Automated agents, powered by Large language models (LLMs), are emerging as the go-to tool for querying information. However, evaluation benchmarks for LLM agents rarely feature natural questions that are both information-seeking and genuinely time-consuming for humans. To address this gap we introduce MoNaCo, a benchmark of 1,315 natural and time-consuming questions that require dozens, and at times hundreds, of intermediate steps to solve -- far more than any existing QA benchmark. To build MoNaCo, we developed a decomposed annotation pipeline to elicit and manually answer real-world time-consuming questions at scale. Frontier LLMs evaluated on MoNaCo achieve at most 61.2% F1, hampered by low recall and hallucinations. Our results underscore the limitations of LLM-powered agents in handling the complexity and sheer breadth of real-world information-seeking tasks -- with MoNaCo providing an effective resource for tracking such progress. The MoNaCo benchmark, codebase, prompts and models predictions are all publicly available at: https://tomerwolgithub.github.io/monaco
@article{arxiv.2508.11133,
title = {MoNaCo: More Natural and Complex Questions for Reasoning Across Dozens of Documents},
author = {Tomer Wolfson and Harsh Trivedi and Mor Geva and Yoav Goldberg and Dan Roth and Tushar Khot and Ashish Sabharwal and Reut Tsarfaty},
journal= {arXiv preprint arXiv:2508.11133},
year = {2025}
}
Comments
Accepted for publication in Transactions of the Association for Computational Linguistics (TACL), 2025. Authors pre-print