Spandana Gella

WebArena-Pro: A Heterogeneous, Multimodal, Reproducible Benchmark for Web Agents

Imene Kerboua

Fatemeh Pesaran zadeh

Xing Han Lu

Weijian Qi

Alexander Miller

Junyi Song

Yunjia Tian

Dongjin Kang

Seyeon Choi

Marzia Nouri

Ewen Gueguen

Matteo Boglioni

Fengyuan Liu

Zeyi Liao

Mengqi Yuan

Yue Li

Alexandre Lacoste

Alexandre Drouin

Huan Sun … (voir 2 de plus)

Gunhee Kim

Siva Reddy

Web agents powered by large language and vision-language models are increasingly applied to realistic browser work that spans heterogeneous … (voir plus)applications, multimodal content, and stateful workflows. However, existing reproducible web-agent benchmarks cover only a small number of web applications drawn from a few software categories, and restrict modality to text and vision. Live benchmarks broaden site coverage but sacrifice reproducibility, since pages and data drift between runs. Moreover, existing benchmarks do not meaningfully evaluate whether agents can understand and use audio and video content embedded within web tasks. To address these gaps, we introduce WebArena-Pro, a benchmark comprising 300 tasks across 20 self-hosted web applications in six domain categories, spanning distinct interface conventions, workflows, and data models. Across the evaluated agents, the best performance is achieved by Gemini 3.1 Pro, which attains 37.0 % success under a 50-step budget, while open-source models' performance does not exceed 27.7% success. Among reproducible, human-curated web agent benchmarks, WebArena-Pro provides the broadest application coverage and the most comprehensive multimodal support to date. The benchmark treats audio and video as core observations alongside text and vision, with dedicated actions for extracting information from each. WebArena-Pro runs each task in isolation and supports reproducible, parallel evaluation. Tasks are authored through a dedicated annotator interface, filtered by LLM-assisted triage, and finally validated by humans before release.

2026-05-22

AIWILD @ International Conference on Machine Learning (publié)

Mem-$π$: Adaptive Memory through Learning When and What to Generate

Xiaoqiang Wang

Chao Wang

Hadi Nekoei

Christopher Pal

Alexandre Lacoste

Bang Liu

Perouz Taslakian

We present Mem-…

2026-05-19

arXiv (prépublication)

Augmenting LLM Reasoning with Dynamic Notes Writing for Complex MultiHop QA

Rishabh Maheshwary

Masoud Hashemi

Khyati Mahajan

Shiva Krishna Reddy Malay

Sai Rajeswar Mudumba

Sathwik Tejaswi Madhusudhan

Vikas Yadav

2026-05-10

Language Resources and Evaluation Conference (publié)

VectorGym: A Multitask Benchmark for SVG Code Generation, Sketching, and Editing

Juan Rodriguez

Haotian Zhang

Abhay Puri

Tianyang Zhang

Rishav Pramanik

Meng Lin

Xiaoqing Xie

Marco Terral

Aly Shariff

Sai Rajeswar

Christopher Pal

We introduce VectorGym, a comprehensive benchmark suite for Scalable Vector Graphics (SVG) that spans generation from text and sketches, com… (voir plus)plex editing, and visual understanding. VectorGym addresses the lack of realistic, challenging benchmarks aligned with professional design workflows. Our benchmark comprises four tasks with expert human-authored annotations: the novel Sketch2SVG task (VG-Sketch); a new SVG editing dataset (VG-Edit) featuring complex, multi-step edits with higher-order primitives; Text2SVG generation (VG-Text); and SVG captioning (VG-Cap). Unlike prior benchmarks that rely on synthetic edits, VectorGym provides gold-standard human annotations that require semantic understanding and design intent. We also propose a multi-task reinforcement learning approach that jointly optimizes across all four tasks using rendering-based rewards. Our method, built on GRPO with curriculum learning, trains a Qwen3-VL 8B model that achieves state-of-the-art performance among open-source models, surpassing much larger models including Qwen3-VL 235B and matching GPT-4o. We also introduce a VLM-as-a-Judge metric for SVG generation, validated through human correlation studies. Our evaluation of frontier VLMs reveals significant performance gaps, positioning VectorGym as a rigorous framework for advancing visual code generation. VectorGym is publicly available on huggingface.co/datasets/ServiceNow/VectorGym.

2026-02-21

arXiv (prépublication)

Grounding Computer Use Agents on Human Demonstrations

Aarash Feizi

Shravan Nayak

Xiangru Jian

Kevin Qinghong Lin

Kaixin Li

Rabiul Awal

Xing Han Lu

Johan Obando-Ceron

Juan A. Rodriguez

Nicolas Chapados

David Vázquez

Adriana Romero-Soriano

Reihaneh Rabbany

Perouz Taslakian

Christopher Pal

Sai Rajeswar

Building reliable computer-use agents requires grounding: accurately connecting natural language instructions to the correct on-screen eleme… (voir plus)nts. While large datasets exist for web and mobile interactions, high-quality resources for desktop environments are limited. To address this gap, we introduce GroundCUA, a large-scale desktop grounding dataset built from expert human demonstrations. It covers 87 applications across 12 categories and includes 56K screenshots, with every on-screen element carefully annotated for a total of over 3.56M human-verified annotations. From these demonstrations, we generate diverse instructions that capture a wide range of real-world tasks, providing high-quality data for model training. Using GroundCUA, we develop the GroundNext family of models that map instructions to their target UI elements. At both 3B and 7B scales, GroundNext achieves state-of-the-art results across five benchmarks using supervised fine-tuning, while requiring less than one-tenth the training data of prior work. Reinforcement learning post-training further improves performance. These results demonstrate the critical role of high-quality, expert-driven datasets in advancing general-purpose computer-use agents.

2025-12-31

International Conference on Learning Representations (Accept (Poster))

Grounding Computer Use Agents on Human Demonstrations

Aarash Feizi

Shravan Nayak

Xiangru Jian

Kevin Qinghong Lin

Kaixin Li

Rabiul Awal

Xing Han Lu

Johan Obando-Ceron

Juan A. Rodriguez

Nicolas Chapados

David Vázquez

Adriana Romero-Soriano

Reihaneh Rabbany

Perouz Taslakian

Christopher Pal

Sai Rajeswar

Building reliable computer-use agents requires grounding: accurately connecting natural language instructions to the correct on-screen eleme… (voir plus)nts. While large datasets exist for web and mobile interactions, high-quality resources for desktop environments are limited. To address this gap, we introduce GroundCUA, a large-scale desktop grounding dataset built from expert human demonstrations. It covers 87 applications across 12 categories and includes 56K screenshots, with every on-screen element carefully annotated for a total of over 3.56M human-verified annotations. From these demonstrations, we generate diverse instructions that capture a wide range of real-world tasks, providing high-quality data for model training. Using GroundCUA, we develop the GroundNext family of models that map instructions to their target UI elements. At both 3B and 7B scales, GroundNext achieves state-of-the-art results across five benchmarks using supervised fine-tuning, while requiring less than one-tenth the training data of prior work. Reinforcement learning post-training further improves performance, and when evaluated in an agentic setting on the OSWorld benchmark using o3 as planner, GroundNext attains comparable or superior results to models trained with substantially more data,. These results demonstrate the critical role of high-quality, expert-driven datasets in advancing general-purpose computer-use agents.

2025-11-09

arXiv (prépublication)

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

Christopher Pal

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… (voir plus)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

Conference on Empirical Methods in Natural Language Processing (publié)

SAFEARENA: Evaluating the Safety of Autonomous Web Agents

Ada Defne Tur

Esin Durmus

Karolina Stanczak

LLM-based agents are becoming increasingly proficient at solving web-based tasks. With this capability comes a greater risk of misuse for ma… (voir plus)licious purposes, such as posting misinformation in an online forum or selling illicit substances on a website. To evaluate these risks, we propose SafeArena, the first benchmark to focus on the deliberate misuse of web agents. SafeArena comprises 250 safe and 250 harmful tasks across four websites. We classify the harmful tasks into five harm categories -- misinformation, illegal activity, harassment, cybercrime, and social bias, designed to assess realistic misuses of web agents. We evaluate leading LLM-based web agents, including GPT-4o, Claude-3.5 Sonnet, Qwen-2-VL 72B, and Llama-3.2 90B, on our benchmark. To systematically assess their susceptibility to harmful tasks, we introduce the Agent Risk Assessment framework that categorizes agent behavior across four risk levels. We find agents are surprisingly compliant with malicious requests, with GPT-4o and Qwen-2 completing 34.7% and 27.3% of harmful requests, respectively. Our findings highlight the urgent need for safety alignment procedures for web agents. Our benchmark is available here: https://safearena.github.io

2025-10-05

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

proceedings.mlr.press

Improving GUI Grounding with Explicit Position-to-Coordinate Mapping

Christopher Pal

2025-10-02

ArXiv (prépublication)

DRBench: A Realistic Benchmark for Enterprise Deep Research

Amirhossein Abaskohi

Tianyi Chen

Miguel Muñoz-Mármol

Curtis Fox

Amrutha Varshini Ramesh

Étienne Marcotte

Christopher Pal

Issam Hadj Laradji

We introduce DRBench, a benchmark for evaluating AI agents on complex, open-ended deep research tasks in enterprise settings. Unlike prior b… (voir plus)enchmarks that focus on simple questions or web-only queries, DRBench evaluates agents on multi-step queries (for example, ``What changes should we make to our product roadmap to ensure compliance with this standard?") that require identifying supporting facts from both the public web and private company knowledge base. Each task is grounded in realistic user personas and enterprise context, spanning a heterogeneous search space that includes productivity software, cloud file systems, emails, chat conversations, and the open web. Tasks are generated through a carefully designed synthesis pipeline with human-in-the-loop verification, and agents are evaluated on their ability to recall relevant insights, maintain factual accuracy, and produce coherent, well-structured reports. We release 15 deep research tasks across 10 domains, such as Sales, Cybersecurity, and Compliance. We demonstrate the effectiveness of DRBench by evaluating diverse DR agents across open- and closed-source models (such as GPT, Llama, and Qwen) and DR strategies, highlighting their strengths, weaknesses, and the critical path for advancing enterprise deep research. Code is available at https://github.com/ServiceNow/drbench.

2025-09-29

ArXiv (prépublication)

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

Sai Rajeswar

Christopher Pal

Scalable Vector Graphics (SVG) offer a powerful format for representing visual designs as interpretable code. Recent advances in vision-lang… (voir plus)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.

2025-09-17

NeurIPS.cc/2025/Conference (poster)