arXiv Computer Vision September 3, 2026 By Xin Zhang, Haochen Wang, Yikang Zhou, Zhuochen Wang, Xiangtai Li, Robby T. Tan

Actor as Its Own Critic: Unifying Region Understanding and Localization via CycleGRPO

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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

Jun 2

An Open-Source Benchmark and Baseline for Multi-temporal Referring Segmentation

arXiv:2606. 00987v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have shown strong visual understanding and language-guided grounding abilities, yet their capacity for multi-temporal visual reasoning remains underexplored.

By Bingyu Li, Da Zhang, Tao Huo, Zhiyuan Zhao, Junyu Gao, Xuelong Li

llms agents computer-vision fine-tuning multimodal benchmarks

arXiv Machine Learning

Jun 2

B-GRTO: Bootstrapped Group Relative Tool Optimization for Referring Segmentation

arXiv:2605. 23500v2 Announce Type: replace-cross Abstract: Segmentation is a fundamental task in computer vision, underpinning pixel-level scene understanding and serving as a cornerstone for applications ranging from autonomous perception to medical image analysis.

By Mario Markov (INSAIT, Sofia University "St. Kliment Ohridski"), Stefan Maria Ailuro (INSAIT, Sofia University "St. Kliment Ohridski"), Mohammad Mahdi (INSAIT, Sofia University "St. Kliment Ohridski"), Luc Van Gool (INSAIT, Sofia University "St. Kliment Ohridski"), Danda Pani Paudel (INSAIT, Sofia University "St. Kliment Ohridski")

llms agents computer-vision reinforcement-learning multimodal benchmarks

arXiv AI

Aug 25

Think with Structured Grounding: Perceptual Reinforcement Learning for Chart and Visual-Tabular Understanding

arXiv:2608.22429v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) capable of thinking with images often rely on external tools for fine-grained perception. However, this relian...

By Changjiang Jiang, Qiannian Zhao, Lei Xin, Jinxiang Xie, Preslav Nakov, Zhuohan Xie

llms reinforcement-learning fine-tuning efficiency multimodal

arXiv Computer Vision

2d ago

A Glance Is All You Need: Single-Pass Fine-Grained Image Captioning with SimLoss

arXiv:2609.00591v1 Announce Type: new Abstract: An image may be worth a thousand words, but most captioning models describe it in only a few. Modern vision-language models produce fluent high-level c...

By Suryaansh Jain, Rahasya Barkur, Vishal G, Ryan Rossi, Franck Dernoncourt, Jack Wang, Koustava Goswami, Nedim Lipka, Puneet Mathur, Samyadeep Basu, Seunghyun Yoon

llms rag fine-tuning efficiency multimodal

arXiv Computation and Language

3d ago

ReVA: A Region-Aware Visual Assistant for Visually Grounded Question Answering

arXiv:2608.28707v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable progress in Visual Question Answering (VQA), yet they continue to struggle with quest...

By Anoop Senthil

llms rag computer-vision nlp multimodal safety