arXiv AI July 15, 2026 By Yubo Zhang, Xiaodong Wang

Self-Evolving In-Context Learning for Direct Pilot-to-Beamformer Design in MU-MISO Systems

arXiv:2607. 11970v1 Announce Type: cross Abstract: We develop an enhanced in-context learning (ICL) framework to improve the performance of pilot-based beamforming in multi-user multiple-input single-output (MU-MISO) systems.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Jul 17

CFM-Bench: A Unified Multi-Domain, Multi-Task Benchmark for Channel Foundation Models

arXiv:2607. 14975v1 Announce Type: new Abstract: Channel foundation models (CFMs) are developing rapidly, with recent studies reporting benefits from pretraining across downstream wireless tasks.

By Yuan Gao, Wenjun Yu, Jun Jiang, Yunfan Li, Xinyu Guo, Shugong Xu

fine-tuning multimodal benchmarks

arXiv AI

Sep 7

Wireless Foundation Models: State-of-the-Art and Open Challenges

The paper surveys wireless foundation models (WFMs), highlighting their role in learning reusable representations from large-scale wireless data for physical-layer tasks. It systematically reviews WFM design components—pretraining, backbone architectures, and downstream adaptation—and categorizes the literature into five task families: signal recognition and demodulation, channel representation learning, RF sensing and localization, beam management, and spectrum sensing and monitoring, including multi-task models. The analysis reveals that while WFMs show promise, evidence of transferability varies across tasks and evaluation settings, and differences in datasets, modalities, architectures, and distribution shifts hinder clear conclusions about effective design choices.

By Alonso M. Pacheco Huachaca, Juan J. Rodriguez Rodriguez, Ahmed Aboulfotouh, Nelson L. S. da Fonseca, Carlos A. Astudillo, Hatem Abou-Zeid

benchmarks

arXiv AI

Jun 2

RA-LWLM: Retrieval-Augmented In-Context Localization with Wireless Foundation Models

arXiv:2606. 01899v1 Announce Type: cross Abstract: Wireless localization is a fundamental capability of sixth-generation (6G) networks.

By Guangjin Pan, Hui Chen, Hei Victor Cheng, Henk Wymeersch

llms rag

arXiv AI

Jul 7

Context Tuning for In-Context Optimization

arXiv:2507. 04221v3 Announce Type: replace-cross Abstract: We introduce Context Tuning, a simple and effective method to significantly enhance few-shot adaptation of large language models (LLMs) without weight updates.

By Jack Lu, Ryan Teehan, Zhenbang Yang, Mengye Ren

llms benchmarks

arXiv AI

Jul 2

Meta-Transfer Learning for mmWave Beam Alignment

arXiv:2607. 00860v1 Announce Type: cross Abstract: Millimeter-wave (mmWave) beam alignment plays a critical role in next-generation wireless systems, yet its efficient implementation remains challenging.

By Ahmet Nuri Cevik, Sinem Coleri

fine-tuning safety

arXiv AI

Aug 18

CM-MAE: A Physics-Guided Cross-Modal Self-Supervised Learning Framework for Vision-Wireless Applications

arXiv:2608. 15972v1 Announce Type: cross Abstract: Synchronized camera and wireless measurements observe the same scene through different physical channels.

By Yubo Zhang, Yiyao Liu

fine-tuning multimodal safety

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Related stories

CFM-Bench: A Unified Multi-Domain, Multi-Task Benchmark for Channel Foundation Models

Wireless Foundation Models: State-of-the-Art and Open Challenges

RA-LWLM: Retrieval-Augmented In-Context Localization with Wireless Foundation Models

Context Tuning for In-Context Optimization

Meta-Transfer Learning for mmWave Beam Alignment

CM-MAE: A Physics-Guided Cross-Modal Self-Supervised Learning Framework for Vision-Wireless Applications