GeoStack: A Framework for Quasi-Abelian Knowledge Composition in VLMs
We address the challenge of knowledge composition in Vision-Language Models (VLMs), where accumulating expertise across multiple domains or tasks typically leads to catastrophic forgetting. We introduce GeoStack (Geometric Stacking), a modular framework that allows independently trained domain experts to be composed into a unified model. By imposing geometric and structural constraints on the adapter manifold, GeoStack ensures the foundational knowledge of the base model is preserved. Furthermore, we mathematically demonstrate a weight-folding property that achieves constant-time inference complexity ($O(1)$), regardless of the number of integrated experts. Experimental results across mul...
| Authors | Pranav Mantini, Shishir K. Shah |
|---|---|
| Primary category | cs.CV |
| Published | 7 May 2026 |
| Last updated at source | 7 May 2026 |
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