Bypassing Shannon's Limit: A Kolmogorov Complexity Approach to Stateless Inverse Data Mapping and Zero-Storage Materialization

The fundamental limits of data compression and transmission, governed by Shannon's Source Coding Theorem, dictate that the physical size of lossless data cannot be reduced below its inherent entropy. Consequently, processing gigabyte-scale structured data (e.g., high-resolution media, foundation model weights) intrinsically suffers from Von Neumann I/O bottlenecks and localized storage constraints. This paper presents a paradigm-shifting architecture that abandons the classical "Store-Transfer-Store" methodology. By conceptualizing data as a deterministic generative output of a synchronized, high-dimensional Mersenne Prime (M52) Lattice Engine, we define a mathematical Inverse Mapping protocol. Instead of compressing the payload, the system computes the Kolmogorov Minimal Description Length — a localized phase coordinate (approx. 4KB) that instructs the deterministic engine to reconstruct the original 4GB data array solely within volatile memory (RAM). This "Zero-Storage Materialization" guarantees 100% bit-for-bit integrity, bypassing physical storage I/O completely and offering unprecedented implications for post-quantum secure, stateless distributed computing.