activity
20242026
most citedThe BrowserGym Ecosystem for Web Agent Research

4 citations · 5 across the 7 of their papers we have counts for

collaborators

8 papers

cs.AI2026

CUBE: A Standard for Unifying Agent Benchmarks

Alexandre Lacoste, Nicolas Gontier, Oleh Shliazhko +23

The proliferation of agent benchmarks has created critical fragmentation that threatens research productivity. Each new benchmark requires substantial custom integration, creating…

cs.LG2026

Privileged Information Distillation for Language Models

Emiliano Penaloza, Dheeraj Vattikonda, Nicolas Gontier +3

Training-time privileged information (PI) can enable language models to succeed on tasks they would otherwise fail, making it a powerful tool for reinforcement learning in hard, lo…

cs.AI2025

JEF-Hinter: Leveraging Offline Knowledge for Improving Web Agents Adaptation

Hadi Nekoei, Aman Jaiswal, Patrice Bechard +7

Large language model (LLM) agents perform well in sequential decision-making tasks, but improving them on unfamiliar domains often requires costly online interactions or fine-tunin…

cs.CL2025

FocusAgent: Simple Yet Effective Ways of Trimming the Large Context of Web Agents

Imene Kerboua, Sahar Omidi Shayegan, Megh Thakkar +7

Web agents powered by large language models (LLMs) must process lengthy web page observations to complete user goals; these pages often exceed tens of thousands of tokens. This sat…

cs.AI2025

How to Train Your LLM Web Agent: A Statistical Diagnosis

Dheeraj Vattikonda, Santhoshi Ravichandran, Emiliano Penaloza +13

LLM-based web agents have recently made significant progress, but much of it has occurred in closed-source systems, widening the gap with open-source alternatives. Progress has bee…

cs.CL2025

LineRetriever: Planning-Aware Observation Reduction for Web Agents

Imene Kerboua, Sahar Omidi Shayegan, Megh Thakkar +6

While large language models have demonstrated impressive capabilities in web navigation tasks, the extensive context of web pages, often represented as DOM or Accessibility Tree (A…

cs.LG2024★ 4 cited

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…

cs.AI2024★ 1 cited

WorkArena++: Towards Compositional Planning and Reasoning-based Common Knowledge Work Tasks

Léo Boisvert, Megh Thakkar, Maxime Gasse +6

The ability of large language models (LLMs) to mimic human-like intelligence has led to a surge in LLM-based autonomous agents. Though recent LLMs seem capable of planning and reas…