arXiv AI June 16, 2026 By Qinwu Xu

Momentum-Guided Semantic Forecasting (MoFore) for Self-Supervised Video Representation Learning

arXiv:2606. 14765v1 Announce Type: cross Abstract: Self-supervised video representation learning has recently advanced through contrastive learning, masked reconstruction, and predictive representation learning.

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 AI

Jun 9

What Makes Video World Model Latents Action-Relevant: Prediction over Reconstruction

arXiv:2606. 07687v1 Announce Type: cross Abstract: Video world models are increasingly used to provide predictive visual representations, yet it remains unclear which pretraining signals induce action-relevant structure in their latent spaces.

By Jewon Yeom, Hanseul Kim, Jeongjae Park, Sungmok Jung, Jaejin Lee, Taesup Kim

diffusion robotics benchmarks

arXiv Computer Vision

6d ago

Learning from Next-Frame Prediction: Autoregressive Video Modeling Encodes Effective Representations

arXiv:2512.21004v2 Announce Type: replace Abstract: Recent advances in pretraining general foundation models have significantly improved performance across diverse downstream tasks. While autoregress...

By Jinghan Li, Yang Jin, Hao Jiang, Yadong Mu, Yang Song, Kun Xu

llms nlp

arXiv Computer Vision

2d ago

Latent-Foresight: End-to-End Learning Predictable Representations for Latent World Models

arXiv:2610.01942v1 Announce Type: new Abstract: Predicting the future evolution of a scene is a fundamental capability for world modeling. Recent work has shown that operating in the feature space of...

By Efstathios Karypidis, Spyros Gidaris, Nikos Komodakis

diffusion nlp

arXiv AI

2d ago

Video Generation Models: A Survey of Post-Training and Alignment

arXiv:2610.00812v1 Announce Type: cross Abstract: Video generation has rapidly progressed from short, low-quality clips to high-resolution, long-duration sequences with complex spatiotemporal dynamic...

By Chaoyu Li, Xiaoyi Gu, Yogesh Kulkarni, Eun Woo Im, Mohammadmahdi Honarmand, Zeyu Wang, Juntong Song, Fei Du, Xilin Jiang, Kexin Zheng, Tianzhi Li, Fei Tao, Pooyan Fazli

diffusion fine-tuning efficiency benchmarks safety

arXiv Machine Learning

Sep 24

ChronoSteer: Bridging Large Language Model and Time Series Foundation Model via Synthetic Cross-Modal Alignment Dataset

ChronoSteer is a decoupled agentic framework that bridges large language models and time series foundation models by learning cross‑modal alignment from synthetic paired supervision. It converts textual events into revision instructions that steer a frozen time‑series model, discretizes these instructions into a compact codebook to reduce semantic divergence, and then refines the predictions with a two‑stage training strategy. The authors also release a leakage‑controlled multimodal benchmark and report a 25.8% improvement in zero‑shot prediction accuracy over the unimodal backbone.

By Chengsen Wang, Qi Qi, Zhongwen Rao, Lujia Pan, Jingyu Wang

llms agents multimodal benchmarks safety

arXiv Machine Learning

Jul 2

MoVA: Learning Asymmetric Dual Projections for Modular Long Video-Text Alignment

arXiv:2607. 00858v1 Announce Type: cross Abstract: Contrastive pre-training has propelled video-text alignment, yet models often inherit the critical limitations of their image-text predecessors like CLIP, resulting in entangled representations.

By Peiyuan Zhu, Shaoan Xie, Zijian Li, Yifan Shen, Namrata Deka, Harsh Shrivastava, Guangyi Chen, Kun Zhang

multimodal safety