arXiv AI June 15, 2026 By Shuqi Ke, Giulia Fanti

Learning What to Predict: Downstream-Guided Task Design for Continued Pretraining

arXiv:2601. 22108v2 Announce Type: replace-cross Abstract: Continued pretraining is optimized with fixed self-supervised tasks but selected by downstream performance, creating a coarse feedback loop in which practitioners evaluate checkpoints, change data mixtures or objectives, and restart runs, while individual updates remain blind to target capabilities.

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.

computer-vision

arXiv AI

Jun 2

VLM4VLA: Revisiting Vision-Language-Models in Vision-Language-Action Models

arXiv:2601. 03309v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models, which integrate pretrained large Vision-Language Models (VLM) into their policy backbone, are gaining significant attention for their promising generalization capabilities.

By Jianke Zhang, Xiaoyu Chen, Qiuyue Wang, Mingsheng Li, Yanjiang Guo, Yucheng Hu, Jiajun Zhang, Shuai Bai, Junyang Lin, Jianyu Chen

llms computer-vision robotics fine-tuning multimodal benchmarks

arXiv AI

Sep 15

AdaVSkip: Adaptive Visual Token Skipping Across Layers For Efficient MLLMs Inference

arXiv:2609.15131v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) require substantial computation to process numerous visual tokens across all transformer layers. Most method...

By Yuyao Sun, Tao Deng, Shuang Li, Deqing Wang

llms reinforcement-learning multimodal

arXiv Computer Vision

Sep 1

Enhancing Self-Supervised Visual Representation Learning via Low-Rank Adapted LLMs

arXiv:2507.00754v3 Announce Type: replace Abstract: The integration of Large Language Model (LLMs) blocks with Vision Transformers (ViTs) holds immense promise for vision-only tasks by leveraging the...

By Selim Kuzucu, Muhammad Ferjad Naeem, Anna Kukleva, Federico Tombari, Bernt Schiele

llms fine-tuning

arXiv Machine Learning

Jul 28

Self-Boosting Vision-Language Models with Noisy Student On-Policy Self-Distillation

arXiv:2607. 23125v1 Announce Type: new Abstract: Post-training enables vision-language models (VLMs) to understand human instructions and perform various downstream tasks.

By Shuai Wang, Daoan Zhang, Zhe Tang, Hao Cheng, Jiaheng Wei

llms reinforcement-learning efficiency multimodal benchmarks

arXiv Computer Vision

Sep 3

Hidden-Shot: Towards One-Shot Task Generalization for Low-Level Vision Generalist Models

Hidden‑Shot introduces an implicit prompt mechanism that extracts task‑specific visual information and merges it with in‑task processing to boost one‑shot performance on new low‑level vision tasks. The method injects this prompt cost‑effectively while minimally altering the base generalist model’s architecture. A data‑driven evaluation framework, C/U assessment, is proposed to systematically test generalization across conventional and unconventional tasks, and experiments on seven and ten datasets show Hidden‑Shot outperforming state‑of‑the‑art models.

By Shao-Jun Xia, Xianzheng Ma, Zichong Meng

llms benchmarks

arXiv Machine Learning

Aug 3

Visual Distribution Anchoring for Efficient Prompt Tuning

arXiv:2607. 28967v1 Announce Type: cross Abstract: Prompt tuning adapts vision--language models with few trainable parameters, but existing approaches trade off efficiency and adaptation: static textual prompts can overfit source classes, image-conditioned prompts add per-instance computation, and multimodal tuning modifies the visual branch.

By Pouya Parsa, Raoof Zare Moayedi, Seongjin Choi

llms multimodal