34 citations · 95 across the 24 of their papers we have counts for
12 papers · 1 filter
DeepTelecom: A Differentiable Multimodal Framework for Channel Twinning in Dynamic Heterogeneous Wireless Environments
Bohao Wang, Tianen Yuan, William Yen +12
Large-scale multimodal data are a key enabler for integrating large generative artificial intelligence models into wireless networks. However, existing wireless datasets offer limi…
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,…
Astar: Learning to Propose Evolution Directions for Self-Evolving Industrial AI Systems
Jinxin Hu, Hao Deng, Haibo Xing +12
Modern AI systems advance through continuous iteration: a loop of proposing evolution directions, implementing code, training, and evaluation. While the latter three stages are inc…
Making Collaborative Signals Count: Graph-Aware Large Language Models for Sequential Recommendation
Fenglin Yan, Bohao Wang, Jian Zhang +5
Large language models (LLMs) have been widely adopted as backbones for recommender systems. However, their language-centric pretraining makes it difficult to capture collaborative…
SmartGR: Hierarchy and Beam-Aware Knowledge Distillation for Generative Recommendation
Ziheng Zhang, Yu Cui, Bohao Wang +6
Generative recommendation (GR) has emerged as a promising paradigm for recommender systems. Scaling up GR models can improve recommendation performance, but it also substantially i…
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…
LBR: Towards Mitigating Length Bias in Large Language Models for Recommendation
Hongchen Li, Bohao Wang, Jingbang Chen +5
Large language models (LLMs) have recently emerged as powerful backbones for recommender systems by reformulating recommendation as a token-level generation task. Despite their pro…
Trustworthy Recommendation in the Era of Large Language Models: Opportunities and Challenges
Bohao Wang, Yu Cui, Zhenxiang Xu +13
The field of recommender systems (RS) is currently undergoing two profound paradigm shifts. From the perspective of objectives, the goal has shifted beyond mere recommendation accu…
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…
RF-GPT: Teaching AI to See the Wireless World
Hang Zou, Yu Tian, Bohao Wang +4
Large language models (LLMs) and multimodal models have become powerful general-purpose reasoning systems. However, radio-frequency (RF) signals, which underpin wireless systems, a…
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…
BEAR: Towards Beam-Search-Aware Optimization for Recommendation with Large Language Models
Weiqin Yang, Bohao Wang, Zhenxiang Xu +5
Recent years have seen a rapid surge in research leveraging Large Language Models (LLMs) for recommendation. These methods typically employ supervised fine-tuning (SFT) to adapt LL…