White Paper on the Zhao Futao Axiom of Material Memory: Yi Equation V2.1

This white paper (DOI: 10.5281/zenodo.18468737) takes the Zhao Futao Axiom of Material Memory as the sole theoretical foundation to finalize and release the universal general framework of the Yi Equation V2.1, representing a core upgrade and application optimization of the V2.0 version. With constraint dimensionality reduction and human-machine collaboration as the core methodologies, this framework constructs a reality-anchored system based on material memory and human intelligence, which converges multi-variable unknown problems into solvable quantitative ones and achieves a fundamental leap from empirical guesswork to scientific equation solving. Addressing the limitations of V2.0 such as ambiguous dimensional representation, limited adaptation boundaries, and inadequate cross-domain operability, this version clarifies the core adaptation principle of "unified universal framework + domain-specific dimensions", adds the universal compatibility design principle, and finalizes the boundaryless three-step implementation method, realizing a fundamental transition from an "self-evolving cognitive model" to a "universal historical dependence dynamic response framework". The core formula structure, symbols and operation logic of the Yi Equation V2.1 remain inviolably unchanged; personalized domain adaptation is achieved through black-box encapsulation, enabling boundaryless coverage of both modern scientific fields such as physics and chemistry, and non-scientific fields of traditional Chinese culture such as I Ching, numerology and traditional Chinese medicine (TCM), thus providing a unified mathematical language for all historically dependent phenomena. The core breakthrough of this framework lies in transforming the long-standing qualitative application of historical experience in various fields into quantifiable and computable dynamic response modeling of historical dependence, while establishing a human-machine symbiotic logic of "humans anchoring reality + machines achieving precise solving". The black-box module features both independence in adaptation and standardized optimal solutions; unknown parameters can be solved by combining the master formula with known historical data and results. Relying on the framework's characteristics, it will accelerate the extraction and finalization of domain-specific standard formulas, enabling a rapid upgrade from empirical parameter testing to data-driven precise solving. Positioned as a finalized phased achievement, a delivery of universal adaptation rules and a benchmark guide for subsequent research, this white paper does not conduct in-depth research or experimental verification in specific fields, but raises the research baseline from V2.0 to V2.1, providing a unified starting point for subsequent researchers and promoting interdisciplinary collaborative research in the field of material memory.