Semantic Bundle AI

A resource-efficient, drop-in semantic management layer for LLMs.

No retraining. No architectural changes. 91.7% memory reduction. Semantic stability.

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The Problem

The rapid scaling of Large Language Models has created three structural challenges:

• Semantic drift: concept embeddings shift across model updates and fine-tuning
• Retraining costs: correcting semantic inconsistency requires expensive retraining
• Memory overhead: storing full embeddings at scale is resource-intensive

The Approach

Semantic Bundle AI introduces a complementary layer that sits on top of existing LLM pipelines:

1. Stable anchor coordinates — project embeddings onto a fixed reference frame to resist drift
2. Semantic bundles — represent concepts as structured objects supporting controlled, localized updates
3. Sparse reconstruction — reconstruct semantics from compressed representations

No LLM retraining required. No architectural modifications. Drop-in compatible with existing embedding pipelines.

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PoC Results

Four experiments validate the framework across stability, consistency, locality, and efficiency.

PoC 1: Anchor Coordinate Stability

Model	Raw embedding variance	Anchor coordinate variance	Ratio
all-mpnet-base-v2	0.6493	0.0339	0.052
all-MiniLM-L6-v2	0.6738	0.0543	0.081

Anchor coordinates reduce intra-cluster variance to 5–8% of raw embedding variance.
Anchor removal robustness: correlation = 1.0000 with up to 2 anchors removed.

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PoC 2: Longitudinal Consistency

Metric	Baseline	Bundle (ρ=0.1)
Cumulative drift (10 steps)	0.1118	0.0687
Drift reduction	—	38.6%
Consistency score (vs. initial)	0.8882	0.9313
Semantic collapse rate	0%	0%

Bundle updates maintain 0.931 consistency with the initial concept after 10 sequential updates.

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PoC 3: Edit Locality

Updating one bundle (Apple + Vision Pro) with update rate ρ=0.1:

• Semantic contamination of unrelated bundles: 32.6% of baseline
• Recommended operating range: ρ < 0.15

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PoC 4: Compression Efficiency

K	Reconstruction similarity	Memory reduction
32	0.855	95.8%
64	0.963	91.7%
100	0.998	87.0%

At K=64: 91.7% memory reduction (45.0 KB → 3.8 KB) with reconstruction similarity of 0.963.

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Quick Start

pip install -r requirements.txt

Then open any notebook in notebooks/:

Notebook	Description
poc_01_anchor_stability.ipynb	Anchor coordinate stability experiment
poc_02_longitudinal.ipynb	Longitudinal consistency experiment
poc_03_edit_locality.ipynb	Edit locality experiment
poc_04_efficiency.ipynb	Compression efficiency experiment

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Papers

Paper	Description	Link
Paper 0	Theoretical framework	10.5281/zenodo.20417222
Paper 1	PoC empirical results	10.5281/zenodo.20417714

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License

The theoretical framework, mathematical formulations, and documentation are licensed under:
CC BY-NC-ND 4.0 — Attribution, NonCommercial, NoDerivatives

The PoC code in notebooks/ is provided for research reproducibility.

For commercial licensing or partnership inquiries: m.saitou@ontheshouldersofgiants.jp

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Contact

• Issues: For technical questions and collaboration proposals
• Email: m.saitou@ontheshouldersofgiants.jp
• Web: ontheshouldersofgiants.jp