Literature Database Entry

basaran2026brain-preprint

Osman Tugay Basaran, Martin Maier and Falko Dressler, "BRAIN: Bayesian Reasoning via Active Inference for Agentic and Embodied Intelligence in Mobile Networks," arXiv, cs.IT, 2602.14033, February 2026.

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 conditions. In this paper, we propose an alternative solution for these challenges: Bayesian reasoning via Active Inference (BRAIN) agent. BRAIN harnesses a deep generative model of the network environment and minimizes variational free energy to unify perception and action in a single closed-loop paradigm. We implement BRAIN as O-RAN eXtended application (xApp) on GPU-accelerated testbed and demonstrate its advantages over standard DRL baselines. In our experiments, BRAIN exhibits (i) robust causal reasoning for dynamic radio resource allocation, maintaining slice-specific quality of service (QoS) targets (throughput, latency, reliability) under varying traffic loads, (ii) superior adaptability with up to 28.3% higher robustness to sudden traffic shifts versus benchmarks (achieved without any retraining), and (iii) real-time interpretability of its decisions through human-interpretable belief state diagnostics.

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Osman Tugay Basaran
Martin Maier
Falko Dressler

BibTeX reference

@techreport{basaran2026brain-preprint,
    author = {Basaran, Osman Tugay and Maier, Martin and Dressler, Falko},
    doi = {10.48550/arXiv.2602.14033},
    title = {{BRAIN: Bayesian Reasoning via Active Inference for Agentic and Embodied Intelligence in Mobile Networks}},
    institution = {arXiv},
    month = {2},
    number = {2602.14033},
    type = {cs.IT},
    year = {2026},
   }
   
   

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Last modified: 2026-04-27