Ekhtesi: Entropic Kernel for Thematic Symmetric Integration

Ekhtesi is a symbolic computation framework that combines entropy, semantic vector projection, and fractal decay kernels to compute resonance across thematic content in structured or unstructured texts. It introduces a formal mathematical model — Phi^Omega — that evaluates thematic alignment across symbolic units using a three-dimensional signal integration mechanism.

Designed for both scientific and philosophical applications, Ekhtesi has been tested on news corpora, religious texts, dialog transcripts, and technical documents. It exhibits strong resilience to symbolic perturbations and outperforms baseline models (e.g., TextRank, TF-IDF, GPT-based extractive summarizers) in thematic fidelity and interpretive depth.

This publication includes formal definitions, algorithmic diagrams, comparative experiments, and future projections including Ekhtesi-Q (quantum logic embedding) and Ekhtesi-MM (multimodal extensions).

It is suitable for researchers in symbolic AI, linguistics, computational epistemology, and information-theoretic modeling.