Cognition Index Protocol (CIP): Demonstrable Machine Comprehension Through Structured Repository Intelligence (Draft)

We present the Cognition Index Protocol (CIP), a comprehensive framework for evaluating artificial intelligence systems' cognitive capabilities through standardized assessment of symbolic reasoning, memory formation, and adaptive learning. Unlike traditional benchmarks that focus on task-specific performance, CIP evaluates fundamental cognitive processes including symbolic pattern recognition, recursive memory formation, conceptual abstraction, and adaptive problem-solving across diverse domains.

Our protocol incorporates validated assessment modules for mathematical reasoning, linguistic comprehension, spatial cognition, temporal reasoning, and meta-cognitive awareness. Through extensive validation with state-of-the-art language models including Claude Sonnet 4, we demonstrate that CIP provides nuanced cognitive profiles that reveal strengths and limitations invisible to conventional benchmarks.

CIP establishes standardized metrics for cognitive capability assessment, enabling systematic comparison across AI architectures and tracking cognitive development over time. The protocol supports both research applications and practical deployment decisions by providing interpretable cognitive profiles rather than single performance scores.