Nicolas Chapados

arxiv.org

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

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… (see more)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

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… (see more)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 (preprint)

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

Christopher Pal

Sai Rajeswar

Human Annotator

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.

2025-10-05

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

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… (see more)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 (preprint)

arxiv.org

WebArena Verified: Reliable Evaluation for Web Agents

Amine El hattami

Megh Thakkar

Christopher Pal

Autonomous web agents increasingly operate in multi-step browser workflows, yet widely used benchmarks can misestimate performance due to un… (see more)derspecified goals and brittle checkers—challenges characteristic of normal benchmark maturation rather than flaws in the paradigm. We present WebArena Verified, a reproducible re-evaluation of WebArena that preserves its containerized environments while strengthening measurement. We audit all 812 tasks, repair misaligned evaluations and clarify ambiguous instructions; replace substring matching with type- and normalization-aware comparators; verify backend state for state-changing tasks; and adopt a structured JSON schema with explicit status codes for deterministic scoring. We provide improved results reporting with template-level macro averages, 95\% confidence intervals, and failure-mode breakdowns. We also introduce WebArena Verified Hard, a 137-task subset that retains difficult cases while reducing evaluation cost by 83\%. On the baseline agent we evaluated, it reduces false negatives by approximately 11\%. WebArena Verified remains drop-in compatible with minimal change to existing agents, supporting faithful and comparable progress. We release our code, data, and evaluation tools in our public repository.

2025-09-27

NeurIPS.cc/2025/Workshop/SEA (poster)

Beyond Naive Prompting: Strategies for Improved Zero-shot Context-aided Forecasting with LLMs

Arjun Ashok

Andrew Robert Williams

Vincent Zhihao Zheng

Irina Rish

Étienne Marcotte

Valentina Zantedeschi

Alexandre Drouin

Forecasting in real-world settings requires models to integrate not only historical data but also relevant contextual information, often ava… (see more)ilable in textual form. While recent work has shown that large language models (LLMs) can be effective context-aided forecasters via naïve direct prompting, their full potential remains underexplored. We address this gap with 4 strategies, providing new insights into the zero-shot capabilities of LLMs in this setting. ReDP improves interpretability by eliciting explicit reasoning traces, allowing us to assess the model's reasoning over the context independently from its forecast accuracy. CorDP leverages LLMs solely to refine existing forecasts with context, enhancing their applicability in real-world forecasting pipelines. IC-DP proposes embedding historical examples of context-aided forecasting tasks in the prompt, substantially improving accuracy even for the largest models. Finally, RouteDP optimizes resource efficiency by using LLMs to estimate task difficulty, and routing the most challenging tasks to larger models. Evaluated on different kinds of context-aided forecasting tasks from the CiK benchmark, our strategies demonstrate distinct benefits over naïve prompting across LLMs of different sizes and families. These results open the door to further simple yet effective improvements in LLM-based context-aided forecasting.

2025-09-22

NeurIPS.cc/2025/Workshop/BERT2S (poster)

Spaced Scheduling for Large Language Model Training

Amine El hattami

Christopher Pal

2025-06-08

TMLR (accepted)

Silent Sabotage: Injecting Backdoors into AI Agents Through Fine-Tuning

Léo Boisvert

Abhay Puri

Chandra Kiran Reddy Evuru

Joshua Kazdan

Avinandan Bose

Quentin Cappart

Maryam Fazel

Sai Rajeswar

Jason Stanley

Alexandre Drouin

Krishnamurthy Dj Dvijotham

The rise of AI agents that can use tools, browse the web and interact with computers on behalf of a user, has sparked strong interest in imp… (see more)roving these capabilities by explicitly fine-tuning the LLMs/VLMs that power these agents. Several researchers have proposed collecting data by letting the agents interact with their environment (e.g., a computer operating system, the web or a collection of APIs exposed as tools), and improve agent performance by fine tuning on this data. In this work, we show that such data collection can be manipulated by adversaries to insert poisoned traces. By modifying just 5% of collected traces, adversaries can embed stealthy bad behaviors into agents—like leaking confidential user information whenever the tool or webpage exposes a trigger. Our results raise important security concerns in the development of AI agents, and underscore the importance of careful scrutiny of all data collection processes used to improve agentic AI.

2025-06-07

ICML.cc/2025/Workshop/WCUA (poster)

Context is Key: A Benchmark for Forecasting with Essential Textual Information

Arjun Ashok

Andrew R. Williams

Étienne Marcotte

Valentina Zantedeschi

Jithendaraa Subramanian

Alexandre Lacoste

Forecasting is a critical task in decision-making across numerous domains. While historical numerical data provide a start, they fail to con… (see more)vey the complete context for reliable and accurate predictions. Human forecasters frequently rely on additional information, such as background knowledge and constraints, which can efficiently be communicated through natural language. However, in spite of recent progress with LLM-based forecasters, their ability to effectively integrate this textual information remains an open question. To address this, we introduce "Context is Key" (CiK), a time-series forecasting benchmark that pairs numerical data with diverse types of carefully crafted textual context, requiring models to integrate both modalities; crucially, every task in CiK requires understanding textual context to be solved successfully. We evaluate a range of approaches, including statistical models, time series foundation models, and LLM-based forecasters, and propose a simple yet effective LLM prompting method that outperforms all other tested methods on our benchmark. Our experiments highlight the importance of incorporating contextual information, demonstrate surprising performance when using LLM-based forecasting models, and also reveal some of their critical shortcomings. This benchmark aims to advance multimodal forecasting by promoting models that are both accurate and accessible to decision-makers with varied technical expertise. The benchmark can be visualized at https://servicenow.github.io/context-is-key-forecasting/v0/.

2025-04-30

International Conference on Machine Learning (poster)

proceedings.mlr.press

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

Juan A. Rodriguez

Chao Wang

Akshay Kalkunte Suresh

Abhay Puri

Xiangru Jian

Pierre-Andre Noel

Sathwik Tejaswi Madhusudhan

Enamul Hoque

Christopher Pal

Issam H. Laradji

David Vázquez

Perouz Taslakian … (see 2 more)

Spandana Gella

Sai Rajeswar

Aligning visual features with language embeddings is a key challenge in vision-language models (VLMs). The performance of such models hinges… (see more) 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 similarity. Existing connectors, such as multilayer perceptrons (MLPs), lack inductive bias to constrain visual features within the linguistic structure of the LLM's embedding space, making them data-hungry and prone to cross-modal misalignment. In this work, we propose a novel vision-text alignment method, AlignVLM, that maps visual features to a weighted average of LLM text embeddings. Our approach leverages the linguistic priors encoded by the LLM to ensure that visual features are mapped to regions of the space that the LLM can effectively interpret. AlignVLM is particularly effective for document understanding tasks, where visual and textual modalities are highly correlated. Our extensive experiments show that AlignVLM achieves state-of-the-art performance compared to prior alignment methods, with larger gains on document understanding tasks and under low-resource setups. We provide further analysis demonstrating its efficiency and robustness to noise.

2025-03-04

ICLR.cc/2025/Workshop/Re-Align (poster)

Societal Alignment Frameworks Can Improve LLM Alignment

Karolina Stanczak

Nicholas Meade

Mehar Bhatia

Hattie Zhou

Konstantin Böttinger

Jeremy Barnes

Jason Stanley

Jessica Montgomery

Richard Zemel

Nicolas Papernot

Denis Therien

Timothy P Lillicrap

Ana Marasovic

Sylvie Delacroix

Gillian K. Hadfield

Siva Reddy

Recent progress in large language models (LLMs) has focused on producing responses that meet human expectations and align with shared values… (see more) - a process coined alignment. However, aligning LLMs remains challenging due to the inherent disconnect between the complexity of human values and the narrow nature of the technological approaches designed to address them. Current alignment methods often lead to misspecified objectives, reflecting the broader issue of incomplete contracts, the impracticality of specifying a contract between a model developer, and the model that accounts for every scenario in LLM alignment. In this paper, we argue that improving LLM alignment requires incorporating insights from societal alignment frameworks, including social, economic, and contractual alignment, and discuss potential solutions drawn from these domains. Given the role of uncertainty within societal alignment frameworks, we then investigate how it manifests in LLM alignment. We end our discussion by offering an alternative view on LLM alignment, framing the underspecified nature of its objectives as an opportunity rather than perfect their specification. Beyond technical improvements in LLM alignment, we discuss the need for participatory alignment interface designs.

2025-03-04

Bi-Align @ International Conference on Learning Representations (poster)