Alexandre Drouin

DoomArena: A framework for Testing AI Agents Against Evolving Security Threats

Léo Boisvert

Abhay Puri

Gabriel Huang

Mihir Bansal

Chandra Kiran Reddy Evuru

Avinandan Bose

Maryam Fazel

Alexandre Lacoste

Jason Stanley

Krishnamurthy Dj Dvijotham

We present DoomArena, a security evaluation framework for AI agents. DoomArena is designed on three principles: 1) It is a plug-in framework… (voir plus) and integrates easily into realistic agentic frameworks like BrowserGym (for web agents) and

2025-06-08

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

How to Train Your LLM Web Agent: A Statistical Diagnosis

Dheeraj Vattikonda

Santhoshi Ravichandran

Emiliano Penaloza

Hadi Nekoei

Megh Thakkar

Thibault Le Sellier de Chezelles

Nicolas Gontier

Miguel Muñoz-Mármol

Sahar Omidi Shayegan

Stefania Raimondo

Xue (Steve) Liu

Laurent Charlin

Alexandre Piché

Alexandre Lacoste

Massimo Caccia

Large language model (LLM) agents for web interfaces have advanced rapidly, yet open-source systems still lag behind proprietary agents. Bri… (voir plus)dging this gap is key to enabling customizable, efficient, and privacy-preserving agents. Two challenges hinder progress: the reproducibility issues in RL and LLM agent training, where results often depend on sensitive factors like seeds and decoding parameters, and the focus of prior work on single-step tasks, overlooking the complexities of web-based, multi-step decision-making. We address these gaps by providing a statistically driven study of training LLM agents for web tasks. Our two-stage pipeline combines imitation learning from a Llama 3.3 70B teacher with on-policy fine-tuning via Group Relative Policy Optimization (GRPO) on a Llama 3.1 8B student. Through 240 configuration sweeps and rigorous bootstrapping, we chart the first compute allocation curve for open-source LLM web agents. Our findings show that dedicating one-third of compute to teacher traces and the rest to RL improves MiniWoB++ success by 6 points and closes 60% of the gap to GPT-4o on WorkArena, while cutting GPU costs by 45%. We introduce a principled hyperparameter sensitivity analysis, offering actionable guidelines for robust and cost-effective agent training.

2025-06-08

ICML.cc/2025/Workshop/WCUA (présentation orale)

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

Léo Boisvert

Abhay Puri

Chandra Kiran Reddy Evuru

Joshua Kazdan

Avinandan Bose

Maryam Fazel

Sai Rajeswar

Jason Stanley

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… (voir plus)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.

2025-06-08

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

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

Andrew Robert Williams

Arjun Ashok

Étienne Marcotte

Valentina Zantedeschi

Jithendaraa Subramanian

Roland Riachi

James Requeima

Alexandre Lacoste

Irina Rish

2025-05-01

ICML.cc/2025/Conference (poster)

Generalization Bounds via Meta-Learned Model Representations: PAC-Bayes and Sample Compression Hypernetworks

Benjamin Leblanc

Mathieu Bazinet

Nathaniel D'Amours

Pascal Germain

Both PAC-Bayesian and Sample Compress learning frameworks have been shown instrumental for deriving tight (non-vacuous) generalization bound… (voir plus)s for neural networks. We leverage these results in a meta-learning scheme, relying on a hypernetwork that outputs the parameters of a downstream predictor from a dataset input. The originality of our approach lies in the investigated hypernetwork architectures that encode the dataset before decoding the parameters: (1) a PAC-Bayesian encoder that expresses a posterior distribution over a latent space, (2) a Sample Compress encoder that selects a small sample of the dataset input along with a message from a discrete set, and (3) a hybrid between both approaches motivated by a new Sample Compress theorem handling continuous messages. The latter theorem exploits the pivotal information transiting at the encoder-decoder junction in order to compute generalization guarantees for each downstream predictor obtained by our meta-learning scheme.

2025-05-01

ICML.cc/2025/Conference (poster)

DoomArena: A framework for Testing AI Agents Against Evolving Security Threats

Léo Boisvert

Mihir Bansal

Chandra Kiran Reddy Evuru

Gabriel Huang

Abhay Puri

Avinandan Bose

Maryam Fazel

Jason Stanley

Alexandre Lacoste

Krishnamurthy Dj Dvijotham

2025-04-18

ArXiv (prépublication)

arxiv.org

Learning to Defer for Causal Discovery with Imperfect Experts

Oscar Clivio

Divyat Mahajan

Perouz Taslakian

Sara Magliacane

Ioannis Mitliagkas

Valentina Zantedeschi

Integrating expert knowledge, e.g. from large language models, into causal discovery algorithms can be challenging when the knowledge is not… (voir plus) guaranteed to be correct. Expert recommendations may contradict data-driven results, and their reliability can vary significantly depending on the domain or specific query. Existing methods based on soft constraints or inconsistencies in predicted causal relationships fail to account for these variations in expertise. To remedy this, we propose L2D-CD, a method for gauging the correctness of expert recommendations and optimally combining them with data-driven causal discovery results. By adapting learning-to-defer (L2D) algorithms for pairwise causal discovery (CD), we learn a deferral function that selects whether to rely on classical causal discovery methods using numerical data or expert recommendations based on textual meta-data. We evaluate L2D-CD on the canonical Tübingen pairs dataset and demonstrate its superior performance compared to both the causal discovery method and the expert used in isolation. Moreover, our approach identifies domains where the expert's performance is strong or weak. Finally, we outline a strategy for generalizing this approach to causal discovery on graphs with more than two variables, paving the way for further research in this area.

2025-03-05

ICLR.cc/2025/Workshop/LLM_Reason_and_Plan (publié)

Learning to Defer for Causal Discovery with Imperfect Experts

Oscar Clivio

Divyat Mahajan

Perouz Taslakian

Sara Magliacane

Ioannis Mitliagkas

Valentina Zantedeschi

Integrating expert knowledge, e.g. from large language models, into causal discovery algorithms can be challenging when the knowledge is not… (voir plus) guaranteed to be correct. Expert recommendations may contradict data-driven results, and their reliability can vary significantly depending on the domain or specific query. Existing methods based on soft constraints or inconsistencies in predicted causal relationships fail to account for these variations in expertise. To remedy this, we propose L2D-CD, a method for gauging the correctness of expert recommendations and optimally combining them with data-driven causal discovery results. By adapting learning-to-defer (L2D) algorithms for pairwise causal discovery (CD), we learn a deferral function that selects whether to rely on classical causal discovery methods using numerical data or expert recommendations based on textual meta-data. We evaluate L2D-CD on the canonical Tübingen pairs dataset and demonstrate its superior performance compared to both the causal discovery method and the expert used in isolation. Moreover, our approach identifies domains where the expert's performance is strong or weak. Finally, we outline a strategy for generalizing this approach to causal discovery on graphs with more than two variables, paving the way for further research in this area.

2025-03-05

ICLR.cc/2025/Workshop/LLM_Reason_and_Plan (publié)

The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applications

Philippe Brouillard

Chandler Squires

Jonas Wahl

Konrad Paul Kording

Karen Sachs

Dhanya Sridhar

Causal discovery aims to automatically uncover causal relationships from data, a capability with significant potential across many scientifi… (voir plus)c disciplines. However, its real-world applications remain limited. Current methods often rely on unrealistic assumptions and are evaluated only on simple synthetic toy datasets, often with inadequate evaluation metrics. In this paper, we substantiate these claims by performing a systematic review of the recent causal discovery literature. We present applications in biology, neuroscience, and Earth sciences - fields where causal discovery holds promise for addressing key challenges. We highlight available simulated and real-world datasets from these domains and discuss common assumption violations that have spurred the development of new methods. Our goal is to encourage the community to adopt better evaluation practices by utilizing realistic datasets and more adequate metrics.

2025-01-28

cclear.cc/CLeaR/2025/Conference (poster)

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

Gaurav Sahu

Abhay Puri

Juan A. Rodriguez

Amirhossein Abaskohi

Mohammad Chegini

Perouz Taslakian

Valentina Zantedeschi

Alexandre Lacoste

David Vazquez

Chris Pal

Sai Rajeswar

Issam Hadj Laradji

2025-01-22

ICLR.cc/2025/Conference (poster)

The BrowserGym Ecosystem for Web Agent Research

Thibault Le Sellier de Chezelles

Maxime Gasse

Alexandre Lacoste

Massimo Caccia

Léo Boisvert

Megh Thakkar

Tom Marty

Rim Assouel

Sahar Omidi Shayegan

Lawrence Keunho Jang

Xing Han Lu

Ori Yoran

Dehan Kong

Frank F. Xu

Siva Reddy

Graham Neubig

Russ Salakhutdinov

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) for web interaction tasks. Many existing benchmarks suffer from fragmentation and inconsistent evaluation methodologies, making it challenging to achieve reliable comparisons and reproducible results. BrowserGym aims to solve this by providing a unified, gym-like environment with well-defined observation and action spaces, facilitating standardized evaluation across diverse benchmarks. Combined with AgentLab, a complementary framework that aids in agent creation, testing, and analysis, BrowserGym offers flexibility for integrating new benchmarks while ensuring consistent evaluation and comprehensive experiment management. This standardized approach seeks to reduce the time and complexity of developing web agents, supporting more reliable comparisons and facilitating in-depth analysis of agent behaviors, and could result in more adaptable, capable agents, ultimately accelerating innovation in LLM-driven automation. 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 all benchmarks currently 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.

2025-01-01

Trans. Mach. Learn. Res. (publié)