@article{Nauman2026, 
author = {Mohammad Nauman},
title = {In-Mig: Geographically Dispersed Agentic LLMs for Privacy-Preserving Artificial Intelligence},
year = {2026},
journal = {Computers, Materials & Continua},
volume = {87},
number = {2},
pages = {46},
keywords = {Mobile agents, large language models (LLMs), privacy-preserving AI, decentralized reasoning, trust and security},
url = {https://www.sciopen.com/article/10.32604/cmc.2026.077259},
doi = {10.32604/cmc.2026.077259},
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 In-Mig, a mobile-agent architecture that integrates LLM reasoning within agents that can migrate across organizational venues. Unlike centralized approaches, In-Mig performs reasoning in situ, 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.}
}