arXiv AI August 19, 2026 By Xiangyi Li, Jiajia Guo, Chao-Kai Wen, Xin Geng, Shi Jin, Zhi-Hua Zhou

Learnware for CSI Feedback: Scene-specific Small Models Can Do Big

The paper proposes a Learnware-based framework for deploying scene‑specific CSI feedback models in 6G systems. A centralized AI data center maintains a catalog of pre‑trained models, each tagged with semantic and statistical specifications. Base stations retrieve the most relevant model using only statistical fingerprints, which reduces data privacy risks, lowers retrieval latency, and cuts fine‑tuning effort, achieving up to 57.7% performance gains over a general model.

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 Machine Learning

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LEMUR 2: Unlocking Neural Network Diversity for AI

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arXiv AI

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PrototypeNAS: Rapid Design of Deep Neural Networks for Microcontroller Units

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By Mark Deutel, Simon Geis, Axel Plinge

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arXiv Machine Learning

Sep 10

FANS: Federated Adaptive Network Search Learning for Heterogeneous Devices

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By Jiaxin Zhang, Xingwei Wang, Bo Yi, Liang Zhao, Alireza Furutanpey, Ziyi Chen, Qiang He, Keqin Li, Schahram Dustdar

efficiency benchmarks safety

arXiv AI

Jul 14

The Universal Language of CSI:Unifying Wireless Sensing Across Devices and Environments

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By Jiayi Chen, Weiting Ou, Guangxu Zhu

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arXiv AI

Jun 24

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training

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By Ismail Labiad, Mathurin Videau, Matthieu Kowalski, Marc Schoenauer, Alessandro Leite, Julia Kempe, Olivier Teytaud

llms benchmarks safety

arXiv Machine Learning

Aug 27

ONNX-Net: Towards Universal Representations and Instant Performance Prediction for Neural Architectures

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By Shiwen Qin, Alexander Auras, Shay B. Cohen, Elliot J. Crowley, Michael Moeller, Linus Ericsson, Jovita Lukasik

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LEMUR 2: Unlocking Neural Network Diversity for AI

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The Universal Language of CSI:Unifying Wireless Sensing Across Devices and Environments

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