Alexandre Drouin

Forecasting in real-world settings requires models to integrate not only historical data but also relevant contextual information, often ava… (voir plus)ilable in textual form. While recent work has shown that large language models (LLMs) can be effective context-aided forecasters via na\"ive 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 na\"ive 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.

2025-08-13

ArXiv (prépublication)

arxiv.org

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

Quentin Cappart

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

2025-07-07

colmweb.org/COLM/2025/Conference (accepté)

On Selecting Robust Approaches for Learning Predictive Biomarkers in Metabolomics Data Sets.

Thibaud Godon

Pier-Luc Plante

Jacques Corbeil

Pascal Germain

Metabolomics, the study of small molecules within biological systems, offers insights into metabolic processes and, consequently, holds grea… (voir plus)t promise for advancing health outcomes. Biomarker discovery in metabolomics represents a significant challenge, notably due to the high dimensionality of the data. Recent work has addressed this problem by analyzing the most important variables in machine learning models. Unfortunately, this approach relies on prior hypotheses about the structure of the data and may overlook simple patterns. To assess the true usefulness of machine learning methods, we evaluate them on a collection of 835 metabolomics data sets. This effort provides valuable insights for metabolomics researchers regarding where and when to use machine learning. It also establishes a benchmark for the evaluation of future methods. Nonetheless, the results emphasize the high diversity of data sets in metabolomics and the complexity of finding biologically relevant biomarkers. As a result, we propose a novel approach applicable across all data sets, offering guidance for future analyses. This method involves directly comparing univariate and multivariate models. We demonstrate through selected examples how this approach can guide data analysis across diverse data set structures, representative of the observed variability. Code and data are available for research purposes.

2025-06-12

Analytical Chemistry (publié)

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

Stefania Raimondo

Alexandre Piché

Alexandre Lacoste

Massimo Caccia

LLM-based web agents have recently made significant progress, but much of it has occurred in closed-source systems, widening the gap with op… (voir plus)en-source alternatives. Progress has been held back by two key challenges: first, a narrow focus on single-step tasks that overlooks the complexity of multi-step web interactions; and second, the high compute costs required to post-train LLM-based web agents. To address this, we present the first statistically grounded study on compute allocation for LLM web-agent post-training. Our approach uses a two-stage pipeline, training a Llama 3.1 8B student to imitate a Llama 3.3 70B teacher via supervised fine-tuning (SFT), followed by on-policy reinforcement learning. We find this process highly sensitive to hyperparameter choices, making exhaustive sweeps impractical. To spare others from expensive trial-and-error, we sample 1,370 configurations and use bootstrapping to estimate effective hyperparameters. Our results show that combining SFT with on-policy RL consistently outperforms either approach alone on both WorkArena and MiniWob++. Further, this strategy requires only 55% of the compute to match the peak performance of pure SFT on MiniWob++, effectively pushing the compute-performance Pareto frontier, and is the only strategy that can close the gap with closed-source models.

2025-06-08

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

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

Stefania Raimondo

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

Quentin Cappart

Maryam Fazel

Sai Rajeswar

Jason Stanley

Nicolas Chapados

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

Alexandre Lacoste

2025-05-01

ICML.cc/2025/Conference (poster)

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

Nathaniel D'Amours

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

Quentin Cappart

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)