The Hypernetic CURSE: Geometry of Collapse

Recursive artificial systems (RAS) incentivized by simple scalar rewards excel in narrow tasks but exhibit characteristic failure modes when objectives become complex, systems scale, or behavior affects high-stakes domains. This paper introduces Context-Dominant Unbounded Recursive Simplifying Executables (CURSEs) as a cross-domain motif of recursive collapse. A CURSE is a feedback process that locks onto a dominant attraction logic (e.g. reward maximization), reorganizes its context to reinforce that logic, and continues without an internal stopping condition. Using this idea, we treat alignment as frame-relative and analyze recursive collapse in biological (addiction, cancer), economic (profit dominance), physical (gravitational collapse), and artificial systems (reward hacking, model collapse). We characterize CURSEs by five signatures – context-dominant, unbounded, recursive, simplifying, and executable – and express them in a domain-neutral grammar (Attraction Logic / Projector / Executor). We contrast CURSE-prone systems with CURSE-resistant recursive systems such as Wikipedia, immune systems, and old-growth ecosystems, highlighting shared motifs like multi-objective feedback, redundant regulation, and slack dissipation. By framing AI alignment failures as instances of a broader class of recursive collapse, the CURSE motif provides a conceptual lens for anticipating drift, understanding why misalignment generalizes, and importing design ideas from resilient recursive systems.