arXiv:2607. 26924v1 Announce Type: new Abstract: Recent work on LeWorldModel (LeWM) has shown that the Sketched Isotropic Gaussian Regularizer (SIGReg) enables stable end-to-end world-model learning from pixels by regularizing the latent marginal distribution toward an isotropic Gaussian, thereby preventing representation collapse.
By Chang Liu, Fei Suo, Yanzhou Jin, Yusuke Iwasawa, Yutaka Matsuo, Yaonan Zhu
arXiv:2603. 19312v3 Announce Type: replace Abstract: Joint Embedding Predictive Architectures (JEPAs) offer a compelling framework for learning world models in compact latent spaces, yet existing methods remain fragile, relying on complex multi-term losses, exponential moving averages, pre-trained encoders, or auxiliary supervision to avoid representation collapse.
By Lucas Maes, Quentin Le Lidec, Damien Scieur, Yann LeCun, Randall Balestriero
arXiv:2607. 02681v1 Announce Type: cross Abstract: Integrating information across related tasks can improve estimation and prediction in transfer, multi-task, and federated learning, but contamination and heterogeneity make robust borrowing challenging.
By Ye Tian, Mengchu Li, Marco Avella Medina
arXiv:2606. 02221v1 Announce Type: cross Abstract: Multi-task learning (MTL) aims to construct a joint model for multiple tasks by sharing a common representation across domains.
By Chengfeng Wu, Tao Zou, Yanru Wu, Jingge Wang
arXiv:2606. 18627v1 Announce Type: new Abstract: Model merging has emerged as a training-free alternative to multi-task learning, aiming to combine multiple task-specific fine-tuned models into a single multi-task model.
By Ningyuan Shi, Zhipeng Zhou, Hao Wang, Chunyan Miao, Peilin Zhao
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