arXiv Computer Vision

VinciCoder: Unifying Multimodal Code Generation via Coarse-to-fine Visual Reinforcement Learning

VinciCoder is a unified framework for multimodal code generation that addresses the limitations of single-task models by training on a large-scale curated corpus of 1.3 M direct generation pairs and 300 k visual‑refinement tasks. It introduces a coarse‑to‑fine Visual Reinforcement Learning (ViRL) approach that uses visual similarity across multi‑scale patches to provide an implementation‑agnostic reward, improving alignment between rendered outputs and input visuals. Experiments on diverse benchmarks show VinciCoder outperforms existing methods, and ablation studies confirm the effectiveness of ViRL.

arXiv AI

Sep 3

TikZilla: Scaling Text-to-TikZ with High-Quality Data and Reinforcement Learning

TikZilla is a new approach to generating TikZ code from textual descriptions, built on a larger, higher‑quality dataset called DaTikZ‑V4 that includes LLM‑generated figure descriptions. The method uses a two‑stage pipeline: supervised fine‑tuning of small Qwen models (3B and 8B) followed by reinforcement learning with an image encoder that provides semantically faithful reward signals. Human evaluations show that TikZilla outperforms its base models by 1.5–2 points on a 5‑point scale, beats GPT‑4o by 0.5 points, and matches GPT‑5 in image‑based tests while remaining much smaller.

By Christian Greisinger, Steffen Eger

llms reinforcement-learning fine-tuning

arXiv Machine Learning

Sep 16

Towards Scalable RLVR: Multimodal Instruction Following Data Synthesis and Distillation

arXiv:2609.16059v1 Announce Type: cross Abstract: Multimodal instruction following (MMIF) is crucial for building generalist agents. However, current training paradigms rely heavily on Supervised Fin...

By Yirong Zeng, Zhang Sai, Yuxian Wang, Yutai Hou, Yufei Liu, Xiao Ding, Bibo Cai

agents reinforcement-learning fine-tuning efficiency multimodal benchmarks

arXiv AI

Jun 3

Vision Inference Former: Sustaining Visual Consistency in Multimodal Large Language Models

arXiv:2605. 18160v2 Announce Type: replace-cross Abstract: In recent years, multimodal large language models (MLLMs) have achieved remarkable progress, primarily attributed to effective paradigms for integrating visual and textual information.

By Xinpeng Dong, Min Zhang, Kairong Han, Xu Tan, Fei Wu, Kun Kuang

llms computer-vision multimodal benchmarks safety

arXiv AI

Sep 2

VectorGym: A Multi-Task Benchmark for SVG Code Generation, Sketching and Editing

arXiv:2603.29852v2 Announce Type: replace-cross Abstract: We introduce VectorGym, a comprehensive benchmark suite for Scalable Vector Graphics (SVG) that spans generation from text and sketches, comp...

By Joan Rodriguez, Haotian Zhang, Abhay Puri, Haoran Dai, Tianyang Zhang, Meng Lin, Rishav Pramanik, Xiaoqing Xie, Marco Terral Rodriguez, Darsh Kaushik, Aly Shariff, Perouz Taslakian, Spandana Gella, Sai Rajeswar, David Vazquez, Christopher Pal, Marco Pedersoli

llms reinforcement-learning multimodal benchmarks

arXiv Machine Learning

2d ago

PrismaCoder: Pushing the Boundaries of Open Multimodal Code Intelligence through Decomposed Rubric Synthesis

arXiv:2610.20538v1 Announce Type: cross Abstract: Multimodal code intelligence connects the symbolic world of programs with the visual world their execution creates. Interest in this capability is bu...

By Qiushi Sun, Jingyang Gong, Yang Liu, Qiaosheng Chen, Zhangyue Yin, Zixian Huang, Hanxu Hu, Jing Xiong, Fangzhi Xu, Haochen Tan, Haoli Bai, Lifeng Shang, Xiaoguang Li, Ben Kao, Qipeng Guo, Lingpeng Kong, Jiahui Gao

reinforcement-learning multimodal data-engineering

arXiv AI

Sep 30

ResComEmb: Effective and Efficient Multimodal Embedding via Residual Homogeneity Compression

arXiv:2609.37225v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) have shown strong potential for universal multimodal representation learning. However, existing methods eith...

By Zijing Cai, Yuzhe Wang, Jingxian Zhu, Fengbin Zhu, Richang Hong

llms rag multimodal

arXiv Machine Learning

Aug 10

Symbolic Graphics Programming with Large Language Models

arXiv:2509. 05208v2 Announce Type: replace-cross Abstract: Large language models (LLMs) excel at program synthesis, yet their ability to produce symbolic graphics programs (SGPs) that render into precise visual content remains underexplored.

By Yamei Chen, Haoquan Zhang, Yangyi Huang, Zeju Qiu, Kaipeng Zhang, Yandong Wen, Weiyang Liu

llms reinforcement-learning multimodal benchmarks

arXiv Computer Vision

Oct 2

VTR-Bench: A Systematic Benchmark for Evaluating Visual Text Rendering in Video Generation

VTR-Bench is a new benchmark designed to evaluate how well video generation models render text within scenes. It includes 300 prompts across five real-world scenarios such as advertisements and scientific videos, and uses an automated pipeline with human alignment to assess text fidelity and scene/motion requirements. Experiments on 11 state‑of‑the‑art models show that even the best performer has a word error rate of 0.250, underscoring widespread challenges in visual text rendering.

By Yu Huang, Jungang Li, Zhiyuan Wang, Yonghua Hei, Song Dai, Jiayu Yang, Deyuan Liu, Xiang Zheng, Xiaoshuang Shi, Hao Cheng, Kaidi Xu

agents diffusion benchmarks

arXiv Computer Vision

Sep 14

Unified Text-Image Generation with Weakness-Targeted Post-Training

The paper introduces a post‑training approach that enables a single inference process to transition from text reasoning to image synthesis, eliminating the need for explicit modality switching. Using the 14B BAGEL model, the authors demonstrate that targeted post‑training data and reward‑weighted training improve multimodal image generation across four independent T2I benchmarks. The study highlights the benefits of joint text‑image generation and strategic data selection for enhancing T2I performance.

By Jiahui Chen, Philippe Hansen-Estruch, Xiaochuang Han, Yushi Hu, Emily Dinan, Amita Kamath, Michal Drozdzal, Reyhane Askari-Hemmat, Luke Zettlemoyer, Marjan Ghazvininejad

llms diffusion multimodal benchmarks

arXiv AI

Jun 26

Semantic Generative Tuning for Unified Multimodal Models

arXiv:2605. 18714v2 Announce Type: replace-cross Abstract: Unified multimodal models (UMMs) strive to consolidate visual understanding and visual generation within a single architecture.

By Songsong Yu, Yuxin Chen, Ying Shan, Yanwei Li

computer-vision multimodal benchmarks

arXiv AI

Jul 10

Reinforcing the Generation Order of Multimodal Masked Diffusion Models

arXiv:2607. 08056v1 Announce Type: cross Abstract: Diffusion Language Models (DLMs) have recently achieved substantial progress in natural language generation tasks.

By Yidong Ouyang, Zhe Wang, Sourav Bhabesh, Dmitriy Bespalov

llms diffusion multimodal benchmarks safety

arXiv Computer Vision

1d ago

Intern Lumina U2: A Multi-Codebook Diffusion Large Language Model for Omni-Visual Understanding and Image Generation

arXiv:2610.20818v1 Announce Type: new Abstract: Unified multimodal generation covers discrete, continuous, and hybrid paradigms. Discrete approaches with single-codebook visual tokenizers align natur...

By Qian Yu, Zongyang Qiu, Juncheng Yan, Hongliang Li, Qi Qin, Keqi Wang, Siqi Luo, Junkui Ju, Yapeng Mi, Tiantian Lu, Tian Liu, Pengyu Xie, Chenhao Ji, Minghui Zhang, Yiang Zhao, Siyu Yan, Haiying Xu, Yanbo Wang, Jianghan Shen, Yuandong Pu, Jiayi Lei, Borui Zhang, Jinxin Shi, Bo Zhang, Yuhang Zang, Shenglong Ye, Lixin Gu, Hongjie Zhang, Linghan Liu, Xinyuan Chen, Yuewen Cao, Lihe Zhang, Xiaohong Liu, Junjun He, Qiangwei Liu, Qi Zhang, Yu Cheng, Tianfan Xue, Hui Xiong, Chang Xu, Yu Qiao, Yihao Liu, Bin Fu

llms rag diffusion multimodal

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