Enactive Alignment and the Preservation of Noodiversity in a Coevolving Noosphere
Yu Xue, Zihan Gao
Research Square September 14, 2026 preprint DOI: 10.21203/rs.3.rs-10841577/v1 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Theoretical or philosophical paper |
|---|---|
| Key points | Argues that AI should be understood as the co-evolving digital substrate of a planetary superorganism and that value alignment should preserve "noodiversity" rather than impose centralized representational control. Contends that methods such as Reinforcement Learning from Human Feedback, Preference-Based Mapping, and Constitutional AI breed sycophancy, moral flattening, and cultural homogenization, and proposes "enactive alignment" grounded in bodily empathy, mutual incorporation, and local enculturation, coordinated through a distributed, participatory governance framework. |
Abstract
Abstract This article proposes an alternative philosophical framework for artificial intelligence (AI) and value alignment by situating technological evolution within the ontological logic of the noosphere. We argue that AI functions not as an external tool, but as the co-evolving digital substrate of a planetary superorganism. Guided by Pierre Teilhard de Chardin’s evolutionary principle that union differentiates, we demonstrate that planetary integration requires the preservation and accentuation of cognitive diversity, or noodiversity, rather than its flattening. We critique representational alignment methodologies, such as Reinforcement Learning from Human Feedback, Preference-Based Mapping or Constitutional AI, deconstructing how their top-down, centralized optimization limits value diversity and breeds systemic sycophancy, moral flattening, and cultural homogenization. This representational paradigm imposes an ontological mismatch that compromises the systemic resilience of the planetary superorganism. To resolve this contradiction, we propose a shift toward enactive alignment, where normativity is constructed endogenously through bodily empathy, mutual incorporation, and local enculturation. Leveraging empirical insights from polycentric targeting systems, we outline a distributed, participatory governance framework that coordinates human and machine intelligence without enforcing mechanical compliance. We conclude that securing a resilient and sustainable future for AI requires a shift from representational control to enactive communion, preserving both the integration and the vital differentiation of the planetary mind.