35 citations · 45 across the 17 of their papers we have counts for
9 papers · 1 filter
AdaptArena: Evaluating Test-Time Personalization of Web Agents
Dongchan Shin, Xing Han Lù, Jiaqi Deng +7
Large language model (LLM) agents have demonstrated strong performance on complex web navigation tasks, yet they remain brittle in real-world settings where user intentions are und…
Apriel-Reasoner: RL Post-Training for General-Purpose and Efficient Reasoning
Rafael Pardinas, Ehsan Kamalloo, David Vazquez +1
Building general-purpose reasoning models using reinforcement learning with verifiable rewards (RLVR) across diverse domains has been widely adopted by frontier open-weight models.…
Overcoming the Modality Gap in Context-Aided Forecasting
Vincent Zhihao Zheng, Étienne Marcotte, Arjun Ashok +4
Context-aided forecasting (CAF) holds promise for integrating domain knowledge and forward-looking information, enabling AI systems to surpass traditional statistical methods. Howe…
The BrowserGym Ecosystem for Web Agent Research
Thibault Le Sellier De Chezelles, Maxime Gasse, Alexandre Drouin +17
The BrowserGym ecosystem addresses the growing need for efficient evaluation and benchmarking of web agents, particularly those leveraging automation and Large Language Models (LLM…
Context is Key: A Benchmark for Forecasting with Essential Textual Information
Andrew Robert Williams, Arjun Ashok, Étienne Marcotte +8
Forecasting is a critical task in decision-making across numerous domains. While historical numerical data provide a start, they fail to convey the complete context for reliable an…
WorkArena: How Capable Are Web Agents at Solving Common Knowledge Work Tasks?
Alexandre Drouin, Maxime Gasse, Massimo Caccia +9
We study the use of large language model-based agents for interacting with software via web browsers. Unlike prior work, we focus on measuring the agents' ability to perform tasks…
Capture the Flag: Uncovering Data Insights with Large Language Models
Issam Laradji, Perouz Taslakian, Sai Rajeswar +6
The extraction of a small number of relevant insights from vast amounts of data is a crucial component of data-driven decision-making. However, accomplishing this task requires con…
Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting
Kashif Rasul, Arjun Ashok, Andrew Robert Williams +15
Over the past years, foundation models have caused a paradigm shift in machine learning due to their unprecedented capabilities for zero-shot and few-shot generalization. However,…
TACTiS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Series
Arjun Ashok, Étienne Marcotte, Valentina Zantedeschi +2
We introduce a new model for multivariate probabilistic time series prediction, designed to flexibly address a range of tasks including forecasting, interpolation, and their combin…