Logic of Existence as a Relational Decoder for Biological Information Networks

This paper was written to demonstrate the potential usefulness of the Logic of Existence (LoE) as a theoretical and computational framework for genomics research powered by artificial intelligence.

While Google’s decade of AI genomics has created extraordinary tools — such as DeepVariant, Enformer, and AlphaGenome — these systems operate primarily on statistical correlations. LoE extends this perspective by introducing a meta-Hamiltonian logic, where biological information is treated not as static data but as a living network of relations.

The goal of this work is to show how LoE can act as a relational decoder: a bridge between deep learning architectures and the underlying informational geometry of the genome. In this view, DNA is not simply read — it resonates. The code of life becomes a dynamic field balancing chaos, order, and entropy, where mutations and regulation emerge as shifts in relational coherence.

By integrating LoE with modern AI models, this paper proposes a path toward a more unified science of life — one capable of understanding not only how genes are sequenced, but why existence organizes itself through them.