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

Spandana Gella

Huan Sun … (voir 2 de plus)

Gunhee Kim

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)

CUBE: A Standard for Unifying Agent Benchmarks

Alexandre Lacoste

Nicolas Gontier

Oleh Shliazhko

Aman Jaiswal

Kusha Sareen

Shailesh Nanisetty

Joan Cabezas

Manuel Del Verme

Omar G. Younis

Simone Baratta

Matteo Avalle

Imene Kerboua

Elron Bandel

Michal Shmueli-Scheuer

Asaf Yehudai

Leshem Choshen

Jonathan Lebensold

Sean Hughes

Massimo Caccia … (voir 6 de plus)

Alexandre Drouin

Tao Yu

Yu Su

Graham Neubig

Dawn Song

The proliferation of agent benchmarks has created critical fragmentation that threatens research productivity. Each new benchmark requires s… (voir plus)ubstantial custom integration, creating an "integration tax" that limits comprehensive evaluation. We propose CUBE (Common Unified Benchmark Environments), a universal protocol standard built on MCP and Gym that allows benchmarks to be wrapped once and used everywhere. By separating task, benchmark, package, and registry concerns into distinct API layers, CUBE enables any compliant platform to access any compliant benchmark for evaluation, RL training, or data generation without custom integration. We call on the community to contribute to the development of this standard before platform-specific implementations deepen fragmentation as benchmark production accelerates through 2026.

2026-03-15

arXiv (prépublication)

arxiv.org

Weasel: Out-of-Domain Generalization for Web Agents via Importance-Diversity Data Selection

Fatemeh Pesaran zadeh

Seyeon Choi

Gunhee Kim

Large language models (LLMs) have enabled web agents that follow natural language goals through multi-step browser interactions. However, ag… (voir plus)ents fine-tuned on specific trajectories and domain often struggle to generalize out of domain, and offline training can be compute-inefficient due to noisy, redundant trajectories and long accessibility-tree (AXTree) states. To address both issues, we propose Weasel, a trajectory selection method for offline training of web agents. Weasel selects a fixed-budget subset of trajectory steps by optimizing an objective that balances unary importance with pairwise diversity over states, websites, and interaction patterns, solving efficiently with a greedy algorithm. We further improve efficiency with action-centered AXTree pruning that keeps only content around the ground-truth action target, and we mitigate style mismatch for reasoning-native models by replacing expert traces with model-generated, style-consistent rationales. Across AgentTrek and NNetNav training datasets, evaluations in WebArena, WorkArena, and MiniWob, and experiments with Qwen2.5-7B, Gemma3-4B, and Qwen3-8B, Weasel improves out-of-domain performance while reducing training cost, producing roughly 9.7-12.5

2026-03-01

LLA @ International Conference on Learning Representations (poster)

Grounding Computer Use Agents on Human Demonstrations

Aarash Feizi

Shravan Nayak

Xiangru Jian

Kevin Qinghong Lin

Kaixin Li

Rabiul Awal

Johan Obando-Ceron

Juan A. Rodriguez

Nicolas Chapados

David Vázquez

Adriana Romero-Soriano

Reihaneh Rabbany

Perouz Taslakian

Christopher Pal

Spandana Gella

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

Johan Obando-Ceron

Juan A. Rodriguez

Nicolas Chapados

David Vázquez

Adriana Romero-Soriano

Reihaneh Rabbany

Perouz Taslakian

Christopher Pal

Spandana Gella

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)

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

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)

arxiv.org

AgentRewardBench: Evaluating Automatic Evaluations of Web Agent Trajectories

Amirhossein Kazemnejad

Karolina Stanczak

Peter Shaw

Christopher J. Pal

Web agents enable users to perform tasks on web browsers through natural language interaction. Evaluating web agents trajectories is an impo… (voir plus)rtant problem, since it helps us determine whether the agent successfully completed the tasks. Rule-based methods are widely used for this purpose, but they are challenging to extend to new tasks and may not always recognize successful trajectories. We may achieve higher accuracy through human evaluation, but the process would be substantially slower and more expensive. Automatic evaluations with LLMs may avoid the challenges of designing new rules and manually annotating trajectories, enabling faster and cost-effective evaluation. However, it is unclear how effective they are at evaluating web agents. To this end, we propose AgentRewardBench, the first benchmark to assess the effectiveness of LLM judges for evaluating web agents. AgentRewardBench contains 1302 trajectories across 5 benchmarks and 4 LLMs. Each trajectory in AgentRewardBench is reviewed by an expert, who answers questions pertaining to the success, side effects, and repetitiveness of the agent. Using our benchmark, we evaluate 12 LLM judges and find that no single LLM excels across all benchmarks. We also find that the rule-based evaluation used by common benchmarks tends to underreport the success rate of web agents, highlighting a key weakness of rule-based evaluation and the need to develop more flexible automatic evaluations. We release the benchmark at: https://agent-reward-bench.github.io

2025-07-06

Conference on Language Modeling (accepté)

DeepSeek-R1 Thoughtology: Let's think about LLM Reasoning

Sara Vera Marjanovi'c

Arkil Patel

Vaibhav Adlakha

Milad Aghajohari

Parishad BehnamGhader

Amirhossein Kazemnejad

Gaurav Kamath

Marius Mosbach

Karolina Stanczak

Large Reasoning Models like DeepSeek-R1 mark a fundamental shift in how LLMs approach complex problems. Instead of directly producing an ans… (voir plus)wer for a given input, DeepSeek-R1 creates detailed multi-step reasoning chains, seemingly"thinking"about a problem before providing an answer. This reasoning process is publicly available to the user, creating endless opportunities for studying the reasoning behaviour of the model and opening up the field of Thoughtology. Starting from a taxonomy of DeepSeek-R1's basic building blocks of reasoning, our analyses on DeepSeek-R1 investigate the impact and controllability of thought length, management of long or confusing contexts, cultural and safety concerns, and the status of DeepSeek-R1 vis-\`a-vis cognitive phenomena, such as human-like language processing and world modelling. Our findings paint a nuanced picture. Notably, we show DeepSeek-R1 has a 'sweet spot' of reasoning, where extra inference time can impair model performance. Furthermore, we find a tendency for DeepSeek-R1 to persistently ruminate on previously explored problem formulations, obstructing further exploration. We also note strong safety vulnerabilities of DeepSeek-R1 compared to its non-reasoning counterpart, which can also compromise safety-aligned LLMs.

2025-04-01

ArXiv (prépublication)

arxiv.org

MMTEB: Massive Multilingual Text Embedding Benchmark

Kenneth Enevoldsen

Isaac Chung

Imene Kerboua

Márton Kardos

Ashwin Mathur

David Stap

Jay Gala

Wissam Siblini

Dominik Krzemiński

Genta Indra Winata

Saba Sturua

Saiteja Utpala

Mathieu Ciancone

Marion Schaeffer

Gabriel Sequeira

Diganta Misra

Shreeya Dhakal

Jonathan Rystrøm

Roman Solomatin

Ömer Çağatan … (voir 66 de plus)

Akash Kundu

Martin Bernstorff

Shitao Xiao

Akshita Sukhlecha

Bhavish Pahwa

Rafał Poświata

Kranthi Kiran GV

Shawon Ashraf

Daniel Auras

Björn Plüster

Jan Philipp Harries

Loïc Magne

Isabelle Mohr

Mariya Hendriksen

Dawei Zhu

Hippolyte Gisserot-Boukhlef

Tom Aarsen

Jan Kostkan

Konrad Wojtasik

Taemin Lee

Marek Šuppa

Crystina Zhang

Roberta Rocca

Mohammed Hamdy

Andrianos Michail

John Yang

Manuel Faysse

Aleksei Vatolin

Nandan Thakur

Manan Dey

Dipam Vasani

Pranjal Chitale

Simone Tedeschi

Nguyen Tai

Artem Snegirev

Michael Günther

Mengzhou Xia

Weijia Shi

Jordan Clive

Gayatri Krishnakumar

Anna Maksimova

Silvan Wehrli

Maria Tikhonova

Henil Panchal

Aleksandr Abramov

Malte Ostendorff

Zheng Liu

Simon Clematide

Lester James Miranda

Alena Fenogenova

Guangyu Song

Ruqiya Bin Safi

Wen-Ding Li

Alessia Borghini

Federico Cassano

Hongjin Su

Jimmy Lin

Howard Yen

Lasse Hansen

Sara Hooker

Chenghao Xiao

Vaibhav Adlakha

Orion Weller

Niklas Muennighoff

Text embeddings are typically evaluated on a limited set of tasks, which are constrained by language, domain, and task diversity. To address… (voir plus) these limitations and provide a more comprehensive evaluation, we introduce the Massive Multilingual Text Embedding Benchmark (MMTEB) - a large-scale, community-driven expansion of MTEB, covering over 500 quality-controlled evaluation tasks across 250+ languages. MMTEB includes a diverse set of challenging, novel tasks such as instruction following, long-document retrieval, and code retrieval, representing the largest multilingual collection of evaluation tasks for embedding models to date. Using this collection, we develop several highly multilingual benchmarks, which we use to evaluate a representative set of models. We find that while large language models (LLMs) with billions of parameters can achieve state-of-the-art performance on certain language subsets and task categories, the best-performing publicly available model is multilingual-e5-large-instruct with only 560 million parameters. To facilitate accessibility and reduce computational cost, we introduce a novel downsampling method based on inter-task correlation, ensuring a diverse selection while preserving relative model rankings. Furthermore, we optimize tasks such as retrieval by sampling hard negatives, creating smaller but effective splits. These optimizations allow us to introduce benchmarks that drastically reduce computational demands. For instance, our newly introduced zero-shot English benchmark maintains a ranking order similar to the full-scale version but at a fraction of the computational cost.

2025-01-21

ICLR.cc/2025/Conference (poster)

Thibault Le Sellier De Chezelles

The BrowserGym Ecosystem for Web Agent Research

Maxime Gasse

Alexandre Lacoste

Massimo Caccia

Lawrence Keunho Jang

Ori Yoran

Dehan Kong

Frank F. Xu

Quentin Cappart

Graham Neubig

Ruslan Salakhutdinov

Nicolas Chapados

The BrowserGym ecosystem addresses the growing need for efficient evaluation and benchmarking of web agents, particularly those leveraging a… (voir plus)utomation and Large Language Models (LLMs). Many existing benchmarks suffer from fragmentation and inconsistent evaluation methodologies, making it challenging to achieve reliable comparisons and reproducible results. In an earlier work, Drouin et al. (2024) introduced BrowserGym which aims to solve this by providing a unified, gym-like environment with well-defined observation and action spaces, facilitating standardized evaluation across diverse benchmarks. We propose an extended BrowserGym-based ecosystem for web agent research, which unifies existing benchmarks from the literature and includes AgentLab, a complementary framework that aids in agent creation, testing, and analysis. Our proposed ecosystem offers flexibility for integrating new benchmarks while ensuring consistent evaluation and comprehensive experiment management. As a supporting evidence, we conduct the first large-scale, multi-benchmark web agent experiment and compare the performance of 6 state-of-the-art LLMs across 6 popular web agent benchmarks made available in BrowserGym. Among other findings, our results highlight a large discrepancy between OpenAI and Anthropic's latests models, with Claude-3.5-Sonnet leading the way on almost all benchmarks, except on vision-related tasks where GPT-4o is superior. Despite these advancements, our results emphasize that building robust and efficient web agents remains a significant challenge, due to the inherent complexity of real-world web environments and the limitations of current models.

2024-12-31

Trans. Mach. Learn. Res. (publié)

Weblinx: Real-World Website Navigation with Multi-Turn Dialogue

Zdeněk Kasner

We propose the problem of conversational web navigation, where a digital agent controls a web browser and follows user instructions to solve… (voir plus) real-world tasks in a multi-turn dialogue fashion. To support this problem, we introduce WEBLINX - a large-scale benchmark of 100K interactions across 2300 expert demonstrations of conversational web navigation. Our benchmark covers a broad range of patterns on over 150 real-world websites and can be used to train and evaluate agents in diverse scenarios. Due to the magnitude of information present, Large Language Models (LLMs) cannot process entire web pages in real-time. To solve this bottleneck, we design a retrieval-inspired model that efficiently prunes HTML pages by ranking relevant elements. We use the selected elements, along with screenshots and action history, to assess a variety of models for their ability to replicate human behavior when navigating the web. Our experiments span from small text-only to proprietary multimodal LLMs. We find that smaller finetuned decoders surpass the best zero-shot LLMs (including GPT-4V), but also larger finetuned multimodal models which were explicitly pretrained on screenshots. However, all finetuned models struggle to generalize to unseen websites. Our findings highlight the need for large multimodal models that can generalize to novel settings. Our code, data and models are available for research: https://mcgill-nlp.github.io/weblinx

2024-04-30

ICML.cc/2024/Conference (spotlight)