arXiv AI October 1, 2026 By Guanyu Zhou, Yida Yin, Wenhao Chai, Shengbang Tong, Xingyu Fu, Zhuang Liu

VisionFoundry: Teaching VLMs Visual Perception with Synthetic Images

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arXiv Computation and Language

Aug 25

Benchmarking and Boosting Multilingual Capabilities of LVLMs via OCR-Centric Reinforcement Learning

The paper introduces PM4Bench, a multimodal, multilingual, multi-task benchmark built on a strictly parallel 10‑language corpus, allowing fair cross‑lingual comparison of Large Vision‑Language Models (LVLMs). It also proposes a vision setting that embeds textual inputs directly into images to better mimic real deployment scenarios. Experiments show OCR performance drives cross‑lingual gaps, leading to an OCR‑centric GRPO training strategy that improves multilingual VQA and reduces disparities without costly supervision.

By Junyuan Gao, Jiahe Song, Jiang Wu, Runchuan Zhu, Guanlin Shen, Shasha Wang, Xingjian Wei, Haote Yang, Weijia Li, Bin Wang, Lijun Wu, Conghui He

llms agents computer-vision reinforcement-learning multimodal benchmarks

arXiv AI

Jul 9

T2T-VICL: Cross-Task Visual In-Context Learning via Implicit Text-Driven VLMs

arXiv:2511. 16107v3 Announce Type: replace-cross Abstract: Visual in-context learning (VICL) solves visual tasks by conditioning on a few input-output demonstrations without any model training.

By Shao-Jun Xia, Huixin Zhang, Zhengzhong Tu

llms multimodal safety

arXiv Computer Vision

Sep 1

Think, Look, and Revise: Inconsistency-Aware Visual Self-Correction in MLLMs

arXiv:2608.29374v1 Announce Type: new Abstract: Tool-augmented multimodal reasoning integrates external tools (e.g., object detection, depth estimation) into multimodal large language models (MLLMs)...

By Yu Cheng, Arushi Goel, Hakan Bilen

llms computer-vision reinforcement-learning multimodal benchmarks

arXiv AI

Jun 6

Image Generators are Generalist Vision Learners

arXiv:2604. 20329v3 Announce Type: replace-cross Abstract: Recent works show that image and video generators exhibit zero-shot visual understanding behaviors, in a way reminiscent of how LLMs develop emergent capabilities of language understanding and reasoning from generative pretraining.

By Valentin Gabeur, Shangbang Long, Songyou Peng, Paul Voigtlaender, Shuyang Sun, Yanan Bao, Karen Truong, Zhicheng Wang, Wenlei Zhou, Jonathan T. Barron, Kyle Genova, Nithish Kannen, Sherry Ben, Yandong Li, Mandy Guo, Suhas Yogin, Yiming Gu, Huizhong Chen, Oliver Wang, Saining Xie, Howard Zhou, Kaiming He, Thomas Funkhouser, Jean-Baptiste Alayrac, Radu Soricut

llms diffusion computer-vision benchmarks

arXiv Machine Learning

Sep 22

Seeing Through Conflicts: Improving Instruction Hierarchy Alignment in Vision-Language Models

arXiv:2609.22234v1 Announce Type: cross Abstract: Instruction hierarchy (IH) alignment teaches language models to prioritize higher-level instructions when inputs conflict. While studied primarily in...

By Nicholas Sansoterra, Zishuo Zheng, Sachin Kumar

llms agents reinforcement-learning multimodal safety

arXiv Machine Learning

Sep 1

SynMulti: Synthetic-to-Real Learning for Multimodal Video Understanding

arXiv:2604.12335v2 Announce Type: replace-cross Abstract: Training multimodal large language models (MLLMs) for video understanding requires large-scale annotated data spanning diverse tasks such as...

By Tanzila Rahman, Renjie Liao, Leonid Sigal

llms computer-vision nlp fine-tuning multimodal