Semantic Bundle AI: A Complementary Layer for LLMs — 91.7% Memory Reduction, 38.6% Drift Reduction, Zero Retraining

Hi HF community,

I’d like to share two preprints on Semantic Bundle AI , a drop-in complementary layer for existing LLMs that addresses three structural problems: semantic drift, difficulty of targeted edits, and memory overhead.

The problem

When you update a concept in an LLM’s embedding space:

• The change drifts across unrelated concepts (semantic drift)
• You can’t surgically edit one concept without contaminating others
• Storing full embeddings at scale is expensive

The approach

Semantic Bundle AI sits on top of existing LLMs — no architectural changes, no retraining required.

• Anchor coordinates : stable reference frames that resist drift
• Semantic bundles : structured concept representations with controlled update locality
• Sparse reconstruction : compress stored embeddings via bundle-based reconstruction

PoC results (4 experiments)

Zero retraining. Zero architectural modifications.

Papers & code

Zenodo (Paper 0 + Paper 1): Search results Code: GitHub - msaitou-glitch/Semantic-Bundle-AI: Official repository for the "Meaning Bundle AI" project. Complementary Layer to LLMs using Stable Coordinate Systems. · GitHub

Limitations (honest)

• Small-scale controlled datasets (15–110 sentences, single domain)
• Stability–ranking tradeoff identified (anchor coordinates improve cluster stability but not ranking consistency)
• Not yet validated at production scale
• Paper 1 under review at SSRN

Looking for critiques, failure cases, and adjacent work. Happy to discuss.