Limits of Governability in Collective Artificial Intelligence Systems

This corpus develops a closed scientific framework for analyzing the limits of governability in collective artificial intelligence systems, grounded in Crowd-Based Dynamics (CBD) and its extension RAG-RES. It treats contemporary AI systems not as individual agents, but as adaptive collective systems subject to structural accumulation, endogenous saturation, regime transitions, and irreversible loss of governability. Document I establishes the structural foundations of collective AI governability, defining algorithmic systems as mimetically accumulating architectures whose governability depends on internal dissipative capacity rather than control, alignment, or performance.Document II formalizes the universal laws governing saturation, conditional reversibility, limited dissipation, dynamic regimes (continuation and reactive divergence), and the Sterking Limit as a boundary of definable governability in algorithmic systems.Document III provides a non-prescriptive diagnostic and cartographic framework, showing how these laws can be used to qualify real AI architectures, identify structural regimes, and understand limits of reversibility without prediction, optimization, or operational coupling.Document IV establishes the epistemic safeguards and conditions of use required to preserve the scientific integrity of the corpus, explicitly prohibiting predictive, normative, prescriptive, or automated instrumentalization of the diagnostics. Taken together, these four documents constitute a complete, invariant, and canonically closed corpus, offering a rigorous structural science of collective AI governability while explicitly excluding control, optimization, and decision-making functions. The corpus is intended for scientific analysis, institutional understanding, and structural audit, not for engineering or governance by algorithmic means.