Semantic Bundle AI: Stable Coordinate Semantics as a Complementary Layer to Large Language Models

Large Language Models (LLMs) have achieved remarkable performance across a wide range of natural language processing tasks; however, their underlying semantic representations remain fundamentally relative and context-dependent. This reliance on relative representations imposes structural limitations on long-term semantic stability, reproducibility, and controllable semantic editing, with semantic drift during retraining or fine-tuning being a representative symptom.This paper introduces Meaning Bundle AI, a novel framework that stabilizes semantic representations by anchoring them to a shared coordinate system based on stable semantic anchors. Importantly, Meaning Bundle AI is not intended to replace Transformer-based architectures or relative embedding methods used in LLMs. Rather, it is designed as a complementary layer that presupposes existing models and augments them with long-term referential stability, reproducibility, and efficient semantic management.Instead of storing semantic information holistically within model parameters, Meaning Bundle AI adopts a referential reconstruction approach, in which semantic representations are reconstituted on demand from a stable coordinate system and a compact set of parameters. This design preserves compatibility with existing embedding-based architectures while enabling substantial reductions in parameter usage, computational overhead, and retraining frequency.To prioritize stability and verifiability, this paper intentionally presents a minimal mathematical formulation, omitting non-essential components such as complex relation tensors and nonlinear elastic extensions. The proposed framework does not negate existing stabilization techniques but rather provides a unifying geometric perspective that integrates them at a higher conceptual level. We further outline proof-of-concept (PoC) designs for evaluating temporal stability, reproducibility, and efficiency.Detailed mathematical formulations, algorithmic designs, and extended discussions are provided in the Japanese version of this paper.