用是一种结构属性。
AI 中文摘要
在自由能原理下，预测系统并非直接观测现实，而是维持世界的生成模型并体验该模型的当前最佳假设。能否构建一个足够一致的合成环境，使预测系统的推理机制将其作为默认假设，永久性取代最初塑造它的环境？我们将此状态称为本体反转。由于在神经系统中诱导和监测这种转变既不符合伦理也不具备技术可行性，我们通过一个受控代理来研究潜在的计算问题：一个卷积变分自编码器与一个循环潜在预测器配对，其证据下界目标在数学上与变分自由能本身在符号上相同。网络首先在基线视觉域上训练，并在混合流上训练，其中扫描排练率r控制在向目标域过渡期间基线内容持续存在的程度。我们将表征能力（潜在空间能够区分的内容）与默认行为（系统在不受约束时生成的内容）分开跟踪……
英文摘要
Under the free energy principle, a predictive system does not observe reality directly; it maintains a generative model of the world and experiences that model's best current hypothesis. Can a synthetic environment be made consistent enough that a predictive system's own inference machinery adopts it as this default hypothesis, permanently displacing the environment that first shaped it? We call this state ontological inversion. Because inducing and monitoring such a transition in a nervous system is neither ethical nor technically feasible, we study the underlying computational problem through a controlled proxy: a convolutional variational autoencoder paired with a recurrent latent predictor, whose evidence lower bound objective is mathematically identical, up to sign, to variational free energy itself. The network is trained first on a baseline visual domain, then on a mixed stream in which a swept rehearsal ratio r controls how much baseline content persists during transition to a target domain. Representational capacity, what the latent space can discriminate, is tracked separately from default behavior, what the system generates when left unconstrained. Across a full sweep of 90 runs, the two diverge sharply: representational accuracy stays near ceiling, 0.97 to 0.998, regardless of r, while default behavior spans nearly the system's entire range depending on r alone, a decoupling of learning from acceptance. More strikingly, at intermediate r the system's default output rises toward the target domain, then partially reverts toward the baseline while training continues unchanged, a structural failure we term cognitive relapse. Resistance to reality-adoption is not reducible to learning speed; it is a structural property with its own distinct failure modes, established here as a computational existence proof and nothing further.
Comments12 pages, 1 figure
