arXiv Machine Learning June 18, 2026 By Shilei Cao, Hehai Lin, Jiashun Cheng, Yang Liu, Guowen Li, Xuehe Wang, Juepeng Zheng, Haoyuan Liang, Meng Jin, Chengwei Qin, Hong Cheng, Haohuan Fu

Task-Adaptive Parameter-Efficient Fine-Tuning for Weather Foundation Models

Read the original on arXiv Machine Learning →

arXiv:2509. 22020v2 Announce Type: replace Abstract: While recent advances in machine learning have equipped Weather Foundation Models (WFMs) with substantial generalization capabilities across diverse downstream tasks, the escalating computational requirements associated with their expanding scale increasingly hinder practical deployment.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jun 5

GenFT: A Generative Parameter-Efficient Fine-Tuning Method for Pretrained Foundation Models

arXiv:2506. 11042v2 Announce Type: replace Abstract: Parameter-efficient fine-tuning (PEFT) has emerged as a resource-efficient strategy for adapting Pretrained Foundation Models (PFMs) by learning a small number of task-specific updates $\Delta W$.

By Guangning Xu, Baoquan Zhang, Michael. K. Ng

llms nlp fine-tuning benchmarks

arXiv Machine Learning

Jun 9

Lost in the Non-convex Loss Landscape: How to Fine-tune the Large Time Series Model?

arXiv:2606. 08578v1 Announce Type: new Abstract: Recently, large time series models (LTSMs) have gained increasing attention due to their similarities to large language models, including flexible context length, scalability, and task generality, outperforming advanced task-specific models.

By Xu Zhang, Peang Wang, Wei Wang

llms fine-tuning benchmarks

arXiv AI

Jun 3

X-Restormer++: 1st Place Solution for the UG2+ CVPR 2026 All-Weather Restoration Challenge

arXiv:2605. 13258v2 Announce Type: replace-cross Abstract: In this work, we present our winning solution for the 8th UG2+ Challenge (CVPR 2026) Track 1: Image Restoration under All-weather Conditions.

By Youwei Pan, Leilei Cao, Yingfang Zhu, Fengjie Zhu

fine-tuning

arXiv Machine Learning

1d ago

UniFed-VLM: Federated Instruction Tuning for Vision-Language Models with Multiple Heterogeneity

arXiv:2608. 15516v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have demonstrated strong performance in multimodal understanding and generation.

By Pengyu Wang, Baochen Xiong, Xiaoshan Yang, Yifan Xu, Zhang Qimeng, Haifeng Chen, Changsheng Xu

llms fine-tuning efficiency multimodal benchmarks safety

arXiv AI

Jun 4

EvoPrompt: Guided Prompt Evolution for Vision-Language Models Adaptation

arXiv:2603. 09493v2 Announce Type: replace-cross Abstract: The adaptation of large-scale vision-language models (VLMs) to downstream tasks with limited labeled data remains a significant challenge.

By Enming Zhang, Jiayang Li, Yanlong Wang, Yanru Wu, Zhenyu Liu, Yang Li

llms rag fine-tuning multimodal benchmarks

arXiv Machine Learning

Jul 2

MMLoP: Multi-Modal Low-Rank Prompting for Efficient Vision-Language Adaptation

arXiv:2602. 21397v2 Announce Type: replace-cross Abstract: Prompt learning has become a dominant paradigm for adapting vision-language models (VLMs) such as CLIP to downstream tasks without modifying pretrained weights.

By Sajjad Ghiasvand, Haniyeh Ehsani Oskouie, Mahnoosh Alizadeh, Ramtin Pedarsani

llms rag multimodal benchmarks safety

Related stories

GenFT: A Generative Parameter-Efficient Fine-Tuning Method for Pretrained Foundation Models

Lost in the Non-convex Loss Landscape: How to Fine-tune the Large Time Series Model?

X-Restormer++: 1st Place Solution for the UG2+ CVPR 2026 All-Weather Restoration Challenge

UniFed-VLM: Federated Instruction Tuning for Vision-Language Models with Multiple Heterogeneity

EvoPrompt: Guided Prompt Evolution for Vision-Language Models Adaptation

MMLoP: Multi-Modal Low-Rank Prompting for Efficient Vision-Language Adaptation