Original Reddit post

I am presenting a theoretical framework for a Deterministic Substrate called the Universal Fluid Method (UFM). This approach moves away from traditional 1D bitstream processing and stochastic vector embeddings towards a 2D Geometric Identity model designed to provide a stable, structural ground truth for machine learning.

  1. The Concept: Data as a 2D Shape Standard computational models treat data as rigid linear sequences. In such systems, shifting a sequence by even a single bit usually causes the identity (the hash) to collapse. The Analogy : Imagine a Lego castle built on a table. Traditional Systems : These identify the castle by its exact coordinates on that table. If you slide the castle two inches, the system sees a “new” object because the coordinates have changed. The UFM Approach : UFM ignores the “table” (the file window) and maps bits onto a 2D Fluid Array . It identifies the castle by the internal geometric relationship of the bricks. Geometric Identity : By calculating the Geometric Centroid and the Total Centroid Signature (TCS) of the “on” bits, the engine establishes an identity that is indifferent to bit-phase offsets or container formats.
  2. Big-Picture Benefits for AI A “Native Memory” for AI : Current models frequently hallucinate because they lack a stable, structural ground truth for information. UFM provides AI with a “Structural Sense of Sight” by identifying data by its physical 2D shape. This allows a model to recognise a pattern instantly even if it has been shifted, moved, or renamed. Universal Vocabulary of Reality : Instead of redundant storage, the method creates a “vocabulary” where novel structures are stored as Primitives and repeating history is replayed through a lossless timeline. Noise as a Resource : Rather than filtering entropy, the method captures novel noise as new primitives. This eventually leads to a state where even random data is composed of known structural primitives.
  3. Technical Proof (Proven so far) We have validated this substrate using a rigorous compliance engine that has passed 24 major tests: The Reversibility Mandate : The engine is a Lossless Ledger . We have achieved 100% bit-exact replay across 1 MB corpora of text, random noise, and binary data. Shift-Invariance Verified : Exhaustive testing confirms that a 1 to 7 bit shift produces an identical identity seed for the same pattern. Geometric Differentiation : The engine uses centroid variance, S = sqrt(vx + vy) , to distinguish structurally distinct patterns. Emergent Symbolism : The system successfully identifies optimal symbol lengths based on local entropy minimisation rather than fixed 8-bit boundaries. Non-Cryptographic Identity : Identity seeds are derived via a polynomial hash of the geometric signature, proving that structural identity does not require “black box” cryptographic hashing.
  4. Collaboration The Source of Truth and a verified reference implementation are now established. We are looking for anyone who could help us take the project to the next level. If you are interested in the application of geometric identity to neural architecture or structural ground truth, we would welcome the opportunity to discuss our findings. submitted by /u/Intelligent-Ad-6805

Originally posted by u/Intelligent-Ad-6805 on r/ArtificialInteligence