Year: 2026
Type: article
Abstract: The central role of local LLMs is not merely to approximate cloud models at a smaller scale, but also to provide a practical user-side implementation of persistent adaptation that is difficult to support universally within shared cloud services. The contribution is an architectural reframing of where user-specific adaptation should be implemented, rather than the proposal of a new component-level learning mechanism. This reframing shifts the design question from model capability to the placement of memory, preference updating, constraint formation, and continual adaptation in hybrid AI systems.
Source: Discover Artificial Intelligence
Author: Kei Itoh
Institution: Ehime University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,590
OpenAlex ID: W7204231554