Rabiul Awal

UI-Vision: A Desktop-centric GUI Benchmark for Visual Perception and Interaction

Shravan Nayak

Xiangru Jian

Kevin Qinghong Lin

Juan A. Rodriguez

Montek Kalsi

M. Tamer Özsu

David Vazquez

Sai Rajeswar

Human Annotator

2025-10-06

Proceedings of the 42nd International Conference on Machine Learning (published)

doi.org

openreview.net

WebMMU: A Benchmark for Multimodal Multilingual Website Understanding and Code Generation

David Vazquez

Juan A. Rodriguez

Sai Rajeswar

ServiceNow

WebMMU Benchmark

We present WebMMU, a multilingual benchmark that evaluates three core web tasks: (1) website visual question answering, (2) code editing inv… (see more)olving HTML/CSS/JavaScript, and (3) mockup-to-code generation. Unlike prior benchmarks that treat these tasks separately, WebMMU unifies them using expert-annotated, real-world web data to assess models'abilities in complex multi-step reasoning, precise element grounding, and functional UI comprehension and coding. Our evaluation shows that while multimodal large language models (MLLMs) perform well on basic information extraction, they struggle with reasoning and grounding, editing code to preserve functionality, and generating design-to-code that maintains hierarchy and supports multilingual content. These findings reveal key limitations in current MLLMs and underscore the need for improved multimodal and cross-lingual reasoning to build future web agents capable of automating diverse web development tasks.

2025-08-22

ArXiv (preprint)

doi.org

arxiv.org

WebMMU: A Benchmark for Multimodal Multilingual Website Understanding and Code Generation

David Vazquez

Juan A. Rodriguez

Sai Rajeswar

ServiceNow

WebMMU Benchmark

We present WebMMU, a multilingual benchmark that evaluates three core web tasks: (1) website visual question answering, (2) code editing inv… (see more)olving HTML/CSS/JavaScript, and (3) mockup-to-code generation. Unlike prior benchmarks that treat these tasks separately, WebMMU unifies them using expert-annotated, real-world web data to assess models'abilities in complex multi-step reasoning, precise element grounding, and functional UI comprehension and coding. Our evaluation shows that while multimodal large language models (MLLMs) perform well on basic information extraction, they struggle with reasoning and grounding, editing code to preserve functionality, and generating design-to-code that maintains hierarchy and supports multilingual content. These findings reveal key limitations in current MLLMs and underscore the need for improved multimodal and cross-lingual reasoning to build future web agents capable of automating diverse web development tasks.

2025-08-22

ArXiv (preprint)

doi.org

arxiv.org

The Promise of RL for Autoregressive Image Editing

Saba Ahmadi

Rabiul Awal

Ankur Sikarwar

Amirhossein Kazemnejad

Ge Ya Luo

Juan A. Rodriguez

Sai Rajeswar

We explore three strategies to enhance performance on a wide range of image editing tasks: supervised fine-tuning (SFT), reinforcement learn… (see more)ing (RL), and Chain-of-Thought (CoT) reasoning. In order to study all these components in one consistent framework, we adopt an autoregressive multimodal model that processes textual and visual tokens in a unified manner. We find RL combined with a large multi-modal LLM verifier to be the most effective of these strategies. As a result, we release EARL: Editing with Autoregression and RL, a strong RL-based image editing model that performs competitively on a diverse range of edits compared to strong baselines, despite using much less training data. Thus, EARL pushes the frontier of autoregressive multimodal models on image editing. We release our code, training data, and trained models at https://github.com/mair-lab/EARL.

2025-08-01

ArXiv (preprint)

doi.org

arxiv.org

The Promise of RL for Autoregressive Image Editing

Saba Ahmadi

Rabiul Awal

Ankur Sikarwar

Amirhossein Kazemnejad

Ge Ya Luo

Juan A. Rodriguez

Sai Rajeswar

We explore three strategies to enhance performance on a wide range of image editing tasks: supervised fine-tuning (SFT), reinforcement learn… (see more)ing (RL), and Chain-of-Thought (CoT) reasoning. In order to study all these components in one consistent framework, we adopt an autoregressive multimodal model that processes textual and visual tokens in a unified manner. We find RL combined with a large multi-modal LLM verifier to be the most effective of these strategies. As a result, we release EARL: Editing with Autoregression and RL, a strong RL-based image editing model that performs competitively on a diverse range of edits compared to strong baselines, despite using much less training data. Thus, EARL pushes the frontier of autoregressive multimodal models on image editing. We release our code, training data, and trained models at https://github.com/mair-lab/EARL.

2025-08-01

ArXiv (preprint)

arxiv.org

The Promise of RL for Autoregressive Image Editing

Saba Ahmadi

Rabiul Awal

Ankur Sikarwar

Amirhossein Kazemnejad

Ge Ya Luo

Juan A. Rodriguez

Sai Rajeswar

We explore three strategies to enhance performance on a wide range of image editing tasks: supervised fine-tuning (SFT), reinforcement learn… (see more)ing (RL), and Chain-of-Thought (CoT) reasoning. In order to study all these components in one consistent framework, we adopt an autoregressive multimodal model that processes textual and visual tokens in a unified manner. We find RL combined with a large multi-modal LLM verifier to be the most effective of these strategies. As a result, we release EARL: Editing with Autoregression and RL, a strong RL-based image editing model that performs competitively on a diverse range of edits compared to strong baselines, despite using much less training data. Thus, EARL pushes the frontier of autoregressive multimodal models on image editing. We release our code, training data, and trained models at https://github.com/mair-lab/EARL.

2025-08-01

arXiv (published)

doi.org

arxiv.org

The Promise of RL for Autoregressive Image Editing

Saba Ahmadi

Rabiul Awal

Ankur Sikarwar

Amirhossein Kazemnejad

Ge Ya Luo

Juan A. Rodriguez

Sai Rajeswar

We explore three strategies to enhance performance on a wide range of image editing tasks: supervised fine-tuning (SFT), reinforcement learn… (see more)ing (RL), and Chain-of-Thought (CoT) reasoning. In order to study all these components in one consistent framework, we adopt an autoregressive multimodal model that processes textual and visual tokens in a unified manner. We find RL combined with a large multi-modal LLM verifier to be the most effective of these strategies. As a result, we release EARL: Editing with Autoregression and RL, a strong RL-based image editing model that performs competitively on a diverse range of edits compared to strong baselines, despite using much less training data. Thus, EARL pushes the frontier of autoregressive multimodal models on image editing. We release our code, training data, and trained models at https://github.com/mair-lab/EARL.

2025-08-01

ArXiv (preprint)

doi.org

arxiv.org

WebMMU: A Benchmark for Multimodal Multilingual Website Understanding and Code Generation

David Vazquez

Juan A. Rodriguez

Sai Rajeswar

We present WebMMU, a multilingual benchmark that evaluates three core web tasks: (1) website visual question answering, (2) code editing inv… (see more)olving HTML/CSS/JavaScript, and (3) mockup-to-code generation. Unlike prior benchmarks that treat these tasks separately, WebMMU unifies them using expert-annotated, real-world web data to assess models' abilities in complex multi-step reasoning, precise element grounding, and functional UI comprehension and coding. Our evaluation shows that while multimodal large language models (MLLMs) perform well on basic information extraction, they struggle with reasoning and grounding, editing code to preserve functionality, and generating design-to-code that maintains hierarchy and supports multilingual content. These findings reveal key limitations in current MLLMs and underscore the need for improved multimodal and cross-lingual reasoning to build future web agents capable of automating diverse web development tasks.

2025-08-01

arXiv (published)

doi.org

CTRL-O: Language-Controllable Object-Centric Visual Representation Learning

Aniket Rajiv Didolkar

Andrii Zadaianchuk

Rabiul Awal

Maximilian Seitzer

Efstratios Gavves

Aishwarya Agrawal

Object-centric representation learning aims to decompose visual scenes into fixed-size vectors called "slots" or "object files", where each … (see more)slot captures a distinct object. Current state-of-the-art models have shown remarkable success in object discovery, particularly in complex real-world scenes, while also generalizing well to unseen domains. However, these models suffer from a key limitation: they lack controllability. Specifically, current object-centric models learn representations based on their preconceived understanding of objects and parts, without allowing user input to guide or modify which objects are represented. Introducing controllability into object-centric models could unlock a range of useful capabilities, such as enabling models to represent scenes at variable levels of granularity based on user specification. In this work, we propose a novel approach that conditions slot representations through guided decomposition, paired with a novel contrastive learning objective, to enable user-directed control over which objects are represented. Our method achieves such controllability without any mask supervision and successfully binds to user-specified objects in complex real-world scenes.

2025-06-10

2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (published)

doi.org

openreview.net

Rendering-Aware Reinforcement Learning for Vector Graphics Generation

Juan A. Rodriguez

Haotian Zhang

Abhay Puri

Aarash Feizi

Rishav Pramanik

Pascal Wichmann

Arnab Mondal

Mohammad Reza Samsami

Rabiul Awal

Perouz Taslakian

Spandana Gella

Sai Rajeswar

David Vazquez

Chris Pal

Marco Pedersoli

Scalable Vector Graphics (SVG) offer a powerful format for representing visual designs as interpretable code. Recent advances in vision-lang… (see more)uage models (VLMs) have enabled high-quality SVG generation by framing the problem as a code generation task and leveraging large-scale pretraining. VLMs are particularly suitable for this task as they capture both global semantics and fine-grained visual patterns, while transferring knowledge across vision, natural language, and code domains. However, existing VLM approaches often struggle to produce faithful and efficient SVGs because they never observe the rendered images during training. Although differentiable rendering for autoregressive SVG code generation remains unavailable, rendered outputs can still be compared to original inputs, enabling evaluative feedback suitable for reinforcement learning (RL). We introduce RLRF(Reinforcement Learning from Rendering Feedback), an RL method that enhances SVG generation in autoregressive VLMs by leveraging feedback from rendered SVG outputs. Given an input image, the model generates SVG roll-outs that are rendered and compared to the original image to compute a reward. This visual fidelity feedback guides the model toward producing more accurate, efficient, and semantically coherent SVGs. RLRF significantly outperforms supervised fine-tuning, addressing common failure modes and enabling precise, high-quality SVG generation with strong structural understanding and generalization.

2025-05-27

ArXiv (preprint)

doi.org

arxiv.org

UI-Vision: A Desktop-centric GUI Benchmark for Visual Perception and Interaction

Shravan Nayak

Xiangru Jian

Kevin Qinghong Lin

Juan A. Rodriguez

Montek Kalsi

M. T. ¨Ozsu

David Vazquez

Sai Rajeswar

Human Annotator

2025-03-19

ArXiv (preprint)

arxiv.org

WebMMU: A Benchmark for Multimodal Multilingual Website Understanding and Code Generation

David Vazquez

Juan A. Rodriguez

Sai Rajeswar

Understanding diverse web data and automating web development presents an exciting challenge for agentic AI. While existing benchmarks addre… (see more)ss isolated web-based tasks—such as website-based Visual Question Answering (VQA) and UI-to-code generation—they lack a unified evaluation suite for assessing web agents that interact with and reason about web environments. We introduce WebMMU, a large-scale benchmark for evaluating AI-driven web agents across multilingual website VQA, HTML/CSS/JavaScript code editing, and sketch-to-code generation. WebMMU provides a comprehensive evaluation suite with real-world website data, multi-step reasoning tasks, and functional UI understanding. Benchmarking state-of-the-art multimodal models on WebMMU reveals significant limitations in web-based reasoning, layout understanding, and structured code generation, particularly in preserving UI hierarchy, handling multilingual content, and producing robust, functional code. While most existing models are optimized for English-only settings, WebMMU highlights the challenges of cross-lingual adaptation in real-world web development. These findings expose critical gaps in current models’ ability to understand website structures, execute user instructions, and generate high-quality web code, underscoring the need for more advanced multimodal reasoning in AI-driven web understanding and development.

2025-03-05

ICLR.cc/2025/Workshop/DL4C (published)

openreview.net

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Rabiul Awal

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Rabiul Awal

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