Core Semantic Fidelity Papers: Preserving Meaning in the Age of Generative AI

This bundle contains the foundational papers of the Semantic Fidelity Lab, part of the Reality Drift Framework (2023–2026). The collection introduces semantic fidelity as a framework for understanding how meaning is preserved, degraded, and measured in artificial intelligence systems. Across nine papers, the bundle examines semantic drift, fidelity decay, recursive compression, constraint collapse, hallucination framing, language as cognitive exhaust, and autopoiesis as a missing variable in AI alignment. Together, these works argue that current AI evaluation frameworks overemphasize accuracy, faithfulness, and coherence while often failing to measure whether intent, nuance, context, and communicative purpose survive transformation. The collection includes papers on semantic fidelity, AI evaluation beyond accuracy, measurement of fidelity decay, the Compression Paradox, constraint collapse, semantic drift, cognitive exhaust, a semantic fidelity lexicon, and autopoiesis in alignment. It is intended as a conceptual and evaluative framework for researchers, AI builders, UX designers, governance practitioners, and others working on meaning preservation in generative systems. This upload includes both PDF versions of the papers and a README overview for repository-style navigation. The README frames the bundle as the core Semantic Fidelity Lab paper set and lists the nine foundational works included in the collection.