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20232026
most citedTelecomGPT: A Framework to Build Telecom-Specfic Large Language Models

7 citations · 11 across the 10 of their papers we have counts for

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5 papers · 1 filter

cs.CL2026

TelecomGPT-R1: Unified Post-Training for Reasoning Across Heterogeneous Telecom Tasks

Bohao Wang, Chenwei Wu, Hang Zou +7

Large language models (LLMs) offer great potential to automate a broad range of telecom engineering tasks by reasoning over standards, network configurations, mathematical models,…

cs.CL2026

TelecomGPT-R1: A Unified Open-Source Reasoner for the Telecom Stack

Bohao Wang, Chenwei Wu, Haoyu Li +8

Telecommunications is a high-leverage domain for large language model (LLM)-based reasoning because routine engineering workflows require joint grounding in normative specification…

cs.LG2026

Neural Computers

Mingchen Zhuge, Changsheng Zhao, Haozhe Liu +16

We propose a new frontier: Neural Computers (NCs) that unify computation, memory, and I/O of traditional computers in a learned runtime state. Our long-term goal is the Completely…

cs.RO2026

Telecom World Models: Unifying Digital Twins, Foundation Models, and Predictive Planning for 6G

Hang Zou, Yuzhi Yang, Lina Bariah +15

The integration of machine learning tools into telecom networks, has led to two prevailing paradigms, namely, language-based systems, such as Large Language Models (LLMs), and phys…

eess.SP2026

Seeing Radio: From Zero RF Priors to Explainable Modulation Recognition with Vision Language Models

Hang Zou, Bohao Wang, Yu Tian +4

Current RF machine-learning pipelines rely on task-specific deep networks for modulation classification and related tasks, but these models require custom architectures and labeled…