arXiv Machine Learning July 16, 2026 By Qi Yu, Ruizhong Qiu, Zhichen Zeng, My T. Thai, Huan Liu, Hanghang Tong

AvAtar: Learning to Align via Active Optimal Transport

Read the original on arXiv Machine Learning →

arXiv:2605. 24395v2 Announce Type: replace Abstract: Alignment plays a fundamental role in many machine learning problems, such as multi-network analysis, multimodal learning, and point cloud registration.

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 Machine Learning.

arXiv Machine Learning

Jul 16

Flatness and Gradient Alignment Are Both Necessary: Spectral-Aware Gradient-Aligned Exploration for Multi-Distribution Learning

arXiv:2605. 07914v2 Announce Type: replace Abstract: Sharpness-aware and gradient-alignment methods have been shown to improve generalization, however each family of methods targets a single geometric property of the loss landscape, while ignoring the other.

By Aristotelis Ballas, Christos Diou

benchmarks safety

arXiv AI

Jun 30

SOTAlign: Semi-Supervised Alignment of Unimodal Vision and Language Models via Optimal Transport

arXiv:2602. 23353v2 Announce Type: replace-cross Abstract: The Platonic Representation Hypothesis posits that neural networks trained on different modalities converge toward a shared statistical model of the world.

By Simon Roschmann, Paul Krzakala, Sonia Mazelet, Quentin Bouniot, Zeynep Akata

llms rag multimodal safety

arXiv AI

3d ago

Prototype-guided Bilateral Alignment Multimodal Federated Learning

arXiv:2609.38925v1 Announce Type: new Abstract: Multimodal federated learning (MFL) has emerged as a pivotal paradigm for leveraging distributed data to enhance model performance. However, existing m...

By Tianchi Liao Tianchi_Liao, Lele Fu, Sheng Huang, Qing Hu, Hong-Ning Dai, Chuan Chen

multimodal benchmarks safety

arXiv Machine Learning

Jun 2

Closing the Alignment-Maturity Gap in Federated Prototype Learning

arXiv:2606. 02172v1 Announce Type: new Abstract: Learning discriminative visual representations from distributed, heterogeneous data is a fundamental challenge in Federated Learning (FL).

By Mario Casado-Diez, Alejandro Dopico-Castro, Ver\'onica Bol\'on-Canedo, Bertha Guijarro-Berdi\~nas

rag benchmarks safety

arXiv Machine Learning

Jun 25

Geometry-Anchored Transport Framework for Exemplar-Free Class-Incremental Learning

arXiv:2606. 25347v1 Announce Type: new Abstract: Exemplar-free class-incremental learning (EFCIL) requires stable decision boundaries within a shifting feature space.

By Hongye Xu, Bartosz Krawczyk

fine-tuning safety

arXiv Machine Learning

Aug 6

Multimodal Alignment Through Joint Kernel Entropic Gromov--Wasserstein Optimal Transport

arXiv:2608. 04234v1 Announce Type: cross Abstract: We study the problem of aligning data from multiple modalities into a shared representation space, focusing on settings where strong pretrained unimodal encoders are available but cross-modal paired data are scarce.

By Yixuan Florence Wu, Yilun Zhu, Naichen Shi

rag diffusion multimodal safety

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Flatness and Gradient Alignment Are Both Necessary: Spectral-Aware Gradient-Aligned Exploration for Multi-Distribution Learning

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