CRHI Operational Architecture: Scoring Systems, Recursive Inference Trees, Convergence Metrics, and Decision Flow

This work develops the operational architecture underlying Constrained Recursive Hypothesis Inference (CRHI). Building upon the foundational CRHI framework, the paper formalizes recursive exploratory reasoning through weighted scoring systems, recursive inference trees, convergence metrics, branch-pruning logic, and hierarchical decision-flow architectures.

The framework is designed to organize uncertainty in complex scientific environments characterized by incomplete observability, nonlinear interactions, unresolved causal structures, and high-dimensional parameter spaces. CRHI operational architecture emphasizes falsifiability, recursive constraint filtering, probabilistic ranking, convergence analysis, and computational tractability while minimizing unconstrained speculation and complexity inflation.

Potential applications include exploratory physics, anomaly classification, inverse engineering, machine-assisted scientific reasoning, probabilistic inference systems, nonlinear systems analysis, signal interpretation, and computational model selection.

Rather than functioning as a theory of anomalous phenomena, CRHI is proposed as a generalized methodological architecture for disciplined exploratory reasoning under uncertainty.