Recursive Compositional Intelligence: Symmetry-Preserving Composition, Meta-Recursive Improvement, and Competitive Resource Dynamics in AI Systems

We present Recursive Compositional Intelligence (RCI) — a framework for AI systems that integrates three principles: (1) symmetry-preserving composition, in which building blocks compose into higher-order structures that are themselves valid building blocks for further composition, preserving a uniform interface across all levels of the hierarchy; (2) meta-recursive improvement, in which the most leveraged target of optimisation is not any particular capability but the capacity to improve capabilities itself, applied recursively — improving how you improve how you improve; and (3) competitive resource dynamics, in which multiple compositional agents compete to construct the most effective structures from a shared, generative pool of building blocks, with selection pressure driving the emergence of superior compositional strategies. These three principles are mutually reinforcing: symmetry-preserving composition enables unbounded hierarchical construction, meta-recursive improvement ensures each level of the hierarchy is more capable than the last, and competitive resource dynamics provide the selection pressure that drives improvement. Together they describe a class of AI system that constructs increasingly powerful structures, recursively improves its own construction process, and evolves its compositional strategies through competition — with each cycle expanding the available resource base for the next. We formalise each principle, establish their interactions, connect the framework to existing work in hierarchical reinforcement learning, neural architecture search, evolutionary computation, and meta-learning, and discuss implications for artificial general intelligence, superintelligence, AI safety, and the democratisation of innovation processes. We analyse the intelligence explosion through the lens of meta-recursive improvement and show that the RCI architecture provides mutually reinforcing mechanisms for recursive capability gain: unbounded meta-recursive improvement, a generative resource pool that expands with each compositional cycle, and competitive dynamics that accelerate discovery.