TelecomGPT‑R1‑9B is an open‑source large language model designed specifically for telecom reasoning tasks. It was trained on a 67,427‑example supervised fine‑tuning corpus that covers protocol, knowledge, modeling, and fault reasoning, and further refined with a two‑stage post‑training process involving low‑rank adaptation and policy optimization. The model tops the GSMA open telco leaderboard and matches state‑of‑the‑art closed‑source reasoners across seven public telecom benchmarks.
By Bohao Wang, Chenwei Wu, Haoyu Li, Hang Zou, Yu Tian, Lina Bariah, Li Wei, Chongwen Huang, Yongliang Shen, Zhaoyang Zhang, Merouane Debbah
The next generation of mobile networks is envisioned as fully AI-native, with AI-RAN architectures embedding small language models (SLMs) to perform reasoning over real-time telemetry. The state-of-th...
The paper introduces CRAFT, a data‑centric fine‑tuning approach that aligns small language models (SLMs) for pre‑hoc reasoning in AI‑native 6G radio access networks (RAN). By automatically generating verified (input, trace, label) triplets and fine‑tuning with low‑rank adaptation, CRAFT achieves high accuracy and F1 scores on TRACTOR and IC xApp datasets while avoiding parse failures that plague RL methods like GRPO. It also reduces energy consumption by 59% compared to GRPO baselines, offering a more sustainable path to auditable AI in 6G RAN.
By Pranshav Gajjar, Vijay K Shah
TeleTables is a benchmark that evaluates large language models on interpreting telecom tables from 3GPP specifications. It contains 2,220 tables in four formats and 500 human‑verified multiple‑choice questions that range from simple retrieval to multi‑step reasoning. Tests on 20 open‑weight LLMs show that closed‑book performance is limited by domain knowledge, while providing the table as context yields high accuracy that still drops with deeper reasoning, evidence scope, and structural complexity.
By Anas Ezzakri, Nicola Piovesan, Mohamed Sana, Antonio De Domenico, Fadhel Ayed, Haozhe Zhang
arXiv:2609.22241v1 Announce Type: new
Abstract: We present H2LooP Telecom Model v1, a domain-specialized large language models fine-tuned for the telecommunications industry. We release two domain-ad...
By Amit Singh, Vedant Nipane, Mayank Goel, Pulkit Agrawal, Sairanjan Mishra
arXiv:2609.38409v1 Announce Type: new
Abstract: Recent progress in large language model reasoning has been driven by benchmarks and reinforcement learning environments with automatically verifiable r...
By \.Ibrahim Ethem Deveci, Funda Tan \c{C}al{\i}k, Bar{\i}\c{s} Deniz Sa\u{g}lam, Duygu Ataman
The paper introduces a structured reasoning framework that leverages large language models (LLMs) for root cause analysis (RCA) in telecom networks. It organizes heterogeneous network telemetry into canonical contexts, enforces decision‑path reasoning, and produces evidence‑grounded explanations to improve fault identification. Experiments on two 5G RCA datasets, TeleLogs and TelecomTS, show that this approach consistently outperforms baseline techniques in diagnostic accuracy and decision consistency.
By Hao Zhou (Jianzhong), Mandar Kulkarni (Jianzhong), Hao Chen (Jianzhong), Yan Xin (Jianzhong), Charlie (Jianzhong), Zhang
arXiv:2606. 04503v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has greatly advanced large reasoning models (LRMs), but it requires timely training on a huge fully-annotated dataset.
By Guangcheng Zhu, Shenzhi Yang, Haobo Wang, Xing Zheng, Yingfan MA, Xuening Feng, Zhongqi Chen, Bowen Song, Weiqiang Wang, Gang Chen
arXiv:2607. 26119v1 Announce Type: new Abstract: Large reasoning models trained via reinforcement learning (RL) have been increasingly shown to outperform their supervised fine-tuned (SFT) counterparts on mathematical reasoning tasks; Yet the mechanistic basis for this advantage remains unclear.
By Antyabha Rahman, Akshaj Gurugubelli, Omar Ankit, Kevin Zhu, Aishwarya Balwani
The paper discusses the challenges of root cause analysis (RCA) in 5G and 6G telecom networks, where complex cross-layer dependencies make diagnosis difficult. It reviews the progression from rule‑based and machine‑learning RCA methods to emerging large language model (LLM) approaches, highlighting issues such as hallucination and unstable reasoning when using vanilla LLMs. The authors propose a structured reasoning framework that organizes network telemetry into canonical contexts, enforces decision‑path reasoning, and generates evidence‑grounded explanations, showing improved diagnostic accuracy on two 5G RCA datasets.
arXiv:2509. 16456v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used in various domains, showing impressive potential on different tasks.
By Jiahao Yu, Zelei Cheng, Xian Wu, Xinyu Xing
arXiv:2510. 19990v2 Announce Type: replace Abstract: The reasoning paradigm, where language models reason before answering, has enabled breakthroughs on tasks such as mathematical problem-solving.
By Zachary Horvitz, Raghav Singhal, Hao Zou, Carles Domingo-Enrich, Zhou Yu, Rajesh Ranganath, Kathleen McKeown