Co-Koala Protocol: A Causal Operator Evaluation Framework for Reasoning, Stability, and Alignment

Co-Koala is a causal operator evaluation framework for testing reasoning
robustness, operator fidelity, perturbation stability, and multi-step inference
integrity in modern AI systems. It provides a principled methodology for
constructing, applying, and analyzing causal operators—transformations that
modify world states, reasoning pathways, task contexts, or inference structures.

The framework evaluates how models respond to causal interventions, structural
perturbations, operator composition, and ambiguous causal branches. Co-Koala
produces interpretable metrics that capture causal fidelity, operator stability,
cross-context consistency, reconstruction accuracy, and resilience to
misapplied or inverted operators.

This technical report describes the causal operator model, testing protocol,
evaluation cycles, perturbation strategies, metrics, failure modes, and
recommendations for robust causal reasoning evaluation. Co-Koala supports LLMs,
multimodal models, agents, tool-using systems, and cognitive architectures such
as CodexOne.