arXiv:2511.02748v2 Announce Type: replace-cross Abstract: We argue that sixth-generation (6G) intelligence is not fluent token prediction but the capacity to imagine and choose -- to simulate future scenarios, weigh trade-offs, and act with calibrated uncertainty. We reframe open radio access network (O-RAN) near-real-time (Near-RT) control via counterfactual dynamics and a world modeling (WM) paradigm that learns an action-conditioned generative state space. This enables quantitative "what-if" forecasting beyond large language models (LLMs) as the primary modeling primitive. Actions such as p
The proliferation of generative AI and the increasing complexity of network management are converging, pushing the demand for more autonomous and intelligent network control systems.
This development signals a fundamental shift in how future networks, like 6G, will be designed and managed, moving beyond traditional predictive models to proactive, generative decision-making.
Network control transitions from reactive pattern recognition to proactive 'what-if' scenario planning and autonomous action, fundamentally altering operational paradigms for telecommunication infrastructure.
- · Telecommunication companies
- · AI software developers
- · Hardware manufacturers for 6G infrastructure
- · Legacy network management software providers
- · Human network operators performing routine tasks
Enhanced efficiency and resilience in future 6G networks through near-real-time intelligent control.
Accelerated development of fully autonomous infrastructure across various sectors, reducing human intervention and operational costs.
Potential for new cyber-physical systems that can self-organize and adapt to complex, unpredictable environments without explicit programming.
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Read at arXiv cs.LG