arXiv AI July 21, 2026 By Jiaqi Zhu, Xincheng Chen, Yuncheng Wu, Zhaojing Luo, Beng Chin Ooi

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift

arXiv:2607. 18072v1 Announce Type: cross Abstract: Generative models trained on a source domain often produce samples that are poorly aligned with shifted target domains, limiting their effectiveness for target-domain data augmentation.

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

diffusion

arXiv Machine Learning

Jul 7

WeightCLIP: Aligning Datasets and Models for Weight Space Learning

arXiv:2607. 03551v1 Announce Type: new Abstract: Weight space learning aims to learn representations of neural network (NN) weights, enabling different downstream tasks.

By Aron Asefaw, Konstantinos Tzevelekakis, Damian Falk, L\'eo Meynent, Damian Borth

diffusion fine-tuning

arXiv AI

1d ago

Bringing Generative Learning to Representation Learning: Self-Supervised Transfer Learning as Distribution Matching

arXiv:2502. 14424v3 Announce Type: replace-cross Abstract: Most self-supervised learning objectives defend against collapse but leave the target representation law unspecified.

By Yuling Jiao, Wensen Ma, Defeng Sun, Hansheng Wang, Yang Wang

fine-tuning benchmarks

arXiv Machine Learning

Jun 2

Efficient Synthetic Network Generation via Latent Embedding Reconstruction

arXiv:2606. 00934v1 Announce Type: cross Abstract: Network data are ubiquitous across the social sciences, biology, and information systems.

By Feifan Jiang, Yinan Bu, Shihao Wu, Gongjun Xu, Ji Zhu

rag diffusion efficiency

arXiv AI

Jun 26

Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes

arXiv:2509. 09960v2 Announce Type: replace-cross Abstract: Synthetic tabular data generation is increasingly essential in machine learning, supporting downstream applications when real-world, high-quality tabular data is insufficient.

By Mingxuan Jiang, Keyang Chen, Yongxin Wang, Yongsheng Zhao, Ziyue Dai, Yicun Liu, Zeping Li, Qiuyang Zhang, Hongyi Nie, Hongbin Zhu, Sen Liu, Guangnan Ye, Hongfeng Chai

llms diffusion fine-tuning benchmarks safety

arXiv AI

Jun 10

ERAlign: Energy-based Representation Alignment of GNNs and LLMs on Text-attributed Graphs

arXiv:2606. 10461v1 Announce Type: cross Abstract: Text-attributed Graphs (TAGs) incorporate textual node attributes with graph structures to describe rich relational semantics.

By Xianlin Zeng, Fan Xia, Xiangyu Chen

llms rag diffusion benchmarks safety

arXiv AI

Jun 2

Improving Visual Representation Alignment Generation with GRPO

arXiv:2606. 00583v1 Announce Type: cross Abstract: Recent diffusion transformers have demonstrated strong image synthesis capabilities but remain inefficient to train due to weak alignment between generative and discriminative representations.

By Shentong Mo, Sukmin Yun

llms rag diffusion safety

Related stories

WeightCLIP: Aligning Datasets and Models for Weight Space Learning

Bringing Generative Learning to Representation Learning: Self-Supervised Transfer Learning as Distribution Matching

Efficient Synthetic Network Generation via Latent Embedding Reconstruction

Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes

ERAlign: Energy-based Representation Alignment of GNNs and LLMs on Text-attributed Graphs

Improving Visual Representation Alignment Generation with GRPO