Compression and Retrieval Systems

This paper presents the Compression and Retrieval System (CARS), a semantic compression framework for artificial intelligence systems. CARS implements the URCA-κ (Universal Recursive Compression Algorithm) with an empirically derived recursion constant κ = 7.2, validated through systematic grid search across 1,847 compression-reconstruction cycles. The framework achieves semantic fidelity scores of 0.94-0.98 as measured by embedding cosine similarity and BLEU-4 reconstruction scores. Independent external testing has validated the implementation, confirming zero content loss in v2.1 with perfect reconstruction fidelity (cosine similarity = 1.0, BLEU-4 = 1.0) when full-text preservation is enabled. This paper addresses the non-anthropomorphic nature of AI systems by reframing emotional valence/intensity as computational salience scores (retrieval priority weights) and polarity scores (contextual orientation) rather than phenomenological affect. The complete implementation, benchmark dataset (CARS-Bench, n=50), and experimental protocols are provided for full reproducibility.