25 citations · 79 across the 21 of their papers we have counts for
5 papers · 2 filters
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…
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…
DeepSeek-R1 Thoughtology: Let's think about LLM Reasoning
Sara Vera Marjanović, Arkil Patel, Vaibhav Adlakha +14
Large Reasoning Models like DeepSeek-R1 mark a fundamental shift in how LLMs approach complex problems. Instead of directly producing an answer for a given input, DeepSeek-R1 creat…
MMTEB: Massive Multilingual Text Embedding Benchmark
Kenneth Enevoldsen, Isaac Chung, Imene Kerboua +83
Text embeddings are typically evaluated on a limited set of tasks, which are constrained by language, domain, and task diversity. To address these limitations and provide a more co…