Abstract
Current conversational interfaces for large language models rely on linear interactions. This linear approach restricts information retrieval and causes navigational context to be lost. Lengthy text inputs are required to refine search spaces, leading to semantic pollution. To address these limitations, a non-linear semantic navigation method is disclosed. User intent is captured through a visual query tree and mapped directly to projection matrices. Specific matrix transformations are applied to local vector regions within a high-dimensional embedding space. These operations rotate, scale, and project the semantic space to amplify specific latent dimensions without requiring lengthy text inputs. Irrelevant branches are mathematically pruned using null space projection. Output density is dynamically adjusted via scaling factors. The primary purpose of this technology is to replace text-heavy prompting with precise geometric control. Consequently, efficient exploration and exact refinement of high-dimensional knowledge bases are achieved.
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Recommended Citation
Chang, John, "Non-Linear Semantic Navigation In Large Language Models Via Matrix Operators", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/11278