We introduce a univariate signal deconvolution method based on the principles of an approach to Artificial General Intelligence in order to build a general-purpose model of models independent of any arbitrarily assumed prior probability distribution. We investigate how non-random data may encode information about the physical properties, such as dimensions and length scales of the space in which a signal or message may have been originally encoded, embedded, or generated. Our multidimensional space reconstruction method is based on information theory and algorithmic probability, so that it is proven to be agnostic vis-a-vis the arbitrarily chosen encoding-decoding scheme, computable or semi-computable method of approximation to algorithmic complexity, and computational model. The results presented in this paper are useful for applications in coding theory, particularly in zero-knowledge one-way communication channels, such as in deciphering messages from unknown generating sources about which no prior knowledge is available and to which no return message can be sent. We argue that this method has the potential to be of great value in cryptography, signal processing, causal deconvolution, life and technosignature detection.

翻译：本文提出了一种基于通用人工智能原理的单变量信号解卷积方法，旨在构建一个不依赖任意先验概率分布的通用模型库。我们研究了非随机数据如何编码关于信号或消息原始编码、嵌入或生成空间物理属性（如维度与尺度）的信息。我们的多维空间重建方法基于信息论与算法概率，被证明对任意选择的编解码方案、可计算或半可计算的算法复杂度近似方法以及计算模型均保持无偏性。本文的研究成果对编码理论具有实际应用价值，尤其适用于零知识单向通信信道——例如在无法获取先验知识且无法发送返回消息的情况下，对来自未知生成源的信号进行破译。我们认为，该方法在密码学、信号处理、因果解卷积以及生命与科技信号探测领域具有重要应用潜力。