@Article{cmc.2026.077259,
AUTHOR = {Mohammad Nauman},
TITLE = {In-Mig: Geographically Dispersed Agentic LLMs for Privacy-Preserving Artificial Intelligence},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {87},
YEAR = {2026},
NUMBER = {2},
PAGES = {--},
URL = {http://www.techscience.com/cmc/v87n2/66655},
ISSN = {1546-2226},
ABSTRACT = {Large Language Models (LLMs) are increasingly utilized for semantic understanding and reasoning, yet their use in sensitive settings is limited by privacy concerns. This paper presents <i>In-Mig</i>, a mobile-agent architecture that integrates LLM reasoning within agents that can migrate across organizational venues. Unlike centralized approaches, In-Mig performs reasoning <i>in situ</i>, ensuring that raw data remains within institutional boundaries while allowing for cross-venue synthesis. The architecture features a policy-scoped memory model, utility-driven route planning, and cryptographic trust enforcement. A prototype using JADE for mobility and quantized Mistral-7B demonstrates practical feasibility. Evaluation across various scenarios shows that In-Mig achieves 92% similarity to centralized baselines, confirming its utility and strong privacy guarantees. These results suggest that migrating, privacy-preserving LLM agents can effectively support decentralized reasoning in trust-sensitive domains.},
DOI = {10.32604/cmc.2026.077259}
}