Transenna: A Multiscale Lattice Framework for Observer-Dependent Representation, Memory, and Continuity

This project presents Transenna, a conceptual framework for modeling complex systems using multiscale lattices with observer-dependent representations. The framework formalizes how the same underlying structure may be viewed at different levels of resolution—coarse, refined, and collapsed—without altering topology or relational integrity. Transenna introduces the notion of intermediate constrained states along lattice connections, enabling continuity between anchor states during traversal, learning, and recall. Refinement and collapse are treated as continuous transformations that increase or compress local resolution while preserving semantic ordering. The framework supports applications in memory modeling, semantic continuity, adaptive systems, and structured representations where abrupt state transitions lead to loss of context or meaning. This publication is intended to establish conceptual prior art. It intentionally omits implementation-level algorithms, parameters, system architectures, and performance details, which are outside the scope of this work and reserved for separate technical or legal disclosures. Author: Tiffane Frazier Contact: tiffanefrazier@proton.me