arXiv:2509.23248v3 Announce Type: replace Abstract: The rapid advancement of large language models (LLMs) has enabled an emergence of agentic artificial intelligence (AI) with powerful reasoning and autonomous decision-making capabilities. This integration with edge computing has led to the development of Mobile Edge General Intelligence (MEGI), which brings real-time, privacy-preserving reasoning to the network edge. However, deploying LLM-based agentic AI reasoning in MEGI environments poses significant challenges due to the high computational demands of reasoning and the limited resources o
The rapid advancement of LLMs is pushing their deployment closer to the edge, necessitating solutions for resource constraints in mobile environments.
This development is crucial for enabling real-time, privacy-preserving AI autonomy on mobile devices, expanding the practical applications of advanced AI.
The feasibility of deploying sophisticated LLM-based autonomous reasoning directly on mobile edge devices shifts from theoretical to practical, despite resource limitations.
- · Mobile edge computing providers
- · AI agent developers
- · Device manufacturers
- · Telecommunications companies
- · Centralized cloud AI providers (for certain use cases)
- · Companies relying on outdated edge infrastructure
Widespread adoption of LLM-powered AI agents on mobile devices becomes more feasible.
Increased demand for specialized edge hardware and optimized AI models.
Enhanced personal AI assistants capable of highly contextual and real-time decision-making without constant cloud connectivity.
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Read at arXiv cs.AI