Drawing inspiration from the golden age of broadcast—when a single transmitter could reach millions with music, drama, and news—RadioText redefines that model for a world constrained by bandwidth and power. Rather than resurrecting purely audio streams, it proposes a versatile framework where expressive, adaptive text—shaped by large language models and encoded by default via the eXtended…

This white paper introduces the concept of Hierarchical Tokens, a novel architectural direction for transformer-based models. Instead of limiting language generation to token-level prediction (word by word), this approach expands the predictive structure to higher-level semantic units such as sentences, paragraphs, sections, chapters, and even domains of knowledge—each treated as composable macro-tokens. By applying the…

This paper addresses the fundamental tension between transparency and performance in artificial general intelligence (AGI) systems. While high-performance models often rely on complex, opaque architectures (e.g., deep neural networks), the demand for explainability and ethical accountability grows increasingly urgent. We introduce a Transparency-Performance Duality Framework (TPDF), which proposes layered system designs that partition cognitive and operational…