Aishwarya Agrawal

Observational Study of Maternal and Fetal Outcome in Posterior Reversible Encephalopathy Syndrome in Eclamptic Women in a Tertiary Care Institute

Prerna Kailashchand Gupta

Meenal Shailesh Sarmalkar

Madhuri A Mehendale

Aishwarya Agrawal

2026-02-02

Journal of South Asian Federation of Obstetrics and Gynaecology (published)

doi.org

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

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.

2025-10-31

Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (published)

doi.org

openreview.net

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

Sai Rajeswar

Autonomous agents that navigate Graphical User Interfaces (GUIs) to automate tasks like document editing and file management can greatly enh… (see more)ance computer workflows. While existing research focuses on online settings, desktop environments, critical for many professional and everyday tasks, remain underexplored due to data collection challenges and licensing issues. We introduce UI-Vision, the first comprehensive, license-permissive benchmark for offline, fine-grained evaluation of computer use agents in real-world desktop environments. Unlike online benchmarks, UI-Vision provides: (i) dense, high-quality annotations of human demonstrations, including bounding boxes, UI labels, and action trajectories (clicks, drags, and keyboard inputs) across 83 software applications, and (ii) three fine-to-coarse grained tasks—Element Grounding, Layout Grounding, and Action Prediction—with well-defined metrics to rigorously evaluate agents’ performance in desktop environments. Our evaluation reveals critical limitations in state-of-the-art models like UI-TARS-72B, including issues with understanding professional software, spatial reasoning, and complex actions like drag-and-drop. These findings highlight the challenges in developing fully autonomous computer-use agents. With UI-Vision, we aim to advance the development of more capable agents for real-world desktop tasks.

2025-10-06

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

proceedings.mlr.press

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

Sai Rajeswar

Human Annotator

2025-10-06

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

doi.org

openreview.net

The Promise of RL for Autoregressive Image Editing

Saba Ahmadi

Rabiul Awal

Ankur Sikarwar

Amirhossein Kazemnejad

Ge Ya Luo

Juan A. Rodriguez

Sai Rajeswar

While image generation techniques are now capable of producing high-quality images that respect prompts which span multiple sentences, the t… (see more)ask of text-guided image editing remains a challenge. Even edit requests that consist of only a few words often fail to be executed correctly. We explore three strategies to enhance performance on a wide range of image editing tasks: supervised fine-tuning (SFT), reinforcement learning (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.

2025-09-17

NeurIPS.cc/2025/Conference (poster)

doi.org

openreview.net

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

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

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 (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

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

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

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

Opening Conference | Building Safer AI for Youth Mental Health

TRAIL: Responsible AI for Professionals and Leaders

Mila Ventures Founder in Residence

Indigenous Pathfinders in AI

Aishwarya Agrawal

Biography

Current Students

Publications

Opening Conference | Building Safer AI for Youth Mental Health

TRAIL: Responsible AI for Professionals and Leaders

Mila Ventures Founder in Residence

Indigenous Pathfinders in AI

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Aishwarya Agrawal

Biography

Current Students

Publications