Against the Monolithic Wireless World Model: Why NextG Needs Composable and Agentic Intelligence

TL;DR

The paper critiques the analogy between large language models and wireless network models, emphasizing the need for composable, agentic architectures over monolithic models for AI-native 6G networks.

Contribution

It highlights structural differences in wireless data and advocates for composable, agentic architectures rather than monolithic models in future wireless networks.

Findings

Wireless data lacks a self-contained, reusable data substrate.

Monolithic models face structural bottlenecks in wireless domains.

Emerging evidence supports composable, agentic network architectures.

Abstract

AI-native 6G visions increasingly invoke wireless foundation models, large multimodal models, and wireless world models as the natural endpoint of AI-native networking, drawing an analogy to recent developments in large language models (LLMs). We argue that this analogy is structurally incomplete. The success of LLMs is based on a broad, reusable, and largely self-contained tokenized data substrate, whereas the wireless domain lacks an equivalent data foundation. Unlike text, code, or images, wireless data such as CSI tensors, IQ samples, or scheduler logs are not self-contained: their meaning is configuration-dependent, simulator-conditioned, task-disaggregated, and weakly grounded in operational feedback, all structural bottlenecks that undermine current pre- and post-training recipes. We therefore argue that monolithic models, including mixture-of-experts (MoE) and wireless world models, are not the most realistic near-term path toward deployable AI-native networks. Instead, emerging evidence points toward composable and agentic network architectures, where general reasoning models orchestrate specialized signal processing models, classical algorithms, digital twins, standards-aware retrieval, and safety checks through explicit programmable interfaces.