The rapid scaling of Large Language Models (LLMs) has intensified structural challengesbeyond raw performance: growing demands on memory, energy consumption, hardwareavailability, and the cost of continuous retraining. At the same time, LLMs representsemantics as relative embeddings with no fixed coordinate system, making long-term semanticconsistency difficult to guarantee under model updates or contextual shifts. This paperpresents empirical proof-of-concept (PoC) results for Semantic Bundle AI 9, a complementaryframework that addresses these challenges simultaneously—without replacing existing LLMinfrastructure and without retraining. By introducing stable anchor-based coordinates andstructured semantic bundles, the framework achieves: (1) over 90% reduction in intra-clustersemantic variance, (2) 38.6% reduction in semantic drift over 10 sequential updates whilemaintaining a consistency score of 0.931, (3) localized edits with update rate ρ < 0.15 limitingsemantic contamination to 32.6% of baseline, and (4) 91.7% memory reduction at K = 64with reconstruction similarity of 0.963. These results establish Semantic Bundle AI as aresource-efficient, drop-in semantic management layer for long-term LLM deployment, andprovide concrete design parameters for practical adoption This paper is the experimental companion to the theoretical framework presented in Paper 0 (DOI: https://doi.org/10.5281/zenodo.20417222).
makoto saitou (Wed,) studied this question.
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