3 citations · 3 across the 3 of their papers we have counts for
3 papers
OpaqueToolsBench: Learning Nuances of Tool Behavior Through Interaction
Skyler Hallinan, Thejas Venkatesh, Xiang Ren +4
Tool-calling is essential for Large Language Model (LLM) agents to complete real-world tasks. While most existing benchmarks assume simple, perfectly documented tools, real-world t…
Lost in the Maze: Overcoming Context Limitations in Long-Horizon Agentic Search
Howard Yen, Yoonsang Lee, Ashwin Paranjape +5
Long-horizon agentic search requires iteratively exploring the web over long trajectories and synthesizing information across many sources, enabling powerful applications like deep…
Promptriever: Instruction-Trained Retrievers Can Be Prompted Like Language Models
Orion Weller, Benjamin Van Durme, Dawn Lawrie +3
Instruction-tuned language models (LM) are able to respond to imperative commands, providing a more natural user interface compared to their base counterparts. In this work, we pre…