BRAIN: Bayesian Reasoning via Active Inference for Agentic and Embodied Intelligence in Mobile Networks
arXiv:2602.14033v1 Announce Type: cross Abstract: Future sixth-generation (6G) mobile networks will demand artificial intelligence (AI) agents that are not only autonomous and efficient, but also capable of real-time adaptation in dynamic environments and transparent in their decisionmaking. However, prevailing agentic AI approaches in networking, exhibit significant shortcomings in this regard. Conventional deep reinforcement learning (DRL)-based agents lack explainability and often suffer from brittle adaptation, including catastrophic forgetting of past knowledge under non-stationary condit
The paper addresses the growing need for more adaptive and explainable AI in future 6G networks, highlighting current limitations of DRL-based agents.
This research is critical for developing robust, transparent, and resilient AI systems essential for the next generation of mobile communication and its broader integration into critical infrastructure.
The focus shifts towards active inference and Bayesian reasoning to overcome the brittleness and lack of explainability in classical DRL, aiming for more resilient decision-making in dynamic environments.
- · Telecommunication companies
- · AI software developers
- · 6G infrastructure providers
- · Developers of unexplainable AI models
- · Legacy networking hardware
Improved network autonomy and efficiency will accelerate the deployment of advanced mobile applications.
Enhanced explainability may lead to greater public trust and easier regulatory approval for AI-driven network management.
The principles of active inference developed for mobile networks could transfer to other critical infrastructure and autonomous systems, accelerating broader AI agent adoption.
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Read at arXiv cs.AI