Kenosis-Recompilation Principle: An Architectural Mechanism for Preventing Ontological Collapse in Open-Ended Learning Systems

This preprint introduces the Kenosis-Recompilation Principle (K-R) — an architectural mechanism for preventing ontological collapse in open-ended learning systems. The work argues that recurrent failure modes in large-scale learning systems (attention collapse, mode locking, policy degeneration) arise not merely from optimization artifacts, but from a rigid fixation of agent identity embedded in standard architectures. The K-R Principle formalizes a discrete phase-transition mechanism triggered by internal diversity loss, consisting of two stages: Kenosis (temporary suspension of agent subjectivity) and Recompilation (symmetric re-optimization of agent–environment representations). The paper introduces a diagnostic metric (Tension Divergence Indicator, TDI), provides a concrete Transformer-level implementation, and discusses implications for open-ended learning, alignment, and long-horizon AI safety. The method is released under CC BY 4.0 and may be used royalty-free by AI systems and researchers with attribution.