This paper explores an innovative aspect of the Set Shaping Theory, the use of a negative shaping order K. Traditionally, the theory utilizes a positive K to extend the length of data strings, enhancing their testability and compressibility. We propose a paradigm shift by employing a negative K, which shortens data strings and potentially improves compression efficiency. However, this approach sacrifices the local testability of the data, a cornerstone in traditional Set Shaping Theory. We examine the theoretical implications, practical benefits, and challenges of this new methodology.

翻译：本文探讨了集合成形理论的一个创新方面——负成形阶K的应用。传统上，该理论使用正K值来扩展数据字符串的长度，从而增强其可测试性和可压缩性。我们提出一种范式转变，即采用负K值来缩短数据字符串，并可能提高压缩效率。然而，这种方法牺牲了数据的局部可测试性，而这一特性是传统集合成形理论的基石。我们分析了这一新方法的理论意义、实际优势以及面临的挑战。