Cognitive Abstraction Layers (CAL): A Research Architecture for Hierarchical Semantic Compression in AI Systems

Key Points

• The research aims to establish a framework for hierarchical semantic compression in AI systems while preserving causal structures for governance.
• Introduces the five-level CAL framework (L0–L4) for semantic compression.
• Adds L3 closure summary focusing on causal conservation characterized by observational invariants.
• Updates the L4 gate-C status note contributing to the CAL architecture.
• Demonstrates preservation of observational invariants Ω₀: Reachability, Coverage, Consistency under semantic compression.
• Emphasizes causal conservation rather than reconstruction fidelity.
• Supersedes prior versions of the CAL concept record.