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Juan A. Rodriguez

10 accepted papers

2026

Grounding Computer Use Agents on Human Demonstrations

ICLR 2026poster

Building reliable computer-use agents requires grounding: accurately connecting natural language instructions to the correct on-screen elements. While large datasets exist for web and mobile interactions, high-quality resources for desktop environments are limited. To address this gap, we introduce…

Cited by 0SourcecodeScholar
2025

AlignVLM: Bridging Vision and Language Latent Spaces for Multimodal Document Understanding

NeurIPS 2025poster

Aligning visual features with language embeddings is a key challenge in vision-language models (VLMs). The performance of such models hinges on having a good connector that maps visual features generated by a vision encoder to a shared embedding space with the LLM while preserving semantic similarit…

Cited by 0SourceScholar
2025

BigDocs: An Open Dataset for Training Multimodal Models on Document and Code Tasks

ICLR 2025poster

Multimodal AI has the potential to significantly enhance document-understanding tasks, such as processing receipts, understanding workflows, extracting data from documents, and summarizing reports. Code generation tasks that require long-structured outputs can also be enhanced by multimodality. Desp…

Cited by 0SourcePDFScholar
2025

InsightBench: Evaluating Business Analytics Agents Through Multi-Step Insight Generation

ICLR 2025poster

Data analytics is essential for extracting valuable insights from data that can assist organizations in making effective decisions. We introduce InsightBench, a benchmark dataset with three key features. First, it consists of 100 datasets representing diverse business use cases such as finance and i…

2025

Rendering-Aware Reinforcement Learning for Vector Graphics Generation

NeurIPS 2025poster

Scalable Vector Graphics (SVG) offer a powerful format for representing visual designs as interpretable code. Recent advances in vision-language models (VLMs) have enabled high-quality SVG generation by framing the problem as a code generation task and leveraging large-scale pretraining. VLMs are pa…

Cited by 0SourceScholar
2025

StarVector: Generating Scalable Vector Graphics Code from Images and Text

AAAI 2025technical

Scalable Vector Graphics (SVG) have become integral to modern image rendering applications due to their infinite scalability and versatility, especially in graphic design and web development. SVGs are essentially long strings of code that adhere to a structured syntax with validity constraints. With…

Cited by 2SourcePDFScholar
2025

StarVector: Generating Scalable Vector Graphics Code from Images and Text

CVPR 2025poster

Scalable Vector Graphics (SVGs) are vital for modern image rendering due to their scalability and versatility. Previous SVG generation methods have focused on curve-based vectorization, lacking semantic understanding, often producing artifacts, and struggling with SVG primitives beyond path curves.…

Cited by 7SourcePDFScholar
2025

The Promise of RL for Autoregressive Image Editing

NeurIPS 2025poster

While image generation techniques are now capable of producing high-quality images that respect prompts which span multiple sentences, the task 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…

Cited by 0SourcecodeScholar
2025

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

ICML 2025poster

Autonomous agents that navigate Graphical User Interfaces (GUIs) to automate tasks like document editing and file management can greatly enhance computer workflows. While existing research focuses on online settings, desktop environments, critical for many professional and everyday tasks, remain und…

Cited by 0SourcePDFScholar
2025

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

EMNLP 2025

We present WebMMU, a multilingual benchmark that evaluates three core web tasks: (1) website visual question answering, (2) code editing involving HTML/CSS/JavaScript, and (3) mockup-to-code generation. Unlike prior benchmarks that treat these tasks separately, WebMMU unifies them using expert-annot

Cited by 0SourcePDFScholar