25 citations · 26 across the 4 of their papers we have counts for
11 papers
Structured Distillation of Web Agent Capabilities Enables Generalization
Xing Han Lù, Siva Reddy
Frontier LLMs can navigate complex websites, but their cost and reliance on third-party APIs make local deployment impractical. We introduce Agent-as-Annotators, a framework that s…
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…
DRBench: A Realistic Benchmark for Enterprise Deep Research
Amirhossein Abaskohi, Tianyi Chen, Miguel Muñoz-Mármol +11
We introduce DRBench, a benchmark for evaluating AI agents on complex, open-ended deep research tasks in enterprise settings. Unlike prior benchmarks that focus on simple questions…
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…
Build the web for agents, not agents for the web
Xing Han Lù, Gaurav Kamath, Marius Mosbach +1
Recent advancements in Large Language Models (LLMs) and multimodal counterparts have spurred significant interest in developing web agents -- AI systems capable of autonomously nav…
AgentRewardBench: Evaluating Automatic Evaluations of Web Agent Trajectories
Xing Han Lù, Amirhossein Kazemnejad, Nicholas Meade +7
Web agents enable users to perform tasks on web browsers through natural language interaction. Evaluating web agents trajectories is an important problem, since it helps us determi…